### From: Lamason, Bill Sent: Friday, July 17, 2015 9:41 PM To: nfrey@omb.eop.gov Cc: aszabo@omb.eop.gov; Vasu, Amy; Harvey, Reid; Culligan, Kevin Subject: EPA's draft updated RIA and revised goal computation document for interagency review

Nathan,

Please see attached the updated RIA and the latest version of the goal computation technical support document (TSD) for interagency review. The latest goal computation TSD contains a new section describing the methodology for producing mass goals. Both documents are accompanied by data spreadsheets.

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

Thank you.

<< EO12866_CPP_2060_AR33_RIA Final_20150717.docx >> << EO12866_CPP_2060_AR33_RIA_Spreadsheet Final_20150717.xlsx >> << 111(d) Technology Rate Quantification and State Goals_071715.docx >> << Baseline, Category-specific rates, and State Goal Quantification (Draft OMB version 7- 16).xlsx >>

### EO12866_CPP 2060 AR33 RIA Final_20150717



# Regulatory Impact Analysis for the Clean Power Plan Final Rule

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

ii [This page intentionally left blank]

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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



# Regulatory Impact Analysis for th e Clean Power Plan Final Rule

U.S. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Pl anning and Standards Research Triangle Park, NC 27711

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

iv CONTACT INFORMATION

This document has been prepared by staff from the Office of Air Quality Planning and Standards, the Office of Atmospheric Program s, and the Office of Policy of the U.S. Environmental Protection Agency. Questions related to this docu ment 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 docum ent from EPA Offices, including the Office of Air Quality Planni ng and Standards, the Office of Atmospheric Programs, and the Office of Policy. Contributions to this report were also made by ICF International and RTI International.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

v 

## T ABLE OF 

## C ONTENTS



### L IST OF 

### T ABLES



### .............................................................................................................................. .x

L IST OF 

### F IGURES 

### ........................................................................................................................ xvii

A CRONYMS



### .............................................................................................................................. ......xx

E XECUTIVE 

### S UMMARY 

### ..............................................................................................................ES-1

ES.1 Background and Context ..................................................................................ES-1 ES.2 Summary of Clean Power Plan Final Rule .......................................................ES-1 ES.3 Illustrative Plan Approaches Examined in RIA ................................................ES-4 ES.4 Emissions Reductions .......................................................................................ES-6 ES.5 Costs .................................................................................................................ES-8 ES.6 Monetized Climate Benefits a nd Health Co-benefits .....................................ES-10 ES.6.1 Estimating Global Climate Benefits ...........................................................ES-13 ES 6.2 Estimating Air Quality Health Co-Benefits ................................................ES-15 ES 6.3 Combined Benefits Estimates .....................................................................ES-18 ES.7 Net Benefits ....................................................................................................ES-20 ES.8 Economic Impacts ..........................................................................................ES-24 ES.9 Employment Im pacts ......................................................................................ES-24 ES.10 References .......................................................................................................ES-26

C HAPTER 

### 1: I NTRODUCTION AND 

### B ACKGROUND FOR THE 

### C LEAN 

### P OWER 

### P LAN 

### .................... 1-1

1.1 Introduction ................................................................................................................ 1 -1 1.2 Legal, Scientific and Economic Basis for this Rulemaking ...................................... 1-1 1.2.1 Statutory Requirement .................................................................................... 1-1 1.2.2 Health and Welfare Impacts from Climate Change ........................................ 1-2 1.2.3 Market Failure ................................................................................................ 1-3 1.3 Summary of Regulator y Analysis .............................................................................. 1-4 1.4 Background for the Final Emission Guidelines ......................................................... 1-4 1.4.1 Base Case and Years of Analysis ................................................................... 1-4 1.4.2 Definition of Affected Sources ....................................................................... 1-5 1.4.3 Regulated Pollutant......................................................................................... 1-6 1.4.4 Emission Guidelines ....................................................................................... 1-6 1.4.5 State Plans....................................................................................................... 1-6 1.5 Organization of the Regulator y Impact Analysis ....................................................... 1-8 1.6 References .................................................................................................................. 1 -8

C HAPTER 

### 2: E LECTRIC 

### P OWER 

### S ECTOR 

### I NDUSTRY 

### P ROFILE 

### ................................................... 2-1

2.1 Introduction ................................................................................................................ 2 -1 2.2 Power Sector Overview ............................................................................................. 2-1 2.2.1 Generation ...................................................................................................... 2-1 2.2.2 Transmission ................................................................................................... 2-9

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vi 2.2.3 Distribution ................................................................................................... 2-10 2.3 Sales, Expenses and Prices ...................................................................................... 2-10 2.3.1 Electricity Prices ........................................................................................... 2-11 2.3.2 Prices of Fossil Fuels Used for Generating Electricity................................. 2-16 2.3.3 Changes in Electricity Intensity of the U.S. Economy Between 2002 to 2012 17 2.4 Deregulation and Restructuring ............................................................................... 2-19 2.5 Emissions of Greenhouse Gases from Electric Utilities .......................................... 2-24 2.6 Carbon Dioxide Control Technologies .................................................................... 2-27 2.6.1 Carbon Capture and Storage ......................................................................... 2-29 2.6.2 Geologic and Geographic Considerati ons for Geologic Sequestration ........ 2-34 2.6.3 Availability of Geologic Sequestratio n in Deep Saline Formations ............. 2-38 2.6.4 Availability of CO 2 

### Storage via Enhanced Oil Recovery (EOR) ................. 2-38 2.7 State Policies on GHG and Clean Energy Re gulation in the Power Sector ............. 2-40 2.8 Revenues and Expenses ........................................................................................... 2-43 2.9 Natural Gas Market .................................................................................................. 2-44 2.10 References ................................................................................................................ 2-4 8

C HAPTER 

### 3: C OST 

### , E MISSIONS 

### , E CONOMIC 

### , AND 

### E NERGY 

### I MPACTS 

### ........................................ 3-1

3.1 Introduction ................................................................................................................ 3 -1 3.2 Overview .................................................................................................................... 3 -1 3.3 Power Sector Modelling Framework ......................................................................... 3-1 3.4 Recent Updates to EPA’s Base Case using IPM (v.5.15) .......................................... 3-4 3.5 State Goals in this Final Rule.................................................................................... 3-5 3.6 Illustrative Plan Approaches Analyzed ...................................................................... 3-8 3.7 Demand-Side Energy Efficiency ............................................................................. 3-13 3.7.1 Demand-Side Energy Efficiency Im provements (Electricity Demand Reductions) .............................................................................................................. 3-13 3.7.2 Demand-Side Energy Efficiency Costs ........................................................ 3-15 3.8 Monitoring, Reporting, and R ecordkeeping Costs .................................................. 3-16 3.9 Projected Power Sector Impacts .............................................................................. 3-19 3.9.1 Projected Emissions (in the Contiguous U.S.) ............................................. 3-19 3.9.2 Projected Compliance Costs (i n the Contiguous U.S.) ................................. 3-22 3.9.3 Projected Compliance Actions fo r Emissions Reductions ........................... 3-24 3.9.4 Projected Generation Mix ............................................................................. 3-26 3.9.5 Projected Incremental Retirements ............................................................... 3-31 3.9.6 Projected Capacity Additions ....................................................................... 3-32 3.9.7 Projected Coal Production and Natura l Gas Use for the Electric Power Sector 34 3.9.8 Projected Fuel Price, Market, a nd Infrastructure Impacts ............................ 3-34 3.9.9 Projected Retail Electricity Prices ................................................................ 3-36 3.9.10 Projected Electricity Bill Impacts ................................................................. 3-41 3.10 Projected Primary PM Emissions from Power Plants .............................................. 3-41 3.11 Limitations of Analysis ............................................................................................ 3-42 3.12 Social Costs .............................................................................................................. 3-4 4 3.13 References ................................................................................................................ 3-4 7

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vii A PPENDIX 

### 3A: A NALYSIS OF 

### P OTENTIAL 

### U PSTREAM 

### M ETHANE 

### E MISSIONS 

### C HANGES IN 

### N ATURAL 

### G AS 

### S YSTEMS AND 

### C OAL 

### M INING 

### ................................................................... 3A-1

3A.1 General Approach ............................................................................................ 3A-2 3A.1.1 Analytical Scope .......................................................................................... 3A-2 3A.1.2 Coal Mining Source Description ................................................................. 3A-3 3A.1.3 Natural Gas Systems Source Description .................................................... 3A-4 3A.1.4 Illustrative Plan Approaches Examined ...................................................... 3A-6 3A.1.5 Activity Drivers ........................................................................................... 3A-6 3A.2 Results .............................................................................................................. 3A-7 3A.3 Uncertainties and Limitations .......................................................................... 3A-8 3A.4 References ........................................................................................................ 3A-9

C HAPTER 

### 4: E STIMATED 

### C LIMATE 

### B ENEFITS AND 

### H UMAN 

### H EALTH 

### C O 

### - BENEFITS 

### ................ 4-1

4.1 Introduction ................................................................................................................ 4 -1 4.2 Estimated Climate Benefits from CO 2 

### ....................................................................... 4-1 4.2.1 Climate Change Impacts ................................................................................. 4-2 4.2.2 Social Cost of Carbon ..................................................................................... 4-3 4.3 Estimated Human Health Co-Benefits ..................................................................... 4-10 4.3.1 Health Impact Assessment for PM 2.5 

### and Ozone .......................................... 4-13 4.3.2 Economic Valuation for H ealth Co-benefits ................................................ 4-18 4.3.3 Benefit-per-ton Estimates for PM 2.5 

### ............................................................. 4-20 4.3.4 Benefit-per-ton Estimates for Ozone ............................................................ 4-21 4.3.5 Estimated Health Co-Benefits Results ......................................................... 4-22 4.3.6 Characterization of Uncertainty in th e Estimated Health Co-benefits ......... 4-36 4.4 Combined Climate Benefits and Health Co-Benefits Estimates.............................. 4-42 4.5 Unquantified Co-benefits ......................................................................................... 4-46 4.5.1 HAP Impacts................................................................................................. 4-48 4.5.2 Additional NO 2 

### Health Co-Benefits ............................................................. 4-52 4.5.3 Additional SO 2 

### Health Co-Benefits .............................................................. 4-53 4.5.4 Additional NO 2 

### and SO 2 

### Welfare Co-Benefits ............................................. 4-54 4.5.5 Ozone Welfare Co-Benefits.......................................................................... 4-55 4.5.6 Carbon Monoxide Co-Benefits ..................................................................... 4-55 4.5.7 Visibility Impairment Co-Benefits ............................................................... 4-56 4.6 References ................................................................................................................ 4-5 6

A PPENDIX 

### 4A: G ENERATING 

### R EGIONAL 

### B ENEFIT 

### - PER 

### -T ON 

### E STIMATES 

### ............................. 4A-1

4A.1 Overview of Benefit-per-Ton Estimates .......................................................... 4A-1 4A.2 Air Quality Modeling for the Pr oposed Clean Power Plan ............................. 4A-2 4A.3 Regional PM 2.5 

### Benefit-per-Ton Estimates for EGUs Derived from Air Quality Modeling of the Proposed Clean Power Plan ................................................... 4A-5 4A.4 Regional Ozone Benefit-per-Ton Estimates .................................................. 4A-14 4A.5 References ...................................................................................................... 4A-17

C HAPTER 

### 5: E CONOMIC 

### I MPACTS



### – M ARKETS 

### O UTSIDE THE 

### U TILITY 

### P OWER 

### S ECTOR 

### ........ 5-1

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

viii 5.1 Introduction ................................................................................................................ 5 -1 5.2 Methods...................................................................................................................... 5 -2 5.3 Summary of Secondary Market Imp acts of Energy Price Changes ........................... 5-3 5.3.1 Share of Total Production Costs ..................................................................... 5-5 5.3.2 Ability to Substitute between Inputs to the Production Process ..................... 5-5 5.3.3 Availability of Substitute Goods and Services ............................................... 5-5 5.4 Effect of Changes in Input Dema nd from Electricity Sector ..................................... 5-6 5.5 Conclusions ................................................................................................................ 5- 6 5.6 References .................................................................................................................. 5 -7

C HAPTER 

### 6: E MPLOYMENT 

### I MPACT 

### A NALYSIS 

### ......................................................................... 6-1

6.1 Introduction ................................................................................................................ 6 -1 6.2 Economic Theory and Employment .......................................................................... 6-2 6.3 Current State of Knowledge Based on the Peer-Reviewed Literature ....................... 6-6 6.3.1 Regulated Sector ............................................................................................. 6-7 6.3.2 Economy-Wide ............................................................................................... 6-8 6.3.3 Labor Supply Impacts ................................................................................... 6-10 6.4 Recent Employment Trends ..................................................................................... 6-11 6.4.1 Electric Power Generation ............................................................................ 6-11 6.4.2 Fossil Fuel Extraction ................................................................................... 6-12 6.4.3 Clean Energy Employment Trends............................................................... 6-14 6.5 Projected Sectoral Employment Changes due to the Final Emission Guidelines .... 6-18 6.5.1 Projected Changes in Employment in Electricity Generation and Fossil Fuel Extraction ......................................................................................................... 6-19 6.5.2 Projected Changes in Employment in Demand-Side Energy Efficiency Activities .................................................................................................................. 6 -26 6.6 Conclusion ............................................................................................................... 6-34 6.7 References ................................................................................................................ 6-3 6

A PPENDIX 

### 6A: E STIMATING 

### S UPPLY 

### S IDE 

### E MPLOYMENT IMPACTS 

### ...................................... 6A-1

6A.1 General Approach ............................................................................................ 6A-1 6A.1.1 Employment Effects Include d In the Analysis ............................................ 6A-2 6A.2 Employment Changes due to Heat Rate Improvements .................................. 6A-3 6A.2.1 Employment Changes Due to Building (or Avoiding) New Generation Capacity .................................................................................................................. 6A- 5 6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements ...................... 6A-8 6A.2.3 Employment Changes due to Coal and Oil/Gas Retirements ...................... 6A-8

C HAPTER 

### 7: S TATUTORY AND 

### E XECUTIVE 

### O RDER 

### A NALYSIS 

### .................................................. 7-1

7.1 Executive Order 12866: Regulatory Planni ng and Review, and Executive Order 13563: Improving Regulation and Regulatory Review .................................................... 7-1 7.2 Paperwork Reduction Act (PRA) ............................................................................... 7-5 7.3 Regulatory Flexibility Act (RFA) .............................................................................. 7-6 7.4 Unfunded Mandates Reform Act (UMRA) ............................................................... 7-7

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

ix 7.5 Executive Order 13132: Federalism .......................................................................... 7-8 7.6 Executive Order 13175: Consultation and Coordination with Indian Tribal Governments ................................................................................................................... 7-12 7.7 Executive Order 13045: Protection of Childre n from Environmental Health Risks and Safety Risks .............................................................................................................. 7-15 7.8 Executive Order 13211: Actions Concerni ng Regulations That Significantly Affect Energy Supply, Distribution, or Use .................................................................... 7-16 7.9 National Technology Transfer and Advancement Act ............................................ 7-16 7.10 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and Low -Income Populations ...................................................... 7-16 7.11 Congressional Review Act (CRA) ........................................................................... 7-19

C HAPTER 

### 8: C OMPARISON OF 

### B ENEFITS AND 

### C OSTS 

### ................................................................ 8-1

8.1 Comparison of Benefits and Costs ............................................................................. 8-1 8.2 Uncertainty Analysis .................................................................................................. 8-5 8.2.1 Uncertainty in Costs and Illust rative Plan Approaches .................................. 8-5 8.2.2 Uncertainty Associated with Estimati ng the Social Cost of Carbon .............. 8-6 8.2.3 Uncertainty Associated with PM 2.5 

### and Ozone Health Co-Benefits Assessment ................................................................................................................. 8- 8 8.3 References .................................................................................................................. 8 -9

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

x 

## L IST OF 

## T ABLES



### Table ES-1. Emission Performance Rates (Adjuste d Output-Weighted-Average Pounds of CO 2 

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

Table ES-2. Climate and Air Pollutant Emission Reductions for the Rate-Based Illustrative Plan Approach .............................................................................................ES-6

Table ES-3. Climate and Air Pollutant Emission Reductions for the Mass-Based Illustrative Plan Appproach ...........................................................................................ES-7

Table ES-4. Projected CO 2 

### Emission Reductions, Relative to 2005 ....................................ES-7

Table ES-5. Compliance Costs for the Illustrative Rate-Based and Mass-Based Plan Approaches .................................................................................................................... ES-9

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

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

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

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

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

Table ES-11. Summary Table of Important Energy Ma rket Impacts (Percent Change from Base Case) .................................................................................................................... ES-24

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

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

Table 2-3. Coal and Natural Ga s Generating Units, by Size, Age, Capacity, and Thermal Efficiency (Heat Rate) ....................................................................................... 2-7

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

Table 2-5. Domestic Emissions of Greenhouse Ga ses, by Economic Sector (million metric tonnes of CO 2 

### equivalent) ................................................................................... 2-25

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

xi Table 2-6. Greenhouse Gas Emissions from the Electricity Sector (Generation, Transmission and Distribution), 2002 and 2012 (million metric tonnes of CO 2 

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

Table 2-7. Fossil Fuel Emission Factors in EP A Base Case 5.14 IPM Power Sector Modeling Application .................................................................................................... 2-27

Table 2-8. Total CO 2 

### Storage Resource (DOE-NETL) ..................................................... 2-36

Table 2-9. Revenue and Expense Statistics for Major U.S. Investor-Owned Electric Utilities for 2010 (nominal $millions) ........................................................................... 2-44

Table 3-1. Statewide CO 2 

### Emission Performance Goals, Ra te-based and Mass-based ....... 3-7

Table 3-2. Demand-Side Energy Efficiency Plan Scenario: Net Cumulative Demand Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales) ............................ 3-14

Table 3-3. Annualized Cost of Demand-Side En ergy Efficiency Plan Scenario [Contiguous U.S.] (at discount rates of 3 per cent and 7 percent, billions 2011$) ......... 3-16
Table 3-4. Years 2020, 2025 and 2030: Summary of State and Industry Annual Respondent Burden and Cost of Reporting and R ecordkeeping Requirements (2011$) .................. 3-19

Table 3-5. Projected CO 2 

### Emission Impacts, Relative to Base Case ................................. 3-20

Table 3-6. Projected CO 2 

### Emission Impacts, Relative to 2005 ......................................... 3-20

Table 3-7. Projected Non-CO 2 

### Emission Impacts, 2020-2030 .......................................... 3-21

Table 3-8. Annualized Compliance Costs Incl uding Monitoring, Reporting and Recordkeeping Costs Requirements (billions of 2011$) ............................................... 3-23

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

Table 3-10. Projected Capacity Factor of Ex isting Coal Steam and Natural Gas Combined Cycle Capacity ............................................................................................. 3-26

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

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

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

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

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

Table 3-16. Power Sector Gas Use ...................................................................................... 3-34

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

xii Table 3-17. Projected Average Minemouth and Deliv ered Coal Prices (2011$/MMBtu) .. 3-36

Table 3-18. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices (2011$/MMBtu) ............................................................................................................. 3-3 6

Table 3-19. 2020 Projected Contiguous U.S. and Re gional Retail Electricity Prices (cents/kWh) ................................................................................................................... . 3-38

Table 3-20. 2025 Projected Contiguous U.S. and Re gional Retail Electricity Prices (cents/kWh) ................................................................................................................... . 3-39

Table 3-21. 2030 Projected Contiguous U.S. and Re gional Retail Electricity Prices (cents/kWh) ................................................................................................................... . 3-40

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

Table 3A-1. Base Year Upstream Methane-Related Emissions in the U.S. GHG Inventory ......................................................................................................................... 3A- 6

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

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

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

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

Table 4-2. Social Cost of CO 2 

### , 2015-2050 (in 2011$) ......................................................... 4-8

Table 4-3. Estimated Global Climate Benefits of CO 2 

### Reductions for the Final Emission Guidelines in 2020 (billions of 2011$) ............................................................ 4-8

Table 4-4. Estimated Global Climate Benefits of CO 2 

### Reductions for the Final Emission Guidelines in 2025 (billions of 2011$) ............................................................ 4-9

Table 4-5. Estimated Global Climate Benefits of CO 2 

### Reductions for the Final Emission Guidelines in 2030 (billions of 2011$) ............................................................ 4-9

Table 4-6. Human Health Effects of Ambient PM 2.5 

### and Ozone ....................................... 4-14

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

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

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$) .......................... 4-24

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

xiii Table 4-10. Emission Reductions of Criteria Pollutant s for the Final Emission Guidelines Rate-based Illustrative Plan Approach in 2020 (thousands of short tons) ..................... 4-24

Table 4-11. Emission Reductions of Criteria Pollutant s for the Final Emission Guidelines Rate-based Illustrative Plan Approach in 2025 (thousands of short tons) ..................... 4-24

Table 4-12. Emission Reductions of Criteria Pollutant s for the Final Emission Guidelines Rate-based Illustrative Plan Approach in 2030 (thousands of short tons) ..................... 4-25

Table 4-13. Emission Reductions of Criteria Pollutant s for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2020 (thousands of short tons) .................... 4-25

Table 4-14. Emission Reductions of Criteria Pollutant s for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2025 (thousands of short tons) .................... 4-25

Table 4-15. Emission Reductions of Criteria Pollutant s for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2030 (thousands of short tons) .................... 4-25

Table 4-16. Summary of Estimated Monetized Health Co-Benefits for the Final Emission Guidelines Rate-bas ed Illustrative Plan Appr oach in 2020 (billions of 2011$) ...................................................................................................................... .... 4-26

Table 4-17. Summary of Estimated Monetized Health Co-Benefits for the Final Emission Guidelines Rate-bas ed Illustrative Plan Appr oach in 2025 (billions of 2011$) ...................................................................................................................... .... 4-26

Table 4-18. Summary of Estimated Monetized Health Co-Benefits for the Final Emission Guidelines Rate-bas ed Illustrative Plan Appr oach in 2030 (billions of 2011$) ...................................................................................................................... .... 4-27

Table 4-19. Summary of Estimated Monetized Health Co-Benefits for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2020 (billions of 2011$) ...................................................................................................................... .... 4-27

Table 4-20. Summary of Estimated Monetized Health Co-Benefits for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2025 (billions o 2011$) * ...................................................................................................................... ... 4-28

Table 4-21. Summary of Estimated Monetized Health Co-Benefits for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2030 (billions of 2011$) ...................................................................................................................... .... 4-28

Table 4-22. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-benefits for the Final Emission Guidelines Rate-based Illustrative Plan Approach in 2020 .......................................................................................................... 4- 29

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

xiv Table 4-23. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-benefits for Final Emission Guidelines Rate-based Illustrative Plan Approach in 2025 .......................................................................................................... 4- 30

Table 4-24. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-Benefits for Final Emission Guidelines Rate-based Illustrative Plan Approach in 2030 .......................................................................................................... 4- 31

Table 4-25. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-benefits for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2020 .......................................................................................................... 4- 32

Table 4-26. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-benefits for Final Emission Guidelines Mass-based Illustrative Plan Approach in 2025 .......................................................................................................... 4- 33

Table 4-27. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-Benefits for Final Emission Guidelines Mass-based Illustrative Plan Approach in 2030 .......................................................................................................... 4- 34

Table 4-28. Population Exposure in the Clean Po wer Plan Proposal Option 1 State Scenario Modeling (used to generate th e benefit-per-ton estimates) Above and Below Various Concentrations Benchmarks in the Underlying Epidemiology Studies .................................................................................................................... ..... 4-40

Table 4-29. Combined Climate Benefits and Health Co-Benefits for Final Emission Guidelines in 2020 (billions of 2011$) .......................................................................... 4-44

Table 4-30. Combined Climate Benefits and Health Co-Benefits for Final Emission Guidelines in 2025 (billions of 2011$)* ........................................................................ 4-44

Table 4-31. Combined Climate Benefits and Health Co-Benefits for Final Emission Guidelines in 2030 (billions of 2011$)* ........................................................................ 4-45

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

Table 4A-1. State Total Annual EGU Emissions for NO X 

### for the 2011 Base Year, 2025 Base Case, and 2025 Clean Power Plan Propos al (Option 1 State) (in thousands of tons) ....................................................................................................................... . 4A-2

Table 4A-2. State Total Annual EGU Emissions for SO 2 

### for the 2011 Base Year, 2025 Base Case, and 2025 Clean Power Plan Propos al (Option 1 State) (in thousands of tons) ........................................................................................................................ . 4A-4

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

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

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

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

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..................................... 4A-11

Table 4A-7. Summary of Regional PM 2.5 

### Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025..................................... 4A-12

Table 4A-8. Summary of Regional PM 2.5 

### Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030..................................... 4A-13

Table 4A-9. Summary of Regional Ozone Benefi t-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$) ....................... 4A-15

Table 4A-10. Summary of Regional Ozone Benefi t-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025 (2011$) ....................... 4A-15

Table 4A-11. Summary of Regional Ozone Benefi t-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030 (2011$) ....................... 4A-15

Table 4A-12. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020..................................... 4A-16

Table 4A-13. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025..................................... 4A-16

Table 4A-14. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030..................................... 4A-16

Table 5-1. Estimated Percentage Changes in Average Energy Prices by Energy Type for the Final Emission Guidelines, Rate-based and Mass-based Illustrative Plan Approaches .................................................................................................................... .. 5-4

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

Table 6-2. Renewable Electricity Genera tion-Related Employment ................................. 6-17

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

Table 6-4. Engineering-Based a 

### Changes in Labor Utilization, Rate-based Scenario (Number of Job-Years b 

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

Table 6-5. Engineering-Based a 

### Changes in Labor Utilization, Mass-Based Illustrative Plan Approach (Number of Job-Years of Employment in a Single Year) .................... 6-26

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xvi Table 6-6. Estimated Demand-Side Energy Effi ciency Employment Impacts: Target 1 percent Growth in Energy Efficiency ............................................................................ 6-31

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

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

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

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

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

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

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

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

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xvii 

## L IST OF 

## F IGURES



### Figure 2-1. New Build and Retired Capacity (MW) by Fuel Type, 2002-2012 ................... 2-4
Figure 2-2. Cumulative Distribution in 2010 of Co al and Natural Gas Electricity Capacity and Generation, by Age .................................................................................... 2-8
Figure 2-3. Fossil Fuel-Fired Electricity Gene rating Facilities, by Size ............................... 2-9
Figure 2-4. Average Retail Electricity Price by State (cents/kWh), 2011 ........................... 2-12
Figure 2-5. Nominal National Average Electricity Prices for Three Major End-Use Categories .................................................................................................................... .. 2-13 Figure 2-6. Relative Increases in Nominal National Average Electricity Prices for Major End-Use Categories, With Inflation Indices .................................................................. 2-14
Figure 2-7. Real National Average Electricity Pr ices (2011$) for Three Major End-Use Categories .................................................................................................................... .. 2-15 Figure 2-8. Relative Change in Real National Av erage Electricity Prices (2011$) for Three Major End-Use Categories .................................................................................. 2-16
Figure 2-9. Relative Real Prices of Fossil Fuels fo r Electricity Generation; Change in National Average Real Price per MBtu Delivered to EGU ........................................... 2-17
Figure 2-10. Relative Growth of Electricity Ge neration, Population and Real GDP Since 2002 .................................................................................................................... .. 2-18 Figure 2-11. Relative Change of Real GDP, Population and Electricity Generation Intensity Since 2002 ....................................................................................................... 2- 19 Figure 2-12. Status of State Electricity Indus try Restructuring Activities ............................ 2-20
Figures 2-13 and 2-14. Capacity and Generation Mix by Ownership Type, 2002 & 2012 .... 2-23
Figures 2-15 and 2-16. Generation Capacity Built and Retired between 2002 and 2012 by Ownership Type ........................................................................................................... 2-2 4 Figure 2-17. Domestic Emissions of Greenhouse Ga ses from Major Sectors, 2002 and 2012 (million metric tonnes of CO 2 

### equivalent) ............................................................ 2-25
Figure 2-18. Marketable products from Syngas Generation ................................................ 2-29
Figure 2-19. Post-Combustion CO 2 

### Capture for a Pulverized Coal Power Plant ................. 2-31
Figure 2-20. Pre-Combustion CO 2 

### Capture for an IGCC Power Plant ................................. 2-32

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xviii Figure 2-19. Geologic Sequestration in the Con tinental United States ................................. 2-35
Figure 2-20. Relative Change Nominal and Real (2011$) Prices of Natural Gas Delivered to the Power Sector ($/MMBtu) .................................................................................... 2-45
Figure 2-21. Relative Change in Real (2011$) Prices of Fossil Fuels Delivered to the Power Sector ($/mmBtu) ............................................................................................... 2-46
Figure 3-1. Illustrative Regions for Demand-Side Energy Efficiency/Renewable Energy Procurement Used in this Analysis ................................................................................ 3-10
Figure 3-2 Generation Mix with the Base Case and 111(d) Options, 2020-2030 (thousand GWh) ............................................................................................................. 3- 29 Figure 3-3. Nationwide Generation: Historical (1 990-2014) and Rate-Based Illustrative Plan Approach Projections (2020, 2025, 2030) ............................................................. 3-30
Figure 3-4. Nationwide Generation: Historical (1990-2014) and Mass-Based Illustrative Plan Approach Projections (2020, 2025, 2030) ............................................................. 3-30
Figure 3-5. Electricity Market Module Regions .................................................................. 3-41
Figure 4-1. Monetized Health Co-benefits of Ra te-based and Mass-based Illustrative Plan Approaches for the Final Em ission Guidelines in 2025 ........................................ 4-35
Figure 4-2. Breakdown of Monetized Health Co-benef its by Precursor Pollutant at a 3% Discount Rate for Rate-based and Mass-base d Illustrative Plan Approaches for the Final Emission Guidelines in 2025 ................................................................................ 4-36
Figure 4-3. Percentage of Adult Populati on (age 30+) by Annual Mean PM 2.5 

### Exposure in the Option 1 State Scenario Clean Power Plan Proposal Modeling (used to generate the benefit-pe r-ton estimates) .......................................................................... 4-41
Figure 4-4. Cumulative Distribution of Adult P opulation (age 30+) by Annual Mean PM 2.5 

### Exposure in the Option 1 State Scenario Clean Power Plan Proposal Modeling (used to generate the benefit-per-ton estimates) ............................................................ 4-42
Figure 4-5. Breakdown of Combined Monetized C limate and Health Co-benefits of Final Emission Guidelines in 2025 for Rate-based and Mass-based Illustrative Plan Approaches and Pollutants (3% discount rate) .............................................................. 4-46
Figure 4A-1. Regional Breakdown ....................................................................................... 4A-6
Figure 6.1. Electric Power Industry Employment ............................................................... 6-12
Figure 6.2. Coal Production Employment ........................................................................... 6-13
Figure 6.3. Oil and Gas Production Employment ............................................................... 6-14

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xix Figure 6.4. Demand-Side Energy Efficiency Employm ent: Jobs per One Million Dollars (2011$) ...................................................................................................................... .... 6-33

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xx 

## 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 DICE Dynamic Integrated Climate and Economy Model DOE U.S. Department of Energy EAB Environmental Appeals Board EC Elemental carbon ECS Energy Cost Share EG Emissions guidelines EGR Enhanced Gas Recovery EGU Electric Generating Unit EIA U.S. Energy Information Administration EMM Electricity Market Module EO Executive Order EOR Enhanced Oil Recovery EPA U.S. Environmental Protection Agency ER Enhanced Recovery FERC Federal Energy Regulatory Commission FGD Flue Gas Desulfurization FOAK First of a Kind FOM Fixed Operating and Maintenance FR Federal Register FRCC Florida Reliability Coordinating Council FUND Framework for Uncertainty, Negotiation, and Distribution Model v GDP Gross Domestic Product GHG Greenhouse Gas

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xxi GS Geologic Sequestration Gt Gigaton H 2 

### S Hydrogen Sulfide HAP Hazardous air pollutant HCl Hydrogen chloride HFC Hydrofluorocarbons HIA Health impact assessment IARC International Agency for Research on Cancer IAM Integrated Assessment Model ICR Information Collection Request IGCC Integrated Gasification Combined Cycle IOU Investor Owned Utility IPCC Intergovernmental Panel on Climate Change IPM Integrated Planning Model IRIS Integrated Risk Information System IRP Integrated Resource Plan ISA Integrated Science Assessment kWh Kilowatt-hour lbs Pounds LCOE Levelized Cost of Electricity LML Lowest measured level LNB Low NO X 

### Burners MATS Mercury and Air Toxics Standards MEA Monoethanolamine MECSA Manufacturing Energy Consumption Survey MeHg Methylmercury MGD Millions of Gallons per Day mg/L Milligrams per Liter mmBtu Million British Thermal Units MW Megawatt MWh Megawatt-hour N 2 

### O Nitrous Oxide NAAQS National Ambient Air Quality Standards NAICS North American Industry Classification System NaOH Sodium Hydroxide NATCARB National Carbon Sequestration Databa se and Geographic Information System NEEDS National Electric Energy Data System NEMS National Energy Modeling System NERC North American Electric Reliability Corporation NETL National Energy Technology Laboratory NGCC Natural Gas Combined Cycle NMMAPS National Morbidity, Mortality Air Pollution Study NOAK Next of a Kind or Nth of a Kind NO X 

### Nitrogen Oxide NRC National Research Council 

### NSPS New Source Performance Standard NSR New Source Review NTTAA National Technology Transfer and Advancement Act OC Organic carbon

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xxii OFA Overfire Air OMB Office of Management and Budget PAGE Policy Analysis of the Greenhouse Gas Effect Model PFC Perfluorocarbons PM

2.5 

### Fine Particulate Matter ppm Parts per Million PRA Paperwork Reduction Act PSD Prevention of Significant Deterioration RCSP Regional Carbon Sequestration Partnerships RADS Relative Airways Dysfunction Syndrome RES Renewable Electricity Standards RFA Regulatory Flexibility Act RGGI Regional Greenhouse Gas Initiative RIA Regulatory Impact Analysis RPS Renewable Portfolio Standards SAB-CASAC Science Advisory Board Clean Air Scientific Advisory Committee SAB-HES Science Advisory Board Health Effects Subcommittee of the Advisory Council on Clean Air Compliance SAB-EEAC Science Advisory Board Environmental Economics Advisory Committee SBA Small Business Administration SBREFA Small Business Regulatory Enforcement Fairness Act SCC Social Cost of Carbon SCPC Super Critical Pulverized Coal SCR Selective Catalytic Reduction SF

6 

### Sulfur Hexafluoride SIP State Implementation Plan 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

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ES-1 

## E XECUTIVE 

## S UMMARY



### This Regulatory Impact Analysis (RIA) discu sses potential benefits, costs, and economic impacts of the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units (herein referred to as “final emission guidelines” or the “Clean Power Plan Final Rule”).

ES.1 Background and Context
The emission of greenhouse gases (GHGs) threat ens Americans' health and welfare by leading to long-lasting changes in our climate. Ca rbon dioxide (CO 2 

### ) is the primary greenhouse gas pollutant, accounting for roughly three-quart ers of global greenhouse gas emissions in 2010 and 82 percent of U.S. greenhouse gas emissions in 2013. Fossil fuel-fired electric generating units (EGUs) are by far the largest emitters of GHGs, primarily in the form of CO 2 

### , among stationary sources in the U.S.

In this action, the Environmental Protection Agency (EPA) is establishing final emission guidelines for states and tribes to follow in developing plans to reduce greenhouse gas emissions from existing fossil fuel-fired EGUs. Specifically, the EPA is establishing: 1) CO 2 

### emission performance rates for two source categories of exis ting fossil fuel-fired EGUs, fossil fuel-fired electric utility steam generating units and stati onary combustion turbines , and 2) guidelines for the development, submittal and implementation of state plans that implement the CO 2 

### emission performance rates. This final ru le will continue progress already underway in the U.S. to reduce

CO 2 

### emissions from the utility power sector.

ES.2 Summary of Clean Power Plan Final Rule
Under CAA section 111(d), states must establis h, in their state plans, emission standards that reflect the degree of emission limitation achievable through the appl ication of the best system of emission reduction (BSER) that, taki ng into account the cost of achieving such reduction and any non-air quality health and envi ronmental impacts and energy requirements, the

Administrator determines has b een adequately demonstrated. 1 

### To determine the BSER for

1 Under CAA section 111(a)(1) and (d), the EPA is authorized to determine the BSER and to calculate the amount of emission reduction achievable through applying the BSER. The state is authorized to identify the standard or

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ES-2 reducing CO 2 

### emissions at affected EGUs, the EPA considered numerous measures that are already being implemented and can be implemente d 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 fall into three main cat egories (or “building bloc ks”) that have been amply demonstrated via their current wi despread use by utilities and states:

1. Reducing the carbon intensity of generation at individual affected coal-fired steam EGUs through heat rate improvements.

2. Substituting increased generation from lower-emitting existing natural gas combined cycle units for reduced generation from higher-emitting affected steam generating

units.

3. Substituting increased generation from zero-emitting generating units for reduced generation from affected fossil fuel-fired generating units.

Specifically, the EPA is establishing 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. The rates are intended to represent CO 2 

### emission rates achievable by 2030 after a 2022-2029 interim pe riod on an output-weighted-average basis collectively by all affected E GUs. The interim and final emission performance rates are presented in the following table:

Table ES-1. Emission Performance Rates (A djusted Output-Weighted-Average Pounds of CO 2 

### Per Net MWh from All Affected Fossil Fuel-Fired EGUs) Subcategory Interim Rate Final Rate
Fossil Fuel-Fired Electric Stea m Generating Units 1,548 1,305 Stationary Combustion Turbines 850 771



### Also, states, and tribes with one or more a ffected EGUs will be required to develop and implement plans to ensure that th eir affected EGUs, individually, in aggregate, or in combination with other measures undertaken by the state, achieve the equivalent of the final CO 2 

### emission

standards of performance that reflects th at amount of emission reduc tion. In addition, the stat e is required to include in its state plan the standards of performance and measures to implement and enforce those standards. The state must submit the plan to the EPA, and the EPA must approve the plan if the standards of performance and implementing and enforcing measures are satisfactory.

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ES-3 performance rates by 2030. The EPA derived statewide rate-based CO 2 

### emissions performance goals as a weighted average of the uniform rate goals with weights base d on baseline generation for the two types of units (fossil steam and stationary combustion turbine) in the state. This blended rate reflects the aggregat e emission rate a state may expect to achieve when its fleet is meeting its technology-specific emission performan ce rates. The Clean Power Plan Final Rule also establishes an 8-year interim compliance pe riod that begins in 2022 with a glide path for meeting interim CO 2 

### emission performance rates separa ted into three steps: 2022-2024, 2025- 2027, and 2028-2029. This results in interim and fi nal statewide goal values unique to each

state’s historical blend of fo ssil steam and stationary combustion turbine generation. Chapter 3 presents finalized equivalent state rate-based CO 2 

### emissions performance goals.

The EPA is providing equivale nt mass-based statewide CO 2 

### emission performance goals for each state, which are also 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 ar e then adjusted to a ccount for the ability

of affected EGUs to increase utilization unde r rate-based compliance by engaging in emission reduction strategies that are in excess of what is necessary to demonstrate compliance if affected EGUs choose to replace their own generation. Fo r more detail on the translation methodology, please refer to the U.S. EPA’s CO 2 

### Emission Performance Rate a nd Goal Computation Technical Support Document for Final Rule, wh ich is available in the docket. 2



### The final guidelines are structured so that st ates and affected EGUs are not required to use each and every one of the measures that the EPA determines constitute the BSER or to apply any one of those measures to the same extent that the EPA determines is achievable at a reasonable cost on a national basis. Instead, in developing its plan, each state will have the flexibility to select the measure or combination of measures it prefers in order to achieve the CO 2 

### emission performance rates for its affected EGUs ormeet the equivalent statewide rate- or massbased CO 2 

### goal. Thus, a state could choose to ach ieve 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 ar e not part of the EPA’s BSER determination.

2 U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units. CO 2 Emission Performance Rate and Goal Computation.

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ES-4 Expressing the states’ obligation in terms of a state-level goal th at can be met in aggregate, including through the adoption of CO 2 

### emission reduction measures th at are not identical to the components of the BSER or are not included in the BSER, rather th an exclusively through source-specific emission performance rates, is entirely permissible. This is precisely because the approach mirrors the foundation of the BSER approach itself, na mely the interconnected nature of the electricity system in which affected EGUs operate. We expect that affected EGUs in each

state will be able to meet the CO 2 

### emission performance rates b ecause the emission performance rates represent the application of the BSER sp ecific to the two categories of affected EGUs.

Given the flexibilities afforded states in complying with the emission guidelines, the benefits, cost and economic impacts reported in this RIA are not definitive estimates. Rather, the impact estimates are instead illustrative of approaches that states may take.

ES.3 Illustrative Plan A pproaches Examined in RIA
In the final emission guidelines, 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 in order to maximize the range of choices that states will have in developing their plans. Because of the range of choices available to states and the lack of a priori knowledge about the specific choices states will make in response to the final goals, this RIA presents two scenarios designed to achieve these goals, which we term the “rate-ba sed” illustrative plan approach and the “massbased” illustrative plan approach.

In this final rule, states may use trading or other multi-unit compliance approaches and technologies or strategies that ar e not explicitly mentioned in any of the three building blocks as part of their overall plans, as long as they achieve th e required emission reduc tions from affected fossil fuel-fired EGUs. In addition, the final rule provides additional options to allow individual EGUs to use creditable out-of-state reductions to achieve required CO 2 

### reductions, without the need for up-front interstate agreements.

The modelled implementation plan approaches reflect states and affected EGUs pursuing building block strategies such as heat rate improvements, shifting generation to less CO 2 

### – intensive generation, and increased deployment of renewable energy, which are more completely described in Chapter 3. However, the modelled stra tegies are not limited to the technologies and measures included in the BSER. For example, inve stment in demand-side energy efficiency is an

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

ES-5 important strategy modelled in the rate-based an d mass-based illustrative plan approached. In the rate-based approach, energy efficiency activitie s are modeled as being used by EGUs as a lowcost method of demonstrating comp liance with their rate-based em issions standards. In the massbased approach, energy efficiency activities are assumed to be adopted by states to lower demand, which in turn reduces the co st of achieving the mass limitations.

Alternative compliance approaches other than those modelled are also possible, which may have different levels and distributions of emissions and electricity generation as well ascosts. While IPM finds a least cost way to achieve the state goals implemented through the ratebased or mass-based emissions cons traints imposed in the illustrativ e plan approaches, individual states or multi-state regional groups may devel op alternate approaches to achieve their state goals.

It is very important to note that the differen ces between the analytical results for the ratebased and mass-based illustrative plan approaches presented in this RIA may not be indicative of likely differences between the approaches if implemented by states and affected EGUs in response to the final guidelines. Rather, the two sets of analyses are intend ed to illustrate two contrasting, stylized implementation approaches to accomplish the emission performance rates

finalized in the Clean Power Plan Final Rule. In other words, if one a pproach performs better than the other on a given metric during a given ti me period, this does not imply this will apply in

all instances in all tim e periods in all places.

To present a complete picture of costs and be nefits of the final emission guidelines, this RIA presents results for the analysis year s 2020, 2025, and 2030. While 2020 is before the first year of the interim compliance period (2022), th e EPA expects states and affected EGUs to perform voluntary activities that will facilitate compliance with interim and final goals. These pre-compliance period activities might include in vestments in renewable energy or demand-side energy efficiency projects, for example, that produce emissions reducti ons in the compliance period. Activities might also include preparat ory investments in transmission capacity or monitoring, reporting, and recordkeeping systems. As a result, there are likely to be benefits and costs in 2020, so these are reported in the illust rative analysis of this RIA. Meanwhile, cost and benefits are estimated in this RIA for 2025, which is intended to represent a central period of the interim compliance time-frame as states and tribes are on glide paths toward fully meeting the

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ES-6 final CO 2 

### emission performance goals. Lastly, the RIA presents costs and benefits for 2030, when the emission performance goals are fully achieved.

ES.4 Emissions Reductions

Table ES-2 shows the emission reductions a ssociated with the modelled rate-based illustrative plan approach.

Table ES-2. Climate and Air Pollutant Em ission Reductions for the Rate-Based Illustrative Plan Approach 1,2 CO 2 (million metric tons) SO 2 (thousand short tons) Annual NO X (thousand of short tons) PM 2.5 (thousands short tons) 2020 Rate-Based Approach Base Case 1,955 1,311 1,333 [X] Final Guidelines 1,892 1,297 1,282 [X] Emissions Change -63 -14 -50 [X] 2025 Rate-Based Approach Base Case 1,964 1,275 1,302 [X] Final Guidelines 1,754 1,097 1,138 [X] Emissions Change -211 -178 -165 [X] 2030 Rate-Based Approach Base Case 2,021 1,314 1,293 [X] Final Guidelines 1,644 996 1,011 [X] Emission Change -376 -318 -282 [X] Source: Integrated Planning Model, 2015.

1 CO 2 emission reductions are used to estimate the climate benefits of the guidelines. SO 2 , NO X , 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.



### In 2020, the EPA estimates that CO 2 

### emissions will be reduced by 63 million metric tons under the rate-based scenario compared to base case levels. In 2025, th e EPA estimates that CO 2 

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

base case levels. CO 2 

### emission reductions increase to 376 m illion metric tons annually in 2030 when compared to the base case emissions. Ta ble ES-2 also shows emission reductions for criteria air pollutants (in short tons). 3

3 Emissions estimates of PM2.5 require additional processing of IPM results. Estimates for PM2.5 reductions for 2025 and 2030 will be provided in the RIA released at th e signature of the final rule. However, the SO2 and NOX reductions account for the large majority of the anticipat ed health co-benefits, so th e temporary omission of PM2.5 reductions does not lead to a significant u nder-estimate of monetized health co-benefits.

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ES-7 Table ES-3 shows the emission reductions associated with the modeled mass-based illustrative plan approach.

Table ES-3. Climate and Air Pollutant Emission Reductions for the Mass-Based Illustrative Plan Appproach 1,2 CO 2 (million metric tons) SO 2 (thousand short tons) Annual NO X (thousand of short tons) PM 2.5 (thousands short tons) 2020 Mass-Based Approach Base Case 1,955 1,311 1,333 [X] Final Guidelines 1,881 1,257 1,272 [X] Emissions Change -74 -54 -60 [X] 2025 Mass-Based Approach Base Case 1,964 1,275 1,302 [X] Final Guidelines 1,724 1,090 1,100 [X] Emissions Change -240 -185 -203 [X] 2030 Mass-Based Approach Base Case 2,021 1,314 1,293 [X] Final Guidelines 1,646 1,034 1,015 [X] Emission Change -375 -280 -278 [X] Source: Integrated Planning Model, 2015.

1 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.



### In 2020, the EPA estimates that CO 2 

### emissions will be reduced by 74 million metric tons under the mass-based approach compared to base ca se levels. In 2025, the EPA estimates that CO 2 

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

to base case levels. CO 2 

### emission reductions increase to 375 million metric tons annually in 2030 when compared to the base case emissions. Ta ble ES-3 also shows emission reductions for criteria air pollutants (in short tons).

Table ES-4 presents CO 2 

### emission reductions relative to 2005.

Table ES-4. Projected CO 2 

### Emission Reductions, Relative to 2005

CO 2 Emissions (million metric tons) CO 2 Emissions: Change from 2005 (million metric tons) CO 2 Emissions Reductions: Percent Change from 2005

2005 2020 2025 2030 2020 2025 2030 Base Case 2,434 -479 -470 -413 -20% -19% -17% Rate-based - -542 -680 -790 -22% -28% -32% Mass-based - -553 -710 -788 -23% -29% -32% Source: Integrated Planning Model, 2015.

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ES-8 In 2020, the EPA estimates that CO 2 

### emissions will be reduced by 542 million metric tons (22 percent) under the rate-b ased approach compared to 2005 levels. In 2025, the EPA estimates that

CO 2 

### emissions will be reduced by 680 million metric tons (28 percent) under the rate-based approach compared to 2005 levels. Under the rate-based approach, CO 2 

### emission reductions increase to 790 million metric tons (32 per cent) in 2030 when compared to 2005 levels.

Under the mass-based approach in 2020, the EPA estimates that CO 2 

### emissions will be reduced by 553 million metric tons (23 percent) under the rate-based approach compared to 2005 levels. In 2025, the EPA estimates that CO 2 

### emissions will be reduced by 710 million metric tons (29 percent) under the mass-based approach co mpared to 2005 levels. Under the mass-based approach, CO 2 

### emission reductions increase to 788 m illion metric tons (32 percent) in 2030 when compared to 2005 levels.

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

ES.5 Costs
The compliance cost estimated for this final action are represented in this analysis as the change in electric power generati on costs between the base case and illustrative plan approach policy cases, including the cost of demand-side energy efficiency measures and costs associated with monitoring, reporting, and recordkeepi ng requirements (MR&R). In the rate-based approach, energy efficiency activ ities are modeled as being used by EGUs as a low-cost method of demonstrating compliance with their rate-b ased emissions standards. In the mass-based approach, energy efficiency activ ities are assumed to be adopted by states to lower demand,

which in turn reduces the cost of achieving the mass limitations. The level of energy efficiency measures is determined outside of IPM and is assumed to be the same in the two illustrative plan approaches. The compliance assumptions, and th erefore the projected “compliance costs” set forth in this analysis, are illustrative in nature and do not represent the full suite of compliance flexibilities states may ultimately pursue.

The annual incremental cost is the projected additional cost of complying with the final rule in the year analyzed and in cludes the net change in the annualized cost of capital investment in new generating sources and heat rate impr ovements at coal-fired steam generating units, the change in the ongoing costs of operating pollution controls, shifts between or amongst various fuels, demand-side energy efficiency measures, and other actions associated with compliance.

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ES-9 The total compliance cost estimate s presented here include the co sts associated with monitoring, reporting, and recordkeeping. 4 

### The costs for both illustrative plan approaches are reflected in Table ES-5 below and discussed more extensively in Chapter 3 of this RIA. All dollar estimates

are in 2011 dollars.

The EPA estimates the annual incremental comp liance cost for the rate-based approach for final emission guidelines to be $2.5 billi on in 2020, $1.0 billion in 2025 and $8.4 billion in 2030, including the costs associated with monitoring, reporting, and recordkeeping. 5 

### The EPA

estimates the annual incremental compliance cost for the mass-based approach for final emission guidelines to be $1.4 billion in 2020, $3.0 bill ion in 2025 and $5.1 billion in 2030, including the

costs associated with monitori ng, reporting, and recordkeeping.

Table ES-5. Compliance Costs for the Illustrative Rate-Based and Mass-Based Plan Approaches Incremental Cost from Base Case (billions of 2011$) Rate-based Approach Mass-based Approach 2020 $2.5 $1.4 2025 $1.0 $3.0 2030 $8.4 $5.1 Source: Integrated Planning Model, 2015, with post-processing to account for exogenous demand-side management energy efficiency costs and monitoring, reporting, and recordkeeping cost s. See Chapter 3 of this RIA for a more complete explanation.



### The costs reported in Table ES-5 represent the estimated incremental electric utility generating costs changes from the base case plus the estimates of demand-side energy efficiency program costs (which are paid by electric utiliti es), demand-side energy efficiency participant costs (which are paid by electric utility consumers), and MR&R costs. For example, in 2030, under the rate-based approach, the incremental el ectric utility generati ng costs decline by about $18.0 billion from the base case. MR&R requi rements in 2030 are estimated at $16.0 million, and demand-side energy efficiency costs in 2030 are estimated to be $26. 3 billion, split equally

4 These costs are estimated outside of the IPM modelling framework as IPM only models the contiguous U.S. and does not incorporate monitoring, reporting, and recordkeep ing requirements specific to the Clean Power Plan Final Rules.

5 The MR&R costs estimates are $67 million in 2020, $16 million in 2025 and $16 million in 2030 and are assumed to be the same for both rate-based and mass-based illustrative plan approaches. Note the MR&R costs in 2020 are related to facilities setting up net energy output monitoring and upgrading data acquisition systems.

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ES-10 between program and participants using a 3 percen t discount rate (see Chap ter 3 of this RIA for more details on these estimates). These cost es timates sum to the $8.4 shown in Table ES-3 and represent the total costs of the rate-based illust rative plan approach in 2030. The same approach applies in each year of analysis for the ra te-based and the mass-based illustrative plan approaches.

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

The compliance costs reported in Table ES-5 are not social costs. These costs represent the estimated expenditures incurred by EGUs and st ates to comply with the BSER goals for the Clean Power Plan Final Rule. These compliance co st estimates are compared to estimates of

social benefits to derive net be nefits of the final emission guidelin es, which are presented later in this Executive Summary. For a more extensive discussion of social co sts and benefits, see Chapter 3 and Chapter 4, respectively, of this RIA.

ES.6 Monetized Climate Benefi ts 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 marginal climate impacts presented

in the Technical Support Document: Technical Updat e of the Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866 (May 2013, Revised July 2015) , henceforth denoted as the current SC-CO 2 

### TSD. 6 

### Also, the range of combined benefits reflects different concentration-response functions for th e air quality health co -benefits, but it does not capture the full range of uncertainty inherent in th e health co-benefits estimates. Furthermore, we were unable to quantify or monetize all of the cl imate benefits and health and environmental cobenefits associated with the final emissi on guidelines, including reducing exposure to SO 2 

### , NO X 

### , and hazardous air pollutants (e.g., mercury), as well as ecosystem effects and visibility

6 Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, De partment of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Domestic Policy Council, Environmental Protection Agency, National Economic Council, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised July 2015). Available at: <https://www.wh itehouse.gov/sites/default/files/om b/inforeg/scc-tsd-final-july-2 015.pdf> Accesse d 7/11/2015.

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ES-11 improvement. The omission of these endpoints from the monetized results should not imply that the impacts are small or unimportant. Table ES-6 provides the list of the quantified and unquantified health and environmenta l benefits in th is analysis.

Table ES-6. Quantified and Unquantified Benefits

Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information

Improved Environment

Reduced climate effects Global climate impacts from CO 2 — 1  SC-CO 2 TSD Climate impacts from ozone and black carbon (directly emitted PM) — — Ozone ISA, PM ISA 2 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-activ ity days (age 18-65)   PM ISA Chronic Bronchitis (age >26) — — PM ISA 2 Emergency room visits fo r cardiovascular effects (all ages) — — PM ISA 2 Strokes and cerebrovascular disease (age 50-79) — — PM ISA 2 Other cardiovascular effects (e .g., other ages) — — PM ISA 3Other respiratory effects (e.g., pulmonary function, nonasthma ER visits, non-bronchit is chronic diseases, other ages and populations) — — PM ISA 3

Reproductive and developmenta l 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 ISA 2

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-activ ity days (age 18–65)   Ozone ISA School absence days (age 5–17)   Ozone ISA Decreased outdoor worker productivity (age 18–65) — — Ozone ISA 2 Other respiratory effects (e.g., prem ature aging of lungs) — — Ozone ISA 3 Cardiovascular and nervous sy stem effects — — Ozone ISA 3 Reproductive and developmen tal effects — — Ozone ISA 3,4

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ES-12 Table ES-6. Continued

Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages) — — NO 2 ISA 2 Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA 2 Respiratory emergency department visits (all ages) — — NO 2 ISA 2 Asthma exacerbation (asthmatics age 4–18) — — NO 2 ISA 2 Acute respiratory symptoms (age 7–14) — — NO 2 ISA 2 Premature mortality — — NO 2 ISA 2,3,4 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 ISA 2 Asthma emergency department visits (all ages) — — SO 2 ISA 2 Asthma exacerbation (asthmatics age 4–12) — — SO 2 ISA 2 Acute respiratory symptoms (age 7–14) — — SO 2 ISA 2 Premature mortality — — SO 2 ISA 2,3,4 Other respiratory effects (e.g ., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA 2,3

Reduced incidence of morbidity from exposure to methylmercury Neurologic effects—IQ loss — — IRIS; NRC, 2000 2 Other neurologic effects (e.g ., developmen tal 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 Improved Environment (co-benefits) Reduced visibility impairment Visibility in Class 1 areas — — PM ISA 2 Visibility in residential areas — — PM ISA 2 Reduced effects on materials Household soiling — — PM ISA 2,3 Materials damage (e.g., corrosi on, increased wear) — — PM ISA 3 Reduced PM deposition (metals and organics) Effects on Individual organism s and ecosystems — — PM ISA 3

Reduced vegetation and ecosystem effects from exposure to

ozone Visible foliar injury on vegetation — — Ozone ISA 2 Reduced vegetation growth and reproduction — — Ozone ISA 2 Yield and quality of co mmercial forest products and crops — — Ozone ISA 2 Damage to urban ornament al plants — — Ozone ISA 3 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 2 Recreational demand associated with forest aesthetics — — Ozone ISA 3 Other non-use effects Ozone ISA 3 Ecosystem functions (e.g., wa ter 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 ISA 2 Tree mortality and decline — — NO x SO x ISA 3 Commercial fishing and fo restry 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., bi ogeochemical cycles) — — NO x SO x ISA 3

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ES-13 Table ES-6. Continued

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., bi ogeochemical 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, ar e used in this RIA to estim ate 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 qualitatively because we do not have sufficient confiden ce in available data or methods.

4 We assess these co-benefits qualitatively because current ev idence is only sugg estive of causality or there are other significant concerns over the strength of the association.



### ES.6.1 Estimating Global Climate Benefits
We estimate the global social benefits of 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 year. It includes a wide range of anticipated climate impacts, such as net changes in agricultu ral productivity and human health, property damage from increased flood ris k, 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 years, using the best science available, and with input from the public. The EPA and other federal agencies have considered the extensive public comments on ways to improve SC-CO 2 

### estimation received

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ES-14 via the notice and comment period that was part of numerous rulemakings. In addition, OMB’s Office of Information and Regulatory Affairs rece ntly issued a response to the public comments it sought through a separate comment period on the approach used to develop the SC-CO 2 

### estimates. 7



### An interagency working group (IWG) that incl uded the EPA and other executive branch entities used three integrated assess ment models (IAMs) to develop SC-CO 2 

### estimates and recommended four global values for us e in regulatory analyses. 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 wo rld, even when they are released in the United States, and the world’s economies are now highly interconnected. Therefore, the SC-CO 2 

### estimates incorporate the worldwide damages cau sed by carbon dioxide emissions in order to reflect the global nature of the problem, and we expect other gove rnments to consider the global consequences of their greenhouse gas emissions wh en setting their own domestic policies. See RIA Chapter 4 for more discussion.

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

new versions of each IAM. The SC-CO 2 

### values was estimated using three integrated assessment models (DICE, FUND, and PAGE) 8 

### , which the IWG harmonized across three key inputs: the probability distribution for equilibrium climat e sensitivity; five scenarios for economic, population, and emissions growth; and three constant 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 9 

### presents and discusses the updated estimates. 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$), and each estim ate increases over time. These SC-CO 2 

### estimates are

associated with different discount rates. The first three estima tes are the model average at 5 percent discount rate, 3 pe rcent, and 2.5 percent, respectively, and the fourth estimate is the 95 th 

### percentile at 3 percent.

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

8 The full models names are as follows: Dynamic Integrat ed Climate and Economy (DIC E); Climate Framework for Uncertainty, Negotiation, and Distribution (FUND); and Po licy Analysis of the Greenh ouse Gas Effect (PAGE).

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

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ES-15 The 2010 SC-CO 2 

### TSD noted a number of limitations to the SC-CO 2 

### analysis, including the incomplete way in which the IAMs capture cat astrophic and non-catastr ophic impacts, their incomplete treatment of adaptation and technologi cal change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Currently integrated assessment models do not assign value to all of the important physical, ecological, and economic impacts of climate change recognized in the c limate change literature because of a lack of precise information on the nature of damages a nd because the science incorporated into these models understandably lags behind the most recen t research. In particular, the IPCC Fourth Assessment Report concluded that “It is very likely that [SC-CO 2 

### estimates] underestimate the damage costs because they cannot include many non-quantifiable impacts.” Nonetheless, these

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

the social benefits of CO 2 

### emission reductions to inform the benefit-cost analysis.

In addition, after careful eval uation of the full range of comments submitted to OMB’s Office of Information and Regulatory Affairs, th e IWG continues to recommend the use of these

SC-CO 2 

### estimates in regulatory impact analysis. W ith the release of the response to comments, the IWG announced plans to obtain expert independent advice from the National Academies of Sciences, Engineering, and Medicine (A cademies) to ensure that the SC-CO 2 

### estimates continue

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

### The Academies process will be informed by the public comments received and focus on the technical merits and challenges of potential approaches to improving the SC-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 

### , NO X 

### , and directly emitted particles), which in tu rn would lower ambient concentrations of PM 2.5 

### and ozone. This co-benefits anal ysis quantifies the monetized benefits associated with the reduced exposure to these two pollutants. 11 

### Unlike the global SC-CO 2 

### estimates, the air quality health co-benefits are only estimated for the contiguous U.S. The estimates of monetized PM 2.5

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

11 We did not estimate the co-benefits associat ed with reducing direct exposure to SO 2 and NO X .

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ES-16 co-benefits include avoided premature deaths (der ived from effect coefficients in two cohort studies [Krewski et al. 2009 and Lepeule et al. 2012] for adults and one for infants [Woodruff et al. 1997]), as well as avoided morbidity effects for ten non-fatal endpoints ranging in severity from lower respiratory symptoms to heart att acks (U.S. EPA, 2012). The estimates of monetized ozone co-benefits include avoided premature d eaths (derived from the range of effect coefficients represented by two shor t-term epidemiology studies [Bell et al. (2004) and Levy et al. (2005)]), as well as avoided morbidity effects for five non-fatal endpoints ranging in severity from school absence days to hospital admissions (U.S. EPA, 2008, 2011).

We use a “benefit-per-ton” approach to estimate the PM 2.5 

### and ozone co-benefits in this RIA. Benefit-per-ton approaches apply an average benefit per ton derived from modeling of benefits of specific air quality scenarios to estim ates of emissions reductions for scenarios where no air quality modeling is available. The benefit- per-ton approach we use in this RIA relies on estimates of human health responses to expos ure to PM and ozone obtained from the peerreviewed scientific literature. These estimates are used in conjunction with population data, baseline health information, air quality data and economic valuation information to conduct health impact and economic benefits assessments.

Specifically, in this analysis, we multip lied the benefit-per-ton estimates by the corresponding emission reductions that were ge nerated from air quali ty modeling of the proposed Clean Power Plan. Similar to the co-ben efits analysis conducted for the RIA for this rule at proposal, we generated regional benefit-per-ton estimate s by aggregating the impacts in

BenMAP 12 

### to the region (i.e., East, West, and Californ ia) rather than aggr egating to the nation.

To calculate the co-benefits for the final emissi on guidelines, we then multiplied the regional benefit-per-ton estimates for the EGU sector by the corresponding emission reductions. All benefit-per-ton estimates reflect the geographic distribution of the modeled emissions, which may not exactly match the emission reductions in this rulemaking, and thus they may not reflect the local variability in population density, me teorology, exposure, baseline health incidence rates, or other local factors for any specific location.

12 BenMAP is a computer program developed by the EPA that calculates the number and economic value of airpollution-related deaths and illnesses. The software incorporates a database that includes many of the concentrationresponse relationships, population files, and health an d economic data needed to quantify these impacts.

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ES-17 Our estimate of the monetized co-benefits is based on the EPA’s interpretation of the best available scientific literature (U.S. EPA, 2009) and methods and supported by the EPA’s Science Advisory Board and the NAS (NRC, 2002). Below are key assumptions underlying the estimates

for PM 2.5 

### -related premature mortality, which account s for 98 percent of the monetized PM 2.5 

### health co-benefits:

1. We assume that all fine particles, re gardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption,

because PM 2.5 

### varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow diffe rentiation of effect estimates by particle type. The PM ISA 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 analysis. Thus, the estimates include health co-benefits from reducing fine particles in areas wi th varied concentrations of PM 2.5 

### , including both

areas that do not meet the National Ambien t Air Quality Standard for fine particles and those areas that are in attainment, dow n to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the ch ange in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premat ure mortality related to PM 2.5 

### exposures occur in a distributed fashion over the 20 years follo wing exposure based on the advice of the SAB-HES (U.S. EPA-SAB, 2004c), which a ffects the valuation of mortality cobenefits at different discount rates.

Every benefits analysis examining the potenti al effects of a change in environmental protection requirements is limited, to some exte nt, by data gaps, model capabilities (such as geographic coverage) and uncertain ties in the underlying scientific and economic studies used to configure the benefit and cost m odels. In addition, given the flex ibilities afforded states in complying with the emission guideli nes, the co-benefits estimated presented in this RIA are not definitive estimates, but are instead illustrative of approaches that states may take. Despite these

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ES-18 uncertainties, we believe this an alysis provides a reasona ble indication of the expected health cobenefits of the air quality emission reductions for the final emission guidelines under a set of reasonable assumptions. This analysis does not include the type of detailed uncertainty

assessment found in the 2012 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, us ing a benefit-per-ton a pproach adds another important source of uncertainty to the benefits estimates.

ES 6.3 Combined Benefits Estimates
The EPA has evaluated the range of potenti al impacts by combin ing all four SC-CO 2 

### values with health co-benefits values at the 3 percent and 7 percent discount rates. Different discount rates are applied to SC-CO 2 

### than to the health co-benefit estimates; because CO 2 

### emissions are long-lived and subsequent dama ges occur over many years. Moreover, several discount rates are applied to SC-CO 2 

### because the literature shows that the estimate of SC-CO 2 

### is sensitive to assumptions about discount rate a nd because no consensus exists on the appropriate rate to use in an intergenerat ional context. The U.S. governme nt centered its attention on the average SC-CO 2 

### at a 3 percent discount rate but empha sized the importance of considering all

four SC-CO 2 

### estimates. Table ES-7 (rate-based illu strative plan approach) and Table ES-8(mass-based illustrative plan approach) provide the combined climate benefits and health cobenefits for the Clean Power Plan Final Rule estimated for 2020, 2025, and 2030 for each

discount rate combination. All dol lar estimates are in 2011 dollars.

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

SC-CO 2 Discount Rate and Statistic** Climate Benefits

Only Climate Benefits plus Health Co-benefits (Discount Rate Applied to Health Co-benefits) 3% 7% In 2020 63 million metric tons CO 2 5% $0.80 $1.5 to $2.6 $1.4 to $2.5 3% $2.8 $3.5 to $4.6 $3.5 to $4.5 2.5% $4.1 $4.9 to $6.0 $4.8 to $5.9 3% (95 th percentile) $8.2 $8.9 to $10 $8.9 to $9.9 In 2025 211 million 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 In 2030 376 million 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 *Benefit 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 cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do no t include reduced health effects fr om 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.

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

SC-CO 2 Discount Rate and Statistic** Climate Benefits

Only Climate Benefits plus Health Co-benefits (Discount Rate Applied to Health Co-benefits) 3% 7% In 2020 74 million metric tons CO 2 5% $0.94 $2.9 to $5.7 $2.8 to $5.3 3% $3.3 $5.3 to $8.1 $5.1 to $7.7 2.5% $4.9 $6.9 to $9.7 $6.7 to $9.3 3% (95 th percentile) $9.7 $12 to $14 $11 to $14 In 2025 240 million metric tons CO 2

5% $3.6 $11 to $21 $10 to $19 3% $12 $19 to $29 $18 to $27 2.5% $17 $24 to $35 $24 to $33 3% (95 th percentile) $35 $42 to $52 $42 to $51 In 2030 375 million metric tons CO 2 5% $6.4 $18 to $34 $17 to $32 3% $20 $32 to $48 $31 to $46 2.5% $29 $41 to $57 $40 to $55 3% (95 th percentile) $60 $72 to $89 $71 to $86 *All benefit estimates are rounded to two significant figu res. 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. Th e 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)). The monetized health co-ben efits do not includ e reduced health effects from direct exposure to NO X , SO 2 , and HAP; ecosystem effects; or vi sibility impairment. See Chapter 4 for more information about these estimates and for more information regarding the uncertainty in these estimates. **Unless otherwise specified, it is the model average.



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

ES.7 Net Benefits
Table ES-9 and ES-10 provide the estimates of the climate benefits, health co-benefits, compliance costs and net benefits of the final emission guidelines for rate-based and mass-based approaches, respectively. Compar ing the rate-based and mass-based illustrative plan approaches

indicates...

There are additional important benefits that the EPA could not monetize. Due to current data and modeling limitations, our estimates of the benefits from reducing CO2 emissions do not include important impacts like ocean acidificati on or potential tipping points in natural or managed ecosystems. Unquantified benefits al so 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), as well as ecosystem effects and visibility

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ES-21 impairment. Upon considering these limitations and uncertainties, it re mains clear that the

benefits of this final rule are subs tantial and far outweigh the costs.

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ES-22 Table ES-9. Monetized Benefits, Compliance Costs, and Net Benefits Under the Ratebased Illustrative Plan Approach (billions of 2011$) a Rate-Based Approach 2020 2025 2030 Climate Benefits b

5% discount rate $0.80 $3.1 $6.4 3% discount rate $2.8 $10 $20 2.5% discount rate $4.1 $15 $29 95th percentile at 3% discount rate $8.2 $31 $61

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $0.7 to $1.8 $0.6 to $1.7 $7.4 to $18 $6.7 to $16 $14 to $34 $13 to $31 Compliance Costs d $2.5 $1.0 $8.4 Net Benefits e $1.0 to $2.1 $1.0 to $2.0 $17 to $27 $16 to $25 $26 to $45 $25 to $43

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient 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 impairment

a All are rounded to two significant figures, so figures may not sum. b The climate benefit estimate in this summ ary 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 percent discount rate, however we emphasize the importance 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 studies. The re duction in premature fatalities each year accounts for over 98 percent of total monetized 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 premat ure mortality because the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by pa rticle type. Estimates in the table are presented for three analytical years with air quality co-b enefits calculated using two discount ra tes. The estimates of co-benefits are annual estimates in each of the analy tical years, reflecting discounting of mortality benefits over the cessation lag between changes in PM 2.5 concentrations and changes in risks of pr emature 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 pres ent value of the benefits of the rule over the full compliance period. d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for the final emission guidelines and a discount rate of approximately 5 percent. This estimate also includes monitoring, recordkeeping, and reporting costs and demand-side energy efficiency progra m 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 an d health estimates based on additional discount rates.

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ES-23 Table ES-10. Monetized Benefits, Complian ce Costs, and Net Benefits under the Massbased Illustrative Plan Approach (billions of 2011$) a Mass-Based Approach 2020 2025 2030 Climate Benefits b

5% discount rate $0.9 $3.6 $6.4 3% discount rate $3.3 $12 $20 2.5% discount rate $4.9 $17 $29 95th percentile at 3% discount rate $9.6 $35 $60

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $2.0 to $4.8 $1.8 to $4.4 $7 to $17 $7 to $16 $12 to $28 $11 to $26 Compliance Costs d $1.4 $3.0 $5.1 Net Benefits e $3.9 to $6.7 $3.7 to $6.3 $16 to $26 $15 to $24 $26 to $43 $25 to $40

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient 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 summ ary 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 percent discount rate, however we emphasize the importance 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 studies. The re duction in premature fatalities each year accounts for over 98 percent of total monetized 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 premat ure mortality because the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by pa rticle type. Estimates in the table are presented for three analytical years with air quality co-b enefits calculated using two discount ra tes. The estimates of co-benefits are annual estimates in each of the analy tical years, reflecting discounting of mortality benefits over the cessation lag between changes in PM2.5 concentrations and changes in risks of premature death (see RIA Chapter 4 for more details), and discounting of morbidity benefits due to the multiple years of costs associated with some illnesses. The estimates are not the pres ent value of the benefits of the rule over the full compliance period. d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for the final emission guidelines and a discount rate of approximately 5 percent. This estimate also includes monitoring, recordkeeping, and reporting costs and demand-side energy efficiency progra m 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 an d health estimates based on additional discount rates.

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ES-24 ES.8 Economic Impacts
The final emission guidelines have importa nt energy market implications. Table ES-11 presents a variety of important energy mark et impacts for 2020, 2025, and 2030 for both the ratebased and mass-based illustrative plan approaches.

Table ES-11. Summary Table of Important En ergy Market Impacts (Percent Change from Base Case)

Rate-Based Mass-Based 2020 2025 2030 2020 2025 2030 Retail electricity prices 3% 1% 1% 3% 2% 0% Price of coal at minemouth -1% -5% -4% -1% -5% -3% Coal production for power sector use -5% -14% -25% -7% -17% -24% Price of natural gas delivered to power sector 5% -8% 2% 4% -3% -2% Natural gas use for electricity generation 3% -1% -1% 5% 0% -4% Non-hydro renewable energy capacity 1% -1% 13% -2% -3% 11%



### Energy market impacts from the guidelines are disc ussed more extensively in Chapter 3 of this

RIA.

Additionally, changes in supply or demand fo r electricity, natural gas, and coal can impact markets for goods and serv ices produced by sectors that us e these energy inputs in the production process or that supply those sectors. Changes in cost of production may result in changes in price and/or quantity produced by th ese sectors and these market changes may affect the profitability of firms and the economic welfar e of their consumers. The EPA recognizes that these final emission guidelines provide flexibil ity, and states implementing the guidelines may choose to mitigate impacts to some markets outside the EGU sector. Similarly, demand for new generation or energy efficiency, for example, can result in changes in production and profitability for firms that s upply those goods and services.

ES.9 Employment Impacts
Executive Order 13563 directs federal agencies to consider the effect of regulations on job creation and employment. According to the Executive Order, “our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, competitiveness, and job creation. It mu st be based on the best available science” (Executive Order 13563, 2011). Alth ough standard benefit-cost an alyses have not typically

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ES-25 included a separate analysis of regulation-induc ed employment impacts, we typically conduct employment analyses. During the current econom ic recovery, employment impacts are of particular concern and questions may ar ise 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 as sociated employment impacts is difficult. The EPA’s illustrative employment analysis includes an estimate of project ed employment impacts associated with these guidelines for the utility power sector, coal and natural gas production, and demand-side energy efficiency activities. These pr ojections are derived, in part, from the detailed model of the utility power sector used for this regulatory analysis, and U.S government data on employment and labor productivity.

In the electricity, coal, and natural gas sect ors, the EPA estimates that these guidelines

could result in a net decrease of approximately 25,200 job-years in 2025 for the final guidelines under the rate-based illustrative plan appr oach and approximately 26,000 job-years in 2025 under the mass-based approach. For 2030 the estima tes of the net decrease in job-years is 31,300 under the rate-based plan, and 33,700 under the mass-ba sed plan. The Agency is also offering an illustrative calculation of potential employment effects due to demand-side energy efficiency programs. Employment impacts from demand-si de energy efficiency programs in 2030 could range from approximately 52,000 to 83,000 jobs unde r the final guidelines. More detail about these analyses can be found in Chapter 6 of this RIA.

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ES-26 ES.10 References

Bell, M.L., A. McDermott, S.L. Ze ger, J.M. Sarnet, and F. Dominici

. 2004. “Ozone and ShortTerm Mortality in 95 U.S. Urban Communities, 1987-2000.” Journal of the American Medical Association. 292(19):2372-8.Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by the Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Co mmerce, Department of Energy, Department of Transportation, Environmental Protection Agen cy, National Economic Council, Office of Energy and Climate Change, Office of Manage ment and Budget, Office of Science and Technology Policy, and Department of Tr easury (February 2010). Available at: <http://www.whitehouse.gov/sites/default/files/o mb/inforeg/for-agencies/Social-Cost-ofCarbon-for-RIA.pdf>. Docket ID EPA-HQ-OAR-2013-0602, Technical Suppor t Document: Technical Update of the Social Cost of Carbon fo r Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with Participation by Council of Economic Advisers, Council on Environmenta l Quality, Department of Agriculture, Department of Commerce, Department of En ergy, Department of Transportation, Domestic Policy Council, Environmental Protection Ag ency, National Economic Council, Office of Management and Budget, Office of Scien ce and Technology Policy, and Department of Treasury (May 2013, Revised July 2015). Also available at: < https://www.whitehouse.gov/sites/ default/files/omb/inforeg/ scc-tsd-final-july-2015.pdf>. Accessed July 15, 2015. Fann, N., K.R. Baker, and C.M. Fulcher. 2012. “Characterizing the PM 2.5 

### -Related Health Benefits of Emission Reductions for 17 Industr ial, Area and Mobile Emission Sectors Across the U.S.” Environment International. 49:41–151. Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, et al. 2009. Extended Follow-Up and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, H ealth Effects Institute, Boston, MA.

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ES-27 Interagency Working Group on Social Cost of Carbon, with partic ipation by Council of Economic Advisers, Council on Environmenta l Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Ec onomic Council, Office of Management and Budget, Office of Science and Technol ogy Policy, and Department of Treasury. Response to to Comments: Social Cost of Carbon for Regul atory Impact Analysis Under Executive Order 12866. July 2015. Available at: <https://www.whitehouse.gov/sites/default/files /omb/inforeg/scc-response-to-commentsfinal-july-2015.pdf.> Accessed July 15, 2015. Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IPCC . Available at: <http://www.ipcc.ch/publications_and_data/public ations_ipcc_fourth_assessment_report_synt hesis_report.htm>. Accessed June 6, 2015. Lepeule, J., F. Laden, D. Dockery, and J. Schw artz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow-Up of th e Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives. 120(7):965-70. Levy, J.I., S.M. Chemerynski, and J.A. Sarnat . 2005. “Ozone Exposure and Mortality: An Empiric Bayes Metaregression Analysis.” Epidemiology. 16(4):458-68. National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee on the Toxicological Effects of Methylmercury .” Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . National Academies Pr ess. Washington, DC. U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for Sulfur Oxides—Health Criteria (Final Report) . National Center for Environmental Assessment – RTP Division, Research Triangl e Park, NC. September. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.c fm?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 Qual ity Planning and Standards Health and Environmental Impacts Division, Air Bene fit and Cost Group Research Triangle Park, NC. March. Available at: < http:// www.epa.gov/ttnecas1/regdata/RIAs/6- ozoneriachapter6.pdf>. Accessed June 4, 2015.

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ES-28 U.S. EPA. 2008c. Integrated Science Assessment fo r Oxides of Nitrogen: Health Criteria (Final Report). Research Triangle Park, NC: National Center for Environmental Assessment. July. Available at < http://cf pub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. July. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.c fm?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.c fm?deid=216546>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2010d. Section 3: Re

‐ analysis of the Benefits of Attaining Alternative Ozone Standar ds to Incorporate Current Methods . Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/s3- supplemental_analysis-updated_benefits11- 5.09.pdf >. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the Final Revisions to the National Ambient Ai r Quality Standards for Particulate Matter. EPA452/R-12-003. Office of Air Qual ity Planning and Standards, Health and Environmental Impacts Division, Research Triangle Pa rk, 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 Photochemica l Oxidants (Final Report). EPA/600/R-10/076F. National Center for Environmental Assessment – RTP Di vision, Research Triangl e Park. Available at: <http://cfpub.epa.gov/ncea/isa/reco rdisplay.cfm?deid=247492#Download>. Accessed June 4, 2015. U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for Existing Stationary Sour ces: Electric Utility Generating Units. CO 2 

### Emission Performance Rate and Goal Computation. Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The relationship between selected causes of postneonatal infant mortality and partic ulate air pollution in the United States.” Environmental Health Perspectives. 105(6): 608-612.

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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 may implement to comply with the Final Carbon Pollution Emission Guidelines for Existing Stationary S ources: Electric Utility Generating Units (herein referred to as “final emission guidelines” or th e “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 en acted as part of the 1970 Clean Air Act Amendments, establishes mechanisms for controlli ng emissions of air pollu tants from stationary sources. This provision requires the EPA to promulgate a list of cat egories of stationary sources that the Administrator, in his or her judgment, finds “causes, or contributes significantly to, air pollution which may reasonably be anticipated to endanger public health or welfare.” 13 

### The EPA has listed more than 60 stationary source categories under this provision. 14 

### Once the EPA lists a source category, the EPA must, under CAA sec tion 111(b)(1)(B), esta blish “standards of performance” for emissions of air pollutants from new sources in the source categories. 15 

### These standards are known as new source performan ce standards (NSPS), and they are national requirements that apply directly to the sources subj ect to them.

When the EPA establishes NSPS for new sour ces in a particular source category, the EPA is also required, under CAA s ection 111(d)(1), to prescribe re gulations for states to submit plans regulating existing s ources in that source category for any air pollutant that, in general, is not regulated under the CAA section 109 require ments for the NAAQS or regulated under the CAA section 112 requirements for hazardous ai r pollutants (HAP). CAA section 111(d)’s mechanism for regulating existing sources differs from the one th at CAA section 111(b) provides

13 CAA §111(b)(1)(A).

14 See 40 CFR 60 subparts Cb – OOOO.

15 CAA §111(b)(1)(B), 111(a)(1).

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1-2 for new sources because CAA section 111(d) contem plates states submitting plans that establish “standards of performance” for the affected s ources and that contain other measures to implement and enforce those standards.

“Standards of performance” are defined unde r CAA section 111(a)(1) as standards for

emissions that reflect the emission limitation achievable from the “bes t system of emission reduction,” considering costs and other factors, that “the Admi nistrator determines has been adequately demonstrated.” CAA section 111(d)(1 ) grants states the authority, in applying a standard of performance to a particular source, to take into account the source’s remaining useful

life or other factors.

Under CAA section 111(d), a state must submit its plan to the EPA for approval, and the EPA must approve the state pl an if it is “satisfactory.” 16 

### If a state does not submit a plan, or if the EPA does not approve a state’s plan, then the EPA must establish a plan for that state. 17 

### Once a state receives the EPA’s approval of its plan, the provisions in the pl an become federally enforceable against the entity responsible fo r noncompliance, in the same manner as the provisions of an approved State Implem entation 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 burning of fossil fuels have ushered in a new epoch wher e human activities will largely determine the

evolution of Earth’s climate. Because CO 2 

### in the atmosphere is long lived, it can effectively lock Earth and future generations into a range of impacts, some of which could become very severe. Therefore, emission reduction choices made toda y matter in determining impacts experienced not just over the next few decades, but in the coming centuries and millennia.” 18



### In 2009, based on a large body of robust and compelling scientific evidence, the EPA Administrator issued the Endangerm ent Finding under CAA section 202(a)(1). 19 

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

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

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

18 National Research Council, Clim ate Stabilization Targets, p.3.
19 “Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“Endangerment Finding”).

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1-3 GHGs in the atmosphere—already at levels unpr ecedented in human history—may reasonably be anticipated to endanger public health and welf are of current and future generations in the

United States.

Since the administrative record concerning the Endangerment Finding closed following the EPA’s 2010 Reconsideration Denial, the climat e 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. A dditionally, a number of major scientific assessments have been released that improve unde rstanding of the climate system and strengthen the case that GHGs endanger public health and we lfare both for current and future generations. These assessments are from the Intergovernment al Panel on Climate Change (IPCC), the U.S. Global Change Research Program (USGCRP), and the National Research Council (NRC). These

and other assessments are discussed in more deta il in the preamble and in Chapter 4 of this Regulatory Impact Assessment (RIA).

1.2.3 Market Failure
Many regulations are promulgated to correct ma rket failures, which lead to a suboptimal

allocation of resources within the free market . Air quality and polluti on control regulations address “negative externalities” whereby the market does not internalize the full opportunity cost of production borne by society as public goods such as air quality are unpriced.

GHG emissions impose costs on society, such as negative health and welfare impacts,

that are not reflected in the market price of the goods produc ed through the polluting process. For this regulatory action the good produced is electricity. These soci al costs associated with the health and welfare impacts are referred to as negative externalities. If a fossil fuel-fired electricity producer pollute s the atmosphere when it generates el ectricity, this cost will be borne not by the polluting firm but by society as a whole. The equilibrium market price of electricity may fail to incorporate the full opportunity cost to society of generating electricity. All else equal, given this externality, th e composition of EGUs used to generate electricity in a free market will not be socially optimal, and the quant ity of electricity generated may not be at the socially optimal level. More electricity may be produced from fossil fuel-fired EGUs than would occur if they had to account for the full opportu nity cost of production including the negative externality. Consequently, absent a regulation on emissions, th e composition of the fleet of

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1-4 EGUs used to generate electricity may not be so cially optimal, and the ma rginal social cost of the last unit of electricity produ ced will exceed its marginal social benefit. This regulation will address this market failure by beginning to in ternalize the negative externality by reducing CO 2 

### emissions from existing EGUs whic h increases social welfare.

1.3 Summary of Regulatory Analysis

In accordance with Executive Order 12866, Executive Order 13563, OMB Circular A-4, and the EPA’s “Guidelines for Preparing Economic Analyses,” the EPA prepared this RIA for this “significant regulatory acti on.” This action is an economical ly significant regulatory action because it is expected to have an annual e ffect on the economy of $100 million or more or adversely affect in a material way the ec onomy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or state, local, or tribal governments

or communities.

This RIA addresses the potential costs, em ission reductions, and benefits of the final emission guidelines that are the focus of this action. Additionally, this RIA includes information about potential impacts on electr icity markets, employment, and markets outside the electricity

sector. In evaluating the impacts of the final guideli nes, we analyzed a number of uncertainties. For example, the analysis includes an evaluation of two illustrative plan approaches that states and affected EGUs may take to accomplish stat e emission performance goals, a rate-based and a mass-based approach. The RIA also examines ke y uncertainties in the estimated benefits of reducing carbon dioxide and other ai r pollutants. For a further disc ussion of key evaluations of uncertainty in the regulatory analyses for this rulemaking, see Chapter 8 of this RIA.

1.4 Background for the Final Emission Guidelines

1.4.1 Base Case and Years of Analysis
The rule analyzed in this RIA finalizes emission guidelines for states to limit CO 2 

### emissions from certain existing EGUs. The base cas e for this analysis, which uses the Integrated Planning Model (IPM), includes stat e rules that have been finaliz ed and/or approved by a state’s legislature or environmental agencies, as well as final federal rules. The IPM Base Case v.5.15

includes the Cross-State Air Pollution Rule (CSAPR), the Mercury and Air Toxics Rule

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1-5 (MATS), the proposed Carbon Pollution Standard s 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 ex tent that they contain measures , permits, or other air-related limitations or requirements. Additional lega lly binding and enforceable commitments for GHG

reductions considered in th e base case are discussed in the documentation for IPM. 20



### Costs and benefits are presente d for illustrative plan approach es for the analysis years of 2020, 2025, and 2030. These years were selected because they represent initial build up, interim, and full implementation years for the two illustrati ve approaches analyzed. Analyses of energy, economic, and employment impacts are presente d for illustrative plan approaches in 2020, 2025,

and 2030. All dollar estimates are presented in 2011 dollars.

1.4.2 Definition of Affected Sources
For the emission guidelines, an affected EGU is any fossil fuel-fired electric utility steam generating unit or stationary combustion turbin e that was in operation or had commenced construction as of January 8, 2014, 21 

### and that meets the following criteria, which differ depending on the type of unit. To be an affected source, such a unit, if it is a steam generating unit or integrated gasification combined cycle (I GCC), must serve a gene rator capable of selling greater than 25 MW to a utility power distribution system and have a base load rating greater than 260 GJ/h (250 MMBtu/h) heat input of fossil fuel (either alone or in combination with any other fuel). If such a unit is a stationary combus tion 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 fr om inclusion in a state plan. For specifics on these criteria see section IV of the preamble.

When considering and understanding applic ability, the following definitions may be helpful. Simple cycle combustion turbine mean s any stationary combustion turbine which does

not recover heat from the combustion turbine en gine exhaust gases for purposes other than

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

21 Under Section 111(a) of the CAA, determination of affect ed sources is based on the date that the EPA proposesaction on such sources. January 8, 2014 is the date the proposed GHG standards of performance for new fossil fuelfired EGUs were published in the Federal Register (79 FR 1430).

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1-6 enhancing the performance of the stationary combustion turbine itself. Combined cycle combustion turbine means any stationary combus tion turbine which recovers heat from the combustion turbine engine exhaust gases to genera te steam that is used to create additional electric power output in a steam turbine. Comb ined heat and power (CHP) combustion turbine means any stationary combustion turbine which recovers heat from the combustion turbine engine exhaust gases to heat wa ter or another medium, generate steam for useful purposes other than exclusively for additional electric generation, or directly uses the he at 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 th ey 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 establis hes for the first time federal emission guidelines for existing power plants that will le ad to significant reductions in CO 2 

### emissions.

1.4.4 Emission Guidelines

In this action, the Environmental Protection Agency (EPA) is establishing final emission guidelines for states and tribes to follow in developing plans to reduce greenhouse gas emissions from existing fossil fuel-fired electric generating units (EGUs). Specifically, the EPA is establishing: 1) carbon dioxide (CO 2 

### ) emission performance rates based on the best system of emission reduction (BSER) for two subcategories of existing fossil fuel-fired EGUs, fossil fuelfired electric utility steam generating units and stationary combustion turbines, and 2) guidelines for the development, submittal and implementation of state plans that implement the 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 power sector.

1.4.5 State Plans

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1-7 After the EPA establishes the emission guidelines, each state 22 

### shall then develop, adopt and submit a state plan under CAA section 111(d ). Each state’s obligat ion under section 111(d) and under these guidelines is to es tablish a standard of performan ce for the affected EGUs in its jurisdiction in order to implement the BSER. The EPA has developed a BSER and a mechanism (e.g. standard of performance (e.g. subcategory sp ecific emission performance rates)) that allows

affected EGUs in all states to access all the components of the BS ER (as well as other alternative but acceptable emission reduction opportunities). Th at standard of performance may expressly incorporate the CO 2 

### emission performance rates the EPA ha s set forth in these guidelines to express the BSER. The EPA has also received numerous comments from a wide range of stakeholders desiring freedom to consider a wide range of standards of performance to implement BSER. Thus, as an alte rnative to incorporating the CO 2 

### emission performance rates, the state may establish standards of performance at different le vels for different EGUs. As a second alternative, the state may adopt measures apart from, or in addition to, standards of performance for its affected EGUs.

These guidelines include subcategory specific CO 2 

### emission performance rates, as well as state-specific rate-based and mass-based CO 2 

### emission goals for affected EGUs that represent the equivalent in aggregate of the CO 2 

### emission performance rates a pplied to the affected EGUs in each state. As already stated, the EPA is establishing rate-based CO 2 

### emission goals for each state, as well as mass-based CO 2 

### goals that represent the equiva lent of each state’s rate based

CO 2 

### goal, in order to ensure that both states a nd affected EGUs enjoy the maximum flexibility and latitude in meeting the requirements of the emission guidelines and that the BSER is fully implemented by each state.

For its plan, a state will be able to choose to either impose federally enforceable emission standards that fully meet the emission guideli nes directly on affected EGUs (the “emission standards” approach) or use a “state measures" approach, which would be composed, at least in part, of measures implemented by the state that are not included as federally enforceable components of the plan. A state measures type plan must include a backstop of federally

22 In this section, the term “state” encompasses the 50 stat es and the District of Columbia, U.S. territories, and any Indian tribe that has been approved by the EPA pursuant to 40 CFR 49.9 as eligible to develop and implement a CAA section 111(d) plan.

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1-8 enforceable standards on affected EGUs that fu lly meet the emission guidelines and that would be triggered if the state measures fail to resu lt in the affected EGUs achieving on schedule the required emission reductions.

1.5 Organization of the Regulatory Impact Analysis
This report presents the EPA’s analysis of the potential benefits, costs, and other economic effects of the final emission guidelines to fulfill the requirements of an RIA. This RIA includes the following chapters:

 Chapter 2, Electric Power S ector Industry Profile

 Chapter 3, Cost, Emissions, Economic, and Energy Impacts

 Chapter 4, Estimated Climate Benefits and Health Co-benefits

 Chapter 5, Economic Impacts – Market s Outside the Electricity Sector

 Chapter 6, Employment Impact Analysis

 Chapter 7, Statutory and Executive Order Analyses

 Chapter 8, Comparison of Benefits and Costs

1.6 References
40 CFR Chapter I [EPA–HQ–OAR–2009–0171; FRL–9091–8] RIN 2060–ZA14, “Endangerment and Cause or Contribute Findi ngs for Greenhouse Gases under Section 202(a) of the Clean Air Act,” Federal Register / Vol. 74, No. 239 / Tuesday, December 15, 2009 / Rules and Regulations. 75 FR 49556. August 13, 2010. “EPA’s Denial of the Petitions to Reconsider the Endangerment and Cause or Contribute Findi ngs 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. Global Change Research Program. Available at <http://nca2014.globalchange.gov>. Accessed June 4, 2015.

National Research Council. Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to Millennia . Washington, DC: The Nati onal Academies Press, 2011.

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1-9 U.S. Environmental Protection Agency. EPA’s Power Sector Modeling Platform v.5.14. March 25, 2015 . Available online at <http://www.epa.gov/ powersectormodeling/psmodel514.html>. Accessed June 4, 2015.

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2-1 

## C HAPTER 

## 2: E LECTRIC 

## P OWER 

## S ECTOR 

## I NDUSTRY 

## P ROFILE



### 2.1 Introduction

This chapter discusses important aspects of the power sector that rela te to the Final Carbon Pollution Emission Guidelines for Existing Stationa ry Sources: Electric Utility Generating Units, including the types of power-s ector 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 abou t existing EPA regulation of the power sector.

In the past decade there have been significant structural changes in the both the mix of generating capacity and in the share of electri city generation supplied by different types of generation. These changes are the result of multiple factors in th e power sector, including normal replacements of older generating units with new un its, changes in the electricity intensity of the US economy, growth and regional changes in the US population, technological improvements in electricity generation from both exis ting and new units, changes in th e prices and availability of different fuels, and substantial growth in elec tricity generation by rene wable and unconventional methods. Many of these trends will continue to c ontribute to the evolutio n of the power sector. The evolving economics of the power sector, in pa rticular the increased natural gas supply and subsequent relatively low natural gas prices, have resulted in mo re gas being utiliz ed 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 thr ough 2012. Projections of new capacity and the impact of this rule on these new sources are discu ssed in more detail in Chapter 4 of this RIA.

2.2 Power Sector Overview

The production and delivery of electricity to cust omers consists of three distinct segments: generation, transmission, and distribution.

2.2.1 Generation

Electricity generation is the first process in th e delivery of electricity to consumers. There are two important aspects of electricity gene ration; capacity and ne t generation. Generating Capacity refers to the maximum amount of produc tion from an EGU in a typical hour, typically

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2-2 measured in megawatts (MW) or gigawatts (1 GW = 1000 MW). Electricity Generation refers to the amount of electricity actua lly produced by EGUs, measured in kilowatt-hours (kWh) or gigawatt-hours (GWh = 1 million kWh). Net gene ration is the amount of electricity that is available to the grid from the EGU (i.e., excluding the amount of electricity generated but used within the generating station for operations). In addition to producing electr icity for sale to the grid, generators perform other se rvices important to reliable elect ricity supply, such as providing backup generating capacity in the event of unexpected changes in demand or unexpected changes in the availability of other generators. Other important services provided by generators include facilitating the re gulation of the voltage of supplied generation.

Individual EGUs are not used to generate electricity 100 percent of the time. Individual EGUs are periodically not needed to meet th e regular daily and seasonal fluctuations of electricity demand. Furthermore, EGUs relying on renewable resources such as wind, sunlight and surface water to generate electricity are routin ely constrained by the availability of adequate wind, sunlight or water at different times of the day and season. Units are also unavailable during routine and unanticipated outages for maintenance. These factors result in the mix of generating capacity types available (e.g., the share of capacity of each type of EGU) being substantially

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

season or year.

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

steam that is released to rotate turbines whic h, in turn, create electric ity. Natural gas combined cycle (NGCC) units have two generating component s operating from a single source of heat. The first cycle is a gas-fired turbine, which generate s electricity directly from the heat of burning natural gas. The second cycle reuses the waste heat from the first cycle to generate steam, which is then used to generate electricity from a st eam turbine. Other EGUs generate electricity by using water or wind to rotate turbines, and a variety of other methods including direct photovoltaic generation also make up a small, but growing, share of the overall electricity supply. The generating capacity includes fossil-fuel -fired units, nuclear un its, and hydroelectric and other renewable sources (see Table 2-1). Ta ble 2-1 also shows the comparison between the generating capacity in 2002 and 2012.

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2-3 In 2012 the power sector consisted of over 19,000 generating units with a total capacity 23 

### of 1,168 GW, an increase of 188 GW (or 19 per cent) from the capacity in 2002 (980 GW). The 188 GW increase consisted primarily of natural gas fired EGUs (134 GW) and wind generators (55 GW), with substantially smaller net increas es and decreases in other types of generating

units.

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

Energy Source Generator Nameplate

Capacity

(MW) % Total Capacit y Generator Nameplate

Capacity

(MW) % Total Capacit y % Increas

e Nameplate Capacity Change (MW) % of Total Capacity Increase Coal 338,199 35% 336,341 29% -1% -1,858 -1% Natural 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. Actu al net generation is presented in Table 2-2. Source: U.S. EIA Electric Power Annual , 2012. Downloaded from EIA Electri city 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 h eat source. This includes Combined Cycle Combustion Turbine (31 percent of 2012 natural gas-fired capacity), Gas Turbine (30 percent), Combined Cycle Steam (19 percent), Steam Turbine (17 percent), and miscellaneous (< 1 percent).

23 As with all data presented in this section, this includ es generating capacity not only at EGUs primarily operated to supply electricity to the grid, but also generating capac ity at commercial and industrial facilities that produce both electricity used onsite as well as dispatched to the grid. Unless otherwise indicated, capacity data presented in this RIA is installed nameplate capacity (also known as nominal capacity), defined by EIA as “The maximum rated output of a generator, prime mover, or other electric power production equipment under specific conditions designated by the manufacturer.” Nameplate capacity is co nsistently reported to regulatory authorities with a common definition, where alternate measur es of capacity (e.g., net summer ca pacity and net winter capacity) can use a variety of definitions and specified conditions.

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2-4 The 19 percent increase in generating capacity is the net impact of newly built generating units, retirements of generating units, and a variet y of increases and decreases to the nameplate capacity of individual exis ting units due to changes in opera ting equipment, changes in emission controls, etc. During the peri od 2002 to 2012, a total of 315,752 MW of new generating capacity was built and brought online, and 64,763 MW existing units were retired. The net effect of the re-rating of existing units redu ced the total capacity by 62,579 MW. The overall net change in capacity was 188,410 MW, as shown in Table 2-1.

The newly built generating capacity was prim arily natural gas (226,605 MW), which was partially offset by gas retirements (29,859 MW). Wind capacity was the s econd largest type of new builds (55,583 MW), augmented by 2,807 MW of solar. 24 

### The overall mix of newly built and retired capacity, along with the net effect, is shown on Figure 2-1.

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

Source: EIA Form 860

24 Partially offset by 87 MW retired older wind or solar capacity. ‐ 50,000 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000

New Build Retirement Net ChangeChangeinCapacity(MW)

Coal Nat Gas Wind & Solar Oil & Other

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2-5 Not displayed: wind and solar retirements = 87 MW, net change in coal capacity = -56 MW



### In 2012, electric generating sources produced a net 4,058 trillion kWh to meet electricity demand, a 5 percent increase from 2002 (3,858 trilli on kWh). As presented in Table 2-2, almost 70 percent of electricity in 2012 was produced th rough the combustion of fo ssil 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 perc ent) was only modestly smaller than the total fossil share in 2002 (71 percent), the mix of fossil fuel generati on changed substantially during that period. Coal generation d eclined by 18 percent and petroleum generation by 72 percent, while natural gas generation increased by 60 percen t. This reflects both the increase in natural gas capacity during that period as well as an increase in the uti lization of new and existing gas EGUs during that period. Wind ge neration also grew from a very small portion of the overall total in 2002 to 4.1 percen t of the 2012 total.

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2-6 Table 2-2. Net Generati on 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 Re view, December 2014. Table 7.2a Elect ricity 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 wh ich are continually present, a nd typically operate throughout all hours of the year. The coal units meet the part of demand that is re latively constant. Although much of the coal fleet operates as base loa d, there can be notable differences across various facilities (see Table 2-3). For ex ample, coal-fired units less th an 100 megawatts (MW) in size compose 37 percent of the total num ber of coal-fired units, but only 6 percent of total coal-fired capacity. Gas-fired generation is bett er able to vary output and is the primary option used to meet the variable portion of the el ectricity load and has histor ically supplied “peak” and “intermediate” power, when there is increased demand for electricity (for example, when businesses operate throughout the day or when peopl e return home from wo rk and run appliances and heating/air-conditioning), versus late at night or very early in the morning, when demand for electricity is reduced.

Table 2-3 also shows comparable data for th e capacity and age distribution of natural gas units. Compared with the fleet of coal EGUs, the natural gas fleet of EGUs is generally smaller and newer. While 55 percent of the coal EGU fl eet is over 500 MW per unit, 77 percent of the

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2-7 gas fleet is between 50 and 500 MW per unit. Many of the largest gas units are gas-fired steamgenerating EGUs.

Table 2-3. Coal and Natural Gas Ge nerating Units, by Size, Age, Capacity, and Thermal Efficiency (Heat Rate)

Unit Size Grouping

(MW) No. Units % of All Units Avg. Age Avg. Net Summer Capacity

(MW) Total Net Summer Capacity

(MW) % Total Capacity Avg. Heat Rate (Btu/kWh) COAL 0 – 24 223 18% 40.7 11.4 2,538 1% 11,733 25 – 49 108 9% 44.2 36.7 3,963 1% 11,990 50 – 99 157 12% 49.0 74.1 11,627 4% 11,883 100 - 149 128 10% 50.6 122.7 15,710 5% 10,971 150 - 249 181 14% 48.7 190.4 34,454 11% 10,620 250 - 499 205 16% 38.4 356.2 73,030 23% 10,502 500 - 749 187 15% 35.4 604.6 113,056 36% 10,231 750 - 999 57 5% 31.4 823.9 46,963 15% 9,942 1000 - 1500 11 1% 35.7 1259.1 13,850 4% 9,732 Total Coal 1257 100% 42.6 250.7 315,191 100% 11,013 NATURAL GAS 0 – 24 1992 37% 37.6 7.0 13,863 3% 13,531 25 – 49 410 8% 21.8 125.0 51,247 12% 9,690 50 – 99 962 18% 15.6 174.2 167,536 39% 8,489 100 - 149 802 15% 23.4 39.9 31,982 8% 11,765 150 - 249 167 3% 28.7 342.4 57,179 13% 9,311 250 - 499 982 18% 24.6 71.1 69,788 16% 12,083 500 - 749 37 1% 40.0 588.8 21,785 5% 11,569 750 - 1000 14 0.3% 35.9 820.9 11,492 3% 10,478 Total Gas 5366 100% 27.7 79.2 424,872 100% 11,652

Source: National Electric Energy Data System (NEEDS) v.5.14 Note: The average heat rate reported is the mean of the h eat 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-st eam units in operation in 2013 or earlie r, and excludes those units in NEEDS with planned retirements in 2014 or 2015.



### In terms of the age of the generating units, 50 percent of the total coal generating capacity has been in service for more than 38 years, whil e 50 percent of the natura l gas capacity has been in service less than 15 years. Figu re 2-2 presents the cumulative ag e distributions of the coal and

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2-8 gas fleets, highlighting the pronounced differences in the ages of the fleets of these two types of fossil-fuel generating capacity. Figure 2-2 al so includes the distri bution of generation.

Figure 2-2. Cumulative Dist ribution in 2010 of Coal a nd Natural Gas Electricity Capacity and Generation, by Age

Source: National Electric Energy Data System (NEEDS) v.5.13 Not displayed: coal units (376 MW total, 1 percent of total) and gas units (62 MW, < .01 percent of total)) over 70 years old for clarity. Figure is limited to coal-steam units in NEEDS v5.13 in operation in 2013 or earlier (excludes ~2,100 MW of coal-fired IGCC and fossil waste capacity ), and excludes those units in NEEDS with planned retirements in 2014 or 2015.



### The locations of existing fossil units in EP A’s National Electric Energy Data System (NEEDS) v.5.13 are shown in Figure 2-3.

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

Source: National Electric Energy Data System (NEEDS) v.5.13 Note: This map displays fossil capacity at facilities in the NEEDS v.5.13 IPM frame. NEEDS v.5.13 reflects generating capacity expected to be on-line at the end of 2015. This includes planned new builds already under construction and planned retirements. In areas with a de nse concentration of facilitie s, some facilities may be obscured.



### 2.2.2 Transmission

Transmission is the term used to describe the bulk transfer of electric ity over a network of high voltage lines, from electric generators to substations where power is stepped down for local distribution. In the U.S. and Canada, there are three separate interconn ected networks of high voltage transmission lines, 25 

### each operating synchronously. Within each of these transmission networks, there are multiple areas where the operation of power plants is monitored and

25 These three network interconnections are the Western Interconnection, comprising the western parts of both the US and Canada (approximately the area to the west of the Rocky Mountains), the Eastern Interconnection, comprising the eastern parts of both the US and Canada (ex cept those part of eastern Ca nada that are in the Quebec Interconnection), and the Electric Reliability Council of Texas (ERCOT) Interconnection, comprising most of Texas.” See map of all NERC interconnections at http://www.nerc.com/AboutNERC/keyplayers/Docum ents/NERC_Interconnections_Color_072512.jpg

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2-10 controlled to ensure that electric ity generation and load are kept in balance. In some areas, the operation of the transmission system is under the c ontrol of a single regional operator; in others,

individual utilities coordinate the operations of thei r generation, transmission, and distribution systems to balance their comm on generation and load needs.

2.2.3 Distribution

Distribution of electrici ty involves networks of lower vo ltage lines and substations that take the higher voltage power from the transmi ssion system and step it down to lower voltage

levels to match the needs of customers. The tr ansmission and distribution system is the classic example of a natural monopoly, in part because it is not practical to have more than one set of lines running from the electricity generating so urces to substations or from substations to

residences and businesses.

Over the last few decades, several jurisdictions in the United States began restructuring the power industry to separate transmission and distribution from gene ration, ownership, and operation. Historically, the transmission system had been developed by vertically integrated utilities, establishing much of the existing transm ission infrastructure. Howe ver, as parts of the country have restructured the industry, transmi ssion infrastructure has also been developed by transmission utilities, electric cooperatives, a nd merchant transmission companies, among others. Distribution, also historically de veloped by vertically integrated utilities, is now often managed by a number of utilities that purchase and sell el ectricity, but do not generate it. As discussed below, electricity restructuring has focused pr imarily on efforts to reorganize the industry to encourage competition in the generation segment of the industry, including ensuring open access of generation to the transmission and distribution services needed to deliver power to consumers. In many states, such efforts have also include d separating generation as sets from transmission

and distribution assets to form distinct economic entities. Transmission and distribution remain price-regulated throughout the country based on th e cost of service.

2.3 Sales, Expenses and Prices

These electric generating sources provide electricity for commercial, industrial and

residential ultimate customers. Each of the three major ultimate cate gories consume roughly a

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2-11 quarter to a third of the total electricity produced 26 

### (see Table 2-4). Some of these uses are highly variable, such as heating and air conditioning in residential and commercial buildings, while others are relatively constant , such as industrial processes that operate 24 hours a day. The

distribution between the end use categories changed very little between 2002 and 2012.

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

Sales/Direct Use

(Billion kWh) Share of Total End Use Sales/Direct Use (Billion kWh) Share of Total End Use
Residential 1,265 35% 1,375 35.9% Commercial 1,104 30% 1,327 34.6% Industrial 990 27% 986 25.7% Transportation NA 7 0.2% Other 106 3% NA Total 3,465 95% 3,695 96%

166 5% 138 4% 3,632 100% 3,832 100% Sales

Direct Use Total End Use 

### 2002 2012

Source: Table 2.2, EIA El ectric Power Annual, 2013 Notes: Retail sales are not equal to net generation (Tab le 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 us e 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 acro ss the United States, di ffering both between the ultimate customer categories and also by state an d region of the country. Electricity prices are typically highest for residential and commercial customers because of the relatively high costs of distributing electricity to indi vidual homes and commercial establishments. The high prices for residential and commercial customers are the result both of the necessary extensive distribution network reaching to virtually every part of the c ountry and every building, and also the fact that generating stations are increas ingly located relatively far from population centers (which increases transmission costs). Industrial custom ers generally pay the lowest average prices,

26 Transportation (primarily urban and regional electrical trains) is a fourth ultima te customer category which accounts less than one percent of electricity consumption.

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2-12 reflecting both their proximity to generating stat ions and the fact that industrial customers receive electricity at higher voltages (which makes transmi ssion more efficient and less expensive). Industrial customers frequently pay variable prices for el ectricity, varying by the season and time of day, while residential and commercial prices historically have been less

variable. Overall industria l customer prices are usually cons iderable closer to the wholesale marginal cost of generating electricity than residential and commercial prices.

On a state-by-state basis, all retail electricity pric es vary considerably. In 2011 the national average retail electricity price (all sectors) was 9.90 cents/KWh, with a range from 6.44 cents (Idaho) to 31.59 (Hawaii). The Northeast, Californ ia and Alaska have average retail prices that

can be as much as double those of other stat es (see Figure 2-4), and Hawaii has the most expensive retail price of electricity in the country.

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

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2-13 Average national overall retail electricity prices increased between 2002 and 2012 by 36.7 percent in nominal (current year $) terms. Th e amount of increase differed for the three major end use categories (residential, commercial and industrial). National aver age residential prices increased the most (40.8 percent), and commercial prices increased the le ast (27.9 percent). The nominal year prices for 2002 thr ough 2012 are shown in Figure 2-5.

Figure 2-5. Nominal National Average El ectricity Prices for Three Major End-Use Categories

Source: EIA AEO 2012, Table 2.4 

### Electricity prices for all three end-use categ ories increased more than overall inflation through this period, measured by either the GDP implicit price deflator (2 3.5 percent) or the consumer price index (CPI-U, which increased by 27.7 percent) 27 

### . Most of these electricity price increases occurred between 2002 and 2008; since 2008 nominal electricity prices have been relatively stable while overall inflation continued to increase. The increase in nominal electricity prices for the major end use categories, as well as increases in the GDP price and CPI-U indices for comparison, are shown in Figure 2-6.

27 Source: Federal Reserve Economic Data, FRB St. Louis. Available at http://research.stlouisfed.org/fred2/. .0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

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

Residential Commercial Industrial Total

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2-14 Figure 2-6. Relative Increases in Nominal Nati onal Average Electricity Prices for Major End-Use Categories, Wi th Inflation Indices

The real (inflation-adjusted) change in average national electricity pric es can be calculated using the GDP implicit price de flator. Figure 2-7 shows real 28 

### (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 s een in the figures, the price for industrial customers has always been lower than for either residential or commercial customers, but the industrial price has been more volatile. While the industria l real price of electricity in 2012 was relatively unchanged from 1960, residential and commercial real pr ices are 23 percent and 28 percent lower respectively than in 1960.

28 All prices in this section are estimated as real 2011 prices adjusted using the GDP implicit price deflator unless otherwise indicated.

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2-15 Figure 2-7. Real National Av erage Electricity Prices (201 1$) for Three Major End-Use Categories

Source: EIA Monthly Energy Review, April 2015, Table 9.8 0 2 4 6 8 10 12 14 16 18

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

## Real Electricity Prices, 1960

‐ 2014 (including taxes)

Residential Commercial Industrial Total

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2-16 Figure 2-8. Relative Change in Real Nati onal Average Electrici ty Prices (2011$) for Three Major End-Use Categories

Source: EIA Monthly Energy Review, April 2015, Table 9.8 

### 2.3.2 Prices of Fossil Fuels Used for Generating Electricity

Another important factor in the changes in electricity prices are the changes in fuel prices for the three major fossil fuels used in electricity generation; coal, natural gas and oil. Relative to real prices in 2002, the national average real pr ice (in 2011$) of coal delivered to EGUs in 2012 had increased by 54 percent, while the real price of natural gas decreased by 22 percent. The real price of oil increased by 203 pe rcent, but with oi l declining as an EGU fuel (in 2012 oil generated only 1 percent of electri city) the doubling of oil prices ha d little overall impact in the electricity market. The combined real delivered price of all fossil fuels in 2012 increased by 23 percent over 2002 prices. Figure 2-9 s hows the relative changes in real price of all 3 fossil fuels

between 2002 and 2012. ‐ 50% ‐ 40% ‐ 30% ‐ 20% ‐ 10%

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

1960 1970 1980 1990 2000 2010ChangeinRealElectricityPrices 

## Relative Change in Electricity Prices, 1960

‐ 2014 (including taxes)

Residential Commercial Industrial Total

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2-17 Figure 2-9. Relative Real Prices of Fossil Fuels for Electricity Generation; Change in National Average Real Price per MBtu Delivered to EGU

Source: EIA AEO 2012, Table 9.9 

### 2.3.3 Changes in Electricity Intensity of the U.S. Economy Between 2002 to 2012

An important aspect of the changes in elec tricity generation (i.e ., electricity demand)

between 2002 and 2012 is that while total net generation increased by 4.9 percent over that period, the demand growth for generation has been low, and in fact was lower than both the population growth (9.2 percent) and real GDP growth (19.8 pe rcent). Figure 2-10 shows the growth of electricity generation, popul ation and real GDP during this period.

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2-18 Figure 2-10. Relative Growth of Electricity Generation, Population and Real GDP Since 2002

Sources: U.S. EIA Monthly Energy Re view, December 2014. Table 7.2a Elect ricity Net Generation: Total (All Sectors). U.S. Census. 

### Because demand for electricity generation grew more slowly than both the population and GDP, the relative electric intensity of the U.S. economy improved (i.e., less electricity used per person and per real dollar of output) during 2002 to 2012. On a per capita basis, real GDP per capita grew by 10.9 percent, increasing fr om $44,900 (in 2011$) per person in 2002 to $49,800/person in 2012. At the same time electri city generation per capita decreased by 3.9 percent, declining from 13.4 MWh/person in 2002 to 12.8 MWh/person in 2012. The combined effect of these two changes improved the overall electricity efficiency of the U.S. market economy. Electricity generation per dollar of real GDP decreased 12.5 per cent, declining from 299 MWh per $1 million of GDP to 261 MWh/$1 million GDP. These relative changes are shown in Figure 2-11. Figures 2-10 and 2-11 clearly show the effects of the 2007 – 2009 recession on both GDP and electricity generation, as well as the effects of the subsequent economic recovery.

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

2-19 Figure 2-11. Relative Change of Real GD P, Population and Electricity Generation Intensity Since 2002

Sources: U.S. EIA Monthly Energy Re view, December 2014. Table 7.2a Elect ricity 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 powe r industry. In addition to reorganizing asset management between companies, restructuri ng sought a functional unbund ling of the generation, transmission, distribution, and anci llary services the power sector has historically provided, with the aim of enhancing competition in th e generation segment of the industry.

Beginning in the 1970s, government policy shifted against traditional regulatory approaches and in favor of de regulation for many important i ndustries, including transportation (notably commercial airlines), communications, and energy, which were all thought to be natural monopolies (prior to 1970) that warranted gove rnmental 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 includ ed the desire for more efficient investment choices, the economic incentive to provide least-cost electric rate s through market competition,

reduced costs of combustion turb ine 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 a nd market restructuring

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

2-20 in the power sector involved the divestiture of generation from utilities, the formation of organized wholesale spot energy markets with ec onomic mechanisms for the rationing of scarce transmission resources during periods of p eak demand, the introduction of retail choice programs, and the establishment of new form s 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 tu rmoil associated with bankruptcy filings of key energy companies. By the end of 2001, restructuri ng had either been dela yed or suspended in eight states that previously enac ted legislation or issu ed 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 re gulatory activity in 2001 (EIA, 2003) (“Not Active” in Figure 2-12). Currently, there are 15 states pl us the District of Columbia where price deregulation of generation (restructur ing) has occurred (“Active” in Figure 2-12). Power sector restructuring is more or less at a standstill; by 2010 there were no active proposals under review by the Federal Energy Regulatory Co mmission (FERC) for actions aimed at wider restructuring, and no additional states have be gun retail deregulation activ ity since that time.

Figure 2-12. Status of State Electric ity Industry Restructuring Activities

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

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

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

2-21 One major effect of the restru cturing and deregulation of the power sector was a significant change in type of ownership of electricity generating units in the states that deregulated prices. Throughout most of the 20th century electricity was supplied by vert ically integrated regulated utilities. The traditional integrated utilities generation, transmission and distribution in their designated areas, and prices were set by cost of service regulations set by state government agencies (e.g., Public Utility Commissions). Deregulation and restructuring resulted in unbundling of the vertical integration structure. Transmissi on and distribution continued to operate as monopolies with cost of service re gulation, while generation shifted to a mix of ownership affiliates of traditional utility ownership and some generation owned and operated by competitive companies known as Independent Po wer Producers (IPP). The resulting generating sector differed by state or re gion, as the power sector adap ted to the restructuring and deregulation requirements in each state.

By 2002 the major impacts of adapting to changes brought about by deregulation and restructuring during the 1990s were largely in place. The result ing ownership mix of generating capacity (MW) in 2002 was 62 perc ent of the generating capacity owned by traditional utilities, 35 percent owned by IPPs 29 

### , and 3 percent owned by commercial and industrial producers. The mix of electricity generated (MWh) was more h eavily weighted towards the utilities, with a distribution in 2002 of 66 percent, 30 percen t and 4 percent for ut ilities, IPPs and commercial/industrial, respectively.

Since 2002 IPPs have expanded faster than tr aditional utiliti es, substantially increasing their share by 2012 of both cap acity (58 percent u tility, 39 percent IPPs, and 3 percent commercial/industrial) and generation (58 percent, 38 percent and 4 percent).

The mix of capacity and genera tion for each of the ownership types is shown in Figures 2- 13 (capacity) and 2-14 (generation). The capac ity and generation data for commercial and industrial owners are not shown on these figures due to the small magnitude of those ownership types. Figures 2-13 and 2-14 present the mi xes in 2002 and 2012. A portion of the shift of

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

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2-22 capacity and generation is due to sales and transfers of generation assets from traditional utilities to IPPs, rather than strictly the result of newly built units.

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

The mix of capacity by fuel types that ha ve been built and reti red between 2002 and 2012 also varies significantly by type of ownership. Figure 2-15 presents the new capacity built during that period, showing that IPPs built the majority of both new wind and so lar generating capacity, as well as somewhat more natural gas capacity than the traditional ut ilities built. Figure 2-16 presents comparable data for the retired capacit y, showing that utilities retired more coal and “other” capacity (mostly oil-fired) than IPPs re tired, while the IPPs retired more natural gas capacity than the utilities retired. The reti red gas capacity was primary (60%) steam and

combustion turbines. 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000

12345Capacity(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

12345Generation(GW) 

## Generation Mix, 2002 & 2012

Series1 Series2 Series3

Series4 Series5 Series6

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

2-24 Figures 2-15 and 2-16. Generation Capaci ty Built and Retired between 2002 and 2012 by Ownership Type

2.5 Emissions of Greenhouse Ga ses from Electric Utilities

The burning of fossil fuels, which generates a bout 69 percent of our el ectricity nationwide, results in emissions of greenhouse gases. The power sector is a major contributor of CO 2 

### in particular, but also cont ributes to emissions of sulfur hexafluoride (SF 6 

### ), CH 4 

### , and N 2 

### O. In 2012, the electricity generation accounted for 38 percent of national CO 2 

### emissions. Including both generation and transmission (a source of SF6), th e power sector accounted for 31 percent of total nationwide greenhouse gas emissions, measured in CO 2 

### equivalent. Table 2-5 and Figure 2-17

show the GHG emissions 30 

### 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 sector and other major emitting economic sectors.

30 CO 2 equivalent data in this section are calculated with the IPCC SAR (Second Assessment Report) GWP potential factors. 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 Type

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 Type

Series1 Series2 Series3 Series4

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

2-25 Table 2-5. Domestic Emissions of Greenhouse Gases, by Economic Sector (million metric tonnes of CO 2 

### equivalent)

2002 2012 Change Between '02 and '12

Sector/Source GHG Emissions % Total GHG Emissions GHG Emissions % Total GHG Emissions Change in Emissions % Change in Emissions % of Total Change in Emissions Electric Power Industry 2,313 33% 2,064 32%

‐ 249

‐ 11% 48% Transportation 1,958 28% 1,837 28%

‐ 121

‐ 6% 23% Industry 1,419 20% 1,278 20%

‐ 140

‐ 10% 27% Agriculture 561 8% 614 9% 53 10%

‐ 10% Commercial 365 5% 353 5%

‐ 12

‐ 3% 2% Residential 374 5% 321 5%

‐ 53

‐ 14% 10% US Territories 52 < 1% 58 1% 6 11%

‐ 1% Total Emissions 7,041 100% 6,526 100%

‐ 516

‐ 7% 100%

Sinks and Reductions -885

‐ 979

‐ 94 11% Net Emissions 6,156 5,546

‐ 610

‐ 10% Source: EPA, 2014 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012”, Table 2-12. Includes

CO 2 , CH 4 , N 2 O and SF 6 emissions.



### Figure 2-17. Domestic Emissions of Greenho use Gases from Major Sectors, 2002 and 2012 (million metric tonnes of CO 2 

### equivalent)

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

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2-26 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 

### emission factors used in IPM v5.14 (same as used in v5.13) are shown in Table 2-7. Coal has higher carbon content than oil or natural gas, and thus releases more CO 2 

### during combustion. Coal emits around 1.7 times as much carbon per unit of energy when bur ned as natural gas (EPA 2013).

Table 2-6. Greenhouse Gas Emissions from the Electricity Sector (Generation, Transmission and Distribution), 2002 and 2012 (million metric tonnes of CO 2 

### equivalent)

2002 2012 Change Between '02 and '12 Gas/Fuel Type or Source GHG Emissions % of Total GHG Emissions from Power Sector GHG Emissions % of Total GHG Emissions from Power Sector Change in GHG Emissions % Change in Emissions

CO 2 2,287 98.9% 2,039 98.8% -248 -11% Fossil Fuel Combustion 2,273 98.2% 2,023 98.0% -250 -11%

Coal 1,890 81.7% 1,511 73.2% -379 -20% Natural Gas 306 13.22% 492 23.85% 187 61% Petroleum 76.8 3.32% 18.8 0.91% -58.0 -76% Geothermal 0.4 0.02% 0.4 0.02% 0.0 0% Incineration of Waste 11.8 0.51% 12.2 0.59% 0.4 3%

Other Process Uses of Carbonates 2.6 0.11% 4.0 0.19% 1.4 54%

CH 4 0.4 0.02% 0.5 0.02% 0.1 25% Stationary Combustion* 0.4 0.02% 0.5 0.02% 0.1 25%

Incineration of Waste + +

N 2 O 12.4 0.54% 18.6 0.90% 6.2 50% Stationary Combustion* 12.0 0.52% 18.3 0.89% 6.3 53%

Incineration of Waste 0.4 0.02% 0.4 0.02% 0.0 0%

SF 6 13.3 0.57% 6.0 0.29% -7.3 -55% Electrical Transmission and Distribution 13.3 0.57% 6.0 0.29% -7.3 -55%

Total GHG Emissions 2,313 2,064 -249 -11%
Source: EPA, 2014 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012”, Table 2-13

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2-27 * Includes only stationary combustion emissions related to the generation of electricity.

** SF 6 is not covered by this rule, which specifica lly regulates GHG emissions from combustion. + Does not exceed 0.05 Tg CO 2 Eq. or 0.05 percent.



### Table 2-7. Fossil Fuel Emission Factors in EPA Base Case 5.14 IPM Power Sector Modeling Application Fuel Type Carbon Dioxide (lb/MMBtu) Coal Bituminous 202.8 – 209.6 Subbituminous 209.2 – 215.8 Lignite 212.6 – 219. Natural Gas 117.1 Fuel Oil Distillate 161.4 Residual 161.4 – 173.9 Biomass 195 Waste Fuels Waste Coal 204.7 Petroleum Coke 225.1 Fossil Waste 321.1 Non-Fossil Waste 0 Tires 189.5 Municipal Solid Waste 91.9

Source: Documentation for IPM Base Case v.5.13, Table 11-5. The emission factors used in Base Case 5.14 are

identical to the emission fact ors in IPM Base Case 5.13.

Note: CO 2 emissions presented here for biomass account for combustion only and do not reflect lifecycle emissions from initial photosynthesis (carbon sink) or harvesting activities an d transportation (carbon source).



### 2.6 Carbon Dioxide Control Technologies

In the power sector, current approaches available for significantly reducing the CO 2 

### emissions of new fossil fuel combustion sources to meet a 1,400 lb CO 2 

### /MWh emission rate include the use of: (1) highly efficiency coal-fir ed designs (e.g., modern supercritical or ultrasupercritical steam units) with up to 40% natural gas co-firing, (2), integr ated coal gasification combined cycle (IGCC) with < 10% CCS or co-fi ring with up to 10 percent natural gas, (3) natural gas combined cycle (NGCC) combustion turbine/steam-turbine units, and/or (4) conventional coal-fired genera tion with carbon capture and stor age (CCS). While CCS is not

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

2-28 included in the BSER framework, it is an emergi ng technology with both new build and retrofit commercial-scale EGUs coming into operati on in 2014 and 2015 in the United States and

Canada. All of these units with CCS have receiv ed substantial subsidies to further develop and demonstrate the feasibility of CCS at a commercial scale, and the costs of these new units with CCS are not indicative of anticipated future cost s of new or retrofit CCS units. CCS is briefly

discussed in this section as existing (but still emerging) technology that may become economically viable in the future.

Investment decisions for the optimal choice in a particular situation of the type of new generating capacity capable of meeting the 1,400 lb CO 2 

### /MWh standard of performance depend in part on the intended primary use of new gene rating capacity. Daily peak electricity demands, involving operation for relatively few hours pe r year, are often most economically met by simple-cycle combustion turbines (CT). Stati onary CTs used for power generation can be installed quickly, at relatively low capital cost. They can be remotely started and loaded quickly, and can follow rapid demand changes. Full-load e fficiencies of large cu rrent technology CTs are typically 30-33 percent but can be has high as 40 percent or more (high heating value basis), as compared to efficiencies of 50 percent or more for new combined-cycle units that recover and

use the exhaust heat otherwise wasted from a CT . A simple-cycle CT’s lower efficiency causes it to burn much more fuel to produce a MWh of electricity than a combined-cycle unit. Thus, when burning natural gas its CO 2 

### emission rate per MWh could be 40-60 percent higher than a

more efficient NGCC unit.

Base load electricity demand can be met with NGCC generation, coal and other fossil-fired steam generation, and IGCC technology, as well as generation from sources that do not emit

CO 2 

### , such as nuclear and hydro. IGCC employs the use of a gasifier to tran sform fossil fuels into synthesis gas (“syngas”) and heat. The syngas is used to fuel a combined cycle generator, and the heat from the syngas conversion can produce steam for the steam turbine portion of the combined cycle generator. Electricity can be generated through this IGCC process somewhat more efficiently than through conventional boiler-steam generators. Additionally, with gasification, some of the syngas can be convert ed into other marketable products such as

fertilizers and chemical feedstocks for Fish er-Tropsch processes to manufacture liquid hydrocarbons (e.g., fuels and lubricants), and CO 2 

### can be captured for use in EOR. Figure 2-18

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2-29 shows the array of products (including electricit y) and by-products that can be produced in a syngas process (NETL).

Figure 2-18. Marketable products from Syngas Generation

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



### 2.6.1 Carbon Capture and Storage

CCS can be achieved through either pre-co mbustion 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). Post-com bustion capture processes remove CO 2 

### from the exhaust gas of a combustion system – such as a utility boiler. It is referred to as “post-combustion capture” because the CO 2 

### is the product of the combustion of the primar y fuel and the capture takes place after the combustion of that fuel. This process is described in more detail in the preamble. (See preamble section V.D.) This proce ss is illustrated for a pulverized coal power

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2-30 plant in Figure 2-19. For post-combustion, a statio n's net generating output will be lower due to the energy needs of the capture process.

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

2-31 Figure 2-19. 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 (“syngas”) under heat a nd pressure and some percentage of the carbon contained in the syngas is captured before combustion. 31 

### For pre-combustion technology, a significant amount of energy is needed to gasify th e fuel(s). This process is illustrated in Figure 2-20. Application of post-combustion CCS with IG CC can be designed to use no water-gas shift, or single- or two-stage shift processe s, to obtain varying percentages of CO 2 

### removal – from a “partial capture” percentage to 90 percent “fu ll capture.” Pre-combustion CCS typically has a lesser impact on net energy output than does post-combustion CCS. For more detail on CCS technology, see the “Report of the Interagenc y Task Force on Carbon Capture and Storage” (2010). 32

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

32 For more information on the cost and performance of CCS, see http://www.netl.doe.gov/energyanalyses/baselin e_studies.html.

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

2-32 Figure 2-20. Pre-Combustion CO 2 

### Capture for an IGCC Power Plant

Source: Interagency Task Force on Carbon Capture and Storage 2010



### Carbon capture technology has been successf ully applied since 1930 on several smaller

scale industrial facilitie s and more recently in a number of demonstration phase projects worldwide for power sector applications. In Oc tober 2014 the first commercial-scale coal-fired capture and storage project for electricity ge neration began operation at the Boundary Dam Power Station in Saskatchewan, Canada. The Boundary Dam Station is owned by the Province of Saskatchewan, and operated by SaskPower, a provincially owned corporation that is the primary electric utility in the Province. The comm ercial-scale demonstration project retrofit Unit 3 (a 130 MW, coal fired built in 1970, and rebuilt in 2013) at a total cost of approximately $1.5 billion (Canadian, or about $1.2 billion US ), including a partial subsidy of $240 million (Canadian) by the Canadian federal govern ment. The carbon capture system is a postcombustion process designed to capture 90 percent of the CO 2 

### emitted by Unit #3. Retrofitting the carbon capture system reduced the capacity of the unit to 110 MW. The majority of the captured CO 2 

### is used for an EOR project in sout hern Saskatchewan. The portion of the CO 2 

### is

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

2-33 being stored in a nearby research and monitori ng geological storage facility, where the captured

CO 2 

### will be injected 3.4 kilometers underground in to a sandstone formation located below the major coal field supplying lignite to Unit # 3. Th e remaining captured CO2 will be injected into deep saline formations.

In the United States, there are two commerci al-scale CCS facilities nearing completion:

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

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

Construction began on the Kemper project in 2010, and the startup is currently scheduled for May, 2016. The Kemper project is constructi ng a new 524 MW lignite unit as well as a 58 MW natural gas unit. Mississippi Power (a di vision of Southern Power) is building and will operate the Kemper County project . The control system is designe d to capture 65 percent of the

CO 2 

### generated by the plant, and is projec ted to capture 3.5 million tons of CO 2 

### per year. The resulting CO 2 

### emission rate is expected to be ab out 800 pounds per MWh produced. The current total cost estimate is $5.6 bill ion, a substantial incr ease from the original $2.4 billion estimate. 33 

### The construction has received a $270 million gran t from the US Department of Energy, 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. 34



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

construction in the United States is the W.A. Parish Petra Nova CCS Project near Houston, Texas. The Parish Petra proj ect is a 50/50 partnership betw een NRG Energy (an integrated electricity company generating a nd supplying electricity to 1.6 m illion customers in Texas) and the Nippon Oil and Gas Explorati on Company. The Parish project will retrofit a post-combustion CCS system on a portion of the flue gas from th e existing 610 MW coal fired Unit # 8. The CCS system will treat a 240 MW slipstream of the flue gas, and is designed to capture 90 percent of

33 The Mississippi Public Utilities Staff authorized an inde pendent monitor to conduct a review of the project. The findings of the review are provided in a summary report available at: http://www.psc.state.ms.us/Insite Connect/InSiteView.aspx?model=IN SITE_CONNECT&queue=CTS_ARCHIVEQ &docid=328417

34 Carbon Capture and Sequestration Technologi es Program at MIT. Accessed 1/23/2015. https://sequestration.mit.edu/tools/projects/kemper.html

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2-34 the CO 2 

### in the treated flue gas. The capacity rati ng of Unit # 8 will not be reduced due to the CCS project because an 85 MW custom-built natu ral gas fired combustion turbine co-generation unit is being built on-site to provide both electricity and steam to the CCS unit. The total cost of the CCS project is estimated to be $1 billion (including a $167 million grant from the US Department of Energy), and the project is e xpected to extract 1.4 – 1.6 million tons of CO 2 

### per year. The construction contract wa s awarded in July, 2014, and operati on 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. 35



### 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 technically feasible and available throughout most of the United States. GS is feasible in different types of geologi c formations 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 effi ciency through a process referred to as enhanced oil recovery (EOR). CO 2 

### may also be used for other types of enhanced recovery, such as for natural gas production. Reservoirs, such as unmineable coal seams, also offer the potential for geologic storage. The geographic availabi lity of deep saline formations , EOR, and un-mineable coal seams is shown in Figure 2-14. Estimates of CO 2 

### storage resources by state compiled by the DOE’s National Carbon Sequestration Databa se and Geographic Information System (NATCARB) and published in DOE’s 2012a Car bon Utilization and Storage Atlas (discussed below) are provided in Table 2-8.

35 US DOE (2010) “Recovery Act: W.A. Parish Post-Combustion CO 2 Capture and Sequestration Project”. http://www.netl.doe.gov/r esearch/proj?k=FE0003311 Accessed 1/23/2015

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

Sources: EPA Greenhouse Gas Reporting Program; Department of Energy, NATCARB; Department of Transportation, National Pipeline Management System.

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2-36 Table 2-8. Total CO 2 

### Storage Resource (DOE-NETL) 36 Million Metric Tons*

State Low Estimate High Estimate

Alabama 122,490 694,380
Alaska 8,640 19,750

Arizona 130 1,170
Arkansas 6,180 63,670
California 33,890 420,630
Colorado 37,610 357,190
Connecticut not assessed by DOE-NETL not assessed by DOE-NETL

Delaware 40 40
District of Columbia not assessed by DOE-NETL not assessed by DOE-NETL

Florida 102,740 555,010 Georgia 145,340 159,050
Hawaii not assessed by DOE-NETL not assessed by DOE-NETL

Idaho 40 390

Illinois 10,020 116,820
Indiana 32,020 68,210

Iowa 10 50

Kansas 10,880 86,340 Kentucky 2,920 7,650
Louisiana 169,500 2,103,980

Maine not assessed by DOE-NETL not assessed by DOE-NETL Maryland 1,860 1,930

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

36 The United States 2012 Carbon Utilization and Storage A tlas, Fourth Edition, U.S Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory (NETL).

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2-37 Table 2-8. Total CO 2 

### Storage Resource, continued

Million Metric Tons*

State Low Estimate High Estimate Michigan 19,050 47,210
Minnesota not assessed not assessed Mississippi 145,010 1,185,030
Missouri 10 170

Montana 84,580 912,720
Nebraska 23,770 113,240

Nevada not assessed not assessed New Hampshire not assessed not assessed New Jersey 0 0
New Mexico 42,760 359,090

New York 4,640 4,640
North Carolina 1,340 18,390
North Dakota 67,090 147,480 Offshore Federal Only 489,840 6,440,090
Ohio 13,460 13,460
Oklahoma 56,950 244,550 Oregon 6,810 93,700 Pennsylvania 22,100 22,100
Rhode Island not assessed not assessed

South Carolina 30,100 34,180
South Dakota 8,760 24,030

Tennessee 430 3,860

Texas 443,800 4,329,930
Utah 25,470 240,910

Vermont not assessed not assessed Virginia 440 2,910 Washington 36,620 496,730 West Virginia 16,650 16,650
Wisconsin 0 0 Wyoming 72,690 684,850

U.S. Total 2,296,680 20,092,180 * States with a “zero” value represent estimates of minimal CO 2 storage resource. States that have not yet been assessed by the RCSPs have been identified.

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2-38 2.6.3 Availability of Geologic Sequestr ation in Deep Saline Formations

DOE and the United States Geological Surv ey (USGS) have independently conducted preliminary analyses of the availability and potential CO 2 

### sequestration capacity of deep saline

formations in the United States. DOE estimat es are compiled by the DOE’s National Carbon Sequestration Database and Geographic Inform ation System (NATCARB) using volumetric models and published in a Carbon Utilization and Storage Atlas. 37 

### DOE estimates that areas of the United States with appropriate geology have a sequestration potential of at least 2,035 billion

metric tons of CO 2 

### in deep saline formations. Accordi ng 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 evaluati on of the technically a ccessible GS resources

for CO 2 

### in U.S. onshore areas and state wa ters using probabilistic assessment. 38 

### The USGS estimates a mean of 3,000 billi on metric tons of subsurface CO 2 

### sequestration potential, including saline and oil and gas reservoirs, across the basins st udied in the United States. As shown in Figure 2-14, there are 39 states for wh ich onshore and offshore deep saline formation storage capacity has been identified. 39



### 2.6.4 Availability of CO 2 

### Storage via Enhanced Oil Recovery (EOR)

Although the regulatory impact an alysis for this rule relies on GS in deep saline formations, the EPA also recognizes the potential for secure ly sequestering CO 2 

### via EOR. EOR has been successfully used at numerous pr oduction fields throughout the United States to increase oil recovery. The oil industry in the Un ited States has over 40 years of experience with EOR. An oil industry study in 2014 identified mo re than 125 EOR projects in 98 fields in the

United States. 40 

### More than half of the projects evalua ted in the study have been in operation for

37 The United States 2012 Carbon Utilization and Storage Atla s, Fourth Edition, U.S. Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory (NETL).

38 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resources—Results: U.S. Geological Survey Circular 1386, p. 41, http://pubs.usgs.gov/circ/1386/.

39 Alaska is not shown in the figure; it has deep saline formation storage capacity, geology amenable to EOR operations, and potential GS capacity in unmineable coal.

40 Koottungal, Leena, 2014, 2014 Worldwide EOR Survey, Oil & Gas Journal, Volume 112, Issue 4, April 7, 2014 (corrected tables appear in Volu me 112, Issue 5, May 5, 2014).

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

2-39 more than 10 years, and many have been in ope ration for more than 30 years. This experience provides a strong foundation for demonstrating successful CO 2 

### injection and monitoring technologies, which are needed for safe and secu re GS that can be used for deployment of CCS across geographically diverse areas.

Currently, 12 states have active EOR operati ons and most have developed an extensive

CO 2 

### infrastructure, including pipelines, to suppor t the continued operation and growth of EOR.

An additional 18 states are within 100 kilomete rs (62 miles) of current EOR operations (see Figure 2-14). 41 

### The vast majority of EOR is conducted in oil reservoirs in the Permian Basin, which extends through southwest Texas and s outheast New Mexico. States where EOR is utilized include Alabama, Colorado, Louisiana, Michigan, Mississippi, New Mexico, Oklahoma, Texas, Utah, and Wyoming.

At the project level, the volume of 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 d ecades, injecting into multiple parts of the field. 42 

### According to data reported to the EPA’s Greenhouse Gas Reporting Program (GHGRP), approximately 60 million metric tons of CO 2 

### were supplied to EOR in the United States in

2013. 43 

### Approximately 70 percent of this total CO 2 

### supplied was produced from natural (geologic) CO 2 

### sources, and approximately 30 percen t was captured from anthropogenic

sources. 44

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

42 Han, Weon S., McPherson, B J., Lichtner, P C., and Wang, F P. “Evaluation of 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.

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

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

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2-40 A DOE-sponsored study has analyzed the geogr aphic availability of applying EOR in 11 major oil producing regions of the United States and found that there is an opportunity to significantly increase the app lication of EOR to areas outsi de of current operations. 45 

### DOEsponsored geologic and engineeri ng analyses show that expandi ng EOR operations into areas additional to the capacity alrea dy identified and applying new me thods and techniques over the next 20 years could utilize 18 billio n metric tons of anthropogenic CO 2 

### and increase total oil production by 67 billion barrels. The availability of anthropogenic CO 2 

### in areas outside of

current sources could drive new EOR projects by making more CO 2 

### locally available.

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

Several states have also esta blished emission performance standards or other measures to

limit emissions of GHGs from new EGUs that are comparable to or more stringent than this rulemaking.

In 2003, then-Governor George Pata ki sent a letter to his coun terparts in the Northeast and Mid-Atlantic inviting them to participate in th e development of a regional cap-and-trade 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 currently participating states include: Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New York, Rhode Is land, and Vermont. The cap covers CO 2 

### emissions from all fossil-fired EGUs greater than 25 MW in particip ating states, and limits total emissions to 91 million short tons in 2014. The 2014 emissions cap is a 51 percent reduction below the initial cap in 2009 to 2011 of 188 million tons. This emissions budget is reduced 2.5 percent annually from

2015 to 2020. RGGI CO 2 

### allowances are sold in a quarter ly 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

45 “Improving Domestic Ener gy Security and Lowering CO 2 Emissions with “Next Generation” CO 2 -Enhanced Oil Recovery”, Advanced Resources Internatio nal, Inc. (ARI), 2011. Available at: http://www.netl.doe.gov /research/energy-analysi s/publications/details?pub =df02ffba-6b4b-4721-a7b4- 04a505a19185.

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2-41 previous record). A total of allowances for 15.3 million tons were sold in the March ’15 auction,

well below the record of 38.7 million tons sold in June ’13 for $3.21.

In September 2006, California Governor Schwar zenegger signed into law Senate Bill 1368. The law limits long-term investments in base load generation by the state's utilities to power plants that meet an emissions performan ce standard jointly esta blished by the California Energy Commission and the California Public Utilities Commission. The Energy Commission has designed regulations that establish a sta ndard for new and existing baseload generation owned by, or under long-term contract to publicly owned utilities, of 1,100 lb CO 2 

### /MWh-net.

In 2006 Governor Schwarzenegger also signed into law Assembly Bill 32, the Global Warming Solutions Act of 2006. This act incl udes a multi-sector GHG cap-and-trade program which covers approximately 85 percent of the st ate GHG emissions. EGUs are included in phase I of the program, which began in 2013. Phase II begins in 2020 and includes upstream sources. The cap is based on a 2 percent reduction from total 2012 expected emissions, and declines 2 percent annually through 2014, th en 3 percent each year un til 2020. The AB32 cap and trade program began functioning in 2011, and functioning market is now operating on the NYMEX futures commodity market. The final 2014 ma rket 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 Gregoi re signed Substitute Senate Bill 6001,

“Baseload Electric Generation Performance” which established statewide GHG emissions reduction goals, and imposed an emission standa rd that applies to any baseload electric generation that commenced operation after June 1, 2008 and is located in Washington, whether or not that generation serves lo ad located within the state. Baseload generation facilities must initially comply with an emission limit of 1,100 lb CO 2 

### /MWh-net. In 2013 the State of Washington revised 46 

### the emission limit to 970 lb CO 2 

### /MWh-net based on a survey of available NGCC generation units co mmercially available in the United States.

46 Washington Department of Commerce, 2013. “Greenhouse Gas Emission Performance Standard for Baseload Electric Generation”. Available at http://www.comme rce.wa.gov/Documents/Concise-Expl-Stmt-WSR-13-06- 074.pdf.

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2-42 In 1997 Oregon required a new baseload ga s fired power plants to meet a CO 2 

### emission standard that was 17 percent below the most ef ficient NGCC unit operating in the United States. In 2000 Oregon established that the effective 17 percent below most efficient was 675 lb

CO 2 

### /MWh-net. In July 2009, Oregon Governor Kulongoski signed Senate Bill 101, which mandated that facilities generati ng baseload electricity, whether ga s- or coal-fired, must have emissions equal to or less than 1,100 lb CO 2 

### /MWh-net regardless of fuel type, and prohibited utilities from entering into long- term purchase agreements for ba seload electricity with out-ofstate facilities that do not meet that standard. Natu ral gas- and petroleum distillate-fired facilities that are primarily used to serve peak demand or to integrate energy from renewable resources are specifically exempted from the performance standard.

In August 2011, New York Governor Cuomo signed the Power NY Act of 2011. Implementing regulations established CO 2 

### emission standards for ne w and modified electric generators greater than 25 MW. The standards vary based on the type of facility: base load

facilities must meet a CO 2 

### standard of 925 lb/MWh-net or 1 20 lb/MMBtu, and peaking facilities

must meet a CO 2 

### standard of 1,450 lbs/MWh-net or 160 lbs/MMBtu.

Several other states have enacted CO 2 

### regulations affecting EGUs that do not set emission limits, but set other regulatory requirements limiting CO 2 

### emissions from EGUs. For example, Montana enacted a law in 2007 requiring the Pub lic Service Commission to limit approvals of new equity interests in or leases of a facility used to generate coal-based el ectricity to facilities that capture and sequester at least half of their CO 2 

### emissions. Minnesota enacted the Next Generation Energy Act in 2007 requiring increase s in power sector greenhouse gas emissions from any new large coal energy facilities built in Minnesota or the impor t of electricity from such a facility located out of state to be offset by equivalent emission reductions. New Mexico enacted legislation in 2007 author izing tax credits and cost rec overy incentives for qualifying coal-fired facilities. To qualify, plants must cap ture and store emissions so that they emit less than 1,100 lbs CO 2 

### /MWh, among other requirements.

Additionally, most stat es have implemented Renewable Portfolio Standards (RPS), or Renewable Electricity Standards (RES). These pr ograms are designed to increase the renewable share of a state’s total electricity generation. Cu rrently 29 states, the District of Columbia, and

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2-43 Guam have enforceable RPS or other mandatory renewable capacity policies, and eight states, Puerto Rico, and Guam have voluntary goals. 47 

### These programs vary widely in structure, enforcement, and scope.

2.8 Revenues and Expenses

Due to lower retail electricity sales, total utility operating revenues declined in 2012 to $271 billion from a peak of almost $300 billi on 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 dampeni ng electricity prices and cons umption (utility revenues), but have also reduced the price a nd cost of fossil fuels and othe r expenses. In 2012 electricity generation was 1.28 percent below the generation in 2011, and has declined in 4 of the past 5

years.

Table 2-9 shows that investor-owned utilit ies (IOUs) earned income of about 13.0 percent compared to total revenues in 2012. The 2012 retu rn on revenue was the third highest year for the period 2002 to 2012 (average: 11.9 per cent range: 10.6 percent to 13.32 percent).

47 EIA 2012a

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2-44 Table 2-9. Revenue and Expense Statistics for Ma jor 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 El ectric Power Annual, 2012
Note: This data does not include information for public utilities, nor for Independent Power Producers (IPPs).



### 2.9 Natural Gas Market

The natural gas market in the United States has historically experi enced significant price volatility from year to year, between seasons within a year, can under go major price swings during short-lived weather events (such as cold snaps leading to short-run spikes in heating demand), and has seen a dramatic shift since 2008 due to increased production from shale formations . Over the last decade, the annual aver age nominal price of gas delivered to the power sector peaked in 2008 at $9.02/mmBtu and has since fallen dramatically to a low of

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2-45 $3.42/mmBtu in 2012. During that time, the daily price 48 

### of natural gas reached as high as $18.48/mmBtu and as low as $2.03. Adjusting fo r inflation using the GDP implicit price deflator, in $2011 the annual average price of natural gas delivered to the power sector peaked at $9.38/mmBtu in 2008 and has fallen dramatically to a low of $3.36 in 2012. The annual natural gas prices in both nominal and r eal (2011$) terms are in Figure 2-22. A comparison of the trends in the real price of natural gas with the real pr ices of delivered coal a nd oil are shown in Figure 2-23. Figure 2-23 shows that while the real pri ce of coal and oil in creased from 2002 to 2012 (+54 percent and +203 percent respec tively), the real price of natu ral gas declined by 22 percent in the same period. Most of the decline in real natural gas prices o ccurred between 2008 (the peak price year) and 2012, during wh ich real gas prices declined by 64 percent while coal and oil prices both increased by 9 per cent. The sharp decline in natu ral gas prices from 2008 to 2012 was primarily caused by the rapid increase in na tural gas production from shale formations.

Figure 2-20. Relative Change Nominal and Re al (2011$) Prices of Natural Gas Delivered to the Power Sector ($/MMBtu)

Source: http://www.eia.gov/totalenergy/data/monthly/#prices. Downloaded 2/15/2015.

48 Henry Hub daily prices. Henry Hub is a major gas distribution hub in Louisiana; Henry Hub prices are generally seen as the primary metric for national gas prices for all end uses. The price of natural gas delivered to electricity generation differs substantially in different regions of the country, and can be higher or lower than the Henry Hub national benchmark price. $0.00 $1.00 $2.00 $3.00 $4.00 $5.00 $6.00 $7.00 $8.00 $9.00 $10.00

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

Nominal Price Real Price

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2-46 Figure 2-21. Relative Change in Real (2011$) Pr ices of Fossil Fuels Delivered to the Power Sector ($/mmBtu)

Source: http://www.eia.gov/totalenergy/data/monthly/#prices. Downloaded 2/15/2015.



### Current and projected natural ga s prices are considerably lowe r 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 res ources and substantially increased the supply of economically recoverable natural gas. According to AEO 2012 (EIA 2012):

Shale gas refers to natural gas that is trapped within shale formations. Shales are finegrained sedimentary rocks that can be rich so urces of petroleum and natural gas. Over the past decade, the combination of horizontal drilling and hydraulic fracturing has allowed access to large volumes of shale gas that we re previously uneconomical to produce. The production of natural gas from shale formati ons has rejuvenated the natural gas industry in the United States.
The U.S. Energy Information Administration' s Annual Energy Outlook 2014 estimates that the United States possessed 2,266 trillion cubic feet (Tcf) of technically recoverable dry natural gas resources as of January 1, 2012. Proven reserv es make up 15 percent of the technically

‐ 50%

0% 50% 100% 150% 200% 250%

2002 2004 2006 2008 2010 2012

Coal Oil Gas Average

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2-47 recoverable total estimate, with the remaining 85 percent from unproven reserves. Natural gas from proven and unproven shale resources acco unts for 611 Tcf of this resource estimate.

Many shale formations, especially the Marcellus 49 

### , are so large that on ly small portions of the entire formations have been intensively pr oduction-tested. Furthermore, estimates from the Marcellus and other emerging fields with few wells already drilled are likely to shift significantly over time as new geological and production information becomes av ailable. Consequently, there is some uncertainty in the estimate of techni cally recoverable resource s, and it is regularly updated as more information is ga ined through drilling and production.

At the 2012 rate of U.S. consumption (about 25. 6 Tcf per year), 2,266 Tcf of natural gas is enough to supply nearly 90 years of use. The AE O 2014 estimate of the shale gas resource base is modestly higher than the AEO 2012 estimat e (2,214 Tcf) of shale gas production, driven by lower drilling costs and continued drilling in shal e plays with high concentr ations of natural gas liquids and crude oil, which have a higher valu e in energy equivalent terms than dry natural

gas. 50



### EIA’s projections of natural ga s conditions did not change su bstantially in AEO 2014 from either the AEO 2012 or 2013, and EIA continues to forecast abundant reserves consistent with the above findings. Recent historic al data reported to EIA is also consistent with these trends, with 2014 being the highest year on record 51 

### for domestic natural gas production. 52

49 The Marcellus formation, underlying most of Pennsylvania and West Virginia, along with portions of New York and Ohio, in 2014 produced 36% of the U.S. total natural gas extracted from shale formations.

50 For more information, see: http://www.eia.gov/fo recasts/archive/aeo11/IF_ all.cfm#prospectshale; http://www.eia.gov/energy_in_br ief/about_shale_gas.cfm

51 The total dry gas production in 2012 from the lower 48 states, including both onshore and offshore production, was 23.97 Tcf, a 1.5% increase from 2013 and a 7.9% total increase from 2011

52 http://www.eia.gov/oiaf/aeo/tablebrowser/#r elease=AEO2014&subject=8-AEO2014&table=72- AEO2014®ion=0-0&cases=ref2014-d102413a

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2-48 2.10 References

Advanced Resources International. 2011. Improving Domestic Energy Security and Lowering CO 2 

### Emissions with “Next Generation” CO 2 

### -Enhanced Oil Recovery (CO 2 

### -EOR) . Available at <http://www.netl.doe.gov/research/energy-an alysis/publications/d etails?pub=df02ffba6b4b-4721-a7b4-04a505a19185>. Accessed June 9, 2015. Clean Energy States Alliance (CESA). The State of State Rene wable Portfolio Standards . June 2013. Available Online at Han, Weon S., McPherson, B J., Lichtner, P C., and Wang, F P. 2010. Evaluation of CO 2 

### trapping mechanisms at the SACROC northern pl atform, Permian basin, Texas, site of 35 years of CO 2 

### injection

. American Journal of Science 310: 282-324. Independent Monitor’s Prudency Evaluation Report for the Kemper County IGCC Project. April 15, 2014. Available online at: http://www.psc.state.ms.us/InsiteConnect/I nSiteView.aspx?model=INSITE_CONNECT&que ue=CTS_ARCHIVEQ&docid=328417. Interagency Task Force on Ca rbon 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: Th e Scientific Basis

. Available at <http://www.grida.no/publications/other/ipcc_ tar/?src=/climate/ipcc_tar/wg1/218.htm>. Accessed June 9, 2015. International Energy Agency (IEA). 2013. Tracking Clean Energy Progress 2013. Input to the Clean Energy Ministerial. Available at <htt p://www.iea.org/etp/tracki ng/>. Accessed June 9, 2015. Koottungal, Leena. 2014. 2014 Worldwide EOR Survey , Oil & Gas Journal, Volume 112, Issue 4, April 7, 2014 (corrected tables app ear in Volume 112, Issue 5, May 5, 2014). National Energy Technology Laboratory (NETL). 2008. Reducing CO 2 

### Emissions by Improving the Efficiency of Existing Coal-fired Power Plant Fleet . Available at <http://www.netl.doe.gov/energy-analyses/pubs /CFPP%20Efficiency-FINAL.pdf>. Accessed June 9, 2015.

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2-49 National Energy Technology Laboratory (NETL). 2012. The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition . Available at <http://www.netl.doe.gov/technologies/carbon_seq/ refshelf/atlasIV/>. Accessed June 9, 2015. National Energy Technology Laboratory (NETL). 2013. Energy Analyses: Cost and Performance Baselines for Fossil Energy Plants. Available at <http://www.netl.doe.gov/energy-analyses/base line_studies.html>. Accessed June 9, 2015. Pacific Northwest National Laboratory (PNNL). 2009. An Assessment of the Commercial Availability of Carbon Dioxide Capture and Storage Technologies as of June 2009. Available at <http://www.pnl.gov/science/pdf/PNNL- 18520_Status_of_CCS_062009.pdf>. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2000. Carbon Dioxide Emissions from the Generation of Electric Po wer in the United States. Available at <ftp://ftp.eia.doe.gov/environment/co2emi ss00.pdf>. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2003. Electric Power Annual 2003. Available at <http://www.eia.gov/electricity /annual/archive/03482003.pdf>. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2009. Electric Power Annual 2009

. Available at <http://www.eia.gov/electricity /annual/archive/03482009.pdf>. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2013. Electric Power Annual 2013. Available at <http://www.eia.gov/electri city/annual/>. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EI A). 2010a. “Status of Electricity Restructuring by State.” Available at <http://www.eia.gov/cneaf/electricity/page/restru cturing/restructure_elect.html>. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2010b. AEO 2010 Retrospective Review

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

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

. Available at <http://www.eia.gov/totalenergy/ data/annual/pdf/aer.pdf>. Accessed June 9, 2015.

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2-50 U.S. Energy Information Administration (U.S. EIA). 2011. Annual Energy Outlook 2011

. Available at <http://www.eia.gov/forecasts/archive/aeo11/>. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2012. Annual Energy Outlook 2012 (Early Release) . Available at <http://www.eia.gov/fo recasts/aeo/>. A ccessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). Today in Energy: Most states have Renewable Portfolio Standards. 2012a. Available at <http://www.eia.gov/todayinenergy/deta il.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/for ecasts/aeo/>. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2015. Monthly Energy Review, April 2015

. Available at <http://www.eia.gov/totalener gy/data/monthly/>. Accessed June 9, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2013. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2011 . Available at <http://www.epa.gov/climatechange/Downloa ds/ghgemissions/US-GHG-Inventory-2013- Main-Text.pdf>. Accessed June 9, 2015. U.S. Geological Survey (USGS) Carbon Dioxi de Storage Resources Assessment Team. 2013. National assessment of geologic carbon dioxide storage resources – Results: U.S. Geological Survey Circular 1386 . Available at <http://pubs.usgs.gov/circ/1386/>. Accessed June 9, 2015

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3-1 

## 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 analysis performed for the Clean Power Plan Final Rule. EPA used the Integrated Planning Model (IPM), developed by ICF International, to conduct most of the analysis discussed in this Chapter. IPM is a dynamic linear programming mode l that can be used to examine air pollution control policies for CO 2 

### , SO 2 

### , NO X 

### , Hg, HCl, and other air pollu tants throughout the contiguous United States for the entire power system. The IPM electricity demand projections are based on projections from the Energy Information Administ ration (EIA), adjusted for demand-side energy efficiency measures that can be reasonably an ticipated to occur under the Clean Power Plan.

3.2 Overview
This chapter of the RIA presents illustra tive analyses of the final rule by making assumptions about the possible approaches that St ates might pursue as they develop their state plans. Over the last decade, EPA has conducte d extensive analyses of regulatory actions affecting the power sector. These efforts support the Agency’s understanding of key variables

that influence the effects of a policy and provide the framewor k for how the Agency estimates

the costs and benefits associated with its actions.

3.3 Power Sector Modelling Framework
The Integrated Planning Model (IPM), develo ped by ICF Consulting, is a state-of-the-art, peer-reviewed, dynamic linear prog ramming model that can be us ed to project power sector

behavior under future business-as-usual conditions and examine prospectiv e air pollution control policies throughout the contiguous Un ited States for the entire elec tric power system. EPA used IPM to project likely future el ectricity market conditions with and without the Clean Power Plan Final Rule. Additional demand side energy efficien cy measures that may be adopted in response to the regulation, and the resul ting changes to future demand pr ojections, are also accounted for in the illustrative plan approach analyses. The level of demand side energy efficiency-driven reductions in electricity demand, and their asso ciated costs, are reported in section 3.6.

IPM is a multi-regional, dynamic, determ inistic linear progra mming model of the contiguous U.S. electric power sector. It provide s forecasts of least cost capacity expansion,

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3-2 electricity dispatch, and emission control st rategies while meeting energy demand and environmental, transmission, dispatch, and reliab ility 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 em ission impacts of prospective environmental policies. The model is designed to reflect electricity markets as accurately as possible. EPA uses

the best available information fr om utilities, industry experts, gas and coal market experts, financial institutions, and government statistics as the basis for the de tailed power sector modeling in IPM. The model documentation provid es additional informa tion on the assumptions discussed here as well as all other model assumptions and inputs. 53



### The model incorporates a detailed representati on of the fossil-fuel supply system that is used to forecast equilibrium fuel prices. The mo del includes an endogenous representation of the North American natural gas supply system through a natural gas module that reflects a partial supply/demand equilibrium of the North American gas market accounting for varying levels of potential power sector and non-power sector gas demand and corresponding gas production and price levels. 54 

### This module consists of 118 supply, demand, and storage nodes and 15 liquefied natural gas re-gasification facility locations that are ti ed together by a series of linkages (i.e., pipelines) that represent the North American na tural gas transmission a nd distribution network.

IPM also endogenously models the partial eq uilibrium of coal supply and EGU coal demand levels throughout the contiguous U.S., taking into account assumed non-power sector demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal transport network, which consists of over four thousand linkages representing rail, barge, and truck and conveyer linkages. The coal supply cu rves in IPM were developed during a thorough bottom-up, mine-by-mine approach that depicts the coal choices and associ ated supply costs that power plants would face if selecting that coal over th e modeling time horizon. The IPM

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

54 See Chapter 10 of EPA’s Base Case usin g IPM (v5.154) documentation, available at: http://www.epa.gov/powersectormodeling

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3-3 documentation outlines the methods and data used to quantify the economically recoverable coal reserves, characteri ze their cost, and build the 36 coal regions’ supply curves. 55



### The costs presented in this RIA include bot h the IPM-projected a nnualized estimates of private compliance costs as well as the estimated costs incurred by utilit ies and ratepayers to achieve demand-side energy effi ciency improvements. The IPM- projected annualized estimates of private compliance costs provided in this analysis are meant to show the increase in production (generating) costs to the power sector in re sponse to the final rule.

To estimate these annualized costs, EPA uses a conventional and widely-accepted approach that applies a capital recovery factor (CRF) multiplier to capital investments and adds that to the annual incremental operating expenses. The CRF is derived from estimates of the cost of capital (private disc ount rate), the amount of insuran ce coverage required, local property taxes, and the life of capital. 56 

### It is important to note that ther e is no single CRF factor applied in the model; rather, the CRF varies across technologi es in the model in or der to better simulate power sector decisionmaking.

While the CRF is used to annualize costs within IPM, a discount rate is used to estimate the net present value of the intertemporal flow of the annualized capital and operating costs. The optimization model then identifies power sector investment decisions that minimize the net present value of all costs over the full planning horizon while satisfying a wide range of demand, capacity, reliability, emissions, a nd other constraints. As explai ned in Chapter 8 of the IPM documentation, the discount rate is derived as a weighted average cost of capital which is a function of capital structure, post-tax cost of debt , and post-tax cost of equity. While the detailed formulation of this rate is presented in the IPM documentation, the rate estimated and used in the current analysis is 4.77 percent.It is important to note that this di scount rate is selected for the purposes of best simulating power sector behavior , and not for the purposes of discounting social

costs or benefits.

55 See Chapter 9 of EPA’s Base Case using IPM (v5.15) documentation, available at: http://www.epa.gov/powersectormodeling

56 See Chapter 8 of EPA’s Base Case using IPM (v5.15) documentation, available at: http://www.epa.gov/powersectormodeling.

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3-4 EPA has used IPM extensively over the past two decades to analyze options for reducing power sector emissions. Previously, the model ha s been used to forecast the costs, emission changes, and power sector impacts for the Clea n Air Interstate Rule, Cross-State Air Pollution Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), and the proposed Carbon Pollution Standards for New Power Plants. Recently IPM has also been used to estimate the air pollution reductions and power sector impacts of water and waste regulations affecting EGUs, including Cooling Water Intakes (316(b)) Rule, Disposal of Coal Combustion Residuals from Electric Utilities (CCR) and Steam Electric Effluent Limitation Guidelines (ELG).

The model and EPA's input assumptions undergo periodic formal peer review. The rulemaking process also provides opportunity for expert review and comment by a variety of stakeholders, including owners and operators of capacity in the electricity sector that is represented by the model, public interest groups, a nd other developers of U. S. electricity sector models. The feedback that the Agency receives provides a highly-detailed review of key input assumptions, model representation, and modeling re sults. IPM has received extensive review by energy and environmental modeling experts in a va riety 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 pe riodically conducted. The model ha s also undergone considerable interagency scrutiny when it was used to conduct ove r a dozen legislative an alyses (performed at Congressional request) over the past decade. The Agency has also used the model in a number of comparative modeling exercises sponsored by St anford University’s Energy Modeling Forum over the past 15 years. IPM has also been employed by states (e.g., for RGGI, the Western Regional Air Partnership, Ozone Transport Assessm ent Group), other Federal and state agencies, environmental groups, and industry, all of whom subject the model to their own review procedures.

3.4 Recent Updates to EPA’s Base Case using IPM (v.5.15)
The “Base Case” for this analysis is a busines s-as-usual scenario that would be expected under market and regulatory conditions in the abse nce of this rule. As such, the IPM base case represents the baseline for this regulatory impact analysis. EP A frequently updates the IPM base

case to reflect the latest availa ble electricity demand forecasts, as well as, expected costs and availability of new and existing generating resources, fuels, and emissions control technologies.

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3-5 EPA’s IPM modeling platform used to anal yze this final rule (v.5.15) incorporates updates to the version of the model used to anal yze 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 th e electric demand forecas t consistent with the AEO 2015 and an update to natural gas suppl y. Additional updates, based on the most up-todate information and/or public comments recei ved 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 technologie s. This IPM modeling platform incorporates federal and most state la ws and regulations whose provisions were either in effect or enacted and clearly delineated in March 2015. This update also includes two non-air federal rules affecting EGUs: Cooling Water In takes (316(b)) Rule a nd Combustion Residuals from Electric Utilities (CCR). A dditionally, all new capacity projected by the model is compliant with Clean Air Act 111(b) standards. For a de tailed account of all updates made to the v.5.15 modeling platform, see the Incremental Documentation for EPA Base Case v.5.15 Using IPM. 57



### EPA also updated the National Electric Ener gy Data System (NEEDS). This database

contains the unit-level data that is used to cons truct 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, oper ating, air emissions, and other data on every generating units in the contiguous U.S. 58



### 3.5 State Goals in this Final Rule
In this final rule, the EPA is establishing CO 2 

### emission performance rates for two categories of existing fossil fuel-fired EGUs, fossil fuel-fired electric utility steam generating units and stationary combustion turbines. The EP A has translated the source category-specific

CO 2 

### emission performance rates into equivalent state-level rate-based and mass-based CO 2 

### goals in order to expand the range of choices that states will have in developing their plans. Due to the range of choices available to states, and the lack of a priori knowledge about th e specific choices states will make in response to the final goals , this RIA presents two scenarios designed to

57 Available at: http://www.epa.gov/powersectormodeling/

58 The NEEDS database can be found on the EPA’s website for the Base Case using IPM (v5.15), <http://www.epa.gov/powersectormodeling >.

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3-6 achieve these goals, which we term the “rate-ba sed” illustrative plan approach and the “massbased” illustrative plan appro ach. Table 3-1 presents the rate-based and mass-based state goals.

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3-7 Table 3-1. Statewide CO 2 

### Emission Performance Goals, Rate-based and Mass-based

Rate-Based(Adjusted Output-WeightedAverage Pounds of CO2 Per Net MWh From All Affected

Fossil Fuel-Fired EGUs) Mass-Based(Adjusted Output-WeightedAverage Metric Tons of CO2 From All Affected Fossil Fuel-Fired EGUs)

State Interim Goal Final Goal Interim Goal Final Goal Alabama 1,156 1,018 56,432,886 51,599,803 Arkansas 1,304 1,130 30,554,805 27,507,539 Arizona 1,173 1,031 29,991,524 27,369,757 California 906 828 46,289,448 43,915,819 Colorado 1,362 1,174 30,279,604 27,117,614 Connecticut 851 786 6,565,914 6,297,085 Delaware 1,022 916 4,592,743 4,274,388 Florida 1,026 919 102,493,187 95,337,918 Lands of the Fort Mojave Tribe 831 771 554,370 533,882 Georgia 1,197 1,049 46,196,486 42,044,096 Iowa 1,505 1,283 25,632,850 22,698,346 Idaho 831 771 1,322,487 1,273,611 Illinois 1,455 1,245 67,852,887 60,305,548 Indiana 1,451 1,242 77,664,415 69,047,576 Kansas 1,518 1,293 22,550,167 19,949,240 Kentucky 1,509 1,286 64,688,636 57,265,616 Louisiana 1,292 1,121 35,659,254 32,138,047 Massachusetts 901 824 11,564,122 10,980,965 Maryland 1,510 1,287 14,703,722 13,015,622 Maine 841 779 1,957,557 1,881,144 Michigan 1,354 1,169 48,129,127 43,130,166 Minnesota 1,413 1,213 23,071,207 20,572,953 Missouri 1,490 1,272 56,757,481 50,313,819 Mississippi 1,060 945 24,799,644 22,955,132 Montana 1,534 1,305 11,603,144 10,253,749 Lands of the Navajo Nation 1,534 1,305 22,276,621 19,685,947 North Carolina 1,311 1,136 51,693,220 46,506,777 North Dakota 1,534 1,305 21,437,569 18,944,473 Nebraska 1,522 1,296 18,742,279 16,576,333 New Hampshire 946 858 3,849,483 3,626,452 New Jersey 885 812 15,808,468 15,058,657 New Mexico 1,325 1,146 12,532,374 11,260,240 Nevada 941 855 13,012,251 12,268,081 New York 1,024 918 30,475,737 28,355,550

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3-8 Ohio 1,383 1,190 74,861,210 66,921,162 Oklahoma 1,222 1,068 40,467,213 36,729,354 Oregon 963 871 7,840,631 7,364,934 Pennsylvania 1,258 1,095 90,105,397 81,483,381 Rhode Island 831 771 3,317,849 3,195,228 South Carolina 1,337 1,156 26,278,911 23,585,275 South Dakota 1,351 1,166 3,571,517 3,201,472 Tennessee 1,410 1,211 28,832,545 25,716,586 Texas 1,187 1,042 188,765,209 171,987,787 Lands of the Uintah and Ouray Reservation 1,534 1,305 2,323,513 2,053,298 Utah 1,368 1,179 24,098,838 21,570,672 Virginia 1,046 934 26,833,268 24,886,275 Washington 1,110 983 10,595,066 9,742,168 Wisconsin 1,364 1,176 28,355,019 25,388,731 West Virginia 1,534 1,305 52,687,755 46,560,398 Wyoming 1,526 1,299 32,456,457 28,697,535



### 3.6 Illustrative Plan Approaches Analyzed
To estimate the costs, benefits, and economic and energy market impacts of implementing the CPP guidelines, the EPA modeled two illustrative plan a pproaches, each at the state level, based on a rate-based approach a nd a mass-based approach. The rate-based plan approach requires affected sources in each state to achieve a si ngle average emissions rate in each period as represented by the state-wide go als. The mass-based plan approach requires

affected sources in each state to limit their aggregate emissions not to exceed the mass goal for

that state.

In each of these scenarios, affected EGUs include:

 Existing fossil steam boilers with nameplate capacity greater than 25 MW

 Existing NGCC units with nameplate capacity greater than 25 MW

In the rate-based scenario, ge neration (or avoided generation) from these additional sources represented in the model is counted toward meeting state goals:

 All renewable capacity (hydro, solar PV , wind, geothermal) that comes online

after 2012

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3-9  Under-construction nuclear 59



###  Demand-side energy efficiency in additi on to levels implicit in base case electricity demand.

In the rate-based illustrative plan approach analyzed in this RIA, the affected EGUs within each state are requ ired to achieve an average emissions ra te that is less than or equal to the state goals for each state. In order meet the goa l for each state, the affected sources in this scenario have the ability to do one or both of the following:

1) generate in amounts within that state such that the average emissions rate is achieved,

and/or

2) include in the average emissions rate calculation new renewable generation or demand-side energy efficiency located outside of the state but within each of the illustrative Interconnection-based regions shown in Figure 3-1 below. 60

59 Includes three nuclear facilities at which construction has already commenced: Watts Barr (TN), Vog tle (GA), and Summer (SC)

60 In this scenario, energy efficiency/r enewable energy procurement is limited to within one of the three illustrative regions, as a conservative modeling approach. Since the in terconnects do not always fo llow state borders, certain states that fall into more than one region were grouped in regions where there was a majo rity of geographic territory (area) or generation. In practice, sources in states that ha ve adopted rate-based plans may be able to procure energy efficiency/renewable energy from states outside of these illustrative regions.

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3-10 Figure 3-1. Illustrative Regions for Dema nd-Side Energy Efficiency/Renewable Energy Procurement Used in this Analysis
This rate-based implementation plan appro ach enables some sources to emit at rates higher than their applicable state goal, as l ong as there is correspondi ng generation coming from

affected sources in that state that emits at a lo wer rate, such that each state’s goal (in lbs/MWh)

is met across all affected sources located within the state, plus generati on (or avoided generation) from energy efficiency/renewable energy which is procured from within the illustrative regions. In this rate-based scenario, specific gene ration (or avoided generation) from energy efficiency/renewable energy procurement may only be used once for compliance toward a state goal; in other words, while emitting sources in a ll states may avail themselves of qualifying energy efficiency/renewable energy across th e illustrative region, no particular energy efficiency/renewable energy MWh can be claimed by more than one emitter as part of reaching a state goal.

While the final rule enables states to achie ve their mass goals with the flexibility of interstate trading, this RIA presents analysis is an illustrative plan approach that conservatively assumes that each state achieves its goal inde pendently. Cooperation between the states that allows for trading across states would provi de EGUs with additional low cost abatement opportunities and would therefore lo wer the overall cost of complian ce across the affected states.

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3-11 Each illustrative plan approach assumes identic al levels of demand-side energy efficiency megawatt-hour (MWh) demand reductions and associated costs, which are specified exogenously and consistent with the energy effi ciency plan scenario performance levels described in section 3.6. Details of the implem entation of the demand reduction are reported in the following section.

The mass-based scenario presented in this chapter includes a 5 percent set-aside of allowances that would be allocated to recogni ze deployment of new rene wable capacity, which is represented by lowering the capital cost of new renewable capacity by the estimated value of the

allowances in the set-aside. The value of the set- aside is estimated in each model run year as the

total allowances in the set-asid es of each state in the contigu ous U.S. multiplied by the projected average allowance price over the co ntiguous U.S. for that year. This total value is then assumed to apply evenly to all new renewable capacity.

The full array of estimates for the benefits, costs, and economic impacts of this action are presented for both the illustrative rate-based a nd mass-based illustrative plan approaches. These illustrative plan approaches are designed to reflect, to the extent possible, the scope and nature of the CPP guidelines. However, there is considerab le uncertainty with regard to the regulatory form and precise measures that states will a dopt to meet the requirements, since there are

considerable flexibilities afforded to the stat es in developing state plans. Nonetheless, the analysis of the benefits, costs, and relevant impacts of the rule attempts to encapsulate some of

those flexibilities in order to inform states and stakeholders of the poten tial 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 an alysis does not spec ify any particular CO 2 

### reduction measure to occur, with the exception of the leve l of demand-side energy efficiency assumed to be adopted in response to the CPP. In other word s, aside from investments in energy efficiency, the analysis allows the power system the flexibility to respond to average emissions rate or mass

constraints on affected sources in the illustrative scenarios to achieve the goals in the most costeffective manner determined by IPM, as specified below.

While IPM produces a cost-minimizing solution to achieve the state goals imposed in the illustrative scenarios, there may be yet lower-co st approaches that the states may adopt to

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3-12 achieve their state goals inasmu ch as states and sources take advantage of emission reduction opportunities in practice, and flexib ilities afforded under the final ru le, that are not represented in this analysis and would yield diffe rent cost and emissions outcomes.

As previously noted, the power sector modeling and analysis presented in this chapter is

intended to be illustrative in nature, and reflects the EPA’s be st assessment of likely impacts of the CPP under a range of approaches that states may adopt. The modeling is designed to reflect the rule’s requirements, includi ng the timing, applicabil ity to sources, and fl exibilities across the power system as accurately as possible to repr esent the nature and scope of the CPP. The analysis is a reasonable expectation of the incremen tal effects of the rule, and is consistent with past EPA analyses of power s ector regulatory requirements (and estimates of the costs and benefits).

For the CPP, the analysis and projections fo r the year 2025 reflect the impacts across the power system of complying with the interim goals, and the an alysis and projections for 2030 reflect the impacts of complying with the fi nal goals. In addition to the 2025 and 2030 projections, modeling results a nd projections are also shown for 2020. There is no regulatory requirement reflected in the 2020 run-year in IPM, consistent with the final rule. These years reflect the basic run-year structur e in IPM, as configured by EPA.

Although the analysis of the C PP does not include estimates of the costs and benefits of the CPP across each year of the rule in a ye ar-by-year manner, the EPA has reflected the structure of the rule, including the interim and the fi nal state goals of the CPP, in a manner that is consistent with the regulatory requirements. This is also consistent with past practice, including analysis of the Clean Air Interstate Rule, the Cross State Air Pollution Rule, the NOx SIP Call, National Ambient Air Quality Standards, the Acid Rain Program, and state rules. These past regulatory and legislative efforts included mode ling and analysis in a similar manner, where select analytic years reflected projections of policy impacts for rules that include multi-year compliance periods.

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3-13 3.7 Demand-Side Energy Efficiency

3.7.1 Demand-Side Energy Efficiency Impro vements (Electricity Demand Reductions) 61



### The final CPP provides states the flexibility to use demand reducti ons resulting from demand-side energy efficiency measures as a com ponent of their plan st rategy, either directly recognized towards compliance w ith a rate-based goal or as a complementary approach for achieving a mass-based goal. The EPA has included in our illustrative plan scenarios (both rateand mass-based) a level of demand reduction that could be achieved, and the associated costs incurred, through implementation of demand-side energy efficiency measures. This “demandside energy efficiency plan scenario” represents a level of performance that has already been demonstrated or is required by policies (e.g., ener gy efficiency resource standards) of leading energy efficiency implementing states, and is cons istent with a demonstrated or required annual pace of performance improvement over time. Th e resulting levels of demand reduction are

consistent with recent studies of achievabl e 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-sid e energy efficiency i nvestments that may be encouraged in response to the final CPP.

For the illustrative demand-side energy effi ciency plan scenario, electricity demand reductions for each state for each year are deve loped by ramping up from a historical basis 62 

### to a target annual incremental demand reduction rate of 1.0 percent of electricity demand over a period of years starting in 2020, and maintaini ng that rate throughout the modeling horizon. Nineteen leading states have either achieved, or have established requirements that will lead them to achieve, this rate of incremental elec tricity demand reduction on an annual basis. Based on historic performance and existi ng 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 percen t is achieved. States already at or above the

61 For a more detailed discussion of the demand-side energy efficiency demand reductions and their associated costs, refer to U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Ge nerating Units. Demand-Side Energy Efficiency.

62 The historical basis of the percentage of reduced electricity consumption di ffers for each state and is drawn from the data reported in Energy Information Administration (EIA) Form 861, 2013, available at http://www.eia.gov/electricity/data/eia861/.

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3-14 1.0 percent target rate are assumed to achieve a 1.0 percent rate beginning in 2020 and sustain

that rate thereafter. The incremental demand reduc tion rate for each state, for each year, is used

to derive cumulative annual el ectricity demand reductions based upon information about the average life of energy efficiency measures and the distribution of meas ure lives across energy efficiency programs. The cumulative annual el ectricity demand reducti on derived using this methodology is used to adjust base case electri city demand levels in the illustrative plan approach modeling.

To reflect the implementation of the illust rative energy efficiency plan scenario in modeling, the IPM base case el ectricity demand was adjusted exogenously to reflect the estimated future-year demand reductions calcul ated as described above. State-level demand reductions were scaled up to accoun t for transmission losses and a pplied to base case generation demand in each model year to derive adjusted demand for each state, reflecting the energy efficiency plan scenario energy reductions. Th e demand adjustments were applied proportionally across all segments (peak and non-p eak) of the load duration curve. 63 

### To reflect the adjusted state-level demand within IPM model regions that cross state borders, en ergy reductions from a bisected state were distributed between the ap plicable IPM model regi ons using a distribution approach based on reported sales in 2013 as a pr oxy for the distribution of energy efficiency investment opportunities.

Table 3-2 summarizes the results of the illust rative demand-side energy efficiency plan

scenario at the national level.

Table 3-2. Demand-Side Energy Efficiency Plan Scenario: Net Cumulative Demand Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales) 2020 2025 2030 Net Cumulative Demand Reduction (GWh) 23,150 194,126 327,092 Net Cumulative Demand Reduction as Percent of BAU Sales 0.59% 4.81% 7.83% Source: U.S. EPA. 2015. Technical Support Document (TSD) for the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units. Demand-Side Energy Efficiency.

63 Details and reasoning for this assumption are included in U.S. EPA. 2015. Technical Support Document (TSD) for the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units. Demand-Side Energy Efficiency.

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3-15 3.7.2 Demand-Side Energy Efficiency Costs 64



### Total costs of achieving the demand-side energy efficiency plan scenario for each year were calculated exogenous to the power sect or modeling. The power system cost impacts resulting from the illustrative plan approach analyses were captured within IPM and include the effects of reduced demand levels driven by the energy efficiency scenar io discussed above. The integration of the exogenously calculated demand-si de energy efficiency scenario costs with the power system cost impacts of the illustrative pl an approaches are discussed in section 3.7.2. In addition to the demand reduction results, the dema nd-side energy efficiency costs were based upon an estimate of the total first-year cost of saved energy (i.e., reduced demand), the average life of the demand-side energy efficiency measures , the distribution of those measure lives, and cost factors as greater levels of demand reductions are achieved. The total first-year cost of saved energy accounts for both the costs of the demand-si de energy efficiency programs, known as the program costs, and the additional cost to electr icity consumers participating in the program (e.g., purchasing a more energy efficient technol ogy), known as the pa rticipant costs.

To calculate total annualized demand-side ener gy efficiency costs, first-year costs for each year for each state were levelized (at 3 pe rcent and 7 percent discount rates) over the

estimated distribution of measure lives and the re sults summed for each year for each state. For example, the 2025 estimate of annualized energy effi ciency cost includes levelized value of firstyear costs for energy efficien cy investments made in 2020 through 2025. The annualized costs rise in each analysis year as additional first-year costs are in curred. The annualized cost results

are summarized below in Table 3-3. The total levelized cost of saved energy was calculated based upon the same inputs and using a 3 percent discount rate resulted in national average values of 9.2 cents per kWh in 2020, 8.6 cents per kWh in 2025, and 8.1 cents per kWh in 2030.

64 For a more detailed discussion of the demand-side energy efficiency cost analysis, refer to the Demand-Side Energy Effici ency TSD.

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3-16 Table 3-3. Annualized Cost of Demand-Side En ergy Efficiency Plan Scenario [Contiguous U.S.] (at discount rates of 3 percent and 7 percent, billions 2011$)

Discount Rate 2018 2020 2025 2030 at 3 percent 0.0 2.1 16.7 26.3 at 7 percent 0.0 2.6 20.6 32.5 Source: U.S. EPA. 2015. Technical Support Document (T SD) the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Gene rating Units. Demand-Side Energy Efficiency. 

### The funding for demand-side energy efficien cy programs (to cover program costs) is typically collected through a sta ndard per kWh surcharge to the ratepayer; the regional retail price impacts analyzed from this RIA’s illustra tive plan approaches assumes the recovery of these program costs thro ugh the following procedure. 65 

### For each state, the first-year energy efficiency program costs are calculated for each year. These costs were distributed between the applicable IPM regions using an approach ba sed on reported sales in 2013 as a proxy for the distribution of energy efficiency investment opportunities. These regionalized energy efficiency program costs were then incorporated into the re gional retail price calcul ation as discussed in

section 3.7.9. 66 

### The U.S. EPA’s Demand-Side Energy Efficiency Technical Support Document (U.S. EPA 2015) provides complete details on the calculations of annualized costs and first-year costs as well as comprehensive results (by state, by year) for the illustrative demand-side energy efficiency plan scenario.

3.8 Monitoring, Reporting, and Recordkeeping Costs
EPA projected monitoring, reporting and reco rdkeeping costs for bot h state entities and affected EGUs for the compliance years 2020, 2 025, 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 st ate’s reduction goal. To calculate the national costs, EPA estimated that 47 states and 1,052 facilities would be affected.

The EPA estimated that the majority of the cost to EGUs would be in calculating net energy output, which is needed whether the stat e plan utilizes a rate-based or a mass-based limit. Since the majority of EGUs do have so me energy usage meters or other equipment

65 The full retail price analysis method is discussed in section 3.7.9 of this chapter.

66 The effect on equilibrium supply and demand of electricity due to changing retail rates to fund energy efficiency programs is not captured in the IPM modeling.

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3-17 available to them, EPA believes a new system fo r calculating net energy output is not needed. Under the final guidelines, states are required to use monitoring and reporting requirements for

their affected EGUs to ensure that th e sources are meeting the appropriate CO 2 

### emission performance rates or emission goals.

The EPA has made it a priority to streamlin e reporting and monitoring requirements. In this rule, the EPA is making implementation as ef ficient as possible for both the states and the affected EGUs by allowing state plans to util ize the current monitoring and recordkeeping requirements and pathways that have already be en 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 major ity of the EGUs that would be affected by a 111(d) state plan to monitor CO 2 

### emissions and report that data using the Emissions Collection and Monitoring Plan System (ECMPS). Additionally since the CO 2 

### hourly data is already reported to the EPA’s ECMPS there is no additional burden associated with the reporting of that data. Since the ECMPS pathway is already in pla ce, the EPA will allow for states to utilize the ECMPS system to facilitate the data reporting of the additional net energy output data required under the emission guidelines. However, because the Acid Rain Program does not require net energy output to be reported, th ere is some additional burden (S hown in Table 3-4) in updating an affected EGUs monitoring system to be able to report the associated net energy output of an

affected EGU.

The EPA estimates that it would take three wo rking months for a tec hnician to retrofit any existing energy meters to meet the require ments set in the state plan. Additionally EPA

believes that 50 hours will be needed for each E GU operator to read the rule and understand how the facility will comply with the rule, based on an average reading rate of 100 words per minute and a projected rule word count of 300,000 words. 67 

### Also, after all modifications are made at a facility to measure net energy output, each E GU’s Data Acquisition System (DAS) would need to be upgraded to supply the rate-based emissions value to either the st ate or EPA’s Emissions

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

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3-18 Collection and Monitoring Plan System (ECMPS). Note the costs to develop net energy output monitoring and to upgrade each facility’s DAS system are one-time costs incurred in 2020. Recordkeeping and reporting costs substant ially decrease for the period 2021-2030. The projected costs for 2020, 2025, and 2030 are summarized below.

In calculating the cost for states to comply, EPA estimates that each state will rely on the equivalent of two full time staff to oversee program implementation, assess progress, develop possible contingency measures, perform state pl an revisions and host the subsequent public meetings if revisions are indeed needed, dow nload data from the ECMPS for their annual reporting and develop their annual EPA report. Th e burden estimate was based on an analysis of similar tasks performed under the Regional Haze Program, whereby states were required to develop their list of elig ible sources, draft implementation plan s, revise initial drafts, identify baseline controls, identify data gaps, identify initial strategies, conduct various reviews, and manage their programs. A total estimate of 78,0 00 hours of labor performed by seven states over a three year period resulted in 3,714 hours per year, per entity. Due to the nature of this final rule whereby we believe the air office and the energy office will both be involved in performing the above-mentioned tasks, we rounded up to the equi valent of two full time staff, which totaled 4,160 hours per year. 68



### In addition, in the absence of specific information, EPA assumed that 27 states with strong, state-driven energy effi ciency programs as documented in the U.S. EPA’s Energy and

Environment Guide to Action 69 

### would submit a plan under the state measures approach, which adds to the state respondent burden. Table 3-4 sh ows estimates of the annual state and industry respondent burden and costs of reporti ng and recordkeeping for 2020, 2025 and 2030.

68 Renewal of the ICR for the Regional Haze Rule, Section 6(a) Tables 1 through 4 based on 7 states’ burden. EPAHQ-OAR-2003-0162-0001.

69 U.S. Environmental Protection Agency. Energy and Environment Guide to Action: State Policies and Best Practices for Advancing Energy Efficiency, Renewable Energy, and Combined Heat and Power, 2015 Edition. Available online at: http://epa.gov/statelocalclimate/resources/action-guide.html.

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3-19 Table 3-4. Years 2020, 2025 and 2030: Summa ry of State and Industry Annual Respondent Burden and Cost of Reporting and Recordkeeping Requirements (2011$)

Nationwide

Totals Total Annual Labor Burden (Hours) Total Annual Labor Costs Total Annualized Capital Costs Total Annual O&M Costs Total Annualized Costs Total Annual Respondent Costs State Year 2020 203,840 14,427,298 0 36,015 36,015 14,463,313 Year 2025 225,920 15,990,066 0 24,500 24,500 16,014,566 Year 2030 225,920 15,990,066 0 24,500 24,500 16,014,566 Industry Year 2020 595,432 51,125,815 0 1,556,500 1,556,500 52,682,315 Year 2025 0 0 0 0 0 0 Year 2030 0 0 0 0 0 0

Total Year 2020 799,272 65,553,113 0 1,592,515 1,592,515 67,145,628 Year 2025 225,920 15,990,066 0 24,500 24,500 16,014,566 Year 2030 225,920 15,990,066 0 24,500 24,500 16,014,566



### 3.9 Projected Power Sector Impacts The following sections present projected im pacts from the two illustrative scenarios described above. The tables present impacts from 2020 (prior to the init ial compliance year), 2025 (representative of the interim compliance pe riod), and 2030 (represen tative of the final compliance period). The narrative focuses on resu lts during the initial and final compliance periods.

3.9.1 Projected Emissions (in the Contiguous U.S.) Under the rate-based approach, EPA projects annual 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 28 percen t to 32 percent below 2005 emissions 70 

### in 2025 and 2030, respectively). For the mass-based approach, EPA projects annual CO 2 

### reductions of 4 percent below the base case in 2020, 12 percent below th e base case in 2025 and 19 percent below base

70 For purposes of these calculations, EPA has used historical CO 2 emissions from eGRID for 2005, which reports EGU emissions as 2,434 million metric tonnes in the contiguous US.

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3-20 case projections in 2030 (reaching 29 per cent to 32 percent below 2005 emissions 71 

### in 2025 and 2030, respectively).

Table 3-5. Projected CO 2 

### Emission Impacts, Relative to Base Case

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

2020 2025 2030 2020 2025 2030 2020 2025 2030 Base Case 1,955 1,964 2,021 Rate-based 1,892 1,754 1,644 -63 -211 -376 -3% -11% -19% Mass-based 1,881 1,724 1,646 -74 -240 -375 -4% -12% -19% Source: Integrated Planning Model run by EPA, 2015



### Table 3-6. Projected CO 2 

### Emission Impacts, Relative to 2005

CO 2 Emissions (MM Tonnes) CO 2 Emissions: Change from 2005 (MM Tonnes) CO 2 Emissions: Percent Change from 2005

2005 2020 2025 2030 2020 2025 2030 Base Case 2,434 -479 -470 -413 -20% -19% -17% Rate-based - -542 -680 -790 -22% -28% -32% Mass-based - -553 -710 -788 -23% -29% -32% Source: Integrated Planning Model run by EPA, 2015



### Under the rate-based illustrative plan appr oach, 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 17 percent reduction of mercury in 2030. Under the mass-based illustrative plan appro ach, EPA projects a 15 percent reduction of

SO 2 

### , 16 percent reduction of NO X 

### , and a 12 percent reduction of mercury in 2025, and a 24 percent reduction of SO 2 

### , 22 percent reduction of NO X 

### , and a 16 percent reduction of mercury in 2030. The projected non-CO 2 

### reductions are summarized below in Table 3-7.

71 For purposes of these calculations, EPA has used historical CO 2 emissions from eGRID for 2005, which reports EGU emissions as 2,434 million metric tonnes in the contiguous US.

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3-21 Table 3-7. Projected Non-CO 2 

### Emission Impacts, 2020-2030

Base Case Rate-based Mass-based Rate-based Mass-based 2020 SO 2 (thousand tons) 1,311 1,297 1,257 -1.0% -4.1% NO X (thousand tons) 1,333 1,282 1,272 -3.8% -4.5% Hg (tons) 6.6 6.4 6.4 -2.8% -3.3% PM 2.5 (thousand tons) [X] [X] [X] [X] [X] 2025 SO 2 (thousand tons) 1,275 1,097 1,090 -14.0% -14.5% NO X (thousand tons) 1,302 1,138 1,100 -12.6% -15.6% Hg (tons) 6.6 5.9 5.8 -10.8% -12.2% PM 2.5 (thousand tons) [X] [X] [X] [X] [X] 2030 SO 2 (thousand tons) 1,314 996 1,034 -24.2% -21.3% NO X (thousand tons) 1,293 1,011 1,015 -21.8% -21.5% Hg (tons) 6.8 5.6 5.8 -17.2% -15.6% 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 se ctor for the final emissions guidelines, the Agency has analyzed the potential changes in upstream methane emissions from the natural gas and coal production sectors that may result from the illustrative approaches examined in this RIA. The EPA assessed whether the net change in upstream methane emissions from natural gas and coal production is likely to be positive or ne gative 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 th e natural gas and coal producti on sectors. This analysis found that the net upstream methane emissions from natural gas systems 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 em issions are small relative to the changes in

direct CO 2 

### emissions from power plants. The proj ections include voluntary and regulatory activities to reduce emissions from coal mining and natural gas and oil systems, including the 2012 Oil and Natural Gas NSPS. In addition, the EPA plans to issue a proposed rule later this

summer that would build on its 2012 Oil and Gas NSPS. When thes e standards are finalized and

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3-22 implemented, they would further reduce projected emissions from natural gas and oil systems. The technical details supporting th is analysis can be found in th e Appendix to this chapter.

3.9.2 Projected Compliance Cost s (in the Contiguous U.S.)
The power industry’s “compliance costs” are repr esented in this analysis as the change in electric power generation costs between the base case and illustrative CPP scenarios, including the cost of demand-side energy efficiency pr ograms and measures and monitoring, reporting, and recordkeeping (MR&R) costs. The system costs re flect the least cost power system outcome in which the sector employs all th e flexibilities assume d 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 indus try expenditures required to meet demand projections while complying with state goals, in cluding the total demand-side energy efficiency

costs. 72 

### The compliance costs for the final emissions guidelines for EGUs in the contiguous U.S. states is forecast using IPM. The cost of dema nd-side energy efficiency programs assumed in the IPM analysis are repor ted in section 3.6.2.

EPA projects that the annual compliance cost of the rate-based illustrative plan scenario are $2.4 billion in 2020, $1.1 billion in 2025, and $8.5 billion in 2030 (Table 3-8). The annual compliance cost of the mass-based illustrative plan approach are estimated to be $1.4 billion in 2020, $3.0 billion in 2025, and $5.1 billion in 2030. Th e annual compliance cost is the projected additional cost of complying with the rule in the year analyzed and includes the net change in the annualized cost of capital investment in new ge nerating sources and heat rate improvements at coal steam facilities, 73 

### the change in the ongoing costs of ope rating pollution controls, the change in expenditures on various fuels (inclusive of ch anges in the price of these fuels), demand-side energy efficiency measures, and other ac tions associated with compliance.

72 The compliance costs also capture the effect of changes in equilibrium fuel prices on the expenditures of the electricity sector to serve demand.

73 See Chapter 2 of the GHG Mitigation Measures TSD an d EPA’s Base Case using IPM (v5.15) documentation, available at: http://www.epa.gov/powersectormodeling

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3-23 Table 3-8. Annualized Compliance Costs Including Monitoring, Reporting and Recordkeeping Costs Requirements (billions of 2011$)

2020 2025 2030 Rate $2.5 $1.0 $8.4 Mass $1.4 $3.0 $5.1 Source: Integrated Planning Model run by EPA, 2015, with post-processing to account for exogenous demand-side energy efficiency costs an d monitoring, reporting, and recordkeeping costs.



### In order to contextualize EPA’s projection of the additional costs in 2030 across the two illustrative plan approaches evaluated in this RIA, it is useful to compare these incremental cost estimates to total project ed power sector expenditures. The powe r sector is expected in the base case to expend over $201 billion in 2030 to generate , transmit, and distribute electricity to enduse consumers. In 2014, according to EIA, the pow er sector generated $389 billion in revenue from retail sales of electricity. For context, the projected costs of complia nce with the final rule amount to a 4 percent increase in the cost of meeting electrici ty demand, while securing public

health and welfare benefits that are several tim es greater (as described in Chapters 4 and 8).

The following example uses projected results fo r the year 2030 to illu strate 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 annua lized expenditures required to supply enough electricity to meet demand declin e by $18 billion (rate) and $21 bill ion (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 by each illustrative plan scenario on electric generators w ould, other things equal, result in an incremental increase in expenditures to supply any given level of elec tricity. However, once electricity demand is

reduced to reflect demand-side energy efficiency improvements, there is a substantial reduction in the expenditures needed to supply a corres pondingly lower amount of electricity demand.

Table 3-9. Total Power Sector Genera ting Costs (IPM) (billions 2011$)

2020 2025 2030 Base Case $166.5 $178.3 $201.3 Rate-based $166.8 $162.6 $183.3 Mass-based $165.7 $164.6 $180.1 Source: Integrated Planning Model run by EPA, 2015

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3-24 In order to reflect the full compliance cost attributable to the CPP scenarios, it is necessary to include the annua lized expenditures needed to secure the demand-side energy efficiency improvements. As described in section 3.6.2, EPA has estimated these energy efficiency-related expenditures to be $26.3 billio n in 2030 (using a 3 percent discount rate). The energy efficiency-related expenditures include costs incurred by partie s administering energy efficiency programs and costs incurred by partic ipants in those programs. As a result, this analysis finds the cost of the rate-based and mass-based illustra tive plan approaches in 2030 to be $8.4 billion and $[X] billion, resp ectively.

3.9.3 Projected Compliance Actions for Emissions Reductions
Heat Rate Improvements (HRI): EPA analysis assumes that the existing coal steam electric generating fleet has, on average, the ability to improve operating efficiency (i.e., reduce the average net heat rate, or the Btu of fuel ener gy 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 re quired fossil fuel results in a lower output-based

CO 2 

### emissions rate (lbs/MWh), as well as a lower va riable cost of electric ity generation. In the modeling conducted for these illustrative plan approaches, coal boilers have the choice to improve heat rates by 4.3 percent in the eastern illustrative co mpliance region, 2.1 percent in the western illustrative compliance regi on, and 2.3 percent in Texas, al l at a capital cost of $100 per

kW. 74 

### The option for heat rate improvement is onl y made available in the illustrative plan approaches, in response to the final rule.

The vast majority of existing coal boilers ar e projected to adopt the aforementioned heat rate improvements. Of the 183 GW of coal pr ojected to operate in 2030, EPA projects that 99 GW of existing coal steam capacity (greater th an 25 MW) will improve operating efficiency (i.e., reduce the average net heat ra te) under the rate-based appr oach by 2030. Under the mass-based approach, EPA projects that 88 GW of the 174 GW of coal projected to operate in 2030 will improve operating efficiency by 2030.

74 The option for heat rate improvement is only made available in the illustrative plan scenarios, and is not available in the base case. For an explanation of the regional diffe rences in average ability to improve heat rates, see GHG Mitigation Measures TSD.

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3-25 Generation Shifting: Another appr oach for reducing the average emission rate from existing units is to shift some ge neration from more CO 2 

### -intensive generation to less CO 2 

### -intensive generation. Compared to the base case, existing coal steam cap acity is, on average, projected to operate at a lower capacity factor for both illust rative plan approaches. Under th e illustrative ra te-based plan approach, the average 20 30 capacity factor is 69 percent, an d under the mass-based approach, the average capacity factor for existing coal steam is 75 percent. Existing natural gas combined cycle units, which are less carbon-intensive than coal steam capacity on an output basis, operate at noticeably higher capacity factor under both illu strative plan approaches, on average. The utilization of existing natural gas combined cycl e capacity is lower than the BSER level of 75 percent 75 

### on an annual average basis in these illustrativ e plan approaches, 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 two (generation shifting) is not deployed to the maxi mum extent in the illustrative plan approaches.

See Table 3-10.

75 See preamble section V.D.

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3-26 Table 3-10. Projected Capacity Factor of Existing Coal Steam and Natural Gas Combined Cycle Capacity Existing Coal Steam Existing Natural Gas Combined Cycle 2020 2025 2030 2020 2025 2030 Base Case 77% 76% 79% 54% 56% 51% Rate-based 78% 75% 69% 56% 60% 61% Mass-based 78% 75% 75% 56% 58% 54% Source: Integrated Planning Model run by EPA, 2015 

### Demand-Side Energy Efficiency: Another approach for reducing emissions from affected EGUs

is to consider reductions in de mand attributable to demand-side energy efficiency measures as discussed in section 3.6. In the illustrative plan ap proaches presented in this RIA, each state is credited for total demand-side energy efficiency implemented in, or procured by, that state, consistent in aggregate with the state-by-state demand reductions that are represented by the demand-side energy efficiency scen ario discussed in section 3.6.1.

Deployment of Cleaner Generati ng Technologies: Another key oppor tunity 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 a nd solar, can displace higher emitting existing sources and while they might be di rectly credited toward the state goals in the

rate-based illustrative scenari o, they are also utilized as a co mpliance option in the mass-based illustrative scenario. Increased deployment results in CO 2 

### reductions in both rate-based and mass-based approaches. See sections below disc ussing projected impacts on generation mix and capacity.

3.9.4 Projected Generation Mix
Table 3-11 and Figure 3-2 show the generati on mix in the base cas e and under the two illustrative plan approaches. In both scenarios, tota l generation declines relative to the base case

as a result of the reduction in total demand attr ibutable to the demand-side energy efficiency applied in the illustrative scenarios, by 5 percent in 2025 and 8 percent in 2030.

Under the rate-based scenario, coal-fired gene ration is projected to decline 12 percent in 2025, and natural-gas-fired generation from existi ng combined cycle capacity is projected to increase 5 percent relative to the base case. The coal-fired fleet in 2030 ge nerates 23 percent less than in the base case, while natural-gas-fired ge neration from existing combined cycles increases 18 percent relative to the base case. Gas-fired generation from new combined cycle capacity

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3-27 decreases in 2025 and 2030, consistent with the decrease in new capaci ty (see section 3.7.6). Relative to the base case, gene ration from non-hydro renewables decreases 1 percent in 2025 and increases 9 percent in 2030.

Similarly, under the mass-based scenario, coal-fired generatio n is projected to decline 15 percent in 2025, and natural-gas- fired generation from existing combined cycle capacity is projected to increase 2 percent relative to the ba se case. The coal-fired fleet in 2030 generates 22 percent less than in the base case, while natu ral-gas-fired generation from existing combined cycles increases 5 percent relative to the base case. Gas-fired genera tion from new combined cycle capacity decreases 8 pe rcent and 36 percent relative to the base case in 2025 and 2030, respectively. Relative to the base case, generation from non- hydro renewables decreases 3 percent in 2025 and incr eases 8 percent in 2030.

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

Base Case Rate-based Mass-based Rate-based Mass-based 2020 Coal 1,462 1,391 1,374 -5% -6% NG Combined Cycle (existing) 1,111 1,126 1,132 1% 2% NG Combined Cycle (new) 33 53 69 61% 111% Combustion Turbine 15 20 17 39% 14% Oil/Gas Steam 51 51 50 0% -1% Non-Hydro Renewables 393 399 385 2% -2% Hydro 310 311 310 0% 0% Nuclear 798 792 804 -1% 1% Other 18 18 18 0% 0% Total 4,190 4,160 4,159 -1% -1% 2025 Coal 1,428 1,256 1,217 -12% -15% NG Combined Cycle (existing) 1,152 1,206 1,179 5% 2% NG Combined Cycle (new ) 113 53 104 -53% -8% Combustion Turbine 23 30 34 31% 46% Oil/Gas Steam 39 21 19 -46% -52% Non-Hydro Renewables 417 414 404 -1% -3% Hydro 340 340 340 0% 0% Nuclear 799 791 804 -1% 1% Other 17 17 18 0% 0% Total 4,328 4,128 4,118 -5% -5% 2030 Coal 1,466 1,131 1,144 -23% -22% NG Combined Cycle (existing) 1,042 1,230 1,090 18% 5% NG Combined Cycle (new) 324 100 207 -69% -36% Combustion Turbine 22 27 32 21% 46% Oil/Gas Steam 22 11 11 -52% -53% Non-Hydro Renewables 450 488 485 9% 8% Hydro 340 341 340 0% 0% Nuclear 783 777 785 -1% 0% Other 17 17 17 0% 0% Total 4,467 4,122 4,110 -8% -8% Note: “Other” mostly includes generation from MSW and fuel cells. Source: Integrated Planning Model run by EPA, 2015

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3-29 Figure 3-2 Generation Mix with the Base Case and 111(d) Options, 2020-2030 (thousand GWh)

Source: Integrated Planning Model run by EPA, 2015 

### Under both the rate-based and mass-based appr oaches, the projected rate of change in coal-fired generation is consistent with recent historical declines in coal-fired generation. Additionally, under both of these appr oaches, the trends for all othe r types will remain consistent

with what their trends would be in the absence of this rule. Specifically, natural-gas fired generation and renewables would be expected to increase without this rule, and both are expected to increase under this rule, with renewa bles increasing at a somewhat greater rate than in the absence of this rule; and nuclear, oil-fired, and ot her types of generation are expected to be little impacted by this rule generation mix is consistent with recent declines in coal-fired generation and increases in gas-fired generation. See Figures 3-3 and 3-4.

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3-30 Figure 3-3. Nationwide Generation: Historic al (1990-2014) and Rate-Based Illustrative Plan Approach Projections (2020, 2025, 2030)

Sources: Historic data (i.e., 1990-2014): U.S. Energy Information Administration, June 2015 Monthly Energy Review, Table 7.2a Electricity Net Generation: Total (All Sectors), Available at <http://www.eia.gov/totalenergy/data/monthly/>. Projected data (i.e., 2020, 2025, 2030): In tegrated Planning Model, 2015. Notes: Historic and projected data include gene ration from the power, industrial, and commercial sectors. Historic data from U.S. EIA reflects all cogeneration, while projections from the Integrated Planning Model reflect net cogeneration.



### Figure 3-4. Nationwide Generation: Histor ical (1990-2014) and Mass-Based Illustrative Plan Approach Projections (2020, 2025, 2030)

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3-31 Sources: Historic data (i.e., 1990-2014): U.S. Energy Information Administration, June 2015 Monthly Energy Review, Table 7.2a Electricity Net Generation: Total (All Sectors), Available at <http://www.eia.gov/totalenergy/data/monthly/>. Projected data (i.e., 2020, 2025, 2030): Integrated Planning Model, 2015. Notes: Historic and projected data include generation from the power, industrial, and commercial sectors. Historic data from U.S. EIA reflects all cogeneration, while projections from the Integrated Planning Model reflect net cogeneration.



### 3.9.5 Projected Increm ental Retirements

Relative to the base case, about 23 GW of add itional coal-fired capacity is projected to be uneconomic to maintain (about 11 percent of all coal -fired capacity projected to be in service in the base case) by 2025 under the ra te-based illustrative scenario. Under the mass-based scenario, about 29 GW of additional coal-fired capacity is projected to be uneconomic to maintain (about 14 percent of all coal-fired capacity projected to be in service in the base case) by 2025. Capacity changes from the base case are shown in Table 3-12.

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

Base Case Rate-based Mass-based Rate-based Mass-based 2020 Coal 208 195 193 -6% -7% NG Combined Cycle (existing) 233 231 232 -1% 0% NG Combined Cycle (new) 4 7 9 62% 113% Combustion Turbine 141 137 137 -3% -3% Oil/Gas Steam 88 81 80 -8% -9% Non-Hydro Renewables 130 132 128 1% -2% Hydro 106 106 106 0% 0% Nuclear 100 100 101 -1% 1% Other 5 5 5 0% 0% Total 1,016 994 992 -2% -2% 2025 Coal 208 187 181 -10% -13% NG Combined Cycle (existing) 233 231 232 -1% 0% NG Combined Cycle (new ) 15 7 14 -52% -9% Combustion Turbine 143 138 137 -4% -4% Oil/Gas Steam 82 71 69 -14% -16% Non-Hydro Renewables 139 137 134 -1% -3% Hydro 112 112 112 0% 0% Nuclear 100 99 101 -1% 1% Other 5 5 5 0% 0% Total 1,037 988 985 -5% -5% 2030 Coal 207 183 174 -11% -16% NG Combined Cycle (existing) 233 231 232 -1% 0% NG Combined Cycle (new) 44 14 27 -68% -38% Combustion Turbine 147 138 136 -6% -7% Oil/Gas Steam 82 70 67 -15% -18% Non-Hydro Renewables 154 174 171 13% 11% Hydro 112 112 112 0% 0% Nuclear 99 98 99 -1% 0% Other 5 5 5 0% 0% Total 1,082 1,025 1,024 -5% -5% Source: Integrated Planning Model run by EPA, 2015 

### 3.9.6 Projected Capacity Additions
Due largely to the electricity demand reducti on attributable to the demand-side energy efficiency improvements applied in the illustrati ve scenarios, the EPA projects less new natural gas combined cycle capacity built under the rate-bas ed scenario than is built in the base case over

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3-33 the period covered by the rule. While this ne w NGCC capacity cannot be directly counted towards the average emissions rate used for compliance in the rate-b ased approach, it can displace some generation from co vered sources and thus indirec tly 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 genera tion to the average

emissions rate in e ach state or region.

Under the rate-based illustra tive scenario, new natural gas combined cycle capacity is projected to decrease by 8 GW in 2025 and 30 GW in 2030 (52 percent and 68 percent decrease

relative to the base ca se). New renewable capacity is proj ected to decrease by about 2 GW (3 percent decrease) below the ba se case in 2025, and increase by 20 GW (27 percent increase) by

2030.

Under the mass-based illustrative scenario, ne w natural gas combined cycle capacity is projected to decrease by 1 GW in 2025 and decrease by 17 GW in 2030 (a 9 percent and 38 percent decrease relative to the base case). New renewable capacity is projected to decrease 4 GW (7 percent) relative to the base case in 2025, and increase 18 GW (24 percent increase) by

2030.

Table 3-13. Projected Capacity Additions, Gas (GW) Cumulative Capacity Additions: Gas Combined Cycle Incremental Cumulative Capacity Additions: Gas Combined Cycle

2020 2025 2030 2020 2025 2030 Base Case 4.4 14.9 44.0 Rate-based 7.1 7.1 13.9 2.7 -7.8 -30.1 Mass-based 9.3 13.6 27.2 4.9 -1.3 -16.8 Source: Integrated Planning Model run by EPA, 2015 

### Table 3-14. Projected Capacity Additions, Renewable (GW) Cumulative Capacity Additions: Renewables Incremental Cumulative Capacity Additions: Renewables 2020 2025 2030 2020 2025 2030 Base Case 39.1 59.1 74.1 Rate-based 40.5 57.4 94.4 1.4 -1.8 20.2 Mass-based 36.7 54.9 91 .9 -2.4 -4.2 17.8 Source: Integrated Planning Model run by EPA, 2015

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3-34 3.9.7 Projected Coal Production and Natural Ga s Use for the Electric Power Sector Coal production is projected to decrease in 2025 and beyond in the illustrative scenarios due to (1) improved heat rates (generating efficiency) at existing coal units, (2) electricity

demand reduction attributable to demand-side ener gy 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 projec ted to occur in the western region.

Table 3-15. Coal Production for the Electric Power Sector, 2025 Coal Production (MM Tons) P ercent Change from Base Case Base Case Rate-based Mass-based Rate-based Mass-based Appalachia 92 71 69 -23% -25%
Interior 250 242 236 -3% -6%

West 379 306 293 -19% -23% Waste Coal 6 6 6 0% 0% Imports 1 1 1 -37% -14%

Total 729 626 606 -14% -17% Source: Integrated Planning Model run by EPA, 2015 

### Power sector natural gas use is projected to decrease by about 1 percent in 2025 and 2030 under the rate-based illustrative pl an scenario. In the mass-based scenario, power sector natural gas use is projected to decreas e by 4.5 percent in 2030. These trends are consistent with the change in generation mix described above in Section 3.7.4.

Table 3-16. Power Sector Gas Use Power Sector Gas Use (TCF) Percent Change in Power Sector Gas Use 2020 2025 2030 2020 2025 2030 Base Case 8.62 9.38 9.72 -based 8.91 9.28 9.59 3.4% -1.0% -1.3% -based 9.02 9.39 9.28 4.6% 0.2% -4.5% Source: Integrated Planning Model run by EPA, 2015



### 3.9.8 Projected Fuel Price, Market, and Infrastructure Impacts
The impacts of the two illustrative plan scen arios on coal and natural gas prices before shipment are shown below in Table 3-17 and Tabl e 3-18 and are attributable to the changes in overall power sector demand for each fuel due to the final guidelines. Coal demand decreases by 2030, resulting in a decrease in the price of coal delivered to the elect ric power sector. In 2030, gas demand and price decrease belo w the base case projections, due to the cumulative impact of

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3-35 demand-side energy efficiency improvements and the consequent reduced overall electricity

demand.

IPM modeling of natural gas prices uses bot h short- and long-term price signals to balance supply and demand for the fuel across the modeled time horizon. As such, it should be understood that the pattern of IPM natural gas pr ice projections over time is not a forecast of natural gas prices incurred by end-use consumers at any particular point in time. The natural gas market in the United States has historically expe rienced some degree of price volatility from year to year, between seasons within a year, and during short- lived weather events (such as cold snaps leading to short-run spikes in heating demand). These short-term price signals are fundamental for allowing the market to successfully align immediate supply and demand needs. However, end-use consumers are typically shielded from ex periencing these rapid fluctuations in natural gas prices by retail rate re gulation and by hedging through longe r-term fuel supply contracts by the power sector. IPM assumes thes e longer-term price arrangements take place “outside of the model” and on top of the “real- time” shorter-term price variati on necessary to align supply and demand. Therefore, the model’s natural gas pr ice projections should not be mistaken for traditionally experienced consumer price impacts related to natural gas, but a reflection of expected average price changes over the period of time represented by the modeling horizon.

There are very small changes to natural gas pipeline infrastructure needs over time, in response to the illustrative plan scenarios. These changes, compared to hi storical deployment of new infrastructure, are very modest. In both the rate-based and mass-base d scenarios, pipeline capacity construction through 2020 is projected to increase by le ss than two percent beyond base case projections. By 2030, however, the total cumu lative pipeline capacity construction built is projected to decrease compared to the base case, consistent with the projected decrease in total demand and natural gas use. The projected increas e in pipeline capacity in the near term is largely the result of building pipelin e capacity a few years earlier than projected in the base case.

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3-36 Table 3-17. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu)

Minemouth Delivered - Electric Power Sector 2020 2025 2030 2020 2025 2030 Base Case 1.55 1.67 1.79 2.38 2.50 2.68 Rate-based 1.54 1.58 1.73 2.34 2.35 2.46 Mass-based 1.54 1.59 1.73 2.35 2.40 2.55 Rate-based -0.8% -5.0% -3.8% -1.7% -6.2% -8.0% Mass-based -0.7% -4.7% -3.2% -1.6% -4.3% -4.6% Source: Integrated Planning Model run by EPA, 2015 

### Table 3-18. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices (2011$/MMBtu)
Henry Hub Delivered - Electric Power Sector

2020 2025 2020 2025 2020 2025 Base Case 5.20 5.12 6.01 5.25 5.17 5.98 Rate-based 5.48 4.73 6.21 5.53 4.77 6.13 Mass-based 5.40 4.97 5.92 5.45 5.00 5.86 Rate-based 5.4% -7.5% 3.3% 5.3% -7.7% 2.5% Mass-based 3.9% -3.0% -1.4% 3.8% -3.2% -2.1% Source: Integrated Planning Model run by EPA, 2015 

### 3.9.9 Projected Retail Electricity Prices
EPA’s analysis of the illustrative rate-based plan scenario shows an increase in the national average (contiguous U.S.) retail electric ity price of less than one percent in both 2025 and 2030, compared to the modeled base case price estimate in those years. Under the illustrative mass-based plan scenario, EPA projects an increa se in the national aver age (contiguous U.S.) retail electricity price of 2 pe rcent in 2025 and 0.01 percent in 2030.

Retail electricity prices embody generati on, transmission, distribution, taxes, and demand-side energy efficiency costs. IPM mode ling projects changes in regional wholesale power prices and capacity payments related to imposition of the represented CPP scenarios that are combined with EIA regional transmission a nd distribution costs to calculate changes to regional retail prices using the Retail Price Model (RPM). 76 

### As described in Section 3.6.2, the funding for demand-side energy efficiency (to co ver program costs) is typically collected through a standard per kWh surcharge to the ra tepayer and the regional retail price impacts

76 See documentation available at: http ://www.epa.gov/powersectormodeling/

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3-37 presented here assume that these costs are recove red by utilities in retail rates. This is an approximation, since not every utility will pa ss through the entirety of demand-side energy efficiency costs (e.g., distribution only utilitie s that generate ERCs for demand-side energy efficiency, sell them to affected EGUs, and acco unt for this revenue in rate determination). Furthermore, this analysis assumes that rate-payers in the state producing zero-emitting generation (or avoided generation) bear the cost s of such production. However, in practice, if such generation is claimed through an ERC in anot her state, part of its costs may ultimately be borne by rate-payers in the ERC-consuming state ra ther than the state in which that zero-emitting generation was located. There are many factors influencing the esti mated retail electricity price impacts, namely projected changes in genera tion mix, fuel prices, and development of new generating capacity. These projected changes vary regionally under each illustrative plan scenario in response to the goals under the tw o scenarios, and they also vary depending upon retail electricity market struct ure (e.g., cost-of-service vs. competitive). In the mass-based approach, treatment of allowance al locations will also have an impact on retail electricity prices. In competitive regions, this RIA assumes that a llowances are freely allocated to generators who then keep 100 percent of the fr eely allocated allowance value w ithout passing this value through to ratepayers through lower retail electricity prices. To the extent that implementing authorities choose to require this allowance value to be pa ssed through to ratepayers (such as by allocating allowances to load-serving entities who could be subject to such a requirement), retail prices

would be lower than those shown here.

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3-38 Table 3-19. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) 2020 Projected Retail Price (cents/k Wh) Percent Change from Base Case Base Case Rate-based Mass-based Rate-based Mass-based ERCT 9.7 9.9 9.9 2.5% 2.1% FRCC 10.5 10.7 10.7 2.0% 1.6% MROE 9.9 10.3 10.3 4.2% 3.8% MROW 8.7 9.0 9.0 2.8% 2.3% NEWE 13.3 14.0 14.0 5.1% 5.5% NYCW 17.4 18.3 18.3 5.0% 5.3% NYLI 14.4 15.1 15.1 4.6% 5.1% NYUP 12.4 13.1 13.1 5.4% 5.3% RFCE 11.1 11.8 11.8 6.1% 6.1% RFCM 10.4 10.9 10.9 4.3% 4.3% RFCW 9.4 9.8 9.8 5.1% 4.8% SRDA 8.6 8.8 8.7 2.1% 1.7% SRGW 8.6 9.0 9.0 4.1% 4.8% SRSE 10.0 10.1 10.1 0.9% 0.5% SRCE 8.0 8.1 8.1 1.1% 0.8% SRVC 9.8 9.9 9.9 1.5% 1.2% SPNO 9.9 9.9 9.9 -0.8% -0.9% SPSO 7.9 8.1 8.1 3.2% 2.4% AZNM 10.9 11.2 11.2 2.1% 2.1% CAMX 14.3 14.8 14.7 3.3% 3.0% NWPP 6.9 7.1 7.1 3.2% 2.9% RMPA 8.7 9.0 8.9 3.1% 2.9% Contiguous U.S. 10.0 10.3 10.3 3.2% 3.0% Note: regions pictured on Figure 3-5.

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3-39 Table 3-20. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) 2025 Projected Retail Price (cents/k Wh) Percent Change from Base Case Base Case Rate-based Mass-based Rate-based Mass-based ERCT 10.7 11.1 10.9 3.8% 1.5% FRCC 10.2 10.2 10.3 -0.2% 1.0% MROE 9.7 10.0 10.0 2.4% 2.6% MROW 8.7 9.0 9.0 2.5% 3.1% NEWE 12.6 12.4 12.7 -1.3% 0.5% NYCW 17.0 16.9 16 .9 -0.5% -0.5% NYLI 14.0 13.7 13 .7 -2.2% -1.7% NYUP 11.8 11.7 11.7 -0.8% -1.3% RFCE 10.3 10.2 10.5 -0.2% 2.1% RFCM 10.4 10.4 10.6 0.5% 1.9% RFCW 9.8 9.7 10.1 -1.4% 2.4% SRDA 8.6 8.6 8.7 0.0% 1.4% SRGW 9.1 9.0 9.3 -0.9% 2.5% SRSE 9.6 9.7 9.8 1.4% 2.1% SRCE 7.8 8.0 8.0 2.6% 3.0% SRVC 9.3 9.5 9.6 1.7% 2.4% SPNO 9.8 10.0 10.2 2.9% 4.3% SPSO 8.1 8.3 8.4 2.7% 4.4% AZNM 10.7 10.9 10.9 2.2% 1.8% CAMX 13.2 13.3 13.5 0.8% 2.4% NWPP 6.8 6.9 7.0 2.1% 2.7% RMPA 8.6 8.7 8.9 2.0% 4.3% Contiguous U.S. 9.9 9.9 10.1 0.9% 2.0% Note: regions pictured on Figure 3-5.

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3-40 Table 3-21. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) 2030 Projected Retail Price (cents/k Wh) Percent Change from Base Case Base Case Rate-based Mass-based Rate-based Mass-based ERCT 11.6 11.4 11.3 -1.4% -2.5% FRCC 10.3 10.8 10.5 4.6% 2.3% MROE 9.7 10.3 10.3 5.9% 6.3% MROW 8.9 9.1 9.1 2.7% 2.8% NEWE 14.3 13.6 13.4 -5.4% -6.9% NYCW 19.2 18.2 18 .0 -5.2% -6.4% NYLI 16.3 14.8 14.6 -9.0% -10.1% NYUP 13.6 12.7 12.5 -7.0% -8.4% RFCE 11.3 10.7 10.6 -5.6% -6.5% RFCM 10.5 10.8 10.7 3.4% 1.7% RFCW 10.4 10.5 10.5 1.2% 0.7% SRDA 9.0 9.3 9.2 3.5% 1.9% SRGW 9.7 9.6 9.7 -0.6% 0.4% SRSE 9.8 10.2 10.0 3.9% 2.1% SRCE 7.8 8.1 8.0 4.3% 3.3% SRVC 9.3 9.6 9.5 3.2% 2.0% SPNO 9.5 9.8 10.1 2.7% 5.8% SPSO 8.7 9.0 8.9 3.9% 2.0% AZNM 10.9 11.2 11.1 2.3% 2.0% CAMX 13.5 13.6 13.7 1.1% 1.4% NWPP 6.9 7.0 7.1 2.2% 2.6% RMPA 8.9 9.0 9.3 0.7% 3.5% Contiguous U.S. 10.3 10.4 10.3 0.8% 0.01% Note: regions pictured on Figure 3-5.

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

Source: EIA (http://www.eia.gov/forecasts/aeo/pdf/nerc_map.pdf)



### 3.9.10 Projected Electri city Bill Impacts
The electricity price changes addressed in section 3.9.9 combine with the significant reductions in electricity demand app lied in the illustrative approaches to affect average electricity bills. The estimated changes to average bills are summarized in Table 3-22. Under the illustrative rate-based plan scenario, EPA estimates an av erage monthly bill increase of 2.7 percent in 2020 and an average bill decrease of 3.8 percent in 2025 and 7 percent in 2030. Under the mass-based scenario, EPA estimates an average bill increas e of 2.4 percent in 2020 and an average bill decrease of 2.7 percent in 2025 and 7.7 percent in 2030. These reduced electricity bills reflect the combined effects of changes in both average reta il rates (driven by compliance approaches taken to achieve the state goals) and lower electr icity demand (driven by demand-side energy efficiency).

Table 3-22. Projected Changes in Average Electricity Bills

2020 2025 2030 Rate-based 2.7% -3.8% -7.0% Mass-based 2.4% -2.7% -7.7%



### 3.10 Projected Primary PM Em issions from Power Plants
IPM is not configured to e ndogenously model primary PM emissions from power plants.

These emissions are calculated as a function of IPM outputs, emission factors and control

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3-42 configuration. IPM-projected fuel use (heat input) is multiplied by PM emission factors (based in part on the presence of PM-rele vant pollution control devices) to determine PM emissions. Primary PM emissions are calculated by a dding the filterable PM and condensable PM

emissions.

Filterable PM emissions for each unit are based on historical information regarding existing emissions controls and types of fuel bur ned 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 plan t type, sulfur conten t of the fuel, and

SO 2 

### /HCl and PM control configurat ions. Although EPA’s analysis is based on the best available emission factors, these emission factors do not a ccount 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 factors, including coal sulfur c ontent, combustion conditions and characteristics of the catalyst used in the SCR, and is likely to vary widely from unit to unit. SCRs are generally designed and operated to minimize increases in condensab le PM. This limitation means that IPM postprocessing is potentially underestimating conden sable PM emissions for units with SCRs. In contrast, it is possible that IPM post-processi ng overestimates condensable PM emissions in a case where the unit is combusting a low-sulf ur coal in the pres ence of a scrubber.

For a more complete description of the met hodologies used to post-process PM emissions from IPM, see “IPM ORL File Generation Meth odology” (March, 2011), avai lable in the docket.

3.11 Limitations of Analysis
EPA’s modeling is based on expe rt judgment of various input assumptions for variables whose outcomes are in fact uncertain. As a genera l matter, the Agency reviews the best available information from engineering studies of air po llution controls, the ability to improve operating efficiency, and new capacity construction costs to support a reasonable modeling framework for analyzing the cost, emission changes, and other impacts of regulatory actions.

The costs presented in this RIA include bot h the IPM-projected a nnualized estimates of private compliance costs as well as the estima ted costs incurred by utilities and program participants to achieve demand-side energy e fficiency improvements. The demand-side energy

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3-43 efficiency costs are developed based on a review of energy efficiency data and studies, and expert judgment. The EPA recognize s that significant variation exis ts in these analyses reflecting data and methodological limitations. The method used for estimating the demand-side energy efficiency costs is discussed in more detail in the Demand-Side Energy Efficiency Technical Support Document (TSD). The evaluation, meas urement and verification (EM&V) of demandside energy efficiency is addressed in the sectio n VIII, State Plans, of the preamble for the final

rule.

The base case electricity demand in IPM v.5.15 is calibrated to reference case demand in AEO 2015. AEO 2015 demand may reflect, to some exte nt, 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 res ource standards). To some degree the implicit representation of state policies in th e EPA’s base case alters the impacts assessment, but the direction and magnitude of change is not known with certainty. This issue is discussed in the Demand-Side Energy Efficiency TSD.

Cost estimates for the final emission guide lines are based on rigorous power sector modeling using ICF’s Inte grated Planning Model. 77 

### IPM assumes “perfect foresight” of market conditions over the time horizon modeled; to the ex tent that utilities an d/or energy regulators misjudge future conditions affecting the ec onomics of pollution control, costs may be

understated as well.

One important element of the final CPP is the flexibility afforded to states as they develop requirements for their existing emitting sources. Each state has discretion on how to best achieve the standards of performa nce and/or state goals. As such, states can apply requirements to sources that achieve greater reductions than required during the interim period, and use those earlier reductions in the final peri od (i.e., banking of reductions).

In the analysis and modeling for the RIA, such flexibilities were not explicitly modeled in the compliance scenarios. Doing so would requi re additional assumptions about the specific

77 Full documentation for IPM can be found at <http:// http://www.epa.gov/powersectormodeling>.

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3-44 opportunities states may choose to ad opt in their plans, including the form of the standard that states apply, the manner in which it is applie d, and the economic signal that such a mechanism provides to sources over time, such that sour ces would have an incentive to make greater reductions earlier. As previously stated, the analysis in the RIA is intended to be illustrative to inform the broad impacts of the rule across th e power sector, and not intended to forecast the specific approaches that individu al states might choose, and how sources might prefer to achieve the emission reductions to reflect each state plan in response to partic ular policy signals or requirements. Not representing banking of earlier reductions into the fina l period is a somewhat conservative analytic approach, given that there is inadequate and incomplete information at this time regarding state plans.

3.12 Social Costs

As discussed in the EPA Guidelines for Pr eparing Economic Analyses, social costs are the total economic burden of a regulatory action. This burden is the sum of all opportunity costs incurred due to the regulatory acti on, where an opportunity cost is the value lost to society of any goods and services that will not be produced an d 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 soci ety. The social costs of a regulatory action will not necessarily be equi valent to the expenditures associated with compliance. Nonetheless, here we use compliance costs as a proxy for social costs. This section provides a qualitative discussion of the relations hip between social costs and compliance cost estimates presented in this chapter.

The cost estimates for the illustrative plan s cenarios presented in this chapter are the sum of expenditures on demand-side energy efficiency and the change in expenditures required by the electricity sector to comply with the final emission guideli nes. These two components are estimated separately. The expenditures require d to achieve the assumed demand reductions through demand-side energy efficien cy programs are estimated usi ng historical data, analysis, and expert judgment. The change in the expenditures required by th e electricity sector to meet demand and maintain compliance are estimate d by IPM and reflect both the reduction in electricity production costs due to the reduction in demand cau sed by the demand-side energy

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3-45 efficiency measures and the in crease in electricity production costs required to achieve the additional emission reductions necessary to comply with the state goals.

As described in section 3.6.1, the illustrative plan approaches assume that, in achieving their goals, demand-side energy efficiency m easures are adopted which lead to demand reductions in each year represented by the illust rative energy efficiency plan scenario. The estimated expenditures required to achieve thos e demand reductions through demand-side energy efficiency are presented in this chapter and de tailed in Chapter 5 of the Demand-Side Energy Efficiency TSD. The social cost of achievi ng these energy savings comes in the form of increased expenditures on technol ogies and/or services that ar e required to lower electricity consumption beyond the business as usual. Un der the assumption of complete and wellfunctioning markets the expenditures required to reduce electricity consumption on the margin will represent society’s opportunity cost of the resources required to produce the energy savings.

Due to the flexibility held by states in implementing their compliance with the final standards these energy efficien cy expenditures may be borne by end-users through direct participant expenditures or electricity rate incr eases, or by producers thro ugh reductions in their profits. While the allocation of these expenditures between consumers and producers is important for understanding the di stributional impact of potential compliance strategies, it does not necessarily affect the opportunity cost requir ed for the production of the energy savings from a social perspective. However, specific desi gn elements of demand-side energy efficiency

measures included to address distributional outcomes may have an effect on the economic efficiency of the programs and therefore the social cost.

Another reason the expenditures associated with demand-side energy efficiency may

differ from social costs is due to differences in the services provided by more energy efficient technologies and services adopted under the prog ram relative to the baseline. For example, if under the program end-users adopted more energy efficient products which were associated with quality or service attributes deemed less desirabl e, then there would be an additional welfare loss

that should be accounted for in social costs but is not necessarily captured in the measure of expenditures. However, there is an analogous pos sibility that in some cases the quality of services, outside of the energy savings, provi ded by the more energy efficient products and practices are deemed more desirable by some end-users. For example, weatherization of

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3-46 buildings to reduced electricity demand associated with cooling will likely have a significant impact on natural gas use associat ed with heating. In either cas e these real welfare impacts are not fully captured by end-use energy efficiency expenditure estimates.

The fact that such quality and service diffe rences may exist in reality but may not be reflected in the price difference between more a nd less energy efficient pr oducts is one potential hypothesis for the energy paradox. The energy para dox is the observation th at end-users do not always purchase products that are more energy efficient when the a dditional cost is less than the reduction in the net present value of expect ed electricity expenditures achieved by those products. 78 

### Such circumstances are present in the analys is presented in this chapter, whereby in some regions the base case and illustrative a pproaches suggest that cost of reducing demand through energy efficiency programs is less than the retail electricity pr ice. In addition to heterogeneity in product services and consumer pr eferences, there are other explanations for the energy paradox, falling both within and outside the neoclassical rational expectations paradigm that is used in benefit/cost analysis. Chapte r 5 of the Demand-Side Energy Efficiency TSD discusses the energy paradox and provides addi tional hypothesis for why consumers may not make energy efficiency investment s that ostensibly seem to be in their own interest. The TSD discussion also provides details on how the presence of additional market failures can lead to levels of energy efficiency investment that may be too low from society’s perspective even if

that is not the case for the end-user. In such cases there is the potenti al for properly designed energy efficiency programs to address the source of under-investment, such as principal-agent problems where there is a disconnect between those making the purchase decision regarding energy efficient investments and energy use and thos e that would receive the benefits associated with reduced energy use through lower electricity bills.

The other component of complian ce cost reported in this chap ter is the change in resource cost (i.e., expenditures) required by the electric ity sector to fulfill the remaining demand while making additional CO 2 

### emissions reductions necessary to co mply with the state goals. Included in the estimate of these compliance costs, develo ped using IPM, are the cost savings associated with the reduction in required electricity ge neration due to the demand reductions from demand-

78 An analogous situation is present when some EGUs have assumed to have the ability to make heat rate improvements at a capital cost that is less than the anticipated fuel expenditure savings.

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3-47 side energy efficiency measures and improvements in heat rate. By shifting the demand curve for electricity, demand-side energy effi ciency reduces the production cost in the sector. The resource

cost estimates from IPM therefor e account for the increased cost of providing electricity while EGUs comply with their obligations net of the reduction in producti on costs due to lower demand resulting from demand-side energy efficiency measures.

3.13 References

Just, R.J., D.L. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy: A Practical Guide to Policy and Project Evaluation , Edwin Elgar Press, Cheltenham UK. U.S. EPA. 2010. EPA Guidelines for Prepari ng Economic Analyses, Chapters 8 and Appendix A. Available at: <http://yosemite.epa.gov/ ee/epa/eed.nsf/webpages/guidelines.htm>. Accessed 7/11/2015. U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: El ectric Utility Generati ng Units. Demand-Side Energy Efficiency.

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3A-1 

## A PPENDIX 

## 3A: A NALYSIS OF 

## P OTENTIAL 

## U PSTREAM 

## M ETHANE 

## E MISSIONS 

## C HANGES IN 

## N ATURAL 

## G AS 

## S YSTEMS AND 

## C OAL 

## M INING



### The purpose of this appendix is to desc ribe the methodology for estimating upstream methane emissions related to natural gas system s and coal mining sectors that may result from the illustrative plan approaches examined in the Regulatory Impact Analysis (RIA). The US Environmental Protection Agency (EPA) assessed whether the net change in upstream methane emissions from natural gas and coal production is likely to be positive or negative and also assessed the potential magnitude of th ese upstream changes relative to CO 2 

### emissions reductions anticipated at power plants from the illustrativ e plan approaches examined in the RIA. In addition to estimating changes in upstream me thane emissions, this assessment included estimating CO 2 

### from the flaring of methane, but did not examine other potential changes in other upstream greenhouse gas emissions changes from na tural gas systems and coal mining sectors.

The methodologies used to project upstream emissions were previously developed for the purpose of the 2014 U.S. Climate Action Report, a nd were subject to peer review and public review as part of the publication of that report. In s ection 3A.1, the overall approach is described in brief. In section 3A.2, results are pres ented. Section 3A.3 discu sses uncertainties and limitations of the analysis. Finally, section 3A.4 c ontains a bibliography of cited resources. In the RIA for the Clean Power Plan proposal (in th en section 3A.3), we presented the detailed methodologies for how methane and flaring-related CO 2 

### projections were estimated for coal mining and natural gas systems. We rely on the same methods in this RIA, so we refer the interested reader to the proposal RIA 79 

### for the detailed methodological discussion. The calculations have been updated to reflect input data from the most recent U.S. GHG Inventory, published in April 2015.

79 Clean Power Plan proposal RIA can be found at < http://www2.epa.gov/sites/production/files/2014- 06/documents/20140602ria-clean-power-plan.pdf>.

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3A-2 3A.1 General Approach

3A.1.1 Analytical Scope
Upstream methane and flaring-related CO 2 

### emissions associated with coal mining and natural gas systems were estimated for 2025 through 2030 using methodologies developed for the 2014 U.S. Climate Action Report (U.S. Depart ment of State 2014). The base year for the projections is 2013, as reported in the 2015 U. S. GHG Inventory (EPA 2015a). The projection methodologies use activity driver data outputs such as coal and natural gas production from the base case and policy scenarios generated by th e Integrated Planning Model (IPM), which was

used in the RIA to model illu strative plan approaches. The pr ojection methodologies use similar activity data and emissions fact ors as are used in the U.S. Greenhouse Gas (GHG) Inventory.

The projection methodologies es timate reductions associated with both voluntary and regulatory programs affecting ups tream methane-related emissions . In the case of the voluntary programs, the rate of reductions is based on the historical average decrea se from these programs over recent years. In the case of regulatory re ductions, the reductions are based on the reduction

rates estimated in the RIAs of relevant regulations. The projec tions include emissions reductions projected to result from the 2012 Oil and Natu ral Gas New Source Performance Standards. The methodologies to estimate upstream emissions were subject to expert p eer review and public

review in the context of the 2014 U.S. Climate Action Report. For more information on the review, or for the detailed methodologies used for non-CO 2 

### source projections in that report, including methane-related emissions from co al production and natural gas systems, see “Methodologies for U.S. Greenhouse Gas Emissions Projections: Non-CO 2 

### and Non-Energy CO 2 

### Sources” (EPA, 2013). Uncertainties and limitations are discussed, including a side case which incorporates additional geographic informati on for estimating methane from coal mining.

The term “upstream emissions” in this document refers to vented, fugitive and flared emissions associated with fuel production, proces sing, transmission, storage, and distribution of fuels prior to fuel combustion in electricity plants. For this analysis, the EPA focused on upstream methane from the natural gas systems and coal mining sectors. In addition, the analysis

included CO 2 

### resulting from flaring in natural gas produc tion. This analysis does not assess other upstream GHG emissions changes, such as CO 2 

### emissions from the combustion of fuel used in

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3A-3 natural gas and coal production ac tivities or other non-combustion CO 2 

### emissions from natural gas systems, such as vented CO 2 

### and CO 2 

### emitted from acid-gas removal processes.

Also, the EPA assessed potential upstream me thane emissions from natural gas systems and coal mining sectors within the domestic U.S ., but did not examine emissions from potential changes in upstream emissions generated by changes in natural ga s and coal production, processing, and transportation act ivities outside of the US. 80 

### Last, the EPA did not assess potential changes in other upstream non-GHG emi ssions, such as nitrogen oxides, volatile organic compounds, and particulate matter. Tabl e 3A-1 presents estimates of the upstream emissions discussed in this analyses for 2013, based on the 2015 U.S. GHG Inventory.

EPA defined the boundaries of this assessment in order to provide targeted insights into the potential net change in methane emissions fr om natural gas systems and coal production activities specifically. CO 2 

### emissions from flared methane are included because regulatory and voluntary programs influence the rate of methane flaring over time and the CO 2 

### remaining after flaring is a methane-related GHG. Because of th e multiple strategies ad opted in the illustrative plan approaches, a more comprehensive assessm ent of upstream GHG emissions would require examination of the broader power sector and rela ted input markets and their potential changes in response to the rule. This analys is would be complex and likely subject to data limitations and substantial uncertainties. Rather, EPA chose to limit the scope of this upstream analysis to evaluate the potential for change s in GHG emissions that may be of significant scale relative to the impacts of the rule and for which EPA had previously-reviewed projection techniques, which are presented in detail below.

3A.1.2 Coal Mining Source Description
Within coal mining, this analysis covers f ugitive methane emissions from coal mining (including pre-mining drainage) and post-mining activities (i.e., coal handling), including both underground and surface mining. Emissions from abandoned mines are not included. Energy-

80 While the analysis does not estimate methane emissions changes outside of the United States, activity factors include imports and exports of natural gas to help estimat e domestic methane emissions related to trade of natural gas, such as emissions from LNG terminals in the US or from pipelines transporting imported natural gas within the US (or transporting natural gas within the US while en route for export).

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3A-4 related CO 2 

### emissions, such as emissions from mining equipment and vehicles transporting coal are not included. Methane, which is contained w ithin coal seams and the surrounding rock strata, is released into the atmosphere when mini ng operations reduce the pressure above and/or surrounding the coal bed. The quantity of methane emitted from these operations is a function of two primary factors: coal rank and coal depth. Coal rank is a measure of the carbon content of the coal, with higher coal ranks corresponding to higher carbon content and generally higher methane content. Pressure increases with dept h and prevents methane from migrating to the surface; as a result, underground mining operations typically emit more methane than surface mining. In addition to emissions from undergr ound and surface mines, post-mining processing of

coal and abandoned mines also release methane. Po st-mining emissions refer to methane retained

in the coal that is releas ed during processing, storage, and transport of the coal.

3A.1.3 Natural Gas Systems Source Description
Within natural gas systems, this analysis covers vented and fugitive methane emissions from the production, processing, transmission and storage, and distribut ion segments of the natural gas system. It also includes CO 2 

### from flaring of natural gas. Not included are vented and fugitive CO 2 

### emissions from natural gas systems, such as vented CO 2 

### emissions removed during natural gas processing, or energy-related CO 2 

### such as emissions from stationary or mobile combustion. The U.S. natural gas system encompasses hundreds of thousands of wells, hundreds of processing facilities, and over a million m iles of transmission and distribution pipelines.

Methane and non-combustion 81 

### CO 2 

### emissions from natural gas systems are generally processrelated, with normal operations, routine mainte nance, and system upsets being the primary contributors. There are four primary stages of th e natural gas system which are briefly described

below.

Production: In this initial stage, we lls are used to withdraw raw gas from underground

formations. Emissions arise from the wells them selves, gathering pipelin es, and well-site gas treatment facilities (e.g., dehydrat ors, separators). Major emissions source categories within the production stage include pneuma tic devices, gas wells with liquids unloading, and gas well

81 In this document, consistent with IPCC accounting term inology, the term “combustion emissions” refers to the emissions associated with the combustion of fuel for useful heat and work, while “non-combustion emissions” refers to emissions resulting from other activities, including flaring and CO 2 removed from raw natural gas.

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3A-5 completions and re-completions (i.e., workovers ) with hydraulic fracturing (EPA 2013). Flaring emissions account for the majority of the non-combustion CO 2 

### emissions within the production stage.
Processing: In this stage, natural gas li quids and various other const ituents from the raw gas are removed, resulting in “pipeline-quali ty” gas, which is then injected into the transmission system. Fugitive methane emissions from compressors, including compressor seals, are the primary emissions source from this stage. In the U.S. GHG Inventory, the majority of non-combustion

CO 2 

### emissions in the processing stage come from acid gas removal units, which are designed to

remove CO 2 

### from natural gas.

Transmission and Storage: Natural gas transmission involve s high-pressure, large-diameter pipelines that transport gas long distances from field production a nd processing areas to distribution systems or large-volume customers such as power plants or chemical plants. Compressor station facilities, which contain la rge reciprocating and turbine compressors, are used to move the gas throughout the U.S. transm ission system. Fugitive methane emissions from these compressor stations and from metering and re gulating stations account for the majority of the emissions from this stage. Pneumatic devi ces and non-combusted engi ne exhaust are also

sources of methane emissions from transmission faci lities. Natural gas is also injected and stored in underground formations, or liquefied and stor ed in above-ground tanks, during periods of lower demand (e.g., summer), and withdrawn, pro cessed, and distributed during periods of higher demand (e.g., winter). Compressors and dehydrators are the primary contributors to emissions from these storage facilities. Emi ssions from LNG import terminals are included within the transportation and storage stage.

Distribution: Distribution pipelines take the high-pressu re gas from the transmission system at “city gate” stations, reduce the pressure, a nd then distribute the gas through primarily underground mains and service lines to individual end users.

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3A-6 Table 3A-1. Base Year Upstream Methane-Rel ated Emissions in the U.S. GHG Inventory

Emissions Source 2013 Emissions (million metric tons CO 2 Eq.) Methane from Coal Mining 64.6

Underground Mining and Post-Mining 52.8 Surface Mining and Post-Mining 11.8 Methane from Natural Gas Systems 157.4 Production 47.0 Processing 22.7 Transmission and Storage 54.4 Distribution 33.3 CO 2 from flaring of natural gas 15.5 Source: 2015 U.S. GHG Inventory (EPA, 2015). A Global Warming Potential of 25 was used to convert methane emissions to CO 2 Eq. 

### In Table 3A-1, CO 2 

### -equivalent methane emissions are presented using the Fourth Assessment Report Global Warming Potential (GWP) of 25.

3A.1.4 Illustrative Plan Approaches Examined
States will ultimately determine optimal approaches to comply with the goals established in this regulatory action. The RIA depicts illu strative plan approaches for the final emissions guidelines, reflecting a rate-based illustrative plan or mass-based illustrative plan approach.

3A.1.5 Activity Drivers IPM-based activity driver projections from base case and illustrative plan approaches underlie the estimates of upstream methane emi ssions. These activity drivers include domestic coal and natural gas production, imports and expo rts, and natural gas consumption. Table 3A-2

and Table 3A-3 summarize the IPM-based coal and natural gas production activity driver results from the baseline and illustrative scenario for the final guidelines. 82



### Under the final guidelines, both th e rate-based and mass-based il lustrative plan approaches result in reduced coal production and little change in natural gas production. We estimate that the illustrative plan approaches will result in reductio ns in coal production of 5 to 6 percent in 2020, 12 to 15 percent in 2025 and 21 to 22 percent in 2030, relative to base case coal production.

82 Uncertainties related to activity drivers are discus sed in the uncertainties and limitations section.

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3A-7 Natural gas production in the illustrative plan appr oaches change by 1 perc ent or less in each of the years of analysis relative to production in the base case.

Table 3A-2. Projected Coal Production Impacts

Coal Production (million short tons) Coal Production Change from Base Case (million short tons) Coal Production Percent Change from Base Case

2020 2025 2030 2020 2025 2030 2020 2025 2030
Base Case 832.4 828.7 860.1 Rate-based 791.5 725.7 674.4 -41.0 -103.0 -186.0 -5% -12% -22% Mass-based 779.9 705.6 678.9 -52.0 -123.0 -181.0 -6% -15% -21%



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

Dry Gas Production (trillion cubic feet) Dry Gas Production Change from Base Case (trillion cubic feet) Dry Gas Production Percent Change from Base Case

2020 2025 2030 2020 2025 2030 2020 2025 2030
Base Case 28.9 30.8 33.0 Rate-based 29.1 30.7 32.9 +0.2 -0.1 -0.1 +1% 0% 0% Mass-based 29.2 30.8 32.6 +0.3 +0.0 -0.4 +1% 0% -1%



### 3A.2 Results
The analytical results (Table 3A-4) for the fi nal guidelines indicates decreases in methane emissions from coal mining of 6 to 8 million metric tons CO 2 

### Eq. in 2025, and about 12 million

metric tons CO 2 

### Eq. in 2030. Methane from natural gas sy stems decreases relative to the base case by 0 to 1 million metric tons CO 2 

### Eq. in 2025 and 1 to 2 million metric tons CO 2 

### Eq. in

2030. CO 2 

### from flaring in natural gas production does not show significant cha nge relative to the

base case.

Based on the actions modelled in the illustrative plan approaches, upstream methane

emissions and CO 2 

### emissions are predicted to decline (s ee Table 3A-4). The final guidelines are predicted to result in a net emissions reduction of about 8 million metric tons CO 2 

### Eq. in 2025

and a net emissions reduction of 13 to 15 million metric tons CO 2 

### Eq. in 2030. These net emissions changes represent the sum of changes in methane from coal mining, methane from

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3A-8 natural gas systems, and CO 2 

### from flaring in natural gas pr oduction. The projections include voluntary and regulatory activities to reduce emissions from coal mining and natural gas and oil systems, including the 2012 Oil and Natural Gas NSPS. In addition, the EPA plans to issue a proposed rule later this summer that would build on its 2012 Oil and Gas NSPS. When these standards are finalized and implemented, they would further reduce projected emissions from natural gas and oil systems.

Table 3A-4. Potential Upstream Emissions Changes Emissions (million metric tons CO 2 Eq.) 2020 2025 2030 Rate-based

Methane from Coal Mining -2.7 -6.8 -12.7 Methane from Natural Gas Systems +1.0 -0.7 -0.6 CO 2 from NG flaring +0.1 -0.0 -0.1 Total Methane + CO 2 -1.6 -7.6 -13.4 Mass-based Methane from Coal Mining -3.5 -8.2 -12.4 Methane from Natural Gas Systems +1.3 -0.1 -2.0 CO 2 from NG flaring +0.2 +0.0 -0.3 Total Methane + CO 2 -2.0 -8.2 -14.6 Note: A Global Warming Potential of 25 was used to convert methane emissions to CO 2 Eq.



### 3A.3 Uncertainties and Limitations
Projections of upstream methane emissions and CO 2 

### emitted from flaring of methane are subject to a range of uncertainties and limitations. These uncertainties and limitations include estimating the effect of the plan approach on activ ity drivers, uncertainty in base year emissions, and uncertainties in changes in emissions fact ors over relatively long periods of time. For example, EPA’s application of IPM relies on EI A projections for coal imports and exports. Consequently, coal imports and exports are not able to fully respond within the IPM framework to significant fluctuations in pow er sector coal demand. To the ex tent international markets may be expected to offset reduced domestic coal demand, changes in U.S. upstream emissions as a result of the policy scenarios would be smaller than what is presented here.

Discussion of uncertainty in hi storical estimates of emissions from coal mining and natural gas systems can be found in the 2015 U.S. GHG I nventory. Projected change s in activity drivers

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3A-9 and emissions factors are based on a combinati on of policy, macroeconomic, energy market, and technology factors which are uncertain in both baseline and illustrative plan approaches. Relatively higher or lower economic growth, or changes in the relativ e prices or availability of various technologies could result in alternative estimates in the net change in upstream methane

emissions and related CO 2 

### emissions.

3A.4 References

IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Available at <http://www.ipcc-nggip.iges.or.jp/public /2006gl/>. Accessed June 4, 2015. U. S. Department of State. 2014. 2014 U.S. Climate Action Report to the UNFCCC. Available at <http://www.state.gov/e/oes/rls/rpts/car6/index.htm>. Accessed June 4, 2015. U. S. Environmental Protection Agency (U.S. EPA). 1993. Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress. EPA-430-R-93-003. U. S. Environmental Protection Agency (U.S. EPA). 2011. Oil and Natural Gas Sector: Standards of Performance for Crude O il and Natural Gas Production, Transmission, and Distribution: Background Technical Suppor t Document for the Proposed Standards

. Available at <http://epa.gov/ airquality/oilandgas/pdfs/20110728tsd.pdf>. Accessed June 4, 2015. U. S. Environmental Protection Agency (U.S. EPA). 2012a. Oil and Natural Gas Sector: New Source Performance Standards and National Emission Standards for Hazardous Air Pollutants Reviews . 40 CFR Parts 60 and 63 [EPA- HQ-OAR-2010-0505; FRL-9665-1], RIN 2060-AP76. Available at <http://www .gpo.gov/fdsys/pkg/FR-2012-08-16/pdf/2012- 16806.pdf>. Accessed June 4, 2015. U. S. Environmental Protection Agency (U.S. EPA). 2012b. Oil and Natural Gas Sector: Standards of Performance for Crude O il and Natural Gas Production, Transmission, and Distribution: Background Supplemental Tech nical Support Document for the Final New Source Performance Standards . Available at <http://epa.gov/airquality/oilandgas/ pdfs/20120418tsd.pdf>. Accessed June 4, 2015. U. S. Environmental Protection Agency (U.S. EPA). 2015. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013 . EPA-430-R-15-004. Available at <http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html>. Accessed June 4, 2015.

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3A-10 U. S. Environmental Protection Agency (U.S. EPA). 2013. Methodologies for U.S. Greenhouse Gas Emissions Projections: Non-CO 2 

### and Non-Energy CO 2 

### Sources. Available at <http://www.state.gov/documents/organ ization/219472.pdf> Accessed June 4, 2015.

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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 Emi ssion Guidelines for Existing Stationary Sources: Electric Utility Generating Units (herea fter referred to as the “final emission guidelines” or “Clean Power Plan Final Rule”) is expected to reduce emi ssions of carbon dioxide (CO 2 

### ) and have ancillary human health benefits (i .e., co-benefits) associated with lower ambient

concentrations of criteri a air pollutants. This chapter describe s the methods used to estimate the

monetized climate benefits and the monetized ai r 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 met hodological limitations prevent th e 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 ecosystem effects and visibility impairment. We qualitatively discuss these unquantif ied benefits in this chapter.

This chapter provides estimates of the monetizedclimate benefits and air quality health cobenefits associated with emission reductions fo r the illustrative rate-based and mass-based illustrative plan approaches across several analysis years and discount rates. The estimated

benefits associated with these emission reduc tions are beyond those ach ieved 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 emissions of CO 2 

### . In this section, we provide a brief overview of th e 2009 Endangerment Finding and climate science assessments released since then. We also prov ide 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 year. Table 4-1 summarizes the quantified and unquantified c limate benefits in this analysis.

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4-2 Table 4-1. Climate Effects Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information Improved Environment Reduced climate effects Global climate impacts from CO 2 — 1  SCC TSD Climate impacts from ozone and black carbon (directly emitted PM) — — Ozone ISA, PM ISA 2 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, ar e used in this RIA to estim ate the welfare effects of quantified changes in CO 2 emissions.

2 We assess these co-benefits qualitatively because we do not have sufficient confiden ce in available data or methods. 

### 4.2.1 Climate Change Impacts
Through the implementation of CAA regula tions, the EPA addresses the negative externalities caused by air pollution. In 2009, th e EPA Administrator found that elevated concentrations of greenhouse gases in the atmo sphere may reasonably be anticipated both to endanger public health and to e ndanger public welfare. It is th ese 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 incr eased likelihood of heat waves, negative impacts on air quality, more intense hurricanes, more frequent and inte nse storms and heavy precipitation, and impacts

on infectious and waterborne diseases. For welfar e, these include reduced water supplies in some regions, increased water polluti on, increased occurrences of fl oods and droughts, rising sea levels and damage to coastal infrastructure, increased peak electricity demand, changes in ecosystems, and impacts on indigenous communities.

The preamble also summarizes new scientific assessments and recent climatic observations. Major scientific assessments released since th e 2009 Endangerment Finding have improved scientific understanding of the climate, and provide even more evidence that GHG emissions endanger public health and welfare for current and future generations. The National Climate Assessment (NCA3), in particular, asse ssed the impacts of climate change on human health in the United States, finding that, Amer icans will be impacted by “increased extreme

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4-3 weather events, wildfire, decreased air quality, th reats to mental health, and illnesses transmitted by food, water, and disease-carriers such as mosqu itoes and ticks.” These assessments also detail the risks to vulnerable groups such as child ren, the elderly and low income households. Furthermore, the assessments present an im proved understanding of the impacts of climate change on public welfare, higher projections of fu ture sea level rise than had been previously estimated, a better understanding of how the warmth in the next century may reach levels that would be unprecedented relative to the precedi ng 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 Anal ysis Under Executive

Order 12866 (May 2013, Revised July 2015) (“current TSD”). 83 

### We refer to these estimates, which were developed by the U.S. government, as “SC-CO 2 

### estimates.” The SC-CO 2 

### is a metric that estimates the monetary value of impact s associated with marginal changes in CO 2 

### emissions in a given year. It includes a wide range of antic ipated climate impacts, such as net changes in agricultural productivity and human health, property damage from increased flood risk, and changes in energy system costs, such as reduced costs for heating and increased costs for air conditioning. It is typically used to assess the avoided damages as a result of regulatory actions (i.e., benefits of rulemakings that lead to an incremental reduction in cumulative global CO 2 

### emissions).

83 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact An alysis Under Executive Order 12866 , Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised July 2015). Available at: <https://www.wh itehouse.gov/sites/default/files/omb/inforeg/ scc-tsd-final-july-2015.pdf> Accessed 7/11/2015.

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4-4 The SC-CO 2 

### estimates used in this analysis were developed over many years, using the best science available, and w ith input from the public. Specif ically, an interagency working group (IWG) that included the EPA and other execu tive branch agencies a nd offices used three integrated assessment models (IAMs) to develop the SC-CO 2 

### estimates and recommended four global values for use in regulatory analyses. 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 mi nor corrections to the SC-CO 2 

### estimates in July 2015.

The SC-CO 2 

### estimates were developed using an ense mble of the three most widely cited integrated assessment models in the economics lite rature with the abilit y 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 c onducting an extensive literature review, the interagency group selected three sets of input parameters (climate sensitivity, socioeconomic and emissions trajectories, and discount rates) to use consistent ly in each model. All other model features were left unchanged, relying on the model developers’ best estimates and judgments, as informed by the literature. Specifically, a common probabilit y distribution for the equilibrium climate sensitivity parameter, which informs the stre ngth of climate’s res ponse to atmospheric GHG concentrations, was used across all three models. In addition, a common range of scenarios for the socioeconomic parameters and emissions forecas ts were used in all three models. Finally, the marginal damage estimates from the three models were estimated using a consistent range of discount rates, 2.5, 3.0, and 5.0 percent. See th e 2010 TSD for a complete discussion of the methods used to develop the estimates and the key uncertainties, and the current TSD for the

latest estimates. 84



### The SC-CO 2 

### estimates represent global measures b ecause of the distinctive nature of the climate change, which is highly unusual in at leas t 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 (g lobal) damages cause d by GHG emissions to address the global nature of th e problem. Second, the U.S. operates in a global and highly

84 See https://www.whitehouse.gov/omb/oira /social-cost-of-carbon for both TSDs.

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4-5 interconnected economy, such that impacts on th e other side of the world can affect our economy. This means that the true costs of climate change to the U.S. are larger than the direct impacts that simply occur within the U.S. Third, climate change represents a classic public goods problem because each country’s reductions benef it everyone else and no c ountry can be excluded from enjoying the benefits of other countries’ redu ctions, even if it provid es no reductions itself. In this situation, the only way to achieve an eco nomically efficient level of emissions reductions is for countries to cooperate in providing mutu ally beneficial reductions beyond the level that would be justified only by their own domestic bene fits. In reference to the public good nature of mitigation and its role in foreign relations, thirteen prominent academics noted that these “are compelling reasons to focus on a global SCC” in a recent article on the SCC (Pizer et al., 2014). In addition, as noted in OMB’s Response to Comments on the SCC, there is no bright line between domestic and global damages. Adverse impacts on other countries can have spillover effects on the United States, partic ularly in the areas of national security, international trade, public health and humanitarian concerns. 85



### The 2010 TSD noted a number of limitations to the SC-CO 2 

### analysis, including the incomplete way in which the integrated a ssessment models capture catastrophic and noncatastrophic impacts, their in complete treatment of adapta tion and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Currently integrated a ssessment models do not assign va lue to all of the important physical, ecological, and economic impacts of clim ate 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 understanda bly lags behind the most recent research. 86 

### The limited amount of research linking climate im pacts to economic damages makes the modeling

exercise even more difficult. Thes e individual limitations do not all work in the same direction in

terms of their influence on the SC-CO 2 

### estimates, though taken togeth er they suggest that the

85 See Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act, 74 Fed. Reg. 66,496, 66,535 (Dec. 15, 2009) and National Research Council 2013a.

86 Climate change impacts and SCC modeling is an area of active research. For example, see: (1) Howard, Peter, “Omitted Damages: What’s Missing from the Social Cost of Carbon.” March 13, 2014, http://costofcarbon.org/files/Omitte d_Damages_Whats_Missing_From_the_Social_Cost_of_Carbon.pdf; and (2) Electric Power Research Institute, “U nderstanding the Social Cost of ca rbon: A Technical Assessment,” October 2014, www.epri.com.

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4-6 SC-CO 2 

### estimates are likely conservative. In pa rticular, the IPCC Fourth Assessment Report (2007), which was the most current IPCC assessm ent available at the time of the IWG’s 2009- 2010 review, concluded that “It is very likely that [SC-CO 2 

### estimates] underestimate the damage costs because they cannot include many non-qua ntifiable impacts.” Since then, the peerreviewed literature has continued to support this conclusion. For example, the IPCC Fifth Assessment 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 analysis to address these concerns.

The EPA and other agencies have conti nued to consider f eedback on the SC-CO 2 

### estimates from stakeholders through a range of channels , including public comments on this rulemaking

and others that use the SC-CO 2 

### in supporting analyses and thr ough regular interactions with stakeholders and research analysts implementing the SC-CO 2 

### methodology used by 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 a nd presentation of the SC-CO 2 

### estimates, and the process by which

the SC-CO 2 

### estimates were derived. Many but not all commenters were supportive of the SCCO 2 

### and its application to this rulemaki ng. Commenters also provided constructive recommendations for potential oppo rtunities to improve the SC-CO 2 

### estimates in future updates. The EPA Response to Comments document provi des a summary and response to the SC-CO 2 

### comments submitted to this rulemaking.

Many of the comments EPA received were si milar to those that OMB’s Office of Information and Regulatory Affairs received in response to a separate request for public comment on the approach used to develop the es timates. 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 analysis. With the release of the response to comments 87 

### , the IWG announced plans to obtain expert independent advice from the National Academie s of Sciences, Engineering, and Medicine

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

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4-7 (Academies) to ensure that the SC-CO 2 

### estimates continue to reflect the best available scientific and economic information on climate change. 88 

### The Academies process will be informed by the public comments received and focus on the techni cal merits and challenges of potential approaches to improving the SC-CO 2 

### estimates in future updates.

Concurrent with OMB’s publication of the response to comments on SC-CO 2 

### and announcement of the Academies process, OMB posted a revised TSD that includes two minor

technical corrections to the current estimates. On e technical correction addressed an inadvertent omission of climate change damages in the last year of analysis (2300) in one model and the second addressed a minor indexing error in an other model. On average the revised SC-CO 2 

### estimates are one dollar less than the mean 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 percent discount rate is slightly larger, as those estimates are heavily influenced by the results from the model th at was affected by th e 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$). 89 

### 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 becau se the literature s hows that the SC-CO 2 

### is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an in tergenerational context (where co sts and benefits are incurred by different generations). The fourth value is the 95 th 

### percentile of the SC-CO 2 

### from all three models at a 3 percent discount ra te. It is included to represent higher-than-expected impacts from temperature change further out in the tails of the SC-CO 2 

### distribution (representing less likely, but potentially catastrophic, outcomes).

Table 4-2 presents the global SC-CO 2 

### estimates for the years 2015 to 2050. In order to calculate the dollar value for emission reductions, the SC-CO 2 

### estimate for each emissions year

88 See https://www.whitehouse.gov/blog/2015/07/02/esti mating-benefits-carbon-dioxide-emissions-reductions.

89 The current version of the TSD is available at: http s://www.whitehouse.gov/sites/de fault/files/omb/inforeg/scctsd-final-july-2015.pdf. The 2010 and 2013 TSDs present SCC in 2007$. The estimates were adjusted to 2011$ using GDP Implicit Price Deflator, http://www.gpo.gov /fdsys/pkg/ECONI-2013-02/pdf/ECONI-2013-02-Pg3.pdf.

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4-8 would be applied to changes in CO 2 

### emissions for that year, and then discounted back to the analysis year using the same discount rate used to estimate the SC-CO 2



### . 90 

### The SC-CO 2 

### increases over time because future emissions are expect ed to produce larger incremental damages as physical and economic systems become more stre ssed in response to greater climate change. Note that the interagency group estimat ed the growth rate of the SC-CO 2 

### directly using the three integrated assessment models rather than assumi ng a constant annual growth rate. This helps to ensure that the estimates are internally consis tent with other modeling assumptions. Tables 4-3 through 4-5 report the incremental climate benef its estimated in three analysis years (2020, 2025, and 2030) for the rate-based and mass-b ased illustrative plan approaches.

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 $110 2020 $13 $45 $66 $130 2025 $15 $49 $72 $150 2030 $17 $53 $77 $160 2035 $19 $58 $83 $180 2040 $22 $64 $89 $190 2045 $24 $68 $94 $210 2050 $28 $73 $100 $230 * The SC-CO 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-CO 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 Mass-Based Million metric tonnes of CO 2 reduced 63 74 5% (average) $0.80 $0.94 3% (average) $2.8 $3.3 2.5% (average) $4.1 $4.9 3% (95 th percentile) $8.2 $9.7 * The SC-CO 2 values are dollar-year and em issions-year specific. SC-CO 2 values represent only a partial accounting of climate impacts.

90 This analysis considered the climate impacts of only CO 2 emission change. As discussed below, the climate impacts 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 fac ilities within these comparisons may also have different emission rates for other climate forcers that will serve a minor role in determining the overall social cost of generation.

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4-9 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 Mass-Based Million metric tonnes of CO 2 reduced 211 240 5% (average) $3.1 $3.6 3% (average) $10 $12 2.5% (average) $15 $17 3% (95 th percentile) $31 $35 * The SC-CO 2 values are dollar-year and em issions-year specific. SC-CO 2 values represent only a partial accounting of climate impacts.



### 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 Mass-Based Million metric tonnes of CO 2 reduced 376 375 5% (average) $6.4 $6.4 3% (average) $20 $20 2.5% (average) $29 $29 3% (95 th percentile) $61 $60 * The SC-CO 2 values are dollar-year and em issions-year specific. SC-CO 2 values represent only a partial accounting of climate impacts. 

### It is important to note that th e climate benefits presented abov e are associated with changes

in CO 2 

### emissions only. Implementing these final em ission guidelines, however, will have an impact on the emissions of other pollutants that would affect the climate. Both predicting reductions in emissions and estimating the climate impacts of these other pollutants, however, is complex. The climate impacts of these other pollu tants have not been calculated for the final emission guidelines. 91



### The other emissions potentially reduced as a re sult of the final emission guidelines include other greenhouse gases (such as methane), aeroso ls and aerosol precursors such as black carbon, organic carbon, sulfur dioxide a nd nitrogen oxides, and ozone precursors such as nitrogen oxides and volatile organic carbon compoun ds. Changes in emissions of these pollutants (both increases and decreases) could directly re sult from changes in electricity generation, upstream fossil fuel extraction and transport, and/or downstream se condary market impacts. Reductions in black carbon or ozone precursors are proj ected to lead to further coo ling, but reductions in the other aerosol species and precursors are projected to lead to warm ing. Therefore, changes in non-CO 2

91 The SC-CO 2 estimates used in this analysis are designed to assess the climate be nefits associated with changes in CO 2 emissions only.

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4-10 pollutants could potentially augment or offset the climate benefits ca lculated here. These pollutants can act in different ways and on di fferent timescales than carbon dioxide. For example, aerosols reflect (and in the case of black carbon, absorb) incoming radiation, whereas greenhouse gases absorb outgoing in frared radiation. In addition, these aerosols are thought to affect climate indirectly by a ltering properties of clouds. Black carbon can also deposit on snow and ice, darkening these surfaces and accelera ting melting. In terms of lifetime, while carbon

dioxide emissions can increase c oncentrations in the atmosphere for hundreds or thousands of years, many of these other pollu tants are short lived and remain in the atmosphere for short periods of time ranging from days to weeks and ca n therefore exhibit larg e spatial and temporal variability.

While the EPA has not quantified the climate impacts of these other pollutants for the final emission guidelines, the Agency has analyzed the potential changes in upstream methane emissions from the natural gas and coal production sectors that may result from the illustrative plan approaches examined in this RIA in th e 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 antici pated at power plants. This assessment included CO 2 

### emissions from the flaring of methane, but did not evaluate potential changes in othe r combustion-related CO 2 

### emissions, such as emissions associated with drilling, mining, processing, and transportation in the natural gas and coal production sectors. This analysis found that the net upstream CH 4 

### emissions from natural gas systems and coal mines and CO 2 

### emissions from flaring of methane will likely decrease under the final emission guidelines. Furthermore, the analysis suggests that the changes in upstream methane em issions are small relative to th e changes in direct emissions from power plants.

4.3 Estimated Human 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

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4-11 PM 2.5 

### , as well as directly emitted fine particles. 92 

### Therefore, 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. Depending on localized concentrations of volatile organic compounds (VOCs), reducing NO X 

### emissions would also reduce human exposure to ozone and the incidence of ozone-related health effects. Although we do not have sufficient data to quantify these impacts in this analysis, reducing emissions of 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 estimate d for the final emission guidelines. A full description of the underlying data, studi es, 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 associat ed with these emission reducti ons are beyond those achieved by previous EPA rulemakings, including MATS.

There are several important cons iderations in assessing the ai r quality-related health cobenefits for a climate-focused rulemaking. Firs t, these estimated hea lth co-benefits do not account for any climate-related air quality chan ges (e.g., increased ambient ozone associated with higher temperatures) but rath er changes in precursor emissi ons affected by this rulemaking. Excluding climate-related air quality changes may underestimate ozone-related health cobenefits. It is unclear how PM 2.5 

### -related health co-benefits wo uld be impacted by excluding climate-related air quality changes since the scie nce is unclear as to how climate change may

affect PM 2.5 

### exposure. Second, the estimated health co-b enefits 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 estimated

health co-benefits are calculated for the contiguous U.S. only. Excluding temperature modification of air pollution risks and international air quality-relat ed health benefits likely leads to underestimation of quantified health co-ben efits (Anenberg et al , 2009, Jhun et al, 2014).

92 We estimate the health co-benefits associated with emission reductions of two categories of directly emitted particles: elemental carbon plus organic carbon (EC+OC) and crustal. Crustal emissions are composed of compounds associated with minerals and metals from th e earth’s surface, including carbonates, silicates, iron, phosphates, copper, and zinc. Often, crustal material repres ents particles not classified as one of the other species (e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).

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4-12 Fourth, as noted earlier, we do not estimate the cl imate 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 Ai r Quality Standards (NAAQS) for PM and ozone

in some areas and assist other areas with attaining these NAAQS. Because the NAAQS RIAs (U.S. EPA, 2012a, 2008b, 2010d) also calculated PM and ozone bene fits, there are important differences worth noting in the design and analy tical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of a ttaining a revised air quali ty standard nationwide based on an array of emission redu ction strategies for different s ources reflecting the application of known and unknown controls, incremental to implementation of existing regulations and

controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not predict, the reduction strategies that States may choose to enact when implementing a revised NAAQS. The setting of a NAAQS does not directly re sult in costs or benefits, and as such, the EPA’s NAAQS RIAs are merely illustrative and th e estimated costs and benefits are not intended

to be added to the costs and benef its of other regulations that resu lt in specific costs of control

and emission reductions. Some of the emissi ons reductions estimated to result from implementation of the final emission guideli nes may achieve some of the air quality improvements that resulted from the hypothesize d attainment strategi es presented in the illustrative NAAQS RIAs. The emissions reducti ons from implementing the final emission guidelines will decrease the remaining amount of emissions reductions needed in non-attainment

areas and reduce the costs and benefits attributable to meeting the NAAQS.

Similar to NAAQS RIAs, the emission reduction scenarios estimated for the final emission guidelines are also illustrative. In contrast to NAAQS RIAs, all of the emission reductions for the illustrative plan approaches would occur in one well-characterized sector (i.e., the EGU sector). In general, the EPA is more confident in the magnitude and location of the emission reductions for rules which require specific emission reductions in a specific sector, for example, the recent Mercury and Air Toxics Standards. As such, emission reductions achieve d under these types of promulgated rules will ultimately be reflected in the baseline of future NAAQS analyses, which

would reduce the incremental cost s and benefits associated wi th attaining revised future NAAQS. The EPA does not re-issue illustrative RIAs outside of the rulemaking process that

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4-13 retroactively update the baseline to account for implementation rules promulgated after an RIA was completed. For more information on the relationship between illustrative analyses, such as for the NAAQS and this final emission guidelines, and implementation rules, please see section 1.3 of the PM NAAQS RIA (U.S. EPA, 2012a).

4.3.1 Health Impact Assessment for PM 2.5 

### and Ozone

The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b)

identified the human health effects associated with ambient PM 2.5 

### exposure, which include premature mortality and a variety of morbidity effects associated with acute and chronic exposures. Similarly, the Integrated Science Assessment for Ozone and Related Photochemical

Oxidants (Ozone ISA) (U.S. EPA, 2013b) identified the human health eff ects associated with ambient ozone exposure, which include premature mortality and a variety of morbidity effects associated with acute and chronic exposures. Ta ble 4-6 identifies the qua ntified and unquantified co-benefit categories captured in the EPA’s health co-benefits estimates for reduced exposure to

ambient PM 2.5 

### and ozone. Although the table below does not list unquantified health effects such as those associated with exposure to SO 2 

### , NO 2 

### , and mercury nor welf are 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 unquantif ied benefit categories is not e xhaustive, nor is quantification of each effect complete.

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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 Improved 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 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-activ ity days (age 18-65)   PM ISA Chronic Bronchitis (age >26) — — PM ISA 1 Emergency room visits fo r 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 ISA 2 Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations) — — PM ISA 2

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc.) — — PM ISA 2,3 Cancer, mutagenicity, and genotoxicity effects — — PM ISA 2,3

Reduced incidence of mortality from exposure to ozone Premature mortality based on short-term study estimates (all ages)   Ozone ISA Premature mortality based on long-term study estimates (age 30–99) — — Ozone ISA 1

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-activ ity 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 ISA 2 Cardiovascular and nervous system effects — — Ozone ISA 2 Reproductive and developmental effects — — Ozone ISA 2,3

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 eviden ce is only suggestive of causality or there are other significa nt concerns over the strength of the association.

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4-15 We follow a “damage-function” approach in ca lculating benefits, which estimates changes in individual health endpoints (spe cific effects that can be associated with changes in air quality) and assigns values to those changes assuming i ndependence of the values for those individual endpoints. Because the EPA rarely has the time or resources to perform new research to measure directly, either health outcomes or their values for regulator y analyses, our estimates are based

on the best available methods of benefits tran sfer, which is the scie nce and art of adapting primary research from similar contexts to estimat e benefits for the environmental quality change under analysis. In addition to transferring informa tion from other contexts to the context of this regulation, we also use a “benefit-per-ton” appro ach to estimate the PM 2.5 

### and ozone co-benefits in this RIA. Benefit-per-ton appr oaches apply an average benef it per ton derived from modeling of benefits of specific air quali ty scenarios to estimates of em issions reductions for scenarios where no air quality modeling is available. Thus, to develop estimates of benefits for this RIA, we are transferring both the underlying health an d economic information from previous studies and information on air quality responses to emi ssions reductions from previous air quality modeling. This section describe s the underlying basis for the h ealth and economic valuation estimates that inform the benefit-per-ton estim ates, and the subsequent section provides an overview of the benefit-per-ton estimates, 93 

### which are described in detail in the appendix to this chapter.

The benefit-per-ton approach we use in this RIA relies on estimates of human health responses to exposure to PM and ozone obtained from the peer-reviewed scientific literature. These estimates are used in conjunction with popul ation data, baseline health information, air quality data and economic valuation information to conduct health impact and economic benefits assessments. These assessments form the key inputs to calculating benefit-per-ton estimates. The next sections provide an overview of the he alth impact assessment (HIA) methodology and additional details on several key elements.

93 We have updated the benefit-per-ton estimates since the proposal RIA. In this RIA, we apply benefit-per-ton estimates that were derived from air quality modeling of the proposed Clean Power Plan (Option 1 State).

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4-16 The HIA quantifies the changes in the inciden ce of adverse health impacts resulting from changes in human exposure to PM 2.5 

### and ozone. We use the envir onmental Benefits Mapping and Analysis Program – Community Edition (BenMA P-CE) (version 1.1) to systematize health impact analyses by applying a database of key input parameters, including population projections, health impact func tions, and valuation functions (A bt Associates, 2012). For this assessment, the HIA is limited to those health effects that are direc tly linked to ambient PM 2.5 

### and ozone concentrations. There may be other indi rect health impacts as sociated with reducing emissions, such as occupational health exposur es. Epidemiological studies generally provide estimates of the relative risks of a particular he alth effect for a given increment of air pollution (often per 10 μg/m 3 

### for PM 2.5 

### or ppb for ozone). These relative risk s 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 information regarding the ep idemiology studies and risk coefficients applied in this analysis, and we briefly elaborate on adult prematur e mortality below. The size of the mortality effect estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascr ibed to reducing risks of premat ure death make mortality risk reduction the most significant health e ndpoint quantified in this analysis.

4.3.1.1 Mortality Concentration- Response Functions for PM 2.5 

### Considering a substantial body of published scie ntific literature a nd reflecting thousands of epidemiology, toxicology, and clinical studi es, the PM ISA documents the association

between elevated PM 2.5 

### concentrations and adverse health e ffects, including increased premature mortality (U.S. EPA, 2009b). The PM ISA, which was twice reviewed by the Clean Air Scientific Advisory Committee of the EPA’s Science Advisory Board (SAB-CASAC) (U.S. EPA-SAB, 2009b, 2009c), concluded th at there is a causal relati onship between mortality and both long-term and short-term exposure to PM 2.5 

### based on the entire body of scientific evidence.

The PM ISA also concluded that the scientific li terature supports the us e of a no-threshold loglinear model to portray the PM-mortality concen tration-response relati onship while recognizing potential uncertainty about the ex act shape of the concentration -response function. In addition to adult mortality discussed in more detail belo w, we use effect coefficients from Woodruff et al. (1997) to estimate PM-rela ted infant mortality.

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4-17 For adult PM-related mortality, we use the e ffect coefficients from the most recent epidemiology studies examining two large popula tion cohorts: the American Cancer Society cohort (Krewski et al. , 2009) and the Harvard Six Cities coho rt (Lepeule et al, 2012). The PM ISA (U.S. EPA, 2009b) concluded that the ACS and Six Cities cohorts produce the strongest evidence of the association between long-term PM2.5 exposure and premature mortality with support from a number of additional cohort st udies. The SAB's Health Effects Subcommittee (SAB-HES) also supported using thes e two cohorts for analyses of the benefits of PM reductions (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities cohort studies have inherent strengths and weaknesses, we present PM2.5 co-benefits estimates based on benefits-per-ton derived using

relative risk estimates from both these cohorts.

As a characterization of uncer tainty regarding the adult PM 2.5 

### -mortality relationship, the EPA graphically 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 describing 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 i ndependent opinions regarding the PM 2.5 

### -mortality concentration-response function. Because the expe rts relied upon the ACS and Six Cities cohort

studies to inform their concen tration-response functi ons, the benefits estimates based on the expert responses generally fall between benefits estimates based on thes e 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 expe rt-derived results is generally consistent with SAB advice (U.S. EPA-SAB, 2008), which r ecommended 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 likely to be substantial”. Although it is possible that newer scientific lite rature could revise the experts’ quantitative responses if elicited again, we believe that these general conclusions are unlikely to change.

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4-18 4.3.1.2 Mortality Concentration-Re sponse Functions for Ozone

In 2008, the National Academies of Scie nce (NRC, 2008) issued a series of recommendations to the EPA regarding the quan tification and valuation of ozone-related shortterm mortality. Chief among these was that “...short- term exposure to ambient ozone is likely to contribute to premature deaths” and the committ ee recommended that “o zone-related mortality

be included in future estimates of the health benefits of reducing oz one exposures...” The NAS also recommended that “...the greatest em phasis be placed on the multicity and NMMAPS [ National Morbidity, Mortal ity, and Air Pollution Study] studies without exclusion of the metaanalyses” (NRC, 2008). In view of the findings of the National Academies panel, we estimate the co-benefits of avoiding short-term ozone mortality using the Bell et al. (2004) NMMAPS analysis, the Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as effect estimates from the three meta-analyses (Bell et al. (2005), Levy et al. (2005), and Ito et al. (2005)). These studies are consistent with th e studies used in th e Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d). 94 

### For simplicity, we report the ozone mortality estimates in this RIA as a range reflecting application of do llar-per-ton estimates based on Bell et al. (2004) and Levy et al. (2005) to represent the lowest and the highest co-benefits esti mates based on these six ozone mortality studies. In addition, we graphically present in Figure 4-1 the estimated co-benefits based on dollar-per-ton estimates derived from all six studies mentioned above as a

characterization of uncerta inty 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 con centrations of air pollution generally lower the risk of future adverse health effects by a sm all amount for a large popu lation. 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 admi ssions, WTP estimates are generally not available, so we use the cost of treating or mitigating the effect. These cost-of-illness (COI) estimates

94 Since the EPA received NAS advice, the Agency publishe d the Ozone ISA (U.S. EPA, 2013b) and the second draft Ozone Health Risk and Exposur e Assessment (U.S. EPA, 2014a). Ther efore, the ozone mortality studies applied in this analysis, while current at the time of the previous Ozone NAAQS RIAs, do not reflect the most updated literature available. The selection of ozone morta lity studies used to estimate benefits in RIAs will be revisited in the forthcoming RIA accompanying the on-going review of the Ozone NAAQS.

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4-19 generally (although not necessarily in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the dire ct expenditures related to treatment but not the value of avoided pain and suffering from the hea lth effect. The unit values applied in this analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 2012a).

Avoided premature deaths account for 98 percen t of monetized PM-rela ted co-benefits and over 90 percent of monetized ozone-related co-b enefits. The economics literature concerning the appropriate method for valuing re ductions in premature mortalit y risk is still developing. The adoption of a value for the projected reduction in th e risk of premature mortality is the subject of continuing discussion within the economics a nd public policy analysis community. Following

the advice of the SAB’s Environmental Econom ics Advisory Committee (SAB-EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating estimates of mortality benefits, because we believe this calculation provid es the most reasonable single estimate of an individual’s willingness to trade off money for reductions in mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people.

The EPA continues work to update its guidan ce on valuing mortality risk reductions, and the Agency consulted several times with the SA B-EEAC on this issue. Until updated guidance is available, the Agency determined that a single, peer-reviewed estimate applied consistently, best

reflects the SAB-EEAC advice it has received. Therefore, the EPA has decided to apply the VSL

that was vetted and endo rsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014) 95 

### while the Agency continues its effort s to update its guidance on this issue. This approach calculates a mean value across VS L estimates derived from 26 labor market and contingent valuation studies published be tween 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 96 

### We then adjust this VSL to account for the currency year and to

95 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), the EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming.

96 In 1990$, this base VSL is $4.8 million.

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4-20 account for income growth from 1990 to the analys is year. Specifically, the VSLs applied in this analysis in 2011$ after adjusting for income growth are $9.9 million for 2020 and $10.1 million

for 2025 and 2030. 97



### The Agency is committed to using scientifical ly sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SABEEAC’s specific recommendations. In the proce ss, the Agency has identified a number of important issues to be consider ed in updating its mortality risk valuation estimates. These are detailed in a white paper, “V aluing 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 fo rmal recommendations 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 morta lity, we discount the value of premature mortality occurring in future years usi ng rates of 3 percent and 7 per cent (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 st ructure of the lag is uncertain, the EPA follo ws the advice of the SAB-HES to assume a segmented lag structur e characterized by 30 percent of mortality reductions in the first year, 50 percent over year s 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 oc curs within the analysis year, the estimated

ozone-related co-benefits are id entical for all discount rates.

4.3.3 Benefit-per-ton Estimates for PM 2.5 

### We 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 esti mates provide the total monetiz ed human health co-benefits (the sum of premature mortality and premat ure morbidity), of reducing one ton of PM 2.5 

### (or

97 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2030 estimates use income growth only through 2024 and are therefore likely underestimates.

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4-21 PM 2.5 

### precursor such as NO X 

### or SO 2 

### ) from a specified source. Speci fically, in this analysis, we multiplied the benefit-per-ton estimates by the corresponding emission reductions that were generated from air quality modeling of the proposed Clean Power Plan.

The method used to calculate the regional be nefit-per-ton estimate s is similar to the average EGU sector estimates used for the propo sal (U.S. EPA, 2013a), but relies on air quality modeling of the proposed Clean Power Plan. Si milar to the proposal, we generated regional benefit-per-ton estimates by aggr egating the impacts in BenMAP to the region (i.e., East, West, and California) rather than aggregating to th e nation. The appendix to this chapter provides additional detail regard ing these calculations.

As noted below in the characte rization of uncertainty, all be nefit-per-ton estimates have inherent limitations. Specificall y, all benefit-per-ton estimates reflect the geographic distribution of the modeled proposal, which may not match th e emission reductions anticipated by the final emission guidelines, and they may not reflec t local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors for any specific location. The regional benefit- per-ton estimates, although less subject to these types of uncertainties than national estimates, still shou ld 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 locati on and magnitude of their impact on PM 2.5 

### levels, 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 be nefit-per-ton estimates for ozone based on air quality modeling for the proposed 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 undere stimates ozone co-benef its in areas with a

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

4-22 longer ozone season such as southern California and Texas. These estimates assume that EGUattributable ozone formation at the regional-level is due to NOx alone. Because EGUs emit little

VOC relative to NO X 

### emissions, it is unlikely that VOCs emitted by EGUs would contribute substantially to regional ozone formation. As not ed above, all benefit-per-ton estimates have

inherent limitations and shoul d be interpreted with caution. We provide more detailed information regarding the generation of these estimates in the appendix to this chapter.

4.3.5 Estimated Health Co-Benefits Results
Tables 4-7 through 4-9 provide the regional benefit-per-ton estimates for three analysis years: 2020, 2025, and 2030. Tables 4-10 throug h 4-12 and 4-13 through 4-15 provide the

emission reductions estimated to occur in each analysis year for the rate-based and mass-based illustrative plan approaches, respectively, by region (i.e., East, West, and California). 98 

### Tables 4- 16 through 4-18 and 4-19 through 4-21 summarize the national monetized PM and ozone-related

health co-benefits estimated to occur in each analysis year for the illustrative rate-based and mass-based plan approaches, respectively, by pr ecursor 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 in cidences associated with the illustrative ratebased and mass-based plan approaches, re spectively, in each analysis year. 99 

### Figure 4-1 provides a visual representation of the range of estimated PM 2.5 

### and ozone-related co-benefits using benefit-per-ton estimates based on concentration-response functions from different studies and expert opinion for the illustrative rate-based and mass-based plan approaches evaluated in 2025 as an illustrative analysis y ear. Figure 4-2 provides a breakdown of the monetiz ed health cobenefits for the rate-based and mass-based plan approaches evaluated in 2025 as an illustrative analysis year by precursor pollutant.

98 See Chapter 3 of this RIA for more information regardin g the expected emission reductions used to calculate the health co-benefits in this chapter. Chapter 3 also provides more information regarding the illustrative plan approach.

99 Incidence estimates were generated using the same “per t on” approach as used to generate the dollar benefit per ton values. See Appendix 4-A for details.

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

4-23 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$)*

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 th e range of epidemiology studies fo r avoided premature mortality for PM 2.5 and

ozone. All estimates are ro unded to two significant figures. The mone tized 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 occu r in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this anal ysis because of the benefitper-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 4-8. 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 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 th e range of epidemiology studies fo r avoided premature mortality for PM 2.5 and

ozone. All estimates are ro unded to two significant figures. The mone tized 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 occu r in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this anal ysis because of the benefitper-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 – Draft – Do Not Cite, Quote or Release During Review***

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 th e range of epidemiology studies fo r avoided premature mortality for PM 2.5 and

ozone. All estimates are ro unded to two significant figures. The mone tized 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 occu r in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this anal ysis because of the benefitper-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 Po llutants for the Final Emission Guidelines Rate-based Illustrative Plan Approa ch 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 13 50 19 West 1 1 0 California 0 0 0 National Total 14 50 19 

### *All emissions shown in the table are rounded, so regional emission reductions may appear to

not sum to national total.

Table 4-11. Emission Reductions of Criteria Po llutants for the Final Emission Guidelines Rate-based Illustrative Plan Approach 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 171 155 67 West 7 8 3 California 1 2 0 National Total 178 165 70 

### *All emissions shown in the table are rounded, so regional emission reductions may appear to

not sum to national total.

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

4-25 Table 4-12. Emission Reductions of Criteria Po llutants for the Final Emission Guidelines Rate-based Illustrative Plan Approach 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 306 263 109 West 11 15 9 California 1 4 0 National Total 318 282 118 

### *All emissions shown in the table are rounded, so regional emission reductions may appear to

not sum to national total.

Table 4-13. Emission Reductions of Criteria Po llutants for the Final Emission Guidelines Mass-based Illustrative Plan Approac h 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 49 57 22 West 4 4 1 California 0 0 0 National Total 54 60 23 

### *All emissions shown in the table are rounded, so regional emission reductions may appear to

not sum to national total.

Table 4-14. Emission Reductions of Criteria Po llutants for the Final Emission Guidelines Mass-based Illustrative Plan Approach 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 156 169 74 West 29 34 14 California 0 0 0 National Total 185 203 88 

### *All emissions shown in the table are rounded, so regional emission reductions may appear to

not sum to national total.

Table 4-15. Emission Reductions of Criteria Po llutants for the Final Emission Guidelines Mass-based Illustrative Plan Approach 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 243 229 99 West 36 48 21 California 1 1 1 National Total 280 279 121

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

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 He alth Co-Benefits for the Final Emission Guidelines Rate-based Illustrative Pl an Approach in 2020 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $0.44 to $0.99 $0.39 to $0.89 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $0.14 to $0.33 $0.13 to $0.30 NOx (as Ozone) $0.12 to $0.52 $0.12 to $0.52 Total $0.70 to $1.8 $0.64 to $1.7 * All estimates are rounded to two si gnificant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced hea lth effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particle s 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-b enefits incorporate the co nversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions . Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 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 He alth Co-Benefits for the Final Emission Guidelines Rate-based Illustrative Plan Approach 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) $0.49 to $2.1 $0.49 to $2.1 Total $7.4 to $18 $6.7 to $16 * All estimates are rounded to two si gnificant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced hea lth effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particle s 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-b enefits incorporate the co nversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions . Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 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 – Draft – Do Not Cite, Quote or Release During Review***

4-27 Table 4-18. Summary of Estimated Monetized He alth Co-Benefits for the Final Emission Guidelines Rate-based Illustrative Plan Approach in 2030 (billions 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 $0.93 to $2.1 NOx (as Ozone) $0.86 to $3.7 $0.86 to $3.7 Total $14 to $34 $13 to $31 * All estimates are rounded to two si gnificant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced hea lth effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particle s 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-b enefits incorporate the co nversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions . Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 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 He alth Co-Benefits for the Final Emission Guidelines Mass-based Illustrative Pl an Approach in 2020 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $1.70 to $3.80 $1.50 to $3.40 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $0.17 to $0.39 $0.16 to $0.36 NOx (as Ozone) $0.14 to $0.61 $0.14 to $0.61 Total $2.00 to $4.8 $1.80 to $4.4 * All estimates are rounded to two si gnificant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced hea lth effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particle s 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-b enefits incorporate the co nversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions . Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 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 – Draft – Do Not Cite, Quote or Release During Review***

4-28 Table 4-20. Summary of Estimated Monetized He alth Co-Benefits for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2025 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $6.0 to $13 $5.4 to $12 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $0.58 to $1.3 $0.52 to $1.2 NOx (as Ozone) $0.56 to $2.4 $0.56 to $2.4 Total $7.1 to $17 $6.5 to $16 * All estimates are rounded to two si gnificant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced hea lth effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particle s 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-b enefits incorporate the co nversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions . Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 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 4-21. Summary of Estimated Monetized He alth Co-Benefits for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2030 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $10 to $23 $9.0 to $20 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $0.87 to $2.0 $0.79 to $1.8 NOx (as Ozone) $0.82 to $3.5 $0.82 to $3.5 Total $12 to $28 $11 to $26 * All estimates are rounded to two si gnificant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced hea lth effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particle s 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-b enefits incorporate the co nversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions . Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 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 – Draft – Do Not Cite, Quote or Release During Review***

4-29 Table 4-22. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-benefits for the Final Emission Guidelines Rate-based Illustrative Plan Approach in 2020* PM 2.5 -related Health Effects Avoided Premature Mortality

Krewski et al. (2009) (adult) 64 Lepeule et al. (2012) (adult) 140 Woodruff et al. (1997) (infant) 0 Avoided Morbidity

Emergency department visits for asthma (all ages) 34 Acute bronchitis (age 8–12) 94 Lower respiratory symptoms (age 7–14) 1,200 Upper respiratory symptoms (asthmatics age 9–11) 1,700 Minor restricted-activity days (age 18–65) 47,000 Lost work days (age 18–65) 7,900 Asthma exacerbation (age 6–18) 4,200 Hospital admissions—respiratory (all ages) 19 Hospital admissions—cardiovascular (age > 18) 23 Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 73 Pooled estimate of 4 studies 8 Ozone-related Health Effects Avoided Premature Mortality

Bell et al. (2004) (all ages) 11 Levy et al. (2005) (all ages) 51 Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 66 Hospital admissions—respiratory causes (ages < 2) 33 Emergency room visits for asthma (all ages) 37 Minor restricted-activity days (ages 18-65) 66,000 School absence days 23,000 * All estimates are rounded to whole numbers w ith two significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx emissions. Confidence intervals are unavailable fo r this analysis because of the incidence-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 Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

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

4-30 Table 4-23. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-benefits for Final Emission Gu idelines Rate-based Illustrative Plan Approach 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) 380 Acute bronchitis (age 8–12) 1,100 Lower respiratory symptoms (age 7–14) 14,000 Upper respiratory symptoms (asthmatics age 9–11) 20,000 Minor restricted-activity days (age 18–65) 530,000 Lost work days (age 18–65) 89,000 Asthma exacerbation (age 6–18) 48,000 Hospital admissions—respiratory (all ages) 220 Hospital admissions—cardiovascular (age > 18) 270 Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 860 Pooled estimate of 4 studies 93 Ozone-related Health Effects Avoided Premature Mortality

Bell et al. (2004) (all ages) 44 Levy et al. (2005) (all ages) 200 Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 280 Hospital admissions—respiratory causes (ages < 2) 130 Emergency room visits for asthma (all ages) 140 Minor restricted-activity days (ages 18-65) 250,000 School absence days 87,000 * All estimates are rounded to whole numbers w ith two significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx emissions. Confidence intervals are unavailable fo r this analysis because of the incidence-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 Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

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

4-31 Table 4-24. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-Benefits for Final Emission Gu idelines Rate-based Illustrative Plan Approach 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 Acute bronchitis (age 8–12) 2,000 Lower respiratory symptoms (age 7–14) 26,000 Upper respiratory symptoms (asthmatics age 9–11) 37,000 Minor restricted-activity days (age 18–65) 970,000 Lost work days (age 18–65) 160,000 Asthma exacerbation (age 6–18) 90,000 Hospital admissions—respiratory (all ages) 440 Hospital admissions—cardiovascular (age > 18) 530 Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 1,700 Pooled estimate of 4 studies 180 Ozone-related Health Effects Avoided Premature Mortality

Bell et al. (2004) (all ages) 73 Levy et al. (2005) (all ages) 330 Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 500 Hospital admissions—respiratory causes (ages < 2) 200 Emergency room visits for asthma (all ages) 220 Minor restricted-activity days (ages 18-65) 400,000 School absence days 140,000 * All estimates are rounded to whole numbers w ith two significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx emissions. Confidence intervals are unavailable fo r this analysis because of the incidence-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 Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

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

4-32 Table 4-25. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-benefits for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2020* PM 2.5 -related Health Effects Avoided Premature Mortality

Krewski et al. (2009) (adult) 200 Lepeule et al. (2012) (adult) 460 Woodruff et al. (1997) (infant) 0 Avoided Morbidity

Emergency department visits for asthma (all ages) 110 Acute bronchitis (age 8–12) 300 Lower respiratory symptoms (age 7–14) 3,800 Upper respiratory symptoms (asthmatics age 9–11) 5,500 Minor restricted-activity days (age 18–65) 150,000 Lost work days (age 18–65) 25,000 Asthma exacerbation (age 6–18) 13,000 Hospital admissions—respiratory (all ages) 59 Hospital admissions—cardiovascular (age > 18) 73 Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 230 Pooled estimate of 4 studies 25 Ozone-related Health Effects Avoided Premature Mortality

Bell et al. (2004) (all ages) 13 Levy et al. (2005) (all ages) 61 Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 78 Hospital admissions—respiratory causes (ages < 2) 40 Emergency room visits for asthma (all ages) 43 Minor restricted-activity days (ages 18-65) 78,000 School absence days 27,000 * All estimates are rounded to whole numbers w ith two significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx emissions. Confidence intervals are unavailable fo r this analysis because of the incidence-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 Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

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4-33 Table 4-26. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-benefits for Final Emission Gu idelines Mass-based Illustrative Plan Approach in 2025* PM 2.5 -related Health Effects Avoided Premature Mortality

Krewski et al. (2009) (adult) 700 Lepeule et al. (2012) (adult) 1,600 Woodruff et al. (1997) (infant) 2 Avoided Morbidity

Emergency department visits for asthma (all ages) 350 Acute bronchitis (age 8–12) 1,000 Lower respiratory symptoms (age 7–14) 13,000 Upper respiratory symptoms (asthmatics age 9–11) 19,000 Minor restricted-activity days (age 18–65) 500,000 Lost work days (age 18–65) 84,000 Asthma exacerbation (age 6–18) 46,000 Hospital admissions—respiratory (all ages) 210 Hospital admissions—cardiovascular (age > 18) 260 Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 810 Pooled estimate of 4 studies 88 Ozone-related Health Effects Avoided Premature Mortality

Bell et al. (2004) (all ages) 51 Levy et al. (2005) (all ages) 230 Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 320 Hospital admissions—respiratory causes (ages < 2) 150 Emergency room visits for asthma (all ages) 160 Minor restricted-activity days (ages 18-65) 290,000 School absence days 100,000 * All estimates are rounded to whole numbers w ith two significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx emissions. Confidence intervals are unavailable fo r this analysis because of the incidence-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 Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

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4-34 Table 4-27. Summary of Avoided Health Incidences from PM 2.5 

### -Related and OzoneRelated Co-Benefits for Final Emission Gu idelines Mass-based Illustrative Plan Approach in 2030* PM 2.5 -related Health Effects Avoided Premature Mortality

Krewski et al. (2009) (adult) 1,200 Lepeule et al. (2012) (adult) 2,600 Woodruff et al. (1997) (infant) 2 Avoided Morbidity

Emergency department visits for asthma (all ages) 440 Acute bronchitis (age 8–12) 1,600 Lower respiratory symptoms (age 7–14) 21,000 Upper respiratory symptoms (asthmatics age 9–11) 30,000 Minor restricted-activity days (age 18–65) 790,000 Lost work days (age 18–65) 130,000 Asthma exacerbation (age 6–18) 74,000 Hospital admissions—respiratory (all ages) 360 Hospital admissions—cardiovascular (age > 18) 430 Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 1,400 Pooled estimate of 4 studies 150 Ozone-related Health Effects Avoided Premature Mortality

Bell et al. (2004) (all ages) 70 Levy et al. (2005) (all ages) 320 Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 470 Hospital admissions—respiratory causes (ages < 2) 200 Emergency room visits for asthma (all ages) 210 Minor restricted-activity days (ages 18-65) 380,000 School absence days 130,000 * All estimates are rounded to whole numbers w ith two significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx emissions. Confidence intervals are unavailable fo r this analysis because of the incidence-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 Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

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4-35 Figure 4-1. Monetized Health Co-benefits of Rate-based and Mass-based Illustrative Plan Approaches for the Final Emission Guidelines in 2025 *

*The PM 2.5 graphs show the estimated PM 2.5 co-benefits at discount rates of 3% and 7% using effect coefficients derived from the Krewski et al. (2009) study and the Lepeule et al. (2012) study, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality (Roman et al. , 2008). The results sh own are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response functions provided in those studies. The ozone graphs show the estimated ozone co-benefits derived from six ozone mortality studies (i.e., Bell et al. (2004), Schwartz (2005), Huang et al. (2005), Bell et al. (2005), Levy et al. (2005), and Ito et al. (2005). Ozone co-benefits occur in the analysis year, so they are the same for all discount rates. These estimates do not include benefits from reductions in CO2. The monetized co-benefits do not include climate benefits from changes in NO 2 and SO 2 or reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. 

### PM 2.5 

### Ozone

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4-36 Low Health Co

‐ benefits High Health Co

‐ benefits

Figure 4-2. Breakdown of Monetized Health Co -benefits by Precursor Pollutant at a 3% Discount Rate for Rate-based and Mass-based Illustrative Plan Approaches for the Final Emission Guidelines in 2025*

* “Low Health Co-benefits” refers to the combin ed health co-benefits estimated using the Bell et al. (2004) mortality 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 analysis using estimated para meters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. This analysis includes many data sources as i nputs, including emission invent ories, air quality data from models (with their associated parameters a nd inputs), population data, population estimates, health effect estimates from epidemiology stud ies, economic data for monetizing co-benefits, SO2 86% Directly emitted PM2.5 (EC+OC)

0% Directly emitted PM2.5 (Crustal) 0%

NOx (as PM2.5) 8%

NOx (as Ozone)

6% 

## Rate

‐ based

SO2 81% Directly emitted PM2.5 (EC+OC) 0% Directly emitted PM2.5 (Crustal) 0%

NOx (as PM2.5) 7%

NOx (as Ozone)

12% 

## Rate

‐ based

SO2 84% Directly emitted PM2.5 (EC+OC) 0%

Directly emitted PM2.5 (Crustal) 0%

NOx (as PM2.5) 8%

NOx (as Ozone)

8% 

## Mass

‐ based

SO2 78% Directly emitted PM2.5 (EC+OC) 0%

Directly emitted PM2.5 (Crustal) 0%

NOx (as PM2.5) 8%

NOx (as Ozone)

14% 

## Mass

‐ based

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

4-37 and assumptions regarding the future state of the world (i.e., regulati ons, 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 an alysis are compounded, even small uncertainties can have large effects on the total quantified bene fits. In addition, the use of the benefit-per-ton approach adds additional uncerta inties beyond those for analyses based directly on air quality modeling. Therefore, the estimates of co-benefit s in each analysis year should be viewed as representative of the general magnitude of co-benefits of the illustrative plan approach, rather

than the actual co-benefits an ticipated from implementing the final emission guidelines.

This RIA does not include the type of detail ed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2012a) or the Ozone NAA QS RIA (U.S. EPA, 2008b) because we lack the necessary air quality modeli ng input and/or monitoring data to run the benefits model. However, the results of the quantitative and qualit ative uncertainty analyses presented in the PM NAAQS RIA and Ozone NAAQS RIA can provide so me information regarding the uncertainty inherent in the estimated co-benefits results pres ented in this analysis. For example, sensitivity analyses conducted for the PM NAAQS RIA indi cate that alternate cessation lag assumptions could change the estimated PM 2.5 

### -related mortality co-benefit s discounted at 3 percent by between 10 percent and –27 percen t and that alternative income growth adjustments could change the PM 2.5 

### -related mortality co-benef its by between 33 percent and

− 14 percent. Although we generally do not calculate confidence interval s for benefit-per-ton estimates and they can provide an incomplete picture about the overall uncertainty in the benefits estimates, the PM NAAQS RIA provides an indicati on of the random sampling error in the health impact and economic valuation functions using Monte Carlo methods. 

### In general, the 95 th 

### percentile

confidence interval for monetized PM 2.5 

### benefits ranges from approxi mately -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 RIAs for which the EPA conducts scen ario-specific air quality modeling, we do not have information on the specific location of the air quality cha nges associated with the final emission guidelines. As such, it is not feasible to estimate the proportion of co-benefits occurring

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

4-38 in different locations, such as designated nona ttainment areas. Instead, we applied benefit-perton estimates, which reflect specific geographic patterns of emissions re ductions and specific air quality and benefits modeling assumptions. For ex ample, these estimates may not reflect local variability in population density, meteorology, exposur e, baseline health incidence rates, or other local factors that might lead to an over-estim ate or under-estimate of th e actual co-benefits of controlling PM and ozone precursors. Use of these benefit-per-ton values to estimate co-benefits may lead to higher or lower bene fit estimates than if co-benefits were calculated based on direct air quality modeling. Great care should be ta ken in applying these estimates to emission reductions occurring in any speci fic location, as these are all ba sed on a broad emission reduction scenario and therefore represen t average benefits-per -ton over the entire region. The benefit-perton for emission reductions in specific locations may be very different than the estimates presented here. To the extent that the geogr aphic distribution of th e emissions reductions achieved by implementing the final emission guidelines is different than the emissions in the air quality modeling of the proposal, the co-benefits may be undere stimated or overestimated.

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

best available scientif ic literature and met hods and supported by the SAB-HES and the National Academies of Science (NRC, 2002). Below are key assumptions underlying the estimates for

PM 2.5 

### -related premature mortality, which accoun ts for 98 percent of the monetized PM 2.5 

### health

co-benefits.

1. We assume that all fine particles, regard less of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

### varies considerably in compos ition across sources, but the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM ISA concluded that “many constituents of PM 2.5 

### can be linked with multiple health effects, and the evidence is not yet sufficient to allow di fferentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b).

2. We assume that the health impact function for fine particles is log-linear without a threshold. Thus, the estimates include health co-benefits from reducing fine particles in areas with varied concentrations of 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.

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

4-39 3. We assume that there is a “cessation” lag between the cha nge in PM exposures and the total realization of changes in mortality effect s. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 

### exposures occur in a distributed fashionover the 20 years following exposure based on the advice of the SAB-HES (U.S. EPASAB, 2004c), which affects the valuation of mortality co-benefits at different discount rates.

In general, we are more confident in the magn itude of the risks we estimate from simulated

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 an alyses (e.g., lowest meas ured level [LML], one

standard deviation below the mean of the air quality data in the study, etc.) allow readers to determine the portion of populati on 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 analysis, we apply two concentration benchmark approaches (LML and one standard deviation below the mean) that have been incorporated into recent RIAs and the

EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in identifying any partic ular point at which our confiden ce in reported associations becomes appreciably less, and the scientific ev idence provides no clear dividing line. However,

the EPA does not view these concentration benc hmarks as a concentration threshold below which we would not quantify health co -benefits of air quality improvements. 100 

### Rather, the cobenefits estimates reported in this RIA are the be st estimates because they reflect the full range of air quality concentrations associated with the emission reduction strategies. The PM ISA

concluded that the scientific evidence collectivel y is sufficient to conclude that the relationship between long-term PM 2.5 

### exposures and mortality is causal and that overall th e studies support the use of a no-threshold log-line ar model to estimate PM-related long-term mortality (U.S. EPA, 2009b).

100 For a summary of the scientific review statemen ts 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 – Draft – Do Not Cite, Quote or Release During Review***

4-40 For this analysis, policy-specific air quality data is not availabl e, and the plan scenarios are illustrative of what states may choose to do. Howeve r, 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-ton estimates for this final RIA using 12 km grid cells across the contiguous U.S. It is important to note that baseline exposure is only one parame ter in the health imp act function, along with baseline incidence rates population and change in air qua lity. In other words, the percentage of the population exposed to air pollution below the LM L is not the same as the percentage of the population experiencing health impa cts as a result of a specifi c emission reduction policy. The most important aspect, which we are unable to quantify without rule -specific air quality modeling, is the shift in expos ure anticipated by implementing the final emission guidelines. Therefore, caution is warranted when interpreting the LML assessment in this RIA because these

results are not consistent w ith results from RIAs that had air quality modeling.

Table 4-28 provides the percentage of th e population exposed above and below two concentration benchmarks (i.e., LML and one sta ndard deviation below the mean) in the Clean Power Plan proposal modeling. Figure 4-3 shows a bar chart of the percentage of the population exposed to various air quality levels in the proposal modeling, and Figure 4-4 shows a

cumulative distribution function of the same data . Both figures identify the LML for each of the major cohort studies.

Table 4-28. Population Exposure in the Clea n Power Plan Proposal Option 1 State Scenario Modeling (used to generate the benefit-per-ton estimates) Above and Below Various Concentrations Benchmar ks in the Underlying Epidemiology Studies *

Epidemiology Study Below 1 Standard Deviation. Below AQ Mean At or Above 1 Standard Deviation

Below AQ Mean Below LML At or Above LML

Krewski et al. (2009) 3% 97% 12% 88% Lepeule et al. (2012) N/A N/A 54% 46% *One standard deviation below the mean is equivalent to the middle of the range between the 10 th and 25 th percentile. For Krewski, the LML is 5.8 μg/m 3 and one standard deviation below the mean is 11.0 μg/m 3 . 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 important to emphasize that although we have lower levels of confidence in levels below the LML for each study, the scientific evidence does not support the existence of a level below which health effects from exposure to PM 2.5 do not occur.

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

4-41 Figure 4-3. Percentage of Adult Po pulation (age 30+) by Annual Mean PM 2.5 

### Exposure in the Option 1 State Scenario Clean Po wer Plan Proposal Modeling (used to generate the benefit-per-ton estimates) Among 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% 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 20PercentageofPopulationExposed

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 – Draft – Do Not Cite, Quote or Release During Review***

4-42 Figure 4-4. Cumulative Distribution of A dult Population (age 30+) by Annual Mean PM 2.5 

### Exposure in the Option 1 State Scen ario Clean Power Plan Proposal Modeling (used to generate the benefit-per-ton estimates)

4.4 Combined Climate Benefits a nd Health Co-Benefits Estimates
In this analysis, we were able to monetize the estimated benefits associated with the

decreased emissions of CO 2 

### and co-benefits of reduced exposure to PM 2.5 

### and ozone, but we

were unable to monetize the co -benefits associated with re ducing exposure to mercury, carbon monoxide, SO 2 

### , and NO 2 

### , as well as ecosystem effects and visibility impairment. In addition, there are expected to be unquantified health a nd welfare impacts associ ated with changes in hydrogen chloride. Specifically, we estimated combin ations of 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 he alth co-benefits at discount ra tes of 3 percent and 7 percent

Among 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 20Cumulativepercentageofpopulationexposed

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 – Draft – Do Not Cite, Quote or Release During Review***

4-43 (as recommended by the EPA’s Guidelines for Preparing Economic Analyses [U.S. EPA, 2014]

and OMB’s Circular A-4 [OMB, 2003]).

Different discount rates are applied to SC-CO 2 

### than to the health co-benefit estimates

because CO 2 

### emissions are long-lived and subse quent damages occur over many years. Moreover, several rates are applied to SC-CO 2 

### because the literature shows that it is sensitive to assumptions about discount rate and because no cons ensus exists on the appropriate rate to use in an intergenerational context. The SC-CO 2 

### interagency group centere d its attention on the 3 percent discount rate but emphasized the im portance of considering all four SC-CO 2 

### estimates. 101 

### The EPA has evaluated the range of pot ential impacts by combining all SC-CO 2 

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

SC-CO 2 

### values with the 3 percent he alth benefit values assumes that there is no difference in discount rates between intragenerati onal and intergenerational impacts.

Tables 4-29 through 4-31 provide the combin ed climate and health benefits for the illustrative plan approaches evaluated for each analysis year: 2020, 2025, and 2030. Figure 4-5

shows the breakdown of the moneti zed benefits by pollutant for the illustrative plan approaches

evaluated in 2025 as an illustrativ e analysis year using a 3 percen t discount rate for both climate

and health benefits.

101 See the 2010 SCC TSD. Docket ID EPA-HQ-OAR-2009-0472-114577 or http://www.whitehouse.gov/sites/defau lt/files/omb/inforeg/for-agencies/Soci al-Cost-of-Carbon-for-RIA.pdf for details.

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4-44 Table 4-29. Combined Climate Benefits and Health Co-Benefits for Final Emission Guidelines in 2020 (billions of 2011$)*

SCC Discount Rate Climate Benefits Only Climate and Health Benefits (Discount Rate Applied to Health Co-Benefits) 3% 7% Rate-based 63 million metric tons CO 2 5% $0.80 $1.5 to $2.6 $1.4 to $2.5 3% $2.8 $3.5 to $4.6 $3.5 to $4.5 2.5% $4.1 $4.9 to $6.0 $4.8 to $5.9 3% (95 th percentile) $8.2 $8.9 to $10 $8.9 to $9.9 Mass-based 74 million metric tons CO 2 5% $0.94 $2.9 to $5.7 $2.8 to $5.3 3% $3.3 $5.3 to $8.1 $5.1 to $7.7 2.5% $4.9 $6.9 to 9.7 $6.7 to $9.3 3% (95 th percentile) $9.6 $12 to $14 $11 to $14 *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 th ey are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health effect s from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment.



### Table 4-30. Combined Climate Benefits and Health Co-Benefits for Final Emission Guidelines in 2025 (billions of 2011$)*

SCC Discount Rate Climate Benefits Only Climate and Health Benefits (Discount Rate Applied to Health Co-Benefits) 3% 7% Rate-based 211 million metric tons 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 240 million metric tons CO 2 5% $3.6 $11 to $21 $10 to $19 3% $12 $19 to $29 $18 to $27 2.5% $17 $24 to $34 $24 to $33 3% (95 th percentile) $35 $42 to $52 $42 to $51 *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 th ey are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health effect s from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment.

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4-45 Table 4-31. Combined Climate Benefits and Health Co-Benefits for Final Emission Guidelines in 2030 (billions of 2011$)*

SCC Discount Rate Climate Benefits Only Climate and Health Benefits (Discount Rate Applied to Health Co-Benefits) 3% 7% Rate-based 376 million 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 375 million metric tons CO 2 5% $6.4 $18 to $34 $17 to $32 3% $20 $32 to $48 $31 to $46 2.5% $29 $41 to $57 $40 to $55 3% (95 th percentile) $60 $72 to $89 $71 to $86 *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 th ey are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health effect s from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment.

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4-46 Low Health Co

‐ benefits High Health Co

‐ benefits

Figure 4-5. Breakdown of Combined Monetize d Climate and Health Co-benefits of Final Emission Guidelines in 2025 for Rate-bas ed and Mass-based Illustrative Plan Approaches and Pollutants (3% discount rate)*

* “Low Health Co-benefits” refers to the combin ed health co-benefits estimated using the Bell et al. (2004) mortality 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 . 

### 4.5 Unquantified Co-benefits

The monetized co-benefits estimated in this RIA reflect a subset of co-benefits attributable

to the health effect reductions associated with ambient fine pa rticles and ozone. Data, time, and resource limitations prevented the EPA from quantifying the impacts to, or monetizing the cobenefits from several important benefit categories, including co-benefits associated with exposure to several HAP (including mercury), SO 2 

### and NO 2 

### , as well as ecosystem effects, and CO2 58% SO2 36% Directly emitted PM2.5 (EC+OC)

0% Directly emitted PM2.5 (Crustal)

0% NOx (as PM2.5)

3% NOx (as Ozone)

3% 

## Rate

‐ based

CO2 37% SO2 51% Directly emitted PM2.5 (EC+OC)

0%

Directly emitted PM2.5 (Crustal)

0% NOx (as PM2.5)

5%
NOx (as Ozone)

7% 

## Rate

‐ based

CO2 62% SO2 32% Directly emitted PM2.5 (EC+OC)

0% Directly emitted PM2.5 (Crustal)

0% NOx (as PM2.5)

3% NOx (as Ozone)

3% 

## Mass

‐ based

CO2 41% SO2 47% Directly emitted PM2.5 (EC+OC)

0%

Directly emitted PM2.5 (Crustal)

0% NOx (as PM2.5)

4%

NOx (as Ozone)

8% 

## Mass

‐ based

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4-47 visibility impairment due to the absence of air quality modeling da ta for these pollutants in this analysis. This does not imply that there are no co -benefits associated with changes in emissions

of HAP or reductions in exposures to SO 2 

### and NO 2 

### . In this section, we provide a qualitative description of these benefits, wh ich are listed in Table 4-32.

Table 4-32. Unquantified Health an d 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 ISA 2,3

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 Genotoxic, immunologi c, and other toxic effects — — IRIS; NRC, 2000 2,3 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 ISA 2

Reduced effects from PM deposition (metals and organics) Effects on Individual organism s and ecosystems — — PM ISA 2

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4-48 Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information

Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation — — Ozone ISA 1 Reduced vegetation growth and reproduction — — Ozone ISA 1 Yield and quality of co mmercial forest products and crops — — Ozone ISA 1 Damage to urban ornament al plants — — Ozone ISA 2 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 1 Recreational demand associated with forest aesthetics — — Ozone ISA 2

Other non-use effects Ozone ISA 2 Ecosystem functions (e .g., water cycling, biogeochemical cycles, ne t primary productivity, leaf-gas exchange, community composition) — — Ozone ISA 2

Reduced effects from acid deposition Recreational fishing — — NO x SO x ISA 1 Tree mortality and decline — — NO x SO x ISA 2 Commercial fishing and fo restry effects — — NO x SO x ISA 2 Recreational demand in te rrestrial and aquatic ecosystems — — NO x SO x ISA 2

Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles) — — NO x SO x ISA 2

Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NO x SO x ISA 2 Coastal eutrophication — — NO x SO x ISA 2 Recreational demand in terrestrial and estuarine ecosystems — — NO x SO x ISA 2

Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NO x SO x ISA 2 Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA 2 Injury to vegetation from NO x exposure — — NO x SO x ISA 2

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 qual itatively because we do not have sufficient confidence in available data or methods.

3 We assess these co-benefits qualitatively because current eviden ce is only suggestive of causality or there are other significa nt concerns over the strength of the association.


### 4.5.1 HAP Impacts Due to methodology and resource limitations, we were unable to estimate the impacts associated with changes in emissions of the hazar dous air pollutants in th is analysis. The EPA’s SAB-HES concluded that “the challenges for as sessing 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, un certainties in emissions invent ories and background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of tracking health

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4-49 progress for diseases, such as cancer, that ha ve 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 rese arch priorities, focu sing on susceptible and vulnerable populations, and improving dos e-response relationships (Gwinn et al. , 2011).

4.5.1.1 Mercury Mercury in the environment is transforme d 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 thes e fish, they consume MeHg. In 2000, the NAS Study was issued which provides a thorough review of the effects of MeHg on human health (NRC, 2000). 102 

### Many of the peer-reviewed articles ci ted in this section are publications originally cited in the Mercury Study. 103 

### In addition, the EPA has conducted literature searches

to obtain other related and more recent publications to comple ment the material summarized by

the NRC in 2000.

In its review of the literature, the NAS f ound neurodevelopmental effects to be the most sensitive and best documented endpoints and appr opriate for establishing a reference dose (RfD) (NRC, 2000); in particular NAS supported the use of results from neurobehavioral or neuropsychological tests. The NAS report noted that studies on an imals reported sensory effects as well as effects on brain development and me mory functions and supported the conclusions based on epidemiology studies. The NAS noted th at their recommended endpoints for a RfD are associated with the ability of children to lear n and to succeed in school. They concluded the following: “The population at highest risk is the children of women who consumed large amounts of fish and seafood during pregnancy. The committee concludes that the risk to that population is likely to be sufficient to result in an increase in the number of children who have to struggle to keep up in school.”

102 National Research Council (NRC). 2000 . Toxicological Effects of Methylmercury . Washington, DC: National Academies Press.

103 U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress , EPA–HQ–OAR– 2009–0234–3054. December. Available on the Internet at <http://www.epa.gov/hg/report.htm>.

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4-50 The NAS summarized data on cardiovascular effects available up to 2000. Based on these and other studies, the NRC concluded that “Alt hough the data base is not as extensive for cardiovascular effects as it is for other end point s (i.e., neurologic effect s), the cardiovascular system appears to be a target for MeHg toxicity in humans and animals.” The NRC also stated

that “additional studies are needed to better ch aracterize the effect of methylmercury exposure on blood pressure and cardiovascular f unction at various stages of life.”

Additional cardiovascular studies have been published since 2000. The EPA did not develop a quantitative dose-re sponse assessment for cardiovascul ar effects associated with MeHg exposures, as there is no consensus amon g scientists on the dose-response functions for these effects. In addition, ther e is inconsistency among available studies as to the association between MeHg exposure and vari ous cardiovascular system eff ects. The pharmacokinetics of some of the exposure measures (such as toena il Hg levels) are not well understood. The studies have not yet received the review and scrutiny of the more well-established neurotoxicity data

base.

The Mercury Study noted that MeHg is not a potent mutagen but is capable of causing chromosomal damage in a number of experiment al systems. The NAS concluded that evidence that human exposure to MeHg caused genetic damage is inconclusive; they note that some earlier studies showing chromosomal damage in lymphocyt es 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 relatio nship between MeHg concentration in hair and DNA damage in lymphocytes, as well as effects on chromosomes. 104 

### Long-term MeHg exposures in this population were believed to occur through consump tion of fish, suggesting that genotoxic effects (largely chromosomal aberrations ) may result from dietary and ch ronic MeHg exposures similar

to and above those seen in the Faroes and Seychelles populations.

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

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4-51 Although exposure to some forms of Hg can resu lt in a decrease in immune activity or an autoimmune response (ATSDR, 1999), evidence for immunotoxic effects of MeHg is limited (NRC, 2000). 105



### Based on limited human and animal data, Me Hg is classified as a “possible” human carcinogen by the International Agency for Research on Cancer (IARC, 1994) 106 

### and in IRIS (U.S. EPA, 2002). 107 

### The existing evidence supporting the pos sibility of carcinogenic effects in humans from low-dose chronic exposures is te nuous. Multiple human ep idemiological studies have found no significant association between Hg exposure and overall cancer incidence, although a few studies have shown an associatio n between Hg exposure and specific types of cancer incidence (e.g., acute leuke mia and liver cancer) (NRC, 2000).

There is also some evidence of reproductiv e and renal toxicity in humans from MeHg exposure. However, overall, human data rega rding reproductive, renal, and hematological toxicity from MeHg are very limited and are based on either studies of the two high-dose poisoning episodes in Iraq and Japan or animal data, rather than epidemiological studies of chronic exposures at the levels of interest in this analysis.

4.5.1.2 Hydrogen Chloride
Hydrogen chloride (HCl) is a corrosive gas that can cause irritation of the mucous membranes of the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour. 108 

### Concentrations in typical

105 Agency for Toxic Substances and Disease Registry (A TSDR). 1999. Toxicological Profile for Mercury. U.S. Department of Health and Human Services , Public Health Service, Atlanta, GA.

106 International Agency for Research on Cancer (IARC). 1994. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans and their Supplements: Beryllium, Cadmium, Mercury, and Exposures in the Glass Manufacturing Industry. Vol. 58. Ja lili, H.A., and A.H. Abbasi. 1961. Poisoning by ethyl mercury toluene sulphonanilide. Br. J. Indust. Med. 18 (Oct.):303-308 (as c ited in NRC, 2000).

107 U.S. Environmental Protection Agency (EPA). 2002. Integrated Risk Information System (IRIS) on Methylmercury. National Center for Environmental Assessment. Office of Research and Development. Available at http://www.epa.gov/iris/subst/0073.htm.

108 Agency for Toxic Substances and Disease Registry (A TSDR). Medical Management Guidelines for Hydrogen Chloride. Atlanta, GA: U.S. Department of Health and Human Services. Available at http://www.atsdr.cdc.gov/mmg/mmg.a sp?id=758&tid=147#bookmark02.

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4-52 human exposure environments are much lower than these levels and rarely exceed the reference

concentration. 109 

### The greatest impact is on the upper respiratory tract; exposure to high concentrations can rapidly lead to swelling and spasm of the throat and suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Exposure to HCl can lead to Reactive Airways Dysfunction Syndrome (RADS), a chemically, or irritant-ind uced type of asthma. Children may be more vulnerable to corrosive agents than adults becau se of the relatively smaller diameter of their airways. Children may also be more vulnerabl e to gas exposure because of increased minute ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not been classified for carcinogenic effects. 110



### 4.5.2 Additional NO 2 

### 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. We were unable to estimate the

health co-benefits asso ciated with reduced NO 2 

### exposure in this analysis. Therefore, this analysis 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 ISA) (U.S. EPA, 2008c) concluded that there is a likely 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 emerge ncy department visits and hospitalizations, respiratory symptoms, airway hyperresponsivene ss, airway inflammation, and lung function. The

NOx ISA also concluded that the relationship between short-term NO 2 

### exposure and premature mortality was “suggestive but not sufficient to in fer a causal relationship,” because it is difficult to attribute the mortality risk effects to NO 2 

### alone. Although the NOx ISA stated that studies

109 Table of Prioritized Chronic Dose-Response Values: http://www2.epa.gov/sites/production/files/2014- 05/documents/table1.pdf

110 U.S. Environmental Protection Agency (U.S. EPA). 1995. “Integrated Risk Information System File of Hydrogen Chloride.” Washington, DC: Research and Development, National Center for Environmental Assessment. This material is available at http://www.epa.gov/iris/subst/0396.htm.

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4-53 consistently reported a relationship between NO 2 

### exposure and mortality, the effect was generally smaller than that for other pollutants such as PM.

4.5.3 Additional SO 2 

### Health Co-Benefits
In addition to being a precursor to PM 2.5 

### , SO 2 

### emissions are also li nked to a variety ofadverse health effects associated with direct exposure. We were unable to estimate the health cobenefits associated with reduced SO 2 

### in this analysis because we do not have air quality modeling data available. Therefore, this analysis only quantifies and monetizes the PM 2.5 

### cobenefits associated with the reductions in SO 2 

### emissions.

Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO 2 

### ISA)

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 system in

humans is bronchoconstriction. Asthmatics ar e more sensitive to the effects of SO 2 

### likely resulting from preexisting inflammation associat ed with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO 2 

### at concentrations between 20 and 100 ppb, both in te rms of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we

identified three short-term morbidity endpoints that the SO 2 

### ISA identified as a “causal relationship”: asthma exacerbation, respiratory -related emergency department visits, and respiratory-related hospitalizations. The differi ng evidence and associated strength of the

evidence for these different effects is described in detail in the SO 2 

### ISA. The SO 2 

### ISA also

concluded that the relations hip between short-term SO 2 

### exposure and prem ature mortality was “suggestive of a causal relationship” because it is difficult to attribut e the mortality risk effects to

SO 2 

### alone. Although the SO 2 

### ISA stated that studies are gene rally consistent in reporting a relationship between SO 2 

### exposure and mortality, there was a lack of robustness of the observed associations to adjustment for other pollutants. We did not quantify these co-benefits due to data

constraints.

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4-54 4.5.4 Additional NO 2 

### and SO 2 

### Welfare Co-Benefits

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur — Ecological Criteria (NOx/SOx ISA) (U.S. EPA, 2008d), SO 2 

### and NOx emissions also contribute to a variety of adverse welfare effects, in cluding those associated with acidic deposition, visibility impairment, and nutrient enrichment . Deposition of nitrog en causes acidification, which can cause a loss of biodiversity of fishes , zooplankton, and macro i nvertebrates in aquatic ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial eco systems. In the northeastern U.S., the surface waters affected by acidification are a source of food for some recreat ional and subsistence fishermen and for other consumers and support seve ral cultural services, including aesthetic and

educational services and recreat ional fishing. Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum to xicity, which can cause reduced root growth, restricting the ability of the plan t to take up water and nutrients. Th ese direct effects can, in turn, increase the sensitivity of these plants to stresses, such as dro ughts, cold temperatures, insect pests, and disease leading to increased mortalit y of canopy trees. Terrestria l acidification affects several important ecological servic es, including declines in habita t for threatened and endangered species (cultural), declines in forest aesthetics (cultural), d eclines 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, exce ss nutrient enrichment can lead to eutrophication. Eutrophication of estuaries can disrupt an importa nt source of food production, part icularly fish and shellfish production, and a variety of cultural ecosystem se rvices, including water-based recreational and

aesthetic services. Terrestrial nut rient enrichment is associated with changes in the types and number of species and biodiversit y in terrestrial systems. Exce ssive nitrogen deposition upsets the balance between native and nonnative plants, ch anging the ability of an area to support biodiversity. When the composition of species ch anges, then fire frequency and intensity can also change, as nonnative grasses fuel more fre quent and more intense wildfires. (U.S. EPA, 2008d)

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4-55 Reductions in emissions of NO 2 

### and SO 2 

### will improve the level of visibility throughout the United States because these gases (and the partic les of nitrate and sulfate formed from these gases) impair visibility by s cattering and absorbing light (U.S. EPA, 2009). Visibility is also referred to as visual air quality (VAQ), and it directly affects pe ople’s enjoyment of a variety of daily activities (U.S. EPA, 2009). Good visibility increases quality of lif e where individuals live and work, and where they travel for recreational activities, including sites of unique public value, such as the Great Smoky Mountains National Park (U. S. EPA, 2009).

4.5.5 Ozone Welfare Co-Benefits
Exposure to ozone has been associated with a wide array of vegetation and ecosystem effects in the published literature (U.S. EPA, 2013b). Sensitivity to oz one is highly variable across species, with over 65 plant species identifi ed as “ozone-sensitive”, many of which occur in state and national parks and forests. These e ffects include those that damage or impair the intended use of the plant or ecosystem. Such eff ects can include reduced growth and/or biomass production in sensitive plant species, including fore st trees, reduced yiel d and quality of crops, visible foliar injury, species composition shif t, and changes in ecosystems and associated ecosystem services.

4.5.6 Carbon Monoxide Co-Benefits
CO in ambient air is formed primarily by the incomplete combus tion of carbon-containing fuels and photochemical reactions in the atmo sphere. The amount of CO emitted from these reactions, relative to carbon dioxide (CO 2 

### ), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that depend on concentratio n and duration of exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded th at short-term exposure to CO is “likely to have a causal relati onship” with cardiovascular morbid ity, particularly in individuals with coronary heart disease. Epidemiologic st udies associate short-te rm CO exposure with increased risk of emergency department visits and hospital admissions. Coronary heart disease includes those who have angina pectoris (cardi ac chest pain), as we ll as those who have

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4-56 experienced a heart attack. Othe r subpopulations potentially at risk include individuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or la te life stages, such as older adults or the developing young. The evidence is suggestive of a cau sal relationship between shor t-term exposure to CO and respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship for birth outcomes and developmental effects follo wing long-term exposure to CO, and for central nervous system effects linked to s hort- 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, 2009b). Fine particles with signifi cant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people’s enjoyment of daily activ ities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in

recreational activities. Particulat e sulfate is the dominant source of regional haze in the eastern U.S. and particulate nitrate is an important cont ributor to light extincti on in California and the upper Midwestern U.S., particularly during wint er (U.S. EPA, 2009b). Previous analyses (U.S. EPA, 2011a) show that visibility co-benefits can be a significant we lfare benefit category. Without air quality modeling, we are unable to es timate visibility relate d benefits, and we are

also unable to determine whether the emission re ductions 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–HQ–OAR–2009–0171; FRL–9091–8] RIN 2060–ZA14, “Endangerment and Cause or Contribute Findi ngs 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.

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4-57 Abt Associates, Inc. 2012. “BenMAP User’s Manual Appendices,” prepared for U.S. Research Triangle Park, NC: U. S. Environmental Prot ection Agency, Office of Air Quality Planning and Standards. Available at: <http://www.epa.gov/air/benmap/models/BenMA PAppendicesOct2012.pdf>. Accessed June 6, 2015. Anenberg SC, West IJ, Fiore AM, Jaffe DA, Prat her MJ, Bergmann D, Cuvelier K, Dentener FJ, Duncan BN, Gauss M, Hess P, Jonson JE, Lupu A, Mackenzie IA, Marmer E, Park RJ, Sanderson MG, Schultz M, Shindell DT, Szopa S, Vivanco MG, Wild O, Zeng G. 2009. Intercontinental impacts of ozone polluti on on human mortality. Environ Science and Technology. 43(17): 6482-7. Bell, M.L., A. McDermott, S.L. Ze ger, J.M. Sarnet, and F. Dominici

. 2004. “Ozone and ShortTerm Mortality in 95 U.S. Urban Communities, 1987-2000.” Journal of the American Medical Association. 292(19):2372-8. Bell, M.L., F. Dominici, and J.M. Samet. 2005. “A Meta-Analysis of Time-Series Studies of Ozone and Mortality with Comparison to the National Morbidity, Mortality, and Air Pollution Study.” Epidemiology . 16(4):436-45. Docket ID EPA-HQ-OAR-2009-0472-114577, Technica l Support Document: Social Cost of Carbon for Regulatory Impact Analysis U nder Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with partic ipation by the Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Tran sportation, Environmental Protection Agency, National Economic Council, Office of Energy a nd Climate Change, Office of Management and Budget, Office of Science and Technology Po licy, and Department of Treasury (February 2010). Available at: <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-o f-Carbon-for-RIA.pdf>.

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4-58 Docket ID EPA-HQ-OAR-2013-0602, Technical Suppor t Document: Technical Update of the Social Cost of Carbon fo r Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with Participation by Council of Economic Advisers, Council on Environmenta l Quality, Department of Agriculture, Department of Commerce, Department of En ergy, Department of Transportation, Domestic Policy Council, Environmental Protection Ag ency, National Economic Council, Office of Management and Budget, Office of Scien ce and Technology Policy, and Department of Treasury (May 2013, Revised July 2015). Also available at: < https://www.whitehouse.gov/sites/ default/files/omb/inforeg/ scc-tsd-final-july-2015.pdf>. Accessed July 15, 2015. 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.” Environmental Health Perspectives. 119(1):125-30. Huang Y., F. Dominici, and M. Bell. 2005. “Bayes ian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Card io-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 Standard s, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available at: <http://www.epa.gov/ttn/ecas/regdata/Uncertain ty/pm_ee_tsd_expert_interview_summaries.p df>. Accessed June 6, 2015. Industrial Economics, Incorporated (IEc). 2009. S ection 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.

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4-59 Interagency Working Group on Social Cost of Carbon, with partic ipation by Council of Economic Advisers, Council on Environmenta l Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Ec onomic Council, Office of Management and Budget, Office of Science and Technol ogy Policy, and Department of Treasury. Response to Comments: Social Cost of Carbon for Regulat ory Impact Analysis Under Executive Order 12866. July 2015. Available at <https://www.whitehouse.gov/sites/default/files /omb/inforeg/scc-response-to-commentsfinal-july-2015.pdf.> Accessed July 15, 2015. Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IPCC . Available at: <http://www.ipcc.ch/publications_and_data/public ations_ipcc_fourth_assessment_report_synt hesis_report.htm>. Accessed June 6, 2015. Intergovernmental Panel on Climate Change (IPCC). 2012: Managing the Risks of Extreme Events and Disasters to Adv ance Climate Change Adaptation . A Special Report of Working Groups I and II of the Intergovernmental Pane l on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Gr oup I to the Fifth Asse ssment Report of the Intergovernmental Panel on Climate Change. Ca mbridge 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 Clim ate 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 Gr oup III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Ca mbridge 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: Analysis and Meta-Analysis.” Epidemiology . 16(4):446-57. Jhun I, Fann N, Zanobetti A, Hubbell B. 2014. Ef fect modification of ozone-related mortality risks by temperature in 97 US cities. Environment International. 73:128-34.

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. 34:451–458. Ren, C. and S. Tong. 2006b. “Temperature Modifies th e Health Effects of Pa rticulate Matter in Brisbane, Australia.” International Journal of Biometeorology. 51:87–96. Ren. C., G.M. Williams, and S. Tong. 2006a. “Does Particulate Matter Modi fy the Association between Temperature and Ca rdiorespiratory Diseases? Environmental Health Persp ectives, 114:1690–1696. Roberts, S. 2004. “Interactions between Partic ulate 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. Conne r, 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 Technol ogy. 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 Respirat ory and Critical Care Medicine. 171(6): 627-31.

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4-62 Sisler, J.F. 1996. Spatial and Seas onal Patterns and Long-Term Vari ability of the Composition of the Haze in the United States: An analysis of data from the IMPROVE network. CIRA Report, ISSN 0737-5352-32, Colorado State University. 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). 2008a. Integrated Science Assessment for Sulfur Oxides—Health Criteria (Final Report) . National Center for Environmental Assessment – RTP Division, Research Triangl e Park, NC. September. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.c fm?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 Qual ity Planning and Standards Health and Environmental Impacts Division, Air Bene fit 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 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.c fm?deid=194645>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2008d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur–Ecologic al Criteria National (Final Report ). National Center for Environmental Assessment – RTP Divisi on, Research Triangle Park, NC. EPA/600/R08/139. December. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.c fm?deid=201485>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmental Assessment – RTP Division, Research Triangle Park, NC. December. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.c fm?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 A ssessment, Research Triangle Park, NC. EPA/600/R-09/019F. January. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.c fm?deid=218686>. Accessed June 4, 2015.

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4-63 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 Triangl e Park, NC. June. Available at: <http://www.epa.gov/ttn/ecas/regdata/Benefits/ thresholdstsd.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 Environmental 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. U.S. Environmental Protection Agency (U.S. EPA). 2010d. Section 3: Re

‐ analysis of the Benefits of Attaining Alternative Ozone Standar ds to Incorporate Current Methods . Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/s3- supplemental_analysis-updated_benefits11- 5.09.pdf >. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 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/cleanaira ctbenefits/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/ma tsriafinal.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S . EPA). 2011c. Regulatory Impact Analysis: 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/boi lersriafinal110221_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/naaq s/standards/pm/data/20110419pmpafinal.pdf>. Accessed June 4, 2015.

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4-64 U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the Final Revisions to the National Ambient Ai r Quality Standards for Particulate Matter. EPA452/R-12-003. Office of Air Qual ity Planning and Standards, Health and Environmental Impacts Division, Research Triangle Pa rk, 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 Environmenta l Impacts Division. June. Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/refiner ies_nsps_ja_final_ria.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2013a. 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 Tr iangle Park, NC. February. Available at: < http://www2.epa.gov/site s/production/files/2014- 10/documents/sourceapportionmentbpttsd.pdf >. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2013b. Integrated Science Assessment of Ozone and Related Photochemica l Oxidants (Final Report). EPA/600/R-10/076F. National Center for Environmental Assessment – RTP Di vision, Research Triangl e Park. Available at: <http://cfpub.epa.gov/ncea/isa/reco rdisplay.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 Envi ronmental 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 Exis ting Power Plants and Emission Standards for Modified and Reconstructed Power Plants . EPA-542/R-14-002. Office of Air Quality Planning and Standards, Research Tr iangle Park, NC. June. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/1 11dproposalRIAfinal0602.pdf>. Accessed June 4, 2015.

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

4-65 U.S. Environmental Protection Agency (U.S. EPA). 2014b. Health Risk and Exposure Assessment for Ozone: Final Report . Office of Air Quality Planni ng 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/ozon e/data/20141021welfarerea.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2014d. Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Quality Standards for Ground-Level Ozone. EPA-452/P-14-006. Office of Air Qual ity Planning and Standards, Research Triangle Park, NC. November. Available at <http://www. epa.gov/ttnecas1/regda ta/RIAs/20141125ria.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Sci ence 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 Workshop (Final Report). EPA-SA B-EC-WKSHP-02-001. January. Available at: <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/34355712EC011A358525719A005B F6F6/$File/ecwkshp02001%2Bappa-g.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2008. Characterizing Uncertainty in Particulate Ma tter Benefits Using Expert Elicitation. EPACOUNCIL-08-002. July. Available at: <http://yosemite.epa.gov/sab/sabproduc t.nsf/0/43B91173651AED9E85257487004EA6CB/$F ile/EPA-COUNCIL-08-002-unsigne d.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2000. An SAB Report on EPA’s White Paper Valuing the Be nefits of Fatal Cancer Risk Reduction. EPA-SAB-EEAC-00-013. July. Available at: <http://yosemite.epa.gov/sab%5CSA BPRODUCT.NSF/41334524148BCCD6852571A70051 6498/$File/eeacf013.pdf>. A ccessed June 4, 2015.

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

4-66 U.S. Environmental Protection Agency—Scien ce Advisory Board (U.S. EPA-SAB). 2004c. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPA-COUNCIL-LTR-05-001. December. Available at: <http://yosemite.epa.gov/sab/sabproduct .nsf/0/39F44B098DB49F3C85257170005293E0/$Fil e/council_ltr_05_001.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2008. Benefits of Reducing Benzene Emissions in Houston, 1990–2020 . EPA-COUNCIL-08-001. July. Available at: <http://yosemite.epa.gov/sab/sabproduc t.nsf/D4D7EC9DAE DA8A548525748600728A83/$Fi le/EPA-COUNCIL-08-001-unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2009b. Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008) . EPA-COUNCIL-09-008. May. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73 ACCA834AB44A10852575BD0064346B/$File/E PA-CASAC-09-008-unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Scien ce Advisory Board (U.S. EPA-SAB). 2009c. Review of Integrated Science Assessment fo r Particulate Matter (Second External Review Draft, July 2009) . EPA-CASAC-10-001. November. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/15 1B1F83B023145585257678006836B9/$File/EPA-CASAC -10-001-unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Scien ce Advisory Board (U.S. EPA-SAB). 2010a. Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act . EPA-COUNCIL-10-001. June. Available at: < http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA 39E48CDB28525774500738776/$File/EPA-COUNCIL- 10-001-unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2011. Review of Valuing Mortality Risk Reductions for Environmental Po licy: A White Paper (December 10, 2010). EPA-SAB-11-011 July

. Available at: <http://yosemite.epa.gov/sab/sabproduct.ns f/298E1F50F844BC23852578DC0059A616/$File/ EPA-SAB-11-011-unsigned.pdf>. Accessed June 4, 2015.

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4-67 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship between Selected of postneonatal infant mortality and particul ate air pollution in the United States.” Environmental Health Perspectives. 105(6): 608-612.

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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 Analysis (RIA). Specifically, this appendix describes the met hods for generating benefit-per-ton estimates by region for the contiguous U.S. for PM 2.5 

### and ozone precursors emitted by the electrical generating unit (EGU) sector in the Final Ca rbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units (hereafter referred to as the “final emission guidelines” or “Clean Po wer Plan Final Rule”).

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

As described in the Technical Support Document: Estimating the Benefit per Ton of Reducing PM 2.5 

### Precursors from 17 Sectors (U.S. EPA, 2013), the general procedure for calculating average benefit-per-ton coefficients gene rally follows three steps. As an example, in order to calculate regional aver age benefit-per-ton estimates fo r 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 propos ed Clean Power Plan.

2. For each grid cell, estimate the health impact s, and the economic value of these impacts, associated with the attributable ambient con centrations using the e nvironmental Benefits Mapping and Analysis Program – Co mmunity Edition (BenMAP-CE v1.1). 111,112 

### Aggregate those impacts and economic values to the three regions of East, West, and California.

3. Divide the regional health impacts attribut able 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 by directly emitted PM 2.5 

### emissions, sulfate benefits are divided by SO 2 

### emissions, nitrate bene fits are divided by NO X 

### emissions, and ozone benefits are divided by ozone-season NO X 

### emissions.

111 When estimating these impacts we apply effect coefficients that relate changes in total PM 2.5 mass to the risk of adverse health outcomes; we do not apply effect coefficients that are differentiated by PM 2.5 species.

112 Previous RIAs have used earlier versions of the BenMAP software. BenMAP-CE v1.1 provides results consistent with earlier versions of BenMAP and is availa ble for download at http://www.epa.gov/air/benmap/.

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

4A-2 4A.2 Air Quality Modeling for the Proposed Clean Power Plan
The EPA ran the Comprehensive Model withExtensions (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 mode l simulations was performed for a nationwide modeling domain 113 

### 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 Powe r Plan proposal scenario using methodologies consistent with the EPA’s air quality mode ling guidance (U.S. EPA, 2007). The air quality modeling results for the 2025 base case served as the baseline for gauging the future year impacts on ozone and annual PM 2.5 

### of the Clean Power Plan pr oposal scenario. The 2025 base

case reflects emissions reductions between 2011 and 2025 that are expected to result from regional and national rules includi ng the Clean Air Interstate Rule (CAIR), the Mercury and Air Toxics Standards (MATS), mobile source rules up through Tier-3, and va rious state emissions control programs and consent decrees. The me thods for estimating the EGU emissions for the proposal are described in Chapter 3 of the RIA for the Clean Power Plan proposal (U.S. EPA, 2014). State total annual EGU emissions for NO X 

### and SO 2 

### for each of the scenarios modeled are provided in Tables 4A-1 and 4A -2, respectively. The data indicat e 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 Total Annual EGU Emissions for NO X 

### for the 2011 Base Year, 2025 Base Case, and 2025 Clean Power Plan Prop osal (Option 1 State) (in thousands of tons)

State 2011 Base Year 2025 Base Case 2025 Clean Power Plan Proposal (Option 1 State) Alabama 63 38 19 Arizona 35 17 4 Arkansas 38 43 9 California 6 33 28 Colorado 51 29 21 Connecticut 1 1 1 Delaware 4 1 1

113 The modeling domain (i.e., region modeled) includes all of the lower 48 states plus adjacent portions of Canada and Mexico) at a spatial resolution of 12 km.

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

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

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

4A-4 Table 4A-2. State Total Annual EGU Emissions for SO 2 

### for the 2011 Base Year, 2025 Base Case, and 2025 Clean Power Plan Prop osal (Option 1 State) (in thousands of tons)

State 2011 Base Year 2025 Base Case 2025 Clean Power Plan Proposal (Option 1 State) Alabama 186 79 45 Arizona 28 18 4 Arkansas 74 30 5 California 1 4 4 Colorado 45 15 10 Connecticut 1 - - Delaware 11 1 1 Florida 95 70 7 Georgia 187 37 12 Idaho - 0 0 Illinois 227 45 48 Indiana 382 126 121 Iowa 100 18 18 Kansas 39 15 15 Kentucky 246 109 119 Louisiana 93 14 11 Maine 1 1 1 Maryland 32 5 9 Massachusetts 23 1 0 Michigan 228 122 95 Minnesota 40 21 12 Mississippi 43 10 3 Missouri 205 80 76 Montana 19 18 17 Nebraska 73 25 24 Nevada 5 1 1 New Hampshire 24 0 0 New Jersey 5 7 1 New Mexico 6 4 4 New York 41 4 2 North Carolina 78 36 33 North Dakota 93 15 14 Ohio 594 105 102 Oklahoma 96 21 6 Oregon 13 1 1 Pennsylvania 338 67 47 Rhode Island 0 - - South Carolina 68 19 12 South Dakota 11 11 7 Tennessee 120 38 31 Texas 426 149 48 Tribal Data 18 19 19 Utah 22 14 10

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

4A-5 State 2011 Base Year 2025 Base Case 2025 Clean Power Plan Proposal (Option 1 State) Vermont 0 0 0 Virginia 75 8 4 Washington 1 1 1 West Virginia 103 78 47 Wisconsin 92 17 11 Wyoming 55 23 17 National Total 4,665 1,504 1,077



### As indicated above, the air quality modeli ng was used to project gridded ozone and

annual PM 2.5 

### concentrations at the 12km 2 

### resolution for the 2025 base case and the Clean Power Plan proposal scenario modeled for this analysis. The air quality modeling results were combined

with monitored ozone and PM2.5 data to cr eate 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-Ton Estimates for EG Us 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 analysis years applied in this RIA (i.e., 2020, 2025, and 2030), we aggregated the benefits results regionally (i.e., East, West, and Californi a), as shown in Figure 4A-1. 114 

### While a small percentage of benefits from emissions reductions in a particular region may occur in one of the other regions, we selected each region to minimize this percenta ge. Thus, the benefits per ton in each region will represent well the match between where the em issions reductions and air quality benefits are occurring. Due to the low emissions of SO 2 

### , NO X 

### , and directly emitted particles from EGUs in California and the high population de nsity, we separated out Californi a in order not to bias the benefit-per-ton estimates fo r the rest of the Western U.S. In or der to calculate the benefit-per-ton estimates, we divided the regional benefits es timates by the corresponding emissions, as shown in Table 4A-1. Lastly, we adjusted the benef it-per-ton estimates for a currency year of 2011$. 115

114 This aggregation is identified as the shapefile “Report Regions” in BenMAP’s grid definitions. 115 Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefit-per-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer Price Index.

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

4A-6 This method provides estimates of the regional average benefit-per-ton for a subset of the major PM 2.5 

### precursors emitted from EGU sources. For precursor emissions of NO X 

### , there is generally a non-linear relationship betw een emissions and formation of PM 2.5 

### . This means that

each ton of NO X 

### reduced would have a different impact on ambient PM 2.5 

### depending on the

initial level of emissions and potentially on the levels of emi ssions of other pollutants. In contrast, SO 2 

### is generally linear in forming PM 2.5 

### . For precursors like NO X 

### which form PM 2.5 

### non-linearly, a marginal benefit-pe r-ton approach would better appr oximate the specific benefits

associated with an emissions reduction scenario for a given set of base case emissions, because it would allow the benefit-per-ton to vary depending on the level of emissions reductions and the baseline emissions levels. However, we do not have sufficient air quality modeling data to calculate marginal benefit-per- ton estimates for the EGU sector. Therefore, using an average benefit-per-ton estimate for NO X 

### adds uncertainty to the co-benefits estimated in this RIA.

Because most of the estimated co-benefits for the proposed guidelines are attributable to

reductions in SO 2 

### emissions, the added uncertainty is likely to be small.

Figure 4A-1. Regional Breakdown

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

4A-7 In this RIA, we estimate emission reductions from EGUs using IPM. 116 

### IPM outputs provide endogenously projecte d unit level emissions of SO 2 

### , NO X 

### , CO 2 

### , Hg, hydrogen chloride (HCl) from EGUs, but carbon monoxide, volatile organic compounds, ammonia and total directly emitted PM 2.5 

### and PM 10 

### emissions are post-calculated. 117 

### In addition, directly emitted particle emissions calculated fr om IPM outputs do not include speci ation, i.e. they are only the

total emissions. In order to conduct ai r quality modeling, directly emitted PM 2.5 

### from EGUs is speciated into components during the emissions modeling process based on emission profiles for EGUs by source classification code . Even though these speciation pr ofiles are not unit-specific, an emission profile based on the source classificati on code is highly sophisticated and reflects the fuel and the unit configuration. Model-predicted concentrations of nitr ate and sulfate include both the directly emitted nitrate and sulfate from speciated PM 2.5 

### and secondarily formed nitrate

and sulfate from emissions of NO X 

### and SO 2 

### , respectively.

In order to estimate the benefits associated with reduced emissions of directly emitted particles without performing air quality modeling, we must determine the fraction of total PM 2.5 

### emissions comprised of elemental carbon and organic carbon (EC+OC) and crustal emissions. 118 

### Based on the work by Fann, Baker, and Fulcher (2012), the national average EC+OC fraction of

emitted 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 stat es 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 averages for the east and west regions at 78% and 81%, respectively. Only four states had crustal fr actions less than 50%. In calculating the PM 2.5 

### cobenefits in this RIA, we estimate the emissi on reductions of EC+OC and crustal emissions by applying the national average fractions (i.e., 78% crustal and 10% EC +OC) to the emission reductions of all directly emitted particles from EGUs. Because the benefit-per-ton estimates for

116 See Chapter 3 of this RIA for additional information regarding the Integrated Planning Model ( IPM).

117 Detailed documentation of this post-processing is available at http://www.epa.gov/powersectormodeling/do cs/v513/FlatFile_Methodology.pdf
118 Crustal emissions are composed of compounds associat ed with minerals and meta ls from the earth’s surface, including carbonates, silicates, iron, phosphates, copper, an d zinc. Often, crustal material represents particles not classified as one of the other species (e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).

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

4A-8 reducing emissions of EC+OC are la rger 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 Ca lifornia and North Dakota.

Although it is possibl e to calculate 95 th 

### percentile confidence inte rvals using the approach described in this appendix (e.g., U.S. EPA, 2011b ), we generally do not calculate confidence intervals for benefit-pe r-ton estimates because of the addi tional unquantified uncertainties that result from the benefit transfer methods, includi ng those related to the tr ansfer of air quality modeling information. Instead, we refer the reader to Chapter 5 of PM NAAQS RIA (U.S. EPA, 2012a) for an indication of the combined random sampling error in th e health impact and economic valuation functions using Mont e 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 con centration-response f unctions 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) and ±46% based on Lepeule et al. (2012). These confidence inte rvals do not reflect other sources of uncertainty inherent wi thin the estimates, such as baseline incidence rates, populations exposed, and transferability of th e effect estimate to diverse lo cations. As a result, the reported confidence intervals and range of estimates gi ve an incomplete picture about the overall uncertainty in the benefits estimates.

Tables 4A-3 through 4A-5 provide the region al benefit-per-ton es timates for the EGU

sector at discount rates of 3% and 7% in 2020, 2025, and 2030 re spectively. The benefit-per-ton values for 2020 and 2030 are based on applying th e air quality modeling from 2025 to population

and health information from 2020 and 2030. Estima ted benefit-per-ton for these years have additional uncertainty relative to 2025 because of potential differences in atmospheric responses

to reductions in PM 2.5 

### precursors in those years, however, these uncertainties are likely to be relatively small. Tables 4A-6 through 4A-8 pr ovide the incidence per ton estimates (which follows the same general methodology as for the be nefit-per-ton calculations ) for the EGU sector in 2020, 2025, and 2030 respectively, for the set of hea lth endpoints used to calculate the benefitper-ton estimates.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-9Table 4A-3. Summary of Regional PM2.5Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Planin 2020 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $32,000 to $71,000 $33,000 to $75,000 $6,200 to $14,000 $95,000 to $210,0007% $28,000 to $64,000 $30,000 to $68,000 $5,600 to $13,000 $85,000 to $190,000Directly emittedPM2.5(EC+OC)3% $140,000 to $310,000 $140,000 to $320,000 $27,000 to $60,000 $370,000 to $830,0007% $120,000 to $270,000 $130,000 to $290,000 $24,000 to $54,000 $330,000 to $740,000Directly emittedPM2.5(Crustal)3% $22,000 to $49,000 $23,000 to $52,000 $11,000 to $25,000 $73,000 to $160,0007% $20,000 to $44,000 $21,000 to $47,000 $9,900 to $22,000 $66,000 to $150,000NOX(as PM2.5)3% $3,000 to $6,800 $3,100 to $7,000 $0,670 to $1,500 $22,000 to $49,0007% $2,700 to $5,600 $2,800 to $6,300 $0,610 to $1,400 $19,000 to $44,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.Table 4A-4. Summary of Regional PM2.5Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Planin 2025 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $35,000 to $78,000 $37,000 to $83,000 $7,100 to $16,000 $110,000 to $240,0007% $31,000 to $70,000 $33,000 to $75,000 $6,400 to $14,000 $97,000 to $220,000Directly emittedPM2.5(EC+OC)3% $150,000 to $340,000 $160,000 to $360,000 $30,000 to $68,000 $410,000 to $930,0007% $130,000 to $290,000 $140,000 to $320,000 $27,000 to $61,000 $370,000 to $830,000Directly emittedPM2.5(Crustal)3% $24,000 to $55,000 $25,000 to $58,000 $12,000 to $28,000 $82,000 to $180,0007% $22,000 to $49,000 $23,000 to $52,000 $11,000 to $25,000 $74,000 to $170,000NOX(as PM2.5)3% $3,200 to $7,300 $3,300 to $7,500 $0,750 to $1,700 $24,000 to $54,0007% $2,900 to $6,000 $3,000 to $6,800 $0,670 to $1,500 $22,000 to $49,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-10Table 4A-5. Summary of Regional PM2.5Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Planin 2030 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $37,000 to $85,000 $40,000 to $89,000 $7,800 to $18,000 $120,000 to $270,0007% $34,000 to $76,000 $36,000 to $81,000 $7,100 to $16,000 $110,000 to $240,000Directly emittedPM2.5(EC+OC)3% $160,000 to $360,000 $170,000 to $380,000 $33,000 to $75,000 $450,000 to $1,000,0007% $150,000 to $320,000 $150,000 to $340,000 $30,000 to $68,000 $410,000 to $920,000Directly emittedPM2.5(Crustal)3% $26,000 to $59,000 $28,000 to $62,000 $14,000 to $31,000 $90,000 to $200,0007% $24,000 to $53,000 $25,000 to $56,000 $13,000 to $28,000 $81,000 to $180,000NOX(as PM2.5)3% $3,400 to $7,800 $3,500 to $8,000 $0,820 to $1,900 $26,000 to $60,0007% $3,100 to $6,400 $3,200 to $7,200 $0,740 to $1,700 $24,000 to $54,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-11Table 4A-6. Summary of Regional PM2.5Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean PowerPlan in 2020*Health EndpointEast West CaliforniaSO2NOXEC+OC Crustal SO2NOXEC+OC Crustal SO2NOXEC+OC CrustalPremature MortalityKrewskiet 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 0.008000Lepeuleet 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 0.018000Woodruffet 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 0.000019MorbidityEmergency department visits for asthma 0.0019000.000190 0.007800 0.001300 0.000290 0.0000310.001200 0.000470 0.005300 0.001400 0.022000 0.004200Acute bronchitis 0.005400 0.000510 0.023000 0.003700 0.001300 0.000200 0.005200 0.002100 0.019000 0.005000 0.077000 0.015000Lower respiratory symptoms 0.069000 0.006500 0.3000000.047000 0.016000 0.002500 0.067000 0.0260000.240000 0.064000 0.970000 0.190000Upper respiratory symptoms 0.098000 0.009300 0.4200000.068000 0.023000 0.003600 0.095000 0.0380000.340000 0.092000 1.400000 0.270000Minor restricted-activity days 2.700000 0.250000 11.000000 1.900000 0.580000 0.078000 2.4000000.920000 9.400000 2.200000 35.000000 6.800000Lost work days 0.450000 0.043000 1.900000 0.310000 0.098000 0.013000 0.410000 0.160000 1.600000 0.380000 6.000000 1.100000Asthma exacerbation 0.240000 0.023000 1.000000 0.1700000.056000 0.008800 0.230000 0.091000 0.840000 0.220000 3.400000 0.650000Hospital Admissions, Respiratory 0.001100 0.000100 0.004500 0.000720 0.000150 0.000015 0.0006400.000260 0.002500 0.000580 0.009400 0.001900Hospital Admissions, Cardiovascular 0.001300 0.000120 0.005600 0.000910 0.000200 0.0000190.000820 0.000330 0.003000 0.000680 0.0110000.002200Non-fatal Heart Attacks (age>18)Peters et al (2001) 0.004100 0.000390 0.018000 0.002800 0.000650 0.000064 0.002800 0.0012000.011000 0.002400 0.041000 0.007900Pooled estimate of 4 studies 0.000450 0.000042 0.001900 0.000310 0.000070 0.000007 0.0003000.000130 0.001100 0.000260 0.004400 0.000850* All estimates are rounded to two significant figures. All fine particles are assumed tohave equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitude of their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-12Table 4A-7. Summary of Regional PM2.5Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean PowerPlan in 2025*Health EndpointEast West CaliforniaSO2NOXEC+OC Crustal SO2NOXEC+OC Crustal SO2NOXEC+OC CrustalPremature MortalityKrewskiet 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 0.008700Lepeuleet 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 0.020000Woodruffet 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 0.000018MorbidityEmergency department visits forasthma0.002000 0.000200 0.006300 0.001300 0.000320 0.0000330.001000 0.000510 0.005500 0.001500 0.018000 0.004400Acute bronchitis 0.005700 0.000520 0.024000 0.003900 0.001300 0.000210 0.005600 0.002200 0.020000 0.005300 0.080000 0.015000Lower respiratory symptoms 0.072000 0.006700 0.3100000.050000 0.017000 0.002700 0.071000 0.0280000.250000 0.067000 1.000000 0.200000Upper respiratory symptoms 0.100000 0.009600 0.4400000.071000 0.024000 0.003800 0.100000 0.0400000.360000 0.096000 1.500000 0.280000Minor restricted-activity days 2.800000 0.250000 12.000000 1.900000 0.610000 0.083000 2.5000000.970000 9.600000 2.300000 36.000000 6.900000Lost work days 0.470000 0.043000 2.000000 0.320000 0.100000 0.014000 0.430000 0.160000 1.600000 0.390000 6.100000 1.200000Asthma exacerbation 0.250000 0.023000 1.100000 0.1700000.059000 0.009300 0.250000 0.097000 0.880000 0.230000 3.500000 0.680000Hospital Admissions, Respiratory 0.001200 0.000110 0.005100 0.000810 0.000180 0.000017 0.0007400.000300 0.002800 0.000650 0.011000 0.002100Hospital Admissions, Cardiovascular 0.001400 0.000130 0.006200 0.001000 0.000220 0.0000220.000930 0.000380 0.003300 0.000750 0.0120000.002400Non-fatal Heart Attacks (age>18)Peters et al (2001) 0.004600 0.000430 0.020000 0.003100 0.000740 0.000071 0.003200 0.0013000.012000 0.002700 0.046000 0.008900Pooled estimate of 4 studies 0.000490 0.000046 0.002100 0.000340 0.000080 0.000008 0.0003400.000140 0.001300 0.000290 0.004900 0.000950* All estimates are rounded to two significant figures. All fine particles are assumed tohave equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitude of their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-13Table 4A-8. Summary of Regional PM2.5Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean PowerPlan in 2030*Health EndpointEast West CaliforniaSO2NOXEC+OC Crustal SO2NOXEC+OC Crustal SO2NOXEC+OC CrustalPremature MortalityKrewskiet 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 0.009600Lepeuleet 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 0.022000Woodruffet 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 0.000017MorbidityEmergency department visits forasthma0.001600 0.000160 0.006600 0.001100 0.000260 0.0000270.001100 0.000420 0.004500 0.001200 0.019000 0.003500Acute bronchitis 0.005900 0.000540 0.025000 0.004100 0.001400 0.000220 0.005900 0.002300 0.021000 0.005400 0.083000 0.016000Lower respiratory symptoms 0.075000 0.006800 0.3200000.052000 0.018000 0.002800 0.075000 0.0300000.260000 0.069000 1.100000 0.200000Upper respiratory symptoms 0.110000 0.009800 0.4600000.074000 0.026000 0.004000 0.110000 0.0420000.370000 0.099000 1.500000 0.290000Minor restricted-activity days 2.900000 0.260000 12.000000 2.000000 0.650000 0.088000 2.7000001.000000 9.800000 2.300000 37.000000 7.100000Lost work days 0.480000 0.043000 2.000000 0.330000 0.110000 0.015000 0.450000 0.170000 1.700000 0.400000 6.300000 1.200000Asthma exacerbation 0.260000 0.024000 1.100000 0.1800000.063000 0.009800 0.260000 0.100000 0.920000 0.240000 3.700000 0.710000Hospital Admissions, Respiratory 0.001300 0.000120 0.005600 0.000900 0.000200 0.000019 0.0008300.000340 0.003200 0.000740 0.012000 0.002400Hospital Admissions, Cardiovascular 0.001600 0.000150 0.006800 0.001100 0.000250 0.0000240.001000 0.000420 0.003800 0.000850 0.0140000.002700Non-fatal Heart Attacks (age>18)Peters et al (2001) 0.005000 0.000460 0.021000 0.003500 0.000830 0.000079 0.003600 0.0015000.014000 0.003100 0.052000 0.010000Pooled estimate of 4 studies 0.000540 0.000049 0.002300 0.000370 0.000090 0.000009 0.0003800.000160 0.001500 0.000330 0.005600 0.001100* All estimates are rounded to two significant figures. All fine particles are assumed tohave equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitude of their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.

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

4A-14 4A.4 Regional Ozone Benefit-per-Ton Estimates
The process for generating the regional ozone be nefit-per-ton estimates is consistent with the process for PM 2.5. 

### Ozone is not directly emitted, and is a non-linear function of NO X 

### 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 NO X 

### emissions. VOC emissions, which are also a precursor to ambient ozone formation, are insi gnificant from the EGU sector relative to both

NO X 

### emissions from EGUs and the total VOC emi ssions inventory. Therefore, we believe that our assumption that EGU-attributable ozone fo rmation at the regional -level is due to NO X 

### alone

is reasonable.

Similar to PM 2.5 

### , this method provides estimates of the regional averag e benefit-per-ton. Due to the non-linear chemistry between NO X 

### emissions and ambient ozone, using an average benefit-per-ton estimate for NO X 

### adds uncertainty to the ozone co-benefits estimated for the proposed guidelines. Because most of the estimat ed 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 th e Clean Power Plan proposal during the ozone-season only (May to September). As shown in Table 4A-1, ozone-season NO X 

### 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 cobenefits in areas with longer oz one seasons such as southern California and Texas. When the underestimated benefit-per-ton estimate is multiplied by ozone-season only NO X 

### emission reductions, this results in an underestimate of the monetized ozone co-b enefits. For illustrative purposes, Tables 4A-9 through 4A -11 provide the ozone benefit- per-ton estimates using both allyear NO X 

### emissions and ozone-season only NO X 

### for 2020, 2025, and 2030, respectively. Tables 4A-12 through 4A-14 provide the ozone seas on incidence-per-ton estimates for 2020, 2025, and 2030, respectively. Similar to PM 2.5 

### , the ozone benefit-per-ton values for 2020 and 2030 are based on applying the air quality modeling from 2025 to population and health information from 2020 and 2030. Estimated benefit-per-ton for these year s have additional uncer tainty relative to 2025 because of potential differences in atmospheri c responses to reductions in ozone precursors

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

4A-15 in those years. Uncertainties may be somewhat larg er in the case of ozone due to high degree of dependence of ozone responses to base line meteorology and emissions levels.

Table 4A-9. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)* Ozone precursor Pollutant National Regional East West California Ozone season 

### NO X $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 ra nge of epidemiology studies for avoided premature mortality for ozone. All estimates are rounded to two significa nt 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 Cl ean Power Plan in 2025 (2011$)* Ozone precursor Pollutant National Regional East West California Ozone season 

### NO X $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 ra nge of epidemiology studies for avoided premature mortality for ozone. All estimates are rounded to two significa nt 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 Cl ean Power Plan in 2030 (2011$)* Ozone precursor Pollutant National Regional East West California Ozone season 

### NO X $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 ra nge of epidemiology studies for avoided premature mortality for ozone. All estimates are rounded to two significa nt figures. The monetized benefits incorporate the conversion from NO X precursor emissions to ambient ozone.

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

4A-16 Table 4A-12. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020* Health Endpoint East West California Premature Mortality – adult

Bell et al. (2004) 0.000600 0.000190 0.001300 Levy et al. (2005) 0.002800 0.000880 0.005800 Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.003500 0.000900 0.006600 Hospital Admissions, Respiratory (ages < 2) 0.001800 0.000780 0.003300 Emergency Room Visits, Respiratory 0.002000 0.000500 0.003900 Acute Respiratory Symptoms 3.500000 1.300000 8.800000 School Loss Days 1.200000 0.490000 3.000000 * All estimates are rounded to two si gnificant figures. The incidence bene fit-per-ton estimates incorporate the conversion from NO X precursor emissions to ambient ozone. These estimates reflect ozone-season NO X emissions.



### Table 4A-13. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025* Health Endpoint East West California Premature Mortality – adult

Bell et al. (2004) 0.000640 0.000210 0.001400 Levy et al. (2005) 0.002900 0.000970 0.006400 Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.004100 0.001100 0.007800 Hospital Admissions, Respiratory (ages < 2) 0.001800 0.000820 0.003400 Emergency Room Visits, Respiratory 0.002000 0.000540 0.004100 Acute Respiratory Symptoms 3.600000 1.400000 8.900000 School Loss Days 1.300000 0.520000 3.200000 * All estimates are rounded to two si gnificant figures. The incidence bene fit-per-ton estimates incorporate the conversion from NO X precursor emissions to ambient ozone. These estimates reflect ozone-season NO X emissions.



### Table 4A-14. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030* Health Endpoint East West California Premature Mortality – adult

Bell et al. (2004) 0.000640 0.000230 0.001800 Levy et al. (2005) 0.002900 0.001100 0.008200 Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.004400 0.001300 0.011000 Hospital Admissions, Respiratory (ages < 2) 0.001800 0.000860 0.004100 Emergency Room Visits, Respiratory 0.002000 0.000580 0.005000 Acute Respiratory Symptoms 3.500000 1.500000 11.000000 School Loss Days 1.200000 0.550000 3.800000 * All estimates are rounded to two si gnificant figures. The incidence bene fit-per-ton estimates incorporate the conversion from NO X precursor emissions to ambient ozone. These estimates reflect ozone-season NO X emissions.

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4A-17 4A.5 References

Abt Associates, Inc. 2010. “User’s Guide: Modele d Attainment Test Software.” Available at: <http://www.epa.gov/scram001/modelingapps_mats.htm>. Accessed June 6, 2015. Abt Associates, Inc. 2012. “BenMAP User’s Manual Appendices,” prepared for U.S. Research Triangle Park, NC: U. S. Environmental Prot ection Agency, Office of Air Quality Planning and Standards. Available at: <http://www.epa.gov/air/benmap/models/BenMA PAppendicesOct2012.pdf>. Accessed June 6, 2015. Bell, M.L., A. McDermott, S.L. Ze ger, J.M. Sarnet, and F. Dominici

. 2004. “Ozone and ShortTerm Mortality in 95 U.S. Urban Communities, 1987-2000.” Journal of the American Medical Association. 292(19):2372-8. Bell, M.L., F. Dominici, and J.M. Samet. 2005. “A Meta-Analysis of Time-Series Studies of Ozone and Mortality with Comparison to the National Morbidity, Mortality, and Air Pollution Study.” Epidemiology . 16(4):436-45. ENVIRON International Corporat ion. 2014. User’s Guide: Comp rehensive Air Quality Model with Extensions, Version 6.1, Novato, CA. Ap ril. Available at <http://www.camx.com>. Accessed June 6, 2015. Fann, N., C.M. Fulcher, and B.J. Hubbell. 2009. “The Influence of Location, Source, and Emission Type in Estimates of the Human H ealth 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 Industr ial, Area and Mobile Emission Sectors Across the U.S.” Environment International. 49:41–151. Fann, N., K.R. Baker, and C.M. Fulcher. 2013. “T he Recent and Future Health Burden of Air Pollution Apportioned Across 23 U.S. Sectors.” Environmental Scientific Technol ogy. 47(8): 3580–3589. Huang Y., F. Dominici, and M. Bell. 2005. “Bayes ian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Card io-Respiratory Mortality.” Environmetrics . 16:547-562. Ito, K., S.F. De Leon, and M. Lippmann. 2005. “Associations between Ozone and Daily Mortality: Analysis and Meta-Analysis.” Epidemiology . 16(4):446-57.

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4A-18 Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, et al. 2009. Extended Follow-Up and Spatial Analysis of the American Cancer Soc iety Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. Lepeule, J., F. Laden, D. Dockery, and J. Schw artz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow-Up of th e Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives. 120(7):965-70. Levy, J.I., S.M. Chemerynski, and J.A. Sarnat . 2005. “Ozone Exposure and Mortality: An Empiric Bayes Metaregression Analysis.” Epidemiology. 16(4):458-68. Roman, H.A., K. D. Walker, T. L. Walsh, L. Conner, H. M. Richmond, B. J. Hubbell, and P. L. Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S.” Environmental Scientific Technology . 42(7):2268-2274. Schwartz, J. 2005. “How Sensitive is the Associ ation between Ozone and Daily Deaths to Control for Temperature?” American Journal of Respirat ory 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 Attain ment of Air Quality Goals for Ozone, PM 2.5 

### , and Regional Haze . Office of Air Quality Planning and St andards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttn/scram/gui dance/guide/final-03-pm-rh-guidance.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2011b Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards . Research Triangle Park, NC: Office of Air Quality Planning and Standards, Health and Envi ronmental Impacts Division. (EPA document number EPA-452/R-11-011, Dece mber). Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/ma tsriafinal.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 Ai r Quality Standards for Particulate Matter. EPA452/R-12-003. Office of Air Qual ity Planning and Standards, Health and Environmental Impacts Division, Research Triangle Pa rk, NC. December. Available at: < http://www.epa.gov/ttnecas1/regdata/RIAs/ finalria.pdf>. Accessed June 4, 2015.

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4A-19 U.S. Environmental Protection Agency (U.S. EPA). 2013. Technical Support Document: Estimating the Benefit per Ton of Reducing PM 2.5 

### Precursors from 17 Sectors . Office of Air Quality Planning and Standards, Research Tr iangle Park, NC. February. Available at: < http://www2.epa.gov/site s/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 Exis ting Power Plants and Emission Standards for Modified and Reconstructed Power Plants . EPA-542/R-14-002. Office of Air Quality Planning and Standards, Research Tr iangle Park, NC. June. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/1 11dproposalRIAfinal0602.pdf>. Accessed June 4, 2015.

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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, natura l gas, and coal potentially resu lting 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 magnit ude of these impacts. We refer to these changes as secondary market impacts.

Under the final emission guideline s, states are not required to use any of the measures that the EPA determines constitute BSER, or use those measures to the same degree of stringency

that the EPA determines is achievable at reas onable cost. Rather, CAA section 111(d) allows each state to determine the appropriate combinat ion of, and the extent of its reliance on, measures for its state plan, by wa y of meeting its state-specific goal. Given the flexibilities afforded states in complying with the emi ssion guidelines, the benefits, cost and economic impacts reported in this RIA are illustrative of actions that states may take. The implementation approaches adopted by the states, and the strate gies adopted by affected EGUs, will ultimately drive the magnitude and timing of secondary impact s from changes in the pr ice of electricity, and the demand for inputs by the electricity sector , on other markets that use and produce these inputs.

The flexibility afforded to states in their st ate plans allows them to encourage compliance methods by affected EGUs, which include desi gn elements that may mitigate or promote particular impacts based on their priorities. For example, states in the Regional Greenhouse Gas

Initiative use the revenues from allowance aucti ons to support direct bi ll assistance for retail consumers, fund investments in clean energy and electricity demand reduction for business consumers, and support employment in the de velopment of clean and renewable energy technologies. In its recent regulations to lim it greenhouse gas (GHG) emi ssions, California’s Air Resources Board designated a portion of allowanc es to be allocated to electric distribution companies in order to mitigate potential electric ity rate increases and their associated impacts. Other states may encourage compliance methods by affected EGUs with particularly robust

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5-2 deployment of renewables, energy efficiency, or natural gas to prom ote manufacturing demand or employment in those sectors. For example, energy efficiency investments may be targeted towards reducing both electricity consumption and natural gas or heating oil consumption, such as weatherization projects. The state plan appr oach and the composition of these programs will influence the effects of complia nce 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 illustra tive plan approaches analyzed. This chapter provides a quantitative assessment of the energy price impacts for these illustrative approaches and a qualitative assessment of th e factors that will in part determine the timing and magnitude

of effects in other markets.

5.2 Methods
One potential quantitative approach to evaluatin g the secondary market impacts is to use a computable general equilibrium (CGE) model. CGE models are able to provide aggregated representations of the whole ec onomy in equilibrium in the ba seline 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), th e EPA used the Economic Model for Policy Analysis (EMPAX) CGE model to estimate the secondary market effects based on the cost impacts projected by the Integrat ed Planning Model (IPM) for the directly regulated sector.

When considering the secondary market imp acts of a regulation bot h the effects of the costs, the benefits of improved air quality, and their interaction may be relevant. Therefore, in the Second Prospective Analysis under Section 812 of the Clean Air Act Amendments the EPA incorporated a set of health be nefits arising from air quality improvement into the EMPAX CGE model when studying the economy-wide impacts of the Clean Air Act (U.S. EPA 2011). While the external Council on Clean Air Compliance Analysis (Council) review of this study stated that inclusion of benefits in an economy-wide model “represent[ed] a significant step forward in benefit-cost analysis” (Hammitt 2010), the EPA recognizes that serious technical challenges

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5-3 remain when attempting to evaluate the benefits and costs of potential re gulatory actions using economy-wide models.

In light of these challenges, the EPA has established a Science Advisory Board (SAB) panel on economy-wide modeling to consider the technical merits and challenges of using this analytical tool to evaluate costs, benefits, a nd economic impacts in regulatory development. In addition, EPA is asking the panel to identify poten tial paths forward for improvements that could address the challenges posed when using economy- wide models to evaluate the effects of regulations. The final panel membership was announced in March 2015 and the first of multiple face-to-face meetings of the SA B panel has been scheduled for October 2015. The EPA will use the recommendations and advice of this panel as an input into its pro cess for improving benefitcost and economic impact analyses used to inform decision-making at the agency.

The advice from the Science Advisory Board (SAB) panel formed specifically to address the subject of economy-wide modeling was not ava ilable in time for this final action. Given the ongoing SAB panel on economy-wide modeling, the unc ertain nature of the ultimate energy price impacts due to the state fl exibility in choosing a plan an d the compliance flexibility for affected EGUs, and the ongoing challenges of accu rately representing costs, benefits, energy efficiency improvements in economy-wide modeli ng, this chapter considers the energy impacts associated with the illustrative plan approaches analyzed and a qualita tive 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 th ese commodities will shift the production costs for sectors that use electricity, natu ral gas, and coal in the produc tion of other goods and services. Such changes in production costs may lead to cha nges in the quantities and/ or prices of the goods or services produced and cha nges in imports and exports.

The EPA used IPM to estimate electricity, natural gas, and coal price changes based on the illustrative plan approaches modeled for this rule. IPM is a multi-regional, dynamic, deterministic linear programming model of the U. S. electric power sector described in more detail in Chapter 3. The Retail Price Model (RPM) uses forecast changes in wholesale prices and

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5-4 the cost of demand-side energy efficiency programs to forecast changes in average retail prices. The prices are average prices over consumer classes and regions weighted by the amount used. Table 5-1 shows these estimated price changes. For other results generated by IPM and the RPM, please refer to Chapter 3.

There are many factors influenci ng the projected natural gas pr ices. IPM (and its integrated gas resource and supply module) models natural gas reserves a ppropriate natural gas supplies

based on a multitude of factors. Since the model si mulates perfect foresight, it anticipates future demand for natural gas and responds accordingly. In addition, IPM (and the natural gas module) are viewing a very long time hor izon (through 2050), such that the impacts in certain years may be responsive to other mode ling assumptions or drivers. The modeling framework is simultaneously solving for all of these key mark et 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-bas ed and Mass-based Illustrative Plan Approaches

Rate-based 2020 2025 2030 Electricity Price Change 3.2% 0.9% 0.8% Delivered Natural Gas Price Change 5.3% -7.7% 2.5% Delivered Coal Price Change -1.7% -6.2% -8.0%

Mass-based 2020 2025 2030 Electricity Price Change 3.0% 2.0% 0.0% Delivered Natural Gas Price Change 3.8% -3.2% -2.1% Delivered Coal Price Change -1.6% -4.3% -4.6%



### For years when the price of electricity, natura l 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. C onversely, for years when prices of these inputs decreased, one would expect increases in produc tion and decreases in market prices within these sectors. Smaller changes in input price changes would l lead to smaller impacts within secondary markets. However, a number of factors in addi tion to the magnitude and sign of the energy price changes, influence the magnitude of the impact on production and market prices for sectors using electricity, natural gas, or coal as inputs to production. These factor s are discussed below.

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5-5 5.3.1 Share of Total Production Costs
The impact of energy price changes in a partic ular sector depends, in part, on the share of total production costs attr ibutable to those commodities. Fo r sectors in which the directly affected inputs are only a small portion of produc tion costs, the impact will be smaller than for sectors in which these inputs make up a greater po rtion of total production co sts. Therefore, more energy-intensive sectors would pot entially experience gr eater cost increases when electricity, natural gas, or coal prices incr ease, but would also experience greater reduced costs when these input prices decrease. 119



### 5.3.2 Ability to Substitute between Inputs to the Production Process
The ease with which producers are able to subs titute other inputs for electricity, natural gas, or coal, or even amongst those commodities, influences the impact of price changes for these inputs. Those sectors with a greater ability to substitute across energy inputs or to other inputs will be able to, at least partially, offset the increase d cost of these in puts 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 Substi tute Goods and Services
The ability of producers in sect ors experiencing change s in their input prices to pass along

the increased costs to their customers in the form of higher prices for their products depends, in part, on the availability of substitutes for the se ctors’ products. Substitutes may be either other domestic products or foreign imports. If close su bstitutes exist, the demand for the product will in general be more elastic and the producers will be less able to pass on the added cost through a price increase.

Such substitution can also take place between foreign and domestic goods within the same sector. Changes in the price of electricity, natural gas, and coal can influence the quantities of goods imported or exported from sectors using these inputs. When the cost of domestic

119 The net direct effect of this rulemaking on the production co sts 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 pr ogram that was adopted to achieve a state goal. That said, those expenditures may lead to other reduced expenditures for the sector, such as reduced natural gas use from weatherization projects.

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5-6 production increases due to more expensive inputs, imports may increase as consumers substitute towards relatively less costly foreign-produced g oods. If imports increase because of a regulation and those imports come from countries with hi gher emissions per unit of production, this can result in foreign emission increa ses that offset some portion of domestic decreases, an effect commonly referred to as “leakage. ” Alternatively, if those imports are less emissions-intensive

than the sectors that have cont racted, emissions may fall even fu rther. The potential for changes in global pollutants such as carbon dioxide (CO 2 

### ) and other GHG emissions is noteworthy. Unlike most criteria pollutants and hazar dous air pollutants, the impacts of CO 2 

### emissions are not affected by the location from whic h 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 ou tside 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 sector s producing these technologies ma y expand. Therefore, while a sector that produces say, wind turbine blades, ma y face higher natural gas and electricity prices, production in that sector may ultimately increas e due to higher demand from the electricity

sector for wind turbines.

5.5 Conclusions
Changes in the price of electri city, natural gas, and coal can affect markets for goods and services produced by sectors that use these energy inputs in the production process. The direction and magnitude of these impacts are influenced by a number of factors. Changes in cost of production may lead to changes in price, quant ity produced, and profitab ility of firms within secondary markets. Furthermore, the demand for certa in inputs in the electr icity sector, as well as changes in the demand for energy efficiency serv ices and products, will also affect secondary markets. If regulation results in changes in domestic markets that lead to an increase in imports, increases in production in countri es with more energy-intensive production may lead to changes

in CO 2 

### emissions elsewhere.

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5-7 5.6 References

Hammitt, J.K. 2010. Review of the Final Inte grated Report for the Second Section 812 Prospective Study of the Benefits and Cost s of the Clean Air Act. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/1E6218D E3BFF682E852577FB005D46F1/$File/EPA-CO UNCIL-11-001-unsigned.pdf.> Accessed June 4, 2015. U.S. Environmental Protection Agency (U .S. EPA). 2008. Final Ozone NAAQS Regulatory Impact Analysis. Office of Air Quality Planni ng and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttn/ecas/r egdata/RIAs/452_R_08_003.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis for the Proposed Federal Transport Rule. Office of Ai r Quality Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/propos altrria_final.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EP A). 2011. The Benefits and Costs of the Clean Air Act from 1990 to 2020, Final Report – Rev A. Office of Air and Radiation, Washington, DC. Available at <http://www.epa.gov/clea nairactbenefits/feb11/ fullreport_rev_a.pdf>. Accessed June 4, 2015.

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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 employment. According to the Exec utive Order, “our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, competitiveness, and job creation. It mu st be based on the best available science” (Executive Order 13563, 2011). Alth ough standard benefit-cost an alyses have not typically included a separate analysis of re gulation-induced employment impacts, 120 

### we typically conduct employment analyses for economically significan t rules. During the current economic recovery, employment impacts are of particular concern an d questions may arise about their existence and magnitude. This chapter discusses and projects potential employment impacts for the utility power, coal and natural gas pr oduction, and demand-side energy e fficiency sectors which may

result from the Final Carbon Pollution Emissi on Guidelines for Existing Stationary Sources: Electric Utility Generating Units (herein referred to as “final emission guidelines” or the “Clean Power Plan Final Rule”). 121



### Section 6.2 describes the theoretical framew ork used to analyze regulation-induced employment impacts, discussing how economic theory alone cannot predict whether such impacts are positive or negative. Section 6.3 presents an overview of the peer-reviewed literature relevant to evaluating the effect of envi ronmental regulation on employment. Section 6.4 provides background regarding recent employment tre nds in the electricity generation, coal and natural gas extraction, renewable energy, and de mand-side energy efficiency-related sectors. Section 6.5 presents the EPA’s quantitative projecti ons of potential employment impacts in these sectors. These projections are ba sed in part on a detailed mode l of the electricity production sector used for this regulatory analysis. A dditionally, this secti on discusses projected

120 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses.

121 The employment analysis in this RIA is part of EPA’s ongoing effort to “conduct continuing evaluations of potential loss or shifts of employment which may result from the administration or enforcement of [the Act]” pursuant to CAA section 321(a).

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6-2 employment impacts due to demand-side energy e fficiency activities. Section 6.6 offers several

conclusions.

6.2 Economic Theory and Employment
Regulatory employment impacts are difficult to disentangle from other economic changes affecting employment decisions over time and across regions and industries. Labor market responses to regulation are complex. They depe nd on labor demand and supply elasticities and possible labor market imperfections (e.g., wage s tickiness, long-term unemployment, etc.). The unit of measurement (e.g., number of jobs, type s of job hours worked, and earnings) may affect observability of that response. Net employment impacts are composed of a mix of potential declines and gains in different areas of the economy (e.g., the directly regulated sector, the environmental protection sector, upstream and downs tream sectors, etc.) over time. In light of these difficulties, economic theory provides a constructive framework for analysis.

Microeconomic theory describes how firms ad just their use of inputs in response to changes in economic conditions. 122 

### Labor is one of many inputs to production, along with capital, energy, and materials. In competitive markets, firms choose inputs and outputs to maximize profit as a function of market pric es and technologi cal constraints. 123,124 

### Berman and Bui (2001) adapt this model to analy ze how environmental regulations affect labor demand. 125 

### They model environmental regulation as effectively requiri ng certain factors of production, such as pollution abatement capital, at levels that firms woul d not otherwise choose. Berman and Bui (2001) model two components that drive changes in firm-level labor demand: output effects and

substitution effects. 126 

### Regulation affects the profit-maxi mizing quantity of output by changing the marginal cost of production. If a regulation causes marginal cost to increase, it will place

122 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9.

123 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization.

124 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs).

125 Morgenstern, Pizer, and Shih (2002) develop a similar model.

126 The authors also discuss a third componen t, the impact of regulation on factor prices, but conclude that this effect is unlikely to be important for large competitive factor ma rkets, such as labor and cap ital. Morgenstern, Pizer and Shih (2002) use a very similar model, but they break the em ployment effect into three part s: 1) a demand effect; 2) a cost effect; and 3) a factor-shift effect.

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6-3 upward pressure on output prices, leading to a decr ease in demand, and resulting in a decrease in production. The output effect desc ribes how, holding labor intensity constant, a decrease in production causes a decrease in labor demand. As noted by Berman and Bui, although many assume that regulations must increases marginal cost, it need not be th e 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 cas e, output could incr ease after firms comply with the regulation. For example, in the context of the current rule, improving the heat rate of utility boiler increases fuel efficiency, lowering margin al production costs, and thereby pot entially 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 lowe rs marginal production costs.

The substitution effect describes how, holdi ng output constant, regulation affects the labor-intensity of production. Although increased environmental regulation may increase use of pollution control equipment and energy to operate that equipment, the impact on labor demand is ambiguous. For example, equipment inspection requirements, specialized waste handling, completing required paperwork, or pollution tech nologies that alter the production process may affect the number of workers necessary to produce a unit of output. Berman and Bui (2001)

model the substitution effect as the effect of regulation on pollution control equipment and expenditures required by the regulation and the co rresponding change in th e labor-intensity of production.

In summary, as output and substitution e ffects may be positive or negative, economic theory alone cannot predict the di rection of the net effect of regulation on labor demand at the level of the regulated firm. Op erating within the bounds of standard economic theory, however, empirical estimation of net employment effects on regulated firms is possi ble when methods and data of sufficient detail and quality are availabl e. The extant literature , however, illustrates difficulties with empirical estimation. For example, there is a paucity of publicly-available data on plant-level employment, thus most studies mu st rely on confidential plant-level employment data from the U.S. Census Bureau, typically combined with pollution abatement expenditure data, that are too dated to be reliably informa tive, or other measures of the stringency of regulation. In addition, the most commonly used empirical methods can only estimate the gross

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6-4 effects of regulation on employme nt (i.e. the impact of that more stringent regulation has on employment relative to less stringent or no regulation), not the net effects.

The conceptual framework described thus far focused on regulatory effects on plant-level decisions within a regulated industry. Employ ment impacts at an individual plant do not necessarily represent impacts for the sector as a whole. The theoretical approach must be modified when applied at the industry level.

At the industry-level, labor demand is more responsive if: (1) th e price elasticity of demand for the product is high, (2) other factors of production can be easily substituted for labor, (3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total production costs. 127 

### For example, if all firms in an indus try are faced with the same regulatory compliance costs and product demand is inelastic, then industry output may not change much, and output of individual fi rms may change slightly. 128 

### In this case, the output effect may be small, while the substitution effect depends on input su bstitutability. Suppose, for example, that new equipment for heat rate improvements requires la bor to install and operate. In this case, the substitution effect may be posit ive, and with a small output effect, the total effect may be positive. As with potential effects for an indivi dual firm, theory cannot determine the sign or magnitude of industry-level re gulatory effects on labor demand. Determining these signs and magnitudes requires additional sector-specific empi rical study. For environmental rules, much of the data needed for these empirical studies is not publicly available, w ould 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 t ypical Regulatory Impact Analysis (RIA).

In addition to changes to labor demand in th e regulated industry, net employment impacts encompass changes in other related sectors. For example, the final guidelines may increase demand for heat rate improving equipment and se rvices. This increased demand may increase revenue and employment in the firms supporting this technology. At the same time, the regulated industry is purchasing the equipment, and thes e costs may impact labor demand at regulated

127 See Ehrenberg & Smith, p. 108.

128 This discussion draws from Berman and Bui (2001), pp. 293.

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6-5 firms. Therefore, it is important to consider the net effect of compliance actions on employment across multiple sectors or industries.

If the U.S. economy is at full employment, even a large-scale environmental regulation is unlikely to have a noticeable imp act on aggregate net employment. 129 

### Instead, labor in affected sectors would primarily be rea llocated from one productive use to another (e.g., from producing electricity or steel to produci ng high efficiency equipment), and net national employment effects from environmental regulation would be small and transitory (e.g., as workers move from one job to another). 130 

### Some workers may retrain or relocate in anticipation of new requirements or require time to search for new jobs, while shor tages in some sectors or regions could bid up wages to attract workers. These adjustment co sts can lead to local labor disruptions. Although the net change in the national workforce is expected to be small, localized reductions in employment may adversely impact individuals a nd communities just as localized increases may have positive impacts.

If, on the other hand, the economy is operati ng at less than full employment, economic theory does not clearly indicate th e direction or magnitude of th e net impact of environmental regulation on employment; it coul d cause either a short-run net in crease or short-run net decrease (Schmalansee and Stavins, 2011). An important research question is how to accommodate unemployment as a structural feature in economic models. This feature may be important in assessing large-scale regulatory impacts on employment (Smith, 2012).

Environmental regulation may also affect labor supply a nd productivity. In particular, pollution and other environmental risks may impact labor productivity or employees’ ability to

work. 131 

### While the theoretical framew ork for analyzing labor supply effects is analogous to that for labor demand, it is more difficult to study em pirically. There is a sm all emerging literature

described in the next section th at uses detailed labor and envi ronmental data to assess these impacts.

129 Full employment is a conceptual target for the econom y where everyone who wants to work and is available to do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero.

130 Arrow et al. 1996; see discussion on bottom of p. 8. In practice, distributional impacts on individual workers can be important, as discussed in later paragraphs of this section.

131 E.g. Graff Zivin and Neidell (2012).

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6-6 To summarize, economic theory provides a framework for analyzing the impacts of environmental regulation on employment. The ne t employment effect incorporates expected employment changes (both positive and negative) in the regulated sect or and other related sectors. Labor demand impacts for regulated firm s, and also for the regulated industry, can be decomposed into output and substitution effect s which may be either negative or positive. Estimation of net employment effects for regulated sectors is possible wh en data of sufficient detail and quality are available. Finally, economic theory suggests that labor supply effects are also possible. In the next section, we discuss the empirical literature.

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

The labor economics literature contains an extensive body of peer-reviewed empirical work analyzing various aspects of labor demand, relying on the theoretical framework discussed in the preceding section. 132 

### This work focuses primarily on e ffects of employment policies such as labor taxes and minimum wages. 133 

### In contrast, the peer-revi ewed empirical literature specifically estimating employment effects of environmental regula tions is growing, but is more limited. In this section, we present an overview of the latter. As discussed in the preceding section on theory, determining th e direction of employment effect s in regulated industries is challenging because of the complexity of the out put and substitution effects. Complying with a new or more stringent regulation may require ad ditional inputs, including labor, and may alter the relative proportions of labor and capital used by regulated firm s (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 regulat ed counties may generate fewer jobs than less regulated ones, but since this study compares more regulated to less regulated counties it cannot address net employment effects at a national level (Greenst one, 2002). Moreover, environmental regulations may affect sectors that support pollution reduction earlier than the regulated industry. Rules are usually announced well in advance of their effective dates and then typically provide a period of

132 Again, see Hamermesh (1993) for a detailed treatment.

133 See Ehrenberg & Smith (2000), Chapter 4: “Employment Effects: Empirical Estimates” for a concise overview.

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6-7 time for firms to invest in technologies and proc ess changes to meet the new requirements. When a regulation is promulgated, the initial response of firms is of ten to order pollution control equipment and services to enable compliance when the regulation becomes effective. Estimates

of short-term increases in de mand for specialized labor within the environmental protection sector have been prepared for several EPA regulat ions in the past, includ ing the Mercury and Air Toxics Standards (MATS). 134 

### Overall, the peer-reviewed literatur e does not contain evidence that environmental regulation has a large impact on net employment (either negative or positive) in the long run across the whole economy.

6.3.1 Regulated Sector
Several empirical studies, including Berman and Bui (2001) and Ferris, Shadbegian, and Wolverton (2014), suggest that regulation-indu ced net employment impacts may be zero or slightly positive, but sma ll in the regulated sector. Gray et al (2014) find that pulp mills that had to comply with both the air a nd water regulations in EPA’s 1998 “Cluster Rule” experienced relatively small, and not always statistically sign ificant, decreases in em ployment. Other research suggests that more highly regulat ed counties may generate fewer jobs than less regulated ones (Greenstone 2002, Walker 2011, 2013). However si nce these latter studies compare more regulated to less regulated counties they overstate the net national impact of regulation to the extent that regulation causes plan ts to locate in one area of the c ountry rather than another. List et al. (2003) find some evidence that this type of geographic relocation may be occurring. Overall, the peer-reviewed literature does not contain evidence that envir onmental regulation has a large impact on net employment (either negative or positive) in the long run across the whole

economy. A small literature examines impacts of environmental regulations on manufacturing employment. Greenstone (2002) and Walker ( 2011, 2013) study the imp act of air quality regulations on manufacturing employ ment, estimating the effects in non-attainment areas relative to attainment areas. Kahn and Mansur ( 2013) study environmental regulatory impacts on geographic distribution of manuf acturing employment, controlling fo r electricity prices and labor regulation (right to work laws ). Their methodology identifies employment impacts by focusing

134 U.S. EPA (2011b).

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6-8 on neighboring counties with differe nt air quality regulations. They find limited evidence that environmental regulations may cause employment to be lower within “county-border-pairs.” This result suggests that regula tion may cause an effective relo cation of labor across a county border, but since one county’s lo ss is another’s gain, such shifts cannot be transformed into an estimate of a national net effect on employment . Moreover this result is sensitive to model specification choices.

The few studies in peer-reviewed journals evaluating employment impacts of policies

that reduce CO 2 

### emissions in the electric power generation sector are in the European context. In a sample of 419 German firms, 13 percent of wh ich were in the electricity sector, Anger and Oberndorfer (2008) find that the ini tial allocation of emission permits did not significantly affect employment growth in the first year of the European Union (EU) Emissions Trading Scheme (ETS). Examining European firms from 1996-2007, Commins et al. (2011) find that a 1 percent increase in energy taxes is associated with a 0.01 percent decrease in employ ees 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 employ ees relative to those th at did not participate, but this effect is not statistically significant.

This literature suggests that the employment 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 effectively put a price on emissions either through taxes or tradable pe rmits. A performance standard may not generate similar employment effects. Moreover, European firms face relative fu el prices and market regulatory structures different from their U.S. counterparts, further co mplicating 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 estim ate the net national employment impacts of environmental regulation. Given the difficulty with estimating national impacts of regulations, EPA has not generally estimated economy-wide employment impact s of its regulations in its benefit-cost analyses. However, in its continuing effort to ad vance the evaluation of costs, benefits, and economic impacts associated with environmental regulation, EPA has formed a

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6-9 panel of experts as part of EPA’s Science Advi sory Board (SAB) to advise EPA on the technical merits and challenges of using economy-wide ec onomic models to evaluate the impacts of its regulations, including the imp act on net national employment. 135 

### Once EPA receives guidance from this panel it will carefully consider this input and then decide if and how to proceed on economy-wide modeling of employment impacts of its regulations.

EPA received several comments regarding the potential net nationa l employment impact of the proposed emission guidelines. Many of thes e comments referred to analyses developed using non-transparent, non- peer reviewed models. 136 

### Additionally, some of the analyses predated the Clean Power Plan proposal. However, one comment was based on an “economy-wide assessment of the employment impacts associat ed with the U.S. Environmental Protection Agency’s (EPA’s) proposed Clean Power Plan” using the Long-term In ter-industry Forecasting Tool (LIFT) model. 137 

### The LIFT model, which is from the Interindustry Forecasting Project (Inforum) at the University of Maryland, has been used in the peer-reviewed academic

literature 138 

### and has also been used to examine the economic impacts of other national policies [Meade (2009); Werling (2011)]. 139 

### The commenter noted that “Wh ile EPA’s analysis provides a reasonable first approximation of the proposed rule’s employment effects, its focus on direct employment impacts does not capture various i ndirect employment impacts that may be of interest to policymakers and the public.” [...] “T hese include the employment impact associated with changes in electricity and other energy prices (both positiv e and negative, depending on the

135 For further information see: http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED?OpenDocument

136 See, for example, comments EP A-HQ-OAR-2013-0602-33150, EPA-HQ-OAR-2013-0602-23140, and EPA-HQOAR-2013-0602-23156, within Docket ID : EPA-HQ-OAR-2013-0602

.

137 See comment EPA-HQ-OAR-2013-0602-22960, within Docket ID : EPA-HQ-OAR-2013-0602

.

138 See, for example, Almon (1991) and Mccarthy (1991).

139 The commenter provides the following description of the LIFT model: “LIFT is a 97-sector dynamic representation of the U.S. national economy. The model combines an interindustry input / output (I-O) formulation with extensive use of regression analysis to employ a ’bottom-up’ approach to macroeconomic modeling. That is, the model works like the actual economy, building macroeconomic totals from details of industry activity, rather than distributing predetermined macroeconomic quantities among industries. The commenter also describes LIFT also captures interactions between in dustries across the economy, enabling the model to gauge how changes in prices, investment, or productivity in one industry cascade across the economy. In the context of the Clean Power Plan, this is an important feature for understanding how the rule’s direct impacts for the electric power sector affect other industries.”

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6-10 year), the productivity impacts a ssociated with heat rate im provements at power plants, households and businesses re-directing expenditure s to other uses because of increased demandside energy efficiency, expenditu res crowded out by energy efficien cy expenditures, and changes in investments for air pollution control devices.”

As mentioned previously, EPA is currently engaged in an SAB process on economy-wide modeling. EPA will not make any determinations on whether modeling the economy-wide impacts of its regulations – includ ing employment impacts - is feas ible and, if so, how and when to do this until it receives guidance from the SA B panel. While the purpose of the SAB process is not to peer review any particular economy-wi de model, it is worth noting that the class of models to which LIFT belongs are encompassed in one of the charge questi ons to the SAB: “Are there other economy-wide modeling approaches th at EPA could consider in conjunction with

CGE models to evaluate the shor t run implications of an air regulation (e.g., macro-economic, disequilibrium, input/output models)? What are the advantages or disadvantages of these approaches?” 140



### 6.3.3 Labor Supply Impacts
The empirical literature on environmenta l regulatory employment impacts focuses primarily on labor demand. However, there is a nascent literature focu sing on regulation-induced effects on labor supply. 141 

### Although this literature is limited by empirical challenges, researchers have found that air quality improvements lead to reductions in lost work days (e.g., Ostro, 1987). Limited evidence suggests worker productivity may also improve when pollution is reduced. Graff Zivin and Neidell (2012) us ed detailed worker-level pr oductivity data from 2009 and 2010, paired with local ozone air quality monitoring data for one large California farm growing multiple crops, with a piece-rate payment structur e. Their quasi-experimental structure identifies an effect of daily variation in monitored ozone levels on productivity. Th ey find “ozone levels well below federal air quality standards have a significant impact on prod uctivity: a 10 parts per

140 See p. 10, http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67 CF77B54734285257BB0004F87ED/$File/Charge+Questions+2 -26-15.pdf

141 For a recent review see Graff-Zivin and Neidell (2013).

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6-11 billion (ppb) decreases in ozone concentrations increases work er productivity by 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654). 142


### This section (section 6.3) has outlined the challenges associated with estimating regulatory effects on both labor demand and supply for specific sectors. These challenges make it difficult to estimate net national employment es timates that would appropriately capture the way in which costs, compliance spending, and en vironmental benefits propagate through the

macro-economy. 6.4 Recent Employment Trends
The U.S. electricity system includes empl oyees that support elect ric power generation, transmission and distribution; the extraction of fossil fuels; renewable energy generation; and supply-side and demand-side ener gy efficiency. This section de scribes recent employment trends in the electricity system.

6.4.1 Electric Power Generation
In 2014, the electric power ge neration, transmission and di stribution sector (NAICS 2211) employed about 390,000 workers in the U.S. 143 

### Installation, mainte nance, and repair occupations accounted for the larges t share of workers (25 percent). 144 

### These categories include inspection, testing, repairing and maintaining of electrical equipment and/or installation and repair of cables used in elect rical power and distribution sy stems. Other major occupation categories include office and administrative s upport (18 percent), production occupations (16 percent), architecture and engineering (10 percent) , business and financial operations (7 percent) and management (7 percent). As shown in Figure 6.1, employment in the Electric Power

142 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this study. In light of this recent research, however, the EP A is considering how best to incorpor ate possible productivity effects in the future.

143 U.S. Bureau of Labor Statistics. “Current Employmen t Survey Seasonally Adjust ed Employment for Electric Power Generation, Transmission, and Distribution (national employment).” Series ID: CES4422110001. Available at <http://www.bls.gov/data/>. Accessed June 9, 2015

.

144 U.S. Bureau of Labor Statistics, Occupational Empl oyment Statistics, May 2014 National Industry-Specific Occupational Employment and Wage Estimates, Electric Power Generation, Transmission, and Distribution (NAICS 2211). Available at: <http://www.bls.gov/oes/current/naics4_221100.htm>.

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6-12 Industry averaged about 420,000 workers 2000 to 2005, declining to an average of about 400,000 workers for the rest of the decade, and th en declining to about 390,000 workers in 2014.

Figure 6.1. Electric Power Industry Employment

6.4.2 Fossil Fuel Extraction

6.4.2.1 Coal Mining
The coal mining sector (NAICS 2121) is prim arily engaged in coal mining and coal mine site development, excluding metal ore mining a nd nonmetallic mineral mining and quarrying. In 2014, BLS reported about 74,000 coal mining empl oyees (Figure 6.2). During the 2000 to 2014, period, coal mining employment peak ed in 2011 at about 87,000 employees. 0 50 100 150 200 250 300 350 400 450 500 

## Power Generation and Supply Employment (NAICS = 2211, Annual Average, 1000s of Employees)

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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). 145 

### This sector includes produc tion of crude petroleum, o il from oil shale and oil sands, production of natural gas, sulfur recovery from natural gas, and recovery of hydrocarbon liquids. Activities include the de velopment of gas and oil fields, exploration activities for crude petroleum and natural gas, drilling, comple ting, and equipping wells, and other production

activities. 146 

### In contrast with coal, and looking at Figure 6.3, there has been a sharp increase in employment in this sector over the past decade.

145 BLS, Current Employment Statistics. Seasonally adju sted employment for oil and gas extraction (national employment), NAICS 211. Series ID: CES1021100001. Ava ilable at <http://www.bls.gov/data/>. Accessed June 9, 2015.

146 U.S. Bureau of Labor Statistics. 20014. Available at: <http://www.bls.gov/iag/tgs/iag211.htm Accessed Feb. 19>.

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6-14 Figure 6.3. Oil and Gas Production Employment

6.4.3 Clean Energy Employment Trends
Clean energy resources, such as energy effi ciency and renewable energy, are used to meet energy demand, reduce peak electricity sy stem loads, and reduce reliance on the most

carbon-intensive sources of electri city. However, there is not a single clean energy sector in

standard national accounts cla ssifications. Renewable generati on is not reported to the BLS separately from other electric power generation. Similarly, ma nufacturers of energy efficient appliances are not reported separately from conventional appliance manufacturers and green building design is not separate from the cons truction sector. Instea d, clean energy technology and services are supported by indus tries throughout the economy.

Without a specific industrial classification, it is difficult to quantify the exact number of clean energy-related jobs or doc ument the trends. Employees enga ged in clean energy can span many job classifications, such as experts require d to design and produce a renewable or energyefficient technology, workers that supply inputs and technicians w ho install service or operate equipment. As such, there are a variety of defini tions of clean or green jobs used, some more expansive than others. 0 50 100 150 200 250 

## Oil and Gas Extraction Employment (NAICS = 211, Annual Average, 1000s of Employees)

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6-15 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 BLS Green Goods and Services surveys have subdivided industrial classifi cations into “green” categories. In both cases the approach was to determine which product classifications, rath er than industries, were green. They multiplied green production by product revenue and defined an industrial sector as green if it met a threshold of green revenue as a proportion of total revenue.

DOC broadly defined green jobs in 2010 as those “created and s upported in businesses that produce green products and services.” 147 

### They further classified green jobs into a broad and a narrow category. The narrow category includes on ly 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 th e 2007 Economic Census to estim ate their contribution to the U.S. economy. The report found that the number of green jobs in 2007 ranged from 1.8 million to 2.4 million jobs, accounting for between 1.5 and 2 percent of total private sector employment. 148


### BLS used an expansive definition of clean or green jobs in 2010 and 2011. It goes beyond direct clean energy-relate d investments and includes “thos e in businesses that produce goods and provide services that benefit the envi ronment or conserve natural resources. These goods and services, which are sold to customers, include research and development, installation, and maintenance services for renewable energy a nd energy efficiency and education and training related to green technologies and practices” but also include recycling and natural resource conservation, such as forestry management. 149 

### Based on surveys acro ss the 325 industries it identified as potential producers of green goods and services, BLS counts approximately 2.3

147 U.S. Department of Commerce Economics and Sta tistics Administration. 2010. “Measuring the Green Economy ,” April. Available at: <http://www.esa.doc.gov/sites/default/files/repo rts/documents/greeneconomyreport_0.pdf>.

148 U.S. Department of Commerce Economics and Statistic s Administration. 2010. “Measuring the Green Economy, April. Available at: <http://www.esa.doc.gov/sites/defa ult/files/reports/documents/greeneconomyreport_0.pdf>.

149 BLS has identified 325 detailed industries (6-digit NAIC S) as potential producers of green goods and services. Available at: <http://w ww.bls.gov/ggs/ggsoverview.ht m>. (Accessed on 1-14-14, last modified date: March 19, 2013.

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6-16 million jobs in the green economy in 2010, rising 7.4 percent to 2.5 million in 2011, 150 

### compared to increases of about one percent across all o ccupations in the entire economy over the same period. 151 

### The table below, Table 6-1, presents BLS green job estimates nationally and for the utility sector.

Table 6-1. U. S. Green Goods and Servi ces (GGS) Employment (annual average) Total GGS Employment Utility GGS Employment Total GGS Growth 2010-11 Utility GGS Growth 2010-11 2010 2,342,562 69,031 NA NA 2011 2,515,200 71,129 7.4% 3.0% Source: Bureau of Labor Statistics 

### 6.4.3.2 Renewable Electricity Gene ration Employment Trends
The DOC report does not separate renewable en ergy data and the BLS data include only privately owned electricity generating facilities. As such, neither sour ce isolates renewable electricity generation employment. For historical trends in this sector, we therefore, rely on a Brookings Institution study, Muro et al. (2011). This study built a na tional database of “clean economy” jobs from the bottom up, veri fying each company individually. 152 

### They include a list of categories similar but not identic al to that of BLS, including agricultural and natural resources conservation, education and compliance, en ergy and resource effi ciency, greenhouse gas reduction, environmental management and recy cling, and renewable energy. This study found about 138,000 jobs in the renewa ble energy sector in 2010, with an overall average annual growth rate of 3.1 percent fr om 2003-2010. Table 6-2 details the national results by energy

source.

150 U.S. Department of Labor, U.S. Bureau of Labor Sta tistics. (n.d.). 2011. “Green Goods and Services 2010-2011.” (Retrieved on January 14, 2014). Available at :< http://www.bls.gov/ggs/ggsoverview.htm>.

151 U.S. Department of Labor, U.S. Bureau of Labor St atistics. 2010. National Occupational Employment and Wage Estimates, United States. Available at: <http://www.bls.gov/oes/2010/may/oes_nat.htm>, May. National Occupational Employment and Wage Estimates, United States http://www.bls.gov/oes/2010/may/oes_nat.htm.

152 <http://www.brookings.edu/~/media/Series /resources/0713_clean_e conomy.pdf> p. 15.

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6-17 Table 6-2. Renewable Electricity Ge neration-Related Employment

Sector Jobs, 2010 2003-2010 Average Annual Growth Rate (%) Biofuels/Biomass 20,680 8.9 Geothermal 2,720 6.7 Hydropower 55,467 -3.6 Renewable Energy Services 1,981 6.3 Solar Photovoltaic 24,152 10.7 Solar Thermal 5,379 18.4 Waste-to-Energy 3,320 3.7 Wave/Ocean Power 371 20.9 Wind 24,294 14.9 Total 138,364 3.1 Source: http://www.brookings.edu/~/media/Series/resou rces/0713_clean_economy.pdf, Appendix A.


### More recent industry data, from 2014, indicate higher employment numbers and growth in the solar sector. 153



### 6.4.3.3 Employment Trends in Demand-Si de Energy Efficiency Activities
U.S. government data used for calculating the historical trends in the demand-side energy efficiency sector come from the BLS green goods and services surveys. BLS reports an energy efficiency category, finding 1.49 million private s ector energy efficiency jobs in 2010 and 1.64

million in 2011.

For a longer term trend the Brookings Institution study (Muro et al. , 2011) built a national database of “clean economy” jobs from the bottom up, verifying each company individually. 154 

### This study found about 428,000 jobs in th e Energy and Resource Efficiency sector in 2010, with an overall average annua l growth rate of 2.6 percent from 2003-2010. Table

6-3 details the result s by energy sector.

153 The Solar Foundation, National Solar Jobs Census 2014. < http://www.thesolarfoundation.org/national-solarjobs-census-2014/>

154 <http://www.brookings.edu/~/media/Series /resources/0713_clean_e conomy.pdf> p. 15

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

Sector Jobs, 2010 2003-2010 Average Annual Growth Rate (%) Appliances 36,608 -3.1 Energy-saving Building Materials 161,896 2.5 Energy-saving Consumer Products 19,210 -2.9 Green Architecture and Construction Services 56,190 6.4 HVAC and Building Control Systems 73,600 3.3 Lighting 14,298 -1.8 Professional Energy Services 49,863 6.9 Smart Grid 15,987 8.6 Total 427,652 2.6 Source: http://www.brookings.edu/~/media/Series/res ources/0713_clean_economy.pdf, Appendix A



### In addition, other research in stitutes and industry groups ha ve clean economy or clean energy employment databases. While definitions and timeframes vary, all show positive employment trends of 1.9 percent or more gr owth in clean energy-re lated 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 rates by placing new orders for efficiency-related or renewabl e energy equipment and services to reduce GHG emissions. Implementing each of the three building blocks used in determining BSER will involve changes in the amount of labor needed in different parts of the utility power sector. Installing and operating new equipm ent or improving heat rate e fficiency could increase labor demand in the electricity generating sector itself , as well as associated equipment and services sectors. Specifically, the direct employment effects of supply-side initiatives include increases in labor demand during the implementation phase for manufacturing, installing, and operating higher efficiency and renewable energy electricity generating assets, as well as making heat rate improvements at existing fossil uni ts. Additional supply-side dire ct employment impacts are the

reductions in labor demand for labor that woul d have been used by less efficient or higher emitting generating assets. Once implemented, increases in operating efficiency and shifting generation to existing or new NGCC units and renewable energy generation will impact the utility power sector’s demand for fossil fu els and plans for EGU retirement and new

construction.

In addition, EPA expects state compliance pl ans to also include demand-side energy efficiency policies and programs that typically change energy consumption patterns of business

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6-19 and residential consumers by reducing the quantity of energy required for a given level of production or service. Demand-side initiatives generally aim to incr ease the use of cost-effective energy efficiency technologies (e.g., including mo re efficient appliances and air conditioning systems, more efficient lighting devices, more efficient design and construction of new homes and businesses), and advance efficiency improve ments in motor systems and other industrial processes. Demand-side initiatives can also dire ctly reduce energy consumption, such as through programs encouraging changing the thermostat during the hours a building is unoccupied or motion-detecting room light switches. Such dema nd-side energy efficiency initiatives directly affect employment by encouraging firms and consum ers to shift to more efficient products and processes than would otherwise be the case. Employment in the sectors that provide these more efficient devices and services would be expected to increase, while empl oyment in the sectors that produce less efficient devices would be expected to contract.

This generation-side employment analysis uses the cost projections from the engineeringbased Integrated Planning Model (IPM) to proj ect labor demand impacts of the final emission guidelines on affected electric utility steam ge nerating units (steam ge nerating units) in the electricity power sector and the fuel production sector (coal and natural gas). These projections

include effects attributable to heat rate im provements, construction of new EGUs, generation shifts, changes in fuel use, and reductions in electricity generation due to demand-side energy efficiency activities. To project labor requirements for demand-side energy efficiency activities, the analysis uses a different appr oach that combines data on hist oric changes in employment and expenditures in the energy efficiency sector with projected changes in ex penditures in the sector arising from state implementation of the emission guidelines. We project labor impacts for the rate-based and mass-based illustrative plan approach. Each scenario draws upon the building blocks described above, as well as assumes th at demand-side energy efficiency plays an important role in compliance strategies

6.5.1 Projected Changes in Employment in Electri city Generation and Fossil Fuel Extraction
The analytical approach us ed in this analysis is a bottom-up engineering method combining EPA’s cost analysis of the emissi on guidelines with data on labor productivity, engineering estimates of the amount and types of labor needed to manufacture, construct, and operate different types of gene rating units, and prevailing wage rates for skilled and general

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6-20 labor categories. This approach is different fr om the economy-wide types of economic analyses discussed in section 6.2. Lacking robust peer -reviewed methods to estimate economy-wide impacts, the engineering-based analysis focu ses on the supply-side direct impact on labor demand in industries closely involved with el ectricity generation. The engineering approach projects labor changes measured as the ch ange in each analysis year in job-years 155 

### employed in the utility power sector and di rectly related sectors (e.g., equi pment manufacturing, fuel supply, EGU construction and generating efficiency services ). For example, this approach projects the amounts and types of labor required to implement improvements in generating efficiency. It then uses the EPA’s estimated effect of efficiency improvements on fuel demand to project reductions in the amount of labor required to produce coal a nd gas. Some of these employment impacts are one-time impacts, such as changes associated with building new NGCC or renewable generating units. Other labor impacts will continue, such as changes associated with operating and maintaining generating units that wi ll be retired, and labor providi ng the fuel supply to the retired

units.

This analysis relies on projections and the co st analysis from IPM, which uses industryspecific data and assumptions to estimate cost s and energy impacts of the final guidelines (see Chapter 3). The EPA uses IPM to predict coal generating capacity that is likely to undertake improvements in heat rate efficiency (HRI). 156 

### IPM also predicts the gui delines’ impacts on fuel use, retirement of existing units , and construction of new ones.

The methods EPA uses to estimate the labor impacts are based on the analytical methods used in many previous EPA regulatory analyses . The most relevant prior analysis was the Regulatory Impact Analysis for the Mercury and Air Toxics Standa rds (MATS). While the methods used in this analysis to estimate the recurring labor impacts (e.g ., labor associated with operating and maintaining ge nerating units, as well as labor needed to mine coal and natural gas) are the same as we used in MATS (with update d data where available), the methods used to

155 Job-years are not individual jobs, but rather the amount of work performed by the equivalent of one full-time individual for one year. For example, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in that year.

156 HRI could include a range of activities in the power plan t to lower the heat rate required to generate a net electrical output. Assuming all other things being equal, a lo wer heat rate is more efficient because more electricity is generated from each ton of coal.

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6-21 estimate the labor associated with installing new capacity and implementing heat rate improvements were developed for the purpose of the Clean Power Plan RIA.

The bottom-up engineering-based labor anal ysis in the MATS RIA primarily was concerned with the labor needs of retrofitting pollution control equipment. A central feature of the supply-side labor analysis for this RIA, however, involves the qua ntity and timing of the labor needs of building new renewable and NGCC units and retiring coal units. The estimated response of the utility power sector involves cha nges in the amount and timing of retirements of existing coal and oil/gas units, as well as ch anges in the amount and timing of building new NGCC units and renewable generating capacity. In addition to the changes in retirements and construction of new units, there are also esti mated changes in the utilization of existing generating units, and changes in th e gas and coal supply sectors.

For example, as presented in Chapter 3, the IPM analysis of the rate-based control scenario finds that in 2025 (part way thr ough 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 scenar io is 49.4 GW less than the estim ated base case capacity (a 4.8 percent net capacity reduction). This 49.4 GW ne t reduction includes more retirements of coal units (an additional 22.9 GW of coal-fired capacity retired) and oil/gas st eam units (an additional 9.3 GW of oil/gas retirement) comp ared to the base case, as well as a reduction in the amount of new natural gas units needed to be built by 2025 (a decrease of 10.9 GW in new capacity from the amount forecast in the base case) and non-hydro renewables (1.7 GW less renewable capacity built). Fossil fuel utilizatio n will also be impacted. The rate-based control scenario finds that in 2025 less coal will be used (102.9 million t ons less, a 14.1 percent decrease from the base case) and less gas will also be used (0.1 TCF less, a 1.0 percent decrease).

An important aspect of the labor analysis is that building new units, and all the associated construction-related labor, occurs before the ne w 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 manufact uring equipment and constructing the new units occurs, and is actually paid for, in a concentrated amount of time before the new capacity begins

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6-22 to generate electricity. IPM assumes 157 

### that new NGCC units take 3 years to build, and both natural gas combustion turbines and wi nd-powered renewables take 2 years.

Avoiding some of the need for new capacity due to both demand and supply efficiency improvements results in both a significant net cost savings to consumers a nd 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 sign ificant labor impacts. A portion of the labor that would have been used to build the new capac ity in the base case will not be employed in the power generation sector with the implementati on of the GHG guidelines, though it likely will be employed in construction elsewhere. Similarly, le ss labor involved with operating and providing

fuel for new units will be needed with the GHG guidelines than in the base case.

A critical component of the overall labor impacts of impl ementing 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 sect ion 6.5.2. All of the labor impacts of the demandside energy efficiency activities are increases in labor needs, which more than offset the loss of supply-side jobs associated w ith the decreasing demand for elect ricity arising from the demandside programs. The IPM labor expenditure proj ections are distributed across different labor categories (e.g., general construc tion labor, boilermakers and engi neering) using data from engineering analyses of labor’s overall share of total expenditures, and apportionment of total labor cost to various labor categories. Hourly labor expenditures (including wages, fringe benefits, and employer-paid costs including taxes, insurance and administrative costs) for each category are used to estimate the labor quantity (m easured in full-time job-years) consistent with the compliance scenario projections. Projected labor impacts arising from changes in fuel demand are primarily derived from labor producti vity data for coal mining (tons mined per employee hour) and natural gas extraction (M MBtu produced/job-year). Tables 6.4 and 6.5 present projected changes relative to th e baseline of four labor categories:

1. manufacturing, engineering and constructi on for building, designing and implementing heat rate improvements;

2. manufacturing and construction for new generating capacity;

157 Table 4.13, IPM 5.13 Documentation.

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6-23 3. operating and maintenance for ex isting generatin g capacity; and

4. extraction of coal and natural gas fuel.

All of the employment estimates presented in Tables 6-4 and 6-5 are estimates occurring in a single year. For the construction-related ( one-time) labor impacts, including the installation of HRI, Tables 6-4 and 6-5 present the average annual impact occurring in each year of three

different intervals. The three intervals are from 2018 through 2020 (a three year interval), during which there are modest labor impacts from th e early changes in the power utility sectors operations, from 2021 through 2025 (five year s), and 2026 through 2029 (5 years). The

construction-related labor analysis are based on the IPM estimates of the net change in capital investment that occurs during each multi-year interval to fund building new units completed during that interval. The new build labor analysis uses the net change in capital investment to estimate the amount and type of labor needed dur ing the interval to bu ild the new capacity. The analysis assumes that the new built labor within each interval is evenly distributed throughout the

interval. Tables 6-4 and 6-5 reflect this assu mption by presenting the av erage labor utilization per year during each of the three intervals.

The HRI-related labor impacts are estimated based on the assumed capital cost of $100/kw (see section 3.7.3). The labor estimates for operating and maintaining generating units annually are based on IPMs estimates of Fixed Operating and Maintenance (FOM) Costs. IPM estimates FOM for each year individually, so the net changes in O&M-related labor estimates in Tables 6-4 and 6-5 are singl e year estimates for 2020, 2025 and 2030. These single year O&M labor estimates are not merely the average a nnual averages labor needs throughout each multiyear interval. There ar e O&M labor changes occurring in th e all years throughout the entire period 2020-2030, but the labor imp acts in each labor category change each year. The fuelrelated labor estimates are also single-year es timates, and not multi-year averages. The labor analysis of the impacts on the fuel extraction indu stries uses IPM’s estimates of the net changes in the amount of coal and natural gas in 2020, 2025 and 2030, which are inherently estimates of the fuel usage in a single year. As with the O& M labor impacts, the fuels-related labor impacts occur in every year throughout 2020-2030, and the labor impact ch anges every year.

It should be noted that the supply-side labor impact estim ates in Tables 6-4 and 6-5 reflect the supply-side changes that will potentia lly occur with each illustrative plan scenario.

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6-24 These labor impacts include not only the impact s of Building Blocks 1 through 3, but also the changes in total generation needed that result fr om the demand-side energy efficiency activities expected to be an important component of st ate compliance strategies. The additional labor impact estimates from demand-side energy effici ency activities are presented below in section

6.5.2.

More details on methodology, assumptions, and da ta sources used to estimate the supplyside labor impacts discussed in this section can be found in Appendix 6A.

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6-25 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 Improvement: Total 0 15,400 2,200 Boilermakers and General Construction 0 11,000 1,600 Engineering and Management 0 2,800 400 Equipment-related 0 1,200 200 Material-related 0 400 0 New Capacity Construction: Total 500 -15,600 400 Renewables 700 -5,000 23,300 Natural Gas -200 -10,600 -22,900

Recurring Changes** 2020 2025 2030 Operation and Maintenance: Total -9,100 -17,000 -19,600 Changes in Renewables 600 -100 1,100 Changes in Gas 300 -1,100 -3,700 Retired Coal -8,000 -13,300 -14,700 Retired Oil and Gas -2,000 -2,500 -2,300 Fuel Extraction: Total 100 -7,800 -13,900 Coal -1,300 -7,300 -13,300 Natural Gas 1,400 -500 -600 Supply-Side Employment Impacts - Qu antified -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 impact s, occurring during each year of the multi-year period during which construction and HRI installation activities occu r. Construction-related figures in table are the average annual job-years in each year between the years in the range. Negative c onstruction job-year es timates occur whenadditional generating capacity must be built in the base case, but is avoided in the final rule due to HRI or demandside energy efficiency programs. **Recurring Changes are job-years asso ciated with annual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating cap acity creates a recurring stre am of positive job-years, while retiring generating capacity, as well as avoided ne w built capacity, create a stream of negative job-years.

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6-26 Table 6-5. Engineering-Based a 

### Changes in Labor Utilization, Mass-Based Illustrative Plan Approach 

# ( Number of Job-Years of Employment in a Single Year)

Construction-related (One-time) Changes* 2018-2020 2021-2025 2026-2030 Heat Rate Improvement: Total 0 14,900 800 Boilermakers and General Construction 0 10,700 600 Engineering and Management 0 2,700 100 Equipment-related 0 1,200 100 Material-related 0 300 0 New Capacity Construction: Total -1,700 -11,100 4,700 Renewables -1,400 -3,500 21,300 Natural Gas -300 -7,600 -16,600

Recurring Changes** 2020 2025 2030 Operation and Maintenance: Total -11,700 -21,200 -25,000 Changes in Renewables -900 -1,000 700 Changes in Gas 500 -400 -2,300 Retired Coal -9,100 -17,000 -20,800 Retired Oil and Gas -2,200 -2,800 -2,600 Fuel Extraction: Total 200 -8,600 -14,300 Coal -1,800 -8,700 -12,200 Natural Gas 2,000 100 -2,100 Supply-Side Employment Impacts - Quantified -13,100 -26,000 -33,700

a Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates (tons coals or MMBtu gas).

b All job-year estimates on this are full-time equivalent (FTE) jobs. Job estimates in the demand-side energy efficiency section (below) include both full-time and part-time jobs. *Construction-related job-year changes are one-time impact s, occurring during each year of the multi-year period during which construction and HRI installation activities occu r. Construction-related figures in table are the average annual job-years in each year between the years in the range. Negative c onstruction job-year es timates occur whenadditional generating capacity must be built in the base case, but is avoided in the final rule due to HRI or demandside energy efficiency programs. **Recurring Changes are job-years asso ciated with annual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating cap acity creates a recurring stre am of positive job-years, while retiring generating capacity, as well as avoided ne w built capacity, create a stream of negative job-years.



### 6.5.2 Projected Changes in Employment in De mand-Side Energy E fficiency Activities While not a component of the best sy stem of emission reduction (BSER), EPA anticipates that this ru le may stimulate investment in cl ean energy technologies and services, resulting in considerable increases in energy e fficiency. We expect these increases in energy efficiency, specifically, to support a significant number of jobs in related industries. For more

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6-27 information on EPA’s illustrative investment levels in demand-side energy efficiency activities, assumed to be adopted in response to the C PP, please see Section 3.6 “Demand-Side Energy Efficiency” in Chapter 3 of this RIA.

In this section, we projec t employment impacts in demand-side energy efficiency activities arising from these guidelines using illustrative calculations . The approach uses information from power sector modeling and proj ected impacts on energy efficiency investments analyzed (see Chapter 3), and U.S. government data on employment and expenditures in energy efficiency. 158 

### This approach is limited by the fact that we do not know which options states will choose for demand-side energy efficiency activ ities and by uncertainties associated with methods. These illustrative 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 employment impacts arising from changes in c onsumer expenditures away from energy towards other sectors. In other words, these projections are not attempts at estimating net national job creation. Also, this approach atte mpts to calculate the number ofemployees (full-time and parttime) rather than job-years as discussed in section 6.5.1.

Investments in demand-side energy efficiency reduce energy required for a given activity by encouraging more efficient technologies (e.g., ENERGY STAR appliances), implementing energy improvements for existing systems (e.g., weat herization of older homes), or encouraging changes in behavior (e.g., reduc ing air conditioning during periods of high electricity demand).

Employment impacts of demand-side ener gy 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

158 Investments in demand-side energy efficiency reduce energy required for a given activity by encouraging more efficient technologies (e.g., ENERGY STAR applian ces), implementing energy improvements for existing systems (e.g., weatherization of older homes), or encouraging changes in behavior (e.g., reducing air conditioning during periods of high electricity demand).

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6-28 policy impacts are due to low investment in ener gy efficiency as described in “energy paradox” literature (Gillingham, Newell, and Palm er (2009), Gillingham and Palmer (2014)). 159


### Two recent articles discuss employment ef fects of demand-side energy efficiency programs. Aldy (2013) describes clean energy in vestments funded by the American Recovery and Reinvestment Act of 2009, which “included more than $90 billion for strategic clean energy investments intended to promote job creation and the deployment of low-carbon technologies” (p. 137), with nearly $20 billion for energy effi ciency investments. The Council of Economic Advisors (CEA) (2011) estimated higher economi c activity and employment than would have

otherwise occurred without the American Rec overy 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 thos e that received the largest shares of stimulus funds: renewable energy, energy effi ciency, and transit. Aldy’s esti mates, while informative, are not directly applicable for employment analys is in this rulemaking as there are important differences in expected employment impacts from a historically la rge fiscal stimulus specifically targeting job creation during a pe riod of exceptionally high unemployment versus environmental regulations taking effect several years from now.

Yi (2013) analyzes clean ener gy policies and employment for U.S. metropolitan areas in 2006, prior to the Recovery Act, to evaluate impacts on clean energy job growth. Implementing an additional state clean energy pol icy tool (renewable energy policies, GHG emissions policies, and energy efficiency polices such as energy e fficiency resource standards, appliance or equipment energy efficiency standards, tax in centives, and public building energy efficiency standards) is associated with 1 percent more clean energy employment within that MSA. These estimates are not transferable to this rulemaking si nce states are likely to change intensity as well as number of clean energy programs.

Lacking a peer-reviewed methodology, we us e the following approach to illustrate possible effects on labor demand in the energy effi ciency sector due to demand-side management strategies. We use U.S. government data a nd divide energy effici ency employment by

159 For more information on this e fficiency paradox see Chapter 3.

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6-29 expenditures on energy efficiency activities to calcu late an estimate of jobs per million dollars. We then multiply this fraction by projected expenditure in energy efficiency activities undertaken in response to these final guidelines.

Data used for calculating employment in energy efficiency sectors comes from the “energy efficiency” industry category of the BLS Green Goods and Services survey. 160 

### Using

BLS Green Goods and Services information on 132 energy efficiency industries, as identified by BLS, 161 

### we adjusted the list to remove ten industries expected to not be directly affected by the rule, e.g. transportation. 162 

### Next we used this detailed list of 122 industries to extract 2011 BLS data on green employment. 163 

### Employment data at the most -detailed industr y level (6-digit NAICS) is available only for a portion of these 122 industries. Therefore we use both the mostdetailed industry level (6-digit NAICS) and al so a more aggregate level (4-digit NAICS) to estimate a range of energy efficiency employme nt with the 2011 BLS Green Goods and Services

data. 164



### BLS does not collect data on energy efficien cy expenditures direc tly, however. Instead,

BLS collects data on the share of revenues asso ciated with green goods and services, at the

establishment level. 165 

### We multiply data on total revenues by NAICS by the share of green revenues reported by BLS to obtain a measure of green revenues by industry. The only U.S.

160 For more details on this survey, see section 6.5.3.1.

161 See detailed listing available here: h ttp://www.bls.gov/ggs/naics_2012.xlsx. Ca tegory 2 is “Energy Efficiency”. More information is available here: http://www.bls.gov/ggs/ggsfaq.htm#3.

162 The ten industries we removed from the list were: NAIC S 483114 Coastal and Great Lakes passenger transport, 483212 Inland water passenger transportation, 485111 Mixe d mode transit systems, 485112 Commuter rail systems, 485113 Bus and other motor vehicle transit systems, 485119 Other urban transit systems, 485210 Interurban and rural bus transportation, 485410 School and employee bus transportation, 485999 All other ground passenger transportation, and 926120 Transportation program administration.

163 BLS Green Goods and Services data is available here: http://download.bls.gov/pub/time.series/gg/ gg.data.1.AllData. A BLS technical no te indicates that the scope of the GGS survey changed between 2010 and 2011 (http://www.bls.gov/news.release/ggqcew.tn.htm). Some industries and establishments that were not previously included in the 2010 survey were included in 2011. Rather than using the change from 2010 to 2011, here we use only the 2011 data.

164 At the 6-digit NAICS level, 27 of the 122 energy efficiency industries listed have employment data available. At the 4-digit NAICS level, 46 of the 122 energy efficien cy industries listed have employment data available.

165 More information is available here : http://www.bls.gov/ggs/ggsfaq.htm#5.

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6-30 Government data source containing this revenue information for all NAICS sectors is the U.S. Economic Census. This Census is conducted at 5-y ear intervals (the late st available year is 2012), however, making it unsuitable for directly pairing with 2011 data from BLS. Instead, we

use U.S. Census Bureau data on total value of shipments by industry, for 2011, from the Annual Survey of Manufacturers. 166 

### The disadvantage of this data sour ce is that the manufacturing sector makes up only 50 percent of the 132 NAICS codes be longing to the energy efficiency sector as defined by the BLS Green Goods and Services surveys, with the remainder in the construction or service sectors. Thus, this analys is implicitly projects that the same number of jobs per dollar are supported in construction and serv ice sectors as in manufacturi ng. Also, the Annual Survey of Manufacturers contains data for some, but not all, detailed industry codes, e.g. 4-digit and 6-digit NAICS. We pair our BLS GGS data by industry, either by 4-digit or 6-digit NAICS, with data from the Annual Survey of Manufact urers. At the more detailed, 6-di git level, 17 industries have data available for both employment and total value of shipments. 167 

### At the less detailed, 4-digit level, 15 industries have data available for both employment and total value of shipments. 168 

### Using this approach we obtain estimates of 2.07 demand-side energy efficiency jobs per million 2011 dollars of expenditure, usi ng the less-detailed industry le vel (4-digit NAICS), and 3.29 demand-side energy efficiency jobs per million 2011 dollars of expenditure, using the moredetailed industry level (6-digit NAICS).

Having calculated estimates of jobs per milli on dollars of energy efficiency expenditure, we use EPA’s illustrative energy effi ciency investment levels of the first-year costs expected for states to attain a target of 1 percent growth in demand-side effi ciency improvements (see Chapter 3.6 of this RIA for more information). If some st ates were to target ra tes of energy efficiency savings greater than one percent, they may see increased energy efficiency employment impacts, relative to the one percent growth assumed in this analysis. The first year cost of saved energy

166 Census data on total value of shipments, by industry, for 2011 is available here: http://factfinder.census.gov/faces/tableservices/jsf/pa ges/productview.xhtml?pid=ASM_2011_31GS101&prodType =table.

167 The 17 industries are: NAICS 321219, 321991, 321992, 327993, 327999, 332913, 332996, 333414, 333415, 334513, 334514, 334515, 335110, 335222, 335311, 335312, and 335999.

168 The 15 industries are: NAICS 314100, 321100, 324100, 326100, 327100, 327300, 327400, 332100, 333300, 334200, 334300, 334400, 335200, 336300, and 337900.

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6-31 accounts for both the costs to the utilities that are funding the demand-side energy efficiency programs (known as the program costs), and the additional cost to the end-user purchasing a more energy efficient technology (known as the participant costs). 169 

### Total costs were divided evenly, 50 percent each, between program costs and participant costs. First-year costs are not annualized; they are the projected expenditures on demand-side energy efficiency activities in that year. As shown in the Demand-Side Energy Efficiency Technical Support Document 170 

### , first-year costs for a 1 percent growth target in energy efficiency activities are projected to be $18.1 billion (2011 $) in 2020. 

### Multiplying this dollar expenditure by the jobs per dollar estimates results in projected employment impacts for demand-side energy e fficiency activities ra nging from 37,570 to 59,700 jobs in 2020 depending on the jobs per million do llars estimate used: low or high. Employment impacts for demand-side energy efficiency activities range from 52,590 to 83,590 jobs in 2025, and from 52,440 to 83,360 jobs in 2030. These es timates are shown in Table 6-6 below.

Table 6-6. Estimated Demand-Side Energy E fficiency Employment Impacts: Target 1 percent Growth in Energy Efficiency

Source Factor 

### Employment impact (jobs)*

2020 2025 2030 EPA low estimate, using BLS and Census data, and power sector modeling projections 2.07 37,570 52,590 52,440 EPA high estimate, using BLS and Census data, and power sector modeling projections 3.29 59,700 83,590 83,360

*Since these figures represent number of employees (full- or part-time) they should not be added to the full-time equivalent job-years reported in Table 6-4 and Table 6-5. Energy efficiency costs are from 1 percent growth target projections for the continental U.S. (excludes Alaska, Hawa ii, and U.S. Territories): $18.1 billion (2011 $) in 2020. First-year energy efficiency costs are the same for rate-b ased and mass-based scenarios. See chapter 3 of this RIA and Demand-Side Energy Efficien cy TSD for more information.



### Although this approach has the advantage of using a range of estimates, derived from U.S. government data, on energy efficiency employment per milllion dollars in industry

169 See Section 3.6.2, “Demand-Side Energy Efficiency Total Costs”, in this RIA, for more information.

170 U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Gene rating Units. Demand-Side Energy Efficiency.

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6-32 shipments, this approach is limited by its focu s on manufacturing sectors and direction of bias (overestimation or underestimation) cannot be determined at this time.

Our estimates of 2.07 to 3.29 demand-side energy efficiency jobs per million dollars of 2011 expenditure fit with other estimates in the lit erature, focused on government data sources, and are on the smaller end of the range. 171 

### Figure 6.4 shows the range of estimates, including EPA low (2.07) and EPA high (3.29). The Departme nt of Commerce report estimates overall employment per million dollar values, rangi ng from 4.65 to 4.85 (Department of Commerce 2010a). 172 

### The report also contains so me case studies, and for those focused on energy efficiency, the Department of Commerce estimates 6.21 jobs pe r million dollars in green buildings activities and 7.53 jobs per million dollars for energy efficiency appliances. 173 

### Lawrence Berkeley National Lab (Goldman et al. 2010) reports a wide range of estim ates: from 2.5 jobs per million dollars for energy service companies (ESCOs), to 8.9 jobs per million dollars for low income

weatherization activities. The Pacific Northwest National Labs report (Anderson et al. 2014), in surveying the literature, estimates 8 jobs per m illion dollars of consumer electricity bill savings and 11 jobs per million dollars of ini tial energy efficiency investments.

171 See Section 6.5.3 for more information on sources in the literature.

172 Calculated values using data on employment and shipments reported in Department of Commerce (2010a), Table 2, p. 12.

173 Calculated values using data on employment and shipments reported in Department of Commerce (2010a), Appendix 2, Table 2B, p. 4. http://www.esa. doc.gov/sites/default/f iles/appendix2_0.pdf

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6-33 Figure 6.4. Demand-Side Energy Efficiency Employment: Jobs per One Million Dollars (2011$)

There is more uncertainty involved in this approach than the standard bottom-up engineering analysis used to estimate electr icity generation and fuel production employment impacts of this rulemaking. For those, the EPA wa s able to identify a li mited set of activities (e.g., constructing a new NGCC power plant), and study associated labor requirements. Demandside energy efficiency improvement s, in contrast, encompass a wide array of activities (subsidies for efficient appliances, “smart meters,” etc.). In addition, there is co nsiderable 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 activ ities similar to the 2011 composition of energy efficiency sectors identif ied by BLS.

In addition, the EPA does not have access to bottom-up information regarding labor requirements for these activities. Use of a consta nt job per dollar fracti on is at best a crude approximation of these labor requirements. The EP A has identified several other limitations of this approach, outlined below. 11 8.9 8 7.53 6.5 6.21 6.2 4.85 4.65 3.29 2.5 2.07

0 5 10 Jobs per Million $ PNNL initial investments LBNL low income weatherization PNNL bill savings DOC EE appliances LBNL state and local gov't EER DOC green buildings LBNL ratepayer funded EE DOC overall high

DOC overall low EPA high LBNL ESCOs EPA low

Sources: EPA calculation, LBNL (2010), PNNL (2014), DOC (2010) 

## Demand-Side EE Employment

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6-34 Job Reclassification. Job numbers in this chapter represent gross changes in the affected sector. As such they may over-estimate impacts to the extent that jobs created displace workers employed elsewhere in the economy. For demand-s ide efficiency activities this potential overstatement is may be higher than in other sect ors. If states encourag e consumers to purchase ENERGY STAR appliances, for example, curren tly employed workers in factories and retail outlets may simply be given a different task. Th is approach, however, would count these workers as jobs created.

Imports. The job per dollar fraction used in th e employment projection is calculated based on jobs per dollar of revenue for domestic firms only. To the extent that spending on demand-side energy efficiency activities goes toward the purchase of imported goods this projection will overstate the U.S. employment im pact of those expenditures.

Fixed Coefficient. Implicit in this approach is the assumption that employment impacts can be projected decades into the futu re on the basis of a single calcu lation from 2011 data. The labor intensity of demand-side energy e fficiency will likely change with technol ogical innovation in the sector. In addition, even ab sent technological change, labor intensity of expenditures will likely change over time as states alter their por tfolio of efficiency activities (e.g., by moving to higher cost activities after e xhausting opportunities for lo w cost efficiency gains).

Non-additional Activities. Here we assume th at all activities financed by demand-side energy efficiency expenditures are additional to what would have been undertaken in the absence of these programs. If utilities finance some actions customers would have undertaken in the absence of these programs (e.g., if a customer receives a rebate for an energy e fficient appliance that would have been purchased without the rebate), these numbers would overestimate employment impacts of the proposed guidelines.

6.6 Conclusion
This chapter presents qualitative and quant itative discussions of potential employment impacts of the final guidelines for electrici ty generation, fuel pr oduction, and demand-side energy efficiency sectors. The qualitative disc ussion identifies challenges associated with estimating net employment effects and discusses anticipated impacts related to the rule. It includes an in-depth discussion of economic theory underlying anal ysis of employment impacts. The employment impacts for regulated firms can be decomposed into output and substitution

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6-35 effects, both of which may be positive or nega tive. Consequently, econo mic theory alone cannot predict the direction or magnitude of a regulation’s employment imp act. It is possible to combine theory with empirical studies spec ific to the regulated firms and ot her relevant sectors if data and methods of sufficient detail and quality are avai lable. Finally, economic theory suggests that environmental regulations may have positive imp acts on labor supply and productivity as well.

We examine the peer-reviewed economics literature analyzing various aspects of labor demand, relying on the above theoretical framewor k. Determining the direction of employment effects in regulated industries is challenging because of the complexity of the output and substitution effects. Complying with a new or more stringent regulation may require additional inputs, including labor, and ma y alter the relative proportions of labor and capital used by regulated firms (and firms in other relevant industries) in their pr oduction processes. The

available literature illustrates some of the diffi culties for empirical estimation: for example, there is a paucity of publicly data on plant-level employment, thus most studies must rely on confidential plant-level employment data from the U.S. Census Bureau, typically combined with pollution abatement expenditure data , that are too dated to be re liably informative, or other measures of the stringency of regulation. In addition, the most commonly used empirical methods can only estimate the gross effects of re gulation on employment (i.e. the impact of that more stringent regulation has on employment relative to less stri ngent or no regulation), not the net effects. Empirical analysis at the industry le vel requires estimates of product demand elasticity; production factor substitut ability; supply elasticity of production factors; and the share of total costs contributed by wages, by indus try, and perhaps even by facility Econometric

studies of environmental rules c onverge on the finding that employment effects, whether positive or negative, have been sm all in regulated sectors.

The illustrative quantitative analysis in th is chapter projects a subset of potential employment impacts in the electricity gene ration, fuel production, and demand-side energy efficiency sectors. States ha ve the responsibility and flexib ility to implement policies and practices for compliance with Final EGU GHG Existing Source Guidelines. As such, given the wide range of approaches that may be used, qua ntifying the associated employment impacts is difficult. EPA’s employment analysis includes projected employment imp acts associated with these guidelines for the electric power industry, coal and natural gas production, and demandside energy efficiency activities. These projections are derived, in part, from a detailed model of

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6-36 the electricity production sector used for this regulatory analysis, and U.S. government data on employment and labor productivity. In the electri city, coal, and natural gas sectors, the EPA estimates that these guidelines could have an employment impact of roughly -8,500 job-years in 2020, -25,000 job-years in 2025, and -30,900 job-years in 2030 for the rate-based scenario. For the mass-based scenario, the EPA estimates that these guidelines could have an employment impact of roughly -13,100 job-years in 2020, - 26,000 job-years in 2025, and -30,900 job-years in 2030 (see Tables 6-4 and 6-5).

Employment impacts from demand-side energy efficiency activities are based on historic data on jobs supported per million dollars of expenditure on energy efficiency. Demand-side energy efficiency employment impacts would approximately range from 37,570 to 59,700 jobs in2020, 52,590 to 83,590 jobs in 2025, and from 52,440 to 83,360 jobs in 2030 for both the ratebased and mass-based approaches and a 1 per cent growth target for energy efficiency expenditures (see Table 6-6).

The IPM-generated job-year numbers for the electricity, coal and natural gas sectors

should not be added to the demand- side efficiency job impacts, si nce the former are reported in full-time equivalent job-years, whereas the latt er do not distinguish betw een full- and part-time employment. Finally, note again that this is an illustrative analysis, and CAA section 111(d)

allows each state to determine the appropriate combination of, a nd the extent of its reliance on, measures for its state plan, by wa y of meeting its state-specific goal. Given the flexibilities afforded states in complying with the emission guidelines, the impacts reported in this chapter

are illustrative of acti ons 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 Systems Research 3(1):1-8.

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6-37 Anderson, D., O. Livingston, D. Belzer, D ., and M. Scott. 2014. “Assessing National Employment Impacts of Investment in Resident ial and Commercial Sector Energy Efficiency: Review and Example Analysis” [Technical Repo rt]. Pacific Northwes t National Laboratory . Available at < http://www.pnnl.gov/main/public ations/external/technical_reports/PNNL23402.pdf >. Accessed June 4, 2015. Anger, N. and U. Oberndorfer. 2008. “Firm Perfo rmance and Employment in the EU Emissions Trading Scheme: An Empirical Assessment for Germany.” Energy Policy . (36):12–22. Arimura, T. H.; S. Li; R. G. Newell, and K. Palmer. 2012 “Cost-Effec tiveness 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 Analysis in Environmental, Health, and Safety Regulati on: 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/rstavin s/Papers/Benefit Cost Analysis in Environmental.AEI.1996.pdf>. Accessed June 5, 2015. Berman, E. and L. T. M. Bui. 2001. “Environm ental 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. Dipe rna. 2008. “Environmental Protection, the Economy, and Jobs: National and Regional Analyses.” Journal of Environmental Management . 86(1): 63-79. Brown, R.S. and L. R. Christensen. 1981. “Estima ting the Elasticities of Substitution in a Model of Partial Static Equilibrium: An Applicati on 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 Competitiveness 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 Economic 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 Longman, Inc., Chapter 4.

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6-38 Executive Order 13563 (January 21, 2011). “Impr oving Regulation and Regulatory Review. Section 1. General Principles of Regulation.” Federal Register 76 (14): 3821-3823. Ferris, A. E., R. J. Shadbegian, A. Wolverton. 2014. “The Effect of E nvironmental Regulation on Power Sector Employment: Phase I of the Title IV SO 2 

### Trading Program.” Journal of the Association of Environmen tal and Resource Economists. 1(4): 521-553. Gillingham, K.; R. Newell and Karen Palmer. 2009. “Energy Efficiency Economics and Policy.” 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 Environmenta l Economics and Policy. 8(1):18-38. Global Insight. 2008. U.S. Metro Economies: Current and Po tential 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 Sect or: Workforce Size and Expectations for Growth. Lawrence Berkeley National Laboratory, LBNL-3987E

. 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. “E nvironment, Health, and Human Capital.” Journal of Economic Literature . 51:(3):689-730. Gray, W., R. J. Shadbegian, C. Wang and M. Meral. 2014 “Do EPA Regulations Affect Labor Demand? Evidence from the Pulp and Paper Industry”, Journal of Environmental Economics and Management. 68: 188-202. Greenstone, M. 2002. “The Impacts of Environm ental 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 Press. Chapter 2. Kahn, M.E. and E. T. Mansur. 2013. “Do Local Energy Prices and Regulation Affect the Geographic Concentration of Employment?” Journal of Public Economics . 101:105–114. Layard, P.R.G. and A. A. Walters. 1978. Microeconomic Theory . McGraw-Hill, Inc. Chapter 9.

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6-39 List, J. A.; D. L. Millimet; P. G. Fred riksson and W. W. McHone. 2003. “Effects of Environmental Regulations on Manufacturing Pl ant 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> . Mccarthy, M.B. 1991. “Lift: Inforum's Model of the U.S. Economy,” Economic Systems Research 3(1):15-36. Meade, D.S. 2009. “An Analysis 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/res ources/0713_clean_economy.pdf>. Accessed June 4, 2015. Ostro, B.D. 1987. “Air Pollution and Morbid ity Revisited: A Specification Test.” Journal of Environmental Economics Management . 14:87-98. Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analysis for the Transport Rule.” White Paper. Boston, MA. Exelon Corp. Smith, K. 2012. “Reflections: In Search 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 Economy: Repowering Jobs, Businesses and Investments Across America

. ” Available at: < http://communitywealth.org/content/clean-ener gy-economy-repowering-jobs-b usinesses-and-investmentsacross-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-jobscensus-2014/>.

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6-40 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 Ad ministration. 2010a. “Measuring the Green Economy.” Available at: <http ://www.esa.doc.gov/Reports/measuring-greeneconomy>. Accessed June 4, 2015. U.S. Department of Commerce, Internati onal Trade Administration. 2010b. Environmental Industries Fact Sheet, using 2008 data from Envi ronmental Business Intern ational, Inc., April. Available at: <http://web.ita.doc.gov/ete/eteinf o.nsf/068f3801d047f26e85256883006ffa54/4878b7e2fc08ac 6d85256883006c452c?OpenDocument>. Accessed June 4, 2015. U.S. Department of Labor, Bureau of Labor Stat istics. 2012a. Databases, Tables & Calculators by Subject. Available at <http://data.bls.gov/ timeseries/LNS14000000>. Acccessed on April 16, 2012. U.S. Department of Labor, Bureau of Labor Statistics (2012b, April 6). “The Employment Situation—March 2012.” News Release. Available at <http://www.bls.gov/news.release/pdf/empsit.pdf>. Accessed April 16, 2012. U.S. Department of Labor, U.S. Bureau of La bor Statistics, (n.d.). “Employment 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 Goods and Services, 2010-2011.” Available at: <http://www.bls.gov/gg s/ggsoverview.htm>. Retrieved January 14, 2014, U.S. Energy Information Agency. 2013. “A nnual Energy Outlook 2014: Early Release.” Available at: http://www.eia.gov/fo recast/aeo/er/early_elecgen.cfm. U.S. Environmental Protection Agency (U.S. EP A). 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/clea nairactbenefits/feb11/ fullreport_rev_a.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the Mercury and Air Toxics Standa rds (MATS). Office of Air Qual ity Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/ma tsriafinal.pdf>. Acessed June 4, 2015.

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6-41 U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: El ectric Utility Generati ng Units. Demand-Side Energy Efficiency.

U.S. International Trade Commission. “Environmen tal and Related Servic es.” Publication No. 4389, Investigation No. 332-533, March, 2013. Available at <http://www.usitc.gov/publications/332/pub4389.pdf>. Accessed June 4, 2015. Walker, W. R. 2011.“Environmental Re gulation and Labor Reallocation." American Economic Review. 101(2): 442-47. Walker, W. R. 2013.“ The Transitional Costs of S ectoral Reallocation: Evidence From the Clean Air Act and the Workforce." The Quarterly Journal of Economi cs 128 (4): 1787-1835. Wei, M., S. Patadia, and D. M. Kammen. 2010. “Putting Renewables And Energy Efficiency to Work: How Many Jobs Can the Clean En ergy Industry Generate in the US?” Energy Policy, 38(2):919-931. Werling, J.F. 2011. “Preliminary Economic Analys is for OSHA’s Proposed Crystalline Silica Rule: Industry and Macroeconomic Impacts”, Fi nal Report for the Occupational Safety and Health Administration, Available at <https://www.osha.gov/silica/Employment_A nalysis.pdf>. Accessed June 9, 2015. Yi, H. 2013. “Clean Energy Policies and Green J obs: An Evaluation of Green Jobs in U.S. Metropolitan Areas.” Energy Policy . 56:644-65.

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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 employment impacts

of the Final Carbon Pollution Emission Guidelin es for Existing Stationary Sources: Electric Utility Generating Units (herein referred to as “f inal emission guidelines” or the “Clean Power Plan Final Rule”). The focus of the employment an alysis is limited to the direct changes in the amount of labor needed in the power, fuels and ge nerating equipment sectors directly influenced by the illustrative plan approaches analyzed for the final emission guidelines. It does not include the ripple effects of these impacts on the broade r economy (i.e., the “multiplier” effect), nor does it include the wider economy-wide e ffects of the changes to the en ergy markets, such as changes in electricity prices.

The methods used to estimate the supply- side employments ar e based on methods previously developed for the Mercury and Air Toxics Standards (MATS) Regulatory Impact Analysis (RIA). The methods used in this analysis to estimate the recurring labor impacts (e.g., labor associated with operating and maintaining generating units, as well as labor needed to mine coal and natural gas) are the same as was used in MATS (with updated da ta where available).

The labor analysis in the MA TS RIA was primarily concerne d with the labor needs of retrofitting pollution control equipment. The an alysis for the Clean Power Plan Final Rule, however, involves the quantity and timing of th e labor needs of building new renewable and natural gas, as well as making heat rate improve ments (HRI) at existing coal fired EGUs. These construction-related compliance activities in the Clean Power Plan Final Rule required developing additional appropriate analytical methods that were not needed for the MATS analysis. The newly developed analytical met hods for the construction -related activities are similar in structure and overall approach to th e methods used in MATS, but required additional data and engineering information not needed in the MATS RIA.

6A.1 General Approach
The analytical approach us ed in this analysis is a bottom-up engineering method combining the EPA’s cost analysis of the final emission guidelines with data on labor productivity, engineering estimates of the amount and types of labor needed to manufacture,

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6A-2 construct, and operate different types of generating units, and pr evailing wage rates for skilled and general labor categories. The approach involved using utility power sector projections and various energy market implications under the fi nal emission guidelines from modeling conducted with the EPA Base Case version 5.15, us ing the Integrated Planning Model (IPM) 174 

### , along with data from secondary sources, to estimate the first order employment impacts for 2020, 2025, and

2030.

Throughout the supply-side labor analysis the engin eering approach projects labor changes measured as the change in each analysis year in job-years 175 

### employed in the power generation and directly related sectors (e .g., equipment manufacturing, fuel supply and generating efficiency services). Job-years are not indivi dual jobs, nor are they necessar ily permanent nor full time jobs. Job-years the amount of work performed by one full time equivalent (FTE) employee in one year. For example, 20 job-years in 2020 may repres ent 20 full-time jobs or 40 half-time jobs in that year, or any combination of full- and part-time workers such that total 20 FTEs.

6A.1.1 Employment Effects Included In the Analysis
The estimates of the employment impacts (b oth positive and negative) are divided into five categories:

 additional employment to make HRI 176 

### at existing coal fired EGUs;

 additional construction-related employment to manufacture and install additional new generating capacity (renewable s, and natural gas combined cycle or combustion turbine units) when needed as part of early compliance actions;

174 Results for this analysis were developed using various outputs from EPA’s Base Case v.5.15 using ICF’s Integrated Planning Model (IPM). This case includes all of the underlying modeling that was developed by EPA with technical support from ICF International, Inc. See http://www.epa.gov/powersectormodeling/ for more information.

175 Job-years are not individual jobs, but rather the amount of work performed by the equivalent of one full-time individual for one year. For example, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in that year.

176 Heat rate improvements could include a range of activities in the power plant to lower the heat rate required to generate a net electrical output. Assuming all other things be ing equal, a lower heat rate is more efficient because less fuel is needed per unit of electric output.

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6A-3  lost construction-related employ ment opportunities due to redu ctions in the total amount of new generating capacity needed to be bui lt in the later years because of reduced overall demand for electricity because of demand-side energy efficiency activities;

 lost operating and maintenance employment opportunities due to increased retirements of coal and small oil/gas units;

 changes (both positive and negative) in coal mining and natural gas extraction employment due to the aggregate net change s in fuel demands arising from all the activities occurring due to compliance w ith the final emission guidelines.

Some of the changes are one-time labor effect s which are associated with the building (or avoiding building) new generating ca pacity and installing HRI. This type of employment effects involves project-specific labor that is used for 2 to 4 years to complete a specific construction and installation type of project. There are other labor effects, how ever, which continue year after year. For example, bringing new generating capacity online creates an ongoing need for labor to operate and maintain the new generating capacity throughout the expected service life of the unit. New generating capacity also creates a need for additional employment to provide the fuel annually to run the new capacity. There are also continuing effects from th e lost operations and maintenance (O&M) and fuel sector labor opport unities from decisions to retire existing capacity, as well as similar lost labor opportuni ties from decisions to reduce a portion of the amount of additional capacity needed in the base case.

6A.2 Employment Changes due to Heat Rate Improvements The employment changes due to HRI were estimated based on the incremental MW capacity estimated to implement such improve ments by 2020 as indicated by the analysis conducted by EPA. The heat rate improvement job impacts were assumed to have all occurred by 2020 and thus this study assumes there will be no HRI related jobs after 2020 (i.e., no permanent O&M related jobs due to HRI for 2025 or 2030) . EPA modeled the heat rate improvements exogenously in IPM using the assumption that all “r elevant” units can improve their heat rate by 6 percent at a capital cost of $100/kW. This study assumes that these investments will occur over a four-year period culminating in 2020. Hence, the per-year cost of heat rate was calculated to be $25/kW, and this cost was us ed in the next step.

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6A-4 This cost was then allocated to four cate gories based on the estimates provided by Andover Technology Partners (ATP), which were adapted from proxy projects in volving installation of combustion control retrofits, such as those installed under the Best Available Retrofit Technology (BART) submissions from coal-fir ed power plants located in Wyoming and Arizona. For more details, refer to the Staudt (2014) report. 177 

### These proxies were chosen to ensure that the types of activities involved and th eir associated costs would be representative of those investments EPA expects power plants to undertake for efficiency upgrades.

Information on cost for these proxies were th en extrapolated to approximate the labor requirements for four broad categories of la bor – boilermakers and general construction, engineering and management support labor, labo r required to produce the equipment in upstream sectors, and labor required to supply the material s (assumed to be primarily steel) in upstream

sectors. More details about these estimates are provided in the Sta udt (2014) report.

Based on the cost allocated in each categorie s and output per worker figures for respective industries in 2020, the employment gains for heat rate improvement were estimated 178 

### for 2020 using the assumptions summarized in Table 6A-1 below. Output per workers in future years were adjusted to account for growth in labor productivity, based on hist orical evidence of productivity growth rates fo r the relevant sectors.

Table 6A-1. Labor Productivity Growth Rate due to Heat Rate Improvement Share of the Total Capital Cost Output/Worker (2020) Labor Productivity Growth Rate Boilermaker and Gen. Const. 40% $78,500 0% Management/Engineering 20% $141,000 1.3% Equipment 30% $458,000 3.2% Materials 10% $424,000 -1.2%

177 Staudt, James, Andover Technology Partners, Inc. Estima ting Labor Effects of Heat Rate Improvements. Report prepared for the Clean Power Plan Proposed Rule, March 6, 2014.

178 Total value of shipments or recei pts in 2007 and total employees were taken from 2007 Economic Census, Statistics by Industry for Mining and Manufacturing sectors. The average annual growth rate of labor productivity was taken from the Bureau of Labor Statistics. Average gr owth rate calculated for years 1992-2007, applied to 2007 productivity to determine 2020 estimates of productivity. For the construction sector, BLS productivity growth rate data was unavailable. Because of this, and lack of relia ble data on construction sect or productivity growth, the output per worker for the construction sector was not foreca sted to 2020, and the most recent available value from 2007 was used.

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6A-5 For these output per worker figures, a power sector construction indus try (NAICS 237130) was used for general construction and boilermakers, Engineering Services (NAICS 54133) was used for the engineering and management compone nt, Machinery Manufacturing (NAICS 333) was used for the equipment sector, and steel manuf acturing (NAICS 33121) was used for materials. Use of machinery manufacturing for equipment and steel for materials was based on an analysis of the types of materials and e quipment needed for these projects, and what EPA determined to be the most appropriate industry sectors for those. For more deta ils, refer to the Staudt (2014) report.

6A.2.1 Employment Changes Due to Building (or Avoiding) New Generation Capacity
Employment changes due to new generation uni ts were based on the incremental changes in capacity (MW), capital costs ($MM), and fi xed operations and maintenance (FOM) costs ($MM) between the policy scenario a nd the base case in a given year.

New capacities were aggregated by genera tion type into the following categories:

• Combined Cycle,

• Combustion Turbine, and

• Renewables (which includes biomass, geothermal, landfill gas, onshore wind, and solar).

For each category, the analysis estimated the im pacts due to both the construction and operating labor requirements for corresponding capacity ch anges. The construction labor was estimated using information on the capital costs, while the operating labor was estimated using the FOM

costs.

Because IPM outputs provide annualized capit al costs ($MM), EPA first converted the annualized capital costs to changes in the total capital investment using the corresponding capital charge rates. 179 

### These total capital investments were then converted to annual capital investments using assumptions about the estimated duration of the construction phase, in order to estimate the

179 Capital charge rates obtained from EPA’s resource, EPA #450R13002: Documentation for EPA Base Case v.5.13 using the Integrated Programming Model (IPM).

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6A-6 annual impacts on construction phase labor. Dura tion estimates were based on assumptions for construction lengths used in EPA’s IPM modeling. 180 

### Specific assumptions used for different generating technologies are show n 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 we re then broken down into four categories: equipment, material (which is assumed to be pr imarily steel), installation labor, and support labor in engineering and management. The percenta ge breakdowns shown in Table 6A-3 were estimated using information provided by Staudt (2014), based primarily on published budgets for new unit assembled in a study for the Nati onal Energy Technology Laboratory (NETL). For more details, refer to the St audt (2014) report. Annual capita l costs for each generation type provided by the IPM output were allo cated according to this breakdown.

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

Equipment Material Labor Eng. and Const. Mgt Renewables 54% 6% 31% 9% Combined Cycle 65% 10% 18% 7% Combustion Turbine 65% 10% 18% 7%



### The short-term construction labor of the new generation units were based on output ($ per worker) figures for the respective sectors. The to tal direct workers per $1 million of output for the baseline year 2007 were forecasted to the years under analysis usi ng the relevant labor productivity growth rate. Table 6A-4 shows the figures for each of the five productivities: general power plant cons truction; engineering and managemen t; material use; equipment use;

180 Ibid.

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6A-7 and plant operators. The resulting values were multiplied by the capital costs to get the job impact.

Table 6A-4. Labor Productivity due to New Generating Capacity Labor Productivity Growth Rate Workers per Million $ (2007) General Power Plant Construction 0.0% 5.7 Engineering and Management 1.3% 5.2 Material Use (Steel) -1.2% 2.0 Equipment Use (Machinery) 3.2% 3.3 Plant Operators 2.8% 10.8



### General installation labor, a ssumed to be mostly related to the general power plant construction phase, was matched with the pow er industry specific construction sector. Engineering/management was matched to the engi neering services sector to determine their respective output per worker. For materials, EP A assumed steel to be the proxy and used the steel manufacturing sector for th is productivity. Equipment was assu med to primarily come from machinery manufacturing sector (such as turbines, engines and fans).

The net labor impact for construction labor fo r a given year was adjusted to account for changes in capacity that has al ready taken place in the prior IPM run year. Because IPM reports cumulative changes for new generating capacity for any given run year, this adjustment ensured that the short-term construction phase job impact s in any given run year does not reflect the cumulative effects of prior construction change s for the given policy scenario. The estimated amount of the change in construction-relate d 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 previous run year (i.e., the la bor estimates derived from the 2025 IPM run year are the average annual labor impacts in 2021 through 2025). The c onstruction labor results for 2020 represent the average labor impacts in 2017 through 2020.

The plant operating employment estimate s used a simpler methodology as the one described above. The operating employment esti mates use the IPM estimated change in FOM costs for the IPM run year. Because the FOM costs are inherently estimates for a single year, the operating employment estimates are for a single year only. While ther e are obviously operating employment effects occurring in every year th roughout the entire IPM estimation period (2017- 2030), the labor analysis only estimates the singl e year labor impacts in the IPM run years: 2020,

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6A-8 2025 and 2030. The total direct workers for $1 m illion and labor productivity growth rate provided for plant operators in Table 6A-4 we re used to estimate the employment impact.

6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements
Employment changes due to plant retirement s 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 ba sic assumption in this analysis was that increased retirements ( over the base case) will lead to reduced FOM expenditures at those plants which were assumed to lead to direct job losses for plant workers.

In order to estimate the total FOM changes due to retirements, EPA first estimated the average FOM costs ($/kW) for existing coal-fired and oil/gas-fired units in the base case, as

shown in Table 6A-5 below. It was assumed that the average FOM cost of existing units in the base case can be used as a proxy for the lost economic output due to fo ssil retirements. Thus, changes in the FOM costs for these retiring uni ts were derived by taking the product of the incremental change in capacity and the average FO M costs. These values were converted to lost employment using data from the Economic Cens us and BLS on the output/worker estimates for the utility sector. 181



### Table 6A-5. Average FOM Cost Assumptions 2020 2025 2030 Coal 65 68 69 Oil and Gas 21 22 22



### Note that the retirement related employment losses are assumed to include losses directly affecting the utility sector, and do not include loss es in upstream sectors th at supply other inputs to the EGU sector (except fuel related job losses, which are estimated separately and discussed in the next section).

6A.2.3 Employment Changes due to Coal and Oil/Gas Retirements
Two types of employment impacts due to project ed fuel use changes were estimated in this section. First, employment losse s due to either reductions or shifts in coal demand were

181 The same specific sources as cited before, however, used workers and total payroll.

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6A-9 estimated using an approach similar to EPA’s co al employment analyses under Title IV of the Clean Air Act Amendments. Using this approach , changes in coal demand (in short tons) for various coal supplying regions were taken from EPA’s base and policy case runs for the proposed EGU GHG NSPS. These changes were converted to job-years using U.S. Energy information Administration (EIA) data on regional coal mining productivity (in short tons per employee hour), using 2008 labor productivity estimates. 182,183



### Specifically, the incremental changes to coal demand were calculated based on the coal supply regions in IPM -- Appal achia, Interior, and West and Wa ste Coal (which was estimated using U.S. total productivity). Worker productivity values used for estimating coal related job impacts are shown in Table 6A-6 below.

Table 6A-6. Labor Productivity due to New Generating Capacity Labor Productivity Coal (Short tons/ employee hour) Appalachia 2.91 Interior 4.81 West 19.91 Waste 5.96 Natural Gas (MMBtu/ employee hour) 126 Pipeline Construction (Workers per $Million) 5.1



### For natural gas demand, labor productivity per unit of natura l gas was unavailable, unlike coal labor productivities used above. Most second ary 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 comb ined 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 respectiv e IPM runs to estimate the job-years for the specific year (converting the TCF of gas used projected by IPM into MMBtu using the

182 From EIA Annual Energy Review, Coal Mining Productivity Data. Used 2008.

183 Unlike the labor productivity estimates for various e quipment resources which were forecasted to 2020 using BLS average growth rates, th is study uses the most recent historical productivity estimates fo r 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 manuf acturing sectors, which would have in troduced a high degree of uncertainty in forecasting productivity growth rates for future years.

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6A-10 appropriate conversion factors). In addition, the pi peline construction costs were estimated using endogenously determined gas market model parame ters in IPM used by EPA for the MATS rule (using assumptions for EPA’s Base Case v4.10). This analysis assumed that the need for additional pipeline would be propor tionate to those projected for the MATS rule and were hence extrapolated from those estimates. 184 

### The job-years associated w ith the pipeline construction were included in the natural gas employment estimates.

184 See U.S. EPA. 2011. Technical Support Document for the Mercury and Air Toxics Standards: Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 2015.

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7-1 

## C HAPTER 

## 7: S TATUTORY AND 

## E XECUTIVE 

## O RDER 

## A NALYSIS



### 7.1 Executive Order 12866: Regulatory Pla nning and Review, and Executive Order 13563: Improving Regulation and Regulatory Review
This final action is an economically signifi cant 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 analysis of the potential costs and benefits

associated with this action.

Consistent with Executive Order 12866 a nd Executive Order 13563, the EPA estimated the costs and benefits for illustrative plan appr oaches 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) gui delines 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 emissions 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 cobenefits, as these reductions are not the primary objective of this rule.

The EPA has used the social cost of carbon estimates presented in the Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis

Under Executive Order 12866 (May 2013, Revised July 2015) (“current TSD”) to analyze CO 2 

### climate impacts of this rulemaking. We refer to these estimates, which were developed by 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 year. The four SC-CO 2 

### estimates are associated with di fferent discount rates (model averag e at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th 

### percentile at 3 perc ent), and each increases over time. In this summary, the EPA provides the estimate of cl imate benefits associated with the SC-CO 2 

### value

deemed to be central in the current TSD: th e model average at 3 pe rcent discount rate.

In the final emission guidelines, 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 in order to maximize the range of choices that states will have in developing their plans. Because of

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7-2 the range of choices available to states and the lack of a priori knowledge about the specific choices states will make in response to the final goals, the Regulatory Impact Analysis (RIA) for this rule analyzed two implementation scenarios designed to achieve these goals, which we term the “rate-based” illustrative plan approach and the “mass-based” illustrative plan approach.

It is very important to note that the differen ces between the analytical results for the ratebased and mass-based illustrative plan approaches presented in the RIA may not be indicative of likely differences between the approaches if implemented by states and affected EGUs in response to the final guidelines. Rather, the two sets of analyses are intend ed to illustrate two contrasting, stylized approaches to accomplish the emission performance rates finalized in the Clean Power Plan Final Rule. In other words, if one approach performs be tter than the other on a given metric during a given time period, this does not imply this will apply in all instances in all time periods in all places.

Table 7-1 and 7-2 provide the estimates of the climate benefits, health co-benefits, compliance costs and net benefits of the final emission guidelines for rate-based and mass-based illustrative plan approaches, respectively. There ar e additional important benefits that the EPA

could not monetize. Due to current data and m odeling limitations, our estimates of the benefits from reducing CO 2 

### emissions do not include important impacts like ocean acidification or potential tipping points in natura l or managed ecosystems. Unquan tified benefits also include climate benefits from reducing emissions of non-CO 2 

### greenhouse gases (e.g., nitrous oxide and methane) and co-benefits from reducing direct exposure to SO 2 

### , NO X 

### and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as from reducing ecosystem effects and visibility impairment. Based upon the foregoing disc ussion, it remains clear that the benefits of this final action are substantial, and far exceed the costs. Additional details on benefits, costs,

and net benefits estimates ar e provided in this RIA.

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7-3 Table 7-1. Monetized Benefits, Compliance Cost s, and Net Benefits Under the Rate-based Illustrative Plan Approach (billions of 2011$) a Rate-Based Approach 2020 2025 2030 Climate Benefits b

5% discount rate $0.80 $3.1 $6.4 3% discount rate $2.8 $10 $20 2.5% discount rate $4.1 $15 $29 95th percentile at 3% discount rate $8.2 $31 $61

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $0.7 to $1.8 $0.6 to $1.7 $7.4 to $18 $6.7 to $16 $14 to $34 $13 to $31 Compliance Costs d $2.5 $1.0 $8.4 Net Benefits e $1.0 to $2.1 $1.0 to $2.0 $17 to $27 $16 to $25 $26 to $45 $25 to $43

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient 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 impairment

a All are rounded to two significant figures, so figures may not sum. b The climate benefit estimate in this summ ary 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 percent discount rate, however we emphasize the importance 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 studies. The re duction in premature fatalities each year accounts for over 98 percent of total monetized 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 premat ure mortality because the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by pa rticle type. Estimates in the table are presented for three analytical years with air quality co-b enefits calculated using two discount ra tes. The estimates of co-benefits are annual estimates in each of the analy tical years, reflecting discounting of mortality benefits over the cessation lag between changes in PM 2.5 concentrations and changes in risks of pr emature 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 pres ent value of the benefits of the rule over the full compliance period. d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for the final emission guidelines and a discount rate of approximately 5 percent. This estimate also includes monitoring, recordkeeping, and reporting costs and demand-side energy efficiency progra m 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 an d health estimates based on additional discount rates.

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7-4 Table 7-2. Monetized Benefits, Compliance Co sts, and Net Benefits under the Mass-based Illustrative Plan Approach (billions of 2011$) a Mass-Based Approach 2020 2025 2030 Climate Benefits b

5% discount rate $0.9 $3.6 $6.4 3% discount rate $3.3 $12 $20 2.5% discount rate $4.9 $17 $29 95th percentile at 3% discount rate $9.6 $35 $60

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $2.0 to $4.8 $1.8 to $4.4 $7 to $17 $7 to $16 $12 to $28 $11 to $26 Compliance Costs d $1.4 $3.0 $5.1 Net Benefits e $3.9 to $6.7 $3.7 to $6.3 $16 to $26 $15 to $24 $26 to $43 $25 to $40

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient 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 summ ary 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 percent discount rate, however we emphasize the importance 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 studies. The re duction in premature fatalities each year accounts for over 98 percent of total monetized 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 premat ure mortality because the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by pa rticle type. Estimates in the table are presented for three analytical years with air quality co-b enefits calculated using two discount ra tes. The estimates of co-benefits are annual estimates in each of the analy tical years, reflecting discounting of mortality benefits over the cessation lag between changes in PM 2.5 concentrations and changes in risks of pr emature 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 pres ent value of the benefits of the rule over the full compliance period. d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for the final emission guidelines and a discount rate of approximately 5 percent. This estimate also includes monitoring, recordkeeping, and reporting costs and demand-side energy efficiency progra m 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 an d health estimates based on additional discount rates.

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7-5 7.2 Paperwork Reduction Act (PRA)
The information collection requirements in this rule have been submitted for approval to

OMB under the PRA. The Information Collection Request (ICR) document prepared by the EPA has been assigned the EPA ICR number 2503.02. Y ou can find a copy of the ICR in the docket for this rule, and it is briefly summarized here . The information collection requirements are not enforceable until OMB approves them.

This rule does not directly impose specific requirements on EGUs located in states, U.S.

territories or areas of Indian country. The rule also does not impose specific requirements on tribal governments that have aff ected 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 either tribal or federa l plans. A tribe would have the opportunity under the Tribal Authority Rule (TAR), but not the obligation, to apply to the EPA for Treatment as State (TAS) for purposes of a CAA section 111(d) plan and, if approved by the EPA, to establish a CAA section 111(d) plan for its area of Indian countr y. To date, no tribe has requested or obtained TAS eligibility for purposes of a CAA section 11 1(d) plan. For areas of Indian country with affected EGUs where a tribe has not applied fo r TAS and submitted any needed plan, if the EPA determines that a CAA section 111(d) plan is ne cessary or appropriate, the EPA would have the responsibility to establish the plans. Because tr ibes are not required to implement section 111(d) plans and because no tribe has yet sought TAS elig ibility for this purpose, this action is not anticipated to impose any information collecti on burden on tribal governments over the 3-year period covered by this ICR.

This rule does impose specific requirements on state and U.S. territory governments with

affected EGUs. The information collection re quirements are based on the recordkeeping and reporting burden associated with developing, im plementing, and enforcing a plan to limit CO 2 

### emissions from existing sources in the utility power sector. These recordkeeping and reporting requirements are specifically authorized by CA A section 114 (42 U.S.C. 7414). All information submitted to the EPA pursuant to the recordkeeping and reporting requirements for which a claim of confidentiality is made is safeguarded according to agency policies set forth in 40 CFR part 2, subpart B.

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7-6 The annual burden for this collection of inform ation for the states (averaged over the first 3 years following promulgation) is estimated to be a range of 529,000 to 859,000 hours at a total annual labor cost of $37.4 to $60.8 million. Th e lower bound estimate reflects the assumption that some states already have energy efficiency and renewable energy programs in place. The higher bound estimate reflects the overly-conserva tive assumption that no states have energy efficiency and renewable energy programs in place.

The annual burden for this collection of inform ation for the two affected U.S. territories, i.e., Puerto Rico and Guam, (averaged over th e first 3 years following promulgation of this action) is estimated to be 23,700 to 38,200 hours at a total annual labo r cost of $1.68 to $2.71

million.

The total annual burden for the federal government associated with th e state collection of information (averaged over the first 3 years following promulgation) is estimated to be 54,900 hours at a total annual labor cost of $3.04 m illion. The total annual burden for the federal government associated with the territorial coll ection of information (a veraged over the first 3 years following promulgation) is estimated to be 2,600 hours at a total annual labor cost of $145,000. Burden is defined at 5 CFR 1320.3(b).

An agency may not conduct or sponsor, a nd 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 liste d in 40 CFR part 9. When OMB approves this ICR, the agency will announce that approval in the Federal Register and publish a technical amendment to 40 CFR part 9 to display the OMB control number for the approved

information collection activities contained in this final rule.

7.3 Regulatory Flexibility Act (RFA)

The EPA certifies that this action will not have a significant economic impact on a

substantial number of small entities unde r the RFA. This action will not impose any requirements on small entities. Specifically, em ission 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 establis h emission standards on ex isting sources, and it is those requirements that could pot entially impact small entities.

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7-7 Our analysis here is consistent with the anal ysis of the analogous situation arising when the EPA establishes NAAQS, which do not impose any requirements on regulated entities. As here, any impact of a NAAQS on small entities w ould only arise when stat es 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 b ecause NAAQS themselves impose no regulations upon small entities).

Nevertheless, the EPA is aware that there is substantial inte rest in the rule among small entities and, as detailed 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) and in section II.D of the preamble to the proposed carbon polluti on 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 a nd electric utility associations, as well as industry leaders and trade association representativ es from various indust ries. While formulating the provisions of the rule, the EPA consider ed the input provided ove r the course of the stakeholder outreach as well as the input provided in the many public comments.

7.4 Unfunded Mandates Reform Act (UMRA)

This action does not contain an unfunded manda te of $100 million or more as described in UMRA, 2 U.S.C. 1531–1538, and does not significan tly or uniquely affect small governments. The emission guidelines do not impose any direct compliance requirements on EGUs located in states, U.S. territories or areas of Indian count ry. As explained in sec tion XII.B above, the rule also does not impose specific requirements on tr ibal 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 E GUs. Specifically, states and U.S. territories are required to develop plans to implement the gu idelines under CAA section 111(d) for affected

EGUs. The burden for states and U.S. territories to develop CAA secti on 111(d) plans in the 3- year period following promulgation of the rule wa s estimated and is listed in section XII.B above, but this burden is estimated to be below $100 million in any one year. Thus, this rule is not subject to the requirements of section 202 or section 205 of the UMRA.

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7-8 This rule is also not subject to the requi rements of section 203 of UMRA because it contains no regulatory require ments that might significantly or uniquely affect small governments. Specifically, the state and U.S. territory governments to which rule requirements apply are not considered small governments.

In light of the interest am ong governmental entities, the EPA conducted outreach with national organizations representing state and local elected officials and U.S. territory and tribal governmental entities while formulating the provis ions of this rule. Sections III.A and XI.F of the preamble to the proposed carbon pollution em ission guidelines for existing EGUs (79 FR 34845-34847; June 18, 2014) and sections II.D and VI.F of the preamble to the proposed carbon pollution emission guidelines for existing EGUs in areas of Indian Country and U.S. Territories (79 FR 65489; November 4, 2014) describes the ex tensive stakeholder outreach the EPA has conducted on setting emission guidelines for existing EGUs. The EPA considered the input provided over the course of the stakeholder outreach as well as the input provided in the many public comments when developing the pr ovisions 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, beca use it imposes substantial direct compliance costs on state or local governments, and the federal government will not provide the funds necessary to pay those costs. As discussed in the Supporting Statement found in the docket for this rulemaking, the development of state plans will entail many hours of staff time to develop and coordinate programs for compliance with th e rule, as well as time to work with state legislatures as appropriate, to develop a plan su bmittal. Consistent with this determination, the EPA provides the following federali sm summary impact statement.

The EPA consulted with state and local offici als early in the pro cess of developing the proposed action to permit them to have meaningf ul and timely input into its development. As described in the Federalism discussion in the pr eamble to the proposed st andards of performance for GHG emissions from new EGUs (79 FR 1501; January 8, 2014), the EPA consulted with state and local officials in the process of developing the proposed standards for newly

constructed EGUs. This outreach addressed planned actions for ne w, reconstructed, modified and existing sources. The EPA invited the following 10 national organizations representing state and

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7-9 local elected officials to a m eeting on April 12, 2011, in Washington DC: (1) National Governors Association; (2) National Conference of State Le gislatures, (3) Council of State Governments, (4) National League of Cities, (5) U.S. Conference of Mayors, (6) National Association of Counties, (7) International C ity/County Management Associati on, (8) National Association of Towns and Townships, (9) County Executives of America, and (10) Environmental Council of States. The National Association of Clean Air Ag encies also participat ed. On February 26, 2014, the EPA re-engaged with those governmental ent ities to provide a pre-proposal update on the emission guidelines for existing EGUs and emissi on standards for modified and reconstructed EGUs. In addition, as described in section III.A of the preamble to the proposed carbon pollution emission guidelines for existing EGUs (79 FR 34845-34847; June 18, 2014), extensive stakeholder outreach conducted by the EPA allowe d state leaders, including governors, state attorneys general, environmen tal commissioners, energy officers, public utility commissioners, and air directors, opportunities to engage w ith EPA officials and pr ovide input regarding reducing carbon pollution from power plants.

In the spirit of Executive Orde r 13132, and consistent with the EPA’s policy to promote

communications between the EPA and state a nd local governments, the EPA specifically solicited comment on the proposed action from st ate and local officials. The EPA received comments from over 400 entities represen ting state and local governments.

Several themes emerged from state and loca l government comments. Commenters raised concerns with the building blocks that comprise the best system of emission reduction (BSER), including the stringency of th e building blocks, and the ti ming of achieving interim CO 2 

### levels. They also identified the potential for electric syst em reliability issues a nd stranded assets due to the proposed timeframe for plan submittals and CO 2 

### emission reductions. In addition, states commented on state plan development and im plementation topics, including state plan approaches, early actions, trading programs, inte rstate crediting for RE, and EPA guidance and

outreach.

Commenters identified overarching con cerns 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 ach ievement of interim CO 2 

### levels. State comment ers, in particular, identified changes to the stringency of the bu ilding blocks, concerns with the timeframe over

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7-10 which reductions must be achieved, and concerns w ith the approaches and measures used for the BSER. For the final rule, in res ponse to stakeholder comments, the EPA has made refinements to the building blocks, the period of time over whic h measures are deployed, and the stringency of

emission limitations that those me asures can achieve in a practi cal and reasonable cost way. The

final BSER reflects those refinements.

To many commenters, the proposal’s 2020 complia nce date, together with the stringency

of the interim CO 2 

### goal, bore significant reliability implicat ions. In this final rule, the agency is addressing those concerns via adjustments to th e compliance timeframe (an 8-year 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 strategi es. The final rule also retains flexibilities presented in the proposal and offers additional opportunities, including opportunities for trading within and between states, and other multi-state plan approaches that will further support electric system reliability. The EPA is also requiring stat es to consult with relevant ISOs/RTOs and/or planning/reliability authoritie s during plan development, and to document recommendations in their plans – and is providing the time for states to do so. Even with this foundation of flexibility in place, these final guidelines further provide states with the option of proposing amendments to approved plans in the event that unanticipated and significant reliability challenges arise.

Commenters provided compelling information i ndicating 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 A ugust 31, 2016, and is allowing states two additional years to submit a final plan, if jus tified (to be submitted by August 31, 2018).

States commented on state plan developmen t and implementation topics that included state plan approaches, early actions being take n into account, trading programs being allowed, interstate crediting for RE bei ng allowed, and guidance and outreach being provided by the EPA. For the state plan approaches, commenters expr essed concerns with th e proposed “portfolio approach” for state plans, including concerns with enforceability of requirements, and identified a “state commitment approach” with backstop measures as an option for state plans. In this final

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7-11 rule, in response to stakeholder comments on the portfolio appro ach 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 prog rams and policies, and states that put in place new programs and policie s early, will be better positioned to achieve the

CO 2 

### goals. Measures that a state takes prior to 2022, or programs already in place by 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 currently existing programs and policies, and states that put in place new programs and policies early, will be better positioned to achieve the goals. RE generating ca pacity and demand-side EE measures that are installed after 2012 will be elig ible 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 emi ssion trading programs. The EPA also received a

number of comments from states and stakeholders about the value of EPA support in developing and/or administering tracking sy stems to support state administ ration of rate-based and massbased emission trading programs. In this fina l rule, states may use trading or averaging approaches and technologies or st rategies that are not explicitly mentioned in any of the three building blocks as part of their overall plans, as long as they achieve the required emission

reductions from affected fossil-fuel -fired EUGs. In addition, in res ponse to concerns from states and power companies that the need for up-front in terstate cooperation in developing multi-state plans could inhibit the developmen t of interstate programs that could lower cost, the final rule provides additional options to allow individual EGUs to use creditable out-of-state reductions to achieve required CO 2 

### reductions, without the need for up-fr ont interstate agreements. The EPA is committed to working with states to provide sup port for tracking of emissions and allowances or credits, to help implement multi-state trading or averaging approaches.

In their comments, many states identified the need for the EPA to provide guidance, including guidance on RE and EE emission meas urement and verification (EM&V), and to maintain regular contact/forums with states th roughout the implementation process. To provide state and local governments and other stakehol ders with an unders tanding of the rule

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7-12 requirements, and to provide efficiencies where possible and reduce the co st and administrative burden, the EPA will continue outreach throughout the plan development and submittal process. Outreach will include opportunities for states to participate in briefings, teleconferences, and meetings about the final rule. The EPA’s 10 region al offices will continue to be the entry point for states, tribes and territories to ask techni cal and policy questions. The agency will host (or partner with appropriate groups to co-host) a number of webina rs about various components of the final rule during the first two months afte r the final rule is i ssued. The EPA will use information from this outreach process to inform th e training and other tools that will be of most use to the states, tribes, and te rritories that are implementing th e final rule. The EPA expects to issue guidance on specific topics, including ev aluation, measurement and verification (EM&V) for RE and demand-side EE, state-community engagement, and resources and financial assistance for RE and demand-side EE. As gui dance documents, tools, templates and other resources become available, the EPA, in consul tation with the U.S. Department of Energy and other federal agencies, will continue to make th ese resources available via a dedicated website.

A list of the state and local government comm enters has been provided to OMB and has been placed in the docket for this rulemaking. In addition, the detailed response to comments

from these entities is contained in the EPA’ s response to comments document on this final rulemaking, which has also been placed in the docket for this rulemaking.

As required by section 8(a) of Executive Order 13132, the EPA included a certification from its Federalism Official stating that the EPA had met the Executive Order’s requirements in a meaningful and timely manner when it sent the draft of this final action to OMB for review pursuant to Executive Order 12866. A copy of the cer tification is included in the public version

of the official record for this final action.

7.6 Executive Order 13175: Consultation a nd Coordination with Indian Tribal Governments
This action has tribal implications. Howeve r, it will neither impose substantial direct costs on federally recognized trib al governments, nor preempt tribal law. Tribes are not required to develop or adopt CAA programs, but they ma y 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 secti on 111(d) for affected EGUs in their areas of

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7-13 Indian country. To the extent th at a tribal government seeks an d attains TAS status for that purpose, these emission guidelines would requir e that planning requirements be met and emission management implementation plans be ex ecuted by the tribes. The EPA notes that this rule does not directly impose sp ecific requirements on affected E GUs, including those located in areas of Indian country, but pr ovides guidance to any tribe ap proved by the EPA to address CO 2 

### emissions from EGUs subject to section 111(d) of the CAA. The EP A also notes that none of the affected EGUs are owned or ope rated by tribal governments.

As described in sections III.A and XI.F of the preamble to the proposed carbon pollution emission guidelines for existing EGUs (79 FR 34845-34847; June 18, 2014) and sections II.D and VI.F of the preamble to the proposed carbon pollution emission gu idelines for existing EGUs in Indian Country and U.S. Territories (79 FR 65489; November 4, 2014), the rule was developed after extensive and vigorous outreach to tribal governments. Th ese tribes expressed varied points of view. Some tribes raised con cerns 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 c oncern about the impact the regulati ons would have on the cost of water covered under treaty to their communities as a result of increased costs to the EGU that provide energy to transport the water to the tribes. Other tribes raised concerns about the impacts of climate change on their communities, resources , ways of life and hunting and treaty rights. The tribes were also interested in the scope of the guidelines being considered by the agency (e.g., over what time period, relationship to stat e and multi-state plans) and how tribes will participate in these planning activities.

The EPA consulted with tribal official s under the EPA Policy on Consultation and Coordination with Indian Tribes early in the pr ocess of developing this action to permit them to have meaningful and timely input into its develo pment. 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 consultati on with the Ute Tribe, the Crow Nation, and the Mandan, Hidatsa, Arikara (MHA) Nation on Ju ly 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 co nsultation and the Navajo Nation’s position on

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7-14 the supplemental proposal. One issu e raised by tribal officials wa s the potential impacts of the June 18, 2014 proposal and the supplemental proposal on tribes with budgets that are dependent on revenue from coal mines and power plants, as well as employment at the mines and power plants. The tribes noted the high unemployment rate s and lack of access to basic services on their

lands. Tribal officials also asked whether the ru les 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 re gulatory requirements. The Na vajo Nation made the following recommendations in their letter of Septembe r 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 Na tion 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 Nava jo people already receive virtually all of their electricity from carbon-free sources (mostly hydroele ctric power) and their us e of electricity is negligible compared to the generation at the power plants; building block 4 is not appropriate for the Navajo Nation because of the inadequate access to electricit y, and the goal should allow for an increase in energy consumption on the Nava jo Nation; the supplemental proposal should

consider the useful life of the power plants located on the Nava jo Nation; and the supplemental proposal should clarify that RE pr ojects located within the Navajo Nation that provide electricity outside the Navajo Nation should be counted toward meeting the relevant state’s RE goals under

the Clean Power Plan.

After issuing the supplemental proposal, the EP A 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 Tr ibe of the Uintah and Ouray Reservation was held on December 16, 2014 and with the Gila River Indian Community on January 15, 2015. The Navajo Nation reiterated the concerns raised duri ng the previous consultati on. Several tribes also again indicated that they wanted to ensure th ey would be included in the development of any tribal or federal plans for areas of Indian count ry. The Fort Mojave Tribe and the Navajo Nation expressed concern with using data from 2012 as th e basis for the goal for their areas of Indian country; in their view, that year was not representative for the affected EGU. On April 28, 2015,

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7-15 the EPA held an additional consultation with th e Navajo Nation. The issues raised by the Navajo Nation during the consultation incl uded whether the EPA has the au thority to set less stringent standards on a case-by-case basis, and a suggest ed “parity glide path” that would account and adjust for the very low electricity usage by the Navajo Nation and promote Navajo Nation economic growth and demand.

As required by section 7(a), the EPA’s Tribal Consultation Official has certified that the requirements of the executive order have been met in a meaningful and timely manner. A copy

of the certification is included in the docket for this action.

7.7 Executive Order 13045: Protection of Ch ildren from Environmental Health Risks and Safety Risks
This action is subject to Executive Orde r 13045 (62 FR 19885, April 23, 1997) because it is an economically significan t regulatory action as defined by Executive Order 12866, and the

EPA believes that the environmental health or safety risk addressed by this action has a disproportionate effect on child ren. Accordingly, the agency ha s 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 pow er plants. The EPA believes that the CO 2 

### emission reductions resulting from implementation of thes e 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 c ited in the EPA’s 2009 Endangerm ent Finding concluded that certain populations and lifestages, including ch ildren, the elderly, and the poor, are most

vulnerable to climate-related health effects. The assessment literature since 2009 strengthens these conclusions by providing more detailed fi ndings regarding these groups’ vulnerabilities and the projected impacts they may experience.

These assessments describe how children’s unique physiological and developmental factors contribute to making them particularly vul nerable to climate change. Impacts to children are expected from heat waves, air pollution, infectious and wate rborne illnesses, and mental health effects resulting from extreme weathe r events. In addition, children are among those especially susceptible to most allergic diseases, as well as health effects associated with heat waves, storms, and floods. Additional health concerns may arise in low income households,

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7-16 especially those with ch ildren, if climate change reduces f ood availability and increases prices, leading to food insecurity within households.

7.8 Executive Order 13211: Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use
This action, which is a significant regulatory action under EO 12866, is likely to have a significant effect on the supply, di stribution, or use of energy. Th e EPA has prepared a Statement of Energy Effects for this action as follows. We estimate a 1 to 2 perc ent change in retail electricity prices on average acr oss the contiguous U.S. in 2025, and a 22 to 23 percent reduction in coal-fired electricity generation as a result of this rule. The EPA projects that utility power sector delivered natural gas prices will incr ease/decrease by up to 2.5 percent in 2030. For more information on the estimated energy effects, please refer to the ec onomic impact analysis for this proposal. The analysis is pr esented in more detail in Chapter 3 of this RIA.

7.9 National Technology Transfer and Advancement Act
This rulemaking does not invol ve technical standards.

7.10 Executive Order 12898: Federal Actions to Address Environm ental Justice in Minority Populations and Low-Income Populations
Executive Order 12898 (59 FR 7629; February 16, 1994) establishes federal executive policy on environmental justice. It s main provision directs federal ag encies, to the greatest extent practicable and permitted by law, to make environmental justice part of their mission by identifying and addressing, as appropriate, disp roportionately high and adverse human health or environmental effects of their programs, polic ies and activities on minority populations and lowincome populations in the U.S. The EPA defines environmental justice as the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementati on, and enforcement of environmental laws, regulations, and policies. The EP A has this goal for all commun ities and persons across this Nation. It will be achieved when everyone en joys the same degree of protection from environmental and health hazards and equal ac cess to the decision-making process to have a healthy environment in which to live, learn, and work.

Leading up to this rulemaking the EPA summar ized the public health and welfare effects of GHG emissions in its 2009 Endangerment Finding. See, section VIII.A of the preamble where

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7-17 the EPA summarizes the public health and welf are impacts from GHG emissions that were detailed in the 2009 Endangerment Finding under CAA section 202(a)(1). 185 

### As part of the Endangerment Finding, the Administrator consid ered climate change risks to minority populations and low-income populat ions, finding that certain pa rts of the population may be especially vulnerable based on their characteri stics or circumstances. Populations that were found to be particularly vulnerable to climate cha nge risks include the poor , the elderly, the very young, those already in poor health, the disa bled, 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 wi th Tribal Governments and Protection of Children. The Administrator placed weight on the f act that certain groups, including children, the elderly, and the poor, are mo st vulnerable to climate- related health effects.

The record for the 2009 Endangerment Finding su mmarizes the strong scientific evidence in the major assessment reports by the U.S. Gl obal Change Research Program (USGCRP), the Intergovernmental Panel on Climate Change (IP CC), and the National Research Council (NRC) of the National Academies that the potential im pacts of climate change raise environmental justice issues. These reports concluded that poo r communities can be especially vulnerable to climate change impacts because they tend to ha ve more limited adaptive capacities and are more dependent on climate-sensitive resources such as local water and food supplies. In addition, Native American tribal communities possess unique vulnerabilities to climate change, particularly those impacted by degradation of na tural and cultural resources within established

reservation boundaries and threats to traditional s ubsistence lifestyles. Tribal communities whose health, economic well-being, and cultural traditions that depend upon the natural environment will likely be affected by the degradation of ecosystem goods and services associated with climate change. The 2009 Endangerment Finding record also specifically noted that Southwest native cultures are especially vulnerable to wa ter quality and availability impacts. Native Alaskan communities are already experiencing di sruptive impacts, including coastal erosion and shifts in the range or abundan ce of wild species crucial to their livelihoods and well-being.

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

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7-18 The most recent assessments continue to st rengthen scientific understanding of climate change risks to minority populations and lo w-income populations in the United States. 186 

### The new assessment literature provides more de tailed findings regarding these populations’ vulnerabilities and projected impacts they ma y experience. In addition, the most recent assessment reports provide new information on how some communities of color (more specifically, populations defined jo intly by ethnic/racial characteri stics and geographic location) may be uniquely vulnerable to climate change hea lth impacts in the United States. These reports find that certain climate change related impacts—including heat wa ves, degraded air quality, and

extreme weather events—have di sproportionate effects on low- income populations and some communities of color, raising environmental justice concerns. Existing health disparities and other inequities in these communities increase thei r vulnerability to the health effects of climate change. In addition, assessment reports also find th at climate change poses particular threats to health, well-being, and ways of life of indigenous peoples in the United States.

As the scientific literature presented above a nd as the 2009 Endangerment Finding illustrates, low income populations and some comm unities of color are especially vulnerable to the health and other adverse impacts of climate change. The EPA believes that communities will benefit from this final rulemaking because this action directly addresses the impacts of climate change by limiting GHG emissions through the establishment of CO 2 

### emission guidelines for existing affected fossil fuel-fired EGUs.

In addition to reducing CO 2 

### emissions, the guidelines finaliz ed in this rulemaking would

reduce other emissions from affected EGUs th at reduce generation due to higher adoption of

186 Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe, Eds., 2014: Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program, 841 pp.

IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach , M.D. Mastrandrea, T.E. Bilir , M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, 1132 pp.

IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects . Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken , M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. M acCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, 688 pp.

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7-19 energy efficiency and renewable energy. These emission reductions will include SO 2 

### and NOx,

which form ambient PM 2.5 

### and ozone in the atmosphere, a nd hazardous air pollutants (HAP), such as mercury and hydrochloric acid. In the final rule revising the annual PM 2.5 

### NAAQS, 187 

### the EPA identified low-income populations as be ing a vulnerable population for experiencing

adverse health effects related to PM exposures. Low-income popul ations have been generally found to have a higher prevalence of pre-existi ng diseases, limited acce ss to medical treatment, and increased nutritional deficiencies, which can increase this population’s risk to PM-related

and ozone-related effects. 188 

### Therefore, in areas where this rulemaking reduces exposure to

PM 2.5 

### , ozone, and methylmercury, low-income populati ons will also benefit from such emissions reductions. The RIA for this rulemaking, included in the docket for this rulemaking, provides additional information regarding th e health and ecosystem effects associated with these emission

reductions.

Additionally, as outlined in the community considerations sect ion 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 hi gh and adverse human health or environmental effects on overburdened communities. The EPA consulted its May 2015, Guidance on Considering Environmental Justice During the Development of Regulatory Actions, when determining what considerations to put in place. 189 

### As described in the community considerations section of the preamble, the EPA also conducted a proximity analys is, which is available in the docket of this rulemaking.

7.11 Congressional Review Act (CRA)
This final action is subject to the CRA, a nd the EPA will submit a rule report to each House of the Congress and to th e Comptroller General of the Un ited States. This action is a “major rule” as defined by 5 U.S.C. 804(2).

187 “National Ambient Air Quality Standards for Particulate Matter, Final Rule,” 78 FR 3086 (Jan. 15, 2013).

188 U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Ce nter for Environmental Assessment – RTP Division. December. Available on the Internet at <http://cfpub.e pa.gov/ncea/cfm/recordis play.cfm?deid=216546>.

189 Guidance on Considering Environmental Justice During the Development of Regulatory Actions. http://epa.gov/environmentaljustice/resources/policy/cons idering-ej-in-rulemaking-guide-final.pdf. May 2015.

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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 illu strative plan scenarios are presented in this chapter of the Regulatory Impact Analysis (RIA) for the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Elec tric Utility Generating Units. As discussed in Chapter 1, the EPA is establishing 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 . Given the flexibilities afforded states in complying with the emission guidelines, the benef its, cost and economic impacts reported in this RIA are not definitive estimates, but are instead illu strative of plan approaches states may take.

The EPA has used the social cost of carbon estimates presented in the Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (May 2013, Revised July 2015) (“current SC-CO 2 

### TSD”) to analyze CO 2 

### climate impacts of this rulemaking. 190 

### We refer to these estimates, which were developed by the U.S. government, as “SC-CO 2 

### estimates.” The SC-CO 2 

### is an estimate of the monetary value of impacts associat ed with a marginal change in CO 2 

### emissions in a given year.

The four SC-CO 2 

### estimates are associated with differe nt discount rates (model average at 2.5 percent discount rate, 3 pe rcent, and 5 percent; 95 th 

### percentile at 3 percen t), and each increases over time. In this comparison of benefits and costs, the EPA provides the estimate of climate

benefits associated with the SC-CO 2 

### value deemed to be central in the current SC-CO 2 

### TSD (the model average at 3 percent discount rate). In addition to reducing CO 2 

### emissions, implementing these final emission guidelines is ex pected 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. Therefore, reducing these emissions would also reduce human exposure to ambient PM 2.5 

### and ozone

190 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised July 2015). Available at: <https://www.whitehouse.gov/sites/defau lt/files/omb/inforeg/scc-tsd-final-july-2015.pdf>.

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8-2 precursors, and the associated PM 2.5 

### and ozone related health effect s. 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 categorie s 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 po tential tipping points in natural or managed ecosystems. Unquantified impacts also include those associated with changes in emissions of other pollutants that affect the climate, such as methane. In addition, the analysis does not quantify co-benefits from reducing exposure to SO 2 

### , NO x 

### , and hazardous air pollutants (e.g., mercury), as well as ecosystem ef fects and visibility impairment.

Based upon the foregoing discussion, it remains clear that this final rule’s combined

climate benefits and human heal th co-benefits associated with the reduction in other air pollutants substantially outweigh the illustrative plan scenario costs.

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8-3 Table 8-1. Monetized Benefits, Costs, a nd Net Benefits Under the Rate-based Illustrative Plan Approach (billions of 2011$) a

Rate-Based Scenario 2020 2025 2030 Climate Benefits b

5% discount rate $0.80 $3.1 $6.4 3% discount rate $2.8 $10 $20 2.5% discount rate $4.1 $15 $29 95th percentile at 3% discount rate $8.2 $31 $61

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $0.7 to $1.8 $0.6 to $1.7 $7.4 to $18 $6.7 to $16 $14 to $34 $13 to $31 Compliance Costs d $2.5 $1.0 $8.4 Net Benefits e $1.0 to $2.1 $1.0 to $2.0 $17 to $27 $16 to $25 $26 to $45 $25 to $43

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient 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 summ ary 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 percent discount rate, however we emphasize the importance 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 studies. The re duction in premature fata lities each year accounts for over 98 percent of total monetized 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 premat ure mortality because the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. d Total costs are approximated by the illustrative plan scenario costs estimated using the Integrated Planning Model for the final emission guidelines and a discount rate of approximately 5 percent. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs.

e The estimates of net benefits in this summary table are calculated using the global SC-CO 2 at a 3 percent discount rate (model average). The RIA includes combined climate an d health estimates based on additional discount rates. f Estimates in the table are presented for three analyti cal 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 va lue of the benefits of the rule over the full compliance period.

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8-4 Table 8-2. Monetized Benefits, Costs, and Net Benefits Under the Mass-based Illustrative Plan Approach Scenario (billions of 2011$) a
Mass-Based Scenario 2020 2025 2030 Climate Benefits b

5% discount rate $0.94 $3.6 $6.4 3% discount rate $3.3 $12 $20 2.5% discount rate $4.9 $17 $29 95th percentile at 3% discount rate $9.6 $35 $60

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7% Air QualityHealth Cobenefits c $2.0 to $4.8 $1.8 to $4.4 $7 to $17 $7 to $16 $12 to $28 $11 to $26

Costs d $1.4 $3.0 $5.1 Net Benefits e $3.9 to $6.7 $3.7 to $6.3 $16 to $26 $15 to $24 $26 to $43 $25 to $40

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient 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 summ ary 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 percent discount rate, however we emphasize the importance 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 studies. The re duction in premature fata lities each year accounts for over 98 percent of total monetized 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 premat ure mortality because the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. d Total costs are approximated by the illustrative plan scenario costs estimated using the Integrated Planning Model for the final emission guidelines and a discount rate of approximately 5 percent. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs.

e The estimates of net benefits in this summar y table are calculated using the global SC-CO 2 at a 3 percent discount rate (model average). The RIA includes combined climate an d health estimates based on additional discount rates. f Estimates in the table are presented for three analyti cal years with air quality co-benefits calculated using two discount rates. The estimates of co-b enefits 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. Th e estimates are not the present value of the benefits of the rule over the full compliance period.

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8-5 8.2 Uncertainty Analysis
The Office of Management and Budget’s circular Regulatory Analysis (Circular A-4) provides guidance on the preparation of regul atory analyses required under E.O. 12866, and requires an uncertainty analysis for rules with annual benefits or costs of $1 billion or more. 191 

### This final rulemaking surpasses that threshold for both benefits and co sts. Throughout the RIA,

we considered a number of sources of uncerta inty, both quantitativel y and qualitatively, on benefits and costs. We summarize three key elem ents of our analysis of uncertainty here:

 Evaluating uncertainty in the illustrative pl an approaches that states will implement,

which influences both costs and benefits.

 Assess uncertainty in the methods used to calc ulate the health co-ben efits associated with

the reduction in PM 2.5 

### and ozone and the use of a benef its-per-ton approach in estimating

these co-benefits.

 Characterizing uncertainty in monetizing climate-related benefits.

Some of these elements are evaluated using probabilistic techniques, whereas for others the underlying likelihoods of cer tain outcomes are unknown and we use scenario analysis to evaluate their potential effect on the be nefits and costs of this rulemaking.

8.2.1 Uncertainty in Costs and Illustrative Plan Approaches
The calculation of the state goa ls is based on an evaluation of methods for reducing the carbon emissions intensity of electri city generation that may be ach ieved at reasonable cost. Our

best estimates of the costs of these methods of intensity reduction are reported within the cost analysis of this rule and are included in the cost modeling in the RIA.

A significant source of uncertainty under this regulation is the ultimate approach states will take to comply with the guidelines, which will affect both the costs and benefits of this rule. For this reason we modeled two potential illustra tive plan scenarios fo r each regulatory option: the rate-based illustrative plan scenario and the mass-based illustrative plan scenario. The cost...

191 Office of Management and Budget (OMB), 2003, Circular A-4 , http://www.whitehouse.gov/omb/circu lars_a004_a-4 and OMB, 2011. Regulatory Impact Analysis: A Primer. http://www.whitehouse.gov /sites/default/files/omb/ inforeg /regpol/circular-a-4_regul atory-impact-analysis-aprimer.pdf

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

8-6 The reductions of CO 2 

### and other pollutants from fossil fuel combustion....

However, from this analysis we see that the net benefits of the two scen arios, given consistent assumptions,

8.2.2 Uncertainty Associated with Es timating the Social Cost of Carbon

The 2010 SC-CO 2 

### TSD noted a number of limitations to the SC-CO 2 

### analysis, including the incomplete way in which the integrated a ssessment models (IAM) capture catastrophic and non-catastrophic impacts, their incomplete trea tment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk

aversion. 192 

### Currently integrated assessment models do not assign value to all of the important physical, ecological, and economic impacts of clim ate change recognized in the climate change literature due to a lack of precise information on the nature of damages and because the science incorporated into these models understandably lags behind the most recent research. These

individual limitations do not all work in the same direction in terms of their influence on the SCCO 2 

### estimates, though taken together they suggest that the SC-CO 2 

### estimates are likely conservative. In particular, the IPCC Fourth Asse ssment Report (2007) concl uded that “It is very likely that [SC-CO 2 

### estimates] underestimate the damage costs because they cannot include many non-quantifiable impacts” and the IPCC Fift h Assessment report ob served that SC-CO 2 

### estimates continue to omit va rious impacts that would lik ely increase damages. The 95 th 

### percentile estimate was included in the recomme nded range for regulatory impact analysis, in part, to address these concerns.

The modeling underlying the development of the SC-CO 2 

### estimates addressed uncertainty in several ways. An ensemble of three IAMs were used to generate the SC-CO 2 

### estimates to capture differences in model struct ures that, in part, reflect uncertainty in the scientific literature about these relationships. Pa rametric uncertainty was explicitly addressed in

192 Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 , Interagency Working Group on Social Cost of Carbon, with participation by the Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Available at: <http://www.whitehouse.gov/sites/default/f iles/omb/inforeg/for-agencies/Socia l-Cost-of-Carbon-for-RIA.pdf>.

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

8-7 each IAM, though to differing degrees, through Monte Carlo simulations in which explicit probability distributions for key parameters we re specified, including the equilibrium climate sensitivity, which represents the long-run re sponsiveness of the climate to increasing GHG concentrations. Furthermore, the analysis considered five differe nt socioeconomic and emissions forecasts to capture the sensitivity of the SC-CO 2 

### estimates to key exogeno us projections used in the modeling. Finally, the results we re calculated for three discount ra tes, which were selected, in part, to reflect uncertainty about how interest rates may change ove r time and the possibility that climate damages are positively correlated with uncertain future economic activity. This analysis produced 45 different distributions of the SC-CO 2 

### estimates for each emissions year. To produce a range of plausible estimates that are manageable in regulatory analysis but still reflects the uncertainty in the results four point estimates were recommended. The use of this range of point estimates in this rulemaking helps to reflect the uncertainty in the SC-CO 2 

### estimates. Chapter 4 of this RIA provides a comprehe nsive discussion about the me thodology and application of the

SC-CO 2 

### ; see 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 uncertain ty and the SC-CO 2 

### estimates in response to a separate request for public comment on the approach used to develop the esti mates. Commenters discussed the analyses and presentation of uncertainty in th e TSD as well as the implications of uncertainty for use of the

SC-CO 2 

### estimates in regulatory impact analysis. In their response, the interagency working group (IWG) acknowledged uncertainty in the SC-CO 2 

### estimates but disagreed with commenters that suggested the uncertainty undermines use of the SC-CO 2 

### estimates in regulatory impact analysis. The IWG went on to note th at 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 analysis involves uncertainty, these analyses can provide

useful information to decision makers and the public. See the IWG Response to Comments for the complete response. 193

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

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8-8 8.2.3 Uncertainty Associated with PM 2.5 

### and Ozone Health Co-Benefits Assessment
Our estimate of the total monetized co-benefits is based on EPA’s interpretation of the

best available scientif ic literature and met hods and supported by the SAB-HES and the National Academies of Science (NRC, 2002). Below are key assumptions underlying the estimates for

PM 2.5 

### -related premature mortality, which accoun ts for 98 percent of the monetized PM 2.5 

### health

co-benefits:

 We assume that all fine particles, re gardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption,

because PM 2.5 

### varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow diffe rentiation of effect estimates by particle type. The PM ISA 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 analysis. Thus, the estimates include health co-benefits from reducing fine particles in areas wi th 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.

 We assume that there is a “cessation” lag between the ch ange in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of pr emature mortality related to PM 2.5 

### exposures occur in a distributed fashion over the 20 years follo wing exposure based on the advice of the SAB-HES (U.S. EPA-SAB, 2004), which affects the valuation of mortality cobenefits at different discount rates. EPA qua ntitatively assessed uncertainty in the air quality health co-benefits, incl uding probabilistic approaches.

In addition, EPA provides the 95 th 

### percentile confidence interval for avoided PM-related premature deaths and the associated economic valuation using two key epidemiology studies. EPA provides the PM-related re sults using alternate concentr ation-response relationship provided by an expert elicitation and alternate ozone-related results using concentration-response

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

8-9 relationships provided by alternate epidemiology studies. In addition, we include an assessment of the distribution of population exposure in the modeling underlyi ng the benefit-per-ton

estimates. For further discussion and characteri zation of those uncertainties influencing the benefit assessment, see Chapter 4 of this RIA.

As noted and described in Chapter 4 of this RIA, we use a benefit- per-ton approach to quantify health co-benefits. All benefit-per-t on have inherent limita tions, including that the estimates reflect the geographic distribution of the modeled sect or emissions, which may not match the emission reductions anticipated by th e final emission guidelines, and they may not reflect local variability in popul ation density, meteorology, exposur e, baseline health incidence rates, or other local factors for any specific location. In addition, these estimates reflect the regional average benefit-pe r-ton for each ambient PM 2.5 

### precursor emitted from EGUs, which assumes a linear atmospheric response to em ission reductions. The regional benefit-per-ton estimates, although less subject to these types of uncertainties th an national estimates, still should be interpreted with caution. Even though we a ssume 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
Docket ID EPA-HQ-OAR-2013-0602, Technical Suppor t Document: Technical Update of the Social Cost of Carbon fo r Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with Participation by Council of Economic Advisers, Council on Environmenta l Quality, Department of Agriculture, Department of Commerce, Department of En ergy, Department of Transportation, Domestic Policy Council, Environmental Protection Ag ency, National Economic Council, Office of Management and Budget, Office of Scien ce and Technology Policy, and Department of Treasury (May 2013, Revised July 2015). Also available at: <https://www.whitehouse.gov/sites/default/files/o mb/inforeg/scc-tsd-final-july-2015.pdf>. Accessed July 15, 2015.

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

8-10 Interagency Working Group on Social Cost of Carbon, with partic ipation by Council of Economic Advisers, Council on Environmenta l Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Ec onomic Council, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury. Response to Comments: Social Cost of Carbon for Regulat ory Impact Analysis Under Executive Order 12866. July 2015. Available at <https://www.whitehouse.gov/sites/default/files /omb/inforeg/scc-response-to-commentsfinal-july-2015.pdf.> Accessed July 15, 2015. Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IPCC . Available at <http://www.ipcc.ch/publications_and_data/public ations_ipcc_fourth_assessment_report_synt hesis_report.htm>. Accessed June 6, 2015. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . National Academies Pr ess. Washington, DC. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPACOUNCIL-LTR-05-001. December. Available at <http://yosemite.epa.gov/sab/sabproduct.ns f/0/39F44B098DB49F3C85257170005293E0/$File/co uncil_ltr_05_001.pdf>. Accessed June 4, 2015. 

### U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmental Assessment – RTP Division, Research Triangle Park, NC. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.c fm?deid=216546>. Accessed June 4, 2015.

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United States Environmental Protection Agency Office of Air Quality Planning and Standards Health and Environmental Impacts Division Research Triangle Park, NC Publication No. EPA-452/R-15-003 July 2015

### Technical Support Document (TSD) for the CAA Sec tion 111(d) Emission Guidelines for Existing Power Plants

Docket ID No. EPA-HQ-OAR-2013-0602



# CO 2 

# Emission Performance Rate and Goal Computation Technical Support Document for CPP Final Rule

U.S Environmental Protection Agency

Office of Air and Radiation

August 2015

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2 Category-Specific Performance Rates and State Goal Setting under 111(d)

This Technical Support Document (TSD) provides info rmation that supports the EPA’s determination of category-specific performance rates for fossil steam and stationary combustion turbine technology categories as well as the state emission rate and mass goals that encompass the likely affected fossil units in a state. 1 

### Section VI of the preamble discusses the ca tegory-specific performance rates more broadly along with some of the changes made between proposal and final based on comment. Section VII of the preamble describes the expression of the category-specific performance standards through a state goal metric reflecting likely covered fossil sources in a state. The Greenhouse Gas (GHG) Mitigation Measures TSD for CPP Final Rule explains the technical basis for the development of the Best System of Emission Reductions (BSER) that inform the cate gory-specific performance rates and the subsequent state goals. This TSD provides detailed explanation of the data and the BSER-based calculations used to determine the category-specific performance rates an d state goals The TSD is organized as follows:

1. BSER factors informing the category-specific performance rates and state emission rate goals

a. Block 1 - Heat rate improvement in the coal steam fleet b. Block 2 - Substitute increased generati on from lower emitting existing NGCC units for reduced generation from highe r emitting fossil steam EGUs

c. Block 3 - Substitute generation from ne w zero emitting renewable energy (RE) generating capacity for reduced genera tion from higher emitting fossil EGUs 2. Form of the Category-specific Performance Rates and State Goals 3. Baseline data used to derive performance rates and state goals

a. Emissions & Generation Integrated Resource Database (eGRID) b. Data sources for affected “under construction” units

c. Region-level baseline 4. Methodology for determining category-specific performance rates 5. Methodology for converting category-specific perfo rmance rates into state emission rate goals 6. Methodology for converting state emi ssion rate goals into mass goals 7. Appendix (attached Excel Workbook)

 Appendix 1 – Underlying 2012 unit-le vel baseline inventory and data

 Appendix 2 - Units that commenced opera tion post 2011, but commenced construction prior to 1/8/14

 Appendix 3 – Underlying stat e-level data, adjustments, and region-level data

 Appendix 4 – Computation of the category-sp ecific performance rates (Interim and Final)

 Appendix 5 - Computation of the State Goal (Interim and Final)

 Appendix 6 –State goal summary table

 Appendix 7 – Calculation for generation ad justment in hydro-intensive states

 Appendix 8 - Category-specific emission rates reflecting BB severability

 Appendix 9 – Description of 111(d) b aseline data sources and development

1 The only gas units currently considered likely affected units under the 111(d) applicability criteria are turbines at NGCC units (both the gas and steam turbines). The remainder of the document will use the term “NGCC” to collectively refer to the output from both the steam and gas turbines at a NGCC unit.

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3 In EPA’s technical evaluation, it assessed the cost and potential of each GHG reducing technology were identified (see GHG Mitigation Measures TSD). EPA relied on a similar building block structure as proposed, but revised the quantification of those building blocks based on comments. These revised building blocks levels were used to derive the cat egory-specific performance rates provided in this final rule. The category-specific performance rates were then used to derive the state rate and mass goals.

1. BSER Factors Informing the Category-specific Pe rformance Rates and State Emission Rate Goals

The GHG Mitigation Measures TSD describes three categories of emission reduction measures (building blocks) used in determining the category-specific performance rates. That document describes EPA’s historical data review and analysis underlying each technology and informing EPA’s assessment of its feasibility and cost-effectiveness as part of a BSER. It also explains how EPA made adjustments to the building blocks based on comments The technology estimates determined through EPA’s analysis and documented in the GHG Mitigation Measures TSD are summarized below.

Figure 1. 2030 Building Block Potential Identified for Each Region

BB1 ‐ HRI Improvement for coal fleet BB2 ‐ TWh of total NGCC Generation at 75% Utilization, (Amount of NGCC Generation Potential

Incremental to Baseline) BB3 ‐ Incremental RE Potential (TWh) Eastern Interconnection 4.3% 988, (253) 438 Western Interconnection 2.1% 306, (108) 161 Texas Interconnection 2.3% 204, (67) 107 *Totals are building block potential only. As evidenced in Section 4-step 8, not all of the building block potential is utilized in establishing BSER category-specific rates and state goals.

The building block data shown above are used to determine category-specific performance rates expressed in a lb/MWh rate. As these building bloc ks reflect both fossil and non-fossil measures, the corresponding category-specific performance rates al so reflect fossil and non-fossil generation through the use of an adjusted emission rate d escribed in the preamble and below.

2. Form of the Category-specific Performance Rates and State Goals

As described in Section VI of the preamble, EPA is promulgating a separate emission rate that quantifies BSER for each technology category covered under 111( d) applicability. Therefore, while similar adjustments are made to the generation levels of a ffected fossil steam and NGCC generation reflecting the building blocks, the adjustments are made and expressed at the technology level rather than the combined affected EGU level:

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4 Exhibit A - Simplified formula demonstrating category-specific emission performance rates

Final – Affected fossil steam and NGCC Generation treated separately for quantifying BSER

BSER for Fossil Steam = BSER adjusted emissions for affected fossil steam sources BSER adjusted generation for affected fossil steam sources + replacement generation

BSER for NGCC = BSER adjusted emissions for affected NGCC sources BSER adjusted generation for affected NGCC sources + replacement generation



### 3. Baseline Data Used to Derive Performance Rates and State Goals

See Section VI.D.2 of the Preamble for a descripti on of EPA’s identification of a baseline data

Adjustments that the EPA made to the 2012 historical data EPA received significant comments regarding unit-level data and applicability status. It has reviewed these comments and updated its 2012 unit-level data acco rdingly to better reflect unit-level operation in that year and unit-level applicability status. The upda ted unit-level data is ava ilable in Appendix One and reflects changes to unit data based on comments. In addition to unit-level data updates, the EPA also made some targeted baseline adjustments at the statelevel to address commenter concerns about the repr esentativeness of baseline y ear-data, even where correctly reported. These are highlighted below, but discussed in more detail in the Preamble Section VI. State-level adjustments:

 EPA adjusted affected fossil baseline generation upwards in states with large hydro generation portfolios (adjustment calculations in appendix 7 and applied in appendix 3).

 EPA adjusted state-level generation upwards in cases where a significant portion of the generation portfolio was off line for maintenan ce (adjustment calculations in appendix 7 and applied in appendix 3).

 EPA adjusted state-level generation upwards to reflect under construction fossil steam and NGCC capacity operating at expected technology-class le vels (including units commencing operation in 2012). (List of units available in appendix 2 and adjustment applied in appendix 3). Once these adjustments were calculate d, EPA summed the baseline data d escribed above at the state and regional-level for the following categories. These to tals reflect the baseline from which the performance rates and state goals are assessed.

- State and regional-level coal steam generation

- State and regional-level coal steam emissions

- State and regional-level oil/gas steam generation

- State and regional-level oil/gas steam emissions

- State and regional-level NGCC generation

- State and regional-level NGCC emissions

- State and regional-level NGCC capacity

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5 All generation values are expressed as net generation. Emission rate values are net emission rates and expressed as lbs/MWh. The NGCC capacity expressed is net summertime capacity in megawatts. At proposal, there was a limited number of high utilizatio n combustion turbines and integrated gasification combined cycle units (IGCCs) determined to be likely affected by 111(d) and placed in a separate “other” category when calculating state goals. In this final rule, the applicability language has been revised, and EPA’s current assessment has not identified any simple -cycle combustion turbines that are likely affected units under this rule. The IGCCs that are likely aff ected by the rule are included with the coal steam totals consistent with their fuel use and reporting under subpart Da.

a. Emissions & Generation Integrated Resource Database (eGRID)

eGRID is an inventory of environmental attribut es of the U.S. electric power system. It is a comprehensive source of air emissions data for the electric power sector, based on available plant-specific data for all U.S. electricity generating plants that pr ovide power to the electric grid and report data to the U.S. government. eGRID integrates many different da ta sources on power plants and power companies, including, but not limited to: the EPA, the Energy Info rmation Administration (EIA), the North American Electric Reliability Corporation (NERC), and the Federal Energy Regulatory Commission (FERC). Emissions data from the EPA are carefully integrated with generation data from EIA to produce useful values like pounds per megawatt-hour (lb/MWh) of emi ssions, which allows direct comparison of the environmental attributes of electricity generation. EPA applied its eGRID methodology for matching the publicly available and reported 2012 emissions and ge neration data. The EPA relies on this most recent data to calculate category-specific performance rates and state goals.

The state and region-level totals for each technology category described in the above bullets are intended to reflect the baseline totals for electric generating units (EGUs) that likely meet the applicability criteria as described in Preamble Section IV.D. 2



### i. b. Data sources for under construction units

At proposal, EPA relied on its National Electric Energy Data System (NEEDS). NEEDS includes basic geographic, operating, capacity, and other data on existing or under cons truction generating units. NEEDS was updated for EPA's new power sector mode ling platform v.5.15 reflecting some of the unitlevel information EPA received in the comment period. For a description of the sources used in preparing NEEDS v.5.15, see Documentation, Chapter 4: Generating Resources. 3 

### Several commenters identified units that were under construction a nd likely covered under the rule’s applicability language, but that had not been identified at Proposal. Per commenter sugg estion, EPA performed an additional review of under construction units using EIA 860 data, NEEDS v.5.15 , and comments. In most cases, commenter and publically available data supported one another. There were several inst ances where commenter and

2 The historical baseline development is described in more detail in Appendix 9

3 Available at http://www.epa.gov/powersectormodeling/

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6 reported data conflicted. In th ese cases, EPA generally relied on the publically available data to determine the likely affected units to ensure consistent treatment across the fleet.

EPA notes that this baseline inventory does not constitu te a final applicability determination. The actual inventory of affected units in a future year may vary from the 2012 baseline inventory of likely affected units.

c. Region-level data

The EPA aggregated unit-level data to the state leve l for purposes of state-specific emission rate and mass goal calculation discussed in Section VII of the Preamble. However, before calculating the state goal and mass equivalents, it further aggregated unit-level to the regional level to calculate the category-specific performance standards. The regions reflect the E astern, Western, and Texas Interconnections. These regions were used when quantifying the best system of emission reductions in order to capture the interstate effects of the building blocks. The rationale for the regional structure is explained in preamble section V.A.3.e. For each region, the EPA made BS ER-related adjustments to the baseline data to determine the effect the three building block abatem ent measures could have on the average fossil steam rate and the average NGCC rate in that region. In making adjustments to region-level data, the EPA is simply identifying the BSER reductions that can be achieved on average at the regional level relative to baseline level. The EPA is not making any assertions about specific units or plant capability. The EPA recognizes the uniqueness and complexity of individual power plants, and is aware that there are sitespecific factors that may prevent some EGUs from achieving performance equal to region-level assumptions for a given technology. Likewise, the EPA also recognizes that some EGUs are capable of, and regularly do, achieve performance levels that surpass the BSER values assumed. In any case, the EPA is not making those unit-level evaluations in th is exercise. The EPA is instead attempting to quantify what is feasible at the regional level based on application of the BSER values to historical regional-level data. Affected EGUs can then meet that emission rate through any particular use of abatement measures and/or emission reduction credits that it chooses. Therefore, the ability or inability of a specific EGU to under/overachieve the assumed t echnology value cannot be taken, on its own, as an indication of the appropriateness of the category- specific performance standards and the state goals estimated using this approach.

The aggregate baseline generation and emission rat es constitute a representative baseline for the power fleet for units likely subject to 111(d) applicability cr iteria. As with other EPA regulations, there may be subsequent applicability determinations post rule finalization that arrive at a different status determination for a particular unit than the one assumed here. Moreove r, the future year invent ory of affected units will inherently vary due to scheduled fleet turnover. While EPA addressed unit-level data comments, there may also be areas where stakeholders disagree over un it-level representation in the baseline. However, it is the regional representation of the power sector based on historical data is what ultimately informs the category-specific emissions rates. The large populatio n size of units embedded in the aggregate regionallevel values used to quantify emission performance rat es limit the ability of any unit-level inventory or data discrepancies to introduce a bias that alters this representation.

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7 EPA received some comment suggesting that it should remove units scheduled to retire from the baseline inventory. It also received comments suggesting th at they should not be removed. Removing units scheduled for retirement would generally result in lo wer category-specific emission rates and state goals. EPA is using 2012 as a representative year among the mo st recently available data and does not try to forecast future generation and emission levels post fleet turnover. Even where fleet turnover is certain, (e.g., a scheduled retirement), the impact of that retirement is not. Removi ng units and generation from the baseline inventory without accounting for the shift in generation to other units would understate the amount of fossil generation in the baseline and distort its representativeness. Accounting for the shift in generation would begin to shift the baseline from a historical-data informed baseline to a projectioninformed baseline. Factoring in retirements and replacing it with projected generation shifts would undermine the merits of relying on a historical da ta set and the certainty of reported data.StillUnderAgencyReview–DonotQuote,Cite,orShare84. Methodology for Determining Category-specific Emission Performance RatesEPA’s methodology for calculating category-specific performance rates is described in the steps below. The implementation of each step isillustrated –using the Eastern Interconnection for year 2030 as an example - in the table below its description.4Step 1:Compile 2012 unit-level data, aggregateto state-level, make baseline adjustments, and sum to regional baseline totals.The EPA begins the category-specific performance rate calculationby starting with 2012 historical data. The underlying unit-level or plant-leveldata reflects actual emissions and generation reported by the facility (See Appendix 9for more detailed explanation). EPA categorized each unit,using the classification system described inAppendix 9, as coal steam, O/G steam, or NGCC. It also flagged units that were considered to beunder construction during the 2012 baseline year. EPA then aggregated the unit-level data to the state level and calculated the state-specificemission rate for each technology category bydividing the total emissions into the total generation. This reflected the unadjusted 2012 baselinedata for units that commenced operation prior to 2012. For states that have likely affected EGUs in two different interconnections, EPAsegmented these states into their relevant interconnect portionsat this step (e.g., the Montana Eastern Interconnection and Montana WesternInterconnection). EPA then made the aforementioned adjustmentsto the state-level values to address concerns addressed by commenters. Thisincluded adding in the expected generation and emissions from likely affected units considered under construction. The resulting state-totalsfollowing these limited adjustments provided a baseline for all likely affected EGUs. Complete data for these steps is available in Appendix 1, 2and 3. See the North Carolina example below illustrating the adjustment made to 2012 data reflecting under construction units.EPA received stakeholder comment noting that the Lee and Dan NGCC plants and the Cliffside coal unit 6 commenced operation partwaythrough 2012 and therefore should be treated as under construction since they were still under construction for part of the year and 2012 data wasnot representative of a full year’s operation. EPA described inpreamble section VI how it incorporated this type of adjustment into its baseline.The example below illustrates where EPA first identified 2012 data from likely affectedunits that were not under construction(columns B & C),then identified under construction capacity (columns D and E), and then adjusted the baseline generation values up to reflect anticipated baselinegeneration values assuming a more representative full-year utilization for these units (columns F & G).Table 1. Example of Adjustment to 2012 Data4As described in the GHG Mitigation Measures TSD, the building blocks have differentassumed levels over the 2022-2030 time frame reflecting deploymentassumptions. Therefore, the rates described below vary by year and the amount of building block specified for that year.StillUnderAgencyReview–DonotQuote,Cite,orShare9A B C D E F G2012 Data for likely affectedunits (excluding underconstruction)Adjustment for Under ConstructionSourcesAdjusted BaselineCoalGeneration(TWh)NGCCGeneration(TWh)Under ConstructionCoal Capacity(MW)Under ConstructionNGCC Capacity(MW)Coal Generation(TWh)NGCCGeneration(TWh)North Carolina50.5715.068252,16554.9225.5215.06 TWh + 8784 hours*2,165 MW*55% capacity factor = 25.52 TWh5Step 2:Aggregate the adjusted historical emissions and generation toa regional level for coal steam, OG steam, and NGCC technology categories.EPA keeps the fossil steam sources separate from the NGCC sources atthis stage to evaluate BSERimpacts separately for each source category.This distinction is illustrated in Exhibit A above. These category-specific values become thebasis for calculating the category-specificperformance emission rates and subsequent state goals. All emission rates provided are on a net basis.Table 2. Regional BaselineA B C D E F GCoal NGCC OG SteamInterconnectionEmissions(1000 shorttons)NetGeneration(GWh)Emissions(1000 shorttons)NetGeneration(GWh)Emissions(1000 shorttons)NetGeneration(GWh)Eastern1,356,0501,230,434328,233734,54252,97974,2415AsdescribedinthepreamblesectionVI,EPAestablisheda55%capacityfactorasrepresentativeofnewNGCCunits(60%fornewcoal)basedonareviewofutilizationpatternsforunitsthatcommencedoperationduringthepreviousfiveyears.The2,165capacityvaluereflectssummertimecapacityandincludestheL.VSuttonPlantwhichwasalsounderconstruction.8,784hoursareusedinsteadof8,760tobeconsistentwiththenumberofhoursinthe2012leapyearforwhichthebaselineispremised.TheunderconstructioncoalcapacityincolumnDreflectsCliffside6whichcommencedoperationin2012,sowasclassifiedasunderconstructionpercommentrecommendation.StillUnderAgencyReview–DonotQuote,Cite,orShare10Step 3:Identify category-specific baseline emission rates for fossil steam and NGCCFossil steam sources include both coal steam and oil/gas steam affected sources, whose datais combined to arrive at a fossil steam emission rateand generation total for each interconnection.This emission rate (in column H) reflects the sum of coal emissions from columnB and O/G steamemissions from column F divided into the baseline generation foreach technology from columns C & G. Because the BSER involvesbothreductions in emissions intensity of sources (e.g., heat rate improvements) and reductions in generation of sources (e.g., shifting from fossil torenewable generation), the baseline emission rate and generation foreach technology source category are utilized to assess thepotential impact ofthe building blocks.Table 3. Baseline Category-specific Emission Rates and Generation.A B C D E FGH I J KCoal NGCC OG SteamFossil SteamNGCCInterconnectionEmissions(1000shorttons)NetGeneration(GWh)Emissions(1000shorttons)NetGeneration(GWh)Emissions(1000shorttons)NetGeneration(GWh)EmissionRate(lb/MWh)NetGeneration(MWh)EmissionRate(lb/MWh)NetGeneration(MWh)Eastern1,356,0501,230,434328,233734,54252,97974,2412,1601,304,674894734,542Eastern Fossil Steam Rate= (coal emissions + OG emissions) =Eastern Fossil steam rate= 1,356,050,000 tons +52,9796,000 tonsCoal gen + OG gen (1,230,434 GWh + 74,241 GWh)Eastern NGCC Rate= NGCC emissions =Eastern NGCC Rate= 328,233 tonsNGCC gen 734,542 GWhStep 4:Calculate regional fossil steam emission rate resultingfrom building block 1 heat rate improvement (HRI).After this baseline data is aggregated for each region, the EPA begins to adjust some of the data values to reflect each building block element ofBSER. The EPA assumes a 2.1% HRI in the Western Interconnection,a 2.3% HRI in the Texas Interconnection, and a 4.3% HRI in the EasternInterconnection applied only to the coal steam fleet. This isreflected by adjusting the coalemissions down by the region-specific HRI percentageand leaving the generation level unchanged.Subsequently, the fossil steam rate forthe region is calculated by adding the adjusted coal emissionsStillUnderAgencyReview–DonotQuote,Cite,orShare11post HRI assumption (column H) with the baseline OG steam emissions(column D) and dividing into the sum of the coal steam (column C) andOG steam generation (column E). There is nochange in the NGCC rate from this step.Table 4. Adjusted Fossil Steam Rate Reflecting Building Block 1A B C D E F G H IBaseline Coal Baseline OG SteamBaselineFossilSteamBB1InterconnectionEmissions(1000short tons)NetGeneration(GWh)Emissions(1000short tons)NetGeneration(GWh)EmissionRate(lb/MWh)BB1HRILevelPost BB1CoalEmissions(1000 shorttons)Fossil SteamEmission RatePost BB1(lb/MWh)Eastern1,356,0501,230,43452,97974,2412,1604.3%1,297,7402,071When the technology emission rate is recalculated with building block 1 reflected in the adjustment to the region’s coal emissions, the region’sfossil steam emission rate drops below its baseline value. Notethat the fossil steam rate reflects the aggregation of both coal and OG steam data.This is not the final category-specific performance rate, ratherit is an adjusted emission rate reflecting the application ofbuilding block 1 beforemoving on to the remaining building blocks. The bold areas in the equation below reflect the values that are adjusted from their baseline level atthis step. In this example, the fossil steam rate drops from abaseline value of 2,160 lb/MWh to 2,071 lb/MWh after building block 1 application.Eastern fossil Steam Rate= (coal emissions *(1-HRI%) + (OG emissions) =Eastern fossil steam rate= (1,356,050,000tons)*0.957+52,979,000tonsCoal gen + OG gen (1,230,434GWh+74,241GWh)Step 5:Calculate regional fossil steam and NGCCgeneration levels resulting from buildingblock 3 (incremental RE generation)Building Block 3 is based on lower-emitting generation replacing higheremitting generation. The GHG Mitigation Measures TSD describes howthe incremental RE generation potential for each region was derived. As explained in the TSD, the BB3 potential is defined asonly incrementalRE generation (incremental relative to 2012 levels). Therefore the computation of category-specific performance rates and state goals for the finalrule only reflect this incrementalRE total. All incremental BB3 RE is assumed to emit zero tons of CO2.StillUnderAgencyReview–DonotQuote,Cite,orShare12For this final rule, EPA assumes that BB3 incremental generation replaces existing fossil generation from the baseline levels.The replacementimpact on each technology category is estimated on a pro-rata basis where the incremental BB3 generation is first identified (column F), and thenapportioned to replace either fossil steam (column D * column F =column I) or NGCC generation (column E * column F = column J)based onthe share of baseline generation each technology category represents.For example, if a region had 100 MWh of potential BB3 generationidentified, and baseline fossil steam accounted for 70% of the region’s generation from affected units and NGCC accounted for 30%, then the 100MWh of incremental RE identified would be assumed to replace 70MWh of fossil steam generation and 30 MWh of NGCC generation. The fossilsteam generation and NGCC generation are decreased by the amount of RE MWh apportioned to that technology (column B – column I)and(column C – column J). The total baseline generation (columns B& C) equals the total remaining generation and renewable generation (columnsF, H, I, and J) reflecting that replacementof fossil sources by incremental RE generation.Table 5. Adjusted Fossil Steam and NGCC Generation Reflecting Building Block 3A B C D E F G H I JBaseline Gen.BB3InterconnectionFossilSteam NetGeneration(GWh)NGCCNetGeneration(GWh)FossilSteam Shareof TotalFossil Gen.NGCCShare ofTotalFossilGen.PotentialBB3(GWh)RemainingFossilSteam(GWh)RemainingNGCCGen(GWh)BB3ReplacingFossil Steam(GWh)BB3ReplacingNGCC(GWh)Eastern1,304,674734,54264%36%438,4451,024,161576,611280,513157,931Eastern Fossil Steam Gen= Baseline Fossil Steam Generation - (Potential BB3 Gen x fossil steam share of total fossil generation)Eastern Fossil steam Gen= 1,304,674 – (438,445 * 64%) = 1,024,161GWhEastern NGCC Gen= Baseline NGCC Generation - (Potential BB3 Gen x NGCC share of total fossil generation)Eastern NGCC Gen = = 734,542 – (438,445 * 36%) = 576,611 GWhStillUnderAgencyReview–DonotQuote,Cite,orShare13Step 6:Calculate regional fossil steam and NGCC generation resulting from building block 2 (incremental NGCC generation)The “Remaining NGCC Generation” field in column C of the belowtable indicates that there is less NGCC generation – relative tobaseline levels- following building block 3 incorporation due to the assumption that some of the incremental RE would replace baseline NGCC generation.Moreover, there is significantly less generation than the potential identified in building block 2 that reflects a 75% utilization. If onlyimplementing building block 3, the NGCC generation levels would be assumed to decrease under a pro-rata replacement approach.However, inthe GHG Mitigation Measures TSD, the EPA described the abatement potential of replacing higher emitting fossil steam generationwith loweremitting gas generation, identified as building block 2. This step of the rate calculation captures the change in source-category generation levelsassociated with building block 2 potential ofa 75% potential utilization for the NGCC fleet.To incorporate building block 2, the regional NGCC fleet summertime capacity is multiplied by8,784 hours (the number of hoursin the 2012year) and then by 75% to get total potential net NGCC generation at a 75% capacity factor (column D). However, this 75% capacity factorrepresents a generation ceiling, and the region’s NGCC generation isonly adjusted up to this ceiling to the extent -that suchNGCC generationincreases can replace remaining fossil steam generation.6Note that the combined remaining fossil steam and NGCC generation from columns Fand G in this table reflect the remainingfossil steam and NGCC generation total afterBB3 (columns B and C). Moreover, columns F and Gcombined with the RE potential assigned to each technology in columns I and J in the previous table sum to the total baseline generation assumedfor each region.Table 6. Adjusted Fossil Steam and NGCC Generation Reflecting Replacement by Building Block 3 and Building Block 2GenerationA B C D E F GPost BB 3BB2RegionRemainingFossilSteam(GWh)RemainingNGCCGen(GWh)NGCC Potentialat 75% CF(GWh)Difference between NGCCgeneration levels at 75%utilization and Post BB3NGCC levels (GWh)RemainingFossil Steam(GWh)RemainingNGCC Gen(GWh)Eastern1,024,160576,611987,956411,345612,816987,9566Theceilingintheinterimperiodislessthanthe75%utilizationlevel.TheBB2deploymentscheduleisdiscussedintheGHGMitigationMeasuresTSD.StillUnderAgencyReview–DonotQuote,Cite,orShare14In the above example, NGCC generation is adjusted upwards by approximately 411 TWh (column E) to 987,956 TWh (column G) (whichequalsthe NGCC fleet generation at 75% utilization) and the fossil steam generation is adjusted down by that same amount (column B -column F).Eastern Fossil Steam Gen= Post BB3 fossil steam gen. - (NGCC Potential at 75% CF – Post BB3 NGCC Gen)7Eastern Fossil steam Gen= 1,024,160 – (987,956 – 576,611) = 612,816 GWhEastern NGCC Gen = Post BB3 NGCC gen +(NGCC Potential at 75% CF – Post BB3 NGCC Gen)Eastern NGCC Gen = 576,611+ (987,956 – 576,611) = 987,956 GWhStep 7:Determine the adjusted category-specific performance rates for each region.Step four estimated the category-specific emission rates post BB1. Steps five and six estimated the category-specific generation levels postbuilding block 3 and 2, respectively. Combining the adjusted emission rates with the adjusted generation allows EPA to calculate a categoryspecific adjusted emission rate reflectingthe expression of BSER onto the baseline.For the regional fossil steam rate, EPA first calculates the numerator. EPA multiplies the fossil steam emission rate from step four (column F)(reflecting the heat rate improvement) by the remaining fossilsteam generation following step six (column J). For BB3, all replacementgeneration was assumed to equal zero so no numerator adjustment was made. As described in the preamble, EPA also captures a portion of the7If(NGCCPotentialat75%CF–PostBB3NGCCGen)isgreaterthanpostBB3fossilsteamgen,thenthefossilsteamgenamountisadjustedtozeroandtheNGCCGenamountisincreasedbythePostBB3fossilsteamgenerationamountthatitreplaced.StillUnderAgencyReview–DonotQuote,Cite,orShare15NGCC generation in the fossil steam ratereflecting the incremental BB2 potential;8this incremental NGCC generation is defined as the amount ofremaining NGCC subsequent to both blocks2 and 3 (column K) minus the amount ofNGCC generation in the baseline (column E).9This amountof NGCC generation is multiplied by the NGCC emission rate from stepthree (column C) to get the amount of incremental NGCC emissionsassigned to the fossil steam emission rate as part of BB2.These emissions from fossil steam sources along with emissions from its replacement sources are then divided by the total amount of remainingfossil steam generation, the RE generation assigned to fossil steam,and the incremental NGCC defined above. This generation is the sum of 1)remaining fossil steam generation post step six (column J), 2)amount of RE generation assigned to fossil steam generation (column H), and 3) theamount of NGCC generation defined above (column K-column E). Dividing this total emissions level by the total generation levels results in aregional fossil steam emission rate reflective of BSER.For the regional NGCC emission rate, EPA performs a similar operation. The NGCC generation post step six (column K) is multiplied by theNGCC baseline emission rate from step three (column C) to estimate the total amount of NGCC emissions post BB3 and BB2. Theseemissionsare then divided into the sum of the NGCC generation post step six (column K) and the amount of BB3 generation apportioned to NGCCgeneration in step five (Column I).10This regional NGCC rate reflects the adjusted NGCC rate reflecting BSER.8As described in the preamble, EPA reflected the incremental NGCC generation (and corresponding emissions) in the fossil steamrate source category rate andcreated a parallel compliance structure for quantifying NGCC ERCs which fossil steam sources may use in compliance. This was necessary to create a fossilsteam source-category rate that reflected generation shifting due to BB2.9EPA also considered quantifying the amount of NGCC generation assigned to fossil steam generation as post step 6 levels minuspost step 5 levels which wouldhave resulted in a lower fossil steam rate. However, this approach would not have resulted in a different stringency because asimilar adjustment would be madewhen measuring and quantifying NGCC ERCs available for compliance. In other words, there would be a lower rate, but more credits available. EPAdetermined that measuring incremental NGCC generation to include in the fossil steam rate was more appropriately done using a baseline level (premised onhistorical generation) as it would facilitate appropriate measurement and identification of NGCC ERCs. EPA took the more conservative approach of definingthe amount of NGCC generation and emissions reassigned to the fossil steam rate as the difference between post BSER levels andbaseline values to ensureconsistency with potential ERC measurement and crediting from a historical baseline level and help ensure that enough ERCs would be available if sources chosethat compliance route. Therefore, theremaining fossil steam and NGCC generation levels appropriately reflect the full building block two and three potential,and the portion of the NGCC emissions and generation levels included in the fossil steam rate appropriately reflect the historical measurement basis for ERCs.10The full NGCC generation (and corresponding emissions) expected under the BSER calculation is included in the NGCC rate, eventhough a portion of it isalso reflected in the fossil steam rate. Failing to do so would leave the NGCC sources with a lower rate than what is expected post BB2 and BB3. Keeping theStillUnderAgencyReview–DonotQuote,Cite,orShare16Eastern Fossil Steam Gen= (Post BB3&2 fossil steam gen x Post BB1 fossil steam emission rate)+(Incremental NGCC Generation x baseline NGCC rate)(Post BB3&2 fossil steam gen + BB3 generation replacing fossil steam + incremental BB2 generation)Eastern Fossil steam Gen= (612,816 GWh * 2,071lb/MWh) + ((987,956 GWh - 734,542 GWh)*894 lb/MWh)612,816 GWh + 280,513 GWh + (987,956 GWh - 734,542 GWh)Eastern NGCC Gen = (Post BB3 NGCC gen x NGCC baseline rate)(Post BB3 NGCC gen + BB3 generation replacing NGCC gen)Eastern NGCC Gen = (987,956 GWh * 894 lb/MWh)(987,956 GWh + 157,931 GWh)Step 8:Identify the least stringent regional rate as the emission performance rate for the technology source categoryAfter completing a regional assessment of building block potential impact on source category-specific rates, EPA evaluated thefossil steam andNGCC rate for each region to identify the region with the leaststringent emission rate. The least stringent (i.e., the highest) fossil steam rate andthe least stringent NGCC emission rate among the three regions are identified and used to establish the source-category emission performancerates described in the preamble.full NGCC generation amount in the NGCC rate, inspite of its replacement impact on fossil steam, recognizesthe dual role it has in terms of complianceresponsibility as an affected source and a mitigation measure under building block 2 that that can offset fossil steam generation.A B C D E F G H I J K LMInterconnectionFossilSteamRateNGCCRateFossilSteamGen NGCCGenFossilSteamRateNGCCEmissionRateBB3ReplacingFossilSteamBB3ReplacingNGCCRemainingFossilSteamRemainingNGCCGenFossilSteamRateNGCCRatelb/MWh lb/MWh GWh GWh lbs/MWh lbs/MWh GWh GWh GWh GWh lb/MWh lb/MWhEastern 2,160 894 1,304,674734,5422,071894 280,513157,931612,816987,9561,305771Table7.AdjustedFossilSteamandNGCCGenerationRatesReflectingallThreeBuildingBlocksBB1HRI BB3ApportionedtoFossil PostBB2 FinalRatesBaselineStillUnderAgencyReview–DonotQuote,Cite,orShare17Table7.IdentifyLeastStringentRateforEachTechnologyCategory(2030)Adjusted RatesRegionFossil Steam Rate(lb/MWh)NGCC Rate(lb/MWh)Eastern Interconnection 1,305 771Western Interconnection 260 660Texas Interconnection 450 754The completion of the previous steps results in a 2030 emission performance rate for each source category. However, as described in the GHGMitigation Measures TSD, the BB2 and BB3 assumed potential changes for each year from 2022through 2030. Thus this procedure is repeatedfor each of those years using the corresponding BB2 and BB3 assumptionsfor that year that reflect the deployment rate for those technologies.11This results in a set of decreasing annual adjusted emission rates for the years 2022-2029. However, this rulemaking issues category-specificemission performance rates for an interim anda final rate. Thus, the interim rate isderived by averaging the annual adjustedemission rates for2022-2029. Once the interim and final rates are determined,EPA rounds any fractional number up to the nearest integer.For the Final CPP Rule category-specific rates:Interim category-specific rate – Average of the adjusted yearly emission rates for the period 2022-2029Final category-specific rate– The 2030 emission rate (as calculated above) becomes the state goal for 2030 and each year thereafter11Theregionwiththeleaststringentratecandifferbyyear.Forthefossilsteamrate,theEasternInterconnectionisthelimitingregioninallyears.FortheNGCCrate,theTexasInterconnectionisthelimitingregionfor2022through2026,andtheEasternInterconnectionisthelimitingregionfor2027through2030.StillUnderAgencyReview–DonotQuote,Cite,orShare18The assumptions used to arrive at the category-specific performance rates are not prescriptive of necessary actions that sources, states, or regionsmust take. As described in the preamble, these values are used only for calculating the emission performance rates and state goals. A state is notrequired to base its state plan on using the same set of measures or the same amount of any measure reflected in these assumptions. Likewise, thestate plan, not these assumptions, determines the range of availablemeasures a source may or must use to comply with the standards ofperformance established for it in the state plan and the extent to which the source may or must rely on any individual measure.5. Methodology for Converting Category-specific Rates into State Emission Rate GoalsTo calculate a state goal in the final CPP, EPA estimates the affected fleet rate for a state if all likely affected baseline sources meet the respectivecategory-specific emission performance rates presented above while generating at the same baseline generation total. For eachstate, EPAcalculated a weighted average of the category-specific fossil steam rate and the category-specific NGCC using the state’s baseline generationlevels for each source category. These aggregate state rates reflect the total emission rate from likely affected units in thestate if they operated atbaseline generation levels while meeting the category-specific rates.For example, the nation-wide 111(d) source category rates determined at the regional level were 1305 lb/MWh and 771 lb/MWh respectively. Thestate of Arizona had baseline affected fossil generation consisting of 25.37 TWh of fossil steam generation and 26.78 TWh of NGCC generation.Arizona’s 2030 state goal metricwould be calculated as follows:Arizona state goal = (Fossil steam source category rate * Fossil steam baseline share of affected generation) + (NGCC source category rate *NGCC baseline share of affected generation)Arizona State Goal = (48.65%*1,305) + (51.35%*771) =1,031 lb/MWh.2022 2023 2024 2025 2026 2027 2028 2029 2030 Interim FinalFossilSteam 1,7411,6811,5921,5461,5001,4531,4041,3551,3041,5341,305NGCC 898877855836817798789780771832771AnnualCategory‐specificRatesStillUnderAgencyReview–DonotQuote,Cite,orShare19EPA uses the representative baseline and calculations described aboveto derive category-specific rates and state emission rategoals. Oncecalculated, the system-wide impacts and feasibility of these state goals are further tested using EPA’s power sector modeling.6.Methodology for Converting State Emission Rate Goals into State Mass GoalsThe calculation of affected EGU mass goals includes two components. First, it includesthe emissions associated with each state’s emission rategoal, which is the product of the state emission rate goal and2012 affected EGU generation. Second, it includes the emissionsassociated with theability of affected EGUs to expand output under rate-based compliance if they deployed the amount of RE quantified under building block 3 thatwas not captured in the ultimate quantification ofthe source category-specific performance rates.The procedure for quantifying this level ofbuilding block 3 generation applies to the values and calculations in Appendix 4. Below is an excerptfrom Appendix 4 that displays building block 3 data and goal rates for 2030:Columns V and W in Appendix 4 display the regional fossil steamand NGCC rates after the full application of the building blocks. Any regionalrates lower than the source category-specific performance rates(1,305 lbs/MWh for fossil steam and 771 lbs/MWh for NGCC) indicate that theregion contains more building block 3 generation potential than is necessary toachieve the source category-specific performance rates in thatregion. In order to quantify the amount of building block 3 generation potential in each region that is beyond the level needed to meet the sourceStillUnderAgencyReview–DonotQuote,Cite,orShare20category-specific performance rates, the EPA designed an optimization algorithm to reduce the region’s building block 3 potential (column N)until the regional rate was equal to the corresponding source category-specific performance rate. The optimization algorithm is designed to:Minimize ‘Potential BB3’ (column N) in each region12for each year by changing values for ‘Potential BB3,’ ‘Fossil Steam Share of TotalFossil,’ and ‘NGCC Share of Total Fossil’ (columns L and M).13Subject to the following constraints:o‘Fossil Steam Share of Total Fossil’ and‘NGCC Share of Total Fossil’ must sum to 100% and neither can exceed 100% nor bebelow 0%. The ‘Share of Total Fossil’ values control how the total amount of building block 3 generation is assigned to eachsubcategory in each region. For example, an 80% value under ‘Fossil Steam Share of Total Fossil’ indicates that 80% of allbuilding block 3 generation in thatparticular region is being applied to the fossil steam subcategory.14o‘Fossil Steam Rate’ must be less than or equal to the source category-specific performance rateo‘NGCC Rate’ must be less than or equal to the source category-specific performance rateAfter minimizing ‘Potential BB3’ for each region according to the procedure described above, the updated Appendix 4 values are:12EachrowisadifferentBSERregion–row7istheEasternInterconnection,row8istheWesternInterconnection,androw9istheTexasInterconnection.13ThefossilsteamandNGCCsharesoftotalgenerationareallowedtovaryinthiscomputationbecauseunderrate‐basedcompliance,theREquantifiedunderbuildingblock3thatwasnotcapturedinthesourcecategory‐specificperformanceratecouldbedeployedandclaimedforcompliancebyeithersteamorNGCCunits,atanypossiblebalanceofgenerationacrossbothsteamandNGCCunits.14NotethatevenwhentheminimizationprocedureincreasestheshareofpotentialBB3generationassignedtoasubcategoryofaffectedEGUs,thetotalamountofbuildingblock3generationassignedtothatsubcategory(i.e.,potentialBB3generationmultipliedbytheshare)isalwaysreducedfromtheoriginalvalue.StillUnderAgencyReview–DonotQuote,Cite,orShare21The difference in ‘Potential BB3’ across all regions for 2030 is166,203,128 MWh, obtained by subtracting the minimum potentialneeded to meetthe source category-specific performance rates in each region (539,826,984 MWh) from the total potential identified in the quantification ofbuilding block 3 (706,030,113 MWh)15. It is this difference in “Potential BB3” that was notcaptured in the ultimate quantification of the sourcecategory-specific performance rates, and that affected EGUs could deploy to expand output and associated emissions under rate-based compliance.Note that the Eastern Interconnection (row 7), as the region whose fossil steam and NGCC rates determined the source category-specificperformance rates in 2030, requires all of the building block 3 generation potential quantified for that region to achieve thefinal goal rates.However, because the final rule would allow sources in the Eastern Interconnection to claim RE from any region for use in compliance, therelevant value for this computational procedure to quantify emissions for mass goals (across all states) is the national-leveldifference in “PotentialBB3” across all regions.Note that in the Texas Interconnection (row 9), the fossil steam rate after the reduction in “Potential BB3” has increased from237 lbs/MWh to1,096 lbs/MWh, which is still below the source category-specific performance rate of 1,305 lbs/MWh. However, the remaining differencebetween the regional fossil steam rate and the source category-specific performance rate for steam cannot be addressed by yet higher reduction inthe region’s “Potential BB3”, because the region would still needall of the remaining “Potential BB3” generation to satisfy the source categoryspecific performance rate for NGCC (as reflected by the “100%” value in column M). The 1,096 lbs/MWh steam rate result from this computation15AllvaluesroundedtothenearestMWh;forexactvaluesrefertoAppendix5.StillUnderAgencyReview–DonotQuote,Cite,orShare22for the Texas Interconnection serves only as an indicator that the computation did not violate the criteria laid out above forcalculating the buildingblock 3 potential that was not captured in the source category-specific performance rates;this value is not used in any computation, including thecomputation below to quantify emissions associated with the ability of affected EGUs toexpand output if they deployed this building block 3potential.The total amount of building block 3 generation not captured inthe source category-specific performance rates for each year isdisplayed below:The next step is to apportion this generation to states on thebasis of each state’s 2012 share of affected EGU generation. The state-levelgeneration total can then be converted into a mass adjustment that reflects the ability of affected EGUs to increase their ownoutput if deployingthis building block 3 generation under rate-based compliance:Mass Adjustment = State Emission Rate Goal * BB3 GenerationNot Captured in Source Category-Specific Performance Rates * 2The mass adjustment reflects the ability of affected EGUs toprocure incremental RE to increase their own generation and emissions if subject toan applicable rate-based standard. Inthat rate-based compliance scenario, every zero-emitting MWh added to the denominator ofan EGU’seffective emission rate would enable that EGU to add another MWh of generation with twice the emissions intensity of the applicable rate-basedstandard, because the average intensity ofthat emitting MWh combined with the zero-emitting MWh would then equal the applicable rate-basedstandard and thus maintain that EGU’s compliance.1616TheassumptionthatoneMWhofincrementalREenablesoneMWhofadditionalaffectedEGUgenerationisconsistentwiththehistoricalperformanceofaffectedsourcesovertimeaswellasexpectedfuturedemandlevels.StillUnderAgencyReview–DonotQuote,Cite,orShare23As an example, an EGU in Arizona in 2030, if subject to (and already compliant with) an emission rate standard equal to the Arizona state goal in2030 of 1,031 lbs/MWh, and assuming it wasalready generating 1,000 MWh for sake of illustration, would be able increase emissions by 2,062lbs, assuming the procurement an incremental MWh of REenables an additional MWh of affected EGU output:ݏ݈ܾ	1,031,000ܹ݄ܯ	1,000൅ݏ݈ܾ	0ܹ݄ܯ	1൅ሺ2	ݔ	1,031ሻݏ݈ܾܹ݄ܯ	1ൌݏ݈ܾ	1,033,062ܹ݄ܯ	1,002ൌݏ݈ܾ	1,031ܹ݄ܯIn this illustrative example, this EGU wasable to remain compliant at the 1,031 lbs/MWh rate while adding a MWh with emissionsof 2,062 lbsand acquiring an incremental MWh of zero-emitting RE.17This example shows why the mass adjustment procedure assumes that the buildingblock 3 potential not captured in the source category-specific compliance rates could allow additional emissions of twice the emission intensityrepresented by the applicable state goal.The final step in calculating an affected EGU mass goal is to simply add the mass associated with the state emission rate to the mass adjustmentdescribed above, using this equation:.Affected EGU Mass Goal = (State Emission Rate Goal * State’s 2012 Affected EGU Generation) + (State Emission Rate Goal * StateApportioned BB3 Generation Not Captured in Source Category-Specific Performance Rates * 2)As an illustration, Arizona’s 2030 affectedEGU mass goal would be calculated as follows:Arizona Affected EGU Mass Goal for 2030 = (1,031 lbs/MWh * 52,154,061 MWh) + (1,031 lbs/MWh * 3,192,419 MWh * 2) = 27,369,757metric tonsAffected EGU mass goal calculations and resultsare available for each state in Appendix 5.17Theemissionsquantifiedthroughthisparticularmassadjustmentapproachcouldalsorepresentavarietyofsource‐specificandfleet‐wideactionsthatcouldresultifaffectedEGUsprocureincrementalREbeyondwhatisrequiredtodemonstratethesourcecategory‐specificperformancerate.StillUnderAgencyReview–DonotQuote,Cite,orShare247.APPENDIXAppendix 1 – Underlying 2012 unit-level baseline inventory and dataSee “Appendix 1-unit-level data” worksheet in the Excel attachment titled “Baseline, Category-specific rates, and State GoalQuantification”Appendix 2 - Units that commenced operation post 2011, but began construction prior to 1/8/14See “Appendix 2 – Under construction” worksheet in the Excel attachment titled “Baseline, Category-specific rates, and State GoalQuantification”. Note, this is largely a subset of the Appendix 1 worksheet.Appendix 3 – Underlying state-level data, adjustmentsSee “Appendix 3 – state-level data” worksheet in the Excel attachment titled “Baseline, Category-specific rates, and State GoalQuantification”Appendix 4 – Computation of the category-specific performance rates (Interim and Final)See “Appendix 4 – category-specific calc.” worksheet in the Excel attachment titled “Baseline, Category-specific rates, and StateGoal Quantification”Appendix 5 - Computation of the state goal (Interim and Final)See “Appendix 5 – State Goals” worksheet inthe Excel attachment titled “Baseline, Category-specific rates, and State GoalQuantification”StillUnderAgencyReview–DonotQuote,Cite,orShare25Appendix 6 –State GoalsState InterimGoal FinalGoal State InterimGoal FinalGoalAlabama 1,1561,018Nebraska 1,5221,296Arizona 1,1731,031Nevada 941855Arkansas 1,3041,130NewHampshire 946858California 906828NewJersey 885812Colorado 1,3621,174NewMexico 1,3251,146Connecticut 851786NewYork 1,024918Delaware 1,022916NorthCarolina 1,3111,136Florida 1,026919NorthDakota 1,5341,305Georgia 1,1971,049Ohio 1,3831,190Idaho 831771Oklahoma 1,2221,068Illinois 1,4551,245Oregon 963871Indiana 1,4511,242Pennsylvania 1,2581,095Iowa 1,5051,283RhodeIsland 831771Kansas 1,5181,293SouthCarolina 1,3371,156Kentucky 1,5091,286SouthDakota 1,3511,166Louisiana 1,2921,121Tennessee 1,4101,211Maine 841779Texas 1,1871,042Maryland 1,5101,287Utah 1,3681,179Massachusetts 901824Virginia 1,046934Michigan 1,3541,169Washington 1,110983Minnesota 1,4131,213WestVirginia 1,5341,305Mississippi 1,060945Wisconsin 1,3641,176Missouri 1,4901,272Wyoming 1,5261,299Montana 1,5341,305StillUnderAgencyReview–DonotQuote,Cite,orShare26Appendix 7 –Adjustments to state-level 2012 baseline dataSee the Excel attachment titled “State-level adjustments”Hydro adjustment– States suggested that 2012 was an abnormal year for hydro, and because of the predominance of hydro in their state, this alsomade it an outlier year for other generation technologies in thestate. EPA assessed this concern for all states using the following filters:1)Using EIA 2012 data, identify the % share of total generation coming from hydro in each state2)Using EIA 1990-2012 data, identify average hydro generation fora state from 1990-2012 and look at the % difference between 2012 hydrogeneration levels and the average hydro generation levels3)Estimate the increase in affected fossil generation that would occur if the difference between the average hydro year and the 2012 hydro yearwas replaced with generation from affected fossil generation.EPA determined that hydro intensive states (greater than 10% generation from hydro), that experienced an outlier year in 2012 (greater than 10%increase in hydro generation relative to observed average between1990-2012), and that would have their state’s affected fossilgenerationsignificantly affected when assuming average hydro generation levels(an adjustment > 5%) had baseline values that were sensitive to fluctuationsin hydro generation and thus increased the fossil generation inthe state from observed 2012 levels to reflect potential generation levels in anaverage hydro year.Unit-outage adjustmentAs explained in the Preamble Section VI, EPA did not generallyview single unit-outages as problematic to its baseline for determining sourcecategory rates or state goals. As regional load levels did notchange subject to the unit outage, the decrease at a particularunit is generally offsetby the increase in generation from other unit(s) in the same state or region. Therefore, EPA views the regional and state-level aggregategeneration totals as robust against unit-level outages. However, it did test for outlier cases where the unit-level outage wassignificant enough topotentially have a large impact on the state goals that EPA provided in section VII. EPA assess this concern for all units byidentifying:1)Units where the generation in 2012 was less than 25% of its 2010 and 2014 totals collectively (signaling a significant outage).2)For units meeting the step 1 criteria, EPA identified those where the generation observed in the non-outage years of 2010 and 2014 yearswas greater than 10% of the state’s total generation (suggesting the replacement generation may be difficult to find in state).The only unit that met this criteria was the 900 MW Sherburne county unit 3 coal unit in Minnesota.EPA adjusted the state’s generation levelvalue up to reflect this unit operating in a typical year.StillUnderAgencyReview–DonotQuote,Cite,orShare27Appendix 8 – Category-specific emission rates under building block severabilitySee “Appendix 9” worksheet in the Excel attachment titled “BB Severability” which replicates EPA quantification of BSERassuming only building blocks 1 and 2StillUnderAgencyReview–DonotQuote,Cite,orShare28Appendix 9- Description of 111(d) baseline data sources and developmentIntroductionThis section describes the methodology used by the EPA to develop the state and region-level baselines used in the goal settingprocess.The 111(d) baseline analysis methodology is based largely on themethodology used to develop theEmissions and Generation Resource IntegratedDatabase (eGRID)18, with certain key differences, which are explained in AppendixA. The 111(d) baseline consists of emission rates in pounds ofCO2per megawatt-hour (MWh) of electricity generation. The baselineis constructed by matching electricity generation data reported to theEnergy Information Administration (EIA) by power plants on forms EIA-86019and EIA-92320with data on CO2emissions submitted by powerplants to the EPA under 40 CFR Part 75.21The process of matching emissions data to generation data and categorizing the EGUs is described in more detail below. The differences betweenthe baseline released for theClean Power Plan Proposed Rule22and the Final Rule are discussed in Section C.Data SourcesThe key data sources used in the construction ofthe 111(d) baseline are listed in Table 1.Table 1. Key data sources used to construct the 111(d) baseline.DataSourceKeyDataEIA‐860Containskeyidentifyinginformation,includingnameplatecapacity,summercapacity,unitoperationalstatus,primemovertype,andfueltype,aswellastheplant’snameandlocation.18U.S.EnvironmentalProtectionAgency,CleanAirMarketsDivision,EmissionsandGenerationResourceIntegratedDatabase(eGRID),availableathttp://www.epa.gov/cleanenergy/energy‐resources/egrid/19EnergyInformationAdministration,FormEIA‐860,availableathttp://www.eia.gov/electricity/data/eia860/20EnergyInformationAdministration,FormEIA‐923,availableathttp://www.eia.gov/electricity/data/eia923/2140CFRPart75,availableathttp://www.ecfr.gov/cgi‐bin/text‐idx?tpl=/ecfrbrowse/Title40/40cfr75_main_02.tpl22https://www.federalregister.gov/articles/2014/06/18/2014‐13726/carbon‐pollution‐emission‐guidelines‐for‐existing‐stationary‐sources‐electric‐utility‐generatingStillUnderAgencyReview–DonotQuote,Cite,orShare29EIA‐923Containsinformationonnetelectricitygenerationandfueluseatthegeneratorlevel,boilerlevel,and/orprimemoverlevel.EPAPart75DataContainsinformationonCO2emissionsandheatinput.EPAPart75emissionsdataispresentedattheunitlevel,wheretheunitisdefinedasthefossilfuel‐fireddevice,whichcouldbeaturbineorboiler(includinganyheatrecoverysteamgenerators(HRSG),ifpresent).EIAdataongenerationandfueluseispresentedatthegenerator,boiler,primemover,andplantlevels.The111(d)baselineanalysismethodologyinvolvesmatchingEPAemissionsdataattheunitlevel(i.e.emissionsfromboilersorturbines),withEIAgenerationdataatthegeneratorlevel.However,thedatadonotalwaysmatchcleanlybetweenthetwodatasources.WhilebothdatasourcesidentifyplantsusingtheOfficeofRegulatoryInformationSystemsPlantLocationcode(ORIScode),theEIAdataidentifiesgeneratorswithageneratorID,andtheEPAdataidentifiesunitswithaunitID.TheORIScodegenerallymatchesbetweendatasources,butthegeneratorIDfromEIAmustbematchedtotheunitIDfromEPAbasedonORIScode,nameplatecapacity,fueltype,primemovertype,andyearofoperation.Furthermore,becausetherearedifferentregulationsgoverningwhichplantsandunitsmustreportdatatotheEIAandtheEPA,theremaybedifferentnumbersofunitsateachplantbetweenthetwodatasets.Additionally,existingandproposedplantsarerequiredtosubmitForms860and923totheEIAiftheplant’stotalgeneratornameplatecapacityis1MWorgreateranditiscapableofsupplyingpowertoordrawingpowerfromtheelectricitygrid.PlantsarerequiredtosubmitemissionsdatatoEPAunder40CFRPart75,generallyifaunitservesageneratorwithanameplatecapacityofgreaterthan25MWwhichproduceselectricityforsale.Baseline Construction ProcessAsdiscussedabove,theconstructionofthe111(d)baselineinvolvesmatchingnetelectricitygenerationdatafromEIAwithdataonCO2emissionsfromEPA.Alloftheexisting,proposed,andretiredunitslistedintheEIA‐860serveasthefoundationforthebaseline,establishingtheuniverseofunits.ElectricitygenerationandCO2emissionsareaddedtothisfoundationusingtheEIA‐923andEPAPart75data.Electricity GenerationForanygivenpowerplant,dataonnetelectricitygenerationfromtheEIA‐923maybeavailableattheunitlevelforsomeunitsorattheprimemoverlevelforotherunits.Ifunit‐leveldataareavailable,thedataareusedinthebaseline.Ifdataareonlyavailableattheprimemoverlevel,thenthesedataaredistributedproportionallybasedonnameplatecapacitytotheunitsatthatplantwiththatprimemover.StillUnderAgencyReview–DonotQuote,Cite,orShare30CO2EmissionsPart75emissionsdatafromEPAarematchedtothegenerator‐leveldatafromEIA.Whenunitscanbematchedexactlybetweenthetwodatasources,theunit‐levelemissionsareusedinthebaseline.WhenoneunitfromtheEPAdataisassociatedwithmorethanonegeneratorfromtheEIAdata(e.g.emissionsfromaboilerthatsuppliessteamtomorethanonegenerator),orifunitsatagivenplantcannotbematchedexactlybetweenthetwodatasources,thetotalemissionsmaybedistributedtogeneratorsbasedontheproportionofnameplatecapacity.Combinedcycleunitsareconsideredasinglesystemandemissionsfromallcomponentsaresummedanddistributedtoallgeneratorsbasedonproportionofnameplatecapacity.BecausetherearedifferentregulationsgoverningwhichplantsandunitsreportdatatoEIAandEPA,therearemoreunitslistedintheEIAdatathanintheEPAdata(forexample,aunitunder25MWmaynotberequiredtoreportemissionsdataunderPart75).ToestimateemissionsforunitsthatarelistedintheEIAdatabutnotintheEPAdata,afuel‐specificemissionfactorismultipliedbyunit‐levelfuelconsumption(millionBritishthermalunits(mmBtu)).ThismethodisbasedonthemethodologyusedbytheIntergovernmentalPanelonClimateChange(IPCC)23andinEPA’sInventoryofU.S.GreenhouseGasEmissionsandSinks.24CO2emissionfactorsforyear2012areobtainedfromtwosources:EPA’sInventoryofU.S.GreenhouseGasEmissionsandSinks:1990‐2012,andtheemissionfactorsusedintheGreenhouseGasReportingProgram,whicharelistedin40CFRPart98.25Theemissionsfactorsusedinthe111(d)baselineanalysisarelistedinSectionD.ThefueluseisbasedonheatinputdatafromEPAPart75data,boiler‐leveldatafromtheEIA‐923,andprimemoverleveldatafromtheEIA‐923.Dataareselectedpreferentiallyinthatorder(e.g.ifheatinputdataareunavailablefromEPA,thenboiler‐leveldatafromEIAareused).Data CorrectionsWhenCO2emissionsfromEPAarematchedwithnetelectricitygenerationdatafromEIA,anemissionsrate(lbs.CO2perMWh)iscalculated.Ifthecalculatedemissionsrateisunreasonablyhigh(>10,000lbs.CO2perMWh)orunreasonablylow(<500lbs.CO2perMWh)foraunit,thenetelectricitydataarecalculatedbasedongrossgenerationdatafromEPA.BecausetheEPAdatacontaingrossgenerationratherthannet23IPCC,2007:TheIntergovernmentalPanelonClimateChange(IPCC),“2006IPCCGuidelinesforNationalGreenhouseGasInventories”,volume2(Energy),April2007.http://www.ipccnggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_2_Ch2_Stationary_Combustion.pdf24EPA,2014:U.S.EnvironmentalProtectionAgency,InventoryofU.S.GreenhouseGasEmissionsandSinks:1990‐2012,Washington,D.C.,April2014.http://epa.gov/climatechange/emissions/usinventoryreport.html25TCR,2014:TheClimateRegistry2014ClimateRegistryDefaultEmissionFactors,WashingtonDC,January10,2014.http://www.theclimateregistry.org/tools‐resources/reporting‐protocols/generalreporting‐protocol/StillUnderAgencyReview–DonotQuote,Cite,orShare31electricitygeneration,netgenerationmustbecalculatedbymultiplyinggrossgenerationbyaunit‐specificnet‐grossconversionfactor.26Incaseswhereanet‐grossconversionfactorisnotavailableforaspecificunit,thecalculationusesanet‐grossconversionfactorfromaplantinthesamestateandwiththesameprimemover.IftheEPAdatadonotincludegrossgenerationforaspecificunit,thecalculationusesdataongrossgenerationfromEIA.27Ifthiscorrectionstillresultsinanemissionsrategreaterthan10,000lbs./MWhorlessthan500lbs./MWh,thenthenetelectricitygenerationdataareleftunchangedandtheoriginalcalculatedrateisretained.Inaddition,forunitsthatreportnegativenetelectricitygeneration(forexample,thefacilityusesmoreelectricitythanitproduces)andCO2emissions,theelectricitygenerationisadjustedusinggrosselectricitygenerationdataasdescribedabove.Thiscorrectionisintendedtoavoidestimatinganegativeemissionsrate.Limitedadjustmentsarealsomadeforseverallikelyaffectedfacilitiesthathadreportedsummertimecapacitysignificantlygreaterthannameplatecapacity.Fortheseunits,EPAreplacedthesummercapacityvaluereportedinEIA860withthenameplatevalue.Inclusion CriteriaInordertocalculatethestate‐levelemissionrateforcoalsteamunits,naturalgascombinedcycleunits,andoilandgassteamunits,theindividualunitsarecategorizedaccordingtothenameplatecapacity,primemovertype,fuel,andoperatingstatus,asshownintable2.Table 2. Criteria for inclusion of units in the 111(d) baseline as likely affected EGUs.CategoryCodeCategoryInclusioncriteriaCOALSTCoalsteamunitsSteamturbineunits(primemover=ST)withcoalasprimaryfuelsource.Nameplatecapacitymustbegreaterthan25MW.26TheseconversionfactorsweredevelopedbyVentyx(nowcalledABBEnterpriseSoftware),aconsultingfirmthatprovidesinformationanddatarelatedtotheelectricitygenerationsector.ThefactorsaredevelopedusingNorthAmericanReliabilityCorporation(NERC)GeneratingAvailabilityDataSystem(GADS),whichcontainsdataongrossandnetgenerationforunitswithanameplatecapacitygreaterthan20MW.Thedataprovidedforthisanalysisareunit‐levelratiosofnetgenerationtogrossgeneration.27EIAsuppliedthegrossgenerationdataforasubsetofgeneratorstoEPA,asthesedataarenotpubliclyavailableintheEIA‐923data.StillUnderAgencyReview–DonotQuote,Cite,orShare32CategoryCodeCategoryInclusioncriteriaNGCCNaturalgascombinedcycleunitsCombinedcycleunitswithnaturalgasasprimaryfuelsource.Ifalloftheturbinecomponentsofthecombinedcycleunit(primemover=CT)haveanameplatecapacitygreaterthan25MW,thenallofthesteamcomponents(primemover=CA)areincluded,regardlessofwhethertheyhaveanameplatecapacitygreaterthan25MW.Otherwise,onlycomponentswithanameplatecapacitygreaterthan25MWareincluded.OGSTOilandgassteamunitsSteamturbineunitswithoilorgasasprimaryfuelsource.Nameplatecapacitymustbegreaterthan25MW.UCCoal–Commencedin2012Coalsteamunitsthatcommencedoperationsin2012UnitsthatwouldotherwisebeclassifiedasCOALST,butwhichcommencedoperationsin2012.DeterminationofwhentheunitcommencedoperationsisbasedontheEIA‐860andpubliccommentsonthe111(d)baselinedevelopedfortheProposedRule.UCNGCC–Commencedin2012NGCCunitsthatcommencedoperationsin2012UnitsthatwouldotherwisebeclassifiedasNGCC,butwhichcommencedoperationsin2012.DeterminationofwhentheunitcommencedoperationsisbasedontheEIA‐860andpubliccommentsonthe111(d)baselinedevelopedfortheProposedRule.UC‐CoalCoalsteamunitsthatareunderconstructionin2012or2013Unitsthatareunderconstructioninthedatayear(EIAunitstatus=U,V,orTS),butwhichwouldlikelybeconsideredCOALSTunitsifoperational.Forthe2012111(d)baseline,unitscanbelistedasUC‐Coaliftheyareunderconstructionin2012or2013.UC‐NGCCNGCCunitsthatareunderconstructionin2012or2013Unitsthatareunderconstructioninthedatayear(EIAunitstatus=U,V,orTS),butwhichwouldlikelybeconsideredNGCCunitsifoperational.Forthe2012111(d)baseline,unitscanbelistedasUC‐NGCCiftheyareunderconstructionin2012or2013.StillUnderAgencyReview–DonotQuote,Cite,orShare33CategoryCodeCategoryInclusioncriteriaEXCLUDEUnitsexcludedfromthe111(d)baselineUnitsmaybeexcludedfromthebaselineforseveralreasons,including:Internalcombustionengineunits;Non‐combustionprimemovers,suchasphotovoltaics,windturbines,andhydropowerunits;Unitsthatusedlessthan10%fossilfuelonaheatinputbasisin2012;Non‐operationalunits,suchasunitsthathaveretiredorareunderconstructionaccordingtoEIAdata;orIndustrialorcommercialunits,includingCHPunitsandnon‐CHPunitsthatsupplylessthanone‐thirdoftheirelectricitytothegrid.*Note also that the inclusion or exclusion of a particularunit in the 111(d) baseline analysis does not necessarily indicate that the unit will meet theapplicability criteria in the Final Rule.State-level baselineThestate‐levelbaselineiscreatedbysummingtheCO2emissionsandnetgenerationfromthegenerator‐levelbaselineforunitsintheCOALST,NGCC,andOGSTcategories.UnitsarealsogroupedbystateandNorthAmericanElectricReliabilityCorporation(NERC)region.NERCregiondataforeachplantaretakenfromtheEIA‐860,whichliststheIndependentSystemOperator/RegionalTransmissionOrganization(ISO/RTO)regionattheplantlevel.ISO/RTOregionsarematchedtoNERCregionsusingtherelationshipsinError!Referencesourcenotfound..28TheemissionsrateiscalculatedbyconvertingtheCO2emissionsfromtonstopoundsbymultiplyingby2,000andthendividingbythenetgeneration.Differences between 111(d) and eGRID Methodologies28ThereareatleasttwofacilitiesinTexas(TenaskaFrontierGeneratingStationandTenaskaGatewayGeneratingStation)thatcansupplyelectricityeithertotheEasternorERCOTNERCregions.TheregionthattheseplantsreportedintheEIA‐860isusedastheNERCregioninthe111(d)baselineanalysis.StillUnderAgencyReview–DonotQuote,Cite,orShare34The methodology used to develop the baseline state-level emission rates for the 111(d) analysis is based largely on the methodology used todevelop the annual editions of the Emissionsand Generation Resource Integrated Database (eGRID), with certain key differences.In general,however, the methodologies are broadly similar: they both involve matching Part 75 CO2emissions data from the EPA Clean Air MarketsDivision (CAMD) with data on electricity generation from EIA.Nevertheless, there are specificcriteria set forth in the CleanPower Plan thatnecessitate slight deviations from the eGRID methodology in the 111(d) baseline analysis.In particular, the Clean Power Plan defines specific criteria that dictate which generating units are to be included in the baseline analysis. TheeGRID methodology is altered slightly to accommodate these inclusion criteria. This Section explains those methodological differences.Emissions assigned to boilerseGRID reports emissions at the boiler level and rolled up to the plant level, but the eGRID methodology does not attempt to assign emissions fromboilers to individual generators. Because the baseline is based on generators(e.g. units with a nameplate capacity greater than 25 MW), the boilerlevel emissions must be assigned to the generators in the 111(d) baseline analysis.Where possible in the 111(d) baseline analysis, the emissions data from EPA are assigned to the generator directly associated with that boiler,according to data from EIA-860. When the emissions are only available at the plant level, or if one boiler is associated with more than onegenerator, or if it is unclear which generator is associatedwith which boiler, the emissionsare proportionally distributed togenerators based onnameplate capacity.Similarly, in the 111(d) baseline analysis, combined cycle unitsare treated as a single system, and the total emissions from the combined cycleunits are distributed to the components (the steam parts andturbine parts) based on proportion of nameplate capacity.Inclusion criteriaIn order to decide which units are included in the estimation ofstate-level rates, it is necessary to determine if they meet the inclusion criteriabased on unit size and type, operating status, fuel use, electricitysales, and capacity factor. For example, coal units with anameplate capacity lessthan 25 MW or with a heat input capacity less than 250 mmBtu/hr. are excluded from the analysis, and therefore the emissions from these units arenot used to calculate the state-level rates. In addition, the111(d) baseline analysis does not include units that use less than 10% fossil fuel on a heatinput basis in 2012, units that are retired or under construction, and certain commercial and industrial units that are not grid connected. However,the data files from the 111(d) baseline analysis still list allof these units, but the “Category” field for these units is listed as “EXCLUDE.”In addition, in the methodology to develop eGRID, units with zero generation are not included in the final eGRID files. Since the 111(d) baselinefiles capture the entire universe of units, the files for the111(d) baseline will list more units than the eGRID files.StillUnderAgencyReview–DonotQuote,Cite,orShare35Outlier emission ratesIn certain cases, when the emissions data from EPA are matchedwith generation data from EIA, it appears that a unit has positive emissions, butzero or negative generation. As a result, the emission rate calculated for this unit will result in a negative value. EPA implemented a correction inthe 111(d) baseline analysis for affected units with negative orunreasonable (e.g., too low or too high) emission rates. For this analysis, EPAconsidered rates that were less than 500 lbs. CO2/MWh or greater than 10,000 lbs. CO2/MWh as unreasonable and used the gross generation, ifavailable, with a net-gross conversion factors to estimate reasonable generation data. The process is described above in the Data Correctionssection. If these corrections resulted in emissionsrates that were still less than 500 lbs. CO2/MWh or greater than 10,000 lbs. CO2/MWh, EPA leftthe generation data unchanged and retainedthe original calculated rate. EPA does notuse this quality assurance correction foroutlier emissionrates in eGRID.Adjustments to emissions from biomassIn eGRID, it is assumed that biomass is carbon neutral and therefore the emissions associated with biomass are adjusted to zero. While the eGRIDplant file reports both the adjusted and unadjusted emissions, the summary tables are based on adjusted emissions.This adjustment is not made in the 111(d) baseline analysis,although units that use less than10% fossil fuel on a heat inputbasis in 2012 areexcluded from the analysis.Tribal landsThe 111(d) analysis includes a total of 4 units from Navajo, Ute,and Fort Mojave tribal lands. These units are included in eGRID but are assignedto states, rather than the appropriate tribal lands.Key Differences between Proposedand Final 111(d) BaselinesThis section outlines the differences betweenthe 111(d) baseline file, created for theProposed Rule (June 2014, hereafter “proposed file”) and theversion of the file created for the Final Rule (hereafter “finalfile”). EPA received public comment on the proposed file and made changesaccordingly. Two changes to the methodology, based on comment, are used to create the final file are as follows:1.Outlier emission rates.In addition to this methodological change, EPA also made non-methodological changes tothe proposed file when creating the final file, including:2.Changes to unit characteristics;3.Changes to the unit categorization; andStillUnderAgencyReview–DonotQuote,Cite,orShare364.Changes to emissions data.Each of these changes are described in more detail below.Methodological Changes Based on Comment1.Outlier emission ratesIn certain cases, when EPA emissions data collected under 40 CFR Part 75 are matched with generation data from EIA, a unit canhave positiveemissions, but zero or negative generation. This may occur if a unit uses more power than it generates. As a result, the emission rate calculated forthis unit would be negative. To correct this issue, EPA estimated the netelectricity generation from these units based on their gross generation andnet-gross conversion factors. Using thismethodology, EPA updated the generation for 95units with negative generation. Of these, 63 units satisfythe criteria for inclusion in the 111(d) baseline analysis. Additionally, EPA also implemented a correction for units with emission rates that areconsidered unreasonable, either too low or toohigh. For this analysis EPA used 500 lbs. CO2/MWh as the cutoff for rates that are too low, and10,000 lbs. CO2/MWh as the cutoff for rates that are too high.For these units EPA applies a correction converting the gross generation to net generation usingnet-gross conversion factors,as describe in theData Corrections section above. If these corrections results inemissions rates that are still less than 500 lbs. CO2/MWH or greater than 10,000 lbs.CO2/MWh, EPA leaves the generation data unchangedand retains the original emissions rate.Using this methodology, EPA updated the generation for 104 units that have “out-of-range” emission rates. Of these, 10 units satisfy the criteriafor inclusion in the 111(d) baseline analysis. EPA was not ableto apply gross to net conversion on 357 units with emissions rates outside of thebounds.Non-Methodological Changes Based on Comment2.Changes to unit characteristicsIn addition to the methodological changes described above, EPA also responded to public comments received on the 111(d) baseline developed forthe Proposed Rule. These comments include updating generation data thathad been misreported to EIA, changing prime movers andfuel types,and changing CHP flags. These changes are detailed for each unitin the unit-level baseline files in the column titled “Changesfrom File forProposed Rule.” EPA also added a column to the baseline files toindicate whether a unit had commenced operations in that datayear. Thiscolumn is populated using a combination of public comments and data from EIA on when the unit commenced operations.StillUnderAgencyReview–DonotQuote,Cite,orShare373.Changes to unit categorizationFor the 2012 baseline final file, EPA made changes to the categorization for coal steam and natural gas combined cycle (NGCC) units that wereunder construction or commenced operations in 2012. In the proposed file, there are 9 units listed as COALST and 46 units listed as NGCC thatcommenced operations in 2012. In the final file, EPA changed thecategory of these units to “UC Coal – commenced in 2012” or “UC NGCC –commenced in 2012”, respectively. There arealso 4 coal steam units and 55 NGCC unitsthat were under construction in either 2012 or 2013according to EIA data that are in the EXCLUDE category in theproposed file, but are now listed as “UC Coal” or “UC NGCC”, respectively, inthe final file.In addition, the proposed file contained additional categories,including simple-cycle turbines (SST), which are not included in calculations for theFinal Rule. EPA changed the category for these units to EXCLUDE.4.Changes to the EPA emissions dataEPA used an updated version of emissions data collected under 40CFR Part 75 in the analysis. The EPA pulled the emissions dataused to createthe proposed file in February 2014, and the data used to create the final file in February 2015. This resulted in changes in emissions for 23 unitsbetween the proposed and final files.StillUnderAgencyReview–DonotQuote,Cite,orShare38Emission FactorsTheemissionfactorslistedinthetablebelowareusedinthe111(d)baselineanalysistoestimateCO2emissions,ifemissionsforagivenunitarenotincludedintheEPAdata.CO2emissionfactorsforyear2012areobtainedfromtwosources:EPA’sInventoryofU.S.GreenhouseGasEmissionsandSinks:1990‐2012,andtheGreenhouseGasReportingProgram(40CFRPart98).FuelCodeFuelTypePrimeMoverEmissionFactor(TonsCO2/mmBtu)ABAgriculturalbyproductsST0.13027BFGBlastfurnacegasST0.05844BGBagasseST0.13027BITBituminouscoalST0.10282BLQBlackliquorST0.10448BUButaneST0.07182COGCokeovengasST0.05844DFODistillatefueloil#2ST0.08152DFODistillatefueloil#2GT0.08152DFODistillatefueloil#2OT0.08152DFODistillatefueloil#2CS0.08152DFODistillatefueloil#2CT0.08152DFODistillatefueloil#2CC0.08152DFODistillatefueloil#2IC0.08152DGDigestergasST0.05739DGDigestergasGT0.05739DGDigestergasOT0.05739DGDigestergasCS0.05739DGDigestergasCT0.05739DGDigestergasCC0.05739DGDigestergasIC0.05739DGDigestergasFC0.05739GEOGeothermalBT0StillUnderAgencyReview–DonotQuote,Cite,orShare39FuelCodeFuelTypePrimeMoverEmissionFactor(TonsCO2/mmBtu)GEOGeothermalST0HYHydrogenST0HYHydrogenGT0HYHydrogenCT0HYHydrogenOT0HYHydrogenCS0HYHydrogenCC0IGCCIntegratedgasificationcombinedcycleburningBITIG0.10282JFJetfuelGT0.07962JFJetfuelIC0.07962JFJetfuelCC0.07962KERKeroseneGT0.08067KERKeroseneIC0.08067LBLiquidbyproductST0.08209LFGLandfillgasST0.05739LFGLandfillgasGT0.05739LFGLandfillgasOT0.05739LFGLandfillgasCS0.05739LFGLandfillgasCT0.05739LFGLandfillgasCC0.05739LFGLandfillgasFC0.05739LIGLignitecoalST0.10771MHMethanolST0.06984MSBMSWbiomasscomponentST0.10339NGNaturalgasST0.05844NGNaturalgasGT0.05844NGNaturalgasOT0.05844NGNaturalgasCS0.05844NGNaturalgasCT0.05844StillUnderAgencyReview–DonotQuote,Cite,orShare40FuelCodeFuelTypePrimeMoverEmissionFactor(TonsCO2/mmBtu)NGNaturalgasCC0.05844NGNaturalgasIC0.05844NGNaturalgasFC0.05844OBGOtherbiomassgasCC0.05739OBGOtherbiomassgasGT0.05739OBGOtherbiomassgasST2.01492OBGOtherbiomassgasFC0.05739OBLOtherbiomassliquidST0.08989OBLOtherbiomassliquidGT0.08989OBLOtherbiomassliquidCT0.08989OBLOtherbiomassliquidOT0.08989OBLOtherbiomassliquidCS0.08989OBLOtherbiomassliquidCC0.08989OBSOtherbiomasssolidST0.11632OGOthergasST0.05844OGOthergasGT0.05844OGOthergasCC0.05844OOOtheroilST0.08152OTLOtherliquidST0.08209OTLOtherliquidGT0.08209OTLOtherliquidOT0.08209OTLOtherliquidCS0.08209OTLOtherliquidCT0.08209OTLOtherliquidCC0.08209OTSOthersolidST0.11289PCPetroleumcokeST0.11256PCPetroleumcokeGT0.11256PCPetroleumcokeCT0.11256PCPetroleumcokeOT0.11256PCPetroleumcokeCS0.11256StillUnderAgencyReview–DonotQuote,Cite,orShare41FuelCodeFuelTypePrimeMoverEmissionFactor(TonsCO2/mmBtu)PCPetroleumcokeCC0.11256PGPropanegasST0.06774PPPaperpelletsST0.10339PRGProcessgasST0.05844RFOResidualfueloil#6ST0.08278RFOResidualfueloilGT0.08278RFOResidualfueloilCC0.08278RGRefinerygasST0.07356SCSyntheticcoalST0.10529SLWSludgewasteST0.11632SUBSubbituminouscoalST0.10711SUNSunPV0TDFTire‐derivedfuelST0.06376WATWaterHY0WCWastecoalST0.10529WDLWoodliquidST0.08989WDSWoodsolidST0.10339WNDWindWS0WNDWindWT0WOWasteoilST0.08209WOWasteoilCC0.08209WOWasteoilGT0.08209StillUnderAgencyReview–DonotQuote,Cite,orShare42Data CodesThe following data codes are codes used bythe EIA in the EIA-860 and EIA-923 forms to indicate a unit’s prime mover, fuel type, and status.PrimeMoverCodePrime Mover DescriptionBA Energy Storage, BatteryBTTurbines Used in a Binary Cycle (including those used for geothermalapplications)CA Combined Cycle Steam PartCCCombined Cycle Total Unit (use only for plants/generators that are in planningstage, for which specific generator details cannot be provided)CE Energy Storage, Compressed AirCP Energy Storage, Concentrated Solar PowerCSCombined Cycle Single Shaft (combustion turbine and steam turbine share asingle generator)CT Combined Cycle Combustion Turbine PartES Energy Storage, OtherFC Fuel CellFW Energy Storage, FlywheelGTCombustion (Gas) Turbine (does not include the combustion turbine part of acombined cycle; see code CT, below)HA Hydrokinetic, Axial Flow TurbineHB Hydrokinetic, Wave BuoyHK Hydrokinetic, OtherHYHydroelectric Turbine (includes turbinesassociated with delivery of water bypipeline)IC Internal Combustion Engine(diesel, piston, reciprocating)OT OtherPS Energy Storage, Reversible Hydraulic Turbine (Pumped Storage)PV PhotovoltaicSTSteam Turbine, including nuclear, geothermal and solar steam (does not includecombined cycle)WS Wind Turbine, OffshoreStillUnderAgencyReview–DonotQuote,Cite,orShare43WT Wind Turbine, OnshoreFuel TypeCodeEnergy Source DescriptionAB Agricultural By-ProductsANT Anthracite CoalBFG Blast Furnace GasBIT Bituminous CoalBLQ Black LiquorDFO Distillate Fuel Oil (including diesel, No. 1, No. 2, and No. 4 fuel oils)GEO GeothermalJF Jet FuelKER KeroseneLFG Landfill GasLIG Lignite CoalMSW Municipal Solid WasteMWH Electricity used for energy storageNG Natural GasNUC Nuclear (including Uranium, Plutonium, and Thorium)OBG Other Biomass Gas (including digester gas, methane, and other biomass gases)OBL Other Biomass LiquidsOBS Other Biomass SolidsOG Other GasOTH OtherPC Petroleum CokePG Gaseous PropanePUR Purchased SteamRC Refined CoalRFO Residual Fuel Oil (incl. Nos. 5 & 6 fuel oils, and bunker C fuel oil)SGC Coal-Derived Synthesis GasSGP Synthesis Gas from Petroleum CokeSLW Sludge WasteSUB Subbituminous CoalSUN SolarStillUnderAgencyReview–DonotQuote,Cite,orShare44Fuel TypeCodeEnergy Source DescriptionTDF Tire-derived FuelsWATWater at a Conventional Hydroelectric Turbine, and water used in Wave BuoyHydrokinetic Technology, Current Hydrokinetic Technology, and TidalHydrokinetic TechnologyWCWaste/Other Coal (incl. anthracite culm,bituminous gob, fine coal, lignite waste,waste coal)WDLWood Waste Liquids excluding Black Liquor (including red liquor, sludge wood,spent sulfite liquor, and other wood-based liquids)WDSWood/Wood Waste Solids (incl. paper pellets, railroad ties, utility poles, woodchips, bark, and wood waste solids)WHWaste heat not directly attributed to a fuel source (WH should only be reportedwhen the fuel source is undetermined, and for combined cycle steam turbines thatdo not have supplemental firing.)WND WindWOWaste/Other Oil (including crude oil,liquid butane, liquid propane, naphtha, oilwaste, re-refined motor oil, sludge oil, tar oil, or other petroleum-based liquidwastes)Unit StatusCodeStatus Code DescriptionIPPlanned new generator canceled, indefinitelypostponed, or no longer in resourceplanLRegulatory approvals pending. Not underconstruction but sitepreparation couldbe underwayOAOut of service – was not used for some or all of the reporting period but isexpected to be returned to service in the next calendar year.OPOperating - in service (commercial operation) and producing some electricity.Includes peaking units that are run on anas needed (intermittent or seasonal)basis.OSOut of service – was not used for some or all of the reporting period and is NOTexpected to be returned to service in the next calendar year.OT OtherStillUnderAgencyReview–DonotQuote,Cite,orShare45PPlanned for installation but regulatory approvals not initiated; Not underconstructionRE Retired - no longer in service and not expected to be returned to service.SBStandby/Backup - available for service but not normally used (has little or nogeneration during the year) for this reporting period.TRegulatory approvals received. Not underconstruction but sitepreparation couldbe underwayTSConstruction complete, but not yet incommercial operation (including low powertesting of nuclear units)UUnder construction, less than or equal to 50 percent complete (based onconstruction time to date of operation)VUnder construction, more than 50 percent complete (based on construction time todate of operation)Description of Baseline Data FieldsThe following table provides a description of the data fields inthe 111(d) baseline file with an indication of the data sources used to populate eachfield.FieldDescriptionSourceCategoryCategorybasedontheinclusioncriteriaofeachgenerator‐‐StateStateinwhichtheplantislocatedEIA‐860PlantNamePlantnameEIA‐860ORISCodeEIAOfficeofRegulatoryInformationSystemsPlantLocationplantorfacilitycodeEIA‐860GeneratorIDGeneratoridentificationcodeEIA‐860FueltypePrimaryfueltypeofthegeneratorEIA‐860PrimemovertypeTheengine,turbine,waterwheel,orsimilarmachinethatdrivesanelectricgenerator;or,forreportingpurposes,adevicethatconvertsEIA‐860StillUnderAgencyReview–DonotQuote,Cite,orShare46FieldDescriptionSourceenergytoelectricitydirectly(e.g.,photovoltaicsolarandfuelcells).NameplateCapacity(MW)ThefullcapacityvalueofpoweroutputfromthegeneratorEIA‐860SummerNameplateCapacity(MW)ThefullcapacityvalueofpoweroutputfromthegeneratorduringthesummerEIA‐860ElectricGeneration(MWh)NetelectricitygenerationoftheunitEIA‐923,EPAPart75dataCarbonDioxideEmissions(tons)TheannualcarbondioxideemissionsfromeachgeneratorintonsEPAPart75data,EIA‐923UNITKEEP(CA<25partofCCwithCT>25)Ifalloftheturbineparts(primemover=CT)ofanNGCCsystemhaveanameplatecapacity>25MW,thenallofthesteamparts(primemover=CA)areincludedinthebaseline,regardlessofwhethertheyhaveanameplatecapacity>25MW.Inthiscase,theUNITKEEPfieldwillbeequalto1.Itwillbeblankotherwise.‐‐SourceCategoryThetypeofindustryinwhichthegeneratorislocated.Optionsincludeelectricutility,independentpowerproducer(IPP),industrial,orcommercial.EIA‐860CognFlagY/NIndicatesthecogenerationstatusofeachgenerator–yes(Y)orno(N).EIA‐860UnitStatusTheoperatingstatusofthegeneratorEIA‐860UnitRetirementYearTheactualorplannedretirementyearofthegeneratorEIA‐860ExclusionDescriptionDescriptionofwhythegeneratorwasexcludedinthe“Category”field‐‐CommencedOperationsinDataYearIfthegeneratorcommencedoperationswithinthedatayear,thefieldismarked“Yes.”Thisfieldisleftblankforallothergenerators.EIA‐860ChangesfromFileforProposedRuleDescriptionofchangesmadetothedatathatdifferfromthebaselinefilecreatedforthe‐‐StillUnderAgencyReview–DonotQuote,Cite,orShare47FieldDescriptionSourceProposedRule,includingchangesmadebasedonpubliccomments.NERCInterconnectionNERCregioninwhichtheplantislocatedEIA‐860