Regulatory Impact Analysis for the Proposed 

Cross-State Air Pollution Rule (CSAPR) for 

the 2008 Ozone National Ambient Air 

Quality Standards (NAAQS) 

 
ii 

EPA-452/R-15-009 

November 2015 

Regulatory Impact Analysis for the Proposed Cross-State Air Pollution Rule 

(CSAPR) for the 2008 Ozone 

National Ambient Air Quality Standards (NAAQS) 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27711 
iii 

CONTACT INFORMATION 

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

Standards, U.S. Environmental Protection Agency. Questions related to this document should be 

addressed to Kathy Kaufman, U.S. Environmental Protection Agency, Office of Air Quality 

Planning and Standards, C439-02, Research Triangle Park, North Carolina 27711 (email: 

kaufman.kathy@epa.gov). 

ACKNOWLEDGEMENTS 

In addition to EPA staff from the Office of Air Quality Planning and Standards, personnel from 

the Office of Atmospheric Programs, the Office of Transportation and Air Quality, and the 

Office of Policy’s National Center for Environmental Economics contributed data and analysis to 

this document. 

 
iv 

GLOSSARY OF TERMS AND ABBREVIATIONS 

The following are abbreviations of terms used in this Regulatory Impact Analysis. 

CAA or Act Clean Air Act 

CAIR Clean Air Interstate Rule 

CAMx Comprehensive Air Quality Model with Extensions 

CBI Confidential Business Information 

CEM Continuous Emissions Monitoring 

CSAPR Cross-State Air Pollution Rule 

EGU Electric Generating Unit 

FIP Federal Implementation Plan 

FR Federal Register 

EPA U.S. Environmental Protection Agency 

GHG Greenhouse Gas 

GW Gigawatts 

ICR Information Collection Request 

IPM Integrated Planning Model 

km Kilometer 

lb/mmBtu Pounds per Million British Thermal Unit 

LNB Low-NOx Burners 

NAAQS National Ambient Air Quality Standard 

NBP NOX Budget Trading Program 

NEI National Emission Inventory 

NOX Nitrogen Oxides 

NSPS New Source Performance Standard 

OFA Overfire Air 

PM2.5 Fine Particulate Matter 

ppb Parts Per Billion 

RIA Regulatory Impact Analysis 

SCR Selective Catalytic Reduction 

SIP State Implementation Plan 
v 

SMOKE Sparse Matrix Operator Kernel Emissions 

SNCR Selective Non-catalytic Reduction 

SO2 Sulfur Dioxide 

TIP Tribal Implementation Plan 

TPY Tons Per Year 

TSD Technical Support Document 
1 
1 
ii 

TABLE OF CONTENTS 

page 

LIST OF TABLES .......................................................................................................................... ii

LIST OF FIGURES ........................................................................................................................ ii

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

Overview ...................................................................................................................................1

ES.1 Identifying Required Emissions Reductions ..........................................................1

ES.2 Baseline and Analysis Year ....................................................................................4

ES.3 Control Strategies and Emissions Reductions ........................................................6

ES.4 Costs ......................................................................................................................7

ES.5 Benefits to Human Health and Welfare ..................................................................8

ES.5.1 Human Health Benefits and Climate Co-benefits ..............................................9

ES.5.2 Combined Health Benefits and Climate Co-Benefits Estimates ......................12

ES.5.3 Unquantified Co-Benefits .................................................................................13

ES.5 Results of Benefit-Cost Analysis ..........................................................................16

E.S.6 References .............................................................................................................17

CHAPTER 1: INTRODUCTION AND BACKGROUND ..........................................................14

Introduction .............................................................................................................................14

1.1 Background ..................................................................................................................14

1.2.1 Role of Executive Orders in the Regulatory Impact Analysis ..........................14

1.2.2 Illustrative Nature of this Analysis ...................................................................14

1.2.3 The Need for Air Quality or Emissions Standards ...........................................14

1.2 Overview and Design of the RIA .................................................................................14

1.2.1 Methodology for Identifying Required Reductions ..........................................14

1.2.2 States Covered by the Proposed Rule ...............................................................14

1.2.3 Regulated Entities .............................................................................................14

1.2.4 Baseline and Analysis Year ..............................................................................14

1.2.5 Emissions Controls and Cost Analysis Approach ............................................14

1.2.6 Benefits Analysis Approach .............................................................................14

1.3 Organization of the Regulatory Impact Analysis.........................................................14

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

Overview ...................................................................................................................................1

2.1 Background ....................................................................................................................1

2.2 Power Sector Overview .................................................................................................2

2.2.1 Generation ..........................................................................................................2

2.2.2 Transmission .....................................................................................................10

2.2.3 Distribution .......................................................................................................11

2.3 Sales, Expenses, and Prices .........................................................................................12

2.3.1 Electricity Prices ...............................................................................................12
iii 

2.3.2 Prices of Fossil Fuels Used for Generating Electricity.....................................17

2.3.3 Changes in Electricity Intensity of the U.S. Economy from 2000 to 2013 ......18

2.4 Deregulation and Restructuring ...................................................................................19

CHAPTER 3: EMISSIONS AND AIR QUALITY MODELING IMPACTS .............................14

Overview .................................................................................................................................14

3.1 Air Quality Modeling Platform ....................................................................................14

3.1.1 Simulation Periods ............................................................................................14

3.1.2 Air Quality Modeling Domain .........................................................................14

3.1.3 Air Quality Model Inputs .................................................................................14

3.2 Development of Emissions Inventories .......................................................................14

3.2.1 2011 Base Year Emissions ...............................................................................14

3.2.2 2017 Baseline Emissions ..................................................................................14

3.2.3 2017 Illustrative Control Case Emissions ........................................................14

3.3 Post-Processing of Air Quality Modeling for Benefits Calculations ...........................14

3.3.1 Converting CAMx Ozone Outputs to Benefits Inputs ......................................14

3.3.2 Converting CAMx PM2.5 Outputs to Benefits Inputs .......................................14

3.4 References ....................................................................................................................14

CHAPTER 4: REGULATORY CONTROL Alternatives ............................................................14

Introduction .............................................................................................................................14

4.1 Background ..................................................................................................................14

4.2 Regulatory Control Alternatives Considered ...............................................................14

4.3 Rationale for Regulatory Control Alternatives Chosen ...............................................14

CHAPTER 5: COST, ECONOMIC, ENERGY, AND EMPLOYMENT IMPACTS ....................1

Overview ...................................................................................................................................1

5.1 Power Sector Modeling Framework ..............................................................................1

5.2 EPA’s Power Sector Modeling Base Case for the Proposal to Update CSAPR ............4

5.2.1 EPA’s IPM Base Case v.5.15 .............................................................................5

5.2.2 The IPM Base Case Used to Analyze the Proposal to Update CSAPR .............6

5.3 Evaluating the Regulatory Control Alternatives ............................................................7

5.3.1 Emission Reduction Assessment ......................................................................10

5.3.2 Compliance Cost Assessment ...........................................................................12

5.3.3 Impacts on Fuel Use, Prices and Generation Mix ............................................17

5.3.4 Effect of Emission Reductions on Downwind Receptors ................................20

5.4 Employment Impacts ...................................................................................................22

5.4.1 Economic Theory and Employment .................................................................23

5.4.1.2 Current State of Knowledge Based on the Peer-Reviewed Literature ......26

5.4.1.3 Regulated Sector ........................................................................................26

5.4.1.4 Economy-Wide ..........................................................................................27

5.4.1.5 Labor Supply Impacts ................................................................................27

5.4.1.6 Conclusion ....................................................................................................28

5.4.2 Recent Employment Trends .............................................................................28
iv 

5.4.2.1 Electric Power Generation .........................................................................28

5.4.2.2 Fossil Fuel Extraction ................................................................................29

5.4.3 Power and Fuels Sector Direct Employment Impacts ......................................31

5.5 Social Costs ......................................................................................................................36

5.6 Secondary Economic Impacts...........................................................................................37

5.6.1 Methods ............................................................................................................37

5.6.2 Summary of Secondary Market Impacts of Energy Price Changes .................39

5.6.3 Share of Total Production Costs .......................................................................40

5.6.4 Ability to Substitute between Inputs to the Production Process .......................41

5.6.5 Availability of Substitute Goods and Services .................................................41

5.6.6 Effect of Changes in Input Demand from Electricity Sector ............................41

5.6.7 Conclusions ......................................................................................................42

5.7 References ....................................................................................................................42

Chapter 6: Estimated Human Health Benefits and Climate Co-benefits .........................................1

6.1 Introduction ....................................................................................................................1

6.2 Estimated Human Health Benefits .................................................................................2

6.2.1 Health Impact Assessment for Ozone and PM2.5................................................4

6.2.1.1 Mortality Effect Coefficients for Short-term Ozone Exposure ...................7

6.2.1.2 PM2.5 Mortality Effect Coefficients for Adults and Infants ........................9

6.2.2 Economic Valuation for Health Benefits ..........................................................14

6.2.3 Benefit-per-ton Estimates for Ozone ................................................................16

6.2.4 Benefit-per-ton Estimates for PM2.5 .................................................................16

6.2.5 Estimated Health Benefits Results ...................................................................17

6.3 Estimated Climate Co-Benefits from CO2 ...................................................................27

6.3.1 Climate Change Impacts ...................................................................................28

6.4 Combined Health Benefits and Climate Co-Benefits Estimates..................................36

6.5 Unquantified Co-benefits .............................................................................................38

6.5.1 HAP Impacts.....................................................................................................40

6.5.2 Additional NO2 Health Co-Benefits .................................................................40

6.5.3 Additional SO2 Health Co-Benefits ..................................................................41

6.5.4 Additional NO2 and SO2 Welfare Co-Benefits .................................................42

6.5.5 Ozone Welfare Co-Benefits ..............................................................................43

6.5.6 Visibility Impairment Co-Benefits ...................................................................43

6.6 References ....................................................................................................................44

6A.1 Overview of Benefit-per-Ton Estimates .................................................................1

6A.2 Air Quality Modeling for the Proposed Transport Rule .........................................1

6A.3 National PM2.5 Benefit-per-Ton Estimates for EGUs Derived from Air

Quality Modeling of the Proposed Transport Rule ...............................................................4

6A.4 Regional Ozone Benefit-per-Ton Estimates ...........................................................7

6A.5 References ...............................................................................................................8

CHAPTER 7: STATUTORY AND EXECUTIVE ORDER REVIEWS .....................................14

Overview .................................................................................................................................14

7.1 Executive Order 12866: Regulatory Planning and Review ........................................14
v 

7.2 Paperwork Reduction Act ............................................................................................14

7.3 Regulatory Flexibility Act ...........................................................................................14

7.3.1 Identification of Small Entities .........................................................................14

7.3.2 Overview of Analysis and Results....................................................................14

7.3.2.1 Methodology for Estimating Impacts of the Proposed CSAPR update 

on Small Entities ...................................................................................................14

7.3.2.2 Results ......................................................................................................14

7.3.3 Summary of Small Entity Impacts....................................................................14

7.4 Unfunded Mandates Reform Act .................................................................................14

7.4.1 Identification of Government-Owned Entities .................................................14

7.4.2 Overview of Analysis and Results....................................................................14

7.4.2.1 Methodology for Estimating Impacts of the proposed CSAPR update 

on Government Entities ........................................................................................14

7.4.2.2 Results ......................................................................................................14

7.4.3 Summary of Government Entity Impacts .........................................................14

7.5 Executive Order 13132: Federalism ............................................................................14

7.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments .......................................................................................................................14

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

Safety Risks .........................................................................................................................14

7.8 Executive Order 13211: Actions that Significantly Affect Energy Supply, 

Distribution, or Use .............................................................................................................14

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

7.10 Executive Order 12898: Federal Actions to Address Environmental Justice 

in Minority Populations and Low-Income Populations ......................................................14

CHAPTER 8: COMPARISON OF BENEFITS AND COSTS .....................................................14

Overview .................................................................................................................................14

8.1 Results ..........................................................................................................................14

8.2 Net Present Value of a Stream of Costs and Benefits ..................................................14
ii 

LIST OF TABLES 

Table ES-1. Projected 2017* EGU Emissions Reductions of NOx, SO2, and CO2 with the 

Proposed NOXx Emissions Budgets and More and Less Stringent Alternatives 

(Tons)** ................................................................................................................................7

Table ES-2 Cost estimates (2011$) for proposal and more and less stringent alternatives .......8

Table ES-3. Summary of Avoided Health Incidences from Ozone-Related and PM2.5-

Related Benefits for the Proposal for 2017* ......................................................................11

Table ES-4. Combined Health Benefits and Climate Co-Benefits for the Proposed 

CSAPR EGU NOX Ozone Season Emissions Budgets and for More and Less 

Stringent Alternatives for 2017 (millions of 2011$)*........................................................13

Table ES-5. Unquantified Health and Welfare Co-benefits Categories ...................................14

Table ES-6. Total Costs, Total Monetized Benefits, and Net Benefits of the Proposal in 

2017 for U.S. (millions of 2011$)a,b,c ................................................................................16

Table 2-1. Existing National Electricity Generating Capacity by Energy Source, 2000 

and 2013 ...............................................................................................................................3

Table 2-2. Net Generation in 2000 and 2013 (Trillion kWh = TWh)......................................6

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

Thermal Efficiency in 2013 (Heat Rate) ..............................................................................7

Table 2-4. Total U.S. Electric Power Industry Retail Sales, 2000 and 2013 (billion 

kWh) 12

Table 3-1 2011 Base Year and 2017 Baseline NOx and VOC Emissions by Sector 

(thousand tons) .....................................................................................................................9

Table 3-2 Illustrative Control Case NOx Emissions and Changes from the 2017 

Baseline .............................................................................. Error! Bookmark not defined.

Table 4-1 Ozone-Season NOX Emissions Budgets (Tons) for Proposed, More Stringent 

and Less Stringent Regulatory Control Alternatives in 2017 and Later ..............................5

Table 5-1 NOX Mitigation Strategies Implemented for Compliance with the Regulatory 

Control Alternatives ...........................................................................................................10

Table 5-2 EGU Ozone Season NOX Emission Reductions (tons) for the Proposal and 

More and Less Stringent Alternatives................................................................................11

Table 5-3 EGU Annual Emission Reductions (tons) for SO2 and CO2 for the Proposal 

and More and Less Stringent Alternatives .........................................................................12
iii 

Table 5-4 Cost Estimates (millions of 2011$) for Proposal and More and Less 

Stringent Alternatives ........................................................................................................14

Table 5-5 Percent Changes* in Coal and Natural Gas Usage by EGUs for the Proposal 

and More and Less Stringent Alternatives for 2017 ..........................................................18

Table 5-6 Percent Changes* in Generation by Major Type for the Proposal and More 

and Less Stringent Alternatives for 2017...........................................................................18

Table 5-7 Changes* in Generating Capacity by Major Type for the Proposal and More 

and Less Stringent Alternatives in 2017 ............................................................................18

Table 5-8 Percent Changes* in Retail Electricity Price for the Proposal and More and 

Less Stringent Alternatives for 2017 & 2020 ....................................................................20

Table 5-9. Estimated Percentage Changes in Average Energy Prices by Energy Type 

for the Proposed Alternative* ............................................................................................40

Table 6-1. Human Health Effects of Ambient Ozone and PM2.5 .............................................5

Table 6-2. Summary of Ozone and PM2.5 Benefit-per-Ton Estimates Based on Air 

Quality Modeling from The Illustrative Control Case in 2017 (2011$)* ..........................18

Table 6-3. Emission Reductions of Criteria Pollutants for the Proposal and More and 

Less Stringent Alternatives in 2017 (thousands of short tons)* ........................................18

Table 6-4. Summary of Estimated Monetized Health Benefits for the Proposal and 

More and Less Stringent Alternatives Regulatory Control Alternatives for 2017 

(millions of 2011$) * .........................................................................................................18

Table 6-5. Summary of Avoided Health Incidences from Ozone-Related and PM2.5-

Related Benefits for the Proposal and More and Less Stringent Alternatives for 

2017* 19

Table 6-8. Climate Effects .....................................................................................................28

Table 6-9. Social Cost of CO2, 2015-2050 (in 2011$ per metric ton)* .................................35

Table 6-10. Estimated Global Climate Co-benefits of CO2 Reductions for the Proposal 

and More and Less Stringent Alternatives for 2017 (millions of 2011$)* ........................35

Table 6-11. Combined Health Benefits and Climate Co-Benefits for the Proposal and 

More and Less Stringent Alternatives for 2017 (millions of 2011$)* ...............................38

Table 6-12. Unquantified Health and Welfare Co-benefits Categories ...................................39

Table 7-1. SBA Size Standards by NAICS Code ............................................................................8

Table 7-2. Projected Impact of the Proposed CSAPR Update on Small Entities in 2017 ............12
iv 

Table 7-3. Summary of Distribution of Economic Impacts of the Proposed CSAPR Update 

on Small Entities in 2017 ...................................................................................................13

Table 7-4. Incremental Annual Costs under the Proposed CSAPR Update Summarized by 

Ownership Group and Cost Category in 2017 (2011$ millions) .......................................14

Table 7-5. Summary of Potential Impacts on Government Entities under the Proposed 

CSAPR Update in 2017 .....................................................................................................20

Table 7-6. Incremental Annual Costs under the Proposed CSAPR Update Summarized by 

Ownership Group and Cost Category (2011$ millions) in 2017 .......................................22

Table 8-1. Total Costs, Total Monetized Benefits, and Net Benefits of the Proposal and 

More or Less Stringent Alternatives for 2017 for U.S. (millions of 2011$)a,b,c ..................2

Table 8-2. Projected 2017* Reductions in Emissions of NOx, SO2, and CO2 with the 

proposed NOx Emissions Budgets and More or Less Stringent Alternatives (Tons)..........4
ii 

LIST OF FIGURES 

Figure ES-1. Nonattainment and Maintenance Receptors Identified for this Proposal and 

Upwind States Linked to these Downwind Air Quality Problems with Respect to the 

2008 Ozone NAAQS ...........................................................................................................3

Figure ES-1. Nonattainment and Maintenance Receptors Identified for this Proposal and 

Upwind States Linked to these Downwind Air Quality Problems with Respect to the 

2008 Ozone NAAQS ...........................................................................................................4

Figure 2-1 National New Build and Retired Capacity (MW) by Fuel Type, 2000-2013 ........5

Figure 2-2 Regional Differences in Generating Capacity (MW), 2013. ..........................................6

Figure 2-3. Cumulative Distribution in 2013 of Coal and Natural Gas Electricity 

Capacity and Generation, by Age ........................................................................................9

Figure 2-4. Fossil Fuel-Fired Electricity Generating Facilities, by Size .................................10

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

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

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

End-Use Categories, With Inflation Indices ......................................................................15

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

Categories ..........................................................................................................................16

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

Three Major End-Use Categories ......................................................................................17

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

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

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

2000 18

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

Intensity Since 2000 ...........................................................................................................19

Figure 2-12. Status of State Electricity Industry Restructuring Activities ................................21

Figures 2-13 & 2-14. Capacity and Generation Mix by Ownership Type, 2002 & 2012 ...........22

Figure 3-1. National air quality modeling domain. .........................................................................2

Figure 5-1 Time series of annual costs for the Proposal and More and Less Stringent 

Alternatives ........................................................................................................................15
iii 

Figure 5-2 Time series of annual costs and annualized costs for the Proposal and More 

and Less Stringent Alternatives .........................................................................................17

Figure 5-3 Electric Power Industry Employment .........................................................................29

Figure 5-4 Coal Production Employment .............................................................................30

Figure 5-5 Oil and Gas Extraction Employment ...........................................................................31

Figure 6-1. Monetized Health Benefits of Proposal for 2017 * ..............................................20

Figure 6-2. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure 

in the Illustrative Modeling (used to generate the benefit-per-ton estimates) ...................26

Figure 6-3. Cumulative Distribution of Adult Population (age 30+) by Annual Mean 

PM2.5 Exposure in the Illustrative Modeling (used to generate the benefit-per-ton 

estimates) ...........................................................................................................................27
ES-1 

EXECUTIVE SUMMARY

Overview 

The primary purpose of this the proposedal rule is to address interstate air quality impacts 

with respect to the 2008 ozone National Ambient Air Quality Standards (NAAQS). The EPA 

promulgated the Cross-State Air Pollution Rule (CSAPR) on July 6, 2011,1 to address interstate 

transport of ozone pollution under the 1997 ozone NAAQS.2 This action proposes toThe 

proposed rule would update CSAPR to address interstate emissions transport of nitrogen oxides 

(NOX) that contribute significantly to nonattainment or interfere with maintenance of the 2008 

ozone NAAQS in downwind states. The proposedal rule also responds to the D.C. Circuit’s July 

28, 2015 remand of certain CSAPR ozone season NOX emissions budgets to the EPA for 

reconsideration. This Regulatory Impact Analysis (RIA) presents the health and welfare benefits 

and climate co-benefits of this the proposal to update CSAPR, and compares the benefits of the 

rule to the estimated costs of implementing the proposed rule for the 2017 analysis year. This 

RIA also reports certain impacts from thisof the proposed rule such as its effect on employment 

and energy prices. This executive summary both explains the analytic approach taken in the RIA 

and summarizes the RIA results. 

ES.1 Identifying Needed Required Emissions Reductions 

As described in the preamble for the proposal, CSAPR provides a 4-step process to 

address the requirements of CAA section 110(a)(2)(D)(i)(I) (sometimes called the “good 

neighbor provision”) for ozone or fine particulate matter (PM2.5) standards: (1) identifying 

downwind receptors that are expected to have problems attaining or maintaining clean air 

standards (i.e., NAAQS); (2) determining which upwind states contribute to these identified 

problems in amounts sufficient to “link” them to the downwind air quality problems; (3) for 

states linked to downwind air quality problems, identifying upwind emissions that significantly 

 

1 See 76 FR 48208 (July 6, 2011) 

2

 CSAPR also addressed interstate transport of fine particulate matter (PM2.5) under the 1997 and 2006 PM2.5 

NAAQS. 
ES-2 

contribute to downwind nonattainment or interfere with downwind maintenance of a standard by 

quantifying available upwind emission reductions and apportioning upwind responsibility among 

linked states; and (4) for states that are found to have emissions that significantly contribute to 

nonattainment or interfere with maintenance of the NAAQS downwind, reducing the identified 

upwind emissions via regional emissions allowance trading programs. This action proposes to 

apply this 4-step process to update CSAPR with respect to the 2008 ozone NAAQS. The 

reductions required by the proposed rule would be achieved through a federal implementation 

plan (FIP) forin any state that does not have an approved state implementation plan (SIP) 

addressing its contribution by the date this rule is finalized. Furthermore, under the FIPs, affected 

EGUs would participate in the CSAPR NOX ozone-season allowance trading program. More 

Ddetails on the methods and results of applying this process can be found in the preamble for 

this proposal, and in Chapter 4 of this RIA. 

Application of the first two steps of this process with respect to the 2008 ozone NAAQS 

provides the analytic basis for proposing that ozone season emissions in 23 eastern states3

 affect 

the ability of downwind states to attain and maintain the 2008 ozone NAAQS. Figure ES-1 

shows the affected states.

 

3

 Alabama, Arkansas, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, 

Missouri, New Jersey, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas, Virginia, 

West Virginia, and Wisconsin.
ES-3 

Figure ES-1. Nonattainment and Maintenance Receptors Identified for this Proposal and 

Upwind States Linked to these Downwind Air Quality Problems with Respect to 

the 2008 Ozone NAAQS 

 Applying Step 3 of this process, this action proposes to quantify electric generating unit 

(EGU) NOX reductions in these 23 eastern states and to update CSAPR ozone season NOX

emissions budgets. A state’s updated CSAPR ozone season NOX emissions budget is the quantity 

of EGU NOX emissions that would remain after reducing significant contribution to 

nonattainment and interference with maintenance of the 2008 ozone NAAQS in an average 

year.4 These updated CSAPR NOX budgets were developed considering EGU NOX reductions 

that are achievable for the 2017 ozone season.5 The EPA applied a multi-factor test in step 3 to 

evaluate EGU NOX reduction potential for 2017 and proposes is proposing to quantify EGU 

 

4

 For example, assuming no abnormal variation in electricity supply due to events such as abnormal meteorology. 

5

 Non-EGU NOx emission control measures and reductions are not included in this proposal. More information on 

non-EGU NOx control measures and the potential for emission reductions can be found in the Non-EGU TSD on 

Emissions and Potential Reductions in 2017, which can be found in the docket for this proposal. 
ES-4 

NOX ozone-season emissions budgets reflecting EGU NOX reduction strategies that are widely 

available at a uniform annualized cost of $1,300 per ton (2011$). This assessment revealed that 

there is significant EGU NOX reduction potential that can be achieved by 2017, which would 

make meaningful and timely improvements in ozone air quality. Applying Step 4 of this process, 

the EPA proposesis proposing to implement these EGU NOX emissions budgets through the 

CSAPR NOX ozone-season allowance trading program. 

For the RIA, in order to implement the OMB Circular A-4 requirement to assess one less 

stringent and one more stringent alternative to the proposal, the EPA is also analyzing EGU NOX

ozone season emissions budgets reflecting NOX reduction strategies that are widely available at a 

uniform cost of $500 per ton (2011$) and strategies that are widely available at a uniform cost of 

$3,400 per ton (2011$). That is, at step 3, applyingThe EPA applies these alternative uniform 

costs for quantifying EGU NOX ozone-season emissions budgets in Step 3 of the 4-step process. 

Applying Step 4 of this process, the EPA proposes to implement these EGU NOX

emissions budgets through the CSAPR NOX ozone-season allowance trading program. 

ES.2 Baseline and Analysis Year 

The proposal sets forth the requirements for states to reduce their significant contribution 

to downwind nonattainment or interference with maintenance of the 2008 ozone NAAQS. To 

develop and evaluate control strategies for addressing these obligationsevaluate the benefits and 

costs of this regulation, it is important to first establish a baseline projection of both emissions 

and air quality in the analysis year of 2017, taking into account currently on-the-books Federal 

regulations, substantial Federal regulatory proposals, enforcement actions, state regulations, 

population, and where possible, economic growth. Establishing this baseline for the analysis then 

allows us to estimate the incremental costs and benefits of the additional emissions reductions 

that will be achieved by the proposal. 

Below is a list of some of the national rules reflected in the baseline. For a more complete 

list of the rules reflected in the air quality modeling, please see the Technical Support Document: 

Preparation of Emissions Inventories for the Version 6.2, 2011 Emissions Modeling Platform 
ES-5 

(U.S. EPA, 2015).6 For a list of those regulations reflected in the compliance and cost modeling 

of the electricity sector, please see “EPA Base Case v.5.15 Using IPM Incremental 

Documentation” August, 2015.

7

 

• Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility 

Generating Units (Final Rule) (U.S. EPA, 2015) 

• Standards of Performance for Greenhouse Gas Emissions from New, Modified, and 

Reconstructed Stationary Sources: Electric Utility Generating Units (Final Rule) (U.S. 

EPA, 2015a) 

• Tier 3 Motor Vehicle Emission and Fuel Standards (U.S. EPA, 2014) 

• 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate 

Average Fuel Economy Standards (U.S. EPA, 2012) 

• Cross State Air Pollution Rule (CSAPR) (U.S. EPA, 2011)8

• Mercury and Air Toxics Standards (MATS) (U.S. EPA, 2011a)9

• Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and 

Heavy-Duty Engines and Vehicles (U.S. EPA, 2011b)10

• C3 Oceangoing Vessels (U.S. EPA, 2010) 

• Reciprocating Internal Combustion Engines (RICE) NESHAPs (U.S. EPA, 2010a) 

 

6

 U.S. Environmental Protection Agency, 2015. Preparation of Emissions Inventories for the Version 6.2, 2011 

Emissions Modeling Platform, Research Triangle Park, NC, 

(http://www.epa.gov/ttn/chief/emch/2011v6/2011v6_2_2017_2025_EmisMod_TSD_aug2015.pdf).

7

 http://www.epa.gov/powersectormodeling/

8

 On July 28, 2015, the D.C. Circuit issued its opinion regarding CSAPR on remand from the Supreme Court, EME 

Homer City Generation, L.P., v. EPA, No. 795 F.3d 118, 129-30, 138 (EME Homer City II)EME Homer City II, No. 

11-1302, slip op. The court largely upheld CSAPR, but remanded to EPA without vacatur certain states’ phase 2 

emissions budgets for reconsideration. Compliance with these remanded emissions budgets was included in the 

baseline modeling for this proposal, which was already underway when this opinion was issued. 

9

 On June 29, 2015, the United States Supreme Court reversed the D.C. Circuit opinion affirming the MATS. The 

EPA is reviewing the decision and will determine any appropriate next steps once the review is complete. MATS is 

included in the baseline for this analysis, and the EPA does not believe including MATS substantially alters the 

results of this analysis, because MATS was remanded, not vacated.

10 This rule is Phase 1 of the Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines (76 FR 57106, 

September 15, 2011) and is included in the 2025 base case. Phase 2 of the Heavy Duty Greenhouse Gas Standards 

for New Vehicles and Engines (80 FR 40138, July 13, 2015) is not included in the 2025 base case because the 

rulemaking was not finalized in time to include in this analysis. 
ES-6 

• Regulation of Fuels and Fuel Additives: Modifications to Renewable Fuel Standard 

Program (RFS2) (U.S. EPA, 2010b) 

• Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel 

Economy Standards; Final Rule for Model-Year 2012-2016 (U.S. EPA, 2010c) 

• Hospital/Medical/Infectious Waste Incinerators: New Source Performance Standards and 

Emission Guidelines: Final Rule Amendments (U.S. EPA, 2009) 

• Emissions Standards for Locomotives and Marine Compression-Ignition Engines (U.S. 

EPA, 2008a) 

• Control of Emissions for Nonroad Spark Ignition Engines and Equipment (U.S. EPA, 

2008b) 

• NOx Emission Standard for New Commercial Aircraft Engines (U.S. EPA, 2005) 

• Regional Haze Regulations and Guidelines for Best Available Retrofit Technology 

Determinations (U.S. EPA, 2005a) 

ES.3 Control Strategies and Emissions Reductions 

The proposal requires EGUs in 23 eastern states to reduce interstate transport of NOX

emissions that significantly contribute to nonattainment or interfere with maintenance of the 

2008 ozone NAAQS. The rule proposes toproposal sets EGU ozone season NOX emissions 

budgets (allowable emission levels) for 2017 and future years. The EPA proposes toproposal 

would implement these reductions through FIPs in any state that does not have an approved good 

neighbor SIP by the date this rule is finalized. Furthermore, under the FIPs, affected EGUs 

would participate in the CSAPR NOX ozone-season allowance trading program. The allowance 

trading program is the remedy in the FIP that achieves the ozone season NOX emissions 

reductions required by the proposed rule. The allowance trading program essentially converts the 

EGU NOX emissions budget for each of the 23 states subject to the FIP into a limited number of 

NOX ozone-season allowances that, on a tonnage basis, equal the state’s ozone season emissions 

budget. 

The EGU ozone season NOX budgets for each state reflect EGU NOX reduction strategies 

that are widely available at a uniform cost of $1,300 per ton of NOX for affected EGUs. 

Specifically, this uniform cost reflects turning on idled selective catalytic reduction (SCR), 

selective non-catalytic reduction (SNCR) and upgrading combustion controls. Furthermore, this 
ES-7 

RIA analyzes regulatory control alternatives based on more and less stringent state emissions 

budgets based on uniform NOX control costs of $3,400 per ton and $500 per ton, respectively. 

Table ES-1 show the emission reductions expected from the proposal and the more and 

less stringent alternatives analyzed. Included in the table are annual and seasonal NOX, sulfur 

dioxide (SO2), and carbon dioxide (CO2) reductions over the contiguous U.S. 

Table ES-1. Projected 2017* EGU Emissions Reductions of NOXx, SO2, and CO2 with the 

Proposed NOXx Emissions Budgets and More and Less Stringent Alternatives 

(Tons)** 

 Proposal More Stringent 

Alternative 

Less Stringent 

Alternative 

NOxX (annual) 90,000 93,000 24,000 

NOxX (ozone season) 85,000 84,68087,000 24,000 

SO2 (annual) 1,200 1,200 1,100 

CO2 (annual) 660,000 614710,000 770,000 

* The forecast of Aannual reductions of co-pollutants in 2017 areis based on 2018 IPM direct model outputs relied 

upon in this RIA to represent 2017 co-pollutant reductions.

 

** NOx and SO2 emissions are reported in English (short) tons; CO2 is reported in metric tons. All estimates rounded 

to two significant figures.

ES.4 Costs and Economic Impacts

The EPA analyzed ozone-season NOX emissions reductions, and as well as the associated 

compliance costs from of the power sector of implementing the EGU NOX ozone-season 

emissions budgets in each of the 23 states, using the Integrated Planning Model (IPM) and its 

underlying data and inputs. All EGU emissions reduction estimates are taken directly from IPM 

modeling results. We use the compliance cost estimate from IPM as a proxy for social costs. 

Regarding costs, aAs currently configured, IPM directly estimates the costs for two of the NOX

reduction strategies: turning on idled existing SCRs and SNCRs, and shifting generation to 

lower-NOX emitting EGUs. The costs of other mitigation measures, which are optimizing

existing operating SCR or SNCR and installing or upgrading NOX combustion controls, are 

estimated outside of IPM. However, these cost estimates are based on inputs to IPM on the cost 

of reducing EGU NOX via these controls but are calculated using IPM inputs. 

One additional aspect of the analysis conducted using IPM is that the existing but idle 

SCRs or SNCRs in the model baseline were are required to be turned on and fully utilized in the 

model for compliance with the emissions budgets. For the proposal, the EPA estimates that, 
ES-8 

given the characteristics of these units and the average cost per ton of NOX reduced to comply 

with the proposed emissions budgets, turning on the existing but idle SCRs is a reasonable and 

cost-effective compliance method to evaluate for this RIA. 

The estimate of the cost of this proposal, therefore, is the combination of NOx costs 

estimated by IPM and additional costs estimated outside of IPM. The cost estimates for the 

proposal and more and less stringent alternatives are presented in Table ES-2.11 All costs are in 

2011 dollars. 

Table ES-2. Cost Eestimates (2011$) for Pproposal and Mmore and Lless Sstringent 

Aalternatives 

Policy Annualized* 

Proposal $92,900,00093,000,000

More Stringent Alternative $95,700,00096,000,000

Less Stringent Alternative $4,700,000 

*Costs are annualized over the period 2016 through 2040 using the 4.77 percent discount rate used in IPM’s 

objective function for minimizing the net present value of the stream of total costs of electricity generation. 

ES.5 Benefits to Human Health and Welfare 

Implementing this proposal to update CSAPR is expected to reduce emissions of ozone 

season NOX. In the presence of sunlight, NOX and VOCs can undergo a chemical reaction in the 

atmosphere to form ozone. Reducing NOX emissions also reduces human exposure to ozone and

the incidence of ozone-related health effects, though this also depends partly on local levels of 

volatile organic compounds (VOCs). In addition, implementing the proposal would reduce 

emissions of NOX throughout the year. Because NOX is also a precursor to formation of ambient 

PM2.5, reducing these emissions would also reduce human exposure to ambient PM2.5 throughout 

the year and would reduce the incidence of PM2.5-related health effects. FurthermoreFinally, 

these emissions reductions would reduce lower ozone and PM2.5 concentrations in regions other 

than beyond those with nonattainment or maintenance receptors that are the subject of to this 

 

11 It should be noted that the costs associated with implementation of compliance with monitoring, recordkeeping, 

and reports requirements are not included within the estimates in this table. Such costs, which are estimated 

separately from the approaches listed above and are found in Chapter 7, are actually negative on net but are very 

small compared to the magnitude of the costs in Table 5-3ES-2. 
ES-9 

proposal, and the RIA accounts for those additional benefits of reducing these pollutants in those 

regions are accounted for in this RIA. 

Based on the IPM modeling results, the The EPA notes thatdoes not expect this proposal, 

as demonstrated by the analysis using IPM, is not expected to significantly change annual power 

sector emissions of SO2, which is also a PM2.5 precursor. Accordingly, this RIA does not 

quantify SO2-related PM2.5 health co-benefits. Additionally, although we do not have sufficient 

data to quantify these impacts in this analysis, reducing emissions of NOX would also reduce 

ambient exposure to nitrogen dioxide (NO2) and its associated health effects. 

In this section, we provide an overview of the monetized ozone benefits and PM2.5-

related co-benefits estimated from NOX reductions for compliance with the proposed CSAPR 

EGU NOX ozone season emissions budgets and for the more and less stringent alternatives. A 

full description of the underlying data, studies, and assumptions is provided in the PM NAAQS 

RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2015).. 

ES.5.1 Human Health Benefits and Climate Co-benefits

We follow a “damage-function” approach in calculating benefits, which estimates 

changes in individual health endpoints (specific effects that can be associated with changes in air 

quality) and assigns values to those changes assuming independence of the values for those 

individual endpoints. Because the EPA rarely has the time or resources to perform new research 

to measure directly either health outcomes or their values for regulatory analyses, our estimates 

are based on the best available methods of benefits transfer, which is the science and art of 

adapting primary research from similar contexts to estimate benefits for the environmental 

quality change under analysis. In addition to transferring information from other contexts to the 

context of this regulation, we also use a “benefit-per-ton” approach to estimate the ozone and 

PM2.5 benefits in this RIA. Benefit-per-ton approaches apply an average benefit- per- ton derived 

from modeling of benefits of specific air quality scenarios to estimates of emissions reductions 

for scenarios where no air quality modeling is available. More information on these approaches 

is available in Chapter 6 of the RIA. Thus, to develop estimates of benefits for this RIA, we are 

transferring both the underlying health and economic information from previous studies and 

information on air quality responses to emissions reductions from other air quality modeling. 
ES-10 

The benefit-per-ton approach we use in this RIA relies on estimates of human health 

responses to exposure to ozone and PM obtained from the peer-reviewed scientific literature. 

These estimates are used in conjunction with population data, baseline health information, air 

quality data and economic valuation information to conduct health impact and economic benefits 

assessments. These assessments form the key inputs to calculating benefit-per-ton estimates. 

The Health Impact Assessment (HIA) for ozone and PM2.5, discussed further in Chapter 6 

of this RIA, quantifies the changes in the incidence of adverse health impacts resulting from 

changes in human exposure to ozone and PM2.5. We use the environmental Benefits Mapping 

and Analysis Program – Community Edition (BenMAP-CE) (version 1.1) to systematize health 

impact analyses by applying a database of key input parameters, including population 

projections, health impact functions, and valuation functions (Abt Associates, 2012). For this 

assessment, the HIA is limited to those health effects that are directly linked to ambient ozone 

and PM2.5 concentrations. Table ES-3 provides national summaries of the reductions in estimated 

health incidences associated with the proposed CSAPR EGU NOx ozone-season emissions 

budgets and for more and less stringent alternatives for 2017. 
ES-11 

Table ES-3. Summary of Avoided Health Incidences from Ozone-Related and PM2.5-

Related Benefits from NOX reductions for the Proposal for 2017* 

Ozone-related Health Effects Proposal More Stringent 

Alternative 

Less Stringent 

Alternative 

Avoided Premature Mortality 

Smith et al. (2009) (all ages) 48 50 14 

Zanobetti and Schwartz (2008) (all ages) 81 83 2423

Avoided Morbidity 

Hospital admissions—respiratory causes (ages > 65) 79 81 2322

Emergency room visits for asthma (all ages) 320 330 9490

Asthma exacerbation (ages 6-18) 93,000 95,000 2726,000 

Minor restricted-activity days (ages 18-65) 240,000 240,000 7067,000 

School loss days (ages 5-17) 77,000 79,000 2322,000 

PM2.5-related Health Effects 

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 21 22 5.6 

Lepeule et al. (2012) (adult) 48 50 13 

Woodruff et al. (1997) (infant) 0<1 <10 <10

Avoided Morbidity 

Emergency department visits for asthma (all ages) 12 12 3.1 

Acute bronchitis (age 8–12) 31 32 8.1 

Lower respiratory symptoms (age 7–14) 390 400 100 

Upper respiratory symptoms (asthmatics age 9–11) 560 570580 150 

Minor restricted-activity days (age 18–65) 16,000 16,000 4,200 

Lost work days (age 18–65) 2,700 2,700 700 

Asthma exacerbation (age 6–18) 580 600 150 

Hospital admissions—respiratory (all ages) 6.4 6.5 1.7 

Hospital admissions—cardiovascular (age > 18) 7.8 8.0 2.1 

Non-Fatal Heart Attacks (age >18) 

Peters et al. (2001) 2526 2625 6.6 

Pooled estimate of 4 studies 2.78 2.87 0.71

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 ozone are based on 

ozone season NOX emissions. Confidence intervals are unavailable for this analysis because of the incidence-per-ton 

methodology. In general, the 95th percentile confidence interval for the health impact function alone ranges from 

approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012). 

There may be other indirect health impacts associated with reducing emissions, such as 

occupational health exposures. Epidemiological studies generally provide estimates of the 

relative risks of a particular health effect for a given increment of air pollution (often per 10 ppb 

for ozone or μg/m3 for PM2.5). These relative risks can be used to develop risk coefficients that 

relate a unit reduction in ozone to changes in the incidence of a health effect. We refer the reader 

to Chapter 6 of this RIA, as well as to the Ozone NAAQS RIA (U.S. EPA, 2015) and PM 

NAAQS RIA (U.S. EPA, 2012a) for more information regarding the epidemiology studies and 

risk coefficients applied in this analysis. 
ES-12 

Co-benefits of the proposed rule is come from reducing emissions of CO2. Chapter 6 of 

this RIA provides a brief overview of the 2009 Endangerment Finding and climate science 

assessments released since then. Chapter 6 also provides information regarding the economic 

valuation of CO2 using the social cost of carbon (SC-CO2), a metric that estimates the monetary 

value of impacts associated with marginal changes in CO2 emissions in a given year. 

ES.5.2 Combined Health Benefits and Climate Co-Benefits Estimates 

In this analysis, we were able to monetize the estimated benefits associated with the 

reduced exposure to ozone and PM2.5 and co-benefits of decreased emissions of CO2.

Specifically, we estimated combinations of health benefits at discount rates of 3 percent and 7 

percent (as recommended by the EPA’s Guidelines for Preparing Economic Analyses [U.S. 

EPA, 2014] and OMB’s Circular A-4 [OMB, 2003]) and climate co-benefits using four SC-CO2

estimates (the average SC-CO2 at each of three discount rates—5 percent, 3 percent, 2.5 

percent—and the 95th percentile SC-CO2 at 3 percent ) (as recommended in the current SC-CO2

TSD12; see Chapter 6 of this RIA for more details). In this analysis we were unable to monetize 

the co-benefits associated with reducing exposure to mercury, carbon monoxide (CO), SO2, and 

NO2, as well as ecosystem effects and visibility impairment. In addition, there are expected to be 

unquantified health and welfare impacts associated with changes in hydrogen chloride. 

Table ES-4 provides the combined health and climate benefits for the proposal and for 

more and less stringent alternatives for the 2017 analysis year. In the table, ranges within the 

total benefits rows reflect multiple studies upon which the estimates of premature mortality were 

derived. 

 

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

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

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

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

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

Department of Treasury (May 2013, Revised July 2015). Available at: https://www.whitehouse.gov/omb/oira/socialcost-of-carbon. 
ES-13 

Table ES-4. Combined Health Benefits and Climate Co-Benefits for the Proposed CSAPR 

EGU NOX Ozone Season Emissions Budgets and for More and Less Stringent 

Alternatives for 2017 (millions of 2011$)* 

SC-CO2 Discount Rate** 

Health and Climate Benefits 

(Discount Rate Applied to Health Co-Benefits) 

Climate CoBenefits Only 

3% 7%

Less Stringent Alternative 

5% $200 190 to $350340 $200 190 to $340330 $7.6 

3% $220 210 to $370360 $220 210 to $360350 $27 

2.5% $230 to $390380 $230 220 to $380370 $41 

3% (95th percentile) $270 260 to $420410 $270 260 to $410400 $78 

Proposal 

5% $680 to $1200 $660 to $1200 $6.5

3% $700 to $1200 $680 to $1200 $23

2.5% $710 to $1300 $690 to $1200 $35

3% (95th percentile) $740 to $1300 $720 to $1200 $66

More Stringent Alternative

5% $700 to $1300 $680 to $1200 $6.5 

3% $720 to $1300 $700 to $1200 $23 

2.5% $730 to $1300 $710 to $1200 $35 

3% (95th percentile) $760 to $1300 $740 to $1300 $66 

*All estimates are rounded to two significant figures. Co-benefits are based on benefit-per-ton estimates. Benefits 

for ozone and PM2.5 co-benefits are based on ozone season NOX emissions. Ozone benefits and PM2.5 co-benefits 

occur in the analysis year, so they are the same for all discount rates. The health benefits range is based on adult 

mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) with Zanobetti 

and Schwartz (2008)). Climate co-benefits are based on reductions in CO2 emissions. The monetized health cobenefits do not include reduced changes in health effects from directly emitted PM2.5, direct exposure to NO2, SO2, 

and hazardous air pollutants; ecosystem effects; or visibility impairment. 

**The SC-CO2 estimates are calculated with four different values of a one ton reduction. See RIA Chapter 6 for a 

complete discussion. 

ES.5.3 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 ozone and fine particles. 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 reduced 

exposure to several hazardous air pollutants (HAP) (including mercury), SO2 and NO2, as well as 

ecosystem effects, and visibility impairment from reduced NOX due to the absence of air quality 

modeling data for these pollutants in this analysis. This does not imply that there are no cobenefits associated with changes in emissions of HAP or reductions changes in exposures to SO2

and NO2. These co-benefits are listed in Table ES-5 below, and discussed more fully in Chapter 

6 of this RIA. 
ES-14 

Table ES-5. Unquantified Health and Welfare Co-benefits Categories 

Category Specific Effect 

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information 

Improved 

Human Health 

Reduced 

incidence of 

morbidity from 

exposure to 

NO2

Asthma hospital admissions 

(all ages) — — NO2 ISA1

Chronic lung disease hospital 

admissions (age > 65) — — NO2 ISA1

Respiratory emergency 

department visits (all ages) — — NO2 ISA1

Asthma exacerbation 

(asthmatics age 4–18) — — NO2 ISA1

Acute respiratory symptoms 

(age 7–14) — — NO2 ISA1

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects 

(e.g., airway 

hyperresponsiveness and 

inflammation, lung function, 

other ages and populations) 

— — NO2 ISA2,3

Reduced 

incidence of 

morbidity from 

exposure to SO2

Respiratory hospital 

admissions (age > 65) — — SO2 ISA1

Asthma emergency 

department visits (all ages) — — SO2 ISA1

Asthma exacerbation 

(asthmatics age 4–12) — — SO2 ISA1

Acute respiratory symptoms 

(age 7–14) — — SO2 ISA1

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects 

(e.g., airway 

hyperresponsiveness and 

inflammation, lung function, 

other ages and populations) 

— — SO2 ISA1,2

Reduced 

incidence of 

morbidity from 

exposure to CO

Cardiovascular effects — — CO ISA 1,2

Respiratory effects — — CO ISA 1,2,3

Central nervous system 

effects — — CO ISA 1,2,3

Premature mortality — — CO ISA 1,2,3

Reduced 

incidence of 

morbidity from 

exposure to 

methylmercury

Neurologic effects—IQ loss — — IRIS; NRC, 20001

Other neurologic effects 

(e.g., developmental delays, 

memory, behavior)

— — IRIS; NRC, 20002

Cardiovascular effects — — IRIS; NRC, 20002,3

Genotoxic, immunologic, 

and other toxic effects — — IRIS; NRC, 20002,3

Improved 

Environment 

Reduced 

visibility 

impairment 

Visibility in Class 1 areas — — PM ISA1

Visibility in residential areas — — PM ISA1

Reduced effects 

on materials 

Household soiling — — PM ISA1,2

Materials damage (e.g., 

corrosion, increased wear) — — PM ISA2
ES-15 

Category Specific Effect 

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information 

Reduced effects 

from PM 

deposition 

(metals and 

organics) 

Effects on Individual 

organisms and ecosystems — — PM ISA2

Reduced 

vegetation and 

ecosystem 

effects from 

exposure to 

ozone 

Visible foliar injury on 

vegetation — — Ozone ISA1

Reduced vegetation growth 

and reproduction — — Ozone ISA1

Yield and quality of 

commercial forest products 

and crops 

— — Ozone ISA1

Damage to urban ornamental 

plants — — Ozone ISA2

Carbon sequestration in 

terrestrial ecosystems — — Ozone ISA1

Recreational demand 

associated with forest 

aesthetics 

— — Ozone ISA2

Other non-use effects Ozone ISA2

Ecosystem functions (e.g., 

water cycling, 

biogeochemical cycles, net 

primary productivity, leafgas exchange, community 

composition) 

— — Ozone ISA2

Reduced effects 

from acid 

deposition 

Recreational fishing — — NOx SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and 

forestry effects — — NOx SOx ISA2

Recreational demand in 

terrestrial and aquatic 

ecosystems 

— — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., 

biogeochemical cycles) — — NOx SOx ISA2

Reduced effects 

from nutrient 

enrichment 

Species composition and 

biodiversity in terrestrial and 

estuarine ecosystems 

— — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

Recreational demand in 

terrestrial and estuarine 

ecosystems 

— — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., 

biogeochemical cycles, fire 

regulation) 

— — NOx SOx ISA2

Reduced 

vegetation 

effects from 

ambient 

exposure to SO2

and NOx

Injury to vegetation from 

SO2 exposure 

— — NOx SOx ISA2

Injury to vegetation from 

NOx exposure 

— — NOx SOx ISA2

Reduced 

ecosystem 

effects from 

Effects on fish, birds, and 

mammals (e.g., reproductive 

effects)

— — Mercury Study RTC2
ES-16 

Category Specific Effect 

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information 

exposure to 

methylmercury

Commercial, subsistence and 

recreational fishing — — Mercury Study RTC1

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

2We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods. 

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

concerns over the strength of the association.

ES.5 Results of Benefit-Cost Analysis 

Below in Table ES-6, we present the primary costs and benefits estimates for 2017. The 

net benefits of the proposal and more and less stringent alternatives reflect the benefits of 

implementing EGU NOX emissions reductions for the affected 23 states via the proposed FIPs 

minus the costs of those emissions reductions. 

The guidelines of OMB Circular A-4 require providing comparisons of social costs and 

social benefits at discount rates of 3 and 7 percent. Ideally, streams of social costs and social 

benefits over time would be estimated and the net present values of each would be compared to 

determine net benefits of the illustrative control strategy. The three different uses of discounting 

in the RIA – (i) construction of annualized costs, (ii) adjusting the value of mortality risk for lags

in mortality risk decreases, and (iii) adjusting the cost of illness for non-fatal heart attacks to 

adjust for lags in follow up costs – are all appropriate. We explain our discounting of benefits in 

Chapter 6 of the RIA, specifically the application , in which we apply discount rates of 3 and 7 

percent to air quality benefits and 2.5, 3, and 5 percent to climate co-benefitsin Chapter 6 of the 

RIA; we explain our discounting of costs, in which we use a single discount rate of 4.77%, 

percent, in Chapter 5. Our estimates of net benefits are the approximations of the net value (in 

2017) of benefits attributable to emissions reductions needed to implement the NOX emissions 

budgets for each state. 

Table ES-6. Total Costs, Total Monetized Benefits, and Net Benefits of the Proposal in 

2017 for U.S. (millions of 2011$)a,b,c

 

 Proposal More Stringent AAA Less Stringent 

Climate Co-Benefits $23 $23 $27 

 

 

Air Quality Health Benefits $70700670 to $1200 $720 690 to $1300 $690190 to $130040

 

Total Benefits $7200 to $1200 $720 to $1300 $210 to $360 
ES-17 

 

 

Annualized Compliance 

Costs

$93 

10

$96 $4.7 

 

Net Benefits $600 to $1100$630 to 

$1100

$620 to $1200$620 to 

$1200

$210 to $360$210 to 

 $360

Non-Monetized Benefitsd Non-quantified climate benefits 

Reductions in exposure to ambient NO2 and SO2

Ecosystem benefits assoc. with reductions in emissions of NOx, SO2, and PM 

Visibility impairment 

a Estimating multiple years of costs and benefits is limited for this RIA by data and resource limitations. As a result, 

we provide compliance costs and social benefits in 2017, using the best available information to approximate social 

compliance costs and social benefits recognizing uncertainties and limitations in those estimates. 

b

 Benefits ranges represent discounting of health benefits and climate co-benefits at a discount rate of 3% percent. 

See Chapter 6 for additional detail and explanation. The costs presented in this table reflect engineeringcompliance

costs annualized at a an approximately 54.77 percent discount rate possible and include monitoring, recordkeeping, 

and reporting costs. See Chapter 5 for additional detail and explanation. 

c All costs and benefits are rounded to two significant figures; rowscolumns may not appear to add correctly. 

 d Non-monetized benefits descriptions are for all three alternatives and are qualitative.

E.S.6 References

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Utility Generating Units (Final Rule), http://www2.epa.gov/cleanpowerplan/clean-powerplan-existing-power-plants. 

U.S. EPA, 2015a. Standards of Performance for Greenhouse Gas Emissions from New, 

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ES-18 

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U.S. EPA, 2008b. Control of Emissions for Nonroad Spark Ignition Engines and Equipment, 

http://www3.epa.gov/nonroad/. 

U.S. EPA, 2005, NOx Emission Standard for New Commercial Aircraft Engines, 

http://www3.epa.gov/otaq/aviation.htm. 

U.S. EPA, 2005a. Regional Haze Regulations and Guidelines for Best Available Retrofit 

Technology Determinations, http://www3.epa.gov/visibility/actions.html. 

Climate Co-Benefits $23 

 

Air Quality Health Benefits $700 to $1200 

Total Benefits $720 to $1200 

Annualized Compliance Costs $93 

10
ES-19 

Net Benefits $630 to $1100 

Non-Monetized Benefits Non-monetized climate benefits 

Reductions in exposure to ambient NO2 and SO2

Reductions in mercury deposition 

Ecosystem benefits assoc. with reductions in emissions of NOx, SO2, and 

Visibility impairment PM

a Estimating multiple years of costs and benefits is limited for this RIA by data and resource limitations. As a 

result, we provide a snapshot of costs and benefits in 2017, using the best available information 

to approximate social costs and social benefits recognizing uncertainties and limitations in those 

estimates. 

b

 Benefits ranges represent discounting of health benefits and climate co-benefits at a discount rate of 3%. See 

Chapter 6 for additional detail and explanation. The costs presented in this table reflect 

compliance costs estimated using IPM annualized at a 4.77 percent discount rate and, to the 

extent possible, monitoring, recordkeeping, and reporting costs. See Chapter 5 for additional 

detail and explanation. 

c All costs and benefits are rounded to two significant figures; rows may not appear to add 

correctly.
1-1 

CHAPTER 1: INTRODUCTION AND BACKGROUND

Introduction 

The EPA is proposing to update the Cross-State Air Pollution Rule (CSAPR) to reduce 

interstate transport of emissions of nitrogen oxides (NOX) that contribute significantly to 

nonattainment or interfere with maintenance of the 2008 Ozone National Ambient Air Quality 

Standard (NAAQS) in downwind states. The primary purpose of the proposal (hereafter referred 

to as the “proposal to update CSAPR”, or simply “the proposal”), is to address interstate air 

quality problems with respect to the 2008 ozone NAAQS. The proposal also responds to the D.C. 

Circuit’s July 28, 2015 remand of certain CSAPR ozone-season NOX emission budgets to the 

EPA for reconsideration. This Regulatory Impact Analysis (RIA) presents the health and welfare 

benefits of the proposal to update CSAPR, and compares the benefits of the rule to the estimated 

costs of implementing the rule in 2017. This chapter contains background information relative to 

the rule and an outline of the chapters of this RIA. 

1.1 Background 

Clean Air Act (CAA or the Act) section 110(a)(2)(D)(i)(I), sometimes called the “good 

neighbor provision,” requires states to prohibit emissions that will contribute significantly to 

nonattainment in, or interfere with maintenance by, any other state with respect to any primary or 

secondary NAAQS.13 The EPA promulgated CSAPR on July 6, 201114 to address interstate 

transport for the 1997 ozone NAAQS and the 1997 and 2006 fine particulate matter (PM2.5) 

NAAQS.15 (See section IV of the preamble to the proposal to update CSAPR for a discussion of 

CSAPR litigation and implementation.) 

As described in the preamble for the proposal, CSAPR provides a 4-step process to 

address the requirements of the good neighbor provision for ozone or PM2.5 standards: (1) 

 

13 The EPA uses the term “states” to include the District of Columbia in this RIA. 

14 See 76 FR 48208 (July 6, 2011) 

15 CSAPR did not evaluate transport obligations for the 2008 ozone standard because the 2008 ozone NAAQS was 

under reconsideration during the analytic work for the ruleCSAPR. 
1-2 

identifying downwind receptors that are expected to have problems attaining or maintaining 

clean air standards (i.e., NAAQS); (2) determining which upwind states contribute to these 

problems in amounts sufficient to “link” them to the downwind air quality problems; (3) for 

states linked to downwind air quality problems, identifying upwind emissions that significantly 

contribute to nonattainment or interfere with maintenance by quantifying upwind NOX

reductions and apportioning upwind responsibility (this step establishes emissions budgets, 

which are remaining allowable emissions after reducing significant contribution to nonattainment 

and interference with maintenance); and (4) for states that are found to have emissions that 

significantly contribute to nonattainment or interfere with maintenance of the NAAQS 

downwind, reducing the identified upwind NOX emissions via regional emissions allowance 

trading programs. In this action, the EPA proposes to apply this 4-step process to update CSAPR 

with respect to the 2008 ozone NAAQS. The reductions required by the proposed rule would be 

achieved through a FIP in any state that does not have an approved SIP addressing its 

contribution by the date this rule is finalized. Furthermore, under the FIPs, affected EGUs would 

participate in the CSAPR NOX ozone-season allowance trading program. DMore details on the 

methods and results of applying this process can be found in the preamble for this proposal and 

in Chapter 4 of this RIA. The 2008 ozone NAAQS is an 8-hour standard that was set at 75 parts 

per billion (ppb). See 73 FR 16436 (March 27, 2008). 

1.2.1 Role of Executive Orders in the Regulatory Impact Analysis 

Several statutes and executive orders apply to any public document. The analyses 

required by these statutes and executive orders are presented in detail in Chapter 7, and below we 

briefly discuss the requirements of Executive Orders 12866 and 13563 and the guidelines of the 

Office Of Management and Budget (OMB) Circular A-4 (U.S. OMB, 2003). 

In accordance with Executive Orders 12866 and 13563 and the guidelines of OMB 

Circular A-4, the RIA analyzes the benefits and costs associated with emissions reductions for 

compliance with the proposal to update CSAPR. OMB Circular A-4 requires analysis of one 

potential alternative standard level more stringent than the proposal and one less stringent than 

the proposal. This RIA evaluates the benefits, costs, and certain impacts of a more and a less 

stringent alternative to the proposal. 
1-3 

1.2.2 Illustrative Nature of this Analysis 

The EPA proposes to implement the proposed EGU NOX emissions budgets via updating 

the CSAPR regional NOX ozone-season allowance trading program. This implementation 

approach provides utilities with the flexibility to determine their own compliance path. This RIA 

develops and analyzes one possible scenario for compliance with the NOx budgets proposed by 

this action and possible scenarios for EGU compliance with more and less stringent alternatives. 

1.2.3 The Need for Air Quality or Emissions Standards 

 OMB Circular A-4 indicates that one of the reasons a regulation may be issued is to 

address a market failure. The major types of market failure include: externalities, market power, 

and inadequate or asymmetric information. Correcting market failures is one reason for 

regulation; it is not the only reason. Other possible justifications include improving the function 

of government, correcting distributional unfairness, or securing privacy or personal freedom. 

 Environmental problems are classic examples of externalities – uncompensated benefits 

or costs imposed on another party as a result of one’s actions. For example, the smoke from a 

factory may adversely affect the health of local residents and soil the property in nearby 

neighborhoods. Pollution emitted in one state may be transported across state lines and affect air 

quality in a neighboring state. If bargaining were costless and all property rights were well 

defined, people would eliminate externalities through bargaining without the need for 

government regulation. 

 From an economics perspective, setting an emissions standard (i.e., EGU NOX ozoneseason emissions budgets in this proposal) is a straightforward remedy to address an externality 

in which firms emit pollutants, resulting in health and environmental problems without 

compensation for those incurring the problems. Setting the emissions standard attempts to 

incentivize those who emit the pollutants to reduce their emissions, which lessens the impact on 

those who suffer the health and environmental problems from higher levels of pollution. 
1-4 

1.2 Overview and Design of the RIA 

1.2.1 Methodology for Identifying Needed Required Reductions 

Application of the first two steps of the CSAPR process (described above) with respect to 

the 2008 ozone NAAQS provides the analytic basis for proposing that ozone season emissions in 

23 eastern states16 affect the ability of downwind states to attain and maintain the 2008 ozone 

NAAQS. Figure 1-1 shows the affected states.

Figure 1-1. Nonattainment and Maintenance Receptors Identified for this Proposal and 

Upwind States Linked to these Downwind Air Quality Problems with Respect to 

the 2008 Ozone NAAQS 

 

16 Alabama, Arkansas, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, 

Missouri, New Jersey, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas, Virginia, 

West Virginia, and Wisconsin 
1-5 

 Applying Step 3 of this process, this action proposes to quantify electric generating unit 

(EGU) NOX reductions in these 23 eastern states and to update CSAPR ozone season NOX

emissions budgets. A state’s CSAPR ozone season NOX emissions budget is the quantity of EGU 

NOX emissions that would remain after reducing significant contribution to nonattainment and 

interference with maintenance of the 2008 ozone NAAQS in an average year.17 These NOX

budgets were developed considering EGU NOX reductions that are achievable for the 2017 ozone 

season.18 The EPA applied a multi-factor test to evaluate EGU NOX reduction potential for 2017 

and proposes to quantify EGU NOX ozone-season emissions budgets reflecting EGU NOX 

reduction strategies that are widely available at a uniform cost of $1,300 per ton (2011$). This 

assessment revealed that there is significant EGU NOX reduction potential that can be achieved 

by 2017, which would make meaningful and timely improvements in ozone air quality. Applying 

Step 4 of this process, the EPA is proposinges to implement these EGU NOX emissions budgets 

through the CSAPR NOX ozone-season allowance trading program.

For the RIA, in order to implement the OMB Circular A-4 requirement to assess one less 

stringent and one more stringent alternative to the proposal, the EPA is also analyzing EGU NOX

ozone season emissions budgets reflecting NOX reduction strategies that are widely available at a 

uniform cost of $500 per ton (2011$) and strategies that are widely available at a uniform cost of 

$3,400 per ton (2011$). 

Applying Step 4 of this process, the EPA proposes to implement these EGU NOX

emissions budgets through the CSAPR NOX ozone-season allowance trading program.

 

17 For example, assuming no abnormal variation in electricity supply due to events such as abnormal meteorology. 

18 Non-EGU NOx emission control measures and reductions are not included in this proposal. More information on 

non-EGU control measures and the potential for emission reductions can be found in the Non-EGU TSD on the 

Assessment of Non-EGU NOx Emission Controls, Cost of Controls, and Time for Compliance s and Potential 

Reductions in 2017, which can be found in the docket for this proposal. 
1-6 

1.2.2 States Covered by the Proposed Rule 

 For each state that would be affected by one of the proposed federal implementation 

plans (FIPs)

19 (as shown in Figure ES-1), and that is already included in the CSAPR NOX ozoneseason trading program to address interstate ozone transport for the 1997 NAAQS, the proposed 

rule would lower EGU NOX ozone-season emissions budgets to reduce ozone transport for the 

2008 ozone NAAQS. One state, Kansas, would have a new CSAPR ozone-season requirement 

under this proposal. The remaining 22 states were included in the original CSAPR ozone-season 

program as to the 1997 ozone NAAQS. 

1.2.3 Regulated Entities 

The proposed rule affects fossil fuel-fired EGUs in these 23 eastern states, and regulates

utilities (electric, natural gas, other systems) which are classified as code 221112 by the North 

American Industry Classification System (NAICS) and have a nameplate capacity of greater than 

25 megawatts (MWe). 

1.2.4 Baseline and Analysis Year 

As described in the preamble, the EPA proposes to align implementation of this rule with 

relevant attainment dates for the 2008 ozone NAAQS, as required by the D.C. Circuit’s decision 

North Carolina v. EPA.

20 The EPA’s final 2008 Ozone NAAQS SIP Requirements Rule revised 

the attainment deadline for ozone nonattainment areas currently designated as moderate from 

December 2018 to July 2018 in accordance with the D.C. Circuit’s decision in NRDC v. EPA.

21

 

Because July 2018 falls during the 2018 ozone season, the 2017 ozone season will be the last full 

season from which data can be used to determine attainment of the NAAQS by the July 2018 

attainment date. We believe that North Carolina compels the EPA to identify upwind reductions 

 

19 Alabama, Arkansas, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, 

Missouri, New Jersey, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas, Virginia, 

West Virginia, and Wisconsin. 

20 531 F.3d 896, 911-12 (D.C. Cir. 2008) (holding that EPA must coordinate interstate transport compliance 

deadlines with downwind attainment deadlines).

21 777 F.3d 456, 469 (D.C. Cir. 2014).
1-7 

and implementation programs to achieve these reductions, to the extent possible, for the 2017 

ozone season. 

The proposal to update CSAPR sets forth the requirements for states to reduce their 

significant contribution to downwind nonattainment and interference with maintenance of the 

2008 ozone NAAQS. To develop and evaluate control strategies for addressing these obligations, 

it is important to first establish a baseline projection of air quality in the analysis year of 2017, 

taking into account currently on-the-books Federal regulations, substantial Federal regulatory 

proposals, enforcement actions, state regulations, population, and where possible, economic 

growth. Establishing this baseline for the analysis then allows us to estimate the incremental 

costs and benefits of the additional emissions reductions that will be achieved by the transport 

rule. 

Furthermore, the analysis in this RIA focuses on benefits, costs and certain impacts in 

2017. as well as cCertain impacts in 2020, such as forecast emissions changes from the 

electricity sector, are also reported in this RIA. The results from the analysis in support of this 

proposal that are reported in this RIA areis limited to these two analysis years as because 

important regulatory actions, including the Clean Power Plan (CPP) and potentially the 2015 

ozone NAAQS, are expected to have an important and uncertain influence on the electricity 

sector in later years, as explained below. For this reason, the EPA expects that most of the 

proposed CSAPR update’s influence on emissions reductions will occur between 2017 and 2020. 

 The first year that EGUs must comply with the CPP is 2022 and investments in 

renewable generation and energy efficiency in 2020 and 2021 are expected to influence 

generation patters in the electricity sector in those years, The CPP is not anticipated to have 

significant interactions with the CPP and the near-term (i.e., starting in 2017) ozone-season EGU 

NOX emission reduction requirements under this proposal. States must submit a state plan, or an 

initial submittal with an extension request, by September 6, 2016 describing how they will 

implement the emissions guidelines of the final CPP. See section VII.E of the preamble for 

further discussion. 

As discussed in the RIA for the final 2015 ozone NAAQS, it is assumed that potential 

nonattainment areas everywhere in the U.S., excluding California, will be designated such that 
1-8 

they are required to attain the revised standard by 2025. Furthermore, as discussed in the memo 

to EPA Regional Administrators, Implementing the 2015 Ozone National Ambient Air Quality 

Standards, implementation of the 2015 ozone NAAQS may use the framework of the CSAPR. 

Doing so may influence compliance with the budgets that are the subject of this proposal. 

In addition to other on-the-books Federal regulations, the baseline for the analysis of the 

benefits, costs and certain impacts of this proposal includes CSAPR phase II NOx ozone-season 

emissions budgets. As discussed in section III.C of the preamble, in EME Homer City II22, the 

D.C. Circuit declared invalid the CSAPR phase 2 ozone-season NOx emissions budgets of 11 

states. Because the proposed rule modeling was performed prior to the D.C. Circuit’s issuance of 

EME Homer City II, the modeling assumes that the baseline for all states includes the emission 

reductions associated with the CSAPR phase 2 ozone-season budgets. Furthermore, as these 

budgets were remanded, it is reasonable to treat them, for the purposes of the analysis in this 

RIA, as on-the-books Federal regulations.23 As discussed in the preamble, this proposal 

incorporates revised emissions budgets that would supplant and replace the budgets promulgated 

in the original CSAPR rule including the remanded budgets. Furthermore, these proposed 

budgets would be effective for the 2017 ozone season, the same period in which the phase 2 

budgets that were invalidated by the court are currently scheduled to become effective. Of the 11 

states for which the CSAPR phase 2 ozone-season NOx emissions budgets were remanded, 9 are 

among the 23 states for which budgets are established in this proposal. They are: Maryland, New 

Jersey, New York, North Carolina, Ohio, Pennsylvania, Texas, Virginia, and West Virginia. The 

baseline and analysis year are discussed in more detail in Chapters 3 and 5 of this RIA.

In addition, on June 29, 2015, the United States Supreme Court reversed the D.C. Circuit 

opinion affirming the MATS. The EPA is reviewing the decision and will determine any 

appropriate next steps once the review is complete. MATS is included in the baseline for this 

 

22 EME Homer City Generation, L.P., v. EPA, No. 795 F.3d 118, 129-30, 138 (EME Homer City II).

23 As discussed in the preamble, in the modeling for the final rulemaking for this rule the implementation of the 4-

step process to address the requirements of CAA section 110(a)(2)(D)(i)(I) will the EPA may no longer assume that 

emissions from sources in states that are affected by the remand are constrained by the ozone-season NOX emissions 

budgets that the court declared invalid in EME Homer City II. If that decision is also applied to the baseline for the 

analysis of the benefits, costs and certain impacts of this rule, then this update to CSAPR is expected to lead to 

greater emission reductions than if the baseline included the phase 2 CSAPR NOX ozone-season emissions budgets. 
1-9 

analysis, and the EPA does not believe including MATS substantially alters the results of this 

analysis, because MATS was remanded, not vacated. 

1.2.5 Emissions Controls and Cost Analysis Approach

 The EPA estimated the control strategies and compliance costs of the proposed rule using 

the Integrated Planning Model (IPM) as well as certain costs that are estimated outside the 

model, but use IPM inputs for their estimation. These cost estimates reflect costs incurred by the 

power sector, and include (but are not limited to) the costs of purchasing, installing, and 

operating NOX control technology, changes in fuel costs, and changes in the generation mix. A 

description of the methodologies used to estimate the costs and economic impacts to the power 

sector is contained in chapter 5 of this RIA.

1.2.6 Benefits Analysis Approach 

 The EPA estimated human health benefits (i.e., mortality and morbidity effects) 

considering an array of health impacts attributable to changes in exposure to ozone and fine 

particulate matter (PM2.5). We estimated these benefits using benefit-per-ton estimates derived 

from the BenMAP tool. The EPA also estimated the climate co-benefits of the proposal. A 

description of the methodologies used to estimate the human health and climate benefits is 

contained in chapter 6 of this RIA. In addition, chapter 6 contains a discussion of welfare cobenefits, such as ecosystem benefits from reduced nitrogen deposition. 

1.3 Organization of the Regulatory Impact Analysis 

 This RIA is organized into the following remaining chapters: 

• Chapter 2: Electric Power Sector Profile. This chapter describes the electric power sector 

in detail. 

• Chapter 3: Emissions and Air Quality Modeling Impacts. The data, tools, and 

methodology used for the air quality modeling are described in this chapter, as well as the 

post-processing techniques used to produce a number of air quality metrics for input into 

the analysis of benefits and costs. 

• Chapter 4: Regulatory Control Scenarios. The chapter summarizes the rationale for the 

three regulatory control alternatives analyzed and how the requirements of these 

alternatives are represented in IPM. 
1-10 

• Chapter 5: Cost, Economic, Employment, and Energy Impacts. The chapter summarizes 

the data sources and methodology used to estimate the costs and other impacts incurred 

by the power sector. 

• Chapter 6: Benefits Analysis Results. The chapter quantifies the health-related benefits of 

the ozone-related air quality improvements associated with the three regulatory control 

alternatives analyzed. 

• Chapter 7: Statutory and Executive Order Impact Analyses. The chapter summarizes the 

Statutory and Executive Order impact analyses. 

• Chapter 8: Comparison of Benefits and Costs. The chapter compares estimates of the 

total benefits with total costs and summarizes the net benefits of the three alternative 

regulatory control scenarios analyzed. 

 
2-1 

CHAPTER 2: ELECTRIC POWER SECTOR PROFILE

Overview 

This chapter discusses important aspects of the power sector that relate to today’s proposal 

to update CSAPR with respect to the interstate transport of emissions of nitrogen oxides (NOX) 

that contribute significantly to nonattainment or interfere with maintenance of the 2008 ozone 

NAAQS in downwind states. This chapter describes types of existing power-sector sources 

affected by the proposed regulation, and provides background on the power sector and electricity 

generating units (EGUs). In addition, this chapter provides some historical background on trends 

in the past decade in the power sector, as well as about existing EPA regulation of the power 

sector. 

2.1 Background 

In the past decade there have been significant structural changes in both the mix of 

generating capacity and in the share of electricity generation supplied by different types of 

generation. These changes are the result of multiple factors in the power sector, including normal 

replacements of older generating units with new units, changes in the electricity intensity of the 

U.S. economy, growth and regional changes in the U.S. population, technological improvements 

in electricity generation from both existing and new units, changes in the prices and availability 

of different fuels, and substantial growth in electricity generation by renewable and 

unconventional methods. Many of these trends will continue to contribute to the evolution of the 

power sector. The evolving economics of the power sector, in particular the increased natural gas 

supply and subsequent relatively low natural gas prices, have resulted in more gas being utilized 

as base load energy in addition to supplying electricity during peak load. This chapter presents 

data on the evolution of the power sector from 2000 through 2013. Projections of future power 

sector behavior and the impact of this rule are discussed in more detail in chapters 3 and 5 of this 

RIA. 
2-2 

2.2 Power Sector Overview 

The production and delivery of electricity to customers consists of three distinct 

segments: generation, transmission, and distribution. 

2.2.1 Generation 

Electricity generation is the first process in the delivery of electricity to consumers. There 

are two important aspects of electricity generation; capacity and net generation. Generating 

Capacity refers to the maximum amount of production an EGU is capable of producing in a 

typical hour, typically measured in megawatts (MW) for individual units, or gigawatts (1 GW = 

1000 MW) for multiple EGUs. Electricity Generation refers to the amount of electricity actually 

produced by an EGU over some period of time, measured in kilowatt-hours (kWh) or gigawatthours (GWh = 1 million kWh). Net Generation is the amount of electricity that is available to the 

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

generating station for operations). Electricity generation is most often reported as the total annual 

generation (or some other period, such as seasonal). In addition to producing electricity for sale 

to the grid, EGUs perform other services important to reliable electricity supply, such as 

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

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

generators include facilitating the regulation of the voltage of supplied generation. 

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

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

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

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

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

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

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

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

season or year. 

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

steam that is released to rotate turbines which, in turn, create electricity. Natural gas combined 
2-3 

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

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

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

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

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

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

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

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

generating capacity in 2000 and 2013. 

In 2013 the power sector consisted of over 19,000 generating units with a total capacity24 

of 1,168 GW, an increase of 301 GW (or 35 percent) from the capacity in 2000 (867 GW). The 

301 GW increase consisted primarily of natural gas fired EGUs (246 GW) and wind generators 

(58 GW), with substantially smaller net increases and decreases in other types of generating 

units. 

Table 2-1. Existing National Electricity Generating Capacity by Energy Source, 

2000 and 2013 

 2000 2013 Change Between '00 and '13 

Energy Source 

Generator 

Nameplate 

Capacity 

(MW) 

% Total 

Capacity 

Generator 

Nameplate 

Capacity 

(MW) 

% Total 

Capacity 

% 

Increase 

Nameplate 

Capacity 

Change 

(MW) 

% of 

Total 

Capacity 

Increase 

Coal 336,247 39% 329,815 29% -2% -6,433 -2% 

Natural Gas 242,602 28% 488,169 42% 101% 245,567 82% 

Nuclear 104,734 12% 104,424 9% 0% -311 0% 

Hydro 95,879 11% 100,182 8% 4% 4,303 1% 

 

24 As with all data presented in this section, this includes generating capacity not only at EGUs primarily operated to 

supply electricity to the grid, but also generating capacity at commercial and industrial facilities that produce both 

electricity used onsite as well as dispatched to the grid. Unless otherwise indicated, capacity data presented in this 

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

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

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

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

use a variety of definitions and specified conditions. Natural Gas information in this chapter (unless otherwise 

stated) reflects data for all generating units using natural gas as the primary fossil heat source. This includes 

Combined Cycle Combustion Turbine, Gas Turbine, and miscellaneous (< 1 percent) 
2-4 

Petroleum 68,080 8% 49,794 5% -27% -18,287 -6% 

Wind 2,394 0.3% 60,712 5.1% 2436% 58,318 19% 

Other 

Renewable 14,047 1.6% 25,748 1.8% 83.3% 11,701 3.9%

Misc 3,079 0.4% 5,180 0.4% 68.2% 2,101 0.7% 

Total 867,062 100% 1,167,995 100% 35% 300,933 100% 

Note: This table presents generation capacity. Actual net generation is presented in Table 2-2. 

Source: U.S. EIA. Downloaded from EIA Electricity Data Browser, Electric Power Plants 

Generating Capacity By energy source, by producer, by state back to 2000 (annual data from 

EIA Form 860). Available online at: <http://www.eia.gov/electricity/data.cfm#gencapacity.> 

Accessed 8/7/15. 

The 35 percent increase in generating capacity is the net impact of newly built generating 

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

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

controls, etc. During the period 2000 to 2013, a total of 408,022 MW of new generating capacity 

was built and brought online, and 83,240 MW existing units were retired. The net effect of the 

re-rating of existing units reduced the total capacity by 23,848 MW. The overall net change in 

capacity was 300,933 MW, as shown in Table 2-1. 

The newly built generating capacity was primarily natural gas (307,764 MW), which was 

partially offset by gas retirements (36,876 MW). Wind capacity was the second largest type of 

new builds (58,477 MW), augmented by solar (6,273 MW).25 The overall mix of newly built and 

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

 

25 Partially offset by 86 MW retired older wind or solar capacity. 
2-5 

Figure 2-1 National New Build and Retired Capacity (MW) by Fuel Type, 2000-201326 

The information in Table 2-1 and Figure 2-1 present information about the generating 

capacity in the entire U.S. The proposal to update CSAPR, however, directly affects EGUs in 23 

eastern states (i.e., the CSAPR 2008 Ozone Region), as discussed in Chapter 1. The share of 

generating capacity from each major type of generation differs between the CSAPR 2008 Ozone 

Region and the rest of the U.S. (non-region). Figure 2-2 shows the mix of generating capacity for 

each region. The overall capacity and generation in the CSAPR 2008 Ozone Region is 60% of 

the national total, reflecting the larger total population in the region. The mix of capacity and 

generation are noticeably different in the two regions. In the CSAPR 2008 Ozone Region, coal 

makes up a significantly larger share of total capacity (34 percent) than it does in the rest of the 

country (19%). The shares of natural gas, however, are quite similar (41% in the region, and 43% 

in the rest of country). The difference in the share of coal’s capacity is balanced by relatively 

more hydro, wind, solar and other capacity in the rest of country compared to the CSAPR 2008 

Ozone Region. 

 

26 Source: EIA Form 860. Not visible: wind and solar retirements = 87 MW, net change in coal 

capacity = -4,186 MW 

-50,000

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

New Build Retirement Net Change

Coal Nat Gas Wind & Solar Oil & Other
2-6 

Figure 2-2 Regional Differences in Generating Capacity (MW), 2013. 

Source: EIA Form 860 Note: “Other” includes petroleum, geothermal, other renewable, waste materials and 

misc. 

In 2013, electric generating sources produced a net 4,058 trillion kWh to meet national 

electricity demand, a 7 percent increase from 2000. As presented in Table 2-2, almost 70 percent 

of electricity in 2013 was produced through the combustion of fossil fuels, primarily coal and 

natural gas, with coal accounting for the largest single share. Although the share of the total 

generation from fossil fuels in 2013 (67 percent) was only modestly smaller than the total fossil 

share in 2000 (71 percent), the mix of fossil fuel generation changed substantially during that 

period. Coal generation declined by 18 percent and petroleum generation by 76 percent, while 

natural gas generation increased by 83 percent. This reflects both the increase in natural gas 

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

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

2000 to 4.1 percent of the 2013 total. 

Table 2-2. Net Generation in 2000 and 2013 (Trillion kWh = TWh) 

2000 2013 Change Between '00 and '13 

 

Net 

Generation 

(TWh) 

Fuel 

Source 

Share 

Net 

Generation 

(TWh) 

Fuel 

Source 

Share 

Net 

Generation 

Change 

(TWh) 

% Change in 

Net 

Generation 

Coal 1,966.3 52% 1,586.0 39% -380.3 -19.3% 

Natural Gas 615.0 16% 1,125.9 28% 510.9 83.1% 

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

In Region Non-Region

Generating Capacity (MW)

Coal Gas Nuclear Hydro Wind & Solar Other
2-7 

Nuclear 753.9 20% 789.0 19% 35.1 4.7% 

Hydro 270.0 7% 264.7 7% -5.3 -2.0% 

Petroleum 111.2 2.9% 26.9 0.7% -84.4 -75.8% 

Wind 5.6 0.1% 167.7 4.1% 162.1 - 

Other Renewable 75.3 2.0% 85.7 2.1% 10.4 13.7% 

Misc 4.8 0.1% 12.4 0.3% 7.6 157.7% 

Total 3,802 100% 4,058 100% 256 7%

Source: U.S. EIA Monthly Energy Review, December 2014. Table 7.2a Electricity Net 

Generation: Total (All Sectors). Available online at: 

<http://www.eia.gov/totalenergy/data/monthly/>. Accessed 12/19/2014 

Coal-fired and nuclear generating units have historically supplied “base load” electricity, 

the portion of electricity loads which are continually present, and typically operate throughout all 

hours of the year. The coal units meet the part of demand that is relatively constant. Although 

much of the coal fleet operates as base load, there can be notable differences across various 

facilities (see Table 2-3). For example, coal-fired units less than 100 megawatts (MW) in size 

compose 37 percent of the total number of coal-fired units, but only 6 percent of total coal-fired 

capacity. Gas-fired generation is better able to vary output and is the primary option used to meet 

the variable portion of the electricity load and has historically supplied “peak” and 

“intermediate” power, when there is increased demand for electricity (for example, when 

businesses operate throughout the day or when people return home from work and run appliances 

and heating/air-conditioning), versus late at night or very early in the morning, when demand for 

electricity is reduced. 

Table 2-3 also shows comparable data for the capacity and age distribution of natural gas 

units. Compared with the fleet of coal EGUs, the natural gas fleet of EGUs is generally smaller 

and newer. While 55 percent of the coal EGU fleet is over 500 MW per unit, 77 percent of the 

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

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

Thermal Efficiency in 2013 (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 
2-8 

0 – 24 223 18% 40.7 11.4 2,538 1% 11,733 

25 – 49 108 9% 44.2 36.7 3,963 1% 11,990 

50 – 99 157 12% 49.0 74.1 11,627 4% 11,883 

100 - 149 128 10% 50.6 122.7 15,710 5% 10,971 

150 - 249 181 14% 48.7 190.4 34,454 11% 10,620 

250 - 499 205 16% 38.4 356.2 73,030 23% 10,502 

500 - 749 187 15% 35.4 604.6 113,056 36% 10,231 

750 - 999 57 5% 31.4 823.9 46,963 15% 9,942 

1000 - 1500 11 1% 35.7 1259.1 13,850 4% 9,732 

Total Coal 1257 100% 42.6 250.7 315,191 100% 11,013

NATURAL GAS 

0 – 24 1992 37% 37.6 7.0 13,863 3% 13,531 

25 – 49 410 8% 21.8 125.0 51,247 12% 9,690 

50 – 99 962 18% 15.6 174.2 167,536 39% 8,489 

100 - 149 802 15% 23.4 39.9 31,982 8% 11,765 

150 - 249 167 3% 28.7 342.4 57,179 13% 9,311 

250 - 499 982 18% 24.6 71.1 69,788 16% 12,083 

500 - 749 37 1% 40.0 588.8 21,785 5% 11,569 

750 - 1000 14 0.3% 35.9 820.9 11,492 3% 10,478 

Total Gas 5366 100% 27.7 79.2 424,872 100% 11,652 

Source: National Electric Energy Data System (NEEDS) v.5.14 

Note: The average heat rate reported is the mean of the heat rate of the units in each size category (as opposed to a 

generation-weighted or capacity-weighted average heat rate.) A lower heat rate indicates a higher level of fuel 

efficiency. Table is limited to coal-steam units in operation in 2013 or earlier, and excludes those units in NEEDS 

with planned retirements in 2014 or 2015. 

 In terms of the age of the generating units, 50 percent of the total coal generating capacity 

has been in service for more than 38 years, while 50 percent of the natural gas capacity has been 

in service less than 15 years. Figure 2-2 presents the cumulative age distributions of the coal and 

gas fleets, highlighting the pronounced differences in the ages of the fleets of these two types of 

fossil-fuel generating capacity. Figure 2-3 also includes the distribution of generation, which is 

similar to the distribution of capacity. 
2-9 

Figure 2-3. Cumulative Distribution in 2013 of Coal and Natural Gas Electricity 

Capacity and Generation, by Age 

Source: National Electric Energy Data System (NEEDS) v.5.15 

Not displayed: coal units (376 MW total, 1 percent of total) and gas units (62 MW, < .01 percent 

of total)) over 70 years old for clarity. Figure is limited to coal-steam units in NEEDS v5.13 in 

operation in 2013 or earlier (excludes ~2,100 MW of coal-fired IGCC and fossil waste capacity), 

and excludes those units in NEEDS with planned retirements in 2014 or 2015. 

The locations of existing fossil units in EPA’s National Electric Energy Data System 

(NEEDS) v.5.15 are shown in Figure 2-4. 
2-10 

Figure 2-4. Fossil Fuel-Fired Electricity Generating Facilities, by Size 

Source: National Electric Energy Data System (NEEDS) v.5.15 

Note: This map displays fossil capacity at facilities in the NEEDS v.5.15 IPM frame. NEEDS 

v.5.15 reflects generating capacity expected to be on-line at the end of 2016. This includes 

planned new builds already under construction and planned retirements. In areas with a dense 

concentration of facilities, some facilities may be obscured. 

2.2.2 Transmission 

Transmission is the term used to describe the bulk transfer of electricity over a network 

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

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

high voltage transmission lines,27 each operating synchronously. Within each of these 

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

and controlled by regional organizations to ensure that electricity generation and load are kept in 

 

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

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

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

Interconnection), and the Texas Interconnection (which encompasses the portion of the Texas electricity system 

commonly known as the Electric Reliability Council of Texas (ERCOT)). See map of all NERC interconnections at 

http://www.nerc.com/AboutNERC/keyplayers/Documents/NERC_Interconnections_Color_072512.jpg 
2-11 

balance. In some areas, the operation of the transmission system is under the control of a single 

regional operator;28 in others, individual utilities29 coordinate the operations of their generation, 

transmission, and distribution systems to balance the system across their respective service 

territories. 

2.2.3 Distribution 

Distribution of electricity involves networks of lower voltage lines and substations that 

take the higher voltage power from the transmission system and step it down to lower voltage 

levels to match the needs of customers. The transmission and distribution system is the classic 

example of a natural monopoly, in part because it is not practical to have more than one set of 

lines running from the electricity generating sources to substations or from substations to 

residences and businesses. 

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

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

operation. Historically, vertically integrated utilities established much of the existing 

transmission infrastructure. However, as parts of the country have restructured the industry, 

transmission infrastructure has also been developed by transmission utilities, electric 

cooperatives, and merchant transmission companies, among others. Distribution, also historically 

developed by vertically integrated utilities, is now often managed by a number of utilities that 

purchase and sell electricity, but do not generate it. As discussed below, electricity restructuring 

has focused primarily on efforts to reorganize the industry to encourage competition in the 

generation segment of the industry, including ensuring open access of generation to the 

transmission and distribution services needed to deliver power to consumers. In many states, 

such efforts have also included separating generation assets from transmission and distribution 

assets to form distinct economic entities. Transmission and distribution remain price-regulated 

throughout the country based on the cost of service. 

 

28 E.g., PMJ Interconnection, LLC, Western Area Power Administration (which comprises 4 sub-regions). 

29 E.g., Los Angeles Department of Power and Water, Florida Power and Light. 
2-12 

2.3 Sales, Expenses, and Prices 

These electric generating sources provide electricity for ultimate commercial, industrial 

and residential customers. Each of the three major ultimate categories consume roughly a quarter 

to a third of the total electricity produced30 (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 2000 and 2013. 

Table 2-4. Total U.S. Electric Power Industry Retail Sales, 2000 and 2013 (billion kWh) 

 2000 2013 

 

Sales/Direct 

Use (Billion 

kWh) 

Share of Total 

End Use 

Sales/Direct 

Use (Billion 

kWh) 

Share of Total End 

Use 

Sales 

Residential 1,192 33% 1,395 36%

Commercial 1,055 29% 1,344 35%

Industrial 1,064 30% 978 25%

Transportation NA 8 0.2%

Other 109 3% NA

Total 

3,421 95% 3,725 96%

Direct Use 171 5% 143 4% 

Total End Use 3,592 100% 3,869 100% 

Source: Table 2.2, EIA Electric Power Annual, 2013 and 2010 

Notes: Retail sales are not equal to net generation (Table 2-2) because net generation includes 

net exported electricity and loss of electricity that occurs through transmission and 

distribution. 

Direct Use represents commercial and industrial facility use of onsite net electricity 

generation; and electricity sales or transfers to adjacent or co-located facilities for which 

revenue information is not available. 

2.3.1 Electricity Prices 

Electricity prices vary substantially across the United States, differing both between the 

ultimate customer categories and also by state and region of the country. Electricity prices are 

typically highest for residential and commercial customers because of the relatively high costs of 

 

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

accounts less than one percent of electricity consumption. 
2-13 

distributing electricity to individual homes and commercial establishments. The higher prices for 

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

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

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

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

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

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

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

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

variable. Overall industrial customer prices are usually considerably closer to the wholesale 

marginal cost of generating electricity than residential and commercial prices. 

On a state-by-state basis, all retail electricity prices vary considerably. In 2013, the national 

average retail electricity price (all sectors) was 10.12 cents/KWh, with a range from 7.09 cents 

(Washington) to 33.26 (Hawaii). 

 

Average national retail electricity prices increased between 2000 and 2013 by 7.1 percent 

in real terms (2011$). The amount of increase differed for the three major end use categories 

(residential, commercial and industrial). National average residential prices increased the most 

(8.7 percent), and commercial prices increased the least (2.3 percent). The real year prices for 

2000 through 2013 are shown in Figure 2-5. 
2-14 

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

Categories

Source: EIA AEO 2013, Table 2.4 

 Most of these electricity price increases occurred between 2002 and 2008; since 2008 

nominal electricity prices have been relatively stable while overall inflation continued to 

increase. The increase in nominal electricity prices for the major end use categories, as well as 

increases in the GDP price and CPI-U indices for comparison, are shown in Figure 2-6. 

.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

2000 2002 2004 2006 2008 2010 2012

Electricity Price (Cents/kWh, 2011$)

Residential Commercial Industrial Total
2-15 

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

End-Use Categories, With Inflation Indices 

 For a longer term perspective, Figure 2-7 shows real31 (2011$) electricity prices for the 

three major customer categories since 1960, and Figure 2-8 shows the relative change in real 

electricity prices relative to the prices since 1960. As can be seen in the figures, the price for 

industrial customers has always been lower than for either residential or commercial customers, 

but the industrial price has been more volatile. While the industrial real price of electricity in 

2014 was relatively unchanged from 1960, residential and commercial real prices are 23 percent 

and 28 percent lower respectively than in 1960. 

 

31 All prices in this section are estimated as real 2011 prices adjusted using the GDP implicit price deflator unless 

otherwise indicated. 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

2000 2002 2004 2006 2008 2010 2012

% Change in Nominal Electricity Prices and Inflation

Indices since 2002

Residential Commercial Industrial CPI-U GDP Price
2-16 

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

Categories 

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

0

2

4

6

8

10

12

14

16

18

1960 1970 1980 1990 2000 2010

Real Electricity Prices (2011$, ¢ /kWh)

Real Electricity Prices, 1960-2014 (including taxes)

Residential Commercial Industrial Total

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

1960 1970 1980 1990 2000 2010

Change in Real Electricity Prices

Relative Change in Electricity Prices,

1960-2014 (including taxes)

Residential Commercial Industrial Total
2-17 

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

Three Major End-Use Categories 

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

2.3.2 Prices of Fossil Fuels Used for Generating Electricity 

Another important factor in the changes in electricity prices are the changes in fuel 

prices32 for the three major fossil fuels used in electricity generation; coal, natural gas and oil. 

Relative to real prices in 2000, the national average real price (in 2011$) of coal delivered to 

EGUs in 2013 had increased by 50.5 percent, while the real price of natural gas decreased by 

22.6 percent. The real price of oil increased by 112 percent, but with oil declining as an EGU 

fuel (in 2013 oil generated only 1 percent of electricity) the doubling of oil prices had little 

overall impact in the electricity market. The combined real delivered price of all fossil fuels in 

2013 increased by 36.9 percent over 2000 prices. Figure 2-9 shows the relative changes in real 

price of all 3 fossil fuels between 2000 and 2013. 

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 2015, Table 9.9 

 

32 Fuel prices in this section are all presented in terms of price per Btu to make the prices comparable. 

-50%

-30%

-10%

10%

30%

50%

70%

90%

110%

130%

150%

2000 2002 2004 2006 2008 2010 2012

Coal Oil Gas Average
2-18 

2.3.3 Changes in Electricity Intensity of the U.S. Economy from 2000 to 2013 

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

between 2000 and 2013 is that while total net generation increased by 6.7 percent over that 

period, the demand growth for generation was lower than both the population growth (12.2 

percent) and real GDP growth (25.4 percent). Figure 2-10 shows the growth of electricity 

generation, population and real GDP during this period. 

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

2000 

Sources: U.S. EIA Monthly Energy Review, December 2014. Table 7.2a Electricity Net 

Generation: Total (All Sectors). U.S. Census. 

 

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

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

per person and per real dollar of output) during 2000 to 2013. On a per capita basis, real GDP per 

capita grew by 11.8 percent, increasing from $44,500 (in 2011$) per person in 2000 to 

$49,800/person in 2013. At the same time electricity generation per capita decreased by 4.8 

percent, declining from 13.4 MWh/person in 2000 to 12.8 MWh/person in 2013. 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 14.9 percent, declining from 

303 MWh per $1 million of GDP to 258 MWh/$1 million GDP. These relative changes are 

-5%

0%

5%

10%

15%

20%

25%

30%

2000 2002 2004 2006 2008 2010 2012

Real GDP Population Generation
2-19 

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. 

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

Intensity Since 2000 

Sources: U.S. EIA Monthly Energy Review, December 2014. Table 7.2a Electricity Net 

Generation: Total (All Sectors). U.S. Census. 

2.4 Deregulation and Restructuring 

The process of restructuring and deregulation of wholesale and retail electricity markets 

has changed the structure of the electric power industry. In addition to reorganizing asset 

management between companies, restructuring sought a functional unbundling of the generation, 

transmission, distribution, and ancillary services the power sector has historically provided, with 

the aim of enhancing competition in the generation segment of the industry. 

Beginning in the 1970s, government policy shifted against traditional regulatory 

approaches and in favor of deregulation for many important industries, including transportation 

-15%

-10%

-5%

0%

5%

10%

15%

2000 2002 2004 2006 2008 2010 2012

Real GDP/Capita Generation/Capita Generation/ Real GDP
2-20 

(notably commercial airlines), communications, and energy, which were all thought to be natural 

monopolies (prior to 1970) that warranted governmental control of pricing. However, 

deregulation efforts in the power sector were most active during the 1990s. Some of the primary 

drivers for deregulation of electric power included the desire for more efficient investment 

choices, the economic incentive to provide least-cost electric rates through market competition, 

reduced costs of combustion turbine technology that opened the door for more companies to sell 

power with smaller investments, and complexity of monitoring utilities’ cost of service and 

establishing cost-based rates for various customer classes. Deregulation and market restructuring 

in the power sector involved the divestiture of generation from utilities, the formation of 

organized wholesale spot energy markets with economic mechanisms for the rationing of scarce 

transmission resources during periods of peak demand, the introduction of retail choice 

programs, and the establishment of new forms of market oversight and coordination. 

The pace of restructuring in the electric power industry slowed significantly in response to 

market volatility in California and financial turmoil associated with bankruptcy filings of key 

energy companies. By the end of 2001, restructuring had either been delayed or suspended in 

eight states that previously enacted legislation or issued regulatory orders for its implementation 

(shown as “Suspended” in Figure 2-12). Eighteen other states that had seriously explored the 

possibility of deregulation in 2000 reported no legislative or regulatory activity in 2001 (EIA, 

2003) (“Not Active” in Figure 2-13). Currently, there are 15 states plus the District of Columbia 

where price deregulation of generation (restructuring) has occurred (“Active” in Figure 2-13). 

Power sector restructuring is more or less at a standstill; by 2010 there were no active proposals 

under review by the Federal Energy Regulatory Commission (FERC) for actions aimed at wider 

restructuring, and no additional states have begun retail deregulation activity since that time. 
2-21 

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

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

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

 One major effect of the restructuring and deregulation of the power sector was a 

significant change in type of ownership of electricity generating units in the states that 

deregulated prices. Throughout most of the 20th century electricity was supplied by vertically 

integrated regulated utilities. The traditional integrated utilities provided generation, transmission

and distribution in their designated areas, and prices were set by cost of service regulations set by 

state government agencies (e.g., Public Utility Commissions). Deregulation and restructuring 

resulted in unbundling of the vertical integration structure. Transmission and distribution 

continued to operate as monopolies with cost of service regulation, while generation shifted to a 

mix of ownership affiliates of traditional utility ownership and some generation owned and 

operated by competitive companies known as Independent Power Producers (IPPs). The 

resulting generating sector differed by state or region, as the power sector adapted to the 

restructuring and deregulation requirements in each state. 

 By the year 2000, the major impacts of adapting to changes brought about by 

deregulation and restructuring during the 1990s were nearing completion. In 2000, traditional 
2-22 

utilities owned 79 percent of U.S. generating capacity (MW) while IPPs33 and commercial and 

industrial producers owned 18 percent and 3 percent of U.S. generating capacity, respectively. 

The mix of electricity generated (MWh) was more heavily weighted towards the utilities, with a 

distribution in 2000 of 81 percent, 15 percent and 4 percent for utilities, IPPs and 

commercial/industrial, respectively. 

 Since 2000, IPPs have expanded faster than traditional utilities, substantially increasing 

their share by 2013 of both capacity (59 percent utility, 38 percent IPPs, and 3 percent 

commercial/industrial) and generation (58 percent, 38 percent and 4 percent). 

 The mix of capacity and generation for each of the ownership types is shown in Figures 

2-13 (capacity) and 2-14 (generation). The capacity and generation data for commercial and 

industrial owners are not shown on these figures due to the small magnitude of those ownership 

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

 

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

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

Capacity (MW)

Capacity Mix, 2002 & 2012

Nuclear Coal Gas Hydro Wind All Other

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000 Generation (GWh)

Generation Mix, 2002 & 2012

Nuclear Coal Gas Hydro Wind All Other
2-23 

types. A portion of the shift of capacity and generation is due to sales and transfers of generation 

assets from traditional utilities to IPPs, rather than strictly the result of newly built units. 
2A-1 
3-1 

CHAPTER 3: EMISSIONS AND AIR QUALITY MODELING IMPACTS

Overview 

 This Chapter describes the methods for estimating emissions and air quality for the 2017 

baseline and 2017 illustrative control case. In Section 3.1, we describe the air quality modeling 

platform, in Section 3.2 we describe the development of emissions inventories used in the air 

quality modeling, and in Section 3.3 we describe the methods for processing the air quality 

modeling outputs to create inputs for estimating benefits. The 2017 baseline and illustrative 

control case air quality model predictions were used to calculate “benefit per ton” factors of 

reduced NOX on both ozone and fine particulate matter (PM2.5) concentrations.34,35 These factors 

were then used to estimate the benefits of the regulatory control alternatives, as described in 

Chapter 6. Details on the air quality modeling are provided in the Air Quality Modeling 

Technical Support Document, which can be found in the docket for this proposed rule.

3.1 Air Quality Modeling Platform 

We use the emissions inputs described in Section 3.2 for national scale applications of the 

Comprehensive Air Quality Model with Extensions (CAMx) modeling system to estimate ozone 

and PM2.5 air quality in the contiguous U.S. CAMx is a three-dimensional grid-based Eulerian 

photochemical model designed to estimate ozone and PM2.5 concentrations over seasonal and 

annual time periods. Because it accounts for spatial and temporal variations as well as 

differences in the reactivity of emissions, CAMx is useful for evaluating the impacts of the rule 

on ozone and PM2.5 concentrations. 

For this analysis we used CAMx to simulate air quality for every hour of every day of the 

year. These model applications require a variety of input files that contain information 

pertaining to the modeling domain and simulation period. In addition to the CAMx model, our 

 

34 The 2017 baseline air quality model predictions were also used to inform the EPA’s ozone transport policy 

analysis by identifying which states significantly contribute to nonattainment or interfere with maintenance of 

downwind receptors. See Ozone Transport Policy Analysis Proposed Rule Technical Support Document, which can 

be found in the docket for this proposed rule. 

35 Note that the baseline underlying the air quality modeling does not reflect the updated IPM emissions baseline 

used to develop costs and benefits in Chapters 5 and 6. See the discussion in section 3.2.2 of this chapter.
3-2 

modeling system includes (1) emissions for a 2011 base year and emissions for the 2017 baseline 

and illustrative control case, (2) meteorological data inputs for the year 2011, and (3) estimates 

of intercontinental transport (i.e., boundary concentrations) from a global photochemical model. 

Using these data, CAMx generates hourly predictions of ozone and PM2.5 component species 

concentrations. The model predictions for the 2011 base year, the 2017 baseline, and the 2017 

illustrative control case were combined with ambient air quality observations to calculate 

seasonal mean ozone air quality metrics and annual mean PM2.5 for the 2017 baseline and 2017 

illustrative control case, which were then used as input for the benefits analysis. 

3.1.1 Simulation Periods 

For use in this benefits analysis, the simulation period modeled by CAMx included 

separate full-year application for each of the three emissions scenarios (i.e., 2011 base year, 2017 

baseline and 2017 illustrative control case). 

3.1.2 Air Quality Modeling Domain 

Figure 3-1 shows the geographic extent of the modeling domain that was used for air 

quality modeling in this analysis. The domain covers the 48 contiguous states, along with the 

southern portions of Canada and the northern portions of Mexico. This modeling domain 

contains 25 vertical layers with a top at about 17,550 meters, or 50 millibars (mb), and horizontal 

grid resolution of 12 km x 12 km. The model simulations produce hourly air quality 

concentrations for each 12 km2

 grid cell across the modeling domain.

Figure 3-1. National air quality modeling domain. 
3-3 

3.1.3 Air Quality Model Inputs 

 CAMx requires a variety of input files that contain information pertaining to the 

modeling domain and simulation period. These include gridded, hourly emissions estimates and 

meteorological data, and initial and boundary conditions. Separate emissions inventories were 

prepared for the 2011 base year, the 2017 baseline, and 2017 illustrative control case. All other 

inputs were specified for the 2011 base year model application and remained unchanged for each 

future-year modeling scenario. 

CAMx requires detailed emissions inventories containing temporally allocated emissions 

for each grid-cell in the modeling domain for each species being simulated, as described in 

Section 3.2. The meteorological data model inputs for the 2011 base year were derived from 

running Version 3.4 of the Weather Research Forecasting Model (WRF). The meteorological 

outputs from WRF include hourly-varying horizontal wind components (i.e., speed and 

direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in 

each vertical layer. The CAMx lateral boundary and initial species concentrations are provided 

by a three-dimensional global atmospheric chemistry and transport model (GEOS-Chem). The 

lateral boundary species concentrations varied with height and time (every 3 hours). 

3.2 Development of Emissions Inventories 

3.2.1 2011 Base Year Emissions 

The 2011 emissions inventories are primarily based on the National Emissions Inventory, 

version 2 (2011NEIv2) for point sources, nonpoint sources, commercial marine vessels (CMV), 

nonroad mobile sources and fires, although the inventories used for modeling often have 

temporal resolution additional to what is available in the NEI. The onroad mobile source 

emissions are similar to those in the 2011NEIv2, but were generated using the official release 

2014 version of the Motor Vehicle Emissions Simulator (MOVES2014) 

(http://www3.epa.gov/otaq/models/moves/), while the 2011NEIv2 emissions were generated 

using a slightly earlier version of MOVES2014. Biogenic emissions and emissions inventories 

for Canada and Mexico are also included in the air quality modeling. The meteorological data 

used to develop and temporally allocate emissions were consistent with the 2011 data used for 

the air quality modeling. 

The emissions inventories and modeling thereof incorporate comments received on the 

Federal Register notices issued for the 2011 and 2018 emissions modeling platforms: the Notice 
3-4 

of Availability of the Environmental Protection Agency's 2011 Emissions Modeling Platform 

issued November 27, 2013 (78 FR 70935) and the Notice of Availability of the Environmental 

Protection Agency's 2018 Emissions Modeling Platform issued January 14, 2014 (79 FR 2437), 

respectively. The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system 

(Houyoux et al., 2000) version 3.6.5 was used to prepare the emissions inventories for CAMx. 

Details regarding the development of the emission inventories and emissions modeling for the 

2011 base year and the 2017 baseline are documented in the Technical Support Document 

Preparation of Emissions Inventories for the Version 6.2, 2011 Emissions Modeling Platform 

(EPA, 2015) and can be found in the docket for this proposed rule. 

3.2.2 2017 Baseline Emissions 

The emission inventories for the 2017 future baseline have been developed using 

projection methods that are specific to emission source type. Future emissions are projected from 

the 2011 base year either by running models to estimate future year emissions from specific 

types of emission sources (e.g., EGUs, and onroad and nonroad mobile sources), or for other 

types of sources by adjusting the base year emissions according to the best estimate of changes 

expected to occur in the intervening years (e.g., non-EGU point and nonpoint sources). The 

biogenic, fire, offshore oil platforms, and Canadian emissions use the same emissions in the base 

and future years.36 For the remaining sectors, rules and specific legal obligations that go into 

effect in the intervening years, along with changes in activity for the sector, are considered when 

possible. The modeled 2017 baseline emission inventories represent predicted emissions that 

account for Federal and State measures promulgated or under reconsideration by December, 

2014. With the exception of speciation profiles for mobile sources and temporal profiles for 

EGUs, the same ancillary data files are used to prepare the future year emissions inventories for 

air quality modeling as were used to prepare the 2011 base year inventories. 

The 2017 baseline inventory for EGUs represents demand growth, fuel resource 

availability, generating technology cost and performance, and other economic factors affecting 

power sector behavior. The EGU emissions for the air quality modeling were developed using 

 

36 The biogenic and fire emissions are normally held constant between base and future years. The offshore and 

Canadian emissions were held constant due to the lack of detailed information available to adequately project those 

emissions to future years. 
3-5 

the IPM version 5.14 base case.37 The IPM base case reflects the expected emissions accounting 

for the effects of environmental rules and regulations, consent decrees and settlements, plant 

closures, units built, control devices installed, and forecast unit construction through the calendar 

year 2017. The 2017 baseline EGU emissions include impacts from the Final Mercury and Air 

Toxics Standards announced on December 21, 201138 and the CSAPR issued on July 6, 2011.39 

The EPA notes that because the modeling for the proposal was performed prior to the D.C. 

Circuit’s issuance of EME Homer City II,

40 that modeling assumed in its baseline for all states 

the emission reductions associated with the CSAPR NOX ozone-season phase 2 emissions 

budgets. 

The Clean Power Plan (CPP) is not included in the modeled IPM 5.14 2017 baseline. The 

EPA notes that after the 2011 base year, the 2017 baseline, and 2017 illustrative control case air 

quality modeling for this proposal were underway, the EPA released an updated IPM base case, 

version 5.15, and the final CPP. In order to reflect all on-the-books policies as well as the most 

current power sector modeling data, the EPA performed an assessment of benefits, costs, and 

impacts of this proposal using the IPM 5.15 base case and including the final CPP , as described 

further in Chapter 5 of this RIA. 

The 2018 emissions output from IPM were adjusted to reflect 2017 emissions levels as 

described in “Calculating 2017 NOx Emissions” (see 

http://www2.epa.gov/airmarkets/calculating-2017-nox-emissions). Temporal allocation was 

 

37 IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. This 

model is described in more detail in Chapter 5 of this RIA. The documentation for version 5.14 can be found on 

EPA”s power sector modeling website: http://www.epa.gov/airmarkets/powersectormodeling.html 

38 In Michigan v. EPA, the Supreme Court reversed on narrow grounds a portion of the D.C. Circuit decision 

upholding the Mercury and Air Toxics Standards, finding that EPA erred by not considering cost when determining 

that regulation of EGUs was "appropriate" pursuant to CAA section 112(n)(1). 135 S.Ct. 192 (2015). The case was 

remanded to the D.C. Circuit for further proceedings, and the Mercury and Air Toxics Standards currently remain in 

place. 

39 We note that the NOx emissions changes that result from the Mercury and Air Toxics Standards and in the 

baseline do not substantially alter the results of the analysis. The NOx reductions based on EPA emissions modeling 

are only 7 percent of the nationwide total for EGUs in 2016, as found by subtraction of emissions in Table 5A-11 

from Table 5A-9 of the Mercury and Air Toxics Standards RIA. 

40 EME Homer City Generation, L.P., v. EPA, No. 795 F.3d 118, 129-30, 138 (EME Homer City II)EME Homer 

City Generation, L.P., v. EPA, No. 11-1302, slip op. at 19, 36 (July 28, 2015)
3-6 

used to process the seasonal emissions outputs from IPM to hourly emissions. To the extent 

possible, this temporal allocation process preserved the emissions patterns from the base year 

(2011), while keeping the maximum emissions below those that occurred in the period 2011-

2014. 

 Projections for most stationary emissions sources other than EGUs (i.e., non-EGUs) 

were developed by using the EPA Control Strategy Tool (CoST) to create post-controls future 

year inventories. CoST is described at http://www3.epa.gov/ttnecas1/cost.htm. The 2017 baseline 

non-EGU stationary source emissions inventory includes all enforceable national rules and 

programs, including the Reciprocating Internal Combustion Engines (RICE) and cement 

manufacturing National Emissions Standards for Hazardous Air Pollutants (NESHAPs) and 

Boiler Maximum Achievable Control Technology (MACT) reconsideration reductions. 

Projection factors and percent reductions for non-EGU point sources reflect comments received 

by EPA in response to 79 FR 2437, along with emissions reductions due to national and local 

rules, control programs, plant closures, consent decrees and settlements. Ancillary reductions to 

criteria air pollutant (CAP) emissions from stationary engines as a result of the Reciprocating 

Internal Combustion Engines (RICE) National Emission Standard for Hazardous Air Pollutants 

(NESHAP) are included. Reductions due to the New Source Performance Standards (NSPS) 

volatile organic compound (VOC) controls for oil and gas sources, and the NSPS for process 

heaters, internal combustion engines, and natural gas turbines are also included. 

Regional projection factors for point and nonpoint oil and gas emissions were developed 

using Annual Energy Outlook (AEO) 2014 (U.S. EIA, 2014) projections from year 2011 to year 

2018.Projected emissions for corn ethanol, cellulosic ethanol and biodiesel plants, refineries and 

upstream impacts represent the Energy Independence and Security Act (EISA) renewable fuel 

standards mandate in the Renewable Fuel Standards Program (RFS2). Airport-specific terminal 

area forecast (TAF) data were used for aircraft to account for projected changes in 

landing/takeoff activity. 

Projection factors for livestock are based on expected changes in animal population from 

2005 Department of Agriculture data, updated according to EPA experts in July 2012; fertilizer 

application ammonia (NH3) emissions projections include upstream impacts representing EISA. 
3-7 

Area fugitive dust projection factors for categories related to livestock estimates are based on 

expected changes in animal population and upstream impacts from EISA. Fugitive dust for paved 

and unpaved roads take growth in VMT and population into account. Residential Wood 

Combustion (RWC) projection factors reflect assumed growth of wood burning appliances based 

on sales data, equipment replacement rates and change outs. These changes include growth in 

lower-emitting stoves and a reduction in higher emitting stoves. Impacts from the NSPS for 

wood burning devices are also included. 

Projection factors for the remaining nonpoint sources such as stationary source fuel 

combustion, industrial processes, solvent utilization, and waste disposal, reflect comments 

received on the projection of these sources as a result of rulemakings and outreach to states on 

emission inventories, and they also include emission reductions due to control programs. Future 

year portable fuel container (PFC) inventories reflect the impact of the final Mobile Source Air 

Toxics (MSAT2) rule along with state comments received in response to 79 FR 2437. 

The MOVES2014-based 2017 onroad emissions41 account for changes in activity data 

and the impact of on-the-books national rules including: the Tier 3 Vehicle Emission and Fuel 

Standards Program, the Light-Duty Vehicle Tier 2 Rule, the Heavy Duty Diesel Rule, the Mobile 

Source Air Toxics Rule, the Renewable Fuel Standard (RFS2), the Light Duty Green House 

Gas/Corporate Average Fuel Efficiency (CAFE) standards for 2012-2016, the Heavy-Duty 

Vehicle Greenhouse Gas Rule, the 2017 and the Later Model Year Light-Duty Vehicle 

Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule (LD 

GHG). The 2017 onroad emissions also include state rules related to the adoption of low 

emission vehicle (LEV) standards, inspection and maintenance programs, Stage II refueling 

controls, and local fuel restrictions. For California, the baseline emissions included most of this 

state’s on-the-books regulations, such as those for idling of heavy-duty vehicles, chip reflash, 

public fleets, track trucks, drayage trucks, and heavy duty trucks and buses. The California 

emissions do not reflect the impacts of the GHG/Smartway regulation, nor do they reflect state 

 

41 The 2017 onroad mobile emissions were derived from a 2018 MOVES model run that was adjusted to account for 

2017. See the emissions modeling TSD (US EPA, 2015)for more details. 
3-8 

GHG regulations for the projection of other emissions sectors because that information was not 

included in the provided inventories. 

The nonroad mobile source emissions for 2017, including those for railroads and 

commercial marine vessel emissions, also include all national control programs. These control 

programs include the Clean Air Nonroad Diesel Rule – Tier 4, the Nonroad Spark Ignition rules, 

and the Locomotive-Marine Engine rule. For ocean-going vessels (Class 3 marine), the 

emissions data reflect the 2005 voluntary Vessel Speed Reduction (VSR) within 20 nautical 

miles, the 2007 and 2008 auxiliary engine rules, the 40 nautical mile VSR program, the 2009 

Low Sulfur Fuel regulation, the 2009-2018 cold ironing regulation, the use of 1% sulfur fuel in 

the Emissions Control Area (ECA) zone, the 2012-2015 Tier 2 NOx controls, the 2016 0.1% 

sulfur fuel regulation in ECA zone, and the 2016 International Marine Organization (IMO) Tier 

3 NOx controls. Non-U.S. and U.S. category 3 commercial marine emissions were projected to 

2017 using consistent methods that incorporated controls based on ECA and IMO global NOx

and SO2 controls. For California, the 2017 emissions for these categories reflect the state’s OffRoad Construction Rule for “In-Use Diesel”, cargo handling equipment rules in place as of 2011 

(see http://www.arb.ca.gov/ports/cargo/cargo.htm), and state rules through 2011 related to 

Transportation Refrigeration Units, the Spark-Ignition Marine Engine and Boat Regulations 

adopted on July 24, 2008 for pleasure craft, and the 2007 and 2010 regulations to reduce 

emissions from commercial harbor craft. 

All modeled 2011 and 2017 emissions cases use 2010 Canada emissions data, which is 

the latest year for which Environment Canada had provided data at the time the modeling was 

performed. No accompanying future-year projected baseline inventories were provided in a form 

suitable for this analysis. For Mexico, emissions compiled from the Inventario Nacional de 

Emisiones de Mexico, 2008 were used for 2011, as that was the latest complete inventory 

available. For the 2017 baseline, projected emissions for the year 2018 based on the 2008 

inventory were used (ERG, 2014). Table 3-1 shows the modeled 2011 and 2017 NOX and VOC 

emissions by sector. Additional details on the emissions by state are given in the emissions 

modeling TSD (US EPA, 2015). 
3-9 

Table 3-1 2011 Base Year and 2017 Baseline NOx and VOC Emissions by Sector (thousand 

tons) 

Sector 2011 NOx 2017 NOx 2011 VOC 2017 VOC 

EGU-point 2,000 1,500 36 40 

NonEGU-point 1,200 1,200 800 810 

Point oil and gas 500 410 160 170 

Wildfires and Prescribed Fires 330 330 4,700 4,700 

Nonpoint oil and gas 650 690 2,600 3,200 

Residential wood combustion 34 35 440 440 

Other nonpoint 760 730 3,700 3,500 

Nonroad 1,600 1,100 2,000 1,400 

Onroad 5,700 3,200 2,700 1,500 

C3 Commercial marine vessel 

(CMV) 130 130 5 6 

Locomotive and C1/C2 CMV 1,100 910 48 35 

Biogenics 1,000 1,000 41,000 41,000 

TOTAL 15,000 11,200 58,000 57,000 

3.2.3 2017 Illustrative Control Case Emissions 

The EPA’s approach to developing IPM v5.14-based emissions for the illustrative control 

case is methodologically consistent with the EPA’s approach to establishing the proposed EGU 

NOX ozone-season emissions budgets to reduce interstate ozone transport for the 2008 ozone 

NAAQS. Specifically, the EPA performed IPM modeling that applied a uniform cost of $1,300 

per ton on EGU NOX emissions. Next, the EPA established illustrative EGU NOX ozone-season 

emissions budgets for the 23 eastern states included in this proposal by multiplying the resulting 

state-specific emissions rate for affected EGUs by the corresponding (i.e., affected EGU) 2014 

heat input. These illustrative EGU NOX ozone-season emissions budgets and their associated 

assurance levels, along with corresponding emission changes for other pollutants as predicted by 

IPM, were modeled in IPM v5.14 to create the illustrative control case. 

The emissions for the illustrative control case were processed for air quality modeling in 

the same way as the 2017 baseline. The only difference in the emissions inventories were the 

EGU emissions. The hourly temporal allocation for the illustrative control case inventories 

preserved the patterns from the 2017 baseline. This was accomplished by generating ratios of 

2017 base hourly emissions to the total seasonal emissions by unit and then applying these ratios 

to the total seasonal emissions from the 2017 illustrative control case. Thus, the same hourly 

temporal patterns in the baseline are reflected in this control case, including any adjustments 
3-10 

made to constrain the hourly 2017 emissions below the maximum levels during the 2011-2014 

period.

3.3 Post-Processing of Air Quality Modeling for Benefits Calculations 

3.3.1 Converting CAMx Ozone Outputs to Benefits Inputs 

 The CAMx model generates predictions of hourly ozone concentrations for every grid 

cell. Future-year estimates of ozone for each of three health benefits metrics for ozone were 

calculated using model predictions. The modeled change in ozone between the 2011 base year 

and the 2017 future baseline and illustrative control case were used to create relative reduction 

factors (RRFs) which were then applied to 2011 ambient ozone concentrations, as described 

below. The health benefits metrics for ozone are May through September seasonal average 8-

hour daily maximum ozone concentrations. The procedures for determining the ozone RRFs for 

these metrics are similar to those described in EPA guidance for modeling attainment of the 

ozone standard (EPA, 2014). This guidance recommends that model predictions be used in a 

relative sense to estimate changes expected to occur in ozone concentrations for a future year 

emissions case. The RRFs and future year ozone concentrations were calculated using EPA’s 

software Modeled Attainment Test Software (MATS) (Abt, 2014). EPA used MATS to estimate 

the ozone impacts of the emissions reductions in the 2017 illustrative control case. 

 For the purposes of projecting future ozone concentrations for input to the benefits 

calculations, we applied MATS using the base year 2011 modeling results and the results from 

the 2017 baseline and 2017 illustrative control case scenarios. In our application of MATS for 

ozone we used the ozone monitoring data centered about 2011 (2010-2012 ozone data) from the 

Aerometric Information Retrieval System (AIRS) as the set of base-year measured 

concentrations. The ambient ozone data and modeled ozone outputs were combined using the 

MATS “eVNA” spatial fusion technique to generate gridded sets of spatial fields (interpolated 

ozone metrics for each modeled 12km grid cell in the modeling domain) for each of the three 

ozone metrics for the 2011 base year period. The ratio of the seasonal average model-predicted 

future case ozone concentrations to the corresponding seasonal average model-predicted 2011 

concentrations in each grid cell (RRF’s) was calculated and then multiplied by the gridded 

interpolated ozone concentrations for each metric to produce gridded ozone concentrations for 
3-11 

the 2017 baseline and 2017 illustrative control case. The resulting gridded files for the 2017 

baseline and illustrative control cases were then input to the Benefits Mapping and Analysis 

Program – Community Edition (BenMAP-CE) (version 1.1)42 BenMAP to calculate benefit per 

ton factors for each metric. Information on the calculation of the benefit per ton factors is 

provided in Chapter 6. 

3.3.2 Converting CAMx PM2.5 Outputs to Benefits Inputs 

The CAMx model43 generates predictions of hourly PM2.5 species concentrations for every 

grid. The species include a primary fraction and several secondary PM2.5 species (e.g., sulfates, 

nitrates, and organics). PM2.5 is calculated as the sum of the primary and the secondary formed 

particles. Future-year estimates of PM2.5 were calculated using RRFs applied to 2010-2012 

ambient PM2.5 and PM2.5 species concentrations, as described below. 

 The procedures for determining the RRFs are similar to those in EPA guidance for 

modeling the PM2.5 NAAQS (EPA, 2014). This guidance recommends that model predictions be 

used in a relative sense to estimate changes expected to occur in each PM2.5 species. The 

modeled attainment test procedure for calculating future year PM2.5 values is described in the 

modeling guidance and is codified in EPA’s MATS. EPA used this procedure to estimate the 

ambient impacts of the emissions reductions in the 2017 illustrative control case. For the 

purposes of projecting future PM2.5 concentrations for input to the benefits calculations, we 

applied the modeled attainment test procedure using the base year 2011 modeling results and the 

results from the 2017 baseline and 2017 illustrative control case. In our application of MATS for 

PM2.5 we used the PM2.5 monitoring data and speciated monitoring data centered about 2011 

(2010-2012) from the state PM2.5 Federal Reference Method (FRM) network, the Chemical 

Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments 

(IMPROVE) network as the set of base-year measured concentrations. The ambient PM2.5 and 

species data and modeled PM2.5 and species outputs were combined using the MATS “eVNA” 

 

42 Abt Associates, Inc. 2012. “BenMAP User’s Manual Appendices,” prepared for U.S. Research Triangle Park, NC: 

U. S. Environmental Protection Agency, Office of Air Quality Planning and Standards. Available at: 

<http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. Accessed June 6, 2015

43 ENVIRON, 2014. User's Guide Comprehensive Air Quality Model with Extensions version 6.11, 

www.camx.com. ENVIRON International Corporation, Novato, CA.
3-12 

spatial fusion technique to generate gridded sets of spatial fields (interpolated annual average 

PM2.5 and species concentrations for each modeled 12km grid cell in the modeling domain) for 

the 2011 base year period. The ratio of the quarterly average model-predicted future case PM2.5 

species concentrations to the corresponding quarterly average model-predicted 2011 species 

concentrations in each grid cell (RRF’s) were calculated and then multiplied by the gridded 

interpolated PM2.5 species concentrations to produce gridded PM2.5 species concentrations for the 

2017 baseline and 2017 illustrative control case. Output files from this process include both 

quarterly and annual mean PM2.5 mass concentrations and PM2.5 species concentrations which are 

then processed to produce BenMAP input files containing annual mean PM2.5 mass 

concentrations for the 2017 baseline and for the 2017 illustrative control case. These data files 

were then input to BenMAP to calculate PM2.5 benefit per ton factors. Information on the 

calculation of the benefit per ton factors is provided in Chapter 6. 

3.4 References 

Abt Associates, 2014. User’s Guide: Modeled Attainment Test Software. 

http://www3.epa.gov/scram001/modelingapps_mats.htm. 

ERG, 2014. Develop Mexico Future Year Emissions Final Report. Available at 

ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/2011emissions/Mexico_Emissions_WA

%204-09_final_report_121814.pdf. 

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S. (2000), 

Emissions inventory development and processing for the Seasonal Model for Regional Air 

Quality (SMRAQ) project, Journal of Geophysical Research – Atmospheres, 105(D7), 9079-

9090. 

U.S. Energy Information Administration, 2014. Annual Energy Outlook, 2014 

(http://www.eia.gov/forecasts/archive/aeo14/).

 U.S. Environmental Protection Agency, 2014. Modeling Guidance for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, Research Triangle 

Park, NC. (http://www3.epa.gov/ttn/scram/guidance/guide/Draft_O3-PMRH_Modeling_Guidance-2014.pdf) 

U.S. Environmental Protection Agency, 2015. Preparation of Emissions Inventories for the 

Version 6.2, 2011 Emissions Modeling Platform, Research Triangle Park, NC. 

(http://www3.epa.gov/ttn/chief/emch/2011v6/2011v6_2_2017_2025_EmisMod_TSD_aug20

15.pdf) 
3A-1 
4-1 

CHAPTER 4: REGULATORY CONTROL ALTERNATIVES

Introduction 

The primary purpose of this proposal is to address interstate air quality impacts with 

respect to the 2008 ozone National Ambient Air Quality Standards (NAAQS). The EPA 

promulgated the Cross-State Air Pollution Rule (CSAPR) on July 6, 2011,44 to address interstate 

transport of ozone pollution under the 1997 ozone NAAQS.45 This action proposes to update 

CSAPR to reduce interstate transport of electricity generating unit (EGU) ozone-season (May 1 

through September 30) emissions of nitrogen oxides (NOX) that contribute significantly to 

nonattainment or interfere with maintenance of the 2008 ozone NAAQS in downwind states. The 

EPA proposes to implement the proposed EGU NOX reductions by setting emissions budgets 

(limits on allowable emissions) that are implemented through the CSAPR NOX ozone-season 

allowance trading program. As described in the preamble, the EPA is proposing transport 

Federal Implementation Plans (FIPs) in this action. The EPA is proposing transport FIPs for 23 

eastern states. The EPA would finalize a FIP for any state that does not have an approved State 

Implementation Plan (SIP) addressing its good neighbor obligation by the date the rule is 

finalized. 

This Regulatory Impact Analysis (RIA) evaluates the benefits, costs and certain impacts 

of three different illustrative regulatory control alternatives. The alternatives differ in the level of 

the NOX emissions budget. One of the alternatives represents the proposed budgets which are 

based on a uniform NOX control cost of $1,300 per ton (2011$), whereas the other two more and 

less stringent alternatives represent budgets based on uniform NOX control costs of $3,400 per 

ton and $500 per ton (2011$), respectively. The regulatory control alternatives also differ in the 

size of the assurance limit, which equals 21 percent of the NOX ozone-season emissions budget, 

for 2017 and following years for each state. The assurance provision limits the total ozone season 

 

44 See 76 FR 48208 (July 6, 2011) 

45 CSAPR also addressed interstate transport of fine particulate matter (PM2.5) under the 1997 and 2006 PM2.5

NAAQS. 
4-2 

NOX emissions from affected EGUs for each state as described below. Other key regulatory 

features of the proposed allowance trading program, such as the ability to bank allowances for 

future use, are the same across all three alternatives. This chapter describes the three alternatives 

analyzed in the RIA. 

4.1 Background 

As described in detail in the preamble as well as in Chapter 1, the proposed rule requires 

23 states in the eastern U.S. to reduce interstate emission transport that significantly contributes 

to nonattainment, or interferes with maintenance, of the 2008 ozone NAAQS by reducing their 

ozone season EGU NOX emissions in 2017 and future years. The rule proposes emissions 

budgets, which are allowable emissions after reducing significant contribution to nonattainment 

and interference with maintenance of the 2008 ozone NAAQS for the emissions of affected 

EGUs in the 23 states. The reductions required by the proposed rule may would be achieved 

through a FIP in any state that does not have an approved SIP addressing its contribution by the 

date this rule is finalized. Furthermore, under the FIPs, affected EGUs would participate in the 

CSAPR NOX ozone-season allowance trading program. The allowance trading program is the 

remedy in the FIP that achieves the ozone season NOX emission reductions required by the 

proposed rule. The allowance trading program essentially converts the EGU NOX emissions 

budget for each of the 23 states subject to the FIP into a limited number of NOX allowances that, 

on a tonnage basis, equal the state’s ozone season emissions budget. EGUs covered by the 

seasonal NOX allowance trading program in the proposed FIPs are able to trade NOX ozoneseason emission allowances among EGUs within their state and across state boundaries, with 

emissions and the use of allowances subject to certain limits. This RIA analyzes the benefits, 

costs, and certain impacts of implementing the regulatory control alternative through the 

allowance trading program assuming that all 23 states are subject to the proposed FIPs. 

In accordance with Executive Orders 12866 and 13563, and the guidelines of OMB 

Circular A-4, this RIA analyzes the benefits and costs associated with emissions controls to 

comply with the proposal to update CSAPR. OMB Circular A-4 requires analysis of one 

potential alternative standard level more stringent the proposed standard and one less stringent 

than the revised standard. In response to this requirement, this RIA analyzes the proposed 

remedy under the FIP as well as a more and a less stringent option (i.e., alternative) to the 
4-3 

proposal under the FIP. The more and less stringent options differ from the proposed remedy in 

that they set different EGU NOX ozone-season emissions budgets for the affected EGUs. 

4.2 Regulatory Control Alternatives Considered 

This RIA analyzes the benefits, costs and certain impacts of the proposed rule. In the 

proposed rule, the EGU ozone-season NOX emissions budgets for each state are based on 

applying a uniform cost of $1,300 per ton (2011$) to affected EGUs. This uniform cost of 

reducing a ton of NOX emissions reflects the cost of NOX reduction technologies that are both 

widely available and can be implemented in the near-term (i.e., by 2017). The budget setting 

process is described in the preamble and in detail in the proposal’s Ozone Transport Policy 

Analysis Technical Support Document (TSD). Furthermore, this RIA analyzes regulatory control 

alternatives with higher and with lower EGU emissions budgets. The EGU emissions budgets in 

these more and less stringent regulatory control alternatives are based on uniform NOX costs of 

$3,400 per ton (2011$) and $500 per ton (2011$), respectively. The rationale for choosing these 

regulatory control alternatives for analysis in the RIA is described in Section 4.3. 

As described in Chapter 5 of this RIA, the benefits, costs, and impacts of the proposed 

rule are estimated, in part, by an economic model estimating how affected EGUs may comply 

with the proposed rule. In addition to the limitation on ozone-season NOX emissions required by 

the proposed EGU emissions budgets for the 23 states, there are four important features of the 

allowance trading program that are represented in the model that may influence the level and 

location of NOX emissions from affected EGUs. They are: the ability of affected EGUs to buy 

and sell ozone-season NOX emissions allowances from one another for compliance purposes; the 

ability of affected EGUs to bank ozone-season NOX allowances for future use; the effect of 

limits on the total ozone-season NOX emissions from affected EGUs in each state required by the 

assurance provisions; and the treatment of banked 2015 and 2016 vintage ozone-season NOX

allowances issued under CSAPR to address interstate ozone transport for the 1997 ozone 

NAAQS. Each of these features of the ozone-season allowance trading program is described 

below. 

Affected EGUs are expected to choose the least-cost method of complying with the 

requirements of the allowance trading program. As described in Chapter 5, in the modeling of 
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EGU compliance, the distribution of ozone season NOX emissions across affected EGUs is 

generally governed by this cost-minimizing behavior. In the analysis, the total ozone season NOX

emissions from affected EGUs are limited to the amount allowed by the sum of the NOX budgets 

across the 23 states. Furthermore, as allowances may be banked for future use, if it is less costly 

to abate ozone season NOX emissions in a current ozone season than to abate emissions in a later 

ozone season, affected EGUs are expected to bank NOX ozone-season allowances in the current 

ozone season for use in the later ozone season. Again, the ability of EGUs to bank allowances is 

represented in the compliance modeling discussed in Chapter 5, and the number of banked 

allowances is based on an outcome that minimizes the cost of complying with the NOX ozoneseason emissions budgets over time. 

While there are no explicit limits on the exchange of allowances between affected EGUs 

and on the banking of 2017 and future vintage NOX ozone-season allowances in the proposal, the 

assurance provisions limit the amount of seasonal NOX emissions by affected EGUs in each of 

the 23 states. The assurance level limits affected EGU emissions over an ozone season to the 

state’s NOX ozone-season emissions budget plus an increment equal to 21 percent of each state’s 

emissions budget. The increment is called the variability limit. See section VII.B.4 of the 

preamble for a discussion of the purpose of the assurance provision and further detail about how 

the variability and assurance limits are determined. Following the approach in CSAPR, iIn the 

proposed rule, if a state exceeds its assurance level in a given year it is assessed a total of 32-to-1 

allowance surrender on the excess tons. Section VII.B.4 of the preamble also explains how EPA 

then determines which EGUs are subject to this surrender requirement. In the analysis in this 

RIA the assurance provisions are represented by a limit on the total amount of ozone season NOX

emissions that can be emitted by affected EGUs in each state. That is, in the analysis, affected 

EGUs do not have the option of surrendering additional allowances at the 32-to-1 rate in the 

compliance modeling. 

As described in section VII.B.5 of the preamble the rule proposes to allow 2015 and 2016 

vintage NOX ozone-season allowances that were issued under CSAPR to address interstate ozone 

transport for the 1997 ozone NAAQS to be used for compliance with this rule that reduces 

interstate ozone transport for the 2008 ozone NAAQS. Specifically, 2015 and 2016 vintage 

CSAPR NOX ozone-season allowances may be used for compliance with this rule from 2017 
4-5 

forward.46 If an EGU in one of the 23 states affected by this proposed rule chooses to do so, it 

must use four pre-2017 vintage allowances to cover one ton of NOX emitted. Based on EPA’s 

expectation of the size of the NOX allowance bank after the 2016 ozone season, the treatment of 

these banked allowances is represented in the modeling as an additional 71,982 tons of NOX

allowances that may be used by affected EGUs during the 2017 ozone season or in later ozone 

seasons. 

Table 4.1 reports the seasonal budget for each of the 23 affected states. It also shows the 

sum of all of the emissions budgets across all 23 states. As described above, in both the proposed 

rule and the analysis in this RIA, emissions from affected EGUs in the entire region cannot 

exceed this sum but for the ability to use banked allowances from previous years. Furthermore, 

as described above, emissions from affected EGUs in a particular state may not exceed the 

state’s budget plus the assurance provision during the ozone season in any year (in 2017 and 

later). 

Table 4-1 Ozone-Season NOX Emissions Budgets (Tons) for Proposed, More Stringent and 

Less Stringent Regulatory Control Alternatives in 2017 and Later 

Proposed Emissions 

Budgets 

Alternative 

More Stringent 

Control Alternative 

Less Stringent Control 

Alternative 

Alabama 9,979 9,931 11,886 

Arkansas 6,949 6,101 7,038 

Illinois 12,078 11,992 12,144 

Indiana 28,284 27,585 33,483 

Iowa 8,351 8,118 8,614 

Kansas 9,272 9,259 9,278 

Kentucky 21,519 20,945 32,783 

Louisiana 15,807 15,378 15,861 

Maryland 4,026 4,026 4,026 

Michigan 19,115 18,624 22,022 

Mississippi 5,910 5,487 6,083 

Missouri 15,323 15,240 15,380 

New Jersey 2,015 2,011 2,016 

New York 4,450 4,391 4,607 

North Carolina 12,275 10,705 12,278 

Ohio 16,660 16,637 20,194 

Oklahoma 16,215 16,215 16,215 

Pennsylvania 14,387 14,358 38,270 

Tennessee 5,481 5,449 5,520 

 

46 Allowances were only issued under CSAPR for the 2015 and 2016 ozone seasons. 
4-6 

Proposed Emissions 

Budgets 

Alternative 

More Stringent 

Control Alternative 

Less Stringent Control 

Alternative 

Texas 58,002 55,864 58,492 

Virginia 6,818 5,834 6,955 

West Virginia 13,390 12,367 22,932 

Wisconsin 5,561 5,511 5,588 

TOTAL 311,867 302,028 371,665 

These regulatory control alternatives are illustrative. For example, the EGUs have 

flexibility in determining how they will comply with the allowance trading program. The way 

that they comply may differ from the methods forecast in the modeling for this RIA. 

4.3 Rationale for Regulatory Control Alternatives Chosen 

As described in the preamble, the $1,300 per ton uniform cost used to set the proposed 

EGU NOX ozone-season emissions budgets for the affected states is based on control 

technologies that are both widely available and can be implemented in the near-term (i.e., by 

2017). The uniform cost of $500 per ton was used to establish NOX ozone-season emissions 

budgets for CSAPR and in this current rulemaking is the cost of fully optimizing postcombustion controls that are already running. The uniform cost of $3,400/ton was used to 

establish NOX ozone-season emissions budgets for the NOX SIP Call and reflects the cost of 

turning on idled existing SNCRs. The proposal takes comment on budgets based on these two 

uniform cost thresholds. Therefore evaluating the benefits and costs of complying with these 

higher and lower budgets both responds to the requirement of Circular A-4 to evaluate a less and 

a more stringent option (i.e., alternatives) and is informative to evaluating alternative control 

stringencies on which the EPA is seeking comment. Achievable emission reductions from higher 

uniform NOX cost levels were also evaluated for this proposal. Budgets are not being proposed 

for this rule based on these higher uniform cost levels as they are based on the application of 

NOX controls that could not be installed in the near term (i.e., by 2017). See section VI of the 

preamble and the Ozone Transport Policy Analysis TSD for further explanation. 
4A-1 
5-1 

CHAPTER 5: COST, ECONOMIC, ENERGY, AND EMPLOYMENT IMPACTS

Overview 

This chapter reports the compliance cost, emissions, economic, energy and employment 

impact analyses performed for the proposal to update CSAPR. The EPA used the Integrated 

Planning Model (IPM), developed by ICF International, to conduct most of the analysis 

discussed in this chapter. The EPA conducted additional analysis using the IPM estimates of 

changes in the power and fuels sector as a basis for examining the potential direct labor impacts 

in those sectors. 

IPM is a dynamic linear programming model of the power sector that can be used to 

examine air pollution control policies affecting NOX, CO2, SO2, mercury (Hg), hydrochloric acid 

(HCl), and other air pollutants throughout the contiguous United States for the entire power 

system. Future-year electricity demand levels are based on projections from the Energy 

Information Administration (EIA). 

This chapter of the RIA presents analyses of the proposed rule that include assumptions 

about the possible actions that EGUs may pursue as they reduce their NOX emissions to comply 

with the proposed EGU NOX ozone-season emissions budgets in the 23-state region. Over the 

past two decades, the EPA has used IPM and other analytical methods described in this chapter 

to conduct extensive analyses of proposed and final federal environmental regulatory actions 

affecting the power sector. These previous analytical efforts support the Agency’s understanding 

of key variables that influence the effects of a policy and provide the framework for how the 

Agency estimates the costs and benefits associated with its actions. 

5.1 Power Sector Modeling Framework 

IPM is a state-of-the-art, peer-reviewed, dynamic linear programming model that can be 

used to project power sector behavior under future business-as-usual conditions, and to examine 

prospective air pollution control policies throughout the contiguous United States for the entire 

electric power system. EPA used IPM to project likely future electricity market conditions with 

and without the proposed updates to CSAPR. 
5-2 

IPM is a multi-regional, dynamic, deterministic linear programming model of the 

contiguous U.S. electric power sector. It provides estimates of least cost capacity expansion, 

electricity dispatch, and emissions control strategies while meeting energy demand and 

environmental, transmission, dispatch, and reliability constraints. The 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 emissions impacts of prospective environmental 

policies. The model is designed to reflect electricity markets as accurately as possible. The EPA 

uses the best available information from utilities, industry experts, gas and coal market experts, 

financial institutions, and government statistics as the basis for the detailed power sector 

modeling in IPM. The model documentation provides additional information on the assumptions 

discussed here as well as all other model assumptions and inputs.47

The model incorporates a detailed representation of the fossil-fuel supply system that is 

used to estimate equilibrium fuel prices. The model includes an endogenous representation of the 

North American natural gas supply system through a natural gas module that reflects a partial 

supply/demand equilibrium of the North American gas market, accounting for varying levels of 

potential power sector and non-power sector gas demand and corresponding gas production and 

price levels.48 This module consists of 118 supply, demand, and storage nodes and 15 liquefied 

natural gas re-gasification facility locations that are tied together by a series of linkages (i.e., 

pipelines) that represent the North American natural gas transmission and distribution network. 

IPM also endogenously models the partial equilibrium of coal supply and EGU coal 

demand levels throughout the contiguous U.S., taking into account assumed non-power sector 

demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal 

transport network, which consists of over four thousand linkages representing rail, barge, and 

 

47 Detailed information andThe documentation of EPA’s Base Case using IPM (v5.15) contains detailed 

information, including all the underlying assumptions, data sources, and architecture parameters. can be found on 

EPA’s website at: The documentation for EPA’s Base Case v.5.15 using IPM consists of a comprehensive 

document for IPM v. 5.13, and an incremental update document for both v.5.14 and v.5.15. All are available at 

available at: 

http://www.epa.gov/airmarkets/powersectormodeling.html 

48 See Chapter 10 of EPA’s Base Case using IPM (v5.15) documentation, available at: 

http://www.epa.gov/airmarkets/powersectormodeling.html
5-3 

truck and conveyer linkages. The coal supply curves in IPM were developed during a thorough 

bottom-up, mine-by-mine approach that depicts the coal choices and associated supply costs that 

power plants would face if selecting that coal over the modeling time horizon. The IPM 

documentation outlines the methods and data used to quantify the economically recoverable coal 

reserves, characterize their cost, and build the 36 coal regions’ supply curves.49 

To estimate the annualized costs of additional capital investments in the power sector, the 

EPA uses a conventional and widely accepted approach that applies a capital recovery factor 

(CRF) multiplier to capital investments and adds that to the annual incremental operating 

expenses. The CRF is derived from estimates of the power sector’s cost of capital (i.e., private 

discount rate), the amount of insurance coverage required, local property taxes, and the life of 

capital.50 It is important to note that there is no single CRF factor applied in the model; rather, 

the CRF varies across technologies, book life of the capital investments, and regions in the 

model in order to better simulate power sector decision-making. 

The EPA has used IPM extensively over the past two decades to analyze options for 

reducing power sector emissions. Previously, the model has been used to estimate the costs, 

emission changes, and power sector impacts for the Clean Air Interstate Rule (U.S. EPA, 2005) , 

the 2011 Cross-State Air Pollution Rule (CSAPR) (U.S. EPA, 2005), the Mercury and Air 

Toxics Standards (MATS) (U.S. EPA, 2011a), the Clean Power Plan (CPP) for Existing Power 

Plants (U.S. EPA, 2015), and the Carbon Pollution Standards for New Power Plants (U.S. EPA, 

2015a). EPA has also used IPM to estimate the air pollution reductions and power sector impacts 

of water and waste regulations affecting EGUs, including Cooling Water Intakes (316(b)) Rule

(U.S. EPA, 2014), Disposal of Coal Combustion Residuals from Electric Utilities (CCR) (U.S. 

EPA, 2015b) and Steam Electric Effluent Limitation Guidelines (ELG) (U.S. EPA, 2015c). 

 

49 See Chapter 9 of EPA’s Base Case using the IPM (v.5.15) documentation. The documentation for EPA’s Base 

Case v.5.15 using IPM consists of a comprehensive document for IPM v. 5.13, and an incremental update document 

for both v.5.14 and v.5.15. All are available at , available at: 

http://www.epa.gov/airmarkets/powersectormodeling.html 

50 See Chapter 8 of EPA’s Base Case using IPM (v5.15) documentation, available at: 

http://www.epa.gov/airmarkets/powersectormodeling.html.
5-4 

The model and EPA's input assumptions undergo periodic formal peer review. The 

rulemaking process also provides opportunity for expert review and comment by a variety of 

stakeholders, including owners and operators of capacity in the electricity sector that is 

represented by the model, public interest groups, and other developers of U.S. electricity sector 

models. The feedback that the Agency receives provides a highly-detailed review of key input 

assumptions, model representation, and modeling results. IPM has received extensive review by 

energy and environmental modeling experts in a variety of contexts. For example, in the late 

1990s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 

prospective studies51 that are periodically conducted. The model has also undergone considerable 

interagency scrutiny when it was used to conduct over a dozen legislative analyses (performed at 

Congressional request) over the past decade. The Agency has also used the model in a number of 

comparative modeling exercises sponsored by Stanford University’s Energy Modeling Forum 

over the past 15 years. IPM has also been employed by states (e.g., for RGGI, the Western 

Regional Air Partnership, Ozone Transport Assessment Group), other Federal and state agencies, 

environmental groups, and industry. 

5.2 EPA’s Power Sector Modeling Base Case for the Proposal to Update CSAPR 

The IPM “bBase cCase” for any regulatory impact analysis is a business-as-usual 

scenario that would be expected under market and regulatory conditions in the absence of the 

rule. As such, an IPM base case represents an element of the baseline for this RIA.52 The EPA 

frequently updates the IPM base case to reflect the latest available electricity demand forecasts 

from the U.S. Energy Information Agency (EIA) as well as expected costs and availability of 

new and existing generating resources, fuels, emission control technologies, and regulatory 

requirements. 

 

51 http://www2.epa.gov/clean-air-act-overview/benefits-and-costs-clean-air-act

52 As described in Chapter 5 of EPA’s Guidelines for Preparing Economic Analyses, the baseline “should 

incorporate assumptions about exogenous changes in the economy that may affect relevant benefits and costs (e.g., 

changes in demographics, economic activity, consumer preferences, and technology), industry compliance rates, 

other regulations promulgated by EPA or other government entities, and behavioral responses to the proposed rule 

by firms and the public.“ (USEPA, 2010). 
5-5 

Our analysis of the proposal to update CSAPR involved two different IPM base cases. The 

EPA used IPM version 5.14 (IPM v.5.14) to provide power sector emissions data for air quality 

modeling. Specifically, IPM v.5.14 was used for the air quality modeling of the a 2017 baseline 

and 2017 illustrative control case. This air quality modeling and IPM v.5.14 are detailed in 

Chapter 3.53 IPM v.5.14 was also used for the air quality modeling to quantify upwind state 

contributions, described in Section V of the preamble for this proposal. 

After these air quality modeling scenarios were underway, the EPA released an updated 

IPM base case, version 5.15 (IPM v.5.15), and the final Clean Power Plan (CPP). The EPA used 

IPM v.5.15 for developing the proposed state NOX emissions budgets discussed in Chapter 4 of 

this RIA, and for analyzing the proposed rule’s cost, benefits and impacts. The EPA relied on 

IPM v.5.15 for these analyses so that the base casebaseline for this proposal RIA would reflect 

all on-the-books policies, including the Clean Power PlanCPP, as well as the most current power 

sector modeling data. Using IPM v.5.15 for these analyses provides EPA with the best 

information available to develop the proposed rule and to provide the public with the most 

current information possible. 

5.2.1 EPA’s IPM Base Case v.5.15 

When the EPA began using the current version of IPM (version 5), the EPA developed a 

comprehensive updated base case (v.5.13), as well as a companion updated database of EGU 

units (the National Electricity Energy Data System, or NEEDS v.5.13) that is used in EPA’s 

modeling applications of IPM. The EPA base case is updated periodically to reflect the annual 

electricity demand forecasts from the EIA, among other data updates. EPA’s IPM modeling 

platform used to analyze this proposed rule (v.5.15) incorporates the version of the model used to 

analyze the potential impacts of the CPP, which was finalized in August, 2015. As discussed 

below in Section 5.2.2, the base case for the proposal to update CSAPR also includes certain 

revisions to the v.5.15 base case used in the CPP analysis. 

 

53 As described in Chapter 3, the baseline used for the air quality modeling used to generate the benefit-per-ton 

estimates used in Chapter 6, differs from the baseline used to estimate the benefits, costs and impacts of the 

proposed rule and more and less stringent alternatives. 
5-6 

The updates to the base case between v.5.14 and v.5.15 include an update to the natural 

gas supply as well as routine calibrations with the EIA’s Annual Energy Outlook (AEO), such as 

updating the electric demand forecast consistent with the AEO 2015.54 Additional updates, based 

on the most up-to-date information and/or public comments received by the EPA, include unitlevel specifications (e.g., pollution control configurations), planned power plant construction and 

closures, and updated cost and performance for onshore wind and utility-scale solar technologies. 

The IPM v.5.15 modeling platform incorporates federal and most state laws and regulations 

whose provisions were either in effect or enacted and clearly delineated by March 2015. This 

update also includes two non-air federal rules affecting EGUs: Cooling Water Intakes (316(b)) 

Rule (U.S. EPA, 2014).. and Combustion Residuals from Electric Utilities (CCR) (U.S. EPA, 

2015b).. Additionally, all new capacity projected by the model is compliant with Clean Air Act 

111(b) standards, including the final standards of performance for GHG emissions from new 

sources55. For a detailed account of all updates made to the v.5.15 modeling platform, see the 

Incremental Documentation for EPA Base Case v.5.15 Using IPM.56 

EPA also updated the National Electric Energy Data System (NEEDS)57. This database 

contains the unit-level data that is used to construct the "model" plants that represent existing and 

committed units in EPA modeling applications of IPM. NEEDS includes detailed information on 

each individual EGU, including geographic, operating, air emissions, and other data on every 

generating unit in the contiguous U.S. 

5.2.2 The IPM Base Case Used to Analyze the Proposal to Update CSAPR 

As discussed above, IPM Base Case v.5.15 was used as the base case in EPA’s analysis 

of the final Clean Power PlanCPP, which was finalized in August, 2015. We are using a different 

IPM base case for this analysis of the proposal to update CSAPR to reflect the fact that the Clean 

 

54 The naming conventions used for EPA’s IPM modeling (such as v.5.15) reflect both the major overall version of 

IPM (v.5) and which iteration of the AEO (currently AEO 2015) that supplies the electricity demand forecast used in 

IPM. 

55 http://www2.epa.gov/cleanpowerplan/carbon-pollution-standards-new-modified-and-reconstructed-power-plants

56 Available at: http://www.epa.gov/airmarkets/powersectormodeling.html/ 

57 http://www2.epa.gov/airmarkets/power-sector-modeling-platform-v515
5-7 

Power Plan is now a finalized and promulgated federal regulation. The Clean Power PlanCPP is 

now a part of the business-as-usual scenario that would be expected under market and existing 

regulatory conditions in the absence of this proposal. 

 The base case used to analyze the proposal to update CSAPR is the IPM configuration 

EPA used to analyze one of the final CPP illustrative policy options. Specifically, the base case 

used in the current analysis is the IPM configuration for the CPP’s “rate-based” illustrative plan 

approach. Using the CPP rate-based IPM configuration for the proposed CSAPR analysis assures 

that the IPM results are the incremental impacts on the power sector of the proposal to update 

CSAPR alone, and avoids “double counting” the impacts of the CPP as well as all other federal 

and state regulations included the v.5.15 base case. Extensive information on the IPM 

configuration and detailed results of the CPP rate-based analysis is available in the Clean Power 

Plan Final Rule – Regulatory Impact Analysis58 for the CPP as well as at EPA’s Power Sector 

Modeling website. 59

The 2017 baseline EGU emissions include impacts from the Final Mercury and Air Toxics 

Standards MATS announced on December 21, 201160 and CSAPR issued on July 6, 2011. The 

EPA notes that because the modeling for the proposal was performed prior to the D.C. Circuit’s 

issuance of EME Homer City II,61 that modeling assumed in its baseline for all states the 

emission reductions associated with the CSAPR NOX ozone-season phase 2 emissions budgets. 

5.3 Evaluating the Regulatory Control Alternatives 

To estimate the costs, benefits, and economic and energy market impacts of the proposal to 

update CSAPR, the EPA conducted quantitative analysis of three regulatory control alternatives: 

the proposed EGU NOX ozone-season emissions budgets that reflect EGU NOX control strategies 

 

58 Available at: http://www2.epa.gov/cleanpowerplan/clean-power-plan-final-rule-regulatory-impact-analysis

59 Available at: http://www.epa.gov/airmarkets/powersectormodeling.html/

60 In Michigan v. EPA, the Supreme Court reversed on narrow grounds a portion of the D.C. Circuit decision 

upholding MATS, finding that EPA erred by not considering cost when determining that regulation of EGUs was 

"appropriate" pursuant to CAA section 112(n)(1). 135 S.Ct. 192 (2015). The case was remanded to the D.C. Circuit 

for further proceedings, and because MATS was remanded but not vacated, MATS currently remain in place. 

61 EME Homer City Generation, L.P., v. EPA, No. 795 F.3d 118, 129-30, 138 (EME Homer City II)EME Homer 

City Generation, L.P., v. EPA, No. 11-1302, slip op. at 19, 36 (July 28, 2015)
5-8 

represented by a uniform NOX cost of $1,300 per ton (2011$); and more and less stringent 

alternative EGU NOX ozone-season emissions budgets that reflect EGU NOX control strategies 

represented by uniform NOX costs of $3,400 per ton and $500 per ton (2011$), respectively. 

Details about these regulatory control alternatives, including state-specific EGU NOX ozoneseason emissions budgets for each alternative, are provided in Chapter 4 of this RIA. 

Before undertaking power sector analysis to evaluate compliance with the regulatory 

control alternatives, the EPA first considered available EGU NOX mitigation strategies that could 

be implemented for the first compliance period (i.e., the 2017 ozone season) assuming that this 

rule is finalized in the summer of 2016. The EPA considered all widely used EGU NOX control 

strategies: fully operating existing sSelective cCatalytic rReduction (SCR) and sSelective nNoncCatalytic rReduction (SNCR) – including optimizing NOX removal by existing, operational 

SCRs and SNCRs as well as turning on and optimizing existing idled SCRs and SNCRs; 

installation of (or upgrading to) state-of-the-art NOX combustion controls; shifting generation to 

units with lower NOX emission rates within the same state; and installing new SCRs and SNCRs. 

EPA determined that the power sector could implement all of these NOX mitigation strategies, 

except installation of new SCRs or SNCRs, for the 2017 ozone-season, assuming this proposed 

rule is finalized in the summer of 2016. As to tThe installation of new SCRs or SNCRs (, the 

amount of time from contract award through commissioning for retrofit) with new SCR or 

SNCR exceeds 18 and 12 months, respectively. It would therefore not be feasible to retrofit new 

SCR or SNCR to achieve EGU NOX reductions in the 2017 ozone season. For more details on 

these assessments, including the assessment of EGU NOX mitigation costs and feasibility, please 

refer to the EGU NOX Mitigation Strategies TSD, in the docket for this rule. 

The EPA notes that, due to limitations on model size, IPM v.5.15 does not have the 

capacity to determine, within the model, whether or not to operate existing EGU postcombustion NOX controls (i.e., SCR or SNCR) that are idle in the base case.62 In order to 

evaluate compliance with the regulatory control alternatives (including the proposal and more 

and less stringent alternatives), the EPA determined, outside the model, whether or not it would 

 

62 The EPA notes that EGUs with idled SCR or SNCR in the base represent a small percentage (less than 10 percent) 

of the EGU fleet that is equipped with NOX post-combustion controls. 
5-9 

be reasonably expected for these controls to operate in order to comply with each of the 

evaluated regulatory control alternatives. After imposing the requirement to operate these control 

systems, IPM then estimated the associated NOX reductions and costs. 

The EGU NOX mitigation strategies that are assumed to operate or be available to reduce 

NOX in order to comply with each of the regulatory control alternatives are shown in Table 5-1; 

more information about the estimated costs of these controls can be found in the EGU NOX

Mitigation Strategies TSD. 
5-10 

Table 5-1. NOX Mitigation Strategies Implemented for Compliance with the Regulatory 

Control Alternatives 

Regulatory Control 

Alternative NOX Controls Implemented 

Less Stringent Alternative 

(1) Fully operating existing SCRs to achieve 0.075 lb/MMBtu NOX emission 

rate (outside IPM) 

(2) Shift generation to minimize costs (within IPM) 

Proposal 

(All controls above) 

(3) Turn on idled SCRs (within IPM) 

(4) Install or upgrade combustion controls (outside IPM) 

More Stringent Alternative (All controls above) 

(5) Turn on idled SNCRs (outside IPM) 

5.3.1 Emission Reduction Assessment 

As discussed in Chapter 3 the EPA determined that NOX emissions in 23 eastern states 

affect the ability of downwind states to attain and maintain the 2008 ozone NAAQS. For these 

23 eastern states, the EPA is proposing to issue Federal Implementation Plans (FIPs) that 

generally update the existing CSAPR NOX ozone-season emission budgets for EGUs and 

implement these budgets via the CSAPR NOX ozone-season allowance trading program. 

The EPA analyzed ozone-season NOX emission reductions from implementing each of the 

three regulatory control alternatives. Specifically, the EPA conducted IPM modeling of each of 

the regulatory control alternatives to evaluate the corresponding power sector emissions 

reductions from complying with the EGU NOX ozone-season emissions budgets that are 

provided in Chapter 4 of this RIA. 

The NOX emissions reductions are presented for two time periods – 2017 (the principal 

year of interest for the proposal to update CSAPR) and 2020. However, this version of IPM was 

not structured to simulate 2017 directly. To evaluate the 2017 ozone season EGU NOX

reductions from compliance with the regulatory control alternatives, EPA developed estimates of 

emissions for 2017 by adjusting IPM’s direct estimates for 2018 to account for a number of 

known differences. For example, these adjustments account for emissions from EGUs that are 

expected to operate in 2017, but that have announced plans to retire by 2018 (and whose futureyear emissions are thus not otherwise represented in IPM’s direct 2018 estimates). The EPA has 

only made such adjustments for ozone season NOX emissions given the air quality objective of 
5-11 

the proposal to address transported ozone pollution by the 2017 ozone season.63 For co-pollutant 

EGU emissions (i.e., annual NOX, SO2, and CO2), the EPA assumes that the 2018 IPM results 

are representative of 2017 with no further adjustment. The EPA believes that this is reasonable 

given that the overall magnitude of the adjustments to ozone season NOX emissions for 2017 is 

relatively small and that the adjustments generally affect both the base case and the regulatory 

control scenarios and therefore have an even smaller influence on the emissions difference 

between the base case and the regulatory control scenarios. 

Table 5-2 presents the reduction in EGU NOX emissions resulting from compliance with 

the regulatory control alternatives (i.e., emissions budgets) in the 23-state region, as well as the 

impact on states not in the region. The emission reductions follow an expected pattern: the less 

stringent alternative produces substantially smaller emission reductions than EPA’s proposed 

emissions budgets, and the more stringent alternative results in modestly more NOX reductions. 

Table 5-2. EGU Ozone Season NOX Emission Reductions (tons) for the Proposal and More 

and Less Stringent Alternatives 

Ozone Season NOX Proposal 

More Stringent 

Alternative 

Less Stringent 

Alternative 

 2017 

23-State Region 85,000 88,000 24,000 

Non-Region -400 -500 -300 

Total 85,000 87,000 24,000 

 2020 

23-State Region 83,000 84,000 24,000 

Non-Region -300 -460 -260 

Total 83,000 84,000 24,000 

In addition to the ozone season NOX reductions, there will also be reductions of other air 

emissions emitted by EGUs burning fossil fuels (i.e., co-pollutants). These other emissions 

include the annual total changes in emissions of NOx, SO2 and CO2. The co-pollutant emission 

reductions are presented in Table 5-3. 

 

63 The 2018 emissions output from IPM were adjusted to reflect 2017 emissions levels as described in 

http://www.epa.gov/airmarkets/documents/ipm/Adjusted2017.pdf 
5-12 

Table 5-3. EGU Annual Emission Reductions (tons) for SO2 and CO2 for the Proposal and 

More and Less Stringent Alternatives 

Annual NOx Proposal 

More Stringent 

Alternative 

Less Stringent 

Alternative 

 2017 

23-State Region 91,000 93,000 24,000 

Non-Region -560 -700 -500 

Total 90,000 93,000 24,000 

 2020 

23-State Region 92,000 94,000 24,000 

Non-Region -1,000 -1,700 -700 

Total 91,000 92,000 24,000 

Annual SO2 

 2017 

23-State Region 2,000 2,160 1,800 

Non-Region -820 -920 -710 

Total 1,200 1,200 1,100 

 2020 

23-State Region 1,700 1,600 1,300 

Non-Region -1,100 -1,500 -910 

Total 610 100 360 

Annual CO2 

 2017 

23-State Region 1,300,000 1,600,000 1,300,000 

Non-Region -670,000 -920,000 -520,000 

Total 660,000 710,000 770,000 

 2020 

23-State Region 1,700,000 2,200,000 1,400,000 

Non-Region -1,400,000 -2,400,000 -1,100,000 

Total 270,000 -250,000 360,000,000 

5.3.2 Compliance Cost Assessment 

This section describes EPA’s approach to quantify the costs for compliance with the 

regulatory control alternatives. IPM directly estimated the costs for three of the NOX mitigation 

strategies: turning on idled SCRs, turning on idled SNCRs, and shifting generation to lower-NOX

emitting EGUs. However, the costs of increasing the use and optimizing the performance of 

existing and operating SCRs and SNCRs, and for installing or upgrading NOX combustion 

controls, were estimated using an incremental analysis step that EPA developed to quantify the 

additional costs of these controls. These methods and analysis relies on data and methods used 

within IPM, including NOX control cost equations used in IPM. Therefore, this analysis is 

consistent with IPM and provides the best available quantification of the costs of these NOX

mitigation strategies, consistent with IPM. 

The following steps illustrate this analytical method and demonstrate the application of this 

method to the EPA’s cost estimate of compliance with the proposal: 
5-13 

Analytical Method

Application to Estimate the Costs of Compliance 

with the Proposal

• Identify the IPM modeled EGU 

NOX reductions that are 

attributable to NOX mitigation 

strategies with costs that are not 

included in the model. 

• Maximizing the use of existing SCRs accounts 

for approximately 33,000 tons of ozone season 

EGU NOX reductions. 

• Installing or upgrading NOX combustion controls 

accounts for approximately 7,800 tons of ozone 

season EGU NOX reductions. 

• Estimate the costs associated with 

these EGU NOX mitigation 

strategies. 

• Maximizing the use of existing SCRs costs 

approximately $500 per ton. 

• Installing or upgrading NOX combustion controls 

costs approximately $1,200 per ton. 

• Calculate the cost of each EGU 

NOX mitigation strategies as 

applied to reduce emissions within 

the model. 

• The cost of reductions from maximizing the use 

of existing SCRs is approximately $17,000,000 

annually for 2017. 

• The cost of reduction from installing or 

upgrading NOX combustion controls is 

approximately $9,400,000 annually for 2017. 

The total costs of compliance with the regulatory control alternatives are estimated as the 

sum of the costs that are modeled within IPM and the costs that are calculated outside the model. 

The results of EPA’s IPM analysis show that, with respect to compliance with the 

proposed EGU NOX emissions budgets, turning on idle existing SCRs provides the largest 

amount of ozone season NOX emission reductions (51 percent), and maximizing the use of 

existing SCRs produces an additional 39 percent of the total ozone season NOX reductions. 

Combustion controls (9 percent) and generation shifting (1 percent) make up the remainder of 

the ozone season NOX reductions. In the more stringent alternative, compliance by turning on 

idle existing SNCRs makes up 3 percent of the total reductions, while the shares attributed to the 

other four mitigation measures are similar to the shares for compliance with the proposed EGU 

NOX emissions budgets. 

The estimates of the changes in the cost of supplying electricity for the regulatory control 

alternatives are presented in Table 5-4. The costs associated with compliance with monitoring, 

recordkeeping, and reports requirements are not included within the estimates in this table and 

can be found in Chapter 7. 
5-14 

Table 5-4. Cost Estimates (millions of 2011$) for Proposal and More and Less Stringent 

Alternatives 

Proposal More Stringent Alternative 

Less Stringent 

Alternative 

Annualized* $92.9 $95.7 $4.7 

Annual 2017 $71.6 $63.4 -$47.6

Annual 2020 $48.6 $20.3 -$27.6 

*Levelized annualized costs over the period 2016 through 2040, discounted using the 4.77 discount rate used in 

IPM’s objective function of minimizing the net present value (NPV) of the stream of total costs of electricity 

generation. 

There are several notable aspects of the results presented on Table 5-4. The most notable 

result in Table 5-4 is that the estimated annual compliance costs for the less stringent alternative 

are negative (i.e., a cost reduction) in 2017 and 2020, although this regulatory control alternative 

reduces NOX emissions by over 25,000 tons as shown in Table 5-3. The estimate of the cost of 

controls determined outside the model are positive. Therefore, the finding of net negative annual 

compliance costs derives from the portion of the annual compliance cost estimate that comes 

from IPM. While seemingly counter-intuitive, such an estimated cost reduction is quite possible 

given the dynamic, perfect foresight linear programing structure of IPM. IPM’s objective 

function is to minimize the discounted net present value (NPV) of a stream of annual total cost of 

generation over a multi-decadal time period. For example, IPM’s perfect foresight structure 

makes it possible that the least cost way of complying may be to delay a new investment 

otherwise expected to occur in the base case. Such a delay could result in a lowering of annual 

cost in an early time period and increases in later time periods. 

A better understanding of the cause of the negative costs for the less stringent alternative 

can come from considering the entire time path of costs, rather than focusing on the 2017 and 

2020 estimates in isolation. Because IPM only reports estimates for certain years, it is necessary 

to estimate the cost values for the years between the years directly simulated. A simple but 

straightforward method is to use a linear interpolation between the annual cost estimates for 

directly simulated years to estimate a potential annual compliance cost time series across all 

years in a given period. Figure 5-1 shows the annual cost estimates, which are the sum of the 

annual compliance cost estimated by IPM and the annual compliance costs estimated outside the 
5-15 

model, for each directly simulated year from 2016 through 2040. It also shows the linearly 

interpolated annual cost between those years for all three of the regulatory control alternatives.64

Figure 5-1. Time Sseries of Aannual Ccosts for the Proposal and More and Less Stringent 

Alternatives 

Figure 5-1 shows that while two of the IPM year cost estimates (2017 and 2020) in the less 

stringent alternative are negative, the rest of the annual costs are positive, resulting in the 

majority of the years between 2016 and 2040 having positive costs. All years have positive costs 

under compliance with the proposed EGU NOX ozone-season emissions budgets and the more 

stringent alternative. 

Using the estimated annual cost time series, it is possible to use a standard NPV 

calculation to estimate an annualized cost (i.e., levelized annual cost) of the annual cost stream 

associated with compliance with each regulatory control alternative.65 For this analysis we first 

 

64 EPA estimated year-specific outside-the-model cost adjustments only for 2017 and 2020. For this analysis of the 

time series of costs, the outside-the-model compliance cost adjustments estimated for 2020 are added to all years 

between 2021 and 2040. While this approach is a relatively rougher estimate of the likely cost of the exogenouslyimposed NOx controls for those years, it is consistent with an assumption that once the CSAPR update is 

implemented it will continue to have similar costs and emission reductions throughout the rest of the model’s 

forecast horizon. 

65 The XNPV() function in Microsoft Excel 2013 was used to calculate the NPV of the variable stream of costs. 

-$50

$0

$50

$100

$150

$200

2016 2020 2024 2028 2032 2036 2040

Annual Costs (Millions of 2011$)

Less Stringent Proposal More Stringent
5-16 

calculated the NPV of the stream of costs from 2016 through 2040. We calculate the NPV in 

2016 of each cost stream using a 4.77 percent discount rate.66 While EPA typically uses a 3 and a 

7 percent discount rate to discount future year social benefits and social costs in regulatory 

impact analyses (USEPA, 2010). , iIn this cost annualization approach we use a 4.77 percent 

rate to be consistent with the optimization approach used within IPM. IPM’s cost minimization 

objective function estimates the lowest possible NPV, and uses a 4.77 percent discount rate to 

calculate the NPV. IPM v.5.15 uses a 4.77 percent discount rate as an estimate of the real 

opportunity cost of capital in the power sector.67

After calculating the NPV of the cost streams, the same 4.77 percent discount rate and 

2016-2040 time period is used to calculate the levelized annualized annual (i.e., annualized) cost 

estimates shown in Table 5-3.68 

Figure 5-2 depicts the annualized cost estimates on top of the time stream of annual costs 

previously presented on Figure 5-1. 

 

66 While IPM also reports the costs for 2050, we do not include the 2050 estimates in this analysis. In this situation, 

extending the present value calculations to include 2050 does not materially change the results. 

67 The IPM Base Case documentation (Section 8.2.1 Introduction to Discount Rate Calculations) states “The real 

discount rate for expenditures (e.g., capital, fuel, variable operations and maintenance, and fixed operations and 

maintenance costs) in the EPA Base Case v.5.13 is 4.77%. This serves as the default discount rate for all 

expenditures.” Section 8.2 in that documentation describes the rationale for using a real opportunity cost of capital 

specific to the power sector, and describes the data used to derive the 4.77 percent rate. 

68 The PMT() function in Microsoft Excel 2013 is used to calculate the level annualized cost from the estimated 

NPV. 
5-17 

Figure 5-2. Time sSeries of aAnnual Ccosts and Aannualized Ccosts for the Proposal 

and More and Less Stringent Alternatives 

As can be seen in Table 5-3 and Figure 5-2, the annualized costs are positive for 

compliance with each of the three regulatory control alternatives. Furthermore, the annualized 

costs have the expected relationship; the annualized costs are lowest for the less stringent 

alternatives, and highest for the more stringent alternative. 

5.3.3 Impacts on Fuel Use, Prices and Generation Mix 

The proposal to update CSAPR is estimated to have a variety of different impacts to the 

power sector. While all the impacts are relatively small in percentage terms, considering the 

potential impacts in addition to the cost and emissions estimates presented previously is an 

important component of assessing the overall impact of the proposal. In this section of the RIA 

we discuss the estimated changes in fuel use, prices of fuel and retail electricity, generation by 

fuel type, and capacity by fuel type. 

Table 5-5 presents the percentage changes in national coal and natural gas usage by EGUs 

in 2017. Percent changes in fuel prices are also shown. As will be seen for the other measures of 

impacts on the power sector, the fuel use estimates in Table 5-5 estimates reflect a modest shift 

to natural gas from coal. 

-$50

$0

$50

$100

$150

$200

2016 2020 2024 2028 2032 2036 2040

Annual Costs (Millions of 2011$)

Less Stringent Proposal More Stringent

Annualized Annualized Annualized
5-18 

Table 5-5. Percent Changes* in Coal and Natural Gas Usage by EGUs for the Proposal and 

More and Less Stringent Alternatives for 2017 

Proposal More Stringent Alternative 

Less Stringent 

Alternative 

Change in Fuel Quantity (TBtu) 

 Coal -0.077% -0.072% -0.091% 

 Natural Gas 0.105% 0.090% 0.134% 

Change in Fuel Prices 

 Coal** -0.211% -0.279% -0.321% 

 Natural Gas*** 0.002% -0.004% 0.004% 

*Fuel use changes measured in TBtu 

**Coal price changes are minemouth prices per MMBtu

***Natural gas price changes are delivered gas prices per MMBtu 

Table 5-6 presents the projected percentage changes in the amount of electricity generation 

nationally in 2017 by 3 major types of electricity generation: coal, gas, and conventional hydro. 

The amount generated by nuclear energy is unchanged in all three alternatives.

Table 5-6. Percent Changes* in Generation by Major Fuel Type for the Proposal and More 

and Less Stringent Alternatives for 2017 

Proposal More Stringent Alternative 

Less Stringent 

Alternative 

Coal -0.0874% -0.079% -0.103% 

Natural Gas 0.094108% 0.101% 0.127% 

Hydro 0.062% 0.064% 0.068% 

Total Generation** 0.009% 0.008% 0.009%

Detail by Region for Proposal 

In-Region Non-Region Total 

Coal -0.185% 0.174% -0.087% 

Natural Gas 0.167% -0.009% 0.094% 

Total Generation -0.009% 0.040% 0.009%

*Changes in GWh generated in 2017. 

**Changes in total generation is from all types of generation nationally, including fossil, non-fossil and renewable 

EGUs dispatching electricity to the grid. 

Table 5-7 presents the projected percentage changes in the amount of generating capacity 

in 2017 for coal, natural gas and total capacity. 

Table 5-7. Changes* in Generating Capacity by Major Fuel Type for the Proposal and 

More and Less Stringent Alternatives in 2017 

Proposal More Stringent Alternative 

Less Stringent 

Alternative 

Percent Change 
5-19 

Proposal More Stringent Alternative 

Less Stringent 

Alternative 

Coal -0.144% -0.136% -0.162% 

Natural Gas -0.006% -0.006% -0.006% 

Total Capacity -0.006% -0.004% -0.015% 

Capacity Change (MW) 

Coal -282 -318 -266 

Natural Gas -24 -95 -22 

Total Capacity -60 -146 -36 

*Changes in generating capacity available in 2017.

The EPA estimated the change in the retail price of electricity (2011$) using the Retail 

Price Model (RPM).69 The RPM was developed by ICF International for the EPA, and uses the 

IPM estimates of changes in the cost of generating electricity to estimate the changes in average 

retail prices. The prices are average prices over consumer classes (i.e.., consumer, commercial 

and industrial) and regions, weighted by the amount of electricity used by each class and in each 

region. The RPM combines the IPM annual cost estimates in each of the 64 IPM regions with 

EIA electricity market data for each of the 22 electricity supply regions in the electricity market 

module of the National Energy Modeling System (NEMS).70

Table 5-8 presents the projected percentage changes in the retail price of electricity for 

the proposal and more and less stringent alternatives. Estimates are presented for both 2017 and 

2020. While all of the estimated changes in prices are quite small, Table 5-8 includes both 

positive and negative retail electricity price changes. This is consistent with the pattern of 

positive and negative annual cost estimates discussed above in Section 5.3.3, and also reflects 

some influence from aggregating results for the 22 NEMS regions into separate estimates for the 

23 -sState region affected by the proposal to update CSAPR, and the rest of the country. 

 

69 See documentation available at: http://www.epa.gov/powersectormodeling/ 

70 See documentation available at: 

http://www.eia.gov/forecasts/aeo/nems/documentation/electricity/pdf/m068(2014).pdf 
5-20 

Table 5-8. Percent Changes* in Retail Electricity Price for the Proposal and More and Less 

Stringent Alternatives for 2017 & 2020 

Base Case Percent Change in Retail Electricity Price 

Price 

(cents/kWh) Proposal 

More Stringent 

Alternative 

Less Stringent 

Alternative 

2017 

 National Price 9.93 -0.069% -0.099% -0.025% 

 23 State Region 9.69 -0.161% -0.371% -0.047% 

 Non-Region 10.75 -0.154% -0.371% -0.027% 

2020 

 National Price 10.33 0.031% 0.034% 0.009% 

 23 State Region 10.26 0.027% 0.028% 0.022% 

 Non-Region 11.11 0.018% 0.022% -0.001% 

*Changes in GW capacity available in 2017.

5.3.4 Effect of Emission Reductions on Downwind Receptors 

As described in Sections V and VI of the preamble, and in the Ozone Transport Policy 

Analysis Proposed Rule TSD, and summarized here, EPA evaluated the effect of the proposed 

rule on nonattainment and maintenance receptors with respect to interstate transport for the 2008 

ozone NAAQS. The 2008 ozone standard is 0.075 parts per million (ppm), annual fourth-highest 

daily maximum 8 hour concentration, averaged over 3 years. As described in Section V of the 

preamble, the nonattainment and maintenance receptors with respect to interstate transport for 

the 2008 ozone NAAQS in 2017 were identified using the Comprehensive Air Quality Model 

with Extensions (CAMx) modeling system. From this CAMx analysis there are 37 receptors in 

10 states evaluated for this proposed update to CSAPR.71 All 37 have a maximum design value 

of 0.076 ppm or higher in 2017. Fourteen of these receptors have an average design value of 

0.076 ppm or higher in 2017.72 Furthermore, as described in the Ozone Transport Policy 

 

71 The CAMx air quality modeling scenario used to identify these receptors was also used as the baseline in Chapter 

3 to estimate benefit-per-ton values for NOX reductions.

72 As described in the preamble, section V.C, the approach for projecting future ozone design values involved the 

projection of an average of up to 3 design value periods, which include the years 2009-2013 (design values for 

2009-2011, 2010-2012, and 2011-2013). The average of the 3 design values creates a “5-year weighted average” 

value. The 5-year weighted average values and the individual design values for 2009-2011, 2010-2012, and 2011-

2013 were projected to 2017. The highest of the three individual values is the “maximum” design value. We are 

proposing to identify nonattainment receptors in this rulemaking as those sites that are violating the NAAQS based 

on current measured air quality and also have projected average design values of 76 parts per billion (ppb) or 

greater. Maintenance-only receptors, i.e., those that are at risk of not maintaining the NAAQS, therefore include 

both (1) those sites with projected average design values above the NAAQS that are currently measuring clean data 

and (2) those sites with projected average design values below the level of the NAAQS, but with projected 

maximum design values of 76 ppb or greater. In addition to the maintenance-only receptors, the 2017 ozone 
5-21 

Analysis Proposed Rule TSD, the forecast ambient concentration at these receptors was adjusted 

to reflect a new IPM base case (IPM v.5.15 with CPP), different from the one that was 

represented in the emissions inventory for the CAMx modeling of the ambient concentrations 

(IPM v.5.14). The results described in this subsection, and in the rest of this RIA except where 

otherwise noted, are from a baseline that includes IPM version 5.15 with CPP. After this 

adjustment to reflect the updated IPM base case, 33 of the 37 receptors have a maximum design 

values of 0.076 ppm or higher in 2017, while 12 have average design values of 0.076 ppm or 

higher in 2017. 

To evaluate the effect of the proposed rule on ambient ozone concentrations at the 

receptors in 2017 relative to the baseline, the EPA first evaluated the response of the electricity 

sector to the proposed rule using IPM. The IPM analysis used to estimate the compliance cost, 

benefits, and impacts of the proposed rule is the same analysis used to identify the effect of the 

proposed rule on the level and spatial pattern of ozone seasonal NOX emissions from EGUs. 

The emissions outputs from IPM are then used to estimate the change in ambient ozone 

concentrations at the 37 receptors. The ozone Air Quality Assessment Tool (AQAT) is used to 

estimate the impact of the upwind states’ EGU NOX reductions on downwind ozone pollution 

concentrations. AQAT is used to forecast both the average and maximum design values at all 

monitors, including the 37 receptors. The ozone AQAT was developed specifically for use in the 

proposed rule’s significant contribution analysis AQAT uses CAMx outputs to calibrate its 

predicted change in ozone concentrations to changes in NOx emissions. See the Ozone Transport 

Policy Analysis Proposed Rule TSD for the air quality estimates and for details on the 

construction of ozone AQAT. 

In summary, the proposed rule is expected to reduce the average design values at the 37 

receptors by 0.61 parts per billion (ppb) on average, and to reduce the maximum design values at 

these receptors by 0.63 ppb on average. At the 12 receptors with an average design values of 

0.076 ppm or greater in the baseline, the proposed rule reduces the difference between the 

baseline average ambient concentration and 0.076 ppm by 14% on average (an average reduction 

 

nonattainment receptors are also maintenance receptors because the maximum design values for each of these sites 

is always greater than or equal to the average design value.
5-22 

of 0.24 ppb). The proposed rule reduces the maximum design values below 0.076 ppm for 7 of 

the 33 receptors. Of the remaining 26 receptors with maximum design values of 0.076 ppm or 

greater, the proposed rule reduces the difference between the baseline maximum design value 

and 0.076 ppm by 21% on average (an average of reduction of 0.37 ppb). The proposed rule is 

therefore expected to provide notable improvements in ambient air quality at these monitors by 

2017. Results for each of the 37 receptors, as well as for the more and less stringent alternatives, 

are described in the Ozone Transport Policy Analysis Proposed Rule TSD.

5.4 Employment Impacts 

Executive Order 13563 directs federal agencies to consider regulatory impacts on job 

creation and employment. According to the Executive Order, “our regulatory system must 

protect public health, welfare, safety, and our environment while promoting economic growth, 

innovation, competitiveness, and job creation. It must be based on the best available science” 

(Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically 

included a separate analysis of regulation-induced employment impacts,73 we typically conduct 

employment analyses for economically significant rules. This section discusses and projects 

potential employment impacts related to today’s proposal, but the employment impacts have not 

been quantified.

74

 

Section 5.5.1 describes the theoretical framework used to analyze regulation-induced 

employment impacts, discussing how economic theory alone cannot predict whether such 

impacts are positive or negative. Section 5.5.2 presents an overview of the peer-reviewed 

literature relevant to evaluating the effect of environmental regulation on employment. Section 

5.5.3 provides background regarding recent employment trends in the electricity generation, coal 

and natural gas extraction sectors. Section 5.5.4 discusses the potential direct employment 

impacts in these sectors. The direct employment impacts are not quantified in this RIA. 

 

73 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses. 

74 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). 
5-23 

5.4.1 Economic Theory and Employment 

Regulatory employment impacts are difficult to disentangle from other economic changes 

affecting employment decisions over time and across regions and industries. Labor market 

responses to regulation are complex. They depend on labor demand and supply elasticities and 

possible labor market imperfections (e.g., wage stickiness, long-term unemployment, etc.). The 

unit of measurement (e.g., number of jobs, types of jobs, hours worked, and earnings) may affect 

observability of that response. Net employment impacts are composed of a mix of potential 

declines and gains in different areas of the economy (e.g., the directly regulated sector, the 

environmental protection sector, upstream and downstream sectors, etc.) over time. In light of 

these difficulties, economic theory provides a constructive framework for analysis. 

Microeconomic theory describes how firms adjust their use of inputs in response to 

changes in economic conditions.75 Labor is one of many inputs to production, along with capital, 

energy, and materials. In competitive markets, firms choose inputs and outputs to maximize 

profit as a function of market prices and technological constraints.76,77 Berman and Bui (2001) 

adapt this model to analyze how environmental regulations affect labor demand.78 They model 

environmental regulation as effectively requiring certain factors of production, such as pollution 

abatement capital, at levels that firms would not otherwise choose. Berman and Bui (2001) 

model two components that drive changes in firm-level labor demand: output effects and 

substitution effects.79 Regulation affects the profit-maximizing quantity of output by changing 

the marginal cost of production. If a regulation causes marginal production cost to increase, it 

will place upward pressure on output prices, leading to a decrease in demand, and resulting in a 

decrease in production. The output effect describes how, holding labor intensity constant, a 

 

75 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9. 

76 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization. 

77 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs). 

78 Morgenstern, Pizer, and Shih (2002) develop a similar model. 

79 The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect 

is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and 

Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 2) a 

cost effect; and 3) a factor-shift effect. 
5-24 

decrease in production causes a decrease in labor demand. As noted by Berman and Bui, 

although many assume that regulations must increase marginal cost, in some cases they may 

decrease it. A regulation could induce a firm to upgrade to less polluting and more efficient 

equipment that lowers the marginal cost of production. In such a case, output could increase after 

firms comply with the regulation. 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 technology is in place it lowers marginal production costs. 

The substitution effect describes how, holding output constant, regulation affects the laborintensity of production. Although increased environmental regulation may increase use of 

pollution control equipment and energy to operate that equipment, the impact on labor demand is 

ambiguous. For example, equipment inspection requirements, specialized waste handling, 

completing required paperwork, or pollution technologies that alter the production process may 

affect the number of workers necessary to produce a unit of output. Berman and Bui (2001) 

model the substitution effect as the effect of regulation on pollution control equipment and 

expenditures required by the regulation and the corresponding change in the labor-intensity of 

production. 

In summary, as output and substitution effects may be positive or negative, economic 

theory alone cannot predict the direction of the net effect of regulation on labor demand. In 

addition, the empirical literature illustrates difficulties with estimation of net employment 

impacts. The most commonly used empirical methods, for example, Greenstone (2002), likely 

overstate employment impacts because they rely on relative comparisons between more 

regulated and less regulated counties, which can lead to “double counting” of impacts when 

production and employment shift from more regulated towards less regulated areas. Thus these 

empirical methods cannot be used to estimate net employment effects.80

The conceptual framework described thus far focused on regulatory effects on plant-level 

decisions within a regulated industry, but employment impacts at an individual plant do not 

necessarily represent impacts for the sector as a whole. At the industry-level, labor demand is 

 

80 See Greenstone (2002) p. 1212. 
5-25 

more responsive if: (1) the price elasticity of demand for the product is high, (2) other factors of 

production can be easily substituted for labor, (3) the supply of other factors is highly elastic, or 

(4) labor costs are a large share of total production costs.81 For example, if all firms in an industry 

are faced with the same regulatory compliance costs and product demand is inelastic, then 

industry output may not change much, and output of individual firms may change slightly.82

 

In addition to changes to labor demand in the regulated industry, net employment impacts 

encompass changes in other related sectors, for example, the environmental protection sector. 

This proposal may increase demand for the nitrogenous reagent (typically ammonia or urea) used 

in SCRs and SNCRs to reduce NOx, which may increase revenue and employment in the firms 

providing these chemicals. 

If the U.S. economy is at full employment, even a large-scale environmental regulation is 

unlikely to have a noticeable impact on aggregate net employment.83 Instead, labor in affected 

sectors would primarily be reallocated from one productive use to another (e.g., from producing 

electricity to manufacturing, installing, or operating and maintaining pollution-abatement 

equipment), and net national employment effects from environmental regulation would be small 

and transitory (e.g., as workers move from one job to another).84 Some workers may retrain or 

relocate in anticipation of new requirements or require time to search for new jobs, while 

shortages in some sectors or regions could bid up wages to attract workers. These adjustment 

costs can lead to local labor disruptions. 

If, on the other hand, the economy is operating at less than full employment, economic 

theory does not clearly indicate the direction or magnitude of the net impact of environmental 

regulation on employment; it could cause either a short-run net increase or short-run net decrease 

(Schmalansee and Stavins, 2011). For example, the Congressional Budget Office considered 

 

81 See Ehrenberg & Smith, p. 108. 

82 This discussion draws from Berman and Bui (2001), pp. 293. 

83 Full employment is a conceptual target for the economy where everyone who wants to work and is available to do 

so at prevailing wages is actively employed. The unemployment rate at full employment is not zero. 

84 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. 
5-26 

EPA’s Mercury Air Toxics Standards and regulations for industrial boilers and process heaters as 

potentially leading to short-run net increases in economic growth and employment, driven by 

capital investments for compliance with the regulations (Congressional Budget Office, 2011). 

Environmental regulation may also affect labor supply and productivity. In particular, reducing 

pollution and other environmental risks may improve labor productivity or employees’ ability to 

work.85 While the theoretical framework for analyzing labor supply effects is analogous to that 

for labor demand, it is more difficult to study empirically. There is a small emerging literature 

that uses detailed labor and environmental data to assess these impacts. 

To summarize, economic theory provides a framework for analyzing the impacts of 

environmental regulation on employment. The net employment effect incorporates expected 

employment changes (both positive and negative) in the regulated sector and other related 

sectors including the environmental protection sector. Labor demand impacts for regulated firms, 

and also for the regulated industry, can be decomposed into output and substitution effects which 

may be either negative or positive. Estimation of net employment effects for regulated sectors is 

possible when data of sufficient detail and quality are available. Finally, economic theory 

suggests that labor supply effects are also possible. In the next section, we discuss the empirical 

literature. 

5.4.1.2 Current State of Knowledge Based on the Peer-Reviewed Literature 

 The peer-reviewed empirical literature specifically estimating employment effects of 

environmental regulations is limited but growing. We summarize it briefly in this section. 

5.4.1.3 Regulated Sector 

Several empirical studies, including Berman and Bui (2001) and Ferris, Shadbegian, and 

Wolverton (2014), suggest that regulation-induced net employment impacts may be zero or 

slightly positive, but small in the regulated sector. Gray et al (2014) find that pulp mills that had 

to comply with both the air and water regulations in EPA’s 1998 “Cluster Rule” experienced 

relatively small, and not always statistically significant, decreases in employment. Other research 

on regulated sectors suggests that employment growth may be lower in more regulated areas 

 

85 E.g. Graff Zivin and Neidell (2012). 
5-27 

(Greenstone 2002, Walker 2011, 2013). However, since these latter studies compare more 

regulated to less regulated counties, this methodological approach likely overstates employment 

impacts to the extent that regulation causes plants to locate in one area of the country rather than 

another, which would lead to “double counting” of the employment impacts. List et al. (2003) 

find some evidence that this type of geographic relocation may be occurring. 

5.4.1.4 Economy-Wide 

Given the difficulty noted above with estimating national impacts of regulations, EPA has 

not generally estimated economy-wide employment impacts of its regulations in its benefit-cost 

analyses. However, in its continuing effort to advance the evaluation of costs, benefits, and 

economic impacts associated with environmental regulation, EPA has formed a panel of experts 

as part of EPA’s Science Advisory Board (SAB) to advise EPA on the technical merits and 

challenges of using economy-wide economic models to evaluate the impacts of its regulations, 

including the impact on net national employment.86 Once EPA receives guidance from this 

panel, it will carefully consider this input and then decide if and how to proceed on economywide modeling of employment impacts of its regulations. 

5.4.1.5 Labor Supply Impacts 

The empirical literature on environmental regulatory employment impacts focuses 

primarily on labor demand. However, there is a nascent literature focusing on regulation-induced 

effects on labor supply.87 Although this literature is limited by empirical challenges, researchers 

have found that air quality improvements lead to reductions in lost work days (e.g., Ostro, 1987). 

Limited evidence suggests worker productivity may also improve when pollution is reduced. 

Graff Zivin and Neidell (2012) used detailed worker-level productivity data from 2009 and 2010, 

paired with local ozone air quality monitoring data for one large California farm growing 

multiple crops, with a piece-rate payment structure. Their quasi-experimental structure identifies 

an effect of daily variation in monitored ozone levels on productivity. They find “ozone levels 

well below federal air quality standards have a significant impact on productivity: a 10 parts per 

 

86 For further information see: 

http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED?OpenDocument 

87 For a recent review see Graff-Zivin and Neidell (2013). 
5-28 

billion (ppb) decreases in ozone concentrations increases worker productivity by 5.5 percent.” 

(Graff Zivin and Neidell, 2012, p. 3654).88

5.4.1.6 Conclusion 

This section has outlined the challenges associated with estimating regulatory effects on 

both labor demand and supply for specific sectors. These challenges make it difficult to estimate 

net national employment estimates that would appropriately capture the way in which costs, 

compliance spending, and environmental improvements propagate through the macro-economy. 

5.4.2 Recent Employment Trends 

The U.S. electricity system includes employees that support electric power generation, 

transmission and distribution; the extraction of fossil fuels; renewable energy generation; and 

supply-side and demand-side energy efficiency. This section describes recent employment trends 

in the electricity system. 

5.4.2.1 Electric Power Generation 

In 2014, the electric power generation, transmission and distribution sector (NAICS 2211) 

employed about 390,000 workers (U.S. BLS, 2015) in the U.S. Installation, maintenance, and 

repair occupations (U.S. BLS, 2014) accounted for the largest share of workers (25 percent). 

These categories include inspection, testing, repairing and maintaining of electrical equipment 

and/or installation and repair of cables used in electrical power and distribution systems. Other 

major occupation categories include office and administrative support (18 percent), production 

occupations (16 percent), architecture and engineering (10 percent), business and financial 

operations (7 percent) and management (7 percent). As shown in Figure 5-3, employment in the 

eElectric pPower iIndustry averaged about 420,000 workers 2000 to 2005, declining to an 

average of about 400,000 workers for the rest of the decade, and then declining to about 390,000 

workers in 2014. 

 

88 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this study. In light 

of this recent research, however, the EPA is considering how best to incorporate possible productivity effects in the 

future. 
5-29 

Figure 5-3. Electric Power Industry Employment 

5.4.2.2 Fossil Fuel Extraction 

Coal Mining. The coal mining sector (NAICS 2121) is primarily engaged in coal mining and 

coal mine site development, excluding metal ore mining and nonmetallic mineral mining and 

quarrying. In 2014, BLS reported about 74,000 coal mining employees (Figure 5-4). During the 

2000 to 2014 period, coal mining employment peaked in 2011 at about 87,000 employees. 

0

50

100

150

200

250

300

350

400

450

500

Power Generation and Supply Employment 

(NAICS = 2211, Annual Average, 1000s of Employees)
5-30 

 

Figure 5-4. Coal Production Employment 

Source: BLS (2014a)

Oil and Gas Extraction. In 2014, there were close to 200,000 employees in the oil and gas 

extraction sector (NAICS 211). This sector includes production of crude petroleum, oil from oil 

shale and oil sands, production of natural gas, sulfur recovery from natural gas, and recovery of 

hydrocarbon liquids. Activities include the development of gas and oil fields, exploration 

activities for crude petroleum and natural gas, drilling, completing, and equipping wells, and 

other production activities. In contrast with coal, Figure 5-5 shows there has been a sharp 

increase in employment in this sector over the past decade. 
5-31 

 

Figure 5-5 Oil and Gas Extraction Employment 

Source: BLS (2014b) 

5.4.3 Power and Fuels Sector Direct Employment Impacts 

As described above, affected EGUs may respond to the CSAPR update by upgrading or 

improving performance of existing combustion controls, or by upgrading, improving, or utilizing 

post-combustion NOX systems already in place. In addition, some net generation may shift from 

higher NOX-emitting EGUs to units with lower NOX emission rates. All of these changes will 

likely involve some amount of change in various types of amount of labor needed in different 

parts of the fuels and utility power sectors. There also may be other labor impacts in sectors that 

provide products and materials used in reducing NOX emissions at EGUs, such as catalysts used 

in SCR control systems. These direct labor impacts will likely include both increased demand for 

certain types of labor in some portions of the affected sectors, and reduced demand for labor in 

other portions of the affected sectors. 

Installing and operating new equipment could change labor demand in the electricity 

generating sector itself, as well as associated equipment and services sectors. Specifically, the 

direct employment effects in the power sector that could occur because of actions taken by the 

2017 ozone season include: 

0

50

100

150

200

250

Oil and Gas Extraction Employment 

(NAICS = 211, Annual Average, 1000s of Employees)
5-32 

• Optimizing NOX removal from existing and operational SCR and SNCR systems; 

• Turning on and optimizing idled SCR and SNCR systems; 

• Installing, optimizing or upgrading combustion-side improvements resulting in reduced 

NOX emissions; 

• Shifting generation from units with higher NOX emission rates to units with lower 

emission rates. 

In addition, there could be directly induced employment impacts (both positive and 

negative) in the labor demand in the fossil fuels industry supplying fuels to the power sector. 

Once implemented, both the potential increases in operating efficiency and NOX reductions, as 

well as and shifting generation to lower NOX emitting assets, could impact the utility power 

sector’s demand for fossil fuels, and hence the demand for labor needed in the coal mining and 

gas extraction sectors. 

The direct net employment impacts of this proposed rule, in terms of the power sector and 

fuels sector, however, are anticipated to be relatively small. This is consistent with the relatively 

small estimated changes in the power sector’s overall cost of generation, as well as relatively 

small changes in generation, fuel use, capacity, and the percent of total generation produced by 

each type of fuel. 

For example, for the proposal in 2017, the estimated impacts relevant to changes in labor 

demand include: 

• The overall total national cost of generation in 2017 increases by 0.046 percent (the 

levelized annualized total cost of generation increases by 0.01 percent); 

• Total net generation increases by 0.009 percent (coal generation decreases by .084 percent, 

and natural gas generation increases by 0.11 percent); 

• The power sector’s total tons of coal used for electricity generation decreases by 0.04 

percent (or 0.09 percent increase in BTUs); 

• Total natural gas increases by 0.11 percent. 

While tThe results of the power sector modeling suggest that because of the very small 

changes in the power and fuels sector, the the direction and magnitude of the potential labor 

impacts are very small in all three regulatory alternatives analyzed. To illustrate this point, t, 
5-33 

because of the very small changes in the power and fuels sector, the direct labor impacts have 

been are not quantified in this repor for the proposed regulation for 2017 and 2020. The labor 

impacts for the more and less stringent alternatives have not been quantified. 

Affected EGUs may respond the proposed requirement for EGUs in 23 eastern states to 

reduce NOX emissions during the ozone season by improving and optimizing existing NOX

emission control systems or to shift generation to generating sources with lower NOX emission 

rates. Meeting the new EGU ozone season NOx budget limits will involve changes in the amount 

of labor needed in different parts of the utility power sector. Installing and operating new 

equipment, optimizing combustion control operations to reduce NOx emissions, and shifting 

generation to other sources could affect labor demand in the electricity generating sector itself, as 

well as associated equipment and services sectors. Specifically, the direct employment effects of 

initiatives at existing fossil EGUs would include increases in labor demand during the 

implementation phase for manufacturing, installing, and operating the NOx emissions controls at 

existing fossil units. Once implemented, increases in operating efficiency and shifting generation 

to existing generation resources will impact the utility power sector’s demand for fossil fuels and 

potentially plans for EGU retirement. 

The generation-side employment analysis uses the cost projections from the engineeringbased Integrated Planning Model (IPM) to project labor demand impacts of the final emission 

guidelines on affected EGUs in the electricity power sector and the fuel production sector (coal 

and natural gas). These projections include effects attributable to improving the NOx control 

performance of combustion control systems, optimizing the operation of post-combustion NOx 

control systems, generation shifts, and changes in fuel use. 

The following section presents the EPA’s quantitative projections of potential employment 

impacts in the electricity generation sector, as well as the impacts in the coal and natural gas fuel 

sectors. These projections are based in part on the IPM estimates of the impact of the proposed 

regulation previously discussed in this RIA. 

5.4.3.1 Methods Used to Estimate Changes in Employment in Electricity Generation and Fuel 

Supply
5-34 

The analytical approach used in this analysis is a bottom-up engineering method 

combining the EPA’s cost analysis of the NOx emission limits with data on labor productivity, 

engineering estimates of the amount and types of labor needed to manufacture, construct, and 

operate different types of NOx control systems, and prevailing wage rates for skilled and general 

labor categories. This approach is different from the economy-wide types of economic analyses 

discussed in section 5.4.1. Lacking robust peer-reviewed methods to estimate economy-wide 

impacts, the engineering-based analysis focuses on the supply-side direct impact on labor 

demand in industries closely involved with electricity generation. The engineering approach 

projects labor changes measured as the change in each analysis year in job-years employed in the 

utility power sector and directly related sectors (e.g., equipment manufacturing and fuel supply). 

Some of the quantified employment impacts in this analysis are one-time impacts, such as 

changes associated with upgrading the combustion controls. Other labor impacts will continue, 

such as changes associated with operating and maintaining generating units that will be retired, 

shifting generation to lower emitting generating units, and changes in the demand for labor 

providing the fuels supplied to the affected fossil-fired EGUs. 

The methods the 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 AnalysesRIAs for Clean Power PlanCPP (2015), and the Cross-State Air 

Pollution RuleCSAPR (2011). The methods used in this analysis to estimate the labor impacts 

(e.g., labor associated with changes in operating and maintaining generating units, as well as 

labor needed to mine coal and natural gas) are the same as we used in the CPP and CSAPR RIAs 

(with updated data where available). 

The bottom-up engineering-based labor analysis in the CPP RIA was primarily was 

concerned with the labor needs of improving CO2 emissions from operating of existing EGUs 

nationwide, and changes in the expected retirements of existing coal-fired EGUs and avoiding 

the need for new gas-fired EGUs. A central feature of the labor analysis for this RIA, however, 

involves the labor needs of upgrading and optimizing NOx control systems on existing EGUs in 

the affected 23- state region. In addition to the changes at EGUs within the 23-state region, there 

are also estimated changes in the utilization of existing generating units in other states, as well as

changes in the gas and coal supply sectors. 
5-35 

The methods and data used to estimate the labor associated with upgrading combustion 

control systems are assumed to be the same as those used to estimate heat rate improvements 

(HRI, another type of combustion control system upgrade) in the CPP RIA. There are two 

components of this assumption. 

• The mix of labor categories needed to implement the combustion control upgrades 

in this NOx analysis (i.e., the share of the labor cost of the upgrades apportioned 

general construction, boilermaker, engineering and management labor) is the same 

as the HRI-related combustion control upgrades needed in the CPP CO2 analysis. 

• The fully loaded labor cost of each labor category is the same for NOx control 

upgrades as for the HRI CO2 upgrades 

5.4.3.2 Estimates of the Changes in Employment in Electricity Generation and Fuel Supply

The estimated labor impacts of the proposed revisions to the NOx budgets from EGUs in 

the 23- state region are presented in Table 5-9. Given the methods the EPA uses to estimate labor 

impacts, it is not possible to directly separate the labor impacts that occur within the 23- state 

region from the labor impacts in the states not in the region. However, all the labor changes 

associated with combustion control upgrades, and optimization of existing post-combustion NOx 

control systems, will occur within the 23 -state region. The fuel supply labor impacts, however, 

will occur both within the 23- state region as well as in other states. This occurs for two reasons. 

First, coal and natural gas used at EGUs throughout the United States are both extracted within 

the 23- state region and in other states. Second, the shifts in fossil-fired generation will also 

occur both with within the 23- state region and in other states. 
5-36 

Table 5-9. Annual Net Employment Impacts for Power and Fuels Sectors of the Proposed 

Optional in 2017 & 2020 

2017 2020 

Upgrades and Optimization 

SCR 70 66 

SNCR* 0 

Combustion Control 67 66 

Sub-Total 137 132 

Plant Retirement 

Coal -160 -95 

Fuel Use Change 

Coal -87 -42 

Natural Gas 50 19 

Subtotal -36 -22 

Net Employment Impact -60 15 

*SNCR optimization is only required in the more stringent alternative. Job-year estimates are derived from IPM 

investment and upgrade estimates, as well as IPM fuel use estimates (tons coals or MMBtu gas). All job-year 

estimates are full-time equivalent (FTE) jobs.

5.5 Social Costs 

As discussed in the EPA Guidelines for Preparing Economic Analyses, social costs are the 

total economic burden of a regulatory action (USEPA, 2010). This burden is the sum of all 

opportunity costs incurred due to the regulatory action, where an opportunity cost is the value 

lost to society of any goods and services that will not be produced and consumed as a result of 

reallocating some resources towards pollution mitigation. Estimates of social costs may be 

compared to the social benefits expected as a result of a regulation to assess its net impact on 

society. The social costs of a regulatory action will not necessarily be equivalent to the 

expenditures associated with compliance. Nonetheless, here we use compliance costs as a proxy 

for social costs. 

The cost estimates for the three alternativeproposed and more or less stringent NOX cost 

threshold scenariosalternatives presented in this chapter are the change in expenditures required 

by the electricity power sector for compliance under each alternative, with a focus on the 

proposed remedy scenario. The change in the expenditures required by the electricity power 

sector to maintain compliance reflect the changes in electricity production costs due to changes 

in electricity demand resulting from application of the NOX control strategies described in 

Chapter 4 and changes in electricity production costs required to achieve the additional emission 
5-37 

reductionsabove necessary to comply with the emissions budgets that are described earlier in this 

RIAin Chapter 4. The changes in electricity production costs and changes in demand are 

estimated by IPM as described earlier in Chapter 5, and the change in expenditures required by 

the electricity power sector to maintain compliance are also described earlier in Chapter 5. 

5.6 Secondary Economic Impacts 

The energy sector impacts presented earlier in Chapter 5 of this RIA include potential 

changes in the prices for electricity, natural gas, and coal potentially resulting from the proposal. 

This chapter addresses the impact of these potential changes on other markets outside of the 

power sector and discusses some of the determinants of the magnitude of these impacts. We refer 

to these changes as secondary market impacts. 

The analysis of costs for this proposal includes strategies for affected states to reduce NOX

emissions from EGUs to comply with the proposed EGU NOX ozone-season emissions budgets 

in 2017. Ultimately, given the flexibilities afforded EGUs and states in complying with the 

proposed rule, the benefits, cost and economic impacts reported in this RIA are illustrativemay 

differ given the compliance approaches affected EGUs adopt of actions that states may take. The 

implementation approaches adopted by the states, and the abatement strategies adopted by 

affected EGUs, will ultimately drive the magnitude and timing of secondary impacts from 

changes in the price of electricity, and the demand for inputs by the electricity sector, on other 

markets that use and produce these inputs. 

To estimate the costs, benefits, and impacts of implementing the proposal, the EPA 

modeled a compliance compliance approach for the proposal and more and less stringent 

alternatives. Chapter 4 provides a description of the proposal and the more and less stringent 

alternatives considered. This chapter provides a quantitative assessment of the energy price 

impacts for these approaches and a qualitative assessment of the factors that will in part 

determine the timing and magnitude of effects in other markets. 

5.6.1 Methods 

One potential quantitative approach to evaluating the secondary market impacts is to use a 

computable general equilibrium (CGE) model. CGE models are able to provide aggregated 
5-38 

representations of the whole economy in equilibrium in the baseline and potentially with 

regulation in place. As such, a 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, 2010a), the EPA used the Economic Model for 

Policy Analysis (EMPAX) CGE model to estimate the secondary market effects based on the 

cost impacts projected by the Integrated Planning Model (IPM)IPM for the directly regulated 

sector. 

When considering the secondary market impacts of a regulation, the effects of the costs, 

the benefits of improved air quality, and their interaction may be relevant. Therefore, in the 

Second Prospective Analysis under Section 812 of the Clean Air Act Amendments, the EPA 

incorporated a set of health benefits arising from air quality improvement into the EMPAX CGE 

model when studying the economy-wide impacts of the Clean Air Act (U.S. EPA 2011). While 

the external Council on Clean Air Compliance Analysis (CouncilCOUNCIL) 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 remain when attempting to evaluate the benefits and costs of potential regulatory 

actions using economy-wide models. 

In light of these challenges, the EPA has established a Science Advisory Board (SAB) 

panel on economy-wide modeling to consider the technical merits and challenges of using this 

analytical tool to evaluate costs, benefits, and economic impacts in regulatory development. In 

addition, EPA is asking the panel to identify potential paths forward for improvements that could 

address the challenges posed when using economy-wide models to evaluate the effects of 

regulations. The final panel membership was announced in March 2015 and the first of multiple 

face-to-face meetings of the SAB panel has been scheduled forwas held October 22 and 23, 

2015. The EPA will use the recommendations and advice of this panel as an input into its process 

for improving benefit-cost and economic impact analyses used to inform decision-making at the 

agency. 
5-39 

The advice from the Science Advisory Board (SAB) panel formed specifically to address 

the subject of economy-wide modeling was not available in time for this final proposed action. 

Given the ongoing SAB panel on economy-wide modeling, and the ongoing challenges of 

accurately representing costs, benefits, energy efficiency improvements in economy-wide 

modeling, this chapter considers the energy impacts associated with the proposed alternatives 

analyzed and a qualitative assessment of the factors that will, in part, determine the timing and 

magnitude of effects in other markets.

5.6.2 Summary of Secondary Market Impacts of Energy Price Changes 

Electricity, natural gas, and coal are important inputs to the production of other goods and 

services. Therefore, changes in the price of these commodities will shift the production costs for 

sectors that use electricity, natural gas, and coal in the production of other goods and services. 

Changes in the types and levels of inputs used by producers in response to electricity and fuel 

price changes may mitigate the production cost changes in these sectors. Such changes in 

production costs may lead to changes in the quantities and/or prices of the goods or services 

produced and changes in imports and exports. 

The EPA used IPM to estimate electricity, natural gas, and coal price changes based on 

compliance with the regulatory control alternatives for this rule. IPM is a multi-regional, 

dynamic, deterministic linear programming model of the U.S. electric power sector described in 

more detail earlier in Chapter 5. The Retail Price Model (RPM) uses estimated changes in 

wholesale prices to estimate changes in average retail prices. The prices are average prices over 

consumer classes and regions weighted by the amount used. Table 5-910 shows these estimated 

price changes for the proposal in 2017 and, for illustrative purposes only, 2020. For other results 

generated by IPM and the RPM, please refer to earlier sections in Chapter 5. 

There are many factors influencing the projected natural gas prices. IPM (and its integrated 

gas resource and supply module) models natural gas natural gas supplies based on a multitude of 

factors. Since the model simulates perfect foresight, it anticipates future demand for natural gas 

and responds accordingly. In addition, IPM (and the natural gas module) are viewing a very long 

time horizon (through 2050), such that the impacts in certain years may be responsive to other 

modeling assumptions or drivers. The modeling framework is simultaneously solving for all of 
5-40 

these key market and policy parameters (both electric and natural gas), resulting in the impacts 

discussed in previous sections and shown in Table 5-9. 

Table 5-109. Estimated Percentage Changes in Average Energy Prices by Energy Type for 

the Proposed Alternative* 

Proposed Rule 2017 2020 

 Electricity Price Change -0.1% -0.2% 

 Delivered Natural Gas Price Change 0.01% -0.1% 

Delivered Coal Price Change -0.3% -0.4% 

 

*A minus sign in front of the number denotes a negative change, or a price decrease. 

For years when the price of electricity, natural gas, or coal increased, one would expect decreases 

in production and increases in market prices in sectors for which these commodities are inputs, 

ceteris paribus. Conversely, for years when prices of these inputs decreased, one would expect 

increases in production and decreases in market prices within these sectors. Smaller changes in 

input price changes would lead to smaller impacts within secondary markets. For compliance 

with the proposal and more and less stringent alternatives these price changes are quite small, or 

even negative, in 2017. Thus, one could readily surmise that the impacts of this proposed rule 

upon secondary markets should be minimal. As seen in Table 5-9, most of the price changes are 

negative, thus showing an estimated decrease in energy prices, albeit very small. The variation 

for these prices at the regional level is not substantially different from the national-level price 

changes, and the variation of energy prices related to the proposal and the regional level can be 

found among the IPM outputs in the public docket for this rulemaking. However, a number of 

factors, in addition to the magnitude and sign of the energy price changes, influence the 

magnitude of the impact on production and market prices for sectors using electricity, natural 

gas, or coal as inputs to production. These factors are discussed below. 

5.6.3 Share of Total Production Costs 

The impact of energy price changes in a particular sector depends, in part, on the share of 

total production costs attributable to those commodities. For sectors in which the directly 

affected inputs are only a small portion of production costs, the impact will be smaller than for 

sectors in which these inputs make up a greater portion of total production costs. Therefore, more 
5-41 

energy-intensive sectors would potentially experience greater cost increases when electricity, 

natural gas, or coal prices increase, but would also experience greater reduced costs when these 

input prices decrease. 

5.6.4 Ability to Substitute between Inputs to the Production Process 

The ease with which producers are able to substitute other inputs for electricity, natural 

gas, or coal, or even amongst those commodities, influences the impact of price changes for 

these inputs. Those sectors with a greater ability to substitute across energy inputs or to other 

inputs will be able to, at least partially, offset the increased cost of these inputs resulting in 

smaller market impacts. Similarly, when prices for electricity, natural gas, or coal decrease, some 

sectors may choose to use more of these inputs in place of other more costly substitutes. 

5.6.5 Availability of Substitute Goods and Services

The ability of producers in sectors experiencing changes in their input prices to pass along 

the increased costs to their customers in the form of higher prices for their products depends, in 

part, on the availability of substitutes for the sectors’ products. Substitutes may be either other 

domestic products or foreign imports. If close substitutes exist, the demand for the product will 

in general be more elastic and the producers will be less able to pass on the added cost through a 

price increase. 

Such substitution can also take place between foreign and domestic goods within the same 

sector. Changes in the price of electricity, natural gas, and coal can influence the quantities of 

goods imported or exported from sectors using these inputs. When the cost of domestic 

production increases due to more expensive inputs, imports may increase as consumers substitute 

towards relatively less costly foreign-produced goods, and vice versa. 

5.6.6 Effect of Changes in Input Demand from Electricity Sector 

 Section 5.7.2 focuses on the effects of changes in energy prices, and possible responses to 

those price changes, on sectors outside of the electricity sector. A change in demand for inputs in 

the electricity sector, as well as changes in demand for energy efficiency services and products, 

will also influence economic activity in other sectors of the economy. For example, EPA 

estimates EGUs will increase use of ammonia and urea to reduce NOx emissions, which could 
5-42 

result in a price driven reduction in demand in other sectors (e.g., fertilizer manufacturing and 

agriculture) using these industrial chemicals. there could be changes in the demand for new 

generation sources such as natural gas combined cycle units, and therefore sectors producing 

technologies such as these may expand. 

5.6.7 Conclusions 

Changes in the price of electricity, natural gas, and coal can affect markets for goods and 

services produced by sectors that use these energy inputs in the production process. The direction 

and magnitude of these impacts are influenced by a number of factors. For example, the more 

producers in these sectors are able to substitute away from the use of these energy inputs, the 

smaller the effect of energy prices changes will be on their production cost. Changes in cost of 

production may lead to changes in price, quantity produced, and profitability of firms within 

secondary markets. Furthermore, the demand inputs in the electricity sector will also affect 

secondary markets. If regulation results in changes in domestic markets that lead to an increase 

in imports, then domestic producers may experience less demand from their consumers, and vice 

versa. 

Modeling choices in IPM influence the estimated changes in electricity, natural gas, and 

coal prices in this RIA. Actual market conditions will ultimately influence the price changes of 

these energy inputs and consequent effects on secondary markets, as will the plan approaches 

that states adopt. 

5.7 References 

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M. Russell; R. Schmalensee; V. K. Smith and R. N. Stavins. 1996. “Benefit-Cost Analysis in 

Environmental, Health, and Safety Regulation: A Statement of Principles.” American 

Enterprise Institute, the Annapolis Center, and Resources for the Future; AEI Press. 

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Environmental.AEI.1996.pdf>. Accessed June 5, 2015. 

Berman, E. and L. T. M. Bui. 2001. “Environmental Regulation and Labor Demand: Evidence 

from the South Coast Air Basin.” Journal of Public Economics. 79(2): 265-295. 

Congressional Budget Office (2011), Statement of Douglas W. Elmendorf, Director, before the 

Senate Budget Committee, “Policies for Increasing Economic Growth and Employment in 
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2012 and 2013” (November 15)<http://www.cbo.gov/sites/default/files/11-15-

Outlook_Stimulus_Testimony.pdf> 

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Addison Wesley Longman, Inc., Chapter 4. 

Executive Order 13563 (January 21, 2011). “Improving Regulation and Regulatory Review. 

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Power Sector Employment: Phase I of the Title IV SO2 Trading Program.” Journal of the 

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American Economic Review. 102(7):3652-73. 

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 

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Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industrial Activity: 

Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of 

Manufactures.” Journal of Political Economy. 110(6): 1175-1219. 

Hamermesh, D. S. 1993. Labor Demand. Princeton, NJ: Princeton University Press. Chapter 2. 

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List, J. A.; D. L. Millimet; P. G. Fredriksson and W. W. McHone. 2003. “Effects of 

Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity 

Score Matching Estimator.” The Review of Economics and Statistics. 85(4): 944-952. 

Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal of 

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Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analysis for the 

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Walker, W. R. 2011.“Environmental Regulation and Labor Reallocation." American Economic 

Review. 101(2): 442-47. 

Walker, W. R. 2013.“The Transitional Costs of Sectoral Reallocation: Evidence From the Clean 

Air Act and the Workforce." The Quarterly Journal of Economics 128 (4): 1787-1835. . 

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National Industry-Specific Occupational Employment and Wage Estimates, Electric Power 
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Generation, Transmission, and Distribution (NAICS 2211)”. Available at: 

<http://www.bls.gov/oes/current/naics4_221100.htm>. Accessed June 9, 2015. 

U.S. Bureau of Labor Statistics (BLS). 2014a. “May 2014 National Industry-Specific 

Occupational Employment and Wage Estimates: NAICS 212100 - Coal Mining”. Available 

at: < http://www.bls.gov/oes/current/naics4_212100.htm#00-0000>. Accessed June 9, 2015. 

U.S. Bureau of Labor Statistics (BLS). 2014b. “May 2014 National Industry-Specific 

Occupational Employment and Wage Estimates: NAICS 212100 – Oil and Gas Extraction”. 

Available at: < http://www.bls.gov/oes/current/naics4_212100.htm>. Accessed June 9, 2015. 

U.S. Bureau of Labor Statistics (BLS). 2015. “Current Employment Survey Seasonally Adjusted 

Employment for Electric Power Generation (national)” and “Current Employment Survey 

Seasonally Adjusted Employment for Transmission, and Distribution (national).” Series ID: 

CES4422110001. Available at <http://www.bls.gov/data/>. Accessed June 9, 2015.

U.S. Energy Information Agency (EIA). 2014. The Electricity Market Module of the National 

Energy Modeling System: Model Documentation 2014. Available at: 

<http://www.eia.gov/forecasts/aeo/nems/documentation/electricity/pdf/m068(2014).pdf>. 

Accessed 9/17/2015. 

U.S. EPA, 2015. Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric 

Utility Generating Units (Final Rule), http://www2.epa.gov/cleanpowerplan/clean-powerplan-existing-power-plants. 

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Modified, and Reconstructed Stationary Sources: Electric Utility Generating Units (Final 

Rule), http://www2.epa.gov/cleanpowerplan/carbon-pollution-standards-new-modified-andreconstructed-power-plants. 

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http://www2.epa.gov/coalash/coal-ash-rule. 

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http://www2.epa.gov/eg/steam-electric-power-generating-effluent-guidelines-2015-final-rule. 

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http://www2.epa.gov/cooling-water-intakes.

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<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/guidelines.htm>. Accessed 9/21/2015. 
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http://archive.epa.gov/airmarkets/programs/cair/web/html/index.html.
5-1 

CHAPTER 6: ESTIMATED HUMAN HEALTH BENEFITS AND CLIMATE COBENEFITS 

6.1 Introduction 

As discussed in Chapter 4, this action proposes to update CSAPR to reduce interstate 

transport of EGU ozone season NOX emissions that contribute significantly to nonattainment or 

interfere with maintenance of the 2008 ozone NAAQS. The EPA proposes to implement the 

proposed EGU NOX reductions by setting emissions budgets that are implemented through the 

CSAPR NOX ozone-season allowance trading program. Implementing this proposal to update 

CSAPR is expected to reduce emissions of NOX during the summer ozone season and provide 

ancillary annual NOX and carbon dioxide (CO2) benefits (i.e., co-benefits). This chapter 

describes the methods used to estimate the monetized ozone-related air quality health benefits, 

fine particulate matter (PM2.5)-related air quality health co-benefits from reductions in NOX

emissions and climate co-benefits from reductions of CO2 emissions. These health benefits are 

associated with reducing exposure to ambient ozone and PM2.5 by reducing emissions of 

precursor pollutants (i.e., NOX). Data, resource, and methodological limitations prevent the EPA 

from monetizing several important co-benefits from reducing emissions of several pollutants, 

including those from reducing direct exposure to NO2, ecosystem effects and visibility 

impairment. We qualitatively discuss these unquantified benefits in this chapter. 

This chapter provides estimates of the monetized air quality health benefits and climate cobenefits associated with emission reductions for the regulatory control alternatives across several 

discount rates. The estimated benefits associated with these emission reductions are beyond 

those achieved by previous EPA rulemakings, including the 2011 CSAPR89 and the finalized 

Clean Power Plan (CPP). 

 

89 As discussed in Chapters 1, 3 and 5, the 2017 baseline EGU emissions for this proposal include impacts from 

CSAPR issued on July 6, 2011. As discussed and elaborated on in Chapter 1, because the modeling for the proposal 

was performed prior to the D.C. Circuit’s issuance of EME Homer City II, 11 that modeling assumed in its baseline 

for all states the emission reductions associated with the CSAPR NOX ozone-season phase 2 emissions budgets. 
A.6-2 

6.2 Estimated Human Health Benefits 

 The proposal to update CSAPR is expected to reduce emissions of ozone season NOX. In 

the presence of sunlight, NOX and VOCs can undergo a chemical reaction in the atmosphere to 

form ozone. Reducing NOX emissions also reduces human exposure to ozone and the incidence 

of ozone-related health effects, though this depends partly on local levels of volatile organic 

compounds (VOCs). The proposal would also reduce emissions of NOX throughout the year. 

Because NOX is also a precursor to formation of ambient PM2.5, reducing these emissions would 

also reduce human exposure to ambient PM2.5 throughout the year and would reduce the 

incidence of PM2.5-related health effects.90 This RIA does not quantify PM2.5-related benefits 

associated with SO2 emission reductions. As discussed in Chapter 3, the EPA does not estimate 

significant SO2 emission reductions as a result of this proposal. Furthermore, these reductions 

would reduce ozone and PM2.5 concentrations in regions other than those with nonattainment 

monitors that are the subject of this proposal, and the benefits of reducing these pollutants in 

those regions are assessed in this Chapter. Reducing emissions of NOX would also reduce 

ambient exposure to NO2 and its associated health effects, though we do not quantify these 

effects because we have not developed a reduced-form technique for estimating NO2 impacts. In 

this section, we provide an overview of the monetized ozone-related benefits and PM2.5-related 

co-benefits estimated for the proposed updated CSAPR EGU NOX ozone season emissions 

budgets and for the more and less stringent alternatives. A full description of the epidemiological 

studies we use, the methods we apply and the tools we employ to quantify the incidence of these 

effects may be found in the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. 

EPA, 2015). The estimated benefits associated with these emissions reductions are additional to 

those achieved by previous EPA rulemakings, including the finalized CPP. 

Implementing these updated CSAPR EGU NOX emissions budgets for the ozone season in 

23 eastern states may reduce ambient ozone and PM2.5 concentrations below the National 

Ambient Air Quality Standards (NAAQS) in some areas and assist other areas with attaining the 

ozone and PM2.5 NAAQS. The NAAQS RIAs (U.S. EPA, 2008, 2012a, 2015) also calculated the 

benefits of attaining alternate ozone and PM NAAQS, and so differences in the design and 

 

90 Additionally, this RIA does not estimate changes in emissions of directly emitted particles. 
A.6-3 

analytical objectives of each RIA are worth noting here. The NAAQS RIAs illustrate the 

potential costs and benefits of attaining a revised air quality standard nationwide based on an 

array of emission reduction strategies for different sources reflecting the application of identified 

and unidentified controls, incremental to implementation of existing regulations and controls 

needed to attain the NAAQS that currently is in effect. In short, NAAQS RIAs hypothesize, but 

do not predict, the strategies that States may choose to enact when implementing a revised 

NAAQS. Setting a NAAQS does not directly result in costs or benefits, and as such, the EPA’s 

NAAQS RIAs are illustrative. The estimated costs and benefits from NAAQS RIAs are not 

intended to be added to the costs and benefits of other regulations that result in specific costs of 

control and prescribe specific emission reductions. For example, some of the emissions 

reductions estimated to result from implementing the proposal to update CSAPR may achieve 

some of the air quality improvements that resulted from the hypothesized attainment strategies 

presented in the NAAQS RIAs. The emissions reductions from implementing the proposal to 

update CSAPR will decrease the remaining amount of emissions reductions needed in 

nonattainment areas to, and reduce the costs to those areas to, meet the 2008 ozone NAAQS. 

Specifically, the anticipated reductions in ozone concentrations from this rule would help areas 

attain and maintain the 2008 ozone NAAQS and achieve some of the air quality improvements 

from the hypothesized attainment strategy from the 2008 ozone NAAQS RIA. These ozone 

improvements would similarly achieve some of the air quality improvements assumed in the 

baseline (i.e., 2008 ozone NAAQS attainment projection) for the 2015 ozone NAAQS RIA. 

As discussed in Chapter 4, the IPM modeling showing compliance with the regulatory 

control alternatives for which emission reductions are estimated in this RIA is also illustrative in 

nature. However, unlike the illustrative control strategies analyzed in NAAQS RIAs described 

above, all of the emission reductions for the illustrative compliance modeling for this proposal 

would occur in one well-characterized sector (i.e., the EGU sector). The EPA is more confident 

in the magnitude and location of the emission reductions for this proposal because it imposes a 

specific requirement that limits emissions from a specific sector. Emission reductions achieved 

under this rule, if finalized, will ultimately be reflected in the baseline of future NAAQS 

analyses and would lower the emissions reductions needed to attain revised future NAAQS. For 

more information on the relationship between illustrative analyses, such as for the NAAQS and 

its associated implementation rules, please see the Ozone NAAQS RIA (U.S. EPA, 2015). 
A.6-4 

6.2.1 Health Impact Assessment for Ozone and PM2.5

The Integrated Science Assessment for Ozone and Related Photochemical Oxidants 

(Ozone ISA) (U.S. EPA, 2013b) identified the human health effects associated with ambient 

ozone exposure, which also include premature mortality and a variety of morbidity effects 

associated with acute and chronic exposures. Similarly, the Integrated Science Assessment for 

Particulate Matter (PM ISA) (U.S. EPA, 2009b) identified the human health effects associated 

with ambient PM2.5 exposure, which include premature mortality and a variety of morbidity 

effects associated with acute and chronic exposures. Table 6-1 identifies the quantified and 

unquantified benefit and co-benefit categories captured in the EPA’s health benefits estimates for 

reduced exposure to ambient ozone and PM2.5. Although the table below does not list 

unquantified health effects or welfare effects, such as acidification and nutrient enrichment, these 

effects are described in detail in Chapters 5 and 6 of the PM NAAQS RIA (U.S. EPA, 2012a) 

and summarized later in this chapter. It is important to emphasize that the list of unquantified 

benefits categories is not exhaustive, nor is quantification of each effect complete. 
A.6-5 

Table 6-1. Human Health Effects of Ambient Ozone and PM2.5

Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More 

Information 

Improved Human Health 

Reduced incidence of 

mortality from 

exposure to ozone 

Premature mortality based on short-term study 

estimates (all ages)   Ozone ISA 

Premature mortality based on long-term study 

estimates (age 30–99) — — Ozone ISA1

Reduced incidence of 

morbidity from 

exposure to ozone 

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

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

Emergency department visits for asthma (all ages)   Ozone ISA 

Minor restricted-activity days (age 18–65)   Ozone ISA 

School absence days (age 5–17)   Ozone ISA 

Decreased outdoor worker productivity (age 18–65) — — Ozone ISA1

Other respiratory effects (e.g., premature aging of

lungs) — — Ozone ISA2

Cardiovascular and nervous system effects — — Ozone ISA2

Reproductive and developmental effects — — Ozone ISA2,3

Reduced incidence of 

premature mortality 

from exposure to 

PM2.5

Adult premature mortality based on cohort study 

estimates and expert elicitation estimates (age >25 

or age >30) 

  PM ISA 

Infant mortality (age <1)   PM ISA

Reduced incidence of 

morbidity from 

exposure to PM2.5

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

Hospital admissions—respiratory (all ages)   PM ISA

Hospital admissions—cardiovascular (age >20)   PM ISA

Emergency room visits for asthma (all ages)   PM ISA

Acute bronchitis (age 8-12)   PM ISA

Lower respiratory symptoms (age 7-14)   PM ISA

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

Asthma exacerbation (asthmatics age 6-18)   PM ISA

Lost work days (age 18-65)   PM ISA

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

Chronic Bronchitis (age >26) — — PM ISA1

Emergency room visits for cardiovascular effects 

(all ages) — — PM ISA1

Strokes and cerebrovascular disease (age 50-79) — — PM ISA1

Other cardiovascular effects (e.g., other ages) — — PM ISA2

Other respiratory effects (e.g., pulmonary function, 

non-asthma ER visits, non-bronchitis chronic 

diseases, other ages and populations) 

— — PM ISA2

Reproductive and developmental effects (e.g., low 

birth weight, pre-term births, etc.) — — PM ISA2,3

Cancer, mutagenicity, and genotoxicity effects — — PM ISA2,3

1

 We assess these co-benefits qualitatively due to data and resource limitations for this analysis, but we have quantified them in 

sensitivity analyses for other analyses. 

2

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

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

concerns over the strength of the association. 

We follow a “damage-function” approach in calculating benefits, which estimates changes 

in individual health endpoints (specific effects that can be associated with changes in air quality) 
A.6-6 

and assigns values to those changes assuming independence of the values for those individual 

endpoints. Because the EPA rarely has the time or resources to perform new research to measure 

directly either health outcomes or their values for regulatory analyses, our estimates are based on 

the best available methods of benefits transfer, which is the science and art of adapting primary 

research from similar contexts to estimate benefits for the environmental quality change under 

analysis. In addition to transferring information from other contexts to the context of this 

regulation, we also use a “benefit-per-ton” approach to estimate the ozone and PM2.5 benefits in 

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

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

no air quality modeling is available. Thus, to develop estimates of benefits for this RIA, we are 

transferring both the underlying health and economic information from previous studies and 

information on air quality responses to emissions reductions from other air quality modeling. 

This section describes the underlying basis for the health and economic valuation estimates that 

inform the benefit-per-ton estimates, and the subsequent section provides an overview of the 

benefit-per-ton estimates, 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 ozone and PM obtained from the peer-reviewed scientific literature. 

These estimates are used in conjunction with population data, baseline health information, air 

quality data and economic valuation information to conduct health impact and economic benefits 

assessments. These assessments form the key inputs to calculating benefit-per-ton estimates. The 

next sections provide an overview of the health impact assessment (HIA) methodology and 

additional details on several key elements. 

The HIA quantifies the changes in the incidence of adverse health impacts resulting from 

changes in human exposure to ozone and PM2.5. We use the environmental Benefits Mapping 

and Analysis Program – Community Edition (BenMAP-CE) (version 1.1) to systematize health 

impact analyses by applying a database of key input parameters, including population 

projections, health impact functions, and valuation functions (Abt Associates, 2012). For this 

assessment, the HIA is limited to those health effects that are directly linked to ambient ozone 

and PM2.5 concentrations. There may be other indirect health impacts associated with reducing 

emissions, such as occupational health exposures. Epidemiological studies generally provide 
A.6-7 

estimates of the relative risks of a particular health effect for a given increment of air pollution 

(often per 10 ppb for ozone or μg/m3 for PM2.5). These relative risks can be used to develop risk

coefficients that relate a unit reduction in pollution (e.g., ozone) to changes in the incidence of a 

health effect. We refer the reader to the Ozone NAAQS RIA (U.S. EPA, 2015) and PM NAAQS 

RIA (U.S. EPA, 2012a) for more information regarding the epidemiology studies and risk 

coefficients applied in this analysis, and we briefly elaborate on adult premature mortality below. 

The size of the mortality effect estimates from epidemiological studies, the serious nature of the 

effect itself, and the high monetary value ascribed to reducing risks of premature death make 

mortality risk reduction the most significant health endpoint quantified in this analysis. 

6.2.1.1 Mortality Effect Coefficients for Short-term Ozone Exposure 

The overall body of evidence indicates that there is likely to be a causal relationship 

between short-term ozone exposure and premature death. The 2013 ozone ISA concludes that the 

evidence suggests that ozone effects are independent of the relationship between PM and 

mortality. (U.S. EPA, 2013a). However, the ISA notes that the interpretation of the potential 

confounding effects of PM on ozone-mortality risk estimates requires caution due to the PM 

sampling schedule (in most cities) which limits the overall sample size available for evaluating 

potential confounding of the ozone effect by PM (U.S. EPA 2013a). 

In 2006, the EPA requested an NAS study to answer the following four key questions 

regarding ozone-related mortality: (1) How did the epidemiological literature to that point 

improve our understanding of the size of the ozone-related mortality effect?; (2) How best can 

EPA quantify the level of ozone-related mortality impacts from short-term exposure?; (3) How 

might EPA estimate the change in life expectancy?; and (4) What methods should EPA use to 

estimate the monetary value of changes in ozone-related mortality risk and life expectancy? 

In 2008, the NAS (NRC, 2008) issued a series of recommendations to the EPA regarding 

the quantification and valuation of ozone-related short-term mortality. Chief among these was 

that “...short-term exposure to ambient ozone is likely to contribute to premature deaths” and the 

committee recommended that “ozone-related mortality be included in future estimates of the 

health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatest 

emphasis be placed on the multi-city and NMMAPS studies without exclusion of the meta-
A.6-8 

analyses” (NRC, 2008). In addition, NAS recommended that EPA “should give little or no 

weight to the assumption that there is no causal association between estimated reductions in 

premature mortality and reduced ozone exposure” (NRC, 2008). In 2010, the Health Effects 

Subcommittee of the Advisory Council on Clean Air Compliance Analysis, while reviewing 

EPA’s The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a), also 

confirmed the NAS recommendation to include ozone mortality benefits (U.S. EPA-SAB, 

2010a). 

In view of the findings of the ozone ISA, the NAS panel, the SAB-HES panel, and the 

CASAC panel, we estimate ozone-related premature mortality for short-term exposure in the 

core health effects analysis using effect coefficients from the Smith et al. (2009) NMMAPS 

analysis and the Zanobetti and Schwartz (2008) multi-city study with several additional studies 

as sensitivity analyses. This emphasis on newer multi-city studies is consistent with 

recommendations provided by the NAS in their ozone mortality report (NRC, 2008). CASAC 

supported using the Smith et al. (2009) and Zanobetti and Schwartz (2008) studies for the ozone 

HREA (U.S. EPA-SAB, 2012, 2014), and these are multi-city studies published more recently 

(as compared with other multi-city studies or meta-analyses included in the sensitivity analyses – 

see discussion below). 

Smith et al. (2009) reanalyzed the NMMAPS dataset, evaluating the relationship between 

short-term ozone exposure and mortality. While this study reproduces the core national-scale 

estimates presented in Bell et al. (2004), it also explored the sensitivity of the mortality effect to

different model specifications including (a) regional versus national Bayes-based adjustment,91

(b) co-pollutant models considering PM10, (c) all-year versus ozone-season based estimates, and 

(d) consideration of a range of ozone metrics, including the daily 8-hour max. In addition, the 

Smith et al. (2009) study did not use the trimmed mean approach employed in the Bell et al. 

 

91 In Bayesian modeling, effect estimates are “updated” from an assumed prior value using observational data. In 

the Smith et al. (2009) approach, the prior values are either a regional or national mean of the individual effect 

estimates obtained for each individual city. The Bayesian adjusted city-specific effect estimates are then 

calculated by updating the selected prior value based on the relative precision of each city-specific estimate and 

the variation observed across all city-specific individual effect estimates. City-specific estimates are pulled 

towards the prior value if they have low precision and/or if there is low overall variation across estimates. Cityspecific estimates are given less adjustment if they are precisely estimated and/or there is greater overall variation 

across estimates. 
A.6-9 

(2004) study in preparing ozone monitor data.92 In selecting effect estimates from Smith et al. 

(2009), we focused on an ozone-only estimate for non-accidental mortality using the 8-hour max 

metric for the warmer ozone season. For the sensitivity analysis, we included a co-pollutant 

model (ozone and PM10) from Smith et al. (2009) for all-cause mortality, using the 8-hour max 

ozone metric for the ozone season. Using a single pollutant model for the core analysis and the 

co-pollutant model in the sensitivity analysis reflects our concern that the reduced sampling 

frequency for days with co-pollutant measurements (1/3 and 1/6) could affect the ability of the 

study to characterize the ozone effect. This choice is consistent with the ozone ISA, which 

concludes that ozone effects are likely to be independent of the relationship between PM and 

mortality (U.S. EPA, 2013a). 

The Zanobetti and Smith (2008) study evaluated the relationship between ozone exposure 

(using an 8-hour mean metric for the warm season June-August) and all-cause mortality in 48 

U.S. cities using data collected between 1989 and 2000. The study presented single pollutant CR functions based on shorter (0-3 day) and longer (0-20 day) lag structures, with the comparison 

of effects based on these different lag structures being a central focus of the study. We used the 

shorter day lag based C-R function since this had the strongest effect and tighter confidence 

interval. We converted the effect estimate from an 8-hour mean metric to an equivalent effect 

estimate based on an 8-hour max to account for the period of the day in which most individuals 

are exposed to ozone. To do this, we used the ozone metric approach wherein the original effect 

estimate (and standard error) is multiplied by the appropriate ozone metric adjustment ratio. 

6.2.1.2 PM2.5 Mortality Effect Coefficients for Adults and Infants 

A substantial body of published scientific literature documents the association between 

elevated PM2.5 concentrations and increased premature mortality (U.S. EPA, 2009b). This body 

of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA 

completed as part of the most recent review of the PM NAAQS, which was twice reviewed by 

the SAB-CASAC (U.S. EPA-SAB, 2009a, 2009b), concluded that there is a causal relationship 

between mortality and both long-term and short-term exposure to PM2.5 based on the entire body 

 

92 There are a number of concerns regarding the trimmed mean approach including (1) the potential loss of temporal 

variation in the data when the approach is used (this could impact the size of the effect estimate), and (2) a lack of 

complete documentation for the approach, which prevents a full reviewing or replication of the technique. 
A.6-10 

of scientific evidence (U.S. EPA, 2009b). The size of the mortality effect estimates from 

epidemiological studies, the serious nature of the effect itself, and the high monetary value 

ascribed to prolonging life make mortality risk reduction the most significant health endpoint 

quantified in this analysis. 

Researchers have found statistically significant associations between PM2.5 and 

premature mortality using different types of study designs. Time-series methods have been used 

to relate short-term (often day-to-day) changes in PM2.5 concentrations and changes in daily 

mortality rates up to several days after a period of exposure to elevated PM2.5 concentrations. 

Cohort methods have been used to examine the potential relationship between community-level 

PM2.5 exposures over multiple years (i.e., long-term exposures) and community-level annual 

mortality rates that have been adjusted for individual level risk factors. When choosing between 

using short-term studies or cohort studies for estimating mortality benefits, cohort analyses are 

thought to capture more of the public health impact of exposure to air pollution over time 

because they account for the effects of long-term exposures, as well as some fraction of shortterm exposures (Kunzli et al., 2001; NRC, 2002). The National Research Council (NRC) stated 

that “it is essential to use the cohort studies in benefits analysis to capture all important effects 

from air pollution exposure” (NRC, 2002, p. 108). The NRC further noted that “the overall effect 

estimates may be a combination of effects from long-term exposure plus some fraction from 

short-term exposure. The amount of overlap is unknown” (NRC, 2002, p. 108-9). To avoid 

double counting, we focus on applying the risk coefficients from the long-term cohort studies in 

estimating the mortality impacts of reductions in PM2.5. 

Over the last two decades, several studies using “prospective cohort” designs have been 

published that are consistent with the earlier body of literature. Two prospective cohort studies, 

often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 2006; 

Lepeule et al., 2012) and the “American Cancer Society” or “ACS study” (Pope et al., 1995; 

Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive analyses of 

ambient PM2.5 concentrations and mortality. These studies have found consistent relationships 

between fine particle indicators and premature mortality across multiple locations in the United 

States. The credibility of these two studies is further enhanced by the fact that the initial 

published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive 
A.6-11 

reexamination and reanalysis by an independent team of scientific experts commissioned by the 

Health Effects Institute (HEI) and by a Special Panel of the HEI Health Review Committee 

(Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 

Six Cities Study and the 1995 ACS study using more recent air quality data and a longer followup period for the ACS cohort provides additional validation of the findings of these original 

studies (Pope et al., 2002, 2004; Laden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). 

The SAB-HES also supported using these two cohorts for analyses of the benefits of PM 

reductions, and concluded, “the selection of these cohort studies as the underlying basis for PM 

mortality benefit estimates [is] a good choice. These are widely cited, well studied and 

extensively reviewed data sets” (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities studies 

have inherent strengths and weaknesses, we present benefits estimates using relative risk 

estimates from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; 

Lepeule et al., 2012). Presenting results using both ACS and Six Cities is consistent with other 

recent RIAs (e.g., U.S. EPA, 2010c, 2011a, 2011c). The PM ISA concludes that the ACS and Six 

Cities cohorts provide the strongest evidence of the association between long-term PM2.5

exposure and premature mortality with support from a number of additional cohort studies 

(described below). 

The extended analyses of the ACS cohort data (Krewski et al., 2009) refined the earlier 

ACS studies by (a) extending the follow-up period by 2 years to the year 2000, for a total of 18 

years; (b) incorporating almost double the number of urban areas; (c) addressing confounding by 

spatial autocorrelation by incorporating ecological, or community-level, co-variates; and (d) 

performing an extensive spatial analysis using land use regression modeling in two large urban 

areas. These enhancements make this analysis well-suited for the assessment of mortality risk 

from long-term PM2.5 exposures for the EPA’s benefits analyses. 

In 2009, the SAB-HES again reviewed the choice of mortality risk coefficients for 

benefits analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have 

not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the 

SAB-HES recommends that EPA not use the Krewski et al. (2009) findings for generating the 

Primary Estimate” (U.S. EPA-SAB, 2010a). Since this time, the Krewski et al. (2009) has 

undergone additional peer review, which we believe strengthens the support for including this 
A.6-12 

study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the 

key mortality studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 

2010b) used risk coefficients drawn from the Krewski et al. (2009) study, the most recent 

reanalysis of the ACS cohort data. The PM risk assessment cited a number of advantages that 

informed the selection of the Krewski et al. (2009) study as the source of the core effect 

estimates, including the extended period of observation, the rigorous examination of model 

forms and effect estimates, the coverage for ecological variables, and the large dataset with over 

1.2 million individuals and 156 MSAs (U.S. EPA, 2010b). The CASAC also provided extensive 

peer review of the PM risk assessment and supported the use of effect estimates from this study 

(U.S. EPA-SAB, 2009a, b, 2010b). 

Consistent with the PM risk assessment (U.S. EPA, 2010b), which was reviewed by the 

CASAC (U.S. EPA-SAB, 2009a, b), we use the all-cause mortality risk estimate based on the 

random-effects Cox proportional hazard model that incorporates 44 individual and 7 ecological 

covariates (RR=1.06, 95% confidence intervals 1.04–1.08 per 10 μg/m3

 increase in PM2.5). The 

relative risk estimate (1.06 per 10 μg/m3 increase in PM2.5) is identical to the risk estimate drawn 

from the earlier Pope et al. (2002) study, though the confidence interval around the Krewski et 

al. (2009) risk estimate is tighter. 

In the most recent Six Cities study, which was published after the last SAB-HES review, 

Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model 

specifications, lower exposures, and averaging time using eleven additional years of cohort 

follow-up that incorporated recent lower exposures. The authors found significant associations 

between PM2.5 exposure and increased risk of all-cause, cardiovascular and lung cancer 

mortality. The authors also concluded that the C-R relationship was linear down to PM2.5

concentrations of 8 μg/m3

 and that mortality rate ratios for PM2.5 fluctuated over time, but 

without clear trends, despite a substantial drop in the sulfate fraction. We use the all-cause 

mortality risk estimate based on a Cox proportional hazard model that incorporates 3 individual 

covariates. (RR=1.14, 95% confidence intervals 1.07–1.22 per 10 μg/m3

 increase in PM2.5). The 

relative risk estimate is slightly smaller than the risk estimate drawn from Laden et al. (2006), 

with relatively smaller confidence intervals. 
A.6-13 

Given that monetized benefits associated with PM2.5 are driven largely by reductions in 

premature mortality, it is important to characterize the uncertainty in this endpoint. In order to do 

so, we utilize the results of an expert elicitation sponsored by the EPA and completed in 2006 

(Roman et al., 2008; IEc, 2006). The results of that expert elicitation can be used as a 

characterization of uncertainty in the C-R functions. 

In addition to the adult mortality studies described above, several studies show an 

association between PM exposure and premature mortality in children under 5 years of age.93

The PM ISA states that less evidence is available regarding the potential impact of PM2.5

exposure on infant mortality than on adult mortality and the results of studies in several countries 

include a range of findings with some finding significant associations. Specifically, the PM ISA 

concluded that evidence exists for a stronger effect at the post-neonatal period and for 

respiratory-related mortality, although this trend is not consistent across all studies. In addition, 

compared to avoided premature deaths estimated for adult mortality, avoided premature deaths 

for infants are significantly smaller because the number of infants in the population is much 

smaller than the number of adults and the epidemiology studies on infant mortality provide 

smaller risk coefficients associated with exposure to PM2.5. 

In 2004, the SAB-HES noted the release of the WHO Global Burden of Disease Study 

focusing on ambient air, which cites several recently published time-series studies relating daily 

PM exposure to mortality in children (U.S. EPA-SAB, 2004). With regard to the cohort study 

conducted by Woodruff et al. (1997), the SAB-HES noted several strengths of the study, 

including the use of a larger cohort drawn from a large number of metropolitan areas and efforts 

to control for a variety of individual risk factors in infants (e.g., maternal educational level, 

maternal ethnicity, parental marital status, and maternal smoking status). Based on these 

findings, the SAB-HES recommended that the EPA incorporate infant mortality into the primary 

benefits estimate and that infant mortality be evaluated using an impact function developed from 

the Woodruff et al. (1997) study (U.S. EPA-SAB, 2004). 

In 2010, the SAB-HES again noted the increasing body of literature relating infant 

mortality and PM exposure and supported the inclusion of infant mortality in the monetized 

 

93 For the purposes of this analysis, we only calculate benefits for infants age 0–1, not all children under 5 years old. 
A.6-14 

benefits (U.S. EPA-SAB, 2010a). The SAB-HES generally supported the approach of estimating 

infant mortality based on Woodruff et al. (1997) but also noted that a more recent study by 

Woodruff et al. (2006) continued to find associations between PM2.5 and infant mortality in 

California. The SAB-HES also noted, “when PM10 results are scaled to estimate PM2.5 impacts, 

the results yield similar risk estimates.” Consistent with The Benefits and Costs of the Clean Air 

Act 1990 to 2020 (U.S. EPA, 2011a), we continue to rely on the earlier 1997 study in part due to 

the national–scale of the earlier study. 

6.2.2 Economic Valuation for Health Benefits

After quantifying the change in adverse health impacts, we estimate the economic value of 

these avoided impacts. Reductions in ambient concentrations of air pollution generally lower the 

risk of future adverse health effects by a small amount for a large population. Therefore, the 

appropriate economic measure is willingness to pay (WTP) for changes in risk of a health effect. 

For some health effects, such as hospital admissions, WTP estimates are generally not available, 

so we use the cost of treating or mitigating the effect. These cost-of-illness (COI) estimates 

generally (although not necessarily in every case) understate the true value of reductions in risk 

of a health effect. They tend to reflect the direct expenditures related to treatment but not the 

value of avoided pain and suffering from the health effect. The unit values applied in this 

analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 

2012a). 

For this proposed rule avoided premature deaths account for over 90 percent of monetized 

ozone-related benefits and 98 percent of monetized PM-related co-benefits. The economics 

literature concerning the appropriate method for valuing reductions in premature mortality risk is 

still developing. The adoption of a value for the projected reduction in the risk of premature 

mortality is the subject of continuing discussion within the economics and public policy analysis 

community. Following the advice of the SAB’s Environmental Economics Advisory Committee 

(SAB-EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating 

estimates of mortality benefits, because we believe this calculation provides the most reasonable 

single estimate of an individual’s willingness to trade off money for reductions in mortality risk 

(U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of small changes 

in mortality risk experienced by a large number of people. 
A.6-15 

The EPA continues work to update its guidance on valuing mortality risk reductions, and 

the Agency consulted several times with the SAB-EEAC on this issue. Until updated guidance is 

available, the Agency determined that a single, peer-reviewed estimate applied consistently, best 

reflects the SAB-EEAC advice it has received. Therefore, the EPA has decided to apply the VSL 

that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses

(U.S. EPA, 2014)94 while the Agency continues its efforts to update its guidance on this issue. 

This approach calculates a mean value across VSL estimates derived from 26 labor market and 

contingent valuation studies published between 1974 and 1991. The mean VSL across these 

studies is $6.3 million (2000$).95 We then adjust this VSL to account for the currency year and to 

account for income growth from 1990 to the analysis year. Specifically, the VSL applied in this 

analysis in 2011$ after adjusting for income growth is $9.9 million for 2017. 

The Agency is committed to using scientifically sound, appropriately reviewed evidence in 

valuing mortality risk reductions and has made significant progress in responding to the SABEEAC’s specific recommendations. In the process, the Agency has identified a number of 

important issues to be considered in updating its mortality risk valuation estimates. These are 

detailed in a white paper, “Valuing Mortality Risk Reductions in Environmental Policy” (U.S. 

EPA, 2010c), which recently underwent review by the SAB-EEAC. A meeting with the SAB on 

this paper was held on March 14, 2011 and formal recommendations were transmitted on 

July 29, 2011 (U.S. EPA-SAB, 2011). The EPA is taking SAB’s recommendations under 

advisement. 

In valuing PM2.5-related premature mortality, we discount the value of premature mortality 

occurring in future years using rates of 3 percent and 7 percent (OMB, 2003). We assume that 

there is a “cessation” lag between changes in PM exposures and the total realization of changes 

in health effects. Although the structure of the lag is uncertain, the EPA follows the advice of the 

SAB-HES to assume a segmented lag structure characterized by 30 percent of mortality 

reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20 

after the reduction in PM2.5 (U.S. EPA-SAB, 2004c). Changes in the cessation lag assumptions 

 

94 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. 

95 In 1990$, this base VSL is $4.8 million. 
A.6-16 

do not change the total number of estimated deaths but rather the timing of those deaths. Because 

short-term ozone-related premature mortality occurs within the analysis year, the estimated 

ozone-related co-benefits are identical for all discount rates. 

6.2.3 Benefit-per-ton Estimates for Ozone 

We used a “benefit-per-ton” approach to estimate the ozone benefits in this RIA. The EPA 

has applied this approach in several previous RIAs (e.g., U.S. EPA, 2011b, 2011c, 2012b, 2014a; 

2015). These benefit-per-ton estimates provide the total monetized human health benefits (the 

sum of premature mortality and premature morbidity) of reducing one ton of NOX (an ozone 

precursor). We generated benefit-per-ton estimates for ozone based on air quality modeling for 

the illustrative control case described in Chapter 3 of this RIA. The estimates correspond to NOX

emissions from U.S. EGUs during the ozone-season (May to September). Because we estimate 

ozone health impacts from May to September only, this approach underestimates ozone benefits 

in areas with a longer ozone season such as Texas. These estimates assume that EGU-attributable 

ozone formation at the regional-level is due to NOX alone. Because EGUs emit little VOC 

relative to NOX emissions, it is unlikely that VOCs emitted by EGUs would contribute 

substantially to regional ozone formation. We provide more detailed information regarding the 

generation of these estimates in the appendix to this chapter. 

As noted below in the characterization of uncertainty, all benefit-per-ton estimates have 

inherent limitations. Specifically, the benefit-per-ton estimates reflect the geographic distribution 

of the modeled illustrative control case, which may not match the geographic distribution of 

emission reductions anticipated for compliance with the regulatory control alternatives, and they 

may not reflect local variability in population density, meteorology, exposure, baseline health 

incidence rates, or other local factors for any specific location. 

6.2.4 Benefit-per-ton Estimates for PM2.5

We used a “benefit-per-ton” approach to estimate the PM2.5 co-benefits in this RIA, which 

represent the total monetized human health co-benefits (the sum of premature mortality and 

premature morbidity), of reducing one ton of PM2.5 (or PM2.5 precursor such as NOX) from a 

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

corresponding emission reductions that were generated from air quality modeling of the 
A.6-17 

illustrative control case. The appendix to this chapter provides additional detail regarding these 

calculations. All benefit-per-ton estimates have inherent limitations and should be interpreted 

with caution. Even though we assume that all fine particles have equivalent health effects, the 

benefit-per-ton estimates vary between precursors depending on the location and magnitude of 

their impact on PM2.5 levels, which drive population exposure. 

6.2.5 Estimated Health Benefits Results 

Table 6-2 provides the benefit-per-ton estimates for the analysis year 2017; because the 

modeled change in SO2 levels was very small, we did not quantify the SO2-related PM2.5

benefits. Table 6-3 provides the emission reductions estimated to occur in the analysis year. 

Table 6-4 summarizes the national monetized ozone-related and PM-related health benefits 

estimated to occur for regulatory control alternatives for the 2017 analysis year, by precursor 

pollutant using discount rates of 3 percent and 7 percent. Table 6-5 provides national summaries 

of the reductions in estimated health incidences associated with the proposal and more and less 

stringent alternatives for the 2017 analysis year.96 Figure 6-1 provides a visual representation of 

the range of estimated ozone and PM2.5-related benefits using benefit-per-ton estimates based on 

concentration-response functions from different studies and expert opinion for the proposal 

evaluated for 2017. 

 

96 Incidence estimates were generated using the same “per ton” approach as used to generate the dollar benefit per 

ton values. See Appendix 6-A for details. 
A.6-18 

Table 6-2. Summary of Ozone and PM2.5 Benefit-per-Ton Estimates Based on Air 

Quality Modeling from the Illustrative Control Case in 2017 (2011$)* 

Pollutant Discount Rate National 

NOX (as Ozone) N/A $5,700 to $9,400 

NOX (as PM2.5) 3% $2,100 to $4,700 

7% $1,900 to $4,300 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone and 

PM2.5. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in the analysis year, so 

they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the 

benefit-per-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits 

ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) 

and Lepeule et al. (2012). 

Table 6-3. Emission Reductions of Criteria Pollutants for the Proposal and More and 

Less Stringent Alternatives in 2017 (thousands of short tons)* 

Proposal 

More Stringent Alternative Less Stringent 

Alternative 

Ozone Season NOX 85,000 87,000 24,000 

All Year NOX 90,000 93,000 24,000 

All Year SO2 380 430 300 

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

total. 

Table 6-4. Summary of Estimated Monetized Health Benefits for the Proposal and 

More and Less Stringent Alternatives Regulatory Control Alternatives for 2017 

(millions of 2011$) * 

Pollutant Proposal More Stringent 

Alternative 

Less Stringent 

Alternative 

NOx (as Ozone) $490 to $790 $500 to $820 $140 to $220 

NOx (as PM2.5) 

3% Discount Rate $190 to $430 $190 to $440 $49 to $110 

7% Discount Rate $170 to $380 $170 to $390 $45 to $100 

Total 

3% Discount Rate $670 to $1,200 $690 to $1,300 $190 to $340 

7% Discount Rate $650 to $1,200 $670 to $1,200 $180 to $330 

* All estimates are rounded to two significant figures so numbers may not sum down columns. The health benefits 

range is based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. 

(2012) with Zanobetti and Schwartz (2008)). The estimated monetized co-benefits do not include climate benefits or 

reduced health effects from direct exposure to NO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 levels, which drive population exposure. The monetized co-benefits incorporate 

the conversion from precursor emissions to ambient fine particles and ozone. Co-benefits for ozone are based on 

ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. 

Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 

95th percentile confidence interval for monetized PM2.5 benefits ranges from approximately -90 percent to +180 

percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The confidence intervals 
A.6-19 

around the ozone mortality estimates are on the order of +/- 60 percent depending on the concentration-response 

function used. 

Table 6-5. Summary of Avoided Health Incidences from Ozone-Related and PM2.5-

Related Benefits for the Proposal and More and Less Stringent Alternatives for 

2017* 

Ozone-related Health Effects Proposal 

More Stringent 

Alternative 

Less Stringent 

Alternative 

Avoided Premature Mortality 

Smith et al. (2009) (all ages) 48 50 14 

Zanobetti and Schwartz (2008) (all ages) 81 83 23 

Avoided Morbidity 

Hospital admissions—respiratory causes (ages > 65) 79 81 22 

Emergency room visits for asthma (all ages) 320 330 90 

Asthma exacerbation (ages 6-18) 93,000 95,000 26,000 

Minor restricted-activity days (ages 18-65) 240,000 240,000 67,000 

School loss days (ages 5-17) 77,000 79,000 22,000 

PM2.5-related Health Effects 

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 21 22 5.6 

Lepeule et al. (2012) (adult) 48 50 13 

Woodruff et al. (1997) (infant) <1 <1 <1 

Avoided Morbidity 

Emergency department visits for asthma (all ages) 12 12 3.1 

Acute bronchitis (age 8–12) 31 32 8.1 

Lower respiratory symptoms (age 7–14) 390 400 100 

Upper respiratory symptoms (asthmatics age 9–11) 560 580 150 

Minor restricted-activity days (age 18–65) 16,000 16,000 4,200 

Lost work days (age 18–65) 2,700 2,700 700 

Asthma exacerbation (age 6–18) 580 600 150 

Hospital admissions—respiratory (all ages) 6.4 6.5 1.7 

Hospital admissions—cardiovascular (age > 18) 7.8 8.0 2.1 

Non-Fatal Heart Attacks (age >18) 

Peters et al. (2001) 26 25 6.6 

Pooled estimate of 4 studies 2.8 2.7 0.71 

* All estimates are rounded to whole numbers with two significant figures.. Co-benefits for ozone are based on 

ozone season NOx emissions. Confidence intervals are unavailable for this analysis because of the incidence-per-ton 

methodology. In general, the 95th percentile confidence interval for the health impact function alone ranges from 

approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012). The confidence intervals around the ozone mortality estimates are on the order of +/- 60 percent 

depending on the concentration-response function used. 

 
A.6-20 

Ozone PM2.5

Figure 6-1. Monetized Health Benefits of Proposal for 2017 * 

*The PM2.5 graphs show the estimated PM2.5 co-benefits at discount rates of 3% and 7% using effect coefficients 

derived from the Krewski et al. (2009) study and the Lepeule et al. (2012) study, as well as 12 effect coefficients 

derived from EPA’s expert elicitation on PM mortality (Roman et al., 2008). The results shown are not the direct 

results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response 

functions provided in those studies. Ozone benefits occur in the analysis year, so they are the same for all discount 

rates. These estimates do not include benefits from reductions in CO2. The monetized co-benefits do not include 

climate benefits from changes in NO2 or reduced health effects from direct exposure to NO2, ecosystem effects, or 

visibility impairment. 

6.2.6 Characterization of Uncertainty in the Estimated Health Benefits 

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. This analysis 
A.6-21 

includes many data sources as inputs, including emission inventories, air quality data from 

models (with their associated parameters and inputs), population data, population estimates, 

health effect estimates from epidemiology studies, economic data for monetizing benefits, and 

assumptions regarding the future state of the world (i.e., regulations, technology, and human 

behavior). Each of these inputs may be uncertain and would affect the estimate of benefits. When 

the uncertainties from each stage of the analysis are compounded, even small uncertainties can 

have large effects on the total quantified benefits. In addition, the use of the benefit-per-ton 

approach adds additional uncertainties beyond those for analyses based directly on air quality 

modeling. Therefore, the estimates of benefits should be viewed as representative of the general 

magnitude of benefits of the regulatory control alternatives for the 2017 analysis year, rather than 

the actual benefits anticipated from implement the proposal. 

This RIA does not include the type of detailed uncertainty assessment found in the Ozone 

NAAQS RIA (U.S. EPA, 2015) or the PM NAAQS RIA (U.S. EPA, 2012a) because we lack the 

necessary air quality modeling input and/or monitoring data to run the benefits model. However, 

the results of the quantitative and qualitative uncertainty analyses presented in the Ozone 

NAAQS RIA and PM NAAQS RIA can provide some information regarding the uncertainty 

inherent in the estimated benefits results presented in this analysis. For example, sensitivity 

analyses conducted for the PM NAAQS RIA indicate that alternate cessation lag assumptions 

could change the estimated PM2.5-related mortality co-benefits discounted at 3 percent by 

between 10 percent and –27 percent and that alternative income growth adjustments could 

change the PM2.5-related mortality benefits by between 33 percent and −14 percent. Although we 

generally do not calculate confidence intervals for benefit-per-ton estimates as they can provide 

an incomplete picture about the overall uncertainty in the benefits estimates, the PM NAAQS 

RIA provides an indication of the random sampling error in the health impact and economic 

valuation functions using Monte Carlo methods. In general, the 95th percentile confidence interval 

for monetized PM2.5 benefits ranges from approximately -90 percent to +180 percent of the 

central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The 95th percentile 

confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 
A.6-22 

We applied benefit-per-ton estimates, which reflect specific geographic patterns of 

emissions reductions and specific air quality and benefits modeling assumptions. For example, 

these estimates may not reflect local variability in population density, meteorology, exposure, 

baseline health incidence rates, or other local factors that might lead to an over-estimate or 

under-estimate of the actual co-benefits of controlling PM and ozone precursors. As such, it is 

not feasible to estimate the proportion of co-benefits occurring in different locations. Use of 

these benefit-per-ton values to estimate benefits may lead to higher or lower benefit estimates 

than if benefits were calculated based on direct air quality modeling. Great care should be taken 

in applying these estimates to emission reductions occurring in any specific location, as these are 

all based on a broad emission reduction scenario and therefore represent average benefits-per-ton 

over the entire region. The benefit-per-ton for emission reductions in specific locations may be 

very different than the estimates presented here. To the extent that the geographic distribution of 

the emissions reductions achieved by implementing the proposal is different than the emissions 

in the air quality modeling of the illustrative control case, the benefits may be underestimated or 

overestimated. 

Our estimate of the total monetized benefits is based on the EPA’s interpretation of the 

best available scientific literature and methods and supported by the SAB-HES and the National 

Academies of Science (NRC, 2002). Below are key assumptions underlying the estimates for 

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

co-benefits. 

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

potent in causing premature mortality. This is an important assumption, because PM2.5 

varies considerably in composition across sources, but the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. The PM ISA 

concluded that “many constituents of PM2.5 can be linked with multiple health effects, and 

the evidence is not yet sufficient to allow differentiation of those constituents or sources 

that are more closely related to specific outcomes” (U.S. EPA, 2009b). 

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

threshold. Thus, the estimates include health co-benefits from reducing fine particles in 

areas with varied concentrations of PM2.5, including both areas that do not meet the fine 

particle standard and those areas that are in attainment, down to the lowest modeled 

concentrations. 
A.6-23 

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

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

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

over the 20 years following exposure based on the advice of the SAB-HES (U.S. EPASAB, 2004c), which affects the valuation of mortality co-benefits at different discount 

rates. 

In general, we are more confident in the magnitude of the risks we estimate from simulated 

PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in 

the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed 

data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML], one 

standard deviation below the mean of the air quality data in the study, etc.) allow readers to 

determine the portion of population exposed to annual mean PM2.5 levels at or above different 

concentrations, which provides some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and 

one standard deviation below the mean) that have been incorporated into recent RIAs and the 

EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties 

inherent in identifying any particular point at which our confidence in reported associations 

becomes appreciably less, and the scientific evidence provides no clear dividing line. However, 

the EPA does not view these concentration benchmarks as a concentration threshold below 

which we would not quantify health benefits of air quality improvements.97 Rather, the cobenefits estimates reported in this RIA are the best estimates because they reflect the full range 

of air quality concentrations associated with the regulatory control alternatives. The PM ISA 

concluded that the scientific evidence collectively is sufficient to conclude that the relationship 

between long-term PM2.5 exposures and mortality is causal and that, overall, the studies support 

the use of a no-threshold log-linear model to estimate PM-related long-term mortality (U.S. EPA, 

2009b). 

We report also the key assumptions associated with our analysis of ozone-related effects: 

 

97 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b). 
A.6-24 

• Key assumption and uncertainties related to modeling of ozone-related premature 

mortality: Ozone-related short-term mortality represents a substantial proportion of total 

monetized benefits (over 94% of the ozone-related-benefits), and these estimates have the 

following key assumptions and uncertainties. We utilize a log-linear impact function 

without a threshold in modeling short-term ozone-related mortality. However, we 

acknowledge reduced confidence in specifying the nature of the C-R function in the range 

of ≤20ppb and below (ozone ISA, section 2.5.4.4). Thus, the estimates include health 

benefits from reducing ozone in areas with varied concentrations of ozone, including both 

areas that do not meet the ozone standard and those areas that are in attainment, down to the 

lowest modeled concentrationsThus, ozone-related premature deaths estimated at or below 

this level are subject to greater uncertainty, but we cannot judge whether (and in what 

direction) these impacts are biased. .

• Avoided premature mortality according to baseline pollutant concentrations: We 

recognize that, in estimating short-term ozone-related mortality, we are less confident in 

specifying the shape of the C-R function at lower ambient ozone concentrations (at and 

below 20 ppb, ozone ISA, section 2.5.4.4). Quantitative uncertainty analyses completed for 

the Ozone NAAQS RIA (U.S. EPA, 2015) found almost 100% of mortality reductions 

occurred above 20 ppb, where we are more confident in specifying the nature of the ozonemortality effect (ozone ISA, section 2.5.4.4). However, as discussed in section 6B.7 of that 

RIA, care must be taken in interpreting these results since the ambient air metric used in 

modeling this endpoint is the mean 8-hour max value in each grid cell (and not the full 

distribution of 8-hour daily max values). Had the latter been used, then the distribution 

would have likely been wider. 

For this analysis, policy-specific air quality data is not available, and the control scenarios 

are illustrative of what states may choose to do. However, we believe that it is still important to 

characterize the distribution of exposure to baseline concentrations. As a surrogate measure of 

mortality impacts, we provide the percentage of the population exposed at each PM2.5

concentration in the baseline of the air quality modeling used to calculate the benefit-per-ton 
A.6-25 

estimates for this RIA using 12 km grid cells across the contiguous U.S.98 It is important to note 

that baseline exposure is only one parameter in the health impact function, along with baseline 

incidence rates population and change in air quality. In other words, the percentage of the 

population exposed to air pollution below the LML is not the same as the percentage of the 

population experiencing health impacts as a result of a specific emission reduction policy. The 

most important aspect, which we are unable to quantify without rule-specific air quality 

modeling, is the shift in exposure anticipated by implementing the proposal. Therefore, caution is 

warranted when interpreting the LML assessment in this RIA because these results are not 

consistent with results from RIAs that had air quality modeling. 

Figure 6-3 shows a bar chart of the percentage of the population exposed to various air 

quality levels, including the LML concentration benchmarks in the illustrative control case 

modeling, and Figure 6-4 shows a cumulative distribution function of the same data. Both figures 

identify the LML for each of the major cohort studies. 

 

98 As described in Chapter 3, the baseline for the air quality modeling used to calculate the benefit-per-ton values 

differs from the baseline used to estimate the benefits, costs, and impacts of this rulemaking. See Chapter 3 for more 

details about the differences between the two baselines. 
A.6-26 

Figure 6-2. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure in 

the Baseline used for the Air Quality Analysis in Chapter 3 

Among the populations exposed to PM2.5 in the baseline:

88% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study 

47% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study 
A.6-27 

Figure 6-3. Cumulative Distribution of Adult Population (age 30+) by Annual Mean 

PM2.5 Exposure in the Baseline used for the Air Quality Analysis in Chapter 3 

6.3 Estimated Climate Co-Benefits from CO2

A co-benefit of this proposal is reducing emissions of CO2. In this section, we provide a 

brief overview of the 2009 Endangerment Finding and climate science assessments released 

since then. We also provide information regarding the economic valuation of CO2 using the 

Social Cost of Carbon (SC-CO2), a metric that estimates the monetary value of impacts 

associated with marginal changes in CO2 emissions in a given year. Table 6-6 summarizes the 

quantified and unquantified climate benefits in this analysis. 

Among the populations exposed to PM2.5 in the baseline: 

88% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study 

47% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study 
A.6-28 

Table 6-6. Climate Effects 

Benefits 

Category 

Specific Effect Effect Has Been 

Quantified 

Effect Has Been 

Monetized 

More 

Information 

Improved Environment 

Reduced 

climate effects 

Global climate impacts from CO2 —1  SCC TSD 

Climate impacts from ozone and 

black carbon (directly emitted PM) 

— — Ozone ISA, PM 

ISA2

Other climate impacts (e.g., other 

GHGs such as methane, aerosols, 

other impacts) 

— — IPCC2

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

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

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

changes in CO2 emissions.

2

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

methods. 

There are several important considerations in assessing the climate-related benefits for an 

ozone air quality-focused rulemaking. First, the estimated health benefits do not account for any 

climate-related air quality changes (e.g., increased ambient ozone associated with higher 

temperatures). Excluding climate-related air quality changes may underestimate ozone-related 

health benefits. It is unclear how PM2.5-related health benefits would be affected by excluding 

climate-related air quality changes since the science is unclear as to how climate change may 

affect PM2.5 exposure. Second, the estimated health benefits also do not consider temperature 

modification of PM2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b). Third, 

the estimated climate co-benefits reported in this RIA reflect global benefits, while the estimated 

health benefits are calculated for the contiguous U.S. only. Excluding temperature modification 

of air pollution risks and international air quality-related health benefits likely leads to 

underestimation of quantified health benefits (Anenberg et al, 2009, Jhun et al, 2014). Fourth, as 

noted earlier, we do not estimate the climate co-benefits associated with reductions in PM and 

ozone precursors. 

6.3.1 Climate Change Impacts 

Through the implementation of CAA regulations, the EPA addresses the negative externalities 

caused by air pollution. In 2009, the EPA Administrator found that elevated concentrations of 

greenhouse gases in the atmosphere may reasonably be anticipated both to endanger public 

health and to endanger public welfare. For health, these include the increased likelihood of heat 
A.6-29 

waves, negative impacts on air quality, more intense hurricanes, more frequent and intense 

storms and heavy precipitation, and impacts on infectious and waterborne diseases. For welfare, 

these include reduced water supplies in some regions, increased water pollution, increased 

occurrences of floods and droughts, rising sea levels and damage to coastal infrastructure, 

increased peak electricity demand, changes in ecosystems, and impacts on indigenous 

communities. 

The preamble also summarizes new scientific assessments and recent climatic observations. 

Major scientific assessments released since the 2009 Endangerment Finding have improved 

scientific understanding of the climate, and provide even more evidence that GHG emissions 

endanger public health and welfare for current and future generations. The National Climate 

Assessment (NCA), in particular, assessed the impacts of climate change on human health in the 

United States, finding that Americans will be affected by “increased extreme weather events, 

wildfire, decreased air quality, threats to mental health, and illnesses transmitted by food, water, 

and disease-carriers such as mosquitoes and ticks.” These assessments also detail the risks to 

vulnerable groups such as children, the elderly and low income households. Furthermore, the 

assessments present an improved understanding of the impacts of climate change on public 

welfare, higher projections of future sea level rise than had been previously estimated, a better 

understanding of how the warmth in the next century may reach levels that would be 

unprecedented relative to the preceding millions of years of history, and new assessments of the 

impacts of climate change on permafrost and ocean acidification. The impacts of GHG emissions 

will be realized worldwide, independent of their location of origin, and impacts outside of the 

United States will produce consequences relevant to the United States. 

6.3.2 Social Cost of Carbon 

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

emission guidelines using the SC-CO2 estimates presented in the Technical Support Document: 

Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive 

Order 12866 (May 2013, Revised July 2015) (“current TSD”).99 We refer to these estimates, 

 

99 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
A.6-30 

which were developed by the U.S. government, as “SC-CO2 estimates.” The SC-CO2 is a metric 

that estimates the monetary value of impacts associated with marginal changes in CO2 emissions 

in a given year. It includes a wide range of anticipated climate impacts, such as net changes in 

agricultural productivity and human health, property damage from increased flood risk, and 

changes in energy system costs, such as reduced costs for heating and increased costs for air 

conditioning. It is typically used to assess the avoided damages as a result of regulatory actions 

(i.e., benefits of rulemakings that lead to an incremental reduction in cumulative global CO2

emissions). 

The SC-CO2 estimates used in this analysis were developed over many years, using the 

best science available, and with input from the public. Specifically, an interagency working 

group (IWG) that included the EPA and other executive branch agencies and offices used three 

integrated assessment models (IAMs) to develop the SC-CO2 estimates and recommended four 

global values for use in regulatory analyses. The SC-CO2 estimates were first released in 

February 2010 and updated in 2013 using new versions of each IAM. As discussed further 

below, the IWG published two minor corrections to the SC-CO2 estimates in July 2015. 

The SC-CO2 estimates were developed using an ensemble of the three most widely cited 

integrated assessment models in the economics literature with the ability to estimate the SC-CO2. 

A key objective of the IWG was to draw from the insights of the three models while respecting 

the different approaches to linking GHG emissions and monetized damages taken by modelers in 

the published literature. After conducting an extensive literature review, the interagency group 

selected three sets of input parameters (climate sensitivity, socioeconomic and emissions 

trajectories, and discount rates) to use consistently in each model. All other model features were 

left unchanged, relying on the model developers’ best estimates and judgments, as informed by 

the literature. Specifically, a common probability distribution for the equilibrium climate 

sensitivity parameter, which informs the strength of climate’s response to atmospheric GHG 

concentrations, was used across all three models. In addition, a common range of scenarios for 

 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and 

Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised July 2015). 

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

7/11/2015. 
A.6-31 

the socioeconomic parameters and emissions forecasts were used in all three models. Finally, the 

marginal damage estimates from the three models were estimated using a consistent range of 

discount rates, 2.5, 3.0, and 5.0 percent. See the 2010 TSD for a complete discussion of the 

methods used to develop the estimates and the key uncertainties, and the current TSD for the 

latest estimates.100 

The SC-CO2 estimates represent global measures because of the distinctive nature of 

climate change, which is highly unusual in at least three respects. First, emissions of most GHGs 

contribute to damages around the world independent of the country in which they are emitted. 

The SC-CO2 must therefore incorporate the full (global) damages caused by GHG emissions to 

address the global nature of the problem. Second, the U.S. operates in a global and highly 

interconnected economy, such that impacts on the other side of the world can affect our 

economy. This means that the true costs of climate change to the U.S. are larger than the direct 

impacts that simply occur within the U.S. Third, climate change represents a classic public goods 

problem because each country’s reductions benefit everyone else and no country can be excluded 

from enjoying the benefits of other countries’ reductions, even if it provides no reductions itself. 

In this situation, the only way to achieve an economically efficient level of emissions reductions 

is for countries to cooperate in providing mutually beneficial reductions beyond the level that 

would be justified only by their own domestic benefits. In reference to the public good nature of 

mitigation and its role in foreign relations, thirteen prominent academics noted that these “are 

compelling reasons to focus on a global SCC” in a recent article on the SCC (Pizer et al., 2014). 

In addition, as noted in OMB’s Response to Comments on the SCC, there is no bright line 

between domestic and global damages. Adverse impacts on other countries can have spillover 

effects on the United States, particularly in the areas of national security, international trade, 

public health and humanitarian concerns.101

The 2010 TSD noted a number of limitations to the SC-CO2 analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and non-

 

100 See https://www.whitehouse.gov/omb/oira/social-cost-of-carbon for both TSDs. 

101 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. 
A.6-32 

catastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. Currently integrated assessment models do not assign value to all of the important 

physical, ecological, and economic impacts of climate change recognized in the climate change 

literature 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.102 The 

limited amount of research linking climate impacts to economic damages makes the modeling 

exercise even more difficult. These individual limitations do not all work in the same direction in 

terms of their influence on the SC-CO2 estimates, though taken together they suggest that the 

SC-CO2 estimates are likely conservative. In particular, the IPCC Fourth Assessment Report 

(2007), which was the most current IPCC assessment available at the time of the IWG’s 2009-

2010 review, concluded that “It is very likely that [SC-CO2 estimates] underestimate the damage 

costs because they cannot include many non-quantifiable impacts.” Since then, the peerreviewed literature has continued to support this conclusion. For example, the IPCC Fifth 

Assessment report observed that SC-CO2 estimates continue to omit various impacts that would 

likely increase damages. The 95th percentile estimate was included in the recommended range 

for regulatory impact analysis to address these concerns. 

The EPA and other agencies have continued to consider feedback on the SC-CO2 estimates 

from stakeholders through a range of channels, including public comments on rulemakings that 

use the SC-CO2 in supporting analyses and through regular interactions with stakeholders and 

research analysts implementing the SC-CO2 methodology used by the interagency working 

group. In addition, OMB’s Office of Information and Regulatory Affairs issued a separate 

request for public comment on the approach used to develop the estimates.103 After careful 

evaluation of the full range of comments submitted to OMB’s Office of Information and 

Regulatory Affairs, the IWG continues to recommend the use of these SC-CO2 estimates in 

 

102 Climate change impacts and SCC modeling is an area of active research. For example, see: (1) Howard, Peter, 

“Omitted Damages: What’s Missing from the Social Cost of Carbon.” March 13, 2014, 

http://costofcarbon.org/files/Omitted_Damages_Whats_Missing_From_the_Social_Cost_of_Carbon.pdf; and (2) 

Electric Power Research Institute, “Understanding the Social Cost of carbon: A Technical Assessment,” October 

2014, www.epri.com. 

103 See https://www.federalregister.gov/articles/2013/11/26/2013-28242/technical-support-document-technicalupdate-of-the-social-cost-of-carbon-for-regulatory-impact 
A.6-33 

regulatory impact analysis. With the release of the response to comments104, the IWG announced 

plans to obtain expert independent advice from the National Academies of Sciences, 

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

the best available scientific and economic information on climate change.105 The Academies’ 

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

and challenges of potential approaches to improving the SC-CO2 estimates in future updates.106 

Concurrent with OMB’s publication of the response to comments on SC-CO2 and 

announcement of the Academies process, OMB posted a revised TSD that includes two minor 

technical corrections to the current estimates. One technical correction addressed an inadvertent 

omission of climate change damages in the last year of analysis (2300) in one model and the 

second addressed a minor indexing error in another model. On average the revised SC-CO2

estimates are one dollar less than the mean SC-CO2 estimates reported in the November 2013 

revision to the May 2013 TSD. The change in the estimates associated with the 95th percentile 

estimates when using a 3% discount rate is slightly larger, as those estimates are heavily 

influenced by the results from the model that was affected by the indexing error. 

The four SC-CO2 estimates are as follows: $12, $41, $63, and $120 per metric ton of CO2

emissions in the year 2017 (2011$).107 The first three values are based on the average SC-CO2

from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SC-CO2 estimates 

for several discount rates are included because the literature shows that the SC-CO2 is quite 

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

 

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

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

106 See 

http://sites.nationalacademies.org/DBASSE/BECS/CurrentProjects/DBASSE_167526?utm_source=All%20DBASS

E%20Newsletters&utm_campaign=e84c13e8c4-

New_Project_the_Social_Cost_of_Carbon&utm_medium=email&utm_term=0_e16023964e-e84c13e8c4-

267347161 for more information about the National Academies process and the status of the project. 

107 The current version of the TSD is available at: https://www.whitehouse.gov/sites/default/files/omb/inforeg/scctsd-final-july-2015.pdf. The 2010 and 2013 TSDs present SC-CO2 in 2007$ per metric ton. The unrounded 

estimates from the current TSD were adjusted to 2011$ using GDP Implicit Price Deflator (1.061374), 

http://www.bea.gov/iTable/index_nipa.cfm. The estimates presented here have been rounded to two significant 

digits. 
A.6-34 

appropriate rate to use in an intergenerational context (where costs and benefits are incurred by 

different generations). The fourth value is the 95th percentile of the SC-CO2 from all three 

models at a 3 percent discount rate. It is included to represent higher-than-expected impacts from 

temperature change further out in the tails of the SC-CO2 distribution (representing less likely, 

but potentially catastrophic, outcomes). 

Table 6-7 presents the global SC-CO2 estimates in metric tons for the years 2015 to 2050. 

In order to calculate the dollar value for emission reductions, the SC-CO2 estimate for each 

emissions year would be applied to changes in CO2 emissions for that year, and then discounted 

back to the analysis year using the same discount rate used to estimate the SC-CO2.

 108, 109 The 

SC-CO2 increases over time because future emissions are expected to produce larger incremental 

damages as physical and economic systems become more stressed in response to greater climate 

change. Note that the interagency group estimated the growth rate of the SC-CO2 directly using 

the three integrated assessment models rather than assuming a constant annual growth rate. This 

helps to ensure that the estimates are internally consistent with other modeling assumptions. 

Table 6-8 reports the incremental climate co-benefits from CO2 emission impacts estimated for 

the proposal and more and less stringent alternatives for the 2017 analysis year. 

 

108 Emission impacts for this rulemaking are shown for the year 2017 and are calculated in short tons. Therefore, the 

conversion factor 0.90718474 metric tons in a short ton was applied to the calculate the benefits in the year 2017. 

109 This analysis considered the climate impacts of only CO2 emission change. As discussed below, the climate 

impacts of other pollutants were not calculated for the proposed guidelines. While CO2 is the dominant GHG 

emitted by the sector, we recognize the representative facilities within these comparisons may also have different 

emission rates for other climate forcers that will serve a minor role in determining the overall social cost of 

generation. 
A.6-35 

Table 6-7. Social Cost of CO2, 2015-2050 (in 2011$ per metric ton)* 

Year 

Discount Rate and Statistic 

5% Average 3% Average 2.5% Average 3% (95th percentile) 

2015 $12 $38 $59 $110 

2017 $12 $41 $63 $120 

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 

* These SC-CO2 values are stated in $/metric ton and rounded to two significant figures. The estimates vary 

depending on the year of CO2 emissions and are defined in real terms, i.e., adjusted for inflation using the GDP 

implicit price deflator. 

Table 6-8. Estimated Global Climate Co-benefits of CO2 Reductions for the Proposal 

and More and Less Stringent Alternatives for 2017 (millions of 2011$)* 

Discount rate and statistic Proposal 

More Stringent 

Alternative 

Less Stringent 

Alternative 

Thousands of short tons of CO2

reduced** 610 610 720 

5% (average) $6.5 $6.5 $7.6 

3% (average) $23 $23 $27 

2.5% (average) $35 $35 $41 

3% (95th percentile) $66 $66 $78 

* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting 

of climate impacts. 

**Emission impacts for this rulemaking are shown for the year 2017 and are calculated in short tons. The 

conversion factor 0.90718474 metric tons in a short ton was applied to the calculate the benefits in the year 2017. 

It is important to note that the climate co-benefits presented above are associated with 

changes in CO2 emissions only. Implementing this proposal, however, will have an impact on the 

emissions of other pollutants that would affect the climate. Both predicting reductions in 

emissions and estimating the climate impacts of these other pollutants, however, is complex. The 

climate impacts of these other pollutants have not been calculated for this proposal.110 

 

110 The SC-CO2 estimates used in this analysis are designed to assess the climate benefits associated with changes in 

CO2 emissions only. 
A.6-36 

The other emissions potentially reduced as a result of the proposal to update CSAPR 

include other greenhouse gases (such as methane), aerosols and aerosol precursors such as black 

carbon, organic carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as 

nitrogen oxides and volatile organic carbon compounds. Changes in emissions of these pollutants 

(both increases and decreases) could directly result from changes in electricity generation, 

upstream fossil fuel extraction and transport, and/or downstream secondary market impacts. 

Reductions in black carbon or ozone precursors are projected to lead to further cooling, but 

reductions in the other aerosol species and precursors are projected to lead to warming. 

Therefore, changes in non-CO2 pollutants could potentially augment or offset the climate cobenefits calculated here. These pollutants can act in different ways and on different timescales 

than carbon dioxide. For example, aerosols reflect (and in the case of black carbon, absorb) 

incoming radiation, whereas greenhouse gases absorb outgoing infrared radiation. In addition, 

these aerosols are thought to affect climate indirectly by altering properties of clouds. Black 

carbon can also deposit on snow and ice, darkening these surfaces and accelerating melting. In 

terms of lifetime, while carbon dioxide emissions can increase concentrations in the atmosphere 

for hundreds or thousands of years, many of these other pollutants are short lived and remain in 

the atmosphere for short periods of time ranging from days to weeks and can therefore exhibit 

large spatial and temporal variability. 

6.4 Combined Health Benefits and Climate Co-Benefits Estimates 

In this analysis, we were able to monetize the estimated benefits associated with the 

reduced exposure to ozone and PM2.5 and co-benefits of decreased emissions of CO2, but we 

were unable to monetize the co-benefits associated with reducing exposure to mercury, carbon 

monoxide, and NO2, as well as ecosystem effects and visibility impairment. In addition, there are 

expected to be unquantified health and welfare impacts associated with changes in hydrogen 

chloride. Specifically, we estimated combinations of health benefits at discount rates of 3 percent 

and 7 percent (as recommended by the EPA’s Guidelines for Preparing Economic Analyses

[U.S. EPA, 2014] and OMB’s Circular A-4 [OMB, 2003]) and climate co-benefits at estimates 

of the SC-CO2 (average SC-CO2 at each of three discount rates—5 percent, 3 percent, 2.5 

percent—and the 95th percentile SC-CO2 at 3 percent) (as recommended by the IWG). 
A.6-37 

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

CO2 emissions are long-lived and subsequent damages occur over many years. Moreover, several 

rates are applied to SC-CO2 because the literature shows that it is sensitive to assumptions about 

discount rate and because no consensus exists on the appropriate rate to use in an 

intergenerational context. The SC-CO2 interagency group centered its attention on the 3 percent 

discount rate but emphasized the importance of considering all four SC-CO2 estimates.111 The 

EPA has evaluated the range of potential impacts by combining all SC-CO2 values with health 

benefits values at the 3 percent and 7 percent discount rates. Combining the 3 percent SC-CO2

values with the 3 percent health benefit values assumes that there is no difference in discount 

rates 

Table 6-9 provides the combined health and climate benefits for the proposal and more and 

less stringent alternatives for the 2017 analysis year. 

 

111 See the 2010 SCC TSD. Docket ID EPA-HQ-OAR-2009-0472-114577 or 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf for 

details. 
A.6-38 

Table 6-9. Combined Health Benefits and Climate Co-Benefits for the Proposal and 

More and Less Stringent Alternatives for 2017 (millions of 2011$)* 

SC-CO2 Discount Rate** 

Health and Climate Benefits 

(Discount Rate Applied to Health Co-Benefits) 

Climate CoBenefits Only 

3% 7%

Proposal 

5% $680 to $1200 $660 to $1200 $6.5 

3% $700 to $1200 $680 to $1200 $23 

2.5% $710 to $1300 $690 to $1200 $35 

3% (95th percentile) $740 to $1300 $720 to $1200 $66 

More Stringent Alternative

5% $700 to $1300 $680 to $1200 $6.5

3% $720 to $1300 $700 to $1200 $23

2.5% $730 to $1300 $710 to $1200 $35

3% (95th percentile) $760 to $1300 $740 to $1300 $66

Less Stringent Alternative 

5% $190 to $340 $190 to $330 $7.6 

3% $210 to $360 $210 to $350 $27 

2.5% $230 to $380 $220 to $370 $41 

3% (95th percentile) $260 to $410 $260 to $400 $78 

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

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone benefits occur in analysis year, so they are the same for all discount rates. The health benefits 

reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions (e.g., from 

Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) with Zanobetti and Schwartz (2008)). The 

monetized health co-benefits do not include reduced health effects from direct exposure to NO2 and HAP; 

ecosystem effects; or visibility impairment. 

**As discussed in section 6.3, the SC-CO2 estimates are calculated with four different values of a one ton reduction. 

6.5 Unquantified Co-benefits 

The monetized co-benefits estimated in this RIA reflect a subset of co-benefits attributable 

to the health effect reductions associated with ambient fine particles and ozone. Data, time, and 

resource limitations prevented the EPA from quantifying the impacts to, or monetizing the cobenefits from several important benefit categories, including co-benefits associated with 

exposure to several HAP and NO2, as well as ecosystem effects, and visibility impairment due to 

the absence of air quality modeling data for these pollutants in this analysis. This does not imply 

that there are no co-benefits associated reductions in exposures to NO2. In this section, we 

provide a qualitative description of these benefits, which are listed in Table 6-10. 
A.6-39 

Table 6-10. Unquantified Health and Welfare Co-benefits Categories 

Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More Information

Improved Human Health 

Reduced incidence of 

morbidity from exposure 

to NO2

Asthma hospital admissions (all ages) — — NO2 ISA1

Chronic lung disease hospital admissions (age > 

65) — — NO2 ISA1

Respiratory emergency department visits (all 

ages) — — NO2 ISA1

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISA1

Acute respiratory symptoms (age 7–14) — — NO2 ISA1

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — NO2 ISA2,3

Reduced incidence of 

morbidity from exposure 

to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISA1

Asthma emergency department visits (all ages) — — SO2 ISA1

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISA1

Acute respiratory symptoms (age 7–14) — — SO2 ISA1

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — SO2 ISA1,2

Improved Environment 

Reduced visibility 

impairment 

Visibility in Class 1 areas — — PM ISA1

Visibility in residential areas — — PM ISA1

Reduced effects on 

materials 

Household soiling — — PM ISA1,2

Materials damage (e.g., corrosion, increased 

wear) — — PM ISA2

Reduced effects from PM 

deposition (metals and 

organics) 

Effects on Individual organisms and ecosystems — — PM ISA2

Reduced vegetation and 

ecosystem effects from 

exposure to ozone 

Visible foliar injury on vegetation — — Ozone ISA1

Reduced vegetation growth and reproduction — — Ozone ISA1

Yield and quality of commercial forest products 

and crops — — Ozone ISA1

Damage to urban ornamental plants — — Ozone ISA2

Carbon sequestration in terrestrial ecosystems — — Ozone ISA1

Recreational demand associated with forest 

aesthetics — — Ozone ISA2

Other non-use effects Ozone ISA2

Ecosystem functions (e.g., water cycling, 

biogeochemical cycles, net primary productivity, 

leaf-gas exchange, community composition) 

— — Ozone ISA2

Reduced effects from 

acid deposition 

Recreational fishing — — NOx SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and forestry effects — — NOx SOx ISA2

Recreational demand in terrestrial and aquatic 

ecosystems 

— — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles) — — NOx SOx ISA2
A.6-40 

Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More Information

Reduced effects from 

nutrient enrichment 

Species composition and biodiversity in terrestrial

and estuarine ecosystems — — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

Recreational demand in terrestrial and estuarine 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles, fire regulation) — — NOx SOx ISA2

Reduced vegetation 

effects from ambient 

exposure to SO2 and NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISA2

Injury to vegetation from NOx exposure — — NOx SOx ISA2

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

2We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods. 

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

concerns over the strength of the association. 

6.5.1 HAP Impacts 

Due to methodology and resource limitations, we were unable to estimate the impacts 

associated with changes in emissions of the hazardous air pollutants in this analysis. The EPA’s 

SAB-HES concluded that “the challenges for assessing progress in health improvement as a 

result of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack 

of exposure-response functions, uncertainties in emissions inventories and background levels, the 

difficulty of extrapolating risk estimates to low doses and the challenges of tracking health 

progress for diseases, such as cancer, that have long latency periods” (U.S. EPA-SAB, 2008). In 

2009, the EPA convened a workshop to address the inherent complexities, limitations, and 

uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations 

from this workshop included identifying research priorities, focusing on susceptible and 

vulnerable populations, and improving dose-response relationships (Gwinn et al., 2011). 

6.5.2 Additional NO2 Health Co-Benefits 

In addition to being a precursor to PM2.5 and ozone, NOx emissions are also linked to a 

variety of adverse health effects associated with direct exposure. We were unable to estimate the 

health co-benefits associated with reduced NO2 exposure in this analysis. Therefore, this analysis 

only quantified and monetized the PM2.5 and ozone co-benefits associated with the reductions in 

NO2 emissions. 
A.6-41 

Following a comprehensive review of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Oxides of Nitrogen —Health Criteria (NOx ISA) 

(U.S. EPA, 2008c) concluded that there is a likely causal relationship between respiratory health 

effects and short-term exposure to NO2. These epidemiologic and experimental studies 

encompass a number of endpoints including emergency department visits and hospitalizations, 

respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. The 

NOx ISA also concluded that the relationship between short-term NO2 exposure and premature 

mortality was “suggestive but not sufficient to infer a causal relationship,” because it is difficult 

to attribute the mortality risk effects to NO2 alone. Although the NOx ISA stated that studies 

consistently reported a relationship between NO2 exposure and mortality, the effect was 

generally smaller than that for other pollutants such as PM. 

6.5.3 Additional SO2 Health Co-Benefits 

In addition to being a precursor to PM2.5, SO2 emissions are also linked to a variety of 

adverse health effects associated with direct exposure. We were unable to estimate the health cobenefits associated with reduced SO2 in this analysis because we do not have air quality 

modeling data available. Therefore, this analysis only quantifies and monetizes the PM2.5 cobenefits associated with the reductions in SO2 emissions. 

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO2 ISA) 

concluded that there is a causal relationship between respiratory health effects and short-term 

exposure to SO2 (U.S. EPA, 2008a). The immediate effect of SO2 on the respiratory system in 

humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2 at 

concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected. Based on our review of this information, we 

identified three short-term morbidity endpoints that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 
A.6-42 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to 

SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 

associations to adjustment for other pollutants. We did not quantify these co-benefits due to data 

constraints. 

6.5.4 Additional NO2 and SO2 Welfare Co-Benefits 

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur —

Ecological Criteria (NOx/SOx ISA) (U.S. EPA, 2008d), SO2 and NOx emissions also contribute 

to a variety of adverse welfare effects, including those associated with acidic deposition, 

visibility impairment, and nutrient enrichment. Deposition of nitrogen causes acidification, 

which can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic 

ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and 

sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern U.S., the surface 

waters affected by acidification are a source of food for some recreational and subsistence 

fishermen and for other consumers and support several cultural services, including aesthetic and 

educational services and recreational fishing. Biological effects of acidification in terrestrial 

ecosystems are generally linked to aluminum toxicity, which can cause reduced root growth, 

restricting the ability of the plant to take up water and nutrients. These direct effects can, in turn, 

increase the sensitivity of these plants to stresses, such as droughts, cold temperatures, insect 

pests, and disease leading to increased mortality of canopy trees. Terrestrial acidification affects 

several important ecological services, including declines in habitat for threatened and endangered 

species (cultural), declines in forest aesthetics (cultural), declines in forest productivity 

(provisioning), and increases in forest soil erosion and reductions in water retention (cultural and 

regulating). (U.S. EPA, 2008d) 

Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. In 

estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of 

estuaries can disrupt an important source of food production, particularly fish and shellfish 

production, and a variety of cultural ecosystem services, including water-based recreational and 

aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and 
A.6-43 

number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets 

the balance between native and nonnative plants, changing the ability of an area to support 

biodiversity. When the composition of species changes, then fire frequency and intensity can 

also change, as nonnative grasses fuel more frequent and more intense wildfires. (U.S. EPA, 

2008d) 

Reductions in emissions of NO2 and SO2 will improve the level of visibility throughout the 

United States because these gases (and the particles of nitrate and sulfate formed from these 

gases) impair visibility by scattering and absorbing light (U.S. EPA, 2009). Visibility is also 

referred to as visual air quality (VAQ), and it directly affects people’s enjoyment of a variety of 

daily activities (U.S. EPA, 2009). Good visibility increases quality of life where individuals live 

and work, and where they travel for recreational activities, including sites of unique public value, 

such as the Great Smoky Mountains National Park (U. S. EPA, 2009). 

6.5.5 Ozone Welfare Co-Benefits 

Exposure to ozone has been associated with a wide array of vegetation and ecosystem 

effects in the published literature (U.S. EPA, 2013b). Sensitivity to ozone is highly variable 

across species, with over 65 plant species identified as “ozone-sensitive”, many of which occur 

in state and national parks and forests. These effects include those that damage or impair the 

intended use of the plant or ecosystem. Such effects can include reduced growth and/or biomass 

production in sensitive plant species, including forest trees, reduced yield and quality of crops, 

visible foliar injury, species composition shift, and changes in ecosystems and associated 

ecosystem services. 

6.5.6 Visibility Impairment Co-Benefits 

Reducing secondary formation of PM2.5 would improve levels of visibility in the U.S. 

because suspended particles and gases degrade visibility by scattering and absorbing light (U.S. 

EPA, 2009b). Fine particles with significant light-extinction efficiencies include sulfates, 

nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct 

significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good 

visibility increases the quality of life where individuals live and work, and where they engage in 

recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern 
A.6-44 

U.S. and particulate nitrate is an important contributor to light extinction in California and the 

upper Midwestern U.S., particularly during winter (U.S. EPA, 2009b). Previous analyses (U.S. 

EPA, 2011a) show that visibility co-benefits can be a significant welfare benefit category. 

Without air quality modeling, we are unable to estimate visibility-related benefits, and we are 

also unable to determine whether the emission reductions associated with the final emission 

guidelines would be likely to have a significant impact on visibility in urban areas or Class I 

areas. 

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A.6-1 
6A-1 

APPENDIX 6A: ESTIMATING THE BENEFIT-PER-TON OF REDUCING PM2.5 AND OZONE 

PRECURSORS

This appendix reports additional detail regarding our approach for estimating the benefit-per-ton 

(BPT) of reducing PM2.5 and ozone precursor emissions. This BPT was applied in Chapter 6 of 

this Regulatory Impact Analysis (RIA). Specifically, this appendix describes the methods for 

estimating these values for the contiguous U.S. for PM2.5 and ozone precursors emitted by the 

electrical generating unit (EGU) sector in the Proposed Ozone Transport Rule. 

 6A.1 Overview of Benefit-per-Ton Estimates 

As described in the Technical Support Document: Estimating the Benefit per Ton of Reducing 

PM2.5 Precursors from 17 Sectors (U.S. EPA, 2013), the general procedure for calculating 

average benefit-per-ton coefficients generally follows three steps. As an example, in order to 

calculate regional average benefit-per-ton estimates for the key precursor pollutants emitted from 

EGU sources, we: 

1. Use air quality modeling to predict changes in ambient concentrations of primary PM2.5, 

nitrate, sulfate, and ozone at a 12km2 grid resolution across the contiguous U.S. that are 

attributable to the proposed Clean Power Plan. 

2. For each grid cell, estimate the health impacts, and the economic value of these impacts, 

associated with the attributable ambient concentrations using the environmental Benefits 

Mapping and Analysis Program – Community Edition (BenMAP-CE v1.1).112,113

Aggregate those impacts and economic values to the United States. 

3. Divide the national health impacts attributable to each precursor, and the national 

monetary value of these impacts, by the amount of associated national precursor 

emissions. That is, nitrate benefits are divided by NOX emissions, and ozone benefits are 

divided by ozone-season NOX emissions. 

6A.2 Air Quality Modeling for the Proposed Transport Rule 

The EPA ran the Comprehensive Model with Extensions (CAMx) photochemical model 

(ENVIRON, 2014) to predict ozone and PM2.5 concentrations for the following emissions 

scenarios: concentrations. The model predictions for the 2011 base year, the 2017 baseline, and 

the 2017 illustrative control case were combined with ambient air quality observations to 

calculate seasonal mean ozone air quality metrics and annual mean PM2.5 for the 2017 baseline 

and 2017 illustrative control case, which were then used as input for the benefits analysis. 

 

112 When estimating these impacts we apply effect coefficients that relate changes in total PM2.5 mass to the risk of adverse health outcomes; we do not apply effect coefficients 

that are differentiated by PM2.5 species. 

113 Previous RIAs have used earlier versions of the BenMAP software. BenMAP-CE v1.1 provides results consistent 

with earlier versions of BenMAP and is available for download at http://www.epa.gov/air/benmap/. 
6A-2 

Each of the CAMx model simulations was performed for a nationwide modeling domain114

using a full year of meteorological conditions for 2011. The modeling for 2011 was used as the 

anchor point for projecting ozone and annual PM2.5 concentration values for the 2017 base case 

and for the 2017 illustrative control scenario using methodologies consistent with the EPA’s air 

quality modeling guidance (U.S. EPA, 2007). The air quality modeling results for the 2017 base 

case served as the baseline for gauging the future year impacts on ozone and annual PM2.5 of the 

illustrative control case scenario. The 2017 base case reflects emissions reductions between 2011 

and 2017 that are expected to result from regional and national rules including the Clean Air 

Interstate Rule (CAIR), the Mercury and Air Toxics Standards (MATS), mobile source rules up 

through Tier-3, and various state emissions control programs and consent decrees. The methods 

for estimating the EGU emissions for the proposal are described in Chapter 3 of the RIA for the 

Transport Rule proposal (U.S. EPA, 2015). State total annual EGU emissions for NOX and SO2

for each of the scenarios modeled are provided in Table 6A-1. The data indicate that, overall 

nationwide, EGU SO2 and NOX emissions with the illustrative control case scenario would be 

about 14% lower than the 2017 base case. 

Table 6A-1. State Total Annual EGU Emissions for NOX for the 2011 Base Year, 2017 

Base Case, and 2017 Transport Rule Proposal (in thousands of tons) 

State 2011 Base Year 2017 Base Case 2017 Transport Rule 

Proposal 

Alabama 63 13,592 10,486 

Arizona 35 16,960 16,975 

Arkansas 38 6,399 6,414 

California 6 3,789 3,789 

Colorado 51 13,467 13,467 

Connecticut 1 1,607 1,607 

Delaware 4 580 580 

Florida 61 0 0 

Georgia 54 30,046 30,433 

Idaho - 7,498 7,510 

Illinois 73 251 248 

Indiana 121 11,002 10,761 

Iowa 40 42,496 31,033 

Kansas 44 8,307 7,935 

Kentucky 92 11,820 11,393 

Louisiana 47 27,201 15,976 

Maine 2 11,162 11,077 

 

114 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. 
6A-3 

State 2011 Base Year 2017 Base Case 2017 Transport Rule 

Proposal 

Maryland 19 1,565 1,565 

Massachusetts 5 7,324 6,138 

Michigan 75 2,219 2,222 

Minnesota 32 22,233 20,635 

Mississippi 26 11,223 11,226 

Missouri 66 8,299 7,579 

Montana 20 18,663 17,793 

Nebraska 37 7,759 7,759 

Nevada 7 14,613 14,577 

New Hampshire 4 4,547 4,546 

New Jersey 6 289 289 

New Mexico 23 3,950 3,090 

New York 22 17,372 17,372 

North Carolina 46 7,911 7,675 

North Dakota 51 17,307 15,341 

Ohio 104 16,423 16,246 

Oklahoma 82 29,249 19,823 

Oregon 5 19,620 18,103 

Pennsylvania 149 800 800 

Rhode Island 0 43,599 17,094 

South Carolina 25 257 255 

South Dakota 11 5,875 6,046 

Tennessee 27 297 297 

Texas 146 5,566 5,493 

Tribal Data 65 59,199 57,146 

Utah 51 24,489 24,489 

Vermont 0 163 163 

Virginia 38 9,201 8,882 

Washington 7 747 747 

West Virginia 58 25,664 14,755 

Wisconsin 32 5,923 5,917 

Wyoming 53 10,796 10,796 

National Total 2,024 609,317 524,543 

 As indicated above, the air quality modeling was used to project gridded ozone and 

annual PM2.5 concentrations at the 12km by 12km resolution for the 2017 base case and the 

illustrative control case scenario modeled for this analysis. The air quality modeling results were 

combined with monitored ozone and PM2.5 data to create projected spatial fields of annual PM2.5

and seasonal mean (May through September) 8-hour daily maximum ozone for the 2017 base 

case and for the proposal scenario. These spatial fields were then used as inputs to estimate the 

health co-benefits of the proposed Transport Rule as described below. 
6A-4 

6A.3 National PM2.5 Benefit-per-Ton Estimates for EGUs Derived from Air Quality 

Modeling of the Proposed Transport Rule 

After estimating the 12km by 12km resolution PM2.5 benefits for each of the analysis years 

applied in this RIA (i.e., 2017), we aggregated the benefits results nationally.115 In order to 

calculate the benefit-per-ton estimates, we divided the regional benefits estimates by the 

corresponding emissions, as shown in Table 4A-1. Lastly, we adjusted the benefit-per-ton 

estimates for a currency year of 2011$.116

This method provides estimates of the national average benefit-per-ton for a subset of the major 

PM2.5 precursors emitted from EGU sources. For precursor emissions of NOX, there is generally 

a non-linear relationship between emissions and formation of PM2.5. This means that each ton of 

NOX reduced would have a different impact on ambient PM2.5 depending on the initial level of 

emissions and potentially on the levels of emissions of other pollutants. In contrast, SO2 is 

generally linear in forming PM2.5. For precursors like NOX which form PM2.5 non-linearly, a 

marginal benefit-per-ton approach would better approximate the specific benefits associated with 

an emissions reduction scenario for a given set of base case emissions, because it would allow 

the benefit-per-ton to vary depending on the level of emissions reductions and the baseline 

emissions levels. However, we do not have sufficient air quality modeling data to calculate 

marginal benefit-per-ton estimates for the EGU sector. Therefore, using an average benefit-perton estimate for NOX adds uncertainty to the co-benefits estimated in this RIA. 

In this RIA, we estimate emission reductions from EGUs using IPM.117 IPM outputs provide 

endogenously projected unit level emissions of SO2, NOX, CO2, Hg, hydrogen chloride (HCl) 

from EGUs, but carbon monoxide, volatile organic compounds, ammonia and total directly 

emitted PM2.5 and PM10 emissions are post-calculated.118 In addition, directly emitted particle 

emissions calculated from IPM outputs do not include speciation, i.e. they are only the total 

emissions. In order to conduct air quality modeling, directly emitted PM2.5 from EGUs is 

speciated into components during the emissions modeling process based on emission profiles for 

EGUs by source classification code. Even though these speciation profiles are not unit-specific, 

an emission profile based on the source classification code reflects the fuel and the unit 

configuration. Model-predicted concentrations of nitrate and sulfate include both the directly 

emitted nitrate and sulfate from speciated PM2.5 and secondarily formed nitrate and sulfate from 

emissions of NOX and SO2, respectively. Because this proposed rule is not expected to greatly 

affect the levels of directly emitted PM2.5 or SO2, we do not quantify a benefit per-ton value for 

either pollutant. 

Although it is possible to calculate 95th percentile confidence intervals using the approach

described in this appendix (e.g., U.S. EPA, 2011b), we generally do not calculate confidence 

intervals for benefit-per-ton estimates because of the additional unquantified uncertainties that 

result from the benefit transfer methods, including those related to the transfer of air quality 

modeling information. Instead, we refer the reader to Chapter 5 of PM NAAQS RIA (U.S. EPA, 

2012a) for an indication of the combined random sampling error in the health impact and 

 

115 This aggregation is identified as the shapefile “Report Regions” in BenMAP’s grid definitions. 

116 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.

117 See Chapter 3 of this RIA for additional information regarding the Integrated Planning Model (IPM).

118 Detailed documentation of this post-processing is available at 

http://www.epa.gov/powersectormodeling/docs/v513/FlatFile_Methodology.pdf 
6A-5 

economic valuation functions using Monte Carlo methods. In general, the 95th percentile 

confidence interval for the total monetized PM2.5 benefits ranges from approximately -90% to 

+180% of the central estimates based on concentration-response functions from Krewski et al.

(2009) and Lepeule et al. (2012). The 95th percentile confidence interval for the health impact 

function alone ranges from approximately ±30% for mortality incidence based on Krewski et al.

(2009) and ±46% based on Lepeule et al. (2012). These confidence intervals do not reflect other 

sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations 

exposed, and transferability of the effect estimate to diverse locations. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the overall 

uncertainty in the benefits estimates. 

Table 6A-3 reports the national benefit-per-ton estimates for the EGU sector at discount rates of 

3% and 7% in 2017. Tables 6A-4 reports the incidence per ton estimates (which follows the 

same general methodology as for the benefit-per-ton calculations) for the EGU sector in 2017 for 

the set of health endpoints used to calculate the benefit-per-ton estimates. 
6A-6 

Table 6A-3. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality 

Modeling from Proposed Transport Rule in 2017 (2011$)* 

Pollutant Discount 

Rate Benefit per ton 

NOX (as 

PM2.5) 

3% $2,100 to $4,700 

7% $1,900 to $4,300 

* The range of estimates reflects the range of epidemiology studies for avoided premature 

mortality for PM2.5. All estimates are rounded to two significant figures. All fine particles are 

assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. 

The monetized benefits incorporate the conversion from precursor emissions to ambient fine 

particles. The estimates do not include reduced health effects from direct exposure to ozone, 

NO2, SO2, ecosystem effects, or visibility impairment. 

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

Quality Modeling from Proposed Transport Rule in 2017* 

Health Endpoint NOx Incidence per ton 

Premature Mortality 

Krewski et al. (2009) – adult 0.00024 

Lepeule et al. (2012) – adult 0.00054 

Woodruff et al. (1997) – infants 0.00001 

Morbidity 

Emergency department visits for asthma 0.00013 

Acute bronchitis 0.00034 

Lower respiratory symptoms 0.00436 

Upper respiratory symptoms 0.00623 

Minor restricted-activity days 0.17575 

Lost work days 0.02947 

Asthma exacerbation 0.00642 

Hospital Admissions, Respiratory 0.00007 

Hospital Admissions, Cardiovascular 0.00009 

Non-fatal Heart Attacks (age>18) 

 Peters et al (2001) 0.00028 

 Pooled estimate of 4 studies 0.00003 

* All estimates are rounded to two significant figures. All fine particles are assumed to have 

equivalent health effects, but the incidence-per-ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence 

benefit-per-ton estimates incorporate the conversion from precursor emissions to ambient fine 

particles. 
6A-7 

6A.4 Regional Ozone Benefit-per-Ton Estimates 

The process for generating the regional ozone benefit-per-ton estimates is consistent with the 

process for PM2.5. Ozone is not directly emitted, and is a non-linear function of NOX and VOC 

emissions. For the purpose of estimating benefit-per-ton for this RIA, we assume that all of the 

ozone impacts from EGUs are attributable to NOX emissions. VOC emissions, which are also a 

precursor to ambient ozone formation, are insignificant from the EGU sector relative to both 

NOX emissions from EGUs and the total VOC emissions inventory. Therefore, we believe that 

our assumption that EGU-attributable ozone formation at the regional-level is due to NOX alone 

is reasonable. 

Similar to PM2.5, this method provides estimates of the regional average benefit-per-ton. Due to 

the non-linear chemistry between NOX emissions and ambient ozone, using an average benefitper-ton estimate for NOX adds uncertainty to the ozone co-benefits estimated for the proposed 

guidelines. 

In the ozone co-benefits estimated in this RIA, we apply the benefit-per-ton estimates calculated 

using NOX emissions derived from modeling the Proposed Transport Rule during the ozoneseason only (May to September). As shown in Table 6A-1, ozone-season NOX emissions from 

EGUs are slightly less than half of all-year NOX emissions. Because we estimate ozone health 

impacts from May to September only, this approach underestimates ozone co-benefits in areas 

with longer ozone seasons such as southern California and Texas. When the underestimated 

benefit-per-ton estimate is multiplied by ozone-season only NOX emission reductions, this results 

in an underestimate of the monetized ozone co-benefits. Table 6A-5 reports the ozone benefitper-ton estimates using ozone-season only NOX for 2017. Table 6A-7 reports the ozone season 

incidence-per-ton estimates for 2017. 

Table 6A-5. Summary of National Ozone Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Transport Rule in 2017 (2011$)* 

Ozone precursor Pollutant Benefit per ton 

Ozone season NOX $4,300 to $20,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature 

mortality for ozone. All estimates are rounded to two significant figures. The monetized benefits 

incorporate the conversion from NOX precursor emissions to ambient ozone. 
6A-8 

Table 6A-6. Summary of National Ozone Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Transport Rule in 2017*

Health Endpoint Incidence per ton 

Premature Mortality – adult 

Smith et al. (2009) 0.000569 

Zanobetti & Schwartz (2008) 0.000956 

Morbidity 

Hospital Admissions, Respiratory (ages < 2 & > 65) 0.000927 

Emergency Room Visits, Respiratory 0.003771 

Acute Respiratory Symptoms 2.781551 

School Loss Days 0.903847 

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates 

incorporate the conversion from NOX precursor emissions to ambient ozone. These estimates 

reflect ozone-season NOX emissions. 

6A.5 References 

Abt Associates, Inc. 2010. “User’s Guide: Modeled Attainment Test Software.” Available at: 

<http://www.epa.gov/scram001/modelingapps_mats.htm>. Accessed June 6, 2015. 

Abt Associates, Inc. 2012. “BenMAP User’s Manual Appendices,” prepared for U.S. Research 

Triangle Park, NC: U. S. Environmental Protection Agency, Office of Air Quality Planning and 

Standards. Available at: 

<http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. Accessed June 6, 

2015. 

Bell, M.L., A. McDermott, S.L. Zeger, J.M. Sarnet, and F. Dominici. 2004. “Ozone and ShortTerm Mortality in 95 U.S. Urban Communities, 1987-2000.” Journal of the American Medical 

Association. 292(19):2372-8. 

Bell, M.L., F. Dominici, and J.M. Samet. 2005. “A Meta-Analysis of Time-Series Studies of 

Ozone and Mortality with Comparison to the National Morbidity, Mortality, and Air Pollution 

Study.” Epidemiology. 16(4):436-45. 

ENVIRON International Corporation. 2014. User’s Guide: Comprehensive Air Quality Model 

with Extensions, Version 6.1, Novato, CA. April. Available at <http://www.camx.com>. 

Accessed June 6, 2015. 

Fann, N., C.M. Fulcher, and B.J. Hubbell. 2009. “The Influence of Location, Source, and 

Emission Type in Estimates of the Human Health Benefits of Reducing a Ton of Air Pollution.” 

Air Quality and Atmospheric Health. 2:169–176. 

Fann, N., K.R. Baker, and C.M. Fulcher. 2012. “Characterizing the PM2.5-Related Health 

Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across the 

U.S.” Environment International. 49:41–151. 

Fann, N., K.R. Baker, and C.M. Fulcher. 2013. “The Recent and Future Health Burden of Air 

Pollution Apportioned Across 23 U.S. Sectors.” Environmental Scientific Technology. 47(8): 

3580–3589. 

Huang Y., F. Dominici, and M. Bell. 2005. “Bayesian Hierarchical Distributed Lag Models for 

Summer Ozone Exposure and Cardio-Respiratory Mortality.” Environmetrics. 16:547-562. 
6A-9 

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. 

Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, et al. 2009. Extended Follow-Up 

and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution and 

Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. 

Lepeule, J., F. Laden, D. Dockery, and J. Schwartz. 2012. “Chronic Exposure to Fine Particles 

and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” 

Environmental Health Perspectives. 120(7):965-70. 

Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An 

Empiric Bayes Metaregression Analysis.” Epidemiology. 16(4):458-68. 

Roman, H.A., K. D. Walker, T. L. Walsh, L. Conner, H. M. Richmond, B. J. Hubbell, and P. L. 

Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient 

Fine Particulate Matter in the U.S.” Environmental Scientific Technology. 42(7):2268-2274. 

Schwartz, J. 2005. “How Sensitive is the Association between Ozone and Daily Deaths to 

Control for Temperature?” American Journal of Respiratory and Critical Care Medicine. 171(6): 

627-31. 

U.S. Environmental Protection Agency (U.S. EPA). 2007. Guidance on the Use of Models and 

Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and 

Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

Available at <http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf>. 

Accessed June 4, 2015. 

U.S. Environmental Protection Agency (U.S. EPA). 2011b Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. Research Triangle Park, NC: Office of Air Quality

Planning and Standards, Health and Environmental Impacts Division. (EPA document number 

EPA-452/R-11-011, December). Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>. Accessed June 4, 2015. 

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the 

Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA452/R-12-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts 

Division, Research Triangle Park, NC. December. Available at: < 

http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf>. Accessed June 4, 2015. 

U.S. Environmental Protection Agency (U.S. EPA). 2013. Technical Support Document: 

Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. February. Available at: < 

http://www2.epa.gov/sites/production/files/2014-10/documents/sourceapportionmentbpttsd.pdf 

>. Accessed June 4, 2015. 

U.S. Environmental Protection Agency (U.S. EPA). 2015. Regulatory Impact Analysis for the 

Carbon Pollution Guidelines for Existing Power Plants and Emission Standards for Modified 

and Reconstructed Power Plants. EPA-542/R-14-002. Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. June. Available at < 

http://www3.epa.gov/airquality/cpp/cpp-final-rule-ria.pdf >. 
6B-1 
7-1 

CHAPTER 7: STATUTORY AND EXECUTIVE ORDER REVIEWS

Overview 

This section explains the statutory and executive orders applicable to EPA rules, and 

discusses EPA’s actions taken pursuant to these orders. 

7.1 Executive Order 12866: Regulatory Planning and Review 

This action is an economically significant regulatory action that was submitted to the 

Office of Management and Budget (OMB) for review. Any changes made in response to OMB 

recommendations have been documented in the docket. This RIA estimates the costs and 

monetized human health and welfare benefits of the proposal. Consistent with Executive Order 

(EO) 12866 and EO 13563, the EPA estimated the costs and benefits for three regulatory control 

alternatives: the proposed EGU NOX ozone-season emissions budgets and more and less 

stringent alternatives. The proposed action would reduce ozone-season NOX emissions from 

EGUs in 23 eastern states. Actions taken to comply with the proposed EGU NOX ozone-season 

emissions budgets would also reduce emissions of other criteria air pollution and hazardous air 

pollution emissions, including annual NOX, and CO2. The benefits associated with these copollutant reductions are referred to as co-benefits, as these reductions are not the primary 

objective of the rule. 

7.2 Paperwork Reduction Act 

The information collection activities in this proposed rule have been submitted for 

approval to the Office of Management and Budget (OMB) under the Paperwork Reduction Act 

(PRA), 44 U.S.C. 3501 et seq. The Information Collection Request (ICR) document that the EPA 

prepared has been assigned EPA ICR number 2527.01. You can find a copy of the ICR in the 

docket for theis proposed rule, and it is briefly summarized here. 

The information generated by information collection activities under CSAPR is used by 

the EPA to ensure that affected facilities comply with the emission limits and other requirements. 

Records and reports are necessary to enable EPA or states to identify affected facilities that may 

not be in compliance with the requirements. The recordkeeping requirements require only the 

specific information needed to determine compliance. These recordkeeping and reporting 

requirements are established pursuant to CAA sections 110(a)(2)(D) and (c) and 301(a) (42 
7-2 

U.S.C. 7410(a)(2)(D) and (c) and 7601(a)) and are specifically authorized by CAA section 114 

(42 U.S.C. 7414). Reported data may also be used for other regulatory and programmatic 

purposes. All information submitted to the EPA for which a claim of confidentiality is made will 

be safeguarded according to EPA policies in 40 CFR part 2, subpart B, Confidentiality of 

Business Information. 

All of the EGUs that would be subject to changed information collection requirements 

under this proposed rule are already subject to information collection requirements under 

CSAPR. Most of these EGUs also are already subject to information collection requirements 

under the Acid Rain Program (ARP) established under Title IV of the 1990 Clean Air Act 

Amendments. Both CSAPR and the ARP have existing approved ICRs or pending renewals: 

EPA ICR Number 2391.03/OMB Control Number 2060-0667 (CSAPR) and EPA ICR Number 

1633.16/OMB Control Number 2060-0258 (ARP). The burden and costs of the information 

collection requirements covered under the CSAPR ICR are estimated as incremental to the 

information collection requirements covered under the ARP ICR. Most of the information used 

to estimate burden and costs in this ICR was developed for the existing CSAPR and ARP ICRs. 

This proposed rule would change the universe of sources subject to certain information 

collection requirements under CSAPR but would not change the substance of any CSAPR 

information collection requirements. The burden and costs associated with the proposed changes 

in the reporting universe are estimated as reductions from the burden and costs under the existing 

CSAPR ICR. (This proposed rule would not change any source’s information collection 

requirements with respect to the ARP.) The EPA intends to incorporate the burden and costs 

associated with the proposed changes in the reporting universe under this rule into the next 

renewal of the CSAPR ICR. 

Respondents/affected entities: Entities potentially affected by this proposed action are 

EGUs in the states of Florida, Kansas, and South Carolina that meet the applicability criteria for 

the CSAPR NOX Ozone Season Trading Program in 40 CFR 97.404. 

Respondent’s obligation to respond: Mandatory (sections 110(a) and 301(a) of the Clean 

Air Act). 

Estimated number of respondents: 116 sources in Florida, Kansas, and South Carolina 

with one or more EGUs. 

Frequency of response: Quarterly, occasionally. 
7-3 

Total estimated burden: reduction of 14,064 hours (per year). Burden is defined at 5 CFR 

1320.3(b). 

Total estimated cost: reduction of $1,472,047 (per year), includes reduction of $450,951 

operation and maintenance costs. 

The burden and cost estimates above reflect the reduction in burden and cost for Florida 

sources with EGUs that would no longer be required to report NOX mass emissions and heat 

input data for the ozone season to the EPA under the proposed rule and that are not subject to 

similar information collection requirements under the Acid Rain Program. Because these EGUs 

would no longer need to collect NOX emissions or heat input data under 40 CFR part 75, the 

estimates above also reflect the reduction in burden and cost to collect and quality assure these 

data and to maintain the associated monitoring equipment. 

The EPA estimates that the proposed rule would cause no change in information 

collection burden or cost for EGUs in Kansas that would be required to report NOX mass 

emissions and heat input data for the ozone season to the EPA or for EGUs in South Carolina 

that would no longer be required to report NOX emissions and heat input data for the ozone 

season to the EPA. The EGUs in both Kansas and South Carolina already are and would remain 

subject to requirements to report NOX mass emissions and heat input data for the entire year to 

the EPA under the CSAPR NOX Annual Trading Program, and the requirements related to ozone 

season reporting are a subset of the requirements related to annual reporting. Similarly, the EPA 

estimates that the proposed rule would cause no change in information collection burden or cost 

for EGUs in Florida that are subject to the Acid Rain Program because of the close similarity 

between the information collection requirements under CSAPR and under the Acid Rain 

Program. 

More information on the ICR analysis is included in the docket for the proposed rule. 

An agency may not conduct or sponsor, and a person is not required to respond to, a 

collection of information unless it displays a currently valid OMB control number. The OMB 

control numbers for the EPA’s regulations in 40 CFR are listed in 40 CFR part 9. 

. 
7-4 

7.3 Regulatory Flexibility Act 

The EPA certifies that this action will not have a significant economic impact on a 

substantial number of small entities under the RFA. The small entities subject to the 

requirements of this action are small businesses, small organizations, and small governmental 

jurisdictions. The EPA has determined that 1 entity (of 7 small entities identified as potentially 

affected) may experience an impact of greater than 3 percent of annual revenues. Details of this 

analysis are presented below. 

The Regulatory Flexibility Act (5 U.S.C. 601 et seq.), as amended by the Small Business 

Regulatory Enforcement Fairness Act (Public Law No. 104 121), provides that whenever an 

agency is required to publish a general notice of proposed rulemaking, it must prepare and make 

available an initial regulatory flexibility analysis, unless it certifies that the proposed rule, if 

promulgated, will not have a significant economic impact on a substantial number of small 

entities (5 U.S.C. 605[b]). Small entities include small businesses, small organizations, and 

small governmental jurisdictions. 

The EPA conducted regulatory flexibility analysis at the ultimate (i.e., highest) level of 

ownership, evaluating parent entities with the largest share of ownership in at least one 

potentially-affected EGU included in EPA’s base case using the IPM v.5.15, used in this RIA.1

This analysis draws on the “parsed” unit-level estimates using IPM results for 2018,2 as well as 

ownership, employment, and financial information for the potentially affected small entities 

drawn from other resources described in more detail below. 

The EPA identified the size of ultimate parent entities by using the Small Business 

Administration (SBA) size threshold guidelines.3

 The criteria for size determination vary by the 

organization/operation category of the ultimate parent entity, as follows: 

• Privately-owned (non-government) entities (see Table 7-1) 

 

1

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

2

 For this analysis, the 2018 parsed file is used to represent 2017 for the purposes of RIA analysis. 

3

 U.S. Small Business Administration (SBA). 2014. Small Business Size Standards. Effective as of July 14, 2014. 

See: http://www.sba.gov/sites/default/files/Size_Standards_Table.pdf. 
7-5 

o Privately-owned entities include investor-owned utilities, non-utility entities, 

and entities with a primary business other than electric power generation. 

o For entities with electric power generation as a primary business, small entities 

are those with less than the threshold number of employees specified by SBA 

for each of the relevant North American Industry Classification System 

(NAICS) sectors (NAICS 2211). 

o For entities with a primary business other than electric power generation, the 

relevant size criteria are based on revenue, assets, or number of employees by 

NAICS sector.4 

• Publicly-owned entities 

o Publicly-owned entities include federal, state, municipal, and other political 

subdivision entities. 

o The federal and state governments were considered to be large. Municipalities 

and other political units with population fewer than 50,000 were considered to 

be small. 

• Rural Electric Cooperatives 

o Small entities are those with fewer than the threshold level of employees or 

revenue specified by SBA for each of the relevant NAICS sectors. 

7.3.1 Identification of Small Entities 

In this analysis, the EPA considered EGUs which meet the following five criteria: 1) 

EGU is represented in NEEDS v5.15; 2) EGU is fossil fuel-fired; 3) EGU is located in a state 

covered by this proposed rule; 4) EGU is neither a cogeneration unit nor solid waste incineration 

unit; 5) EGU capacity is 25MW or larger. The EPA next refined this list of EGUs, narrowing it 

to those that exhibit at least one of the following changes under the proposal, in comparison to 

the baseline. 

 

4

 Certain affected EGUs are owned by ultimate parent entities whose primary business is not electric power 

generation. 
7-6 

• Summer fuel use (BTUs) changes by +/- 1% or more 

• Summer generation (GWh) changes by +/- 1% or more 

• NOx summer emissions (tons) changes by +/- 1% or more 

 Based on these criteria, the EPA identified a total of 318 potentially affected EGUs 

warranting examination in this RFA analysis. Next, we determined power plant ownership 

information, including the name of associated owning entities, ownership shares, and each 

entity’s type of ownership. We primarily used data from SNL and Ventyx, supplemented by 

limited research using publicly available data.5 Majority owners of power plants with affected 

EGUs were categorized as one of the seven ownership types.6

 These ownership types are: 

1. Investor-Owned Utility (IOU): Investor-owned assets (e.g., a marketer, independent 

power producer, financial entity) and electric companies owned by stockholders, etc. 

2. Cooperative (Co-Op): Non-profit, customer-owned electric companies that generate 

and/or distribute electric power. 

3. Municipal: A municipal utility, responsible for power supply and distribution in a small 

region, such as a city. 

4. Sub-division: Political subdivision utility is a county, municipality, school district, 

hospital district, or any other political subdivision that is not classified as a municipality 

under state law. 

5. Private: Similar to an investor-owned utility, however, ownership shares are not openly 

traded on the stock markets. 

6. State: Utility owned by the state. 

7. Federal: Utility owned by the federal government. 

 

5

 SNL Financial data covers the energy market and other industries, and includes detailed immediate and ultimate 

ownership at the EGU level. For more information, see: www.snl.com. The Ventyx Energy Velocity Suite database 

consists of detailed ownership and corporate affiliation information at the EGU level. For more information, see: 

www.ventyx.com. 

6

 Throughout this analysis, the EPA refers to the owner with the largest ownership share as the “majority owner” 

even when the ownership share is less than 51 percent. 
7-7 

Next, the EPA used both the Hoover’s online database and the SNL database to identify 

the ultimate owners of power plant owners identified in the SNL and Ventyx databases. This was 

necessary, as many majority owners of power plants (listed in SNL or Ventyx) are themselves 

owned by other ultimate parent entities (listed in Hoover’s or SNL).7

 In these cases, the ultimate 

parent entity was identified via Hoover’s or SNL, whether domestically or internationally owned. 

The EPA followed SBA size standards to determine which non-government ultimate 

parent entities should be considered small entities in this analysis. These SBA size standards are 

specific to each industry, each having a threshold level of either employees, revenue, or assets 

below which an entity is considered small. SBA guidelines list all industries, along with their 

associated NAICS code and SBA size standard. Therefore, it was necessary to identify the 

specific NAICS code associated with each ultimate parent entity in order to understand the 

appropriate size standard to apply. Data from Hoover’s was used to identify the NAICS codes for 

most of the ultimate parent entities. In many cases, an entity that is a majority owner of a power 

plant is itself owned by an ultimate parent entity with a primary business other than electric 

power generation. Therefore, it was necessary to consider SBA entity size guidelines for the 

range of NAICS codes listed in Table 7-1. This table represents the range of NAICS codes and 

areas of primary business of ultimate parent entities which are majority owners of potentially 

affected EGUs in the EPA’s IPM base case. 

 

7

 The Hoover’s Inc. online platform includes company records that can contain NAICS codes, number of employees, 

revenues, and assets. For more information, see: http://www.hoovers.com
7-8 

Table 7-1. SBA Size Standards by NAICS Code 

NAICS 

Code 

NAICS Description SBA Size Standard 

221112 Fossil Fuel Electric Power Generation 750 employees 

221118 Other Electric Power Generation 250 employees 

221122 Electric Power Distribution 1,000 employees 

221210 Natural Gas Distribution 500 employees 

238210 Electrical Contractors and Other Wiring Installation Contractors $15 million in revenue 

324110 Petroleum Refineries 1,500 employees 

325180 Other Basic Inorganic Chemical Manufacturing 1,000 employees 

325320 Pesticide and Other Agricultural Chemical Manufacturing 500 employees 

331313 Alumina Refining and Primary Aluminum Production 1,000 employees 

333613 Mechanical Power Transmission Equipment Manufacturing 500 employees 

424720 Petroleum and Petroleum Products Merchant Wholesalers 

(except Bulk Stations and Terminals) 

100 employees 

486210 Pipeline Transportation of Natural Gas $27.5 million in revenue 

522110 Commercial Banking $550 million in assets 

522220 Sales Financing $38.5 million in revenue 

523120 Securities Brokerage $38.5 million in revenue 

523910 Miscellaneous Intermediation $38.5 million in revenue 

523930 Investment Advice $38.5 million in revenue 

524126 Direct Property and Casualty Insurance Carriers 1,500 employees 

525120 Health and Welfare Funds $32.5 million in revenue 

525990 Other Financial Vehicles $32.5 million in revenue 

541611 Administrative Management and General Management 

Consulting Services 

$15 million in revenue 

551112 Offices of Other Holding Companies $20.5 million in revenue 

Note: Based on size standards effective at the time the EPA conducted this analysis (SBA size standards, effective 

July 14, 2014) 

Source: SBA, 2014 

The EPA compared the relevant entity size criterion for each ultimate parent entity to the 

SBA threshold value noted in Table 7-1. We used the following data sources and methodology to 

estimate the relevant size criterion values for each ultimate parent entity: 

1. Employment, Revenue, and Assets: The EPA used the Hoover’s database as the 

primary source for information on ultimate parent entity employee numbers, revenue, and 

assets.8 In parallel, the EPA also considered estimated revenues from affected EGUs 

based on analysis of parsed-file estimates for the proposal. The EPA assumed that the 

ultimate parent entity revenue was the larger of the two revenue estimates. In limited 

 

8

 Estimates of sales were used in lieu of revenue estimates when revenue data was unavailable. 
7-9 

instances, supplemental research was also conducted to estimate an ultimate parent 

entity’s number of employees, revenue, or assets. 

2. Population: Municipal entities are defined as small if they serve populations of less than 

50,000. The EPA primarily relied on data from the Ventyx database and the U.S. Census 

Bureau to inform this determination. Supplemental research of individual municipalities 

was also conducted in some instances. 

Ultimate parent entities for which the relevant measure is less than the SBA size criterion 

were identified as small entities and carried forward in this analysis. In the case of one entity, 

data limitations prevented the comparison of the entity against its appropriate SBA size standard. 

For the purposes of this analysis, the EPA assumed that this entity is a small entity. Overall, the 

EPA identified 16 potentially affected EGUs owned by 7 small entities included in the EPA’s 

Base Case. 

7.3.2 Overview of Analysis and Results 

 This section presents the methodology and results for estimating the impact of the 

proposed CSAPR update to small entities in 2017 based on the following endpoints: 

• annual economic impacts of the proposed CSAPR update on small entities, and 

• ratio of small entity impacts to revenues from electricity generation. 

7.3.2.1 Methodology for Estimating Impacts of the Proposed CSAPR update on Small 

Entities 

 An entity can comply with the proposed CSAPR update through some combination of the 

following: optimizing existing SCR and SNCRs, turning on idled SCR or SNCR controls, 

upgrading to state of the art combustion controls, using allocated allowances, purchasing 

allowances, or reducing emissions through a reduction in generation or improved efficiency. 

Additionally, units with more allowances than needed can sell these allowances in the market. 

The chosen compliance strategy will be primarily a function of the unit’s marginal control costs 

and its position relative to the marginal control costs of other units. 

To attempt to account for each potential control strategy, the EPA estimates compliance 

costs as follows: 
7-10 

CCompliance = ∆ COperating+Retrofit + ∆ CFuel + ∆ CAllowances + ∆ CTransaction + ∆ R 

where C represents a component of cost as labeled, and ∆ R represents the value of foregone 

electricity generation, calculated as the difference in revenues between the base case and the 

proposed CSAPR update in 2017. 

In reality, compliance choices and market conditions can combine such that an entity may 

actually experience a savings in any of the individual components of cost. Under the proposed 

CSAPR update, some units will forgo some level of electricity generation (and thus revenues) to 

comply and this impact will be lessened on these entities by the projected increase in electricity 

prices under the proposed CSAPR update. On the other hand, those increasing generation levels 

will see an increase in electricity revenues and as a result, lower net compliance costs. If entities 

are able to increase revenue more than an increase in fuel cost and other operating costs, 

ultimately they will have negative net compliance costs (or savings). Overall, small entities are 

not projected to install relatively costly emissions control retrofits, but may choose to do so in 

some instances. Because this analysis evaluates the total costs along each of the compliance 

strategies laid out above for each entity, it inevitably captures savings or gains such as those 

described. As a result, what we describe as cost is really more of a measure of the net economic 

impact of the rule on small entities. 

For this analysis, the EPA used IPM-parsed output to estimate costs based on the 

parameters above, at the unit level. These impacts were then summed for each small entity, 

adjusting for ownership share. Net impact estimates were based on the following: operating and 

retrofit costs, sale or purchase of allowances, and the change in fuel costs or electricity 

generation revenues under the proposed CSAPR update relative to the base case. These 

individual components of compliance cost were estimated as follows: 

(1) Operating and retrofit costs: Using the IPM-parsed output for the base case and 

the proposed CSAPR update, the EPA identified units that install control 

technology under the proposed CSAPR update, and what technology was 

installed. The equations for calculating retrofit costs were adopted from the EPA’s 

version of IPM. The model calculates the capital cost (in $/MW); the fixed 

operation and maintenance (O&M) cost (in $/MW-year); the variable O&M cost 

(in $/MWh); and the total annual cost for units projected to optimize and/or turn 

on existing idled SCR or SNCR. 

(2) Sale or purchase of allowances: To estimate the value of allowances holdings, 

allocated allowances were subtracted from projected emissions, and the difference 
7-11 

was then multiplied by $1,300 per ton. $1,300 per ton is the marginal cost of NOX

reductions used to set the proposed budgets in the proposed CSAPR update. 

While this is a reasonable approximation, it is possible that the actual allowance 

price could be lower. Units were assumed to purchase or sell allowances to 

exactly cover their projected emissions under the proposed CSAPR update. 

(3) Fuel costs: The change in fuel expenditures under the proposed CSAPR update 

was estimated by taking the difference in projected fuel expenditures between the 

IPM estimates for the proposed CSAPR update and the base case. 

(4) Value of electricity generated: To estimate the value of electricity generated, the

projected level of electricity generation is multiplied by the regional-adjusted 

retail electricity price ($/MWh) estimate, for all entities except those categorized 

as Private in Ventyx. For private entities, the EPA used wholesale electricity price 

instead retail electricity price because most of the private entities are independent 

power producers (IPP). IPPs sell their electricity to wholesale purchasers and do 

not own transmission facilities and thus their revenue was estimated with 

wholesale electricity prices. 

(5) Administrative costs: Because most affected units are already monitored as a 

result of other regulatory requirements, the EPA considered the primary 

administrative cost to be transaction costs related to purchasing or selling 

allowances. The EPA assumed that transaction costs were equal to 1.5 percent of 

the total absolute value of the difference between a unit’s allocation and projected 

NOX emissions. This assumption is based on market research by ICF 

International.

7.3.2.2 Results 

The potential impacts of the proposed CSAPR update on small entities are summarized in 

Table 7-2. All costs are presented in $2011. The EPA estimated the annual net compliance cost 

to small entities to be approximately - $38.3 million in 2017 or savings of $38.3 million. The fact 

that the net compliance costs for all entities are actually net savings does not mean that each 

small entity would benefit from the proposal to update CSAPR. The net savings are driven by 

entities that are able to increase their revenues by increasing generation. 
7-12 

Table 7-2. Projected Impact of the Proposed CSAPR Update on Small Entities in 2017 

EGU 

Ownership 

Type

Number of 

Potentially 

Affected 

Entities

Total Net 

Compliance 

Cost ($2011 

millions)

Number of Small 

Entities with 

Compliance Costs 

>1% of Generation 

Revenues

Number of Small 

Entities with 

Compliance Costs 

>3% of Generation 

Revenues

Cooperative 2 $0.2 0 0 

Investor-Owned 

Utility 1 -$38 0 0 

Municipal 2 -$0.1 1 1 

Private 2 -$0.4 0 0 

Total 7 -$38.3 1 1 

Note: The total number of entities with costs greater than 1 percent or 3 percent of revenues includes only entities 

experiencing positive costs. A negative cost value implies that the group of entities experiences a net 

savings under the proposed CSAPR update. 

Source: IPM analysis

The EPA assessed the economic and financial impacts of the rule using the ratio of 

compliance costs to the value of revenues from electricity generation, focusing in particular on 

entities for which this measure is greater than 1 percent. Although this metric is commonly used 

in the EPA impact analyses, it makes the most sense when as a general matter an analysis is 

looking at small businesses that operate in competitive environments. However, small businesses 

in the electric power industry often operate in a price -regulated environment where they are able 

to recover expenses through rate increases. Given this, the EPA considers the 1 percent measure 

in this case a crude measure of the price increases these small entities will be asking of rate 

commissions or making at publicly owned companies. 

Of the 7 small entities considered in this analysis, 1 entity may experience compliance 

costs greater than 1 percent of generation revenues in 2017. Since this entity is not projected to 

operate in the base case, we are unable to compare the estimated compliance costs to generation 

revenues. However, we note that this entity is located in a cost of service market, where typically 

we expect entities should be able to recover all of their costs of complying with the proposed 

CSAPR update. Entities that experience negative net costs under the proposed CSAPR update 

are excluded from these totals. The EPA has concluded that there is no significant economic 

impact on a substantial number of small entities (No SISNOSE) for this rule. The number of 
7-13 

entities with compliance costs exceeding 3 percent of generation revenues is also included in 

Table 7-2. 

The distribution across entities of economic impacts as a share of base case revenue is 

summarized in Table 7-3. Since there are few potentially-impacted small entities included in this 

analysis, the distributions of economic impacts on each ownership type are in general fairly tight. 

Note that one municipal entity is not projected to operate in the base case, and we are therefore 

unable to compare the estimated compliance costs to generation revenues. We estimate a positive 

compliance cost for this entity. 

Table 7-3. Summary of Distribution of Economic Impacts of the Proposed CSAPR Update 

on Small Entities in 2017 

EGU Ownership 

Type

Capacity-Weighted 

Average Economic 

Impacts as a % of 

Generation Revenues

Min Max

Cooperative 0.3% 0.1% 0.5% 

Investor-owned utility -60.1% -60.1% -60.1% 

Municipal* -0.5% -0.5% N/A* 

Private -2.3% -4.2% -1.5% 

*Note: One municipal entity is not projected to operate in the base case, and we are therefore unable to compare 

the estimated compliance costs to generation revenues. 

Source: IPM analysis 
7-14 

The separate components of annual costs to small entities under the proposed CSAPR 

update are summarized in Table 7-4. The most significant components of incremental cost to 

these entities under the proposed CSAPR update are due to lower electricity revenues and 

increased fuel costs. Fuel costs increase over all ownership groups except the ones under the 

ownership type “Private” because an entity with the second largest generation under “Private” is 

projected to cut its generation by 25 percent under the proposed CSAPR update, which translates 

to lower fuel costs for the whole group. Additionally, increases in electricity generation revenue, 

shown as cost savings or negative costs are experienced by cooperative, investor-owned utility, 

municipal and subdivision entities. This is due largely to the projected increase in electricity 

prices under the proposed CSAPR update. Among the private category, however, reduced 

generation by the one entity with a large share of generation leads to higher net costs for the 

entire category. Our data suggests this entity owns a group of combined cycle units and which 

are presumably marginal units in their respective load segments under the base case. 

Table 7-4. Incremental Annual Costs under the Proposed CSAPR Update Summarized by 

Ownership Group and Cost Category in 2017 (2011$ millions) 

EGU 

Ownership 

Type

Operating 

Cost

Net Purchase 

of Allowances Fuel Cost

Lost 

Electricity 

Revenue

Administrative 

Cost

Cooperative $0.1 $0.2 -$0.5 $0.4 $0.00

InvestorOwned Utility $1.9 $0.0 $26.8 -$66.7 $0.00

Municipal $0.1 $0.0 -$0.2 -$0.1 $0.00

Private -$0.5 -$0.3 -$3.9 $4.4 $0.00

Source: IPM analysis. 

7.3.3 Summary of Small Entity Impacts 

The EPA examined the potential economic impacts to small entities associated with this 

rulemaking based on assumptions of how the affected states will implement control measures to 

meet their emissions. To summarize, of the 7 small entities potentially affected, 1 may 

experience compliance costs in excess of 1 percent of revenues in 2017, based on assumptions of 

how the affected states implement control measures to meet their emissions budgets as set forth 

in this rulemaking. Potentially affected small entities experiencing compliance costs in excess of 
7-15 

1 percent of revenues have some potential for significant impact resulting from implementation 

of the proposed CSAPR update. However, as noted above, it is the EPA’s position that because 

this entity does not operate in a competitive market environment, they should generally be able 

to pass the costs of complying with the proposed CSAPR update on to rate-payers. 

The EPA has lessened the impacts for small entities by excluding all units smaller than 

25 MW. This exclusion, in addition to the exemptions for cogeneration units and solid waste 

incineration units, eliminates the burden of higher costs for a substantial number of small entities 

located in the 23 states for which the EPA is proposing FIPs. 

7.4 Unfunded Mandates Reform Act 

 Title II of the UMRA of 1995 (Public Law 104-4)(UMRA) establishes requirements for 

federal agencies to assess the effects of their regulatory actions on state, local, and Tribal 

governments and the private sector. Under Section 202 of the UMRA, 2 U.S.C. 1532, the EPA 

generally must prepare a written statement, including a cost-benefit analysis, for any proposed or 

final rule that includes any Federal mandate that may result in the expenditure by State, local, 

and Tribal governments, in the aggregate, or by the private sector, of $100,000,000 or more in 

any one year. A Federal mandate is defined under Section 421(6), 2 U.S.C. 658(6), to include a 

Federal intergovernmental mandate and a Federal private sector mandate. A Federal 

intergovernmental mandate, in turn, is defined to include a regulation that would impose an 

enforceable duty upon State, Local, or Tribal governments, Section 421(5)(A)(i), 2 U.S.C. 

658(5)(A)(i), except for, among other things, a duty that is a condition of Federal assistance, 

Section 421(5)(A)(i)(I). A Federal private sector mandate includes a regulation that would 

impose an enforceable duty upon the private sector, with certain exceptions, Section 421(7)(A), 2 

U.S.C. 658(7)(A). 

Before promulgating an EPA rule for which a written statement is needed under Section 

202 of the UMRA, Section 205, 2 U.S.C. 1535, of the UMRA generally requires the EPA to 

identify and consider a reasonable number of regulatory alternatives and adopt the least costly, 

most cost-effective, or least burdensome alternative that achieves the objectives of the rule. 

Moreover, section 205 allows the EPA to adopt an alternative other than the least costly, most 
7-16 

cost-effective or least burdensome alternative if the Administrator publishes with the final rule 

an explanation why that alternative was not adopted. 

Furthermore, as the EPA stated in the proposal, the EPA is not directly establishing any 

regulatory requirements that may significantly or uniquely affect small governments, including 

Tribal governments. Thus, under the proposed CSAPR update, the EPA is not obligated to 

develop under Section 203 of the UMRA a small government agency plan. 

The EPA did analyze the economic impacts of the proposed CSAPR update on 

government entities, however. This analysis does not examine potential indirect economic 

impacts associated with the proposed CSAPR update, such as employment effects in industries 

providing fuel and pollution control equipment, or the potential effects of electricity price 

increases on industries and households. 

7.4.1 Identification of Government-Owned Entities 

In this analysis, the EPA considered EGUs which meet the following five criteria: 1) 

EGU is represented in NEEDS v5.15; 2) EGU is fossil-fuel fired; 3) EGU is located in a state 

covered by this proposed rule; 4) EGU is neither a cogeneration unit nor solid waste incineration 

unit; and 5) EGU capacity is 25 MW or larger. 

The EPA next refined this list of EGUs, narrowing it to those that exhibit at least one of 

the following changes under the proposal, in comparison to the base case. 

• Summer fuel use (BTUs) changes by +/- 1% or more 

• Summer generation (GWh) changes by +/- 1% or more 

• NOx summer emissions (tons) changes by +/- 1% or more 

 From the inventory of units meeting the criteria above, the EPA used Ventyx data to 

identify state and municipality-owned utilities and subdivisions in the proposed CSAPR update 

region. The EPA then used IPM-parsed output to associate these plants with individual 

generating units. The EPA identified nine municipality-owned utilities that are potentially 

affected by the proposed CSAPR update. 
7-17 

7.4.2 Overview of Analysis and Results 

 After identifying potentially affected government entities, the EPA estimated the impact 

of the proposed CSAPR update in 2017 based on the following: 

• total impacts of compliance on government entities; and 

• ratio of government entity impacts to revenues from electricity generation. 

The financial burden to owners of EGUs under the proposed CSAPR update is composed 

of compliance and administrative costs. This section outlines the compliance and administrative 

costs for the nine potentially affected government-owned units in the proposed CSAPR update 

region. 

7.4.2.1 Methodology for Estimating Impacts of the proposed CSAPR update on 

Government Entities 

 An entity can comply with the proposed CSAPR update through any combination of the 

following: optimizing existing SCR and SNCRs, turning on idled SCR or SNCR controls, 

upgrading to state of the art combustion controls, using allocated allowances, purchasing 

allowances, or reducing emissions through a reduction in generation or improved efficiency. 

Additionally, units with more allowances than needed can sell these allowances on the 

market. The chosen compliance strategy will be primarily a function of the unit’s marginal 

control costs and its position relative to the marginal control costs of other units. 

To attempt to account for each potential control strategy, the EPA estimates compliance costs 

as follows: 

CCompliance = ∆ COperating+Retrofit + ∆ CFuel + ∆ CAllowances + ∆ CTransaction + ∆ R 

where C represents a component of cost as labeled, and ∆ R represents the retail value of 

foregone electricity generation. 

 In reality, compliance choices and market conditions can combine such that an entity may 

actually experience a savings in any of the individual components of cost. Under the 

proposed CSAPR update, for example, some units will forgo some level of electricity 

generation (and thus revenues) to comply, this impact will be lessened on these entities by 
7-18 

the projected increase in electricity prices under the proposed CSAPR update, while those not 

reducing generation levels will see an increase in electricity revenues. Because this analysis 

evaluates the total costs along each of the compliance strategies laid out above for each 

entity, it inevitably captures savings or gains such as those described. As a result, what we 

describe as cost is really more of a measure of the net economic impact of the rule on small 

entities. 

 In this analysis, the EPA used IPM-parsed output for the base case and the proposed 

CSAPR update to estimate compliance cost at the unit level. These costs were then summed 

for each small entity, adjusting for ownership share. Compliance cost estimates were based 

on the following: operating and retrofit costs, sale or purchase of allowances, and the change 

in fuel costs or electricity generation revenues under the proposed CSAPR update relative to 

the base case. These components of compliance cost were estimated as follows: 

(1) Operating and retrofit costs: Using the IPM-parsed output for the base case and 

the proposed CSAPR update, the EPA identified units that install control 

technology under the proposed CSAPR update and the technology installed. The 

equations for calculating retrofit costs were adopted from the EPA’s version of 

IPM. The model calculates the capital cost (in $/MW); the fixed operation and 

maintenance (O&M) cost (in $/MW-year); the variable O&M cost (in $/MWh); 

and the total annual cost for units projected to optimize and/or turn on existing 

idled SCR or SNCR. 

(2) Sale or purchase of allowances: To estimate the value of allowances holdings, 

allocated allowances were subtracted from projected emissions, and the difference 

was then multiplied by $1,300 per ton. $1,300 per ton is the marginal annualized 

cost of NOX reductions used to set the proposed budgets. While this is a 

reasonable approximation, it is possible that the actual allowance price could be 

lower. Units were assumed to purchase or sell allowances to exactly cover their 

projected emissions under the proposed CSAPR update. 
7-19 

(3) Fuel costs: The change in fuel expenditures under the proposed CSAPR update 

was estimated by taking the difference in projected fuel expenditures between the 

proposed CSAPR update and the base case. 

(4) Value of electricity generated: To estimate the value of electricity generated, the 

projected level of electricity generation is multiplied by the regional-adjusted 

retail electricity price ($/MWh) estimate, for all entities except those categorized 

as Private in Ventyx. For private entities, the EPA used wholesale electricity price 

instead retail electricity price because most of the private entities are independent 

power producers (IPP). IPPs sell their electricity to wholesale purchasers and do 

not own transmission facilities and thus their revenue was estimated with 

wholesale electricity prices. 

(5) Administrative costs: Because most affected units are already monitored as a 

result of other regulatory requirements, the EPA considered the primary 

administrative cost to be transaction costs related to purchasing or selling 

allowances. The EPA assumed that transaction costs were equal to 1.5 percent of 

the total absolute value of the difference between a unit’s allocation and projected 

NOX emissions. This assumption is based on market research by ICF 

International. 

7.4.2.2 Results 

A summary of economic impacts on government owned entities is presented in 

Table 7-5. According to the EPA’s analysis, the total net economic impact on 

government -owned entities (state- and municipality-owned utilities and subdivisions) is 

expected to be negative in 2014.9 Note that we expect the proposed CSAPR update to 

potentially have an impact on only one category of government-owned entities 

(municipality-owned entities). 

 

9All costs are reported in 2011 dollars. 
7-20 

Table 7-5. Summary of Potential Impacts on Government Entities under the Proposed 

CSAPR Update in 2017 

EGU Ownership 

Type

Potentially 

Affected Entities

Projected 

Annualized 

Costs ($2011 

millions) 

Number of 

Government 

Entities with 

Compliance 

Costs >1% of 

Generation 

Revenues

Number of 

Government 

Entities with 

Compliance 

Costs >3% of 

Generation 

Revenues

Municipal 9 -$4.6 4 2 

Total 9 -$4.6 4 2 

Note: The total number of entities with costs greater than 1 percent or 3 percent of revenues includes only entities 

experiencing positive costs, and includes one entity for which we are unable to estimate base case generation 

revenues due to projected closure in the base case. A negative cost value implies that the group of entities 

experiences a net savings under the proposed CSAPR update. 

As was done for the small entities analysis, the EPA further assessed the economic and 

financial impacts of the rule using the ratio of compliance costs to the value of revenues from 

electricity generation in the base case, also focusing specifically on entities for which this 

measure is greater than 1 percent.10 The EPA projects that four government entities will have 

compliance costs greater than 1 percent of revenues from electricity generation in 2017. One 

municipal entity is not projected to operate in the base case, and we are therefore unable to 

compare the estimated compliance costs to generation revenues. We include this entity in the 

>3% category. The majority of the units that have higher costs are not expected to make 

operational changes as a result of this rule (e.g., turn on controls). Their increased costs are 

largely due to a change in generation level, which results in either a decrease in electricity 

revenue or increased fuel cost, coupled with a relatively low base case revenue estimate. The 

EPA notes that increased fuel costs are often passed through to rate-payers as common practice 

in many areas of the U.S. due to fuel adder arrangements instituted by state public utility 

commissions. Entities that are projected to experience negative compliance costs under the 

proposed CSAPR update are not included in those totals. This approach is more indicative of a 

 

10 Neither the costs nor the revenues of units that retire under the proposed CSAPR update are included in this 

portion of the analysis. Because these units are better off retiring under the proposed CSAPR update than continuing 

operation, the true cost of the rule on these units is not represented by our modeling. The true cost of the proposed 

CSAPR update for these units is the differential between their costs in the base case and the costs of meeting their 

customers’ demand under the rule. 
7-21 

significant impact when an analysis is looking at entities operating in a competitive market 

environment. Government-owned entities do not operate in a competitive market environment 

and therefore will be able to recover expenses under the proposed CSAPR update through rate 

increases. Given this, the EPA considers the 1 percent measure in this case a crude measure of 

the extent to which rate increases will be made at publicly owned companies. 

For municipality-owned entities, the capacity-weighted average economic impact as a 

share of base case revenue is 4.6 percent. This average is heavily influenced by an outlier, for 

which the maximum economic impact as a share of base case revenues is 470 percent. This 

entity holds two small combustion turbines that are not projected to make any operational 

changes (e.g., turn on controls), but rather is projected to operate more under the proposed 

CSAPR update in 2017 than it was projected to operate in the base case. The relatively large 

impact estimate on a percent basis results from the low level of generation and thus revenue in 

the base case. This is the only entity that experiences economic impacts that are significantly 

higher than the capacity-weighted average for this group. The next highest economic impact in 

this group is 2.1 percent. 

Additionally, four of the nine entities are projected to experience a negative economic 

impact as a share of base case revenue, which implies that this group of four entities experiences 

a net savings under the proposed CSAPR update. 

The various components of annual incremental cost under the proposed CSAPR update to 

government entities are summarized in Table 7-6. In 2017, municipals are a net purchaser of 

allowances, and experience both an increase in fuel expenditures and an increase in electricity 

revenue under the proposed CSAPR update. Incremental fuel costs are positive because these 

entities are projected to increase generation and face higher fuel prices. Overall, increases in total

electricity revenue by government entities under the proposed CSAPR update exceed the 

increases in fuel and operating costs. 
7-22 

Table 7-6. Incremental Annual Costs under the Proposed CSAPR Update Summarized by 

Ownership Group and Cost Category (2011$ millions) in 2017 

EGU 

Ownership 

Type

Retrofit + 

Operating 

Cost

Net Purchase 

of Allowances Fuel Cost

Lost 

Electricity 

Revenue

Administrative 

Cost

Municipal $2.7 $0.7 $1.4 -$9.4 $0.0

Source: IPM analysis

7.4.3 Summary of Government Entity Impacts 

The EPA examined the potential economic impacts on government-owned entities 

associated with this rulemaking based on assumptions of how the affected states will implement 

control measures to meet their emissions. According to the EPA’s analysis, the total net 

economic impact on government-owned entities is expected to be -$4.6 million in 2017 or a net 

savings of $4.6 million. This does not mean that each government entity will experience net 

savings as the overall net savings is driven by some entities garnering savings. Of the nine 

government entities considered in this analysis, four may experience compliance costs in excess 

of 1 percent of revenues in 2017, based on our assumptions of how the affected states implement 

control measures to meet their emissions budgets as set forth in this rulemaking. 

Government entities projected to experience compliance costs in excess of 1 percent of 

revenues have some potential for significant impact resulting from implementation of the 

proposed CSAPR update. However, as noted above, it is the EPA’s position that because these 

government entities can pass on their costs of compliance to rate-payers, they will not be 

significantly affected. 

7.5 Executive Order 13132: Federalism 

This action does not have federalism implications. It will not have substantial direct 

effects on the states, on the relationship between the national government and the states, or on 

the distribution of power and responsibilities among the various levels of government. 
7-23 

7.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments 

This action has tribal implications as specified in Executive Order 13175. 

However, it will neither impose substantial direct compliance costs on federally 

recognized tribal governments, nor preempt tribal law. 

This action proposes to implement EGU NOX ozone-season emissions 

reductions in 23 eastern states. However, at this time, none of the existing or 

planned EGUs affected by this rule are owned by tribes or located in Indian 

country. This action may have tribal implications if a new affected EGU is built in 

Indian country. Additionally, tribes have a vested interest in how this proposed 

rule would affect air quality. 

In developing CSAPR, which was promulgated on July 6, 2011 to address 

interstate transport of ozone pollution under the 1997 ozone NAAQS,11 the EPA 

consulted with tribal officials under the EPA Policy on Consultation and 

Coordination with Indian Tribes early in the process of developing that regulation 

to permit them to have meaningful and timely input into its development. A 

summary of that consultation is provided in 76 FR 48346 (August 8, 2011). 

The EPA received comments from several tribal commenters regarding 

the availability of CSAPR allowance allocations to new units in Indian country. 

The EPA responded to these comments by instituting Indian country new unit setasides in the final CSAPR. In order to protect tribal sovereignty, these set-asides 

are managed and distributed by the federal government regardless of whether 

CSAPR in the adjoining or surrounding state is implemented through a FIP or 

SIP. While there are no existing affected EGUs in Indian country covered by this 

proposal, the Indian country set-asides will ensure that any future new units built 

in Indian country will be able to obtain the necessary allowances. This proposal 

 

11 CSAPR also addressed interstate transport of fine particulate matter (PM2.5) under the 1997 and 2006 PM2.5 

NAAQS. 
7-24 

maintains the Indian country new unit set-aside and adjusts the amounts of 

allowances in each set-aside according to the same methodology of the original 

CSAPR rule. 

The EPA has informed tribes of our development of this proposal through 

a National Tribal Air Association – EPA air policy conference call (January 29, 

2015). The EPA plans to further consult with tribal officials under the EPA Policy 

on Consultation and Coordination with Indian Tribes early in the process of 

developing this regulation to permit them to have meaningful and timely input 

into its development. The EPA will facilitate this consultation before finalizing 

this proposed rule. 

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 the proposed rule. 

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

Safety Risks 

The EPA interprets EO 13045 (62 FR 19885, April 23, 1997) as applying to those 

regulatory actions that concern health or safety risks, such that the analysis required under 

section 5–501 of the Order has the potential to influence the regulation. This action is not subject 

to EO 13045 because it does not involve decisions on environmental health or safety risks that 

may disproportionately affect children. The EPA believes that the ozone-related benefits, PM2.5-

related co-benefits, and CO2-related co-benefits would further improve children’s health. 

7.8 Executive Order 13211: Actions that Significantly Affect Energy Supply, 

Distribution, or Use 

This action, which is a significant regulatory action under EO 12866, is likely to have a 

significant effect on the supply, distribution, or use of energy. The EPA notes that one aspect of 

this proposal that may affect energy supply, disposition or use is the EPA’s proposing and taking 

comment on a range of options with respect to use of 2015 vintage and 2016 vintage CSAPR 

NOX ozone-season allowances for compliance with 2017 and later ozone-season requirements. 
7-25 

The EPA has prepared a Statement of Energy Effects for the proposed regulatory control 

alternative as follows. We estimate a much less than 1 percent change in retail electricity prices 

on average across the contiguous U.S. in 2017, and a much less than 1 percent change in coalfired electricity generation in 2017 as a result of this rule. The EPA projects that utility power 

sector delivered natural gas prices will change by less than 1 percent in 2017. For more 

information on the estimated energy effects, please see chapter 5 of this RIA. 

7.9 National Technology Transfer and Advancement Act 

The proposed rulemaking does not involve technical standards. 

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

Minority Populations and Low-Income Populations 

The EPA believes the human health or environmental risk addressed by 

this proposal will not have potential disproportionately high and adverse human 

health or environmental effects on minority, low-income or indigenous 

populations. 

The EPA notes this action proposes to update CSAPR to reduce interstate 

ozone transport with respect to the 2008 ozone NAAQS. The rule uses EPA’s 

authority in CAA section 110(a)(2)(d) to reduce (nitrogen oxides) NOX pollution 

that significantly contributes to downwind ozone nonattainment or maintenance 

areas. As a result, the rule will reduce exposures to ozone in the mostcontaminated areas (i.e., areas that are not meeting the 2008 ozone National 

Ambient Air Quality Standards (NAAQS)). In addition, the rule separately 

identifies both nonattainment areas and maintenance areas. This requirement 

reduces the likelihood that areas close to the level of the standard will exceed the 

current health-based standards in the future. The EPA proposes to implement 

these emission reductions using the CSAPR EGU NOX ozone-season emissions 

trading program with assurance provisions. 

The EPA recognizes that many environmental justice communities have 

voiced concerns in the past about emission trading and the potential for any 

emission increases in any location. The EPA believes that CSAPR mitigated these 

concerns and that this proposal, which applies the CSAPR framework to reduce 
7-26 

interstate ozone pollution and implement these reductions, will also minimize 

community concerns. The EPA seeks comment from communities on this 

proposal. 

Ozone pollution from power plants have both local and regional 

components: part of the pollution in a given location—even in locations near 

emission sources—is due to emissions from nearby sources and part is due to 

emissions that travel hundreds of miles and mix with emissions from other 

sources. 

It is important to note that the section of the Clean Air Act providing 

authority for this rule, section 110(a)(2)(D), unlike some other provisions, does 

not dictate levels of control for particular facilities. CSAPR allows sources to 

trade allowances with other sources in the same or different states while firmly 

constraining any emissions shifting that may occur by requiring a strict emission 

ceiling in each state (the assurance level). In addition, assurance provisions in the 

rule outline the allowance surrender penalties for failing to meet the assurance 

level; there are additional allowance penalties as well as financial penalties for 

failing to hold an adequate number of allowances to cover emissions. 

This approach reduces EGU emissions in each state that significantly 

contribute to downwind nonattainment or maintenance areas, while allowing 

power companies to adjust generation as needed and ensure that the country’s 

electricity needs will continue to be met. The EPA maintains that the existence of 

these assurance provisions, including the penalties imposed when triggered, will 

ensure that state emissions will stay below the level of the budget plus variability 

limit. 

In addition, all sources must hold enough allowances to cover their 

emissions. Therefore, if a source emits more than its allocation in a given year, 

either another source must have used less than its allocation and be willing to sell 

some of its excess allowances, or the source itself had emitted less than its 

allocation in one or more previous years (i.e., banked allowances for future use). 

In summary, the CSAPR minimizes community concerns about localized 

hot spots and reduces ambient concentrations of pollution where they are most 
7-27 

needed by sensitive and vulnerable populations by: considering the science of 

ozone transport to set strict state emissions budgets to reduce significant 

contributions to ozone nonattainment and maintenance (i.e., the most polluted) 

areas; implementing air quality-assured trading; requiring any emissions above 

the level of the allocations to be offset by emission decreases; and imposing strict 

penalties for sources that contribute to a state’s exceedance of its budget plus 

variability limit. In addition, it is important to note that nothing in this final rule 

allows sources to violate their title V permit or any other federal, state, or local 

emissions or air quality requirements. 

Finally, it is also important to note that CAA section 110(a)(2)(d), which 

addresses transport of criteria pollutants between states, is only one of many 

provisions of the CAA that provide EPA, states, and local governments with 

authorities to reduce exposure to ozone in communities. These legal authorities 

work together to reduce exposure to these pollutants in communities, including 

for minority, low-income, and tribal populations, and provide substantial health 

benefits to both the general public and sensitive sub-populations. 

The EPA has informed communities of our development of this proposal through an 

Environmental Justice community call (January 28, 2015) and a National Tribal Air Association 

– EPA air policy conference call (January 29, 2015). The EPA plans to further consult with 

communities early in the process of developing this regulation to permit them to have 

meaningful and timely input into its development. The EPA will facilitate this engagement 

before finalizing the proposed rule. 
7-1 
8-1 
8-1 

CHAPTER 8: COMPARISON OF BENEFITS AND COSTS

Overview 

The EPA performed an illustrative analysis to estimate the costs, human health benefits, 

and climate co-benefits of compliance with the proposed and more and less stringent alternatives. 

The EPA is proposing EGU NOx ozone season emissions budgets for 23 states.1

 The emissions 

reductions evaluated in the proposal reflect EGU NOX reduction strategies that are achievable for 

the 2017 ozone season. The EPA proposes to quantify EGU NOX ozone-season emissions 

budgets reflecting EGU NOX reduction strategies that are widely available at a uniform 

annualized cost of $1,300 per ton (2011$). For the RIA, in order to implement the OMB Circular 

A-4 requirement to assess one less stringent and one more stringent alternative to the proposal, 

the EPA is also analyzing EGU NOX ozone season emissions budgets reflecting NOX reduction 

strategies that are widely available at a uniform cost of $500 per ton (2011$) and strategies that 

are widely available at a uniform cost of $3,400 per ton (2011$). This chapter summarizes these 

results. 

8.1 Results 

As shown in Chapter 5, the estimated social annualized costs to implement the proposal, 

as described in this document, are approximately $93 million (2011 dollars). As shown in 

Chapter 6, the total estimated combined benefits from implementation of the proposal are 

approximately $660 to $1,300 million in 2017 (2011 dollars, based on a discount rate of 3 

percent and 7 percent for health benefits (ozone benefits and PM2.5 co-benefits), a range of 2.5% 

to 5% for climate co-benefits, and rounded to three two significant figures). EPA can thus 

calculate the net benefits of the proposal by subtracting the estimated social annualized costs 

from the estimated benefits in 2017. The annual net benefits of the proposal are approximately 

$700 63700 to $1,121200 million (based on air quality benefits discounted at 3 percent, the 

central estimate of CO2 co-benefits, and annualized cost estimates) using a 3 percent discount 

rate or $680 650580 to $1,1200 million (based on air quality benefits discounted at 7 percent, the 

central estimate of CO2 co-benefits, and annualized cost estimates)using a 7 percent discount 

 

1

 Alabama, Arkansas, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, 

Missouri, New Jersey, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas, Virginia, 

West Virginia, and Wisconsin. 
8-2 

rate. Therefore, the EPA expects that implementation of this rule, based solely on economic 

efficiency criteria, will provide society with a significant net gain in social welfare, 

notwithstanding the expansive set of health and environmental effects we were unable to 

quantify. Further quantification of directly emitted PM2.5-, mercury-, acidification-, and 

eutrophication-related impacts would increase the estimated net benefits of the rule. Table 8-1 

presents a summary of the benefits, costs, and net benefits of the proposal and also the more and 

less stringent alternatives. 

Table 8-1. Total Costs, Total Monetized Benefits, and Net Benefits of the Proposal and 

More or Less Stringent Alternatives for 2017 for U.S. (millions of 2011$)a,b,c 

 

Proposal More Stringent 

AAA 

Less Stringent 

Climate Co-Benefits $23 $23 $27 

 

Air Quality Health Benefits $670 to $1200 $690 to $1,300 $190 to $340 

 

Total Benefits $700 to $1200 $720 to $1300 $210 to $360 

 

Annualized Compliance Costs $93 

10

$96 $4.7

 

Net Benefits $6020 to $11200 $620 to $1200 $210 to $360

 

Non-Monetized Benefitsd Non-quantified climate benefits 

Reductions in exposure to ambient NO2 and SO2

Reductions in HAP, including mercury deposition 

Ecosystem benefits assoc. with reductions in emissions of NOx, SO2, and PM 

Visibility impairment 

a Estimating multiple years of costs and benefits is limited for this RIA by data and resource limitations. As a result, 

we provide compliance costs and social benefits in 2017, using the best available information to approximate 

compliance costs and social benefits recognizing uncertainties and limitations in those estimates. 

b

 Benefits ranges represent discounting of health benefits and climate co-benefits at a discount rate of 3 percent. See 

Chapter 6 for additional detail and explanation. The costs presented in this table reflect compliance costs annualized 

at 4.77 percent discount rate possible and monitoring, recordkeeping, and reporting costs. See Chapter 5 for 

additional detail and explanation. 

c All costs and benefits are rounded to two significant figures; columns may not appear to add correctly. 

 d Non-monetized benefits descriptions are for all three alternatives and are qualitative. 

 

 

Proposal More Stringent 

Alternative

Less Stringent 

Climate Co-Benefits $23 23 27 

 

Air Quality Health Benefits $700 670 to $1200 $720 690 to 

$1300 $1300

$220 190 to 370 

 
8-3 

 

 

Total Benefits $720 to $1200 $740 720 to 

$1300

$250 to $400 

 

Annualized Compliance Costs $93 $96 $5 

 

Net Benefits $630 to $1100 $640 to $1200 $250 to $400 

 

Non-Monetized Benefitsd Non-monetized climate benefits 

Reductions in exposure to ambient NO2 and SO2 

Reductions in mercury deposition 

Ecosystem benefits assoc. with reductions in 

emissions of NOx, SO , and PM

 

Visibility impairment 

 

a Estimating multiple years of costs and benefits is limited for this RIA by data and resource limitations. As a result, 

we provide a snapshot of costs and benefits in 2017, using the best available information to approximate social costs 

and social benefits recognizing uncertainties and limitations in those estimates. 

b

 Benefits ranges represent discounting of health benefits and climate co-benefits at a discount rate of 3%. See 

chapter 6 for additional detail and explanation. The costs presented in this table reflect engineering costs annualized 

at an approximately 5 percent discount rate to the extent possible and monitoring, recordkeeping, and reporting 

costs. See chapter 5 for additional detail and explanation. 

c All costs and benefits are rounded to two significant figures; rows may not appear to add correctly. 

 d Non-monetized benefits descriptions are for all three alternatives and are qualitative. 

In accordance with Circular A-4 Guidance (OMB, 2003), the EPA also analyzed the costs 

and benefits of two regulatory control alternatives that impose relatively more stringent and 

relatively less stringent EGU NOx emissions budgets, compared to the proposed rule. These 

alternatives are illustrative of the cost and benefit impacts of varying program stringency. They 

are designed to show the effects of more stringent and less stringent NOx reduction requirements 

in a regulatory structure that is otherwise the same as the proposed NOx emissions budgets. Air 

quality modeling was not conducted for the proposal or the more and less stringent alternatives. 

EPA applied a benefit-per-ton approach appropriate for deriving benefits for the evaluated 

regulatory control alternatives, as described in Chapter 6. The proposal and alternatives’ social 

costs are estimated using the IPM model, as described in Chapter 5. Table 8-2 presents the 

projected emissions reductions for ozone season NOx, as well as reductions in co-pollutant 

annual NOX, annual SO2, and annual CO2, in 2017 under the proposed rule and the more and less 

stringent alternatives. 
8-4 

Table 8-2. Projected 2017* Reductions in Emissions of NOx, SO2, and CO2 with the 

proposed NOx Emissions Budgets and More or Less Stringent Alternatives 

(Tons) 

 Proposal More Stringent 

Alternative 

Less Stringent 

Alternative 

NOx (annual) 90,000 93,000 24,000

NOx (ozone season) 85,000 8487,000 2524,000

SO2 (annual) 3901,20380 1,20430 1,1300

CO2 (annual short tons) 6610,000 7610,000 7720,000

*Annual reductions are based on 2018 IPM direct model outputs relied upon in this RIA to represent 2017 copollutant reductions 

 In this RIA, we quantify an array of adverse health impacts attributable to ozone and 

PM2.5. The Integrated Science Assessment for Ozone and Related Photochemical Oxidants 

(“Ozone ISA”) (U.S. EPA, 2013a) identifies the human health effects associated with ozone 

exposure, which include premature death and a variety of illnesses associated with acute (dayslong) and chronic (months to years-long) exposures. Similarly, the Integrated Science 

Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009) identifies the human health 

effects associated with ambient particles, which include premature death and a variety of 

illnesses associated with acute and chronic exposures. Air pollution can affect human health in a 

variety of ways.,\.

8.2 Net Present Value of a Stream of Costs and Benefits 

The EPA believes that providing comparisons of social costs and social benefits at 

discount rates of 3 and 7 percent is appropriate to the extent this is possible given available 

models and techniques. The three different uses of discounting in the RIA – (i) construction of 

annualized costs, (ii) adjusting the value of mortality risk for lags in mortality risk decreases, and

(iii) adjusting the cost of illness for non-fatal heart attacks to adjust for lags in follow up costs -- 

are all appropriate. We explain our discounting of benefits in Chapter 6 of the RIA, specifically 

the application, in which we apply discount rates of 3 and 7 percent to air quality benefits and 

2.5, 3, and 5 percent to climate co-benefits; in Chapter 6 of the RIA; we explain our discounting 

of costs, in which we use a single discount rate of 4.77 percent%, in Chapter 5. Our estimates of 

net benefits are the approximations of the net value (in 2017) of benefits attributable to emissions 

reductions needed to attain just for the year 2017.
8-5 

The EPA presents annualized costs and benefits in a single year for comparison in this RIA 

because there are a number of methodological complexities associated with calculating the net 

present value (NPV) of a stream of costs and benefits for a NAAQS. While NPV analysis allows 

evaluation of alternatives by summing the present value of all future costs and benefits, insights 

into how costs will occur over time are limited by underlying assumptions and data. Calculating 

a present value (PV) of the stream of future benefits also poses special challenges, which we 

describe below. In addition, the method requires definition of the length of that future time 

period, which is not straightforward for this analysis and subject to uncertainty. We provide 

annualized costs of compliance instead of using NPV or alternatives in this RIA, and our 

explanation for this is in Chapter 5. 

Further, because we do not know when a facility will stop using a control measure or 

change to another measure based on economic or other reasons, the EPA assumes the control 

equipment and measures applied in the illustrative control strategies remain in service for their 

full useful life. As a result, the annualized cost of controls in a single future year is the same 

throughout the lifetimes of control measures analyzed, allowing the EPA to compare the 

annualized control costs with the benefits in a single year for consistent comparison. 

The EPA’s RIAs for air quality rules generally report the estimated net benefits of 

improved air quality for a single year. The estimated NPV can better characterize the stream of 

benefits and costs over a multi-year period. However, calculating the PV of improved air quality 

is generally quite data-intensive and costly. Further, the results are sensitive to assumptions 

regarding the time period over which the stream of benefits is discounted. 

The theoretically appropriate approach for characterizing the PV of benefits is the life table 

approach. The life table, or dynamic population, approach explicitly models the year-to-year 

influence of air pollution on baseline mortality risk, population growth and the birth rate—

typically for each year over the course of a 50-to-100 year period (U.S. EPA SAB, 2010; Miller, 

2003). In contrast to the pulse approach2

, a life table models these variables endogenously by 

following a population cohort over time. For example, a life table will “pass” the air pollution-

 

2

 The pulse approach assumes changes in air pollution in a single year and affects mortality estimates over a 20-year 

period. 
8-6 

modified baseline death rate and population from year to year; impacts estimated in year 50 will 

account for the influence of air pollution on death rates and population growth in the preceding 

49 years. 

Calculating year-to-year changes in mortality risk in a life table requires some estimate of 

the annual change in air quality levels. It is both impractical to model air quality levels for each 

year and challenging to account for changes in federal, state and local policies that will affect the 

annual level and distribution of pollutants. For each of these reasons, the EPA has not generally 

reported the PV of benefits for air rules but has instead pursued a pulse approach. While we 

agree that providing the NPV of a stream of costs and benefits could be informative, based on 

these reasons we are not able to provide the NPV of that stream in this RIA. 
8-1 
9-1