Regulatory Impact Analysis for the 

Final Clean Air Interstate Rule
EPA-452/R-05-002

March 2005

Regulatory Impact Analysis for the Final

Clean Air Interstate Rule

U.S. Environmental Protection Agency

Office of Air and Radiation

Air Quality Strategies and Standards Division, 

Emission, Monitoring, and Analysis Division

and 

Clean Air Markets Division
iii

CONTENTS

Section Page

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

1.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.1.1 Health Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

1.1.2 Welfare Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

1.1.3 Uncertainty in the Benefits Estimates . . . . . . . . . . . . . . . . . . . . 1-6

1.2 Not All Benefits Quantified . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9

1.3 Costs and Economic Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9

1.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-12

1.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-13

2. INTRODUCTION AND BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

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

2.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.3 Regulated Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

2.4 Baseline and Years of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.5 Control Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.6 Benefits of Emission Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

2.7 Cost of Emission Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

2.8 Organization of the Regulatory Impact Analysis . . . . . . . . . . . . . . . . . 2-4

3. EMISSIONS AND AIR QUALITY IMPACTS . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1 Emissions Inventories and Estimated Emissions Reductions . . . . . . . . 3-1
iv

3.2 Air Quality Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

3.2.1 PM Air Quality Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

3.2.1.1 Modeling Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

3.2.1.2 Simulation Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

3.2.1.3 Model Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

3.2.1.4 CMAQ Model Evaluation . . . . . . . . . . . . . . . . . . . . . . 3-8

3.2.1.5 Converting CMAQ Outputs to Benefits Inputs . . . . 3-12

3.2.1.6 PM Air Quality Results . . . . . . . . . . . . . . . . . . . . . . 3-14

3.2.2 Ozone Air Quality Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . 3-15

3.2.2.1 Modeling Domain . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17

3.2.2.2 Simulation Periods . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17

3.2.2.3 Nonemissions Modeling Inputs . . . . . . . . . . . . . . . . 3-17

3.2.2.4 Model Performance for Photochemical Ozone . . . . . 3-18

3.2.2.5 Converting CAMx Outputs to Full-Season 

Profiles for Benefits Analysis . . . . . . . . . . . . . . . . . . 3-20

3.2.2.6 Ozone Air Quality Results . . . . . . . . . . . . . . . . . . . . 3-21

3.2.3 Visibility Degradation Estimates . . . . . . . . . . . . . . . . . . . . . . . 3-21

3.3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-25

4. BENEFITS ANALYSIS AND RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1 Benefit Analysis—Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . 4-12

4.1.1 Valuation Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-14

4.1.2 Growth in WTP Reflecting National Income Growth 

Over Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-16

4.1.3 Methods for Describing Uncertainty . . . . . . . . . . . . . . . . . . . . 4-19

4.1.4 Demographic Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-24

4.1.5 Health Benefits Assessment Methods . . . . . . . . . . . . . . . . . . . 4-25

4.1.5.1 Selecting Health Endpoints and Epidemiological 

Effect Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-26

4.1.5.2 Uncertainties Associated with Health Impact 

Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-40

4.1.5.3 Baseline Health Effect Incidence Rates . . . . . . . . . . 4-46

4.1.5.4 Selecting Unit Values for Monetizing Health 

Endpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-50

4.1.6 Human Welfare Impact Assessment . . . . . . . . . . . . . . . . . . . . 4-65

4.1.6.1 Visibility Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-65

4.1.6.2 Agricultural, Forestry and other VegetationRelated Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-70

4.1.6.3 Benefits from Reductions in Materials Damage . . . . 4-72

4.1.6.4 Benefits from Reduced Ecosystem Damage . . . . . . . 4-73

4.2 Benefits Analysis—Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-73

4.2.1 Potential Benefits of the New Jersey and Delaware Proposal . 4-77
v

4.3 Probabilistic Analysis of Uncertainty in the Benefits Estimates . . . . . 4-77

4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-84

4.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-85

5. QUALITATIVE ASSESSMENT OF NONMONETIZED BENEFITS . . . . . . 5-1

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.2 Atmospheric Deposition of Sulfur and Nitrogen—Quantification 

of Impacts for the Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.3 Atmospheric Deposition of Sulfur and Nitrogen—Impacts on 

Aquatic, Forest, and Coastal Ecosystems . . . . . . . . . . . . . . . . . . . . . . . 5-3

5.3.1 Freshwater Acidification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4

5.3.1.1 Water/Watershed Modeling . . . . . . . . . . . . . . . . . . . . 5-6

5.3.1.2 Description of the MAGIC Model and Methods . . . . 5-7

5.3.1.3 Model Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-7

5.3.1.4 Model Implementation . . . . . . . . . . . . . . . . . . . . . . . . 5-8

5.3.1.5 Calibration Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 5-9

5.3.1.6 MAGIC Modeling Results . . . . . . . . . . . . . . . . . . . . 5-11

5.3.1.7 Study of Benefits of Natural Resource 

Improvements in the Adirondacks . . . . . . . . . . . . . . 5-12

5.3.2 Forest Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-13

5.3.3 Coastal Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15

5.3.4 Potential Other Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-17

5.4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-17

6. ELECTRIC POWER SECTOR PROFILE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.1 Power-Sector Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.1.1 Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.1.2 Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

6.1.3 Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

6.2 Deregulation and Restructuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

6.3 Pollution and EPA Regulation of Emissions . . . . . . . . . . . . . . . . . . . . . 6-5

6.4 Pollution Control Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-6

6.5 Regulation of the Power Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

6.6 Cap and Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-9
vi

6.7 Clean Air Interstate Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-9

6.8 Price Elasticity of Electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-11

7. COST, ECONOMIC, AND ENERGY IMPACTS . . . . . . . . . . . . . . . . . . . . . . 7-1

7.1 Modeling Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

7.2 Projected SO2 and NOx Emissions and Reductions . . . . . . . . . . . . . . . . 7-4

7.3 Projected Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-5

7.4 Projected Control Technology Retrofits . . . . . . . . . . . . . . . . . . . . . . . . 7-5

7.5 Projected Generation Mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

7.6 Projected Capacity Additions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-11

7.7 Projected Coal Production for the Electric Power Sector . . . . . . . . . . 7-11

7.8 Projected Retail Electricity Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-11

7.9 Projected Fuel Price Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-15

7.10 Key Differences in EPA Model Runs for Final CAIR Modeling . . . . 7-15

7.11 Projected Primary PM Emissions from Power Plants . . . . . . . . . . . . . 7-16

7.12 Limitations of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17

7.13 Significant Energy Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-21

7.14 Industry-Sector Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-21

7.15 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-22

8. STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSES . . . . . . . 8-1

8.1 Small Entity Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

8.1.1 Identification of Small Entities . . . . . . . . . . . . . . . . . . . . . . . . . 8-3

8.1.2 Overview of Analysis and Results . . . . . . . . . . . . . . . . . . . . . . . 8-4

8.1.2.1 Methodology for Estimating Impacts of CAIR 

on Small Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-5

8.1.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-7
vii

8.1.3 Summary of Small Entity Impacts . . . . . . . . . . . . . . . . . . . . . . 8-10

8.2 Unfunded Mandates Reform Act (UMRA) Analysis . . . . . . . . . . . . . 8-11

8.2.1 Identification of Government-Owned Entities . . . . . . . . . . . . 8-13

8.2.2 Overview of Analysis and Results . . . . . . . . . . . . . . . . . . . . . . 8-14

8.2.2.1 Methodology for Estimating Impacts of CAIR on

Government Entities . . . . . . . . . . . . . . . . . . . . . . . . . 8-14

8.2.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-16

8.2.3 Summary of Government Entity Impacts . . . . . . . . . . . . . . . . 8-18

8.3 Paperwork Reduction Act . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-20

8.4 Children’s Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-21

8.5 Tribal Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-21

8.6 Environmental Justice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-22

8.7 Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-23

9. COMPARISON OF BENEFITS AND COSTS . . . . . . . . . . . . . . . . . . . . . . . . . 9-1

9.1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-3

Appendix A: Benefits and Costs of the Clean Air Interstate Rule, the Clean Air Visibility 

Rule, and the Clean Air Interstate Rule plus the Clean Air Visibility Rule . . A-1

Appendix B: Supplemental Analyses Addressing Uncertainties in the Benefits Analyses . . B-1

Appendix C: Sensitivity Analyses of the Key Parameters in the Benefits Analysis . . . . . . . . C-1

Appendix D: Sensitivity Analyses of Key Parameters in the Cost and Economic Impact

Analysis and a Listing of IPM Runs in Support of CAIR . . . . . . . . . . . . . . . . D-1

Appendix E: CAIR Industry-Sector Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

Appendix F: Additional Technical Information Supporting the Benefits Analysis . . . . . . . . F-1

Appendix G: Health-Based Cost-Effectiveness of Reductions in Ambient PM2.5

Associated with CAIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G-1
viii

LIST OF FIGURES

Number Page

1-1 Comparative Assessment of Relative Range of Uncertainty in Estimated 

Avoided Incidence of Premature Mortality Using Classical Statistical Error 

and the Pilot Expert-Based Characterizations of Uncertainty . . . . . . . . . . . . . . 1-7

2-1 Final CAIR Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

3-1 CMAQ Modeling Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

3-2 CAMx Eastern U.S. Modeling Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-18

4-1 Key Steps in Air Quality Modeling Based Benefits Analysis . . . . . . . . . . . . . . 4-7

4-2 CAIR Final Rule Visibility Improvements in Class I Areas in the 

Southeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-70

4-3 Results of Illustrative Application of Pilot Expert Elicitation: Annual 

Reductions in Premature Mortality in 2015 Associated with the Clean 

Air Interstate Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-81

4-4 Results of Illustrative Application of Pilot Expert Elicitation: Dollar 

Value of Annual Reductions in Premature Mortality in 2015 Associated 

with the Clean Air Interstate Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-83

5-1 Percentage Reduction of All Forms of Sulfur Deposition for the Years 

2010 and 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-2

5-2 Percentage Reduction of All Forms of Nitrogen Deposition for the Years 

2010 and 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-2

5-3 CAIR Nitrogen and Sulfur Deposition Reduction in the Adirondacks, 

New England, and the Blue Ridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

5-4 CAIR Nitrogen Deposition Reductions in Hydrologic Regions . . . . . . . . . . . 5-16

6-1 Status of State Electricity Industry Restructuring Activities 

(as of February 2003) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5

6-2 Emissions of SO2 and NOx from the Power Sector (2003) . . . . . . . . . . . . . . . . 6-6

7-1 CAIR Modeled Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

7-2 SO2 Emissions from the Power Sector in 2010 and 2015 With and 

Without CAIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

7-3 NOX Emissions from the Power Sector in 2010 and 2015 With and 

Without CAIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7
ix

7-4 Ozone Season NOX Emissions from the Power Sector in 2010 and 2015 

With and Without CAIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-8

7-5 Generation Mix with and without CAIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10

7-6 Current Coal Production Levels and Projected Production with CAIR . . . . . 7-12

7-7 Regional Electricity Prices with and without CAIR . . . . . . . . . . . . . . . . . . . . 7-13

7-8 NERC Power Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14

7-9 Final CAIR Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-16
x

LIST OF TABLES

Number Page

1-1 Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air 

Interstate Rule (billions of 1999$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2

1-2 Clean Air Interstate Rule: Estimated Reduction in Incidence of Adverse 

Health Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

1-3 Estimated Monetary Value of Reductions in Incidence of Health and 

Welfare Effects (in millions of 1999$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

1-4 Unquantified and Nonmonetized Benefits of the Clean Air Interstate Rule . . 1-10

1-5 Unquantified Costs of the Clean Air Interstate Rule . . . . . . . . . . . . . . . . . . . . 1-12

3-1 Emissions Sources and Basis for Current and Future-Year Inventories . . . . . . 3-2

3-2 Summary of Modeled Baseline Emissions for Lower 48 States . . . . . . . . . . . . 3-3

3-3 Summary of Modeled Emissions Changes for the Clean Air Interstate 

Rule: 2010 and 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

3-4 Model Performance Statistics for CAIR CMAQ 2001 . . . . . . . . . . . . . . . . . . 3-11

3-5 Selected Performance Evaluation Statistics from the CMAQ 2001 

Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-12

3-6 Summary of Base Case PM Air Quality and Changes Due to Clean 

Air Interstate Rule: 2010 and 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-15

3-7 Distribution of PM2.5 Air Quality Improvements Over Population Due 

to Clean Air Interstate Rule: 2010 and 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . 3-16

3-8 Model Performance Statistics for Hourly Ozone in the Eastern U.S. 

CAMx Ozone Simulations: 1995 Base Case . . . . . . . . . . . . . . . . . . . . . . . . . 3-19

3-9 Summary of CAMx Derived Population-Weighted Ozone Air Quality 

Metrics for Health Benefits Endpoints Due to Clean Air Interstate Rule: 

Eastern U.S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-22

3-10 Summary of Deciview Visibility Impacts at Class I Areas in the 

CAIR Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-23

4-1 Estimated Monetized Benefits of the Final CAIR . . . . . . . . . . . . . . . . . . . . . . . 4-2

4-2 Human Health and Welfare Effects of Pollutants Affected by the 

Final CAIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

4-3 Elasticity Values Used to Account for Projected Real Income Growth . . . . . 4-18

4-4 Adjustment Factors Used to Account for Projected Real Income Growth . . . 4-19

4-5 Primary Sources of Uncertainty in the Benefits Analysis . . . . . . . . . . . . . . . . 4-21
xi

4-6 Summary of Considerations Used in Selecting C-R Functions . . . . . . . . . . . . 4-27

4-7 Endpoints and Studies Used to Calculate Total Monetized Health Benefits . . 4-30

4-8 Studies Examining Health Impacts in the Asthmatic Population Evaluated 

for Use in the Benefits Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-41

4-9 Baseline Incidence Rates and Population Prevalence Rates for Use in 

Impact Functions, General Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-48

4-10 Asthma Prevalence Rates Used to Estimate Asthmatic Populations in 

Impact Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-50

4-11 Unit Values Used for Economic Valuation of Health Endpoints (1999$) . . . 4-52

4-12 Expected Impact on Estimated Benefits of Premature Mortality Reductions 

of Differences Between Factors Used in Developing Applied VSL and

Theoretically Appropriate VSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-57

4-13 Alternative Direct Medical Cost of Illness Estimates for Nonfatal 

Heart Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-62

4-14 Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial

Infarction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-62

4-15 Women with Children: Number and Percentage in the Labor Force, 

2000, and Weighted Average Participation Rate . . . . . . . . . . . . . . . . . . . . . . . 4-64

4-16 Clean Air Interstate Rule: Estimated Reduction in Incidence of 

Adverse Health Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-74

4-17 Estimated Monetary Value in Reductions in Incidence of Health and 

Welfare Effects (in millions of 1999$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-75

5-1 Acidification Changes in Water Bodies as a Result of CAIR . . . . . . . . . . . . . 5-12

6-1 Existing Electricity Generating Capacity by Energy Source, 2002 . . . . . . . . . . 6-1

6-2 Total U.S. Electric Power Industry Retail Sales in 2003 (Billion kWh) . . . . . . 6-2

6-3 Electricity Net Generation in 2003 (Billion kWh) . . . . . . . . . . . . . . . . . . . . . . . 6-2

6-4 Emissions of SO2 and NOx in 2003 and Percentage of Emissions in the 

CAIR Affected Region (tons) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10

6-5 Current Electricity Net Generation and EPA Projections for 2010 and 

2015 (Billion kWh) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10

7-1 CAIR Annual Emissions Caps (Million Tons) . . . . . . . . . . . . . . . . . . . . . . . . . 7-2

7-2 Projected Emissions of SO2 and NOx with the Base Case (No Further 

Controls) and with CAIR (Million Tons) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-5

7-3 Annualized Regional Cost of CAIR and Marginal Cost of SO2 and NOx

Reductions with CAIR ($1999) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

7-4 Pollution Controls by Technology with the Base Case (No Further Controls) 

and with CAIR (GW) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

7-5 Generation Mix with the Base Case (No Further Controls) and with CAIR

(Thousand GWhs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10

7-6 Total Coal and Natural Oil/Gas-Fired Capacity by 2020 (GW) . . . . . . . . . . . 7-11
xii

7-7 Coal Production for the Electric Power Sector with the Base Case (No 

Further Controls) and with CAIR (Million Tons) . . . . . . . . . . . . . . . . . . . . . . 7-12

7-8 Projected Regional Retail Electricity Prices with the Base Case (No 

Further Controls) and with CAIR (Mills/kWh) . . . . . . . . . . . . . . . . . . . . . . . . 7-13

7-9 Retail Electricity Prices by NERC Region with the Base Case (No 

Further Controls) and with CAIR (Mills/kWh) . . . . . . . . . . . . . . . . . . . . . . . . 7-14

7-10 Henry Hub Natural Gas Prices and Average Minemouth Coal Prices with the

Base Case (No Further Controls) and with CAIR ($1999) . . . . . . . . . . . . . . . 7-15

8-1 Potentially Regulated Categories and Entities . . . . . . . . . . . . . . . . . . . . . . . . . . 8-2

8-2 Projected Impact of CAIR on Small Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-4

8-3 Summary of Distribution of Economic Impacts of CAIR on Small Entities . . . 8-9

8-4 Incremental Annualized Costs under CAIR Summarized by Ownership 

Group and Cost Category ($1999 millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-10

8-5 Summary of Potential Impacts on Government Entities under CAIR . . . . . . . 8-13

8-6 Distribution of Economic Impacts on Government Entities under CAIR . . . . 8-18

8-7 Incremental Annualized Costs under CAIR Summarized by Ownership 

Group and Cost Category ($1,000,000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-19

9-1 Summary of Annual Benefits, Costs, and Net Benefits of the Clean 

Air Interstate Rule (billions of 1999 dollars) . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-2
1-1

Synopsis

EPA has estimated the benefits and costs of the Clean Air Interstate Rule and finds

that the rule results in estimated annual net benefits of $71.4 or $60.4 in 2010 and $98.5 or

$83.2 billion in 2015. These alternate net benefit estimates reflect differing assumptions

about the social discount rate used to estimate the social benefits and costs of the rule. The

lower estimates reflect a discount rate of 7 percent and the higher estimates a discount rate

of 3 percent. In 2015, the total annual quantified benefits are $101 or $86.3 billion and the

annual social costs are $2.6 or $3.1 billion—benefits outweigh social costs in 2015 by a

ratio of 39 to 1 or 28 to 1 (3 percent and 7 percent discount rates respectively). An

alternative comparison of the annual benefits of the rule to the estimated private costs to the

electric generating industry in 2015 result in benefits outweighing costs by a ratio of 25 to 1

(benefits of $101 billion compared to costs of $3.6 billion). These estimates do not include

the value of benefits or costs that we cannot monetize. Upon consideration of the

uncertainties and limitations in the analysis, it remains clear that the benefits of CAIR are

substantial and far outweigh the costs.

CHAPTER 1

EXECUTIVE SUMMARY

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

the costs of the Clean Air Interstate Rule (CAIR) and compares the benefits to the costs of

implementing CAIR in 2010 and 2015. 

1.1 Results

A comparison of the benefits and costs of the rule in 2010 and 2015 is shown in

Table 1-1. The benefits and costs reported for CAIR in Table 1-1 represent estimates for a

complete CAIR program that includes the CAIR promulgated rule and the concurrent

proposal to include annual sulfur dioxide (SO2) and nitrogen oxide (NOx) controls for New

Jersey and Delaware. The modeling used to provide these estimates also assumes annual

SO2 and NOx controls for Arkansas that are not a part of the complete CAIR program

resulting in a slight overstatement of the reported benefits and costs.
1-2

Table 1-1. Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air

Interstate Rulea

 (billions of 1999$)

Description 2010 2015

Social costsb

3 percent discount rate $1.91 $2.56

7 percent discount rate $2.14 $3.07

Social benefitsc,d,e

3 percent discount rate 73.3 + B 101 + B

7 percent discount rate 62.6 + B 86.3 + B

Health-related benefits:

3 percent discount rate 72.1 99.3

7 percent discount rate 61.4 84.5

Visibility benefits 1.14 1.78

Net benefits (benefits-costs)e,f

3 percent discount rate $71.4 + B $98.5 + B

7 percent discount rate $60.4 + B $83.2 + B

a All estimates are rounded to three significant digits and represent annualized benefits and costs anticipated

for the year 2010 and 2015. Estimates relate to the complete CAIR program including the CAIR

promulgated rule and the proposal to include SO2 and annual NOX controls for New Jersey and Delaware.

Modeling used to develop these estimates assumes annual SO2 and NOx controls for Arkansas resulting in a

slight overstatement of the reported benefits and costs for the complete CAIR program.

b Note that costs are the annualized total costs of reducing pollutants including NOx and SO2 for the EGU

source category in the CAIR region. 

c As this table indicates, total benefits are driven primarily by PM-related health benefits. The reduction in

premature fatalities each year accounts for over 90 percent of total monetized benefits in 2015. Benefit

estimates in this table are nationwide (with the exception of ozone and visibility) and reflect NOx and SO2

reductions. The analysis assumes that States will choose to achieve CAIR caps solely from the EGU source

category. Ozone benefits represent benefits in the eastern United States. Visibility benefits represent

benefits in Class I areas in the southeastern United States.

d Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all

unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and

monetized are listed in Table 1-4.

e Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure described in

Chapter 4. Results reflect 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines

for preparing economic analyses (U.S. EPA, 2000; OMB, 2003).

f Net benefits are rounded to the nearest $100 million. Columnar totals may not sum due to rounding.
1

 Although well over 90 percent of the expected benefits of this rule are derived from reductions in SO2 and

NOx, a small portion of EPA’s projected benefits are a result of reductions in primary PM from power

plants. Although this reduction is not required by the rule, it is a potential ancillary benefit of installing

certain SO2 control technologies.

2

 These estimates account for growth in the public’s willingness to pay for reductions in health and

environmental risks and account for growth in real gross domestic product per capita between the present

and 2015. Benefit estimates reflect the use of 3 percent and 7 percent discount rates consistent with the U.S.

Environmental Protection Agency (EPA) and the Office of Management and Budget (OMB) guidelines for

preparing economic analyses (U.S. EPA, 2000; OMB, 2003). 

1-3

Details of the important analysis assumptions, including entities regulated, baseline,

analysis year, control scenario, and other relevant analysis assumptions are discussed in

Chapter 2 of this report. 

1.1.1 Health Benefits

CAIR is expected to yield significant health benefits by reducing emissions of two

key contributors to fine particle and ozone formation. Sulfur dioxide contributes to the

formation of fine particle pollution (PM2.5), and nitrogen oxide contributes to the formation

of both PM2.5 and ground-level ozone.1

Our analyses suggest CAIR would yield benefits in 2015 of $101 billion (based on a

3 percent discount rate) and $86.3 billion (based on a 7 percent discount rate) that includes

the value of avoiding approximately 17,000 premature deaths, 22,000 nonfatal heart attacks,

12,300 hospitalizations for respiratory and cardiovascular diseases, 1.7 million lost work

days, 500,000 school absences, and 10.6 million days when adults restrict normal activities

because of respiratory symptoms exacerbated by PM2.5 and ozone pollution.2

We also estimate substantial additional health improvements for children from

reductions in upper and lower respiratory illnesses, acute bronchitis, and asthma attacks. See

Table 1-2 for a list of the annual reduction in health effects expected in 2010 and 2015 and

Table 1-3 for the estimated value of those reductions.

1.1.2 Welfare Benefits

The term welfare benefits covers both environmental and societal benefits of reducing

pollution, such as reductions in damage to ecosystems, improved visibility and

improvements in recreational and commercial fishing, agricultural yields, and forest 
1-4

Table 1-2. Clean Air Interstate Rule: Estimated Reduction in Incidence of Adverse

Health Effectsa

2010 2015

Health Effect Incidence Reduction 

PM-Related Endpoints:

Premature mortalityb,c

 Adult, age 30 and over

 Infant, age <1 year 

13,000

29

17,000

36

Chronic bronchitis (adult, age 26 and over) 6,900 8,700

Non-fatal myocardial infarction (adults, age 18 and older) 17,000 22,000

Hospital admissions—respiratory (all ages)d 4,300 5,500

Hospital admissions—cardiovascular (adults, age >18)e 3,800 5,000

Emergency room visits for asthma (age 18 years and younger) 10,000 13,000

Acute bronchitis (children, age 8–12) 16,000 19,000

Lower respiratory symptoms (children, age 7–14) 190,000 230,000

Upper respiratory symptoms (asthmatic children, age 9–18) 150,000 180,000

Asthma exacerbation (asthmatic children, age 6–18) 240,000 290,000

Work loss days (adults, age 18–65) 1,400,000 1,700,000

Minor restricted-activity days (MRADs) (adults, age 18–65) 8,100,000 9,900,000

Ozone-Related Endpoints

Hospital admissions—respiratory causes (adult, 65 and older)f 610 1,700

Hospital admissions—respiratory causes (children, under 2) 380 1,100

Emergency room visit for asthma (all ages) 100 280

Minor restricted-activity days (MRADs) (adults, age 18–65) 280,000 690,000

School absence days 180,000 510,000

a Incidences are rounded to two significant digits. These estimates represent benefits from CAIR nationwide. The

modeling used to derive these incidence estimates are reflective of those expected for the final CAIR program including

the CAIR promulgated rule and the proposal to include SO2 and annual NOx controls for New Jersey and Delaware. 

Modeling used to develop these estimates assumes annual SO2 and NOx controls for Arkansas resulting in a

slight overstatement of the reported benefits and costs for the complete CAIR program. b Premature mortality benefits associated with ozone are not analyzed in the primary analysis. c Adult premature mortality based upon studies by Pope et al., 2002. Infant premature mortality is based upon studies by

Woodruff, Grillo, and Schoendorf, 1997. d Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD),

pneumonia, and asthma. e Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart disease,

dysrhythmias, and heart failure. f Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for COPD and

pneumonia.
1-5

Table 1-3. Estimated Monetary Value of Reductions in Incidence of Health and

Welfare Effects (in millions of 1999$)a,b

2010 2015

Health Effect Pollutant Estimated Value of Reductions

Premature mortalityc,d

 Adult >30 years

 3% discount rate

 7% discount rate

 Child <1 year

PM2.5 $67,300

$56,600

$168

$92,800

$78,100

$222

Chronic bronchitis (adults, 26 and over) PM2.5 $2,520 $3,340

Nonfatal acute myocardial infarctions 

 3% discount rate

 7% discount rate

PM2.5 $1,420

$1,370

$1,850

$1,790

Hospital admissions for respiratory causes PM2.5, O3 $45.2 $78.9

Hospital admissions for cardiovascular causes PM2.5 $80.7 $105

Emergency room visits for asthma PM2.5,O3 $2.84 $3.56

Acute bronchitis (children, age 8–12) PM2.5 $5.63 $7.06

Lower respiratory symptoms (children, 7–14) PM2.5 $2.98 $3.74

Upper respiratory symptoms (asthma, 9–11) PM2.5 $3.80 $4.77

Asthma exacerbations PM2.5 $10.3 $12.7

Work loss days PM2.5, $180 $219

Minor restricted-activity days (MRADs) PM2.5,O3 $422 $543

School absence days O3 $12.9 $36.4

Worker productivity (outdoor workers, 18–65) O3 $7.66 $19.9

Recreational visibility, 81 Class I areas PM2.5 $1,140 $1,780

Monetized Totale 

 Base Estimate:

 3% discount rate

 7% discount rate 

PM2.5,O3 $73,300 + B

$62,600 + B

$101,000 + B

$86,300 + B a Monetary benefits are rounded to three significant digits. These estimates represent benefits from CAIR nationwide for

NOx and SO2 emission reductions from electricity-generating units (EGU) sources (with the exception of ozone and

visibility benefits). Ozone benefits relate to the eastern United States. Visibility benefits relate to Class I areas in the

southeastern United States. The benefit estimates reflected relate to the final CAIR program that includes the CAIR

promulgated rule and the proposal to include SO2 and annual NOx controls for New Jersey and Delaware. Modeling

used to develop these estimates assumes annual SO2 and NOx controls for Arkansas resulting in a slight

overstatement of the reported benefits and costs for the complete CAIR program. b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis year (2010 or

2015). c Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure described in Chapter 4. 

Results show 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic

analyses (U.S. EPA, 2000; OMB, 2003). d Adult premature mortality based upon studies by Pope et al., 2002. Infant premature mortality based upon studies by

Woodruff, Grillo, and Schoendorf, 1997. e B represents the monetary value of health and welfare benefits not monetized. A detailed listing is provided in

Table 1-4.
1-6

productivity. Although we are unable to monetize all welfare benefits, EPA estimates CAIR

will yield welfare benefits of $1.8 billion in 2015 (1999$) for visibility improvements in

southeastern Class I (national park) areas. 

1.1.3 Uncertainty in the Benefits Estimates

As part of an overall program to improve the Agency’s characterization of

uncertainties in health benefits analyses, we present two types of probabilistic approaches to

characterize uncertainty. The first approach generates a distribution of benefits based on the

classical statistical error expressed in the underlying health effects and economic valuation

studies used in the benefits modeling framework. The second approach uses the results from

a pilot expert elicitation project designed to characterize key aspects of uncertainty in the

ambient PM2.5/mortality relationship, and augments the uncertainties in the mortality

estimate with the statistical error reported for other endpoints in the benefit analysis. Both

approaches provide insights into the likelihood of different outcomes and about the state of

knowledge regarding the benefits estimates.

The uncertainty estimates have the strength of presenting a statistical measure of the

uncertainty in the underlying studies serving as the basis for the estimates used in the

analysis. However, this approach captures only a limited portion of the uncertainty about the

parameters. The 5th and 95th percentile points of the distributions are based on statistical

error and cross-study variability and provide some insight into how uncertain our estimate is

with regard to those sources of uncertainty. However, it does not capture other sources of

uncertainty regarding the model specification and other inputs to the model, including

emissions, air quality, and aspects of the health science not captured in the studies, such as

the likelihood that PM is causally related to premature mortality and other serious health

effects.

Figure 1-1 presents box plots of the distributions of the reduction in PM2.5-related

premature mortality based on the C-R distributions provided by each expert, as well as that

for our primary estimate, based on the statistical error associated with Pope et al. (2002). 

The distributions are depicted as box plots with the diamond symbol (—) showing the mean,

the dash (–) showing the median (50th percentile), the box defining the interquartile range

(bounded by the 25th and 75th percentiles), and the whiskers defining the 90 percent

confidence interval (bounded by the 5th and 95th percentiles of the distribution). Our

primary estimate based on the Pope et al. (2002) study shows that the average number of

premature deaths avoided in 2015 is 17,000 (at a value of $100 billion) with the 5th and 95th

percentiles of the distribution ranging from 6,000 to 27,000 fewer mortalities. The figure 
1-7

22,800

11,600

2,800

7,100

13,700

17,000

0

10,000

20,000

30,000

40,000

50,000

60,000

Pope et al (2002)

Statistical Error

Expert A Expert B Expert C Expert D Expert E

Reduction in Premature Mortality in 2015

Note: Distributions labeled Expert A - Expert E are based on individual expert responses. The distribution labeled Pope et al. (2002) Statistical Error is based on the mean and standard 

error of the C-R function from the study.

Distribution of 

Primary 

Estimate

Distributions Based on Application of Pilot Expert Elicitation Results

Figure 1-1. Comparative Assessment of Relative Range of Uncertainty in Estimated

Avoided Incidence of Premature Mortality Using Classical Statistical Error and the

Pilot Expert-Based Characterizations of Uncertainty

shows that the average annual number of premature deaths avoided in 2015 based on the

pilot expert elicitation ranges from approximately 3,000 (based on the judgments of Expert

C), which is valued at $23 billion to 23,000 (based on the judgments of Expert E), which is

valued at $140 billion. The confidence intervals vary across experts with all experts

estimating zero at the 5th percentile and the 95th percentile ranging from 10,000 to 54,000

fewer mortalities.

As part of the CAIR analysis, we conducted a variety of supplemental analyses

designed to provide the reader with an understanding of the degree of uncertainty that may

be associated with the benefits resulting from implementation of this regulation. Because

estimates of premature mortality contribute the most to the monetized benefits, our efforts

focused on the sensitivity of the final benefits estimate to analytic judgments regarding this

Note: The results of the Pilot Expert Elicitation are presented here as an illustration of EPA’s initial

efforts to characterize the uncertainties associated with the estimate of benefits from the PM2.5/mortality

relationship. The Pilot was limited in scope and does not address inherent differences in the thought

processes and background information used by each expert to express their distribution. Based on

findings from the Pilot and a favorable peer review of the Pilot, EPA is conducting a full-scale expert

elicitation to better characterize uncertainty in the mortality estimate for future regulatory analyses. See

Appendix B for a full description of the two approaches used to characterize uncertainty.
1-8

relationship. Specifically, we conducted analyses designed to characterize the degree of

uncertainty in the slope (magnitude) of the PM2.5 concentration-response function, the form

of the PM2.5 concentration-response function (i.e., the potential for a threshold), and the

cessation lag (i.e., temporal relationship between cessation of exposure and reduction in

adverse health effects). Both discrete and probabilistic approaches were used to characterize

the uncertainty associated with the concentration response function.

These supplemental analyses yield the following insights:

C Use of statistical error associated with the ACS (Pope et al., 2002) estimate for

the concentration response function for PM2.5—premature mortality as well as the

statistical error associated with the concentration response functions for each of

the other health endpoints to describe the probability distribution of total benefits

yields a distribution in which the 95th percentile is nearly twice the mean ($100

billion in 2015) and 5th percentile is one fourth the mean. The overall range from

5th to 95th percentile on the total benefits estimate represents one order of

magnitude ($26 billion to $210 billion).

C Description of the probability distribution of the concentration response function

for PM2.5—premature mortality using the results from the pilot expert elicitation

(rather than the estimate based on the statistical error associated with the ACS

cohort) yields a larger degree of uncertainty because the elicitation exercise was

designed to encompass a broader set of model uncertainty. The mean annual

benefits for each expert elicited during the pilot project range from approximately

$16 billion to $130 billion in 2015

C Substitution of the steeper concentration response function for PM2.5—premature

mortality from the Six Cities study increases the value of the total benefits from

$101 billion to $208 billion in 2015.

C Substitution of the most plausible alternative lag structures has little overall

impact on the estimate of total benefits (reductions are on the order of 5 to 15

percent).

C The assessment of alternative assumptions regarding the existence (and level) of a

threshold in the PM2.5 premature mortality concentration response function

highlights the sensitivity of the analysis to this assumption. Only 5percent of the

estimated premature mortality is due to changes in exposure above 15mg/m3,

while over 84 percent of the premature morality related benefits are due to

changes in PM2.5 concentrations occurring above 10ug/m3

.
1-9

C Estimates of premature mortality from ozone exposure may result in an additional

500 premature deaths avoided and an increase in the estimated health benefits of

CAIR by approximately $3 billion annually.

In addition to these mortality related supplemental analyses, we also conducted analyses

related to non-health (welfare) effects, including visibility and household cleaning costs. 

Based on these analyses, expanded coverage of welfare effects could increase benefits by

over $500 million. Other welfare effects have been quantified such as nitrogen and sulfur

deposition reductions in the CAIR region, acidification reductions in lakes the Adirondacks

and the northeastern US, and reduced nitrogen deposition to the Chesapeake Bay. While

monetized estimates of these benefits could not be examined even in sensitivity analyses, it is

likely these benefit categories are significant in terms of the total ecological endpoints.

1.2 Not All Benefits Quantified

EPA was unable to quantify or monetize all of the health and environmental benefits

associated with CAIR. EPA believes these unquantified benefits are substantial, including

the value of increased agricultural crop and commercial forest yields, visibility

improvements, reductions in nitrogen and acid deposition and the resulting changes in

ecosystem functions, and health and welfare benefits associated with reduced mercury

emissions. Table 1-4 provides a list of these benefits. 

1.3 Costs and Economic Impacts

For the affected region, the projected annual incremental private costs of CAIR to the

power industry are $2.36 billion in 2010 and $3.57 billion in 2015. These costs represent the

total cost to the electricity-generating industry of reducing NOx and SO2 emissions to meet

the caps set out in the rule. Estimates are in 1999 dollars. Costs of the rule are estimated

using the Integrated Planning Model and assume firms make decisions using costs of capital 

ranging from 5.34 percent to 6.74 percent.

In estimating the net benefits of regulation, the appropriate cost measure is “social

costs.” Social costs represent the welfare costs of the rule to society. These costs do not

consider transfer payments (such as taxes) that are simply redistributions of wealth. The

social costs of this rule are estimated to be $1.91 billion in 2010 and $2.56 billion in 2015

assuming a 3 percent discount rate. These costs become $2.14 billion in 2010 and $3.07

billion in 2015, if one assumes a 7 percent discount rate. 
1-10

Table 1-4. Unquantified and Nonmonetized Effects of the Clean Air Interstate Rule

Pollutant/Effect Effects Not Included in Primary Estimates—Changes in:

Ozone—Healtha • Premature mortalityb

• Chronic respiratory damage

• Premature aging of the lungs

• Nonasthma respiratory emergency room visits

• Increased exposure to UVb

Ozone—Welfare • Yields for:

– commercial forests,

– fruits and vegetables, and

– commercial and noncommercial crops

• Damage to urban ornamental plants

• Recreational demand from damaged forest aesthetics

• Ecosystem functions

• Increased exposure to UVb

PM—Healthc • Premature mortality: short-term exposuresd

• Low birth weight

• Pulmonary function

• Chronic respiratory diseases other than chronic bronchitis

• Nonasthma respiratory emergency room visits

• Exposure to UVb (+/-)e

PM—Welfare • Visibility in many Class I areas 

• Residential and recreational visibility in non-Class I areas

• Soiling and materials damage

• Ecosystem functions

• Exposure to UVb (+/-)e

Nitrogen and Sulfate

Deposition—Welfare

• Commercial forests due to acidic sulfate and nitrate deposition

• Commercial freshwater fishing due to acidic deposition

• Recreation in terrestrial ecosystems due to acidic deposition

• Existence values for currently healthy ecosystems 

• Commercial fishing, agriculture, and forests due to nitrogen deposition 

• Recreation in estuarine ecosystems due to nitrogen deposition

• Ecosystem functions

• Passive fertilization due to nitrogen deposition

Mercury Health • Incidence of neurological disorders

• Incidence of learning disabilities

• Incidence of developmental delays

• Potential reproductive effectsf

• Potential cardiovascular effectsf, including:

– Altered blood pressure regulationf

– Increased heart rate variabilityf

– Incidence of myocardial infarctionf

Mercury Deposition

Welfare

• Impacts on birds and mammals (e.g., reproductive effects)

• Impacts to commercial, subsistence, and recreational fishing

(continued)
1-11

Table 1-4. Unquantified and Nonmonetized Effects of the Clean Air Interstate Rule

(continued)

a In addition to primary economic endpoints, there are a number of biological responses that have been associated with

ozone health effects including increased airway responsiveness to stimuli, inflammation in the lung, acute inflammation

and respiratory cell damage, and increased susceptibility to respiratory infection. The public health impact of these

biological responses may be partly represented by our quantified endpoints.

b Premature mortality associated with ozone is not currently included in the primary analysis. Recent evidence suggests

that short-term exposures to ozone may have a significant effect on daily mortality rates, independent of exposure to PM. 

EPA is currently conducting a series of meta-analyses of the ozone mortality epidemiology literature. EPA will consider

including ozone mortality in primary benefits analyses once a peer-reviewed methodology is available.

c In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM

health effects including morphological changes and altered host defense mechanisms. The public health impact of these

biological responses may be partly represented by our quantified endpoints.

d While some of the effects of short term exposures are likely to be captured in the estimates, there may be premature

mortality due to short term exposure to PM not captured in the cohort study upon which the primary analysis is based.

e May result in benefits or disbenefits. See discussion in Section 5.3.4 for more details.

f These are potential effects as the literature is insufficient.

Retail electricity prices are projected to increase roughly 2.0 to 2.7 percent with

CAIR in the 2010 and 2015 time frame and then drop below 2.0 percent thereafter. The

effects of CAIR on natural gas prices and the power-sector generation mix is also small, with

a 1.6 percent or less increase in gas prices projected from 2010 to 2020. There will be a

continued reliance on coal-fired generation, which is projected to remain at roughly

50 percent of total electricity generated. A relatively small amount of coal-fired capacity,

about 5.3 GW (1.7 percent of all coal-fired capacity and 0.5 percent of all generating

capacity), is projected to be uneconomic to maintain. In practice units projected to be

uneconomic to maintain may be “mothballed,” retired, or kept in service to ensure

transmission reliability in certain parts of the grid. For the most part, these units are small

and infrequently used generating units that are dispersed throughout the CAIR region. As

demand grows in the future, additional coal-fired generation is projected to be built under

CAIR and utilization of coal-fired units will increase. Because of this, coal production is

projected to increase from 2003 levels by about 15 percent in 2010 and by 25 percent by

2020, and we expect greater coal production in Appalachia and the Interior coal regions of

the country with CAIR. Overall, the impacts of CAIR are modest, particularly in light of the

large projected benefits of CAIR.
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1.4 Limitations

Every analysis examining the potential benefits and costs of a change in

environmental protection requirements is limited to some extent by data gaps, limitations in

model capabilities (such as geographic coverage), and variability or uncertainties in the

underlying scientific and economic studies used to configure the benefit and cost models. 

Despite these uncertainties, we believe this benefit-cost analysis provides a reasonable

indication of the expected economic benefits and costs of CAIR in future years.

For this analysis, such uncertainties include possible errors in measurement and

projection for variables such as population growth and baseline incidence rates; uncertainties

associated with estimates of future-year emissions inventories and air quality; variability in

the estimated relationships between changes in pollutant concentrations and the resulting

changes in health and welfare effects; and uncertainties in exposure estimation. We have

used sensitivity analyses to address these limitations where possible. 

EPA’s cost estimates assume that all States in the CAIR region fully participate in the

cap and trade programs that reduce SO2 and NOx emissions from EGUs. The cost

projections also do not take into account the potential for advancements in the capabilities of

pollution control technologies for SO2 and NOx removal and other compliance strategies,

such as fuel switching or the reductions in their costs over time. EPA projections also do not

take into account demand response (i.e., consumer reaction to electricity prices) because the

consumer response is likely to be relatively small, but the effect on lowering private

compliance costs may be substantial. Costs may be understated since an optimization model

was employed and the regulated community may not react in the same manner to comply

with the rules. The Agency also did not factor in the costs and/or savings for the government

to operate the CAIR program as opposed to other air pollution compliance programs and

transactional costs and savings from CAIR’s effects on the labor supply. A listing of

possible unquantified costs associated with the CAIR program are shown in Table 1-5.

Table 1-5. Unquantified Costs of the Clean Air Interstate Rule

Effects Not Quantified

Employment shifts as workers are retrained at the same company or re-employed elsewhere in the economy.

Costs of running and administering the program to State and Federal Government.

Certain relatively small permitting costs associated with Title IV that new program entrants face.
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1.5 References

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine

Particulate Air Pollution.” Journal of the American Medical Association

287:1132-1141. 

U.S. Environmental Protection Agency (EPA). September 2000. Guidelines for Preparing

Economic Analyses. EPA 240-R-00-003. 

U.S. Office of Management and Budget (OMB). 2003. Circular A-4 Guidance to Federal

Agencies on Preparation of Regulatory Analysis.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected 

Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and

Particulate Air Pollution in the United States.” Environmental Health Perspectives

105(6):608-612.
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CHAPTER 2

INTRODUCTION AND BACKGROUND

2.1 Introduction

For this rulemaking, the U.S. Environmental Protection Agency (EPA) has assessed

the role that transported emissions from upwind states play in contributing to unhealthy

levels of PM2.5 and 8-hour ozone in downwind states. Based on this assessment, the Clean

Air Interstate Rule (CAIR) requires air emissions reductions from upwind states. This

document presents the health and welfare benefits of CAIR and compares the benefits of this

rule to the estimated costs of implementing the rule in 2010 and 2015. This chapter contains

background information relative to the rule and an outline of the chapters of the report.

2.2 Background

Congress recognized that interstate pollution transport from upwind states can

contribute to unhealthy pollution levels in downwind states. Therefore, the Clean Air Act

(CAA) contains provisions in Section 110(a)(2)(D) that require upwind states to eliminate

emissions that contribute significantly to nonattainment downwind. Under Section

110(a)(2), states are required to submit plans to EPA within 3 years of issuance of a revised

National Ambient Air Quality Standard (NAAQS). Among other requirements, these plans

are required to prohibit emissions in the state that contribute significantly to nonattainment

downwind.

EPA’s final rule finds that 28 states and the District of Columbia contribute

significantly to nonattainment, or interfere with maintenance, of the NAAQS for PM2.5

and/or 8-hour ozone in downwind states. EPA requires these upwind states to revise their

State Implementation Plans (SIPs) to include control measures to reduce emissions of SO2

and/or NOx. SO2 is a precursor to PM2.5 formation, and NOx is a precursor to both ozone and

PM2.5 formation. Reducing upwind precursor emissions will assist the downwind PM2.5 and

8-hour ozone nonattainment areas in achieving the NAAQS. Moreover, attainment would be

achieved in a more equitable, cost-effective manner than if each nonattainment area

attempted to achieve attainment by implementing local emissions reductions alone. The

relevant regions for PM2.5 and ozone significant contribution are depicted in Figure 2-1. 
2-2

States controlled for both SO2 and NOx

States not covered by CAIR

States controlled for Ozone Season NOx

Figure 2-1. Final CAIR Region

The estimates presented in this report represent the benefits and costs for a final CAIR

program that includes the final promulgated CAIR and the proposal to include SO2 and

annual NOx controls for New Jersey and Delaware in CAIR. The modeling used to provide

these estimates also assumes annual SO2 and NOx controls for Arkansas that are not a part of

the complete CAIR program resulting in a slight overstatement of the reported benefits and

costs.

2.3 Regulated Entities

This action does not directly regulate emissions sources. Instead, it requires states to

revise their SIPs to include control measures to reduce emissions of NOx and SO2. The

emissions reduction requirements that would be assigned to the states are based on controls

that are known to be highly cost-effective for EGUs. EPA modeled emission cap-and-trade

programs phased in over time beginning with SO2 and NOX caps in 2010 and lowering these

emission caps in 2015. The timing of emission caps was decided on the basis of when

control actions would be needed to help the states in their NAAQS attainment efforts,

feasibility of installing emission controls, and other factors. However, states would have the

flexibility to choose the sources to control and how to control them. Although states have

the flexibility to control pollution from sources other than EGUs, the analysis conducted

assumes controls for EGUs only.
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2.4 Baseline and Years of Analysis

The final rule on which this analysis is based sets forth the requirements for states to

eliminate their significant contribution to downwind nonattainment of ozone and PM2.5

NAAQS. To reduce this significant contribution, EPA requires that certain states reduce

their emissions of SO2 and NOx. The Agency considered all promulgated CAA requirements

and known state actions in the baseline used to develop the estimates of benefits and costs

for this rule. EPA did not consider actions states may take to implement the ozone and PM2.5

NAAQS standards in the baseline for this analysis. The years 2010 and 2015 are used in this

analysis. The year of 2010 was chosen as one of the analysis years, because this year

represents the date of Phase I of the rule and is in close proximity to the 2009 phase date for

NOx. The year 2015 represents the year in which Phase II of the rule is anticipated to be

implemented. All estimates presented in this report represent annualized estimates of the

benefits and costs of CAIR in 2010 and 2015 rather than the net present value of a stream of

benefits and costs in these particular years of analysis.

2.5 Control Scenario

The analysis conducted assumes that a cap-and-trade program will be used to achieve

the emission reduction requirements from the electric power industry. All fossil-fuel electric

generating units (EGUs) over 25 megawatt (MW) capacity within the CAIR region are

covered by the program. With the complete CAIR program (CAIR final plus the New Jersey

and Delaware proposal), EPA would establish regional emission budgets (caps) for SO2 and

NOx to address the transport problem. In this final rule, these requirements would effectively

establish annual emission caps in 2010 for SO2 and NOx of 3.7 million tons and 1.5 million

tons, respectively. These emission budgets (caps) would be lowered in 2015 to provide

annual SO2 and NOx emission caps of 2.6 million tons and 1.3 million tons, respectively, in

the control region. Banking of emissions is allowed in the program. These caps were

derived by determining the amount of emissions of SO2 and NOx that EPA believes can be

controlled from EGUs in a highly cost-effective manner. When fully implemented, this

would result in nationwide SO2 emissions of approximately 3.4 million tons. This is

significantly lower than the 8.95 million tons of SO2 emissions allowed under the current

Title IV Acid Rain SO2 Trading Program. For the final CAIR promulgated rule (exclusive of

the New Jersey and Delaware proposal) emission caps are 3.6 million tons for SO2 and 1.5

million tons for NOx in 2010. These estimates become 2.5 million tons and 1.3 million tons

in 2015.
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2.6 Benefits of Emission Controls

The benefits of CAIR are discussed in Chapters 4 and 5 of this report. Annual

monetized benefits of $101 billion (3 percent discount rate) or $86.3 billion (7 percent

discount rate) are expected for CAIR in 2015. Despite the fact that the final CAIR program

is comparable in most respects to the proposed rule, the benefits reported for the final rule

exceed the estimates reported in the Benefits of the Proposed Interstate Air Quality Rule

(OAR-2003-0053-0175, January 2004). There are several reasons for the increase in

monetized benefit estimates for the final rule. These reasons include increased SO2 emission

reductions, geographical changes in the location of emission reductions with great reductions

occurring near population centers, and additional direct PM2.5 emissions reductions for the

final rule. 

2.7 Cost of Emission Controls 

EPA analyzed the costs of CAIR using the Integrated Planning Model (IPM). EPA

has used this model in the past to analyze the impacts of regulations on the power sector. 

EPA estimates the private industry costs of the rule to the power sector to be $3.57 billion in

2015 (1999 dollars). In estimating the net benefits of the rule, EPA uses social costs of the

rule that represent the costs to society of this rule. The social costs of the rule are estimated

to be $2.56 or $3.07 billion in 2015 (3 percent and 7 percent discount rates, respectively). A

description of the methodology used to model the costs and economic impacts to the power

sector is discussed in Chapter 7 of this report.

2.8 Organization of the Regulatory Impact Analysis

This report presents EPA’s analysis of the benefits, costs, and other economic effects

of the final CAIR to fulfill the requirements of a Regulatory Impact Analysis (RIA). This

RIA includes the following chapters:

C Chapter 3, Emissions and Air Quality Impacts, describes emission inventories and

air quality modeling that are essential inputs into the benefits assessment.

C Chapter 4, Benefits Analysis and Results, describes the methodology and results

of the benefits analysis.

C Chapter 5, Qualitative Assessment of Nonmonetized Benefits, describes benefits

that are not monetized for this rulemaking.

C Chapter 6, Electric Power Sector Profile, describes the industry potentially

affected by the rule.
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C Chapter 7, Cost, Economic, and Energy Impacts of the Rule, describes the

modeling conducted to estimate the cost, economic, and energy impacts to the

affected sources.

C Chapter 8, Statutory and Executive Order Impact Analyses, describes the small

business, unfunded mandates, paperwork reduction act, and other analyses

conducted for the rule to meet statutory and Executive Order requirements.

C Chapter 9, Comparison of Benefits and Costs, shows a comparison of the social

benefits to social costs of the rule.

C Appendix A, Benefits and Costs of the Clean Air Interstate Rule, Clean Air

Visibility Rule, and the Clean Air Interstate Rule Plus the Clean Air Visibility

Rule

C Appendix B, Supplemental Analyses Addressing Uncertainties in the Benefits

Analyses

C Appendix C, Sensitivity Analyses of Key Parameters in the Benefits Analysis 

C Appendix D, Sensitivity Analysis of Key Parameters in the Cost and Economic

Impact Analysis and Listing of IPM Runs in Support of CAIR

C Appendix E, CAIR Industry Sector Impacts

C Appendix F, Additional Technical Information Supporting the Benefits Analysis

C Appendix G, Health-Based Cost-Effectiveness of Reductions in Ambient PM2.5

Associated With CAIR
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SECTION 3

EMISSIONS AND AIR QUALITY IMPACTS

This chapter summarizes the emissions inventories and air quality modeling that

serve as the inputs to the benefits analysis of this rule as detailed in Chapter 4. EPA uses

sophisticated photochemical air quality models to estimate baseline and post-control ambient

concentrations of ozone and PM and deposition of nitrogen and sulfur for each year. The

estimated changes in ambient concentrations are then combined with monitoring data to

estimate population-level exposures to changes in ambient concentrations for use in

estimating health effects. Modeled changes in ambient data are also used to estimate

changes in visibility and changes in other air quality statistics that are necessary to estimate

welfare effects.

Section 3.1 of this chapter provides a summary of the baseline emissions inventories

and the emissions reductions that were modeled for this rule. Section 3.2 provides a

summary of the methods for and results of estimating air quality for the 2010 and 2015 base

cases and control scenarios for the purposes of the benefits analysis. There are separate

sections for PM, ozone, and visibility.

3.1 Emissions Inventories and Estimated Emissions Reductions

The technical support document for emissions inventories discusses the development

of the 2001, 2010, and 2015 baseline emissions inventories for the benefits analysis of this

final rule. The emission sources and the basis for current and future-year inventories are

listed in Table 3-1. Tables 3-2 and 3-3 summarize the baseline emissions of NOX and SO2

and the change in the emissions from EGUs that were used in modeling air quality changes.

For details on EPA’s projected emissions for the EGU sector, see Chapter 7 of this RIA.
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Table 3-1. Emissions Sources and Basis for Current and Future-Year Inventories 

Sector Emissions Source 2001 Base Year Future-Year Base Case Projections

EGU Power industry

electric generating

units (EGUs)

2001 data from

Acid Rain

Trading Program

Integrated Planning Model (IPM)

Non-EGU Non-Utility Point,

including pointsource fugitive

dust

2001 National

Emission

Inventory (NEI)

(1) Department of Energy (DOE) fuel use

projections, (2) Regional Economic Model, Inc.

(REMI) Policy Insight® model, (3) decreases to

REMI results based on trade associations, Bureau

of Labor Statistics (BLS) projections and Bureau of

Economic Analysis (BEA) historical growth from

1987 to 2002, (4) control assumptions

Average

Fire

Wildfire,

prescribed burning

Same as future

year

Average fires from 1996 through 2002 (based on

state-total acres burned), with the same emissions

rates and county distributions of emissions as in the

2001 NEI

Average

Fire

Agricultural

burning, open

burning

2001 NEI 2001 NEI

Ag Livestock NH3 2002 Preliminary

NEIc

2010 and 2015 emissions estimated with the same

approach as was used for the 2002 preliminary NEIc

Ag Fertilizer NH3 2001 NEI 2001 NEI

Area All other stationary

area sources,

including areasource fugitive

dust

1999 NEI, version

3 grown to 2001

(1) DOE fuel use projections, (2) REMI Policy

Insight model, (3) decreases to REMI results based

on trade associations, BLS projections and BEA

historical growth from 1987-2002

On-road Highway vehicles MOBILE6.2

model

Projected vehicle miles traveled same as IAQR

proposal; emissions from MOBILE6.2 model

Nonroad Locomotives,

commercial marine

vessels, and

aircraft

2001 NEI; CMV

adjusted to new

national totals

from OTAQ

Grown based on national totals from OTAQ, using

state/county distribution of emissions from the 2001

NEI

Nonroad All other nonroad

vehicles

NONROAD2004

model

NONROAD 2004 model

a This table documents only the sources of data for the U.S. inventory. The sources of data used for Canada

and Mexico are explained in the technical support document (TSD) and were held constant from the base

year to the future years.

b All fugitive dust emissions were adjusted downward using county-specific transportable fractions needed as

part of the current state of the art in air quality modeling.

c ftp://ftp.epa.gov/EmisInventory/prelim2002nei/nonpoint/documentation/nh3inventorydraft_jan2004.pdf.
3-3

Table 3-2. Summary of Modeled Baseline Emissions for Lower 48 States 

Pollutant Emissions (tons)

Sourcea NOX SO2

2001 Baseline

EGUs 4,937,398 10,901,127

Non-EGUs 2,942,618 2,958,692

Average Fire 238,931 49,108

Area 1,462,276 1,295,146

On-road 8,064,067 271,026

Nonroad 4,050,655 433,250

Total, All Sources 21,695,945 15,908,349

2010 Base Case

EGUs 3,672,929 9,903,882

Non-EGUs 2,931,360 3,189,864

Average Fire 238,931 49,108

Area 1,630,411 1,408,990

Mobile 4,683,085 27,435

Nonroad 3,282,338 219,029

Total, All Sources 16,439,055 14,798,308

2015 Base Case

EGUs 3,708,658 9,079,214

Non-EGUs 3,183,499 3,422,915

Average Fire 238,931 49,108

Area 1,702,154 1,480,348

Mobile 3,152,562 30,824

Nonroad 2,912,382 232,627

Total, All Sources 14,898,186 14,295,035

a The “ag” sector does not have emissions of NOx and SO2.
3-4

3.2 Air Quality Impacts

This section summarizes the methods for and results of estimating air quality for the

2010 and 2015 base cases and control scenarios for the purposes of the benefits analysis. 

EPA has focused on the health, welfare, and ecological effects that have been linked to air

quality changes. These air quality changes include the following:

1. Ambient particulate matter (PM2.5)—as estimated using a national-scale

applications of the Community Multi-Scale Air Quality (CMAQ) model; 

2. Ambient ozone—as estimated using regional-scale applications of the

Comprehensive Air Quality Model with Extensions (CAMx); and

3. Visibility degradation (i.e., regional haze), as developed using empirical

estimates of light extinction coefficients and efficiencies in combination with

CMAQ modeled reductions in pollutant concentrations.

Table 3-3. Summary of Modeled Emissions Changes for the Clean Air Interstate Rule:

2010 and 2015 

Pollutant

Item NOX SO2

2010 Emission Reductionsa

Absolute Tons 1,245,038 3,620,280

Percentage of Base EGU Emissions 33.9% 36.6%

Percentage of All Manmade Emissions 7.6% 24.5%

2015 Emission Reductionsa

Absolute Tons 1,535,821 3,967,777

Percentage of Base EGU Emissions 41.4% 43.7%

Percentage of All Manmade Emissions 10.3% 27.8%

a Note that the emission changes only occur within the affected transport region; however, the percentage

reductions reflect the change as a share of baseline emissions for the lower 48 states as presented in

Table 3-2.
1

Given the focus of this rule on secondarily formed particles (e.g., sulfates) it is important to employ a Eulerian

model such as CMAQ. The formation and fate of secondarily formed pollutants typically involve emissions

of precursor pollutants (e.g., SO2) from a multitude of widely dispersed sources coupled with chemical and

physical processes which are best addressed using an air quality model that employs a Eulerian grid model

design.

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The air quality estimates in this section are based on the emission changes

summarized in the preceding section. These air quality results are in turn associated with

human populations and ecosystems to estimate changes in health and welfare effects. In

Section 3.2.1, we describe the estimation of PM air quality using CMAQ, and in

Section 3.2.2, we cover the estimation of ozone air quality using CAMx. Lastly, in

Section 3.2.3, we discuss the estimation of visibility degradation.

3.2.1 PM Air Quality Estimates

We use the emissions inputs summarized above with a national-scale application of

the Community Multi-scale Air Quality (CMAQ) modeling system to estimate PM air

quality in the contiguous United States. CMAQ is a three-dimensional grid-based Eulerian

air quality model designed to estimate annual particulate concentrations and deposition over

large spatial scales (e.g., over the contiguous United States). Consideration of the different

processes that affect primary (directly emitted) and secondary (formed by atmospheric

processes) PM at the regional scale in different locations is fundamental to understanding

and assessing the effects of pollution control measures that affect PM, ozone, and deposition

of pollutants to the surface.1

 Because it accounts for spatial and temporal variations as well

as differences in the reactivity of emissions, CMAQ is useful for evaluating the impacts of

the rule on U.S. PM concentrations. Our analysis applies the modeling system to the entire

United States for the six emissions scenarios: a 2001 base year, a 2010 baseline projection

and a 2010 projection with controls, and a 2015 baseline projection and a 2015 projection

with controls. 

The CMAQ version 4.3 was employed for this CAIR modeling analysis (Byun and

Schere, 2004). This version reflects updates in a number of areas to improve performance

and address comments from the peer review, including (1) the formation of nitrates based on

updated gaseous/heterogeneous chemistry and a current inorganic nitrate partitioning

module, (2) a state-of-the-science Secondary Organic Aerosol (SOA) module that includes a

more comprehensive gas-particle partioning algorithm from both anthropogenic and biogenic

SOA, (3) an in-cloud sulfate chemistry that accounts for the nonlinear sensitivity of sulfate
3-6

formation to varying pH, and (4) the updated CB-IV gas-phase chemistry mechanism and

aqueous chemistry mechanism that provide a comprehensive simulation of aerosol precursor

oxidants.

CMAQ simulates every hour of every day of the year and, thus, requires a variety of

input files that contain information pertaining to the modeling domain and simulation period. 

These include hourly emissions estimates and meteorological data in every grid cell, as well

as a set of pollutant concentrations to initialize the model and to specify concentrations along

the modeling domain boundaries. These initial and boundary concentrations were obtained

from output of a global chemistry model. As discussed below, we use the model predictions

in a relative sense by first determining the ratio of species predictions between the 2001 base

year and each future-year scenario. The calculated relative change is then combined with the

corresponding ambient species measurements to project concentrations for the future case

scenarios. The annual mean PM air quality is used as input to the health and welfare

concentration-response (C-R) functions of the benefits analysis. The following sections

provide a more detailed discussion of each of the steps in this evaluation and a summary of

the results.

3.2.1.1 Modeling Domain

As shown in Figure 3-1, the modeling domain encompasses the lower 48 states and

extends from 126 degrees to 66 degrees west longitude and from 24 degrees north latitude to

52 degrees north latitude. The modeling domain is segmented into rectangular blocks

referred to as grid cells. The model actually predicts pollutant concentrations for each of

these grid cells. For this application the horizontal grid cells are roughly 36 km by 36 km. 

In addition, the modeling domain contains 14 vertical layers with the top of the modeling

domain at about 16,200 meters, or 100 mb. Within the domain each vertical layer has 16,576

grid cells.

3.2.1.2 Simulation Periods

For use in this benefits analysis, the simulation periods modeled by CMAQ included

separate full-year application for each of the five emissions scenarios (i.e., 2001 base year

and the 2010 and 2015 base cases and control scenarios).
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Figure 3-1. CMAQ Modeling Domain

3.2.1.3 Model Inputs

CMAQ 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 2001 base year and each of the future-year base cases and control

scenarios. All other inputs were specified for the 2001 base year model application and

remained unchanged for each future-year modeling scenario.
3-8

CMAQ requires detailed emissions inventories containing temporally allocated

emissions for each grid cell in the modeling domain for each species being simulated. The

previously described annual emission inventories were preprocessed into model-ready inputs

through the emissions preprocessing system. Details of the preprocessing of emissions are

provided in the Clean Air Interstate Rule Emissions Inventory Technical Support Document

(Emissions Inventory TSD) (EPA, 2005). Meteorological inputs reflecting 2001 conditions

across the contiguous United States were derived from version 5 of the Mesoscale Model

(MM5). These inputs include 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 lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry and transport model (GEOS-CHEM). The lateral

boundary species concentrations varied with height and time (every 3 hours). Terrain

elevations and land use information were obtained from the U.S. Geological Survey database

at 10 km resolution and aggregated to the roughly 36 km horizontal resolution used for this

CMAQ application. 

3.2.1.4 CMAQ Model Evaluation

An operational model performance evaluation for PM2.5 and its related speciated

components (e.g., sulfate, nitrate, elemental carbon, organic carbon) as well as deposition of

ammonium, nitrate, and sulfate for 2001 was performed to estimate the ability of the CMAQ

modeling system to replicate base-year concentrations. This evaluation principally

comprises statistical assessments of model versus observed pairs that were paired in time and

space on a daily or weekly basis, depending on the sampling period of measured data. The

statistics are presented separately for the entire domain, the East, and the West (using the

100th meridian to divide the eastern and western United States). In addition, scatterplots of

seasonal average and annual average predictions versus observations paired by site are

included in the model performance evaluation. A spatial analysis was also performed for

sulfate and nitrate to examine how well the modeling platform (year-specific meteorology,

anthropogenic and biogenic emissions, and boundary conditions representative of 2001)

predicts the spatial patterns and gradients evident from the observations. The details of these

graphical analyses can be found in the Clean Air Interstate Rule Air Quality Modeling

Technical Support Document (Air Quality Modeling TSD).
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For PM2.5 species, this evaluation includes comparisons of model predictions to the

corresponding measurements from the Clean Air Status and Trends Network (CASTNet)

and the Speciation Trend Network (STN) in addition to measurements from the Interagency

Monitoring of PROtected Visual Environments (IMPROVE). The CASTNet dry deposition

monitoring network contained a total of 79 sites in 2001, with a total number of 56 sites

located in the East and 23 sites located in the West. Sulfate and total nitrate data were used

in the evaluation. CASTNet data are collected and reported as weekly average data. The

data are collected in filter packs that sample the ambient air continuously during the week. 

The sulfate data are of high quality because sulfate is a stable compound. However, the

particulate nitrate concentration data collected by CASTNet are known to be problematic and

subject to volatility because of the length of the sampling period. CASTNet also reports a

total nitrate measurement, which is the combination of particulate nitrate and nitric acid. 

Because the total nitrate measurement is not affected by this sampling problem, it is

considered a more reliable measurement. Therefore, we chose to use the total nitrate data

and not to use the particulate nitrate data in this evaluation. 

The EPA STN network began operation in 1999 to provide nationally consistent

speciated PM2.5 data for the assessment of trends at representative sites in urban areas. STN

reports mass concentrations and PM2.5 constituents, including sulfate, nitrate, ammonium,

and elemental and organic carbon. Most STN sites collect data on a frequency of 1 in every

3 days, (some supplemental sites are collected 1 in every 6 days). For the 2001 analysis,

CMAQ predictions were evaluated against 133 STN sites (105 sites in the East and 28 sites

in the West). 

The IMPROVE network is a cooperative visibility monitoring effort between EPA,

federal land management agencies, and state air agencies. Data are collected at Class I areas

across the United States mostly at national parks, national wilderness areas, and other

protected pristine areas. Approximately 134 IMPROVE rural/remote sites had complete

annual PM2.5 mass and/or PM2.5 species data for 2001. Eighty-six sites were in the West, and

forty-eight sites were in the East. IMPROVE data are collected once in every 3 days. 

The principal evaluation statistics used to evaluate CMAQ performance are the

fractional bias and fractional error. Fractional bias is defined as: 
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Fractional bias is a useful model performance indicator because it has the advantage of

equally weighting positive and negative bias estimates. Fractional error is similar to

fractional bias except the absolute value of the difference is used so that the error is always

positive. Fractional error is defined as:

These metrics were calculated annually for all IMPROVE, CASTNet, STN, and National

Atmospheric DePosition (NADP) sites for the East and West individually. 

Currently, there are no universally accepted performance criteria for judging the

adequacy of PM2.5 model performance. However, performance can be judged by comparison

to model performance results found by other groups in the air quality modeling community. 

In this respect, we have compared our CMAQ 2001 model performance results to the range

of performance found in other recent regional PM2.5 model applications by other groups. 

These modeling studies represent a broad range of modeling analyses that cover various

models, model configurations, domains, years and/or episodes, chemical mechanisms, and

aerosol modules. The fractional bias and fractional error statistics were calculated using the

predicted-observed pairs for the full year of 2001 and for each season, separately. The

statistics for the full year are provided in Table 3-4. Overall, the performance is within the

range or close to that found by other groups in recent applications. It should be noted that

nitrate and sulfate are the two species most relevant for CAIR. Model performance statistics

for these two species during the seasons when the concentrations are highest in the East (i.e.,

summer for sulfate and winter for nitrate) are provided in Table 3-5. The general range of

model performance for summer sulfate and winter nitrate compares favorably to fractional

bias and fractional error statistics from the better performing model applications found by

others in the modeling community, as follows: 
3-11

Table 3-4. Model Performance Statistics for CAIR CMAQ 2001

CAIR CMAQ 2001 Annual Fractional Bias

(%)

Fractional Error

(%)

PM2.5

 Total Mass

STN East –12 44

West –51 64

IMPROVE East 8 43

West 14 57

Sulfate

STN East 8 45

West –32 52

IMPROVE East 7 40

West –2 49

CASTNet East –2 24

West –35 50

Nitrate

STN East –12 86

West –92 115

IMPROVE East –32 107

West –44 115

Total Nitrate 

(NO3 + HNO3)

CASTNet East 16 81

West –60 105

Elemental Carbon

STN East 32 63

West –20 67

IMPROVE East –23 51

West –13 66

Organic Carbon

STN East –3 75

West –31 66

IMPROVE East –10 52

West 51 76

C summer sulfate is in the range of –10 percent to +30 percent for fractional bias

and 35 percent to 50 percent for fractional error and

C winter nitrate is in the range of +50 percent to +70 percent for fractional bias and

85 percent to 105 percent for fractional error.

Thus, CMAQ is considered appropriate for use in projecting changes in future year PM2.5

concentrations and the resultant health/economic benefits due to the emissions reductions. 
3-12

Table 3-5. Selected Performance Evaluation Statistics from the CMAQ 2001

Simulation

Eastern United States CMAQ 2001

Fractional Bias (%) Fractional Error (%)

Sulfate

(Summer)

STN 14 44

IMPROVE 10 42

CASTNet 3 22

Nitrate 

(Winter)

STN 15 73

IMPROVE 21 92

3.2.1.5 Converting CMAQ Outputs to Benefits Inputs

CMAQ generates predictions of hourly PM species concentrations for every grid. 

The species include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron

size range), a primary fine fraction (corresponding to PM less than 2.5 microns in diameter),

and several secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as

the sum of the primary fine fraction and all of the secondarily formed particles. Future-year

estimates of PM2.5 were calculated using relative reduction factors (RRFs) applied to 2002

ambient PM2.5 and PM2.5 species concentrations. A gridded field of PM2.5 concentrations was

created by interpolating Federal Reference Monitor ambient data and IMPROVE ambient

data. Gridded fields of PM2.5 species concentrations were created by interpolating EPA

speciation network (ESPN) ambient data and IMPROVE data. The ambient data were

interpolated to the CMAQ 36 km grid. 

The procedures for determining the RRFs are similar to those in EPA’s draft

guidance for modeling the PM2.5 standard (EPA, 2000). This guidance has undergone

extensive peer review and is anticipated to be finalized this year. The guidance recommends

that model predictions be used in a relative sense to estimate changes expected to occur in

each major PM2.5 species. The procedure for calculating future-year PM2.5 design values is

called the “Speciated Modeled Attainment Test (SMAT).” EPA used this procedure to

estimate the ambient impacts of the CAIR NPR emissions controls. The SMAT procedures

for the No Further Remediation (NFR) have been revised. Full documentation of the revised

SMAT methodology is contained in the Air Quality Modeling TSD. 
3-13

The revised SMAT uses an FRM mass construction methodology that results in

reduced nitrates (relative to the amount measured by routine speciation networks, such as

ESPN), higher mass associated with sulfates (reflecting water included in FRM

measurements), and a measure of organic carbonaceous mass that is derived from the

difference between measured PM2.5 and its noncarbon components. This characterization of

PM2.5 mass also reflects crustal material and other minor constituents. The resulting

characterization provides a complete mass balance. It does not have any unknown mass that

is sometimes presented as the difference between measured PM2.5 mass and the characterized

chemical components derived from routine speciation measurements. The revised SMAT

methodology uses the following PM2.5 species components: sulfates, nitrates, ammonium,

organic carbon mass, elemental carbon, crustal, water, and blank mass (a fixed value of 0.5

ug/m3

). In each grid cell, the PM2.5 component species mass adds up to interpolated PM2.5

mass.

For the purposes of projecting future PM2.5 concentrations for input to the benefits

calculations, we applied the SMAT procedure using the base-year 2001 modeling scenario

and each of the future-year scenarios. In our application of SMAT we used temporally

scaled speciated PM2.5 monitor data from 2002 as the set of base-year measured

concentrations. Temporal scaling is based on ratios of model-predicted future case PM2.5

species concentrations to the corresponding model-predicted 2001 concentrations. Output

files from this process include both quarterly and annual mean PM2.5 mass concentrations,

which are then manipulated within SAS to produce a BenMAP input file containing 364

daily values (created by replicating the quarterly mean values for each day of the appropriate

season). 

The SMAT procedures documented in the Air Quality Modeling TSD are applicable

for projecting future nonattainment counties and downwind receptor areas for the transport

analysis. Those procedures are the same as those performed for the PM benefits analysis

with the following exceptions:

1) The benefits analysis uses interpolated PM2.5 data that cover all of the grid

cells in the modeling domain (covering the entire country), whereas the

nonattainment analysis is performed at each ambient monitoring site in the

East using measured PM2.5 data (only the species data are interpolated). 
2

For the purpose of this analysis “East” is defined as the U.S. portion of the modeling domain east of 100

degrees longitude, and similarly “West” is defined as the U.S. portion of the domain west of 100 degrees

longitude.

3-14

2) The benefits analysis is anchored by the interpolated PM2.5 data from the

single year of 2002, whereas the nonattainment analysis uses a 5-year

weighted average (1999–2003) of PM2.5 design values at each monitoring site.

3.2.1.6 PM Air Quality Results

Table 3-6 summarizes the projected PM2.5 concentrations for the 2010 and 2015 base

cases and changes associated with the rule. The table includes the annual mean

concentration averaged across all model grid cells in the East and West,2 separately, along

with the average change between base and control concentrations. We also provide the

population-weighted average that better reflects the baseline levels and predicted changes for

more populated areas of the nation. This measure, therefore, better reflects the potential

benefits of these predicted changes through exposure changes to these populations. As

shown, the average annual mean concentrations of PM2.5 across populated eastern U.S. grid

cells declines by roughly 7.1 percent (or 0.73 μg/m3) and 8.7 percent (or 0.89 μg/m3) in 2010

and 2015, respectively. The population-weighted average mean concentration declined by

8.1 percent (or 0.96 μg/m3) in 2010 and 9.8 percent (or 1.15 μg/m3) in 2015, and this change

is larger in absolute terms than the spatial average for both years. This indicates the rule

generates greater absolute air quality improvements in more populated urban areas. 

Table 3-7 provides information on the populations in 2010 and 2015 that will

experience improved PM air quality. Significant populations live in areas with meaningful

reductions in annual mean PM2.5 concentrations resulting from the rule. As shown, in 2015,

almost 63 percent of the U.S. population located in the eastern 37-state modeling domain is

predicted to experience reductions of greater than 0.5 μg/m3. This is an increase from the

54 percent of the U.S. population that is expected to experience such reductions in 2010. 

Furthermore, over 40 percent of this population will benefit from reductions in annual mean

PM2.5 concentrations of greater than 1 μg/m3

, and almost 23 percent will live in areas with

reductions of greater than 1.5 μg/m3

. 
3-15

Table 3-6. Summary of Base Case PM Air Quality and Changes Due to Clean Air

Interstate Rule: 2010 and 2015

2010 2015

Region PM2.5 (μg/m3

) Base Case Changea

Percent

Change Base Case Changea

Percent

Change

East

Averageb Annual Mean 10.36 –0.73 –7.1 10.28 –0.89 –8.7

Population-Weighted

Average Annual Mean

11.91 –0.96 –8.1 11.79 –1.15 –9.8

West

Average Annual Mean 6.04 –0.02 –0.3 6.07 –0.03 –0.5

Population-Weighted

Average Annual Meanc

12.38 –0.01 –0.1 12.50 –0.01 –0.1

a The change is defined as the control case value minus the base case value. b Calculated as the average across all grid cells in the U.S. portion of the region. c Calculated by summing the product of the population and the projected annual mean PM concentration for

each grid cell then dividing this sum by the total population.

3.2.2 Ozone Air Quality Estimates

We use the emissions inputs summarized earlier in this chapter with a regional-scale

application of CAMx to estimate ozone air quality in the East. CAMx is a Eulerian threedimensional photochemical grid air quality model designed to calculate the concentrations of

both inert and chemically reactive pollutants by simulating the physical and chemical

processes in the atmosphere that affect ozone formation. Version 3.10 of the CAMx model

was employed for these analyses. 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 final rule on ozone concentrations.

Our analysis applies the modeling system separately to the Eastern United States for

five emissions scenarios: a 2001 baseline, a 2010 baseline projection and a 2010 projection

with controls, and a 2015 baseline projection and a 2015 projection with controls. Further

discussion of this modeling, including an evaluation of model performance relative to

observed ozone, is provided in the Air Quality Modeling TSD. As discussed in Chapter 4, 
3-16

Table 3-7. Distribution of PM2.5 Air Quality Improvements Over Population Due to

Clean Air Interstate Rule: 2010 and 2015

Change in Annual Mean

PM2.5 Concentrations (μg/m3

)

a

2010 Populationb 2015 Population

Number

(millions) Percent (%)

Number

(millions) Percent (%)

) PM2.5 Conc # 0.25 81.7 26.8% 80.8 26.5%

0.25 > ) PM2.5 Conc # 0.5 58.4 19.2% 31.9 10.5%

0.5 > ) PM2.5 Conc # 0.75 36.2 11.9% 54.8 18.0%

0.75 > ) PM2.5 Conc # 1.0 24.0 7.9% 26.1 8.6%

1.0 > ) PM2.5 Conc # 1.25 41.1 13.5% 16.3 5.4%

1.25 > ) PM2.5 Conc # 1.5 21.8 7.2% 37.5 12.3%

1.5 > ) PM2.5 Conc # 1.75 14.5 4.8% 26.4 8.7%

1.75 > ) PM2.5 Conc # 2.0 8.9 2.9% 16.8 5.5%

) PM2.5 Conc > 2.0 17.7 5.8% 26.2 8.6%

a The change is defined as the control case value minus the base case value.

b Population counts and percentages are for the fraction of the continental U.S. population located in the

modeling domain considered in modeling health benefits for the rule.

we use the relative predictions from the model by combining the 2001 base-year and each

future-year scenario with current ambient air quality observations to determine the expected

change in 2010 or 2015 ozone concentrations due to the rule. These results are used solely in

the benefits analysis.

The CAMx modeling system requires a variety of input files that contain information

pertaining to the modeling domain and simulation period. These include gridded, dayspecific emissions estimates and meteorological fields, initial and boundary conditions, and

land-use information. As applied to the Eastern United States, the model segments the area

into square blocks called grids (roughly equal in size to counties), each of which has several

layers of air conditions that are considered in the analysis. Using these data, the CAMx

model generates predictions of hourly ozone concentrations for every grid. We used the

results of this process to develop 2010 and 2015 ozone profiles at monitor sites by

normalizing the CAMx predictions to the observed ozone concentrations at each monitor

site. For areas (grids) without ozone monitoring data, we interpolated ozone values using

data from monitors surrounding the area. After completing this process, we calculated daily
3-17

and seasonal ozone metrics to be used as inputs to the health and welfare C-R functions of

the benefits analysis. The following sections provide a more detailed discussion of each of

the steps in this evaluation and a summary of the results.

3.2.2.1 Modeling Domain 

The modeling domain representing the Eastern United States is the same as that used

previously for Ozone Transport Assessment Group and for the On-highway Tier-2

rulemaking. As shown in Figure 3-2, this domain encompasses most of the Eastern United

States from the East coast to mid-Texas and consists of two grids with differing resolutions. 

The modeling domain extends from 99 degrees to 67 degrees west longitude and from 26

degrees to 47 degrees north latitude. The inner portion of the modeling domain shown in

Figure 3-2 uses a relatively fine grid of approximately 12 km consisting of nine vertical

layers. The outer area has less horizontal resolution. The grid cell size in the outer grid is

approximately 36 km with the same nine vertical layers. The vertical height of the modeling

domain is 4,000 meters above ground level for both areas. 

3.2.2.2 Simulation Periods

For use in this benefits analysis, the simulation periods modeled by CAMx included

several multiday periods when ambient measurements recorded high ozone concentrations. 

A simulation period, or episode, consists of meteorological data characterized over a block of

days that are used as inputs to the air quality model. We modeled three periods during the

summer of 1995: June 12–24, July 5–15, and August 7–21. Collectively, these periods

contain episodes of high ozone in various portions of the East. The six emissions scenarios

(1995 base year, 2001 base year, 2010 base and control, 2015 base and control) were

simulated for all three episodes. The periods modeled include three “ramp-up” days to

initialize the model, but the results for these days are not used in this analysis.

3.2.2.3 Nonemissions Modeling Inputs

The meteorological data required for input into CAMx (e.g., wind, temperature,

vertical mixing) were developed by separate meteorological models. The gridded

meteorological data for the three historical 1995 episodes were developed using the Regional

Atmospheric Modeling System (RAMS), version 3b. This model provided needed data at

every grid cell on an hourly basis. These meteorological modeling results were evaluated

against observed weather conditions before being input into CAMx, and it was concluded

that the model fields were adequate representations of the historical meteorology. A more
3-18

Figure 3-2. CAMx Eastern U.S. Modeling Domain

Note: The inner area represents fine grid modeling at 12 km resolution. The outer area represents the coarse

grid modeling at 36 km resolution.

detailed description of the settings and assorted input files employed in these applications is

provided in the Air Quality Modeling TSD. 

The modeling assumed background pollutant levels at the top and along the periphery

of the domain. Initial conditions were also assumed to be relatively clean. Given the rampup days and the expansive domains, it is expected that these assumptions will not affect the

modeling results. The development of model inputs is discussed in greater detail in the Air

Quality Modeling TSD, which is available in the docket for this rule.

3.2.2.4 Model Performance for Photochemical Ozone

A performance evaluation of CAMx for the three 1995 episodes was conducted prior

to CAIR, in support of the Nonroad Diesel Engine Rule. A summary of model performance
3-19

from the study is provided here. In this analysis, a series of performance statistics was

calculated for the Eastern U.S. domain as well as the four quadrants of this domain and

multiple subregions. The model performance evaluation consisted solely of comparisons

against ambient surface ozone data. 

Three primary statistical metrics were used to assess the overall accuracy of the baseyear modeling simulations:

C Mean normalized bias is defined as the average difference between the hourly

model predictions and observations (paired in space and time) at each monitoring

location, normalized by the magnitude of the observations.

C Mean normalized gross error is defined as the average absolute difference

between the hourly model predictions and observations (paired in space and time)

at each monitoring location, normalized by the magnitude of the observations.

C Average accuracy of the peak is defined as the average difference between peak

daily model predictions and observations at each monitoring location, normalized

by the magnitude of the observations.

In general, the model tends to slightly underestimate observed ozone. When all

hourly observed ozone values greater than a 60 ppb threshold are compared to their model

counterparts for the 30 episode modeling days in the eastern domain, the mean normalized

bias is –1.1 percent and the mean normalized gross error is 20.5 percent. As shown in

Table 3-8, the model generally underestimates observed ozone values for the June and July

episodes but predicts higher than observed amounts for the August episode.

Table 3-8. Model Performance Statistics for Hourly Ozone in the Eastern U.S. CAMx

Ozone Simulations: 1995 Base Case

Episode

Average Accuracy of

the Peak

Mean Normalized

Bias

Mean Normalized

Gross Error

June 1995 –7.3 –8.8 19.6

July 1995 –3.3 –5.0 19.1

August 1995 9.6 8.6 23.3
3

The ozone season for this analysis is defined as the 5-month period from May to September; however, to

estimate certain crop yield benefits, the modeling results were extended to include months outside the 5-

month ozone season.

4

Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at

least nine hourly observations per day [8 am to 8 pm] during the ozone season).

3-20

At present, there are no guidance criteria by which one can determine if a regional

ozone modeling exercise is exhibiting adequate model performance. These base-case

simulations were determined to be acceptable based on comparisons to previously completed

model rulemaking analyses (e.g., OTAG, Tier-2, and Heavy-Duty Engine). The modeling

completed for this rule exhibits less bias and error than any past regional ozone modeling

application done by EPA. Thus, the model is considered appropriate for use in projecting

changes in future-year ozone concentrations and the resultant health/economic benefits due

to the emissions reductions.

In addition, the CAMx modeling results were also evaluated at a “local” level to

ensure that areas determined to need the emissions reductions based on projected

exceedances of the ozone standard were not unduly influenced by local overestimation of

ozone in the model base year. As detailed in the Air Quality Modeling TSD, performance

statistics were computed for each of 51 local subregions within the modeling domain. These

performance statistics were compared to the recommended performance ranges for urban

attainment modeling (EPA, 1999). The results indicate that model performance for the June

episode was within the recommended ranges for 69 percent of the local areas examined. For

the July and August episodes, the percentage of local areas with performance within the

recommended ranges was 80 percent and 61 percent, respectively. 

3.2.2.5 Converting CAMx Outputs to Full-Season Profiles for Benefits Analysis

This study extracted hourly, surface-layer ozone concentrations for each grid cell

from the standard CAMx output file containing hourly average ozone values. These model

predictions are used in conjunction with the observed concentrations obtained from the

Aerometric Information Retrieval System (AIRS) to generate ozone concentrations for the

entire ozone season.3,4 The predicted changes in ozone concentrations from the future-year

base case to future-year control scenario serve as inputs to the health and welfare C-R

functions of the benefits analysis (i.e., the Environmental Benefits Mapping and Analysis

Program [BenMAP]). 
5

The 8-km grid squares contain the population data used in the health benefits analysis model, BenMAP. See

Chapter 4 for a discussion of this model.

6

This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi

Neighbor Averaging (EVNA) spatial interpolation.

3-21

To estimate ozone-related health and welfare effects for the contiguous United States,

full-season ozone data are required for every BenMAP grid-cell. Given available ozone

monitoring data, we generated full-season ozone profiles for each location in the contiguous

48 states in two steps: (1) we combined monitored observations and modeled ozone

predictions to interpolate hourly ozone concentrations to a grid of 8 km by 8 km population

grid cells, and (2) we converted these full-season hourly ozone profiles to an ozone measure

of interest, such as the daily average.5,6

3.2.2.6 Ozone Air Quality Results

This section provides a summary of the predicted ambient ozone concentrations from

the CAMx model for the 2010 and 2015 base cases and changes associated with the rule. 

Table 3-9 provides those ozone metrics for grid cells in the Eastern United States that enter

the C-R functions for health benefits endpoints. The population-weighted average reflects

the baseline levels and predicted changes for more populated areas of the nation. This

measure, therefore, will better reflect the potential benefits of these predicted changes

through exposure changes to these populations.

3.2.3 Visibility Degradation Estimates

Visibility degradation is often directly proportional to decreases in light transmittal in

the atmosphere. Scattering and absorption by both gases and particles decrease light

transmittance. To quantify changes in visibility, our analysis computes a light-extinction

coefficient, based on the work of Sisler (1996), which shows the total fraction of light that is

decreased per unit distance. This coefficient accounts for the scattering and absorption of

light by both particles and gases and accounts for the higher extinction efficiency of fine

particles compared to coarse particles. Fine particles with significant light-extinction

efficiencies include sulfates, nitrates, organic carbon, elemental carbon (soot), and soil

(Sisler, 1996).
3-22

Table 3-9. Summary of CAMx Derived Population-Weighted Ozone Air Quality

Metrics for Health Benefits Endpoints Due to Clean Air Interstate Rule: Eastern U.S.

2010 2015

Statistica

Base

Case Changeb

Percent

Changeb

Base

Case Changeb

Percent

Changeb

Population-Weighted Average (ppb)d

Daily 1-Hour Maximum Concentration 51.82 –0.50 –1.0% 50.77 –1.36 –2.7%

Daily 8-Hour Average Concentration 42.59 –0.39 –1.0% 41.84 –1.05 –2.5%

Daily 12-Hour Average Concentration 40.09 –0.37 –1.0% 39.41 –0.97 –2.5%

Daily 24-Hour Average Concentration 30.15 –0.27 –1.0% 29.73 –0.67 –2.3%

a These ozone metrics are calculated at the CAMX grid-cell level for use in health effects estimates based on

the results of spatial and temporal Voronoi Neighbor Averaging. Except for the daily 24-hour average,

these ozone metrics are calculated over relevant time periods during the daylight hours of the “ozone

season” (i.e., May through September). For the 8-hour average, the relevant time period is 9 am to 5 pm,

and for the 12-hour average it is 8 am to 8 pm.

b The change is defined as the control-case value minus the base-case value. The percentage change is the

“Change” divided by the “Base Case” and then multiplied by 100 to convert the value to a percentage. 

d Calculated by summing the product of the projected CAMx grid-cell population and the estimated CAMx

grid cell seasonal ozone concentration and then dividing by the total population.

Based on the light-extinction coefficient, we also calculated a unitless visibility

index, called a “deciview,” which is used in the valuation of visibility. The deciview metric

provides a scale for perceived visual changes over the entire range of conditions, from clear

to hazy. Under many scenic conditions, the average person can generally perceive a change

of one deciview. The higher the deciview value, the worse the visibility. Thus, an

improvement in visibility is a decrease in deciview value. 

Table 3-10 provides visibility improvements expected to occur in specific parks in

the CAIR region. As shown, major parks in the Eastern United States, including the Great

Smokey Mountains and Shenandoah, are expected to see significant improvements in

visibility. By 2015, on the 20 percent of worst visibility days, the Great Smokey Mountains

National Park is expected to see improvements of over 2.5 deciviews (9 percent), and

Shenandoah National Park is expected to see improvements of over 3.3 deciviews (12

percent). Under average light conditions, these represent improvements in visual range by

close to 7 miles in the Great Smokies and over 10 miles in Shenandoah.
3-23

Table 3-10. Summary of Deciview Visibility Impacts at Class I Areas in the CAIR Regiona,b

Federal Class I Area

2010 2015

Change in

Average of 20%

Worst Days

Percent Change

in Average of

20% Worst Days

Change in 

Annual 

Average

Percent Change

in Annual

Average

Change in

Average of 20%

Worst Days

Percent Change

in Average of

20% Worst Days

C

A

Acadia, ME 0.88 3.98 0.36 2.59 1.00 4.54 Boundary Waters Canoe Area,

MN

0.26 1.36 0.12 0.95 0.29 1.52 

Brigantine, NJ 1.88 6.90 0.93 4.59 2.07 7.54 

Caney Creek, AR 1.08 4.30 0.42 2.17 1.32 5.24 *Chassahowitzka, FL 0.90 3.76 0.47 2.45 1.66 6.92 *Dolly Sods, WV 2.39 9.03 1.44 7.41 2.75 10.54 *Everglades, FL 0.42 2.13 0.17 1.16 0.49 2.43 Great Gulf, NH 1.32 5.87 0.44 3.10 1.56 6.95 *Great Smoky Mountains, TN 1.85 6.40 0.97 4.65 2.61 9.12 Isle Royale, MI 0.31 1.42 0.17 1.29 0.38 1.76 *James River Face, VA 2.09 7.55 1.21 5.93 2.45 8.96 *Joyce Kilmer—Slickrock,

TN

1.85 6.40 0.97 4.65 2.61 9.12 

*Linville Gorge, NC 1.71 6.22 1.00 5.18 2.14 7.92 

Lye Brook, VT 1.76 7.20 0.61 4.31 2.10 8.64 *Mammoth Cave, KY 1.68 5.61 0.94 4.20 2.45 8.31 Mingo, MO 0.82 2.97 0.50 2.41 0.95 3.46 Moosehorn, ME 0.82 3.83 0.30 2.10 0.92 4.30 *Okefenokee, GA 0.99 3.87 0.63 3.21 1.44 5.64 
3-24

Table 3-10. Summary of Deciview Visibility Impacts at Class I Areas in the CAIR Region (continued)

Federal Class I Area

2010 2015

Change in

Average of 20%

Worst Days

Percent Change

in Average of

20% Worst Days

Change in 

Annual 

Average

Percent Change

in Annual

Average

Change in

Average of 20%

Worst Days

Percent Change

in Average of

20% Worst Days

C

A

*Otter Creek, WV 2.47 9.22 1.45 7.43 2.94 11.14 Presidential Range - Dry, NH 1.50 6.70 0.52 3.75 1.76 7.93 *Cape Romain, SC 1.01 4.17 0.77 4.07 1.44 5.98 Roosevelt Campobello, ME 0.80 3.76 0.29 2.03 0.94 4.41 Seney, MI 0.65 2.64 0.23 1.67 0.78 3.16 *Shenandoah, VA 2.81 10.23 1.69 8.54 3.31 12.27 *Sipsey, AL 1.45 5.27 0.81 3.83 2.06 7.55 *Swanquarter, NC 1.45 6.04 0.80 4.39 1.86 7.85 Upper Buffalo, AR 0.67 2.74 0.31 1.72 0.80 3.27 Voyageurs, MN 0.12 0.68 0.09 0.74 0.12 0.72 *Wolf Island, GA 0.84 3.28 0.60 3.06 1.12 4.37 

a The change is defined as the base case value minus the control case value. b The percent change is the “Change” divided by the “Base Case” and then multiplied by 100 to convert the value to a percentage

* Visibility Benefits were monitized for this park.
3-25

3.3 References

Byun, D., and K.L. Schere. March 2004. “Review of the Governing Equations,

Computational Algorithms, and Other Components of the Models-3 Community

Multiscale Air Quality (CMAQ) Modeling System.” Submitted to the Journal of

Applied Mechanics Reviews.

Sisler, J.F. July 1996. Spatial and Seasonal Patterns and Long Term Variability of the

Composition of the Haze in the United States: An Analysis of Data from the

IMPROVE Network. Fort Collins, CO: Cooperative Institute for Research in the

Atmosphere, Colorado State University.

U.S. Environmental Protection Agency (EPA). 1999. Draft Guidance on the Use of Models

and Other Analyses in Attainment Demonstrations for the 8-Hour Ozone NAAQS,

Office of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. Environmental Protection Agency (EPA). 2000. Draft Guidance for Demonstrating

Attainment of the Air Quality Goals for PM2.5 and Regional Haze; Draft 1.1, Office

of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. Environmental Protection Agency (EPA). 2005. Clean Air Interstate Rule Air Quality

Modeling Technical Support. Office of Air Quality Planning and Standards. 

Research Triangle Park, N.C.

U.S. Environmental Protection Agency (EPA). 2005. Clean Air Interstate Rule Emission

Inventory Technical Support Document. Office of Air Quality Planning and

Standards. Research Triangle Park, NC.
4-1

CHAPTER 4

BENEFITS ANALYSIS AND RESULTS

This chapter reports EPA’s analysis of a subset of the public health and welfare

impacts and associated monetized benefits to society of CAIR. EPA is required by

Executive Order (E.O.) 12866 to estimate the benefits and costs of major new pollution

control regulations. Accordingly, the analysis presented here attempts to answer three

questions: (1) what are the physical health and welfare effects of changes in ambient air

quality resulting from reductions in precursors to particulate matter (PM) including NOx and

SO2 emissions? (2) what is the monetary value of the changes in these effects attributable to

the final rule? and (3) how do the monetized benefits compare to the costs? It constitutes one

part of EPA’s thorough examination of the relative merits of this regulation. 

The analysis presented in this chapter uses a methodology generally consistent with

benefits analyses performed for the recent analysis of Nonroad Diesel Engines Tier 4

Standards and the proposed Clear Skies Act of 2003 (EPA, 2003c). The benefits analysis

relies on three major modeling components:

1) Calculation of the impact of CAIR on EGUs assuming a cap-and-trade program

based on the national inventory of precursors to PM, specifically NOx and SO2.

2) Air quality modeling for 2010 and 2015 to determine changes in ambient

concentrations of ozone and PM, reflecting baseline and postcontrol emissions

inventories.

3) A benefits analysis to determine the changes in human health and welfare, both in

terms of physical effects and monetary value, that result from the projected

changes in ambient concentrations of various pollutants for the modeled

standards.

A wide range of human health and welfare effects are linked to the emissions of NOx

and SO2 from EGUs and the resulting impact on ambient concentrations of ozone and PM. 

Potential human health effects associated with PM2.5 range from premature mortality to

morbidity effects linked to long-term (chronic) and shorter-term (acute) exposures (e.g.,

respiratory and cardiovascular symptoms resulting in hospital admissions, asthma
1

Short-term exposure to ambient ozone has also been linked to premature death. EPA is currently evaluating the

epidemiological literature examining the relationship between ozone and premature mortality, sponsoring

three independent meta-analyses of the literature. EPA will consider including ozone mortality in primary

benefits analyses once a peer-reviewed methodology is available.

4-2

exacerbations, and acute and chronic bronchitis [CB]). Exposure to ozone has also been

linked to a variety of respiratory effects including hospital admissions and illnesses resulting

in school absences. Some studies, including a recent multi-city analysis of 95 major U.S.

urban areas (Bell et al., 2004), have linked short term ozone exposures with premature

mortality.1 Welfare effects potentially linked to PM include materials damage and visibility

impacts, while ozone can adversely affect the agricultural and forestry sectors by decreasing

yields of crops and forests. Although methods exist for quantifying the benefits associated

with many of these human health and welfare categories, not all can be evaluated at this time

because of limitations in methods and/or data. Table 4-1 summarizes the annual monetized

health and welfare benefits associated with CAIR for 2 years, 2010 and 2015. Table 4-2 lists

the full complement of human health and welfare effects associated with PM and ozone and

identifies those effects that are quantified for the primary estimate and those that remain

unquantified because of current limitations in methods or available data. 

Figure 4-1 illustrates the major steps in the benefits analysis. Given baseline and

post-control emissions inventories for the emission species expected to affect ambient air

quality, we use sophisticated photochemical air quality models to estimate baseline and postcontrol ambient concentrations of ozone and PM and deposition of nitrogen and sulfur for

each year. The estimated changes in ambient concentrations are then combined with 

Table 4-1. Estimated Monetized Benefits of the Final CAIR

Total Benefitsa, b (billions 1999$)

2010 2015

Using a 3% discount rate $73.3 + B $101 + B

Using a 7% discount rate $62.6 + B $86.3 + B

a For notational purposes, unquantified benefits are indicated with a “B” to represent the sum of additional

monetary benefits and disbenefits. A detailed listing of unquantified health and welfare effects is provided

in Table 4-2.

b Results reflect the use of two different discount rates: 3 and 7 percent, which are recommended by EPA’s

Guidelines for Preparing Economic Analyses (EPA, 2000b) and OMB Circular A-4 (OMB, 2003). Results

are rounded to three significant digits for ease of presentation and computation.
4-3

Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAIR

Pollutant/Effect

Quantified and Monetized in

Base Estimatesa

Quantified and/or Monetized

Effects in Sensitivity Analyses Unquantified Effects 

Ozone/Healthb Hospital admissions: respiratory 

Emergency room visits for asthmaMinor restricted-activity daysSchool loss days

Premature mortality: short termexposuresc

Asthma attacksCardiovascular emergency roomvisitsAcute respiratory symptoms

Chronic respiratory damagePremature aging of the lungsNonasthma respiratory emergency Increased exposure to UVb

Ozone/Welfare Decreased outdoor worker

productivity

Yields for:  - Commercial forests - Fruits and vegetables, and - Other commercial and noncoDamage to urban ornamental planRecreational demand from damagEcosystem functionsIncreased exposure to UVb
4-4

Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAIR (continued)

Pollutant/Effect

Quantified and Monetized in

Base Estimatesa

Quantified and/or Monetized

Effects in Sensitivity Analyses Unquantified Effects 

PM/Healthd Premature mortality based on

cohort study estimateseBronchitis: chronic and acuteHospital admissions: respiratoryand cardiovascularEmergency room visits for asthmaNonfatal heart attacks (myocardialinfarction)Lower and upper respiratory illnessMinor restricted-activity daysWork loss days Asthma exacerbations (asthmaticpopulation)Respiratory symptoms (asthmaticpopulation)Infant mortality

Premature mortality: short termexposuresf

Subchronic bronchitis cases

Low birth weightPulmonary functionChronic respiratory diseases other Nonasthma respiratory emergency UVb exposure (+/-)g

PM/Welfare Visibility in Southeastern Class I

areas

Visibility in northeastern andMidwestern Class I areasHousehold soiling

Visibility in western U.S. Class I Visibility in residential and non-CUVb exposure (+/-)g
4-5

Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAIR (continued)

Pollutant/Effect

Quantified and Monetized in

Base Estimatesa

Quantified and/or Monetized

Effects in Sensitivity Analyses Unquantified Effects 

Nitrogen andSulfateDeposition/Welfare

Commercial forests due to acidic depositionCommercial freshwater fishing duRecreation in terrestrial ecosystemdepositionCommercial fishing, agriculture, anitrogen depositionRecreation in estuarine ecosystemdepositionEcosystem functionsPassive fertilization

SO2/Health Hospital admissions for respirator

Respiratory symptoms in asthmat

NOx/Health Lung irritation

Lowered resistance to respiratory Hospital admissions for respirator
4-6

Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAIR (continued)

Pollutant/Effect

Quantified and Monetized in

Base Estimatesa

Quantified and/or Monetized

Effects in Sensitivity Analyses Unquantified E

Mercury Health Incidences of neurological disord

Incidences of learning disabilitiesIncidences in developmental delayPotential cardiovascular effectsh, i

 - Altered blood pressure regulat

 - Increased heart rate variability - Incidences of Myocardial infarPotential reproductive effectsh

MercuryDepositionWelfare

Impact on birds and mammals (e.gImpacts to commercial, subsistencfishing

a Primary quantified and monetized effects are those included when determining the primary estimate of total monetized benefits 

C for a more complete discussion of the benefit estimates.

b In addition to primary economic endpoints, there are a number of biological responses that have been associated with ozone hea

airway responsiveness to stimuli, inflammation in the lung, acute inflammation and respiratory cell damage, and increased susce

infection. The public health impact of these biological responses may be partly represented by our quantified endpoints.

c Premature mortality associated with ozone is not currently included in the primary analysis. Recent evidence suggests that shor

may have a significant effect on daily mortality rates, independent of exposure to PM. EPA is currently conducting a series of m

mortality epidemiology literature. EPA will consider including ozone mortality in primary benefits analyses once a peer-reviewavailable. 

d In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health 

morphological changes and altered host defense mechanisms. The public health impact of these biological responses may be pa

quantified endpoints.

e Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but relative risk estimates ma

effects due to shorter term exposures (see Kunzli, 2001 for a discussion of this issue).

f While some of the effects of short term exposure are likely to be captured by the cohort estimates, there may be additional prem

term PM exposure not captured in the cohort estimates included in the primary analysis.

g May result in benefits or disbenefits. See Section 5.3.4 for more details.h These are potential effects as the literature is insufficient.
2

The term “impact function” as used here refers to the combination of a) an effect estimate obtained from the

epidemiological literature, b) the baseline incidence estimate for the health effect of interest in the modeled

population, c) the size of that modeled population, and d) the change in the ambient air pollution metric of

interest. These elements are combined in the impact function to generate estimates of changes in incidence

of the health effect. The impact function is distinct from the C-R function, which strictly refers to the

estimated equation from the epidemiological study relating incidence of the health effect and ambient

pollution. We refer to the specific value of the relative risk or estimated coefficients in the epidemiological

study as the “effect estimate.” In referencing the functions used to generate changes in incidence of health

effects for this RIA, we use the term “impact function” rather than C-R function because “impact function”

includes all key input parameters used in the incidence calculation.

4-7

Emissions inventories

(2001 CEM, 1996 NEI, 

MOBILE 5b and 6 PART5 

model, NONROAD2002) 

Air quality monitoring data

AIRS (ozone), FRM (total 

PM), STN (speciated PM)

Model baseline and

post-control ambient air 

quality (REMSAD, CAM-X)

Model population exposure to 

changes in ambient concentrations

Estimate expected changes in 

human health outcomes

Estimate monetary value of 

changes in human health

Adjust monetary values for growth 

in real income to year of analysis

Sum health and welfare monetary 

values to obtain total monetary benefits

Concentration response 

functions

Incidence and 

prevalence rates for 

health endpoints

Population and 

demographic data (with 

growth projections)

Valuation functions 

Interpolation of projected air 

concentration surfaces (base and control) 

Estimate 

expected 

changes in 

welfare

(visibility)

Estimate 

monetary 

value of 

changes in 

welfare 

effects

Valuation 

functions

•BenMAP-derived 

(ozone)

•SMAT-derived 

(PM2.5)

BenMAP

integrated 

model

Income elasticities

GDP projections

INPUTS PROCESSES INPUTS

Emissions inventories

(2001 CEM, 1996 NEI, 

MOBILE 5b and 6 PART5 

model, NONROAD2002) 

Air quality monitoring data

AIRS (ozone), FRM (total 

PM), STN (speciated PM)

Model baseline and

post-control ambient air 

quality (REMSAD, CAM-X)

Model population exposure to 

changes in ambient concentrations

Estimate expected changes in 

human health outcomes

Estimate monetary value of 

changes in human health

Adjust monetary values for growth 

in real income to year of analysis

Sum health and welfare monetary 

values to obtain total monetary benefits

Concentration response 

functions

Incidence and 

prevalence rates for 

health endpoints

Population and 

demographic data (with 

growth projections)

Valuation functions 

Interpolation of projected air 

concentration surfaces (base and control) 

Estimate 

expected 

changes in 

welfare

(visibility)

Estimate 

monetary 

value of 

changes in 

welfare 

effects

Valuation 

functions

•BenMAP-derived 

(ozone)

•SMAT-derived 

(PM2.5)

BenMAP

integrated 

model

Income elasticities

GDP projections

INPUTS PROCESSES INPUTS

Figure 4-1. Key Steps in Air Quality Modeling Based Benefits Analysis

monitoring data to estimate population-level potential exposures to changes in ambient

concentrations for use in estimating health effects. Modeled changes in ambient data are also

used to estimate changes in visibility and changes in other air quality statistics that are

necessary to estimate welfare effects. Changes in population exposure to ambient air

pollution are then input to impact functions2 to generate changes in the incidence of health
4-8

effects, or changes in other exposure metrics are input to dose-response functions to generate

changes in welfare effects. The resulting effects changes are then assigned monetary values,

taking into account adjustments to values for growth in real income out to the year of

analysis (values for health and welfare effects are in general positively related to real income

levels). Finally, values for individual health and welfare effects are summed to obtain an

estimate of the total monetary value of the changes in emissions.

The benefits discussed in this chapter represent the estimates based upon emission

changes anticipated for the final CAIR program (final CAIR plus the proposal to include SO2

and annual NOx controls for New Jersey and Delaware in the final CAIR program) with one

exception. The benefits estimated in this report are slightly overstated due to the inclusion of

emission reductions for SO2 and annual NOx controls for Arkansas. Thus, the analysis

presented reflects the EPA’s best estimate of the benefits for a complete CAIR program

assuming New Jersey and Delaware become a part of the CAIR region for PM2.5 as well as

ozone, but these benefits are slightly overstated due to use of modeling that includes

Arkansas in the CAIR region for SO2 and annual NOx controls. 

On September 26, 2002, the National Research Council (NRC) released a report on

its review of the Agency’s methodology for analyzing the health benefits of measures taken

to reduce air pollution. The report focused on EPA’s approach for estimating the health

benefits of regulations designed to reduce concentrations of ambient PM.

In its report, the NRC said that EPA has generally used a reasonable framework for

analyzing the health benefits of PM-control measures. It recommended, however, that the

Agency take a number of steps to improve its benefits analysis. The current analysis reflects

the following suggestions of that NRC report:

C estimate benefits for intervals, such as every 5 years, rather than a single year;

C clearly state the projected baseline statistics used in estimating health benefits,

including those for air emissions, air quality, and health outcomes;

C when appropriate, use data from non-U.S. studies to broaden age ranges to which

current estimates apply and to include more types of relevant health outcomes;

In addition, the NRC recommended that EPA move the assessment of uncertainties

from its ancillary analyses into its base analyses by conducting probabilistic, multiple-source

uncertainty analyses. However, for this rule, given the limited data available for such a

complex uncertainty assessment, EPA made the decision only to summarize the results of an

ancillary probabilistic uncertainty analysis to provide context for sources of uncertainty
4-9

reflecting statistical error in the base analysis. The EPA followed the NRC

recommendations that the probabilistic assessment should be based on available data and

expert judgment.

The NRC made a number of recommendations for improving EPA’s approach and

found that the studies selected by the Agency for use in its benefits analyses were generally

reasonable choices. In particular, the NRC agreed with EPA’s decision to use cohort studies

for estimating premature mortality benefits. It also concluded that the Agency’s selection of

the American Cancer Society (ACS) study for the evaluation of PM-related premature

mortality was reasonable, although it noted the publication of new cohort studies that the

Agency should evaluate. Since the publication of the NRC report, EPA has reviewed new

cohort studies, including reanalyses of the ACS study data and has carefully considered these

new study data in developing the analytical approach for CAIR (see below).

In addition to the NRC report, EPA received technical guidance and input regarding

its methodology for conducting PM- and ozone-related benefits analysis from the Health

Effects Subgroup (HES) of the SAB Council reviewing the 812 blueprint (SAB-HES, 2004)

and the Office of Management and Budget (OMB) through ongoing discussions regarding

methods used in conducting regulatory impact analyses (RIAs), and developments during the

collaboration on the recent Nonroad Diesel rulemaking. EPA addressed many of the

comments received from the NRC, the SAB-HES, and OMB in developing the analytical

approach for the recent Nonroad Diesel Rule RIA. These improvements are also reflected in

this analysis for the final CAIR. 

Recommendations from OMB regarding RIA methods have focused on the approach

used to characterize uncertainty in the benefits estimates generated for RIAs and the

approach used to value mortality estimates. EPA is currently developing a comprehensive

integrated strategy for characterizing the impact of uncertainty in key elements of the

benefits modeling process (e.g., emissions modeling, air quality modeling, health effects

incidence estimation, valuation) on the health impact and monetized benefits estimates that

are generated. A subset of this effort, which has recently been completed and peer reviewed,

was a pilot expert elicitation designed to characterize uncertainty in the estimation of PM-
3

Expert elicitation is a formal, highly structured and well documented process whereby expert judgments,

usually of multiple experts, are obtained (Ayyub, 2002). 

4-10

related mortality resulting from both short-term and long-term exposure.3 The peer review of

the pilot expert elicitation was generally favorable. We provide a detailed description of the

pilot in Appendix B, along with a summary of results in Section 4.3.

We have also reflected advances in data and methods in air quality modeling,

epidemiology, and economics in developing this analysis. Updates to the assumptions and

methods used in estimating PM2.5-related and ozone-related benefits since the analysis for the

proposed rule include the following:

Air Quality

C Use of CMAQ-based predictions for ambient PM2.5 and component species.

C Use of an updated SMAT approach for developing PM2.5 air modeling results. 

For the CAIR proposal analysis, we used temporally scaled speciated PM2.5

monitor data from 2001–2002, reconstructed into total PM2.5 mass based on

2000–2002 design values and kriged to 12 kilometer grids (nested within the

standard 36 km REMSAD grid structure). Temporal scaling was based on ratios

of future modeled REMSAD data to 2001 REMSAD model data, using REMSAD

modeling conducted at the 36 km grid resolution. For this analysis of the final

rule, we used a modified method that is based on the future to 2001 modeled

CMAQ speciated outputs and spatially interpolated speciated monitor data (see

Chapter 3 for more details).

C The CAIR proposal analysis was limited to the Eastern U.S. For the final rule

analysis, PM benefits are estimated for the entire U.S., to account for the transport

of PM precursor emissions from the CAIR domain to the western states. The

ozone benefits assessment is still limited to the eastern U.S. due to limitations in

the models for ozone formation in the western states. 

Valuation

C In generating the monetized benefits for reductions in premature mortality in the

primary analysis, a 20-year segmented lag structure will be used to characterize

the relationship between the time when exposure to ambient PM2.5 is changed and

the time when reductions in premature mortality are expected to occur.
4-11

Uncertainty

C In addition to the primary estimate of the benefits of reduced premature mortality,

we characterize uncertainty using a probabilistic range of benefits based on

statistical uncertainty as captured in the standard errors associated with the Pope

et al (2002) epidemiological study, and model uncertainties obtained from the

pilot expert elicitation. Uncertainty in some other elements of the model are

characterized by statistical uncertainty as captured in either standard errors on

epidemiological effect estimates or variability in published estimates of valuation

estimates. 

The benefits estimates generated for the final CAIR are subject to a number of

assumptions and uncertainties, which are discussed throughout this document. For example,

key assumptions underlying the primary estimate for the mortality category include the

following:

1) Inhalation of fine particles is causally associated with premature death at

concentrations near those experienced by most Americans on a daily basis. 

Although biological mechanisms for this effect have not yet been completely

established, the weight of the available epidemiological and experimental

evidence supports an assumption of causality. 

2) All fine particles, regardless of their chemical composition, are equally potent

in causing premature mortality. This is an important assumption, because PM

produced via transported precursors emitted from EGUs may differ

significantly from direct PM released from automotive engines and other

industrial sources. However, no clear scientific grounds exist for supporting

differential effects estimates by particle type. 

3) The C-R function for fine particles is approximately linear within the range of

ambient concentrations under consideration. Thus, the estimates include

health benefits from reducing fine particles in areas with varied

concentrations of PM, including both regions that are in attainment with the

fine particle standard and those that do not meet the standard. 

4) The forecasts for future emissions and associated air quality modeling are

valid. Although recognizing the difficulties, assumptions, and inherent

uncertainties in the overall enterprise, these analyses are based on

peer-reviewed scientific literature and up-to-date assessment tools, and we

believe the results are highly useful in assessing this rule.
4-12

In addition to the quantified and monetized benefits summarized above, a number of

additional categories are not currently amenable to quantification or valuation. These

include reduced acid and particulate deposition damage to cultural monuments and other

materials, reduced ozone effects on forested ecosystems, and environmental benefits due to

reductions of impacts of acidification in lakes and streams and eutrophication in coastal

areas. Additionally, we have not quantified a number of known or suspected health effects

linked with PM and ozone for which appropriate health impact functions are not available or

which do not provide easily interpretable outcomes (i.e., changes in forced expiratory

volume [FEV1]). As a result, monetized benefits generated for the primary estimate may

underestimate the total benefits attributable to the final regulatory option. 

Benefits estimates for the final CAIR were generated using BenMAP, a computer

program developed by EPA that integrates a number of the modeling elements used in

previous RIAs (e.g., interpolation functions, population projections, health impact functions,

valuation functions, analysis and pooling methods) to translate modeled air concentration

estimates into health effects incidence estimates and monetized benefits estimates. BenMAP

provides estimates of both the mean impacts and the distribution of impacts (more

information on BenMAP can be found at http://www.epa.gov/ttn/ecas/ benmodels.html).

In general, this chapter is organized around the steps illustrated in Figure 4-1. In

Section 4.1, we provide an overview of the data and methods that were used to quantify and

value health and welfare endpoints and discuss how we incorporate uncertainty into our

analysis. In Section 4.2, we report the results of the analysis for human health and welfare

effects (the overall benefits estimated for the final CAIR are summarized in Table 4-1). 

Details on the emissions inventory and air modeling are presented in Chapter 3. 

4.1 Benefit Analysis—Data and Methods

Given changes in environmental quality (ambient air quality, visibility, nitrogen, and

sulfate deposition), the next step is to determine the economic value of those changes. We

follow a “damage-function” approach in calculating total benefits of the modeled changes in

environmental quality. This approach estimates changes in individual health and welfare

endpoints (specific effects that can be associated with changes in air quality) and assigns

values to those changes assuming independence of the individual values. Total benefits are

calculated simply as the sum of the values for all nonoverlapping health and welfare

endpoints. This imposes no overall preference structure and does not account for potential

income or substitution effects (i.e., adding a new endpoint will not reduce the value of

changes in other endpoints). The “damage-function” approach is the standard approach for
4-13

most cost-benefit analyses of environmental quality programs and has been used in several

recent published analyses (Banzhaf et al., 2002; Levy et al., 2001; Levy et al., 1999; Ostro

and Chestnut, 1998). 

To assess economic value in a damage-function framework, the changes in

environmental quality must be translated into effects on people or on the things that people

value. In some cases, the changes in environmental quality can be directly valued, as is the

case for changes in visibility. In other cases, such as for changes in ozone and PM, a health

and welfare impact analysis must first be conducted to convert air quality changes into

effects that can be assigned dollar values.

For the purposes of this RIA, the health impacts analysis is limited to those health

effects that are directly linked to ambient levels of air pollution and specifically to those

linked to ozone and PM. There may be other, indirect health impacts associated with

implementing emissions controls, such as occupational health impacts for coal miners. 

These impacts may be positive or negative, but in general, for this set of control options, they

are expected to be small relative to the direct air pollution-related impacts.

The welfare impacts analysis is limited to changes in the environment that have a

direct impact on human welfare. For this analysis, we are limited by the available data to

examine impacts of changes in visibility. We also provide qualitative discussions of the

impact of changes in other environmental and ecological effects, for example, changes in

deposition of nitrogen and sulfur to terrestrial and aquatic ecosystems, but we are unable to

place an economic value on these changes.

We note at the outset that EPA rarely has the time or resources to perform extensive

new research to measure either the health outcomes or their values for this analysis. Thus,

similar to Kunzli et al. (2000) and other recent health impact analyses, our estimates are

based on the best available methods of benefits transfer. Benefits transfer is the science and

art of adapting primary research from similar contexts to obtain the most accurate measure of

benefits for the environmental quality change under analysis. Adjustments are made for the

level of environmental quality change, the sociodemographic and economic characteristics of

the affected population, and other factors to improve the accuracy and robustness of benefits

estimates.
4

For many goods, WTP can be observed by examining actual market transactions. For example, if a gallon of

bottled drinking water sells for $1, it can be observed that at least some people are willing to pay $1 for such

water. For goods not exchanged in the market, such as most environmental “goods,” valuation is not as

straightforward. Nevertheless, a value may be inferred from observed behavior, such as sales and prices of

products that result in similar effects or risk reductions (e.g., nontoxic cleaners or bike helmets). 

Alternatively, surveys can be used in an attempt to directly elicit WTP for an environmental improvement.

5

In general, economists tend to view an individual’s WTP for an improvement in environmental quality as the

appropriate measure of the value of a risk reduction. An individual’s willingness to accept (WTA)

compensation for not receiving the improvement is also a valid measure. However, WTP is generally

considered to be a more readily available and conservative measure of benefits.

4-14

4.1.1 Valuation Concepts

In valuing health impacts, we note that reductions in ambient concentrations of air

pollution generally lower the risk of future adverse health effects by a fairly small amount for

a large population. The appropriate economic measure is willingness to pay4 (WTP) for

changes in risk prior to the regulation (Freeman, 1993).5 Adoption of WTP as the measure of

value implies that the value of environmental quality improvements depends on the

individual preferences of the affected population and that the existing distribution of income

(ability to pay) is appropriate. For some health effects, such as hospital admissions, WTP

estimates are generally not available. In these cases, we use the cost of treating or mitigating

the effect as the measure of benefits. These cost of illness (COI) estimates generally

understate the true value of reductions in risk of a health effect, because they do not include

the value of avoided pain and suffering from the health effect (Harrington and Portney, 1987;

Berger et al., 1987).

One distinction in environmental benefits estimation is between use values and

nonuse values. Although no general agreement exists among economists on a precise

distinction between the two (see Freeman [1993]), the general nature of the difference is

clear. Use values are those aspects of environmental quality that affect an individual’s

welfare directly. These effects include changes in product prices, quality, and availability;

changes in the quality of outdoor recreation and outdoor aesthetics; changes in health or life

expectancy; and the costs of actions taken to avoid negative effects of environmental quality

changes. 

Nonuse values are those for which an individual is willing to pay for reasons that do

not relate to the direct use or enjoyment of any environmental benefit but might relate to

existence values and bequest values. Nonuse values are not traded, directly or indirectly, in

markets. For this reason, measuring nonuse values has proven to be significantly more
6

Concerns about the reliability of value estimates from CV studies arose because research has shown that bias

can be introduced easily into these studies if they are not carefully conducted. Accurately measuring WTP

for avoided health and welfare losses depends on the reliability and validity of the data collected. There are

several issues to consider when evaluating study quality, including but not limited to 1) whether the sample

estimates of WTP are representative of the population WTP; 2) whether the good to be valued is

comprehended and accepted by the respondent; 3) whether the WTP elicitation format is designed to

minimize strategic responses; 4) whether WTP is sensitive to respondent familiarity with the good, to the

size of the change in the good, and to income; 5) whether the estimates of WTP are broadly consistent with

other estimates of WTP for similar goods; and 6) the extent to which WTP responses are consistent with

established economic principles. 

4-15

difficult than measuring use values. The air quality changes produced by CAIR cause

changes in both use and nonuse values, but the monetary benefits estimates are almost

exclusively for use values. 

More frequently than not, the economic benefits from environmental quality changes

are not traded in markets, so direct measurement techniques cannot be used. There are three

main nonmarket valuation methods used to develop values for endpoints considered in this

analysis: stated preference (or contingent valuation [CV]), indirect market (e.g., hedonic

wage), and avoided cost methods. 

The stated preference or CV method values endpoints by using carefully structured

surveys to ask a sample of people what amount of compensation is equivalent to a given

change in environmental quality. There is an extensive scientific literature and body of

practice on both the theory and technique of stated preference-based valuation. Welldesigned and well-executed stated preference studies are valid for estimating the benefits of

air quality regulations.6

 Stated preference valuation studies form the basis for valuing a

number of health and welfare endpoints, including the value of mortality risk reductions, CB

risk reductions, minor illness risk reductions, and visibility improvements.

Indirect market methods can also be used to infer the benefits of pollution reduction. 

The most important application of this technique for our analysis is the calculation of the

VSL for use in estimating benefits from mortality risk reductions. No market exists where

changes in the probability of death are directly exchanged. However, people make decisions

about occupation, precautionary behavior, and other activities associated with changes in the

risk of death. By examining these risk changes and the other characteristics of people’s

choices, it is possible to infer information about the monetary values associated with changes

in mortality risk (see Section 4.1.5).
7

Income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent

change in income.

4-16

Avoided cost methods are ways to estimate the costs of pollution by using the

expenditures made necessary by pollution damage. For example, if buildings must be

cleaned or painted more frequently as levels of PM increase, then the appropriately

calculated increment of these costs is a reasonable lower-bound estimate (under most

conditions) of true economic benefits when PM levels are reduced. Avoided costs methods

are also used to estimate some of the health-related benefits related to morbidity, such as

hospital admissions (see Section 4.1.5).

4.1.2 Growth in WTP Reflecting National Income Growth Over Time

Our analysis accounts for expected growth in real income over time. Economic

theory argues that WTP for most goods (such as environmental protection) will increase if

real incomes increase. There is substantial empirical evidence that the income elasticity7 of

WTP for health risk reductions is positive, although there is uncertainty about its exact value. 

Thus, as real income increases, the WTP for environmental improvements also increases. 

Although many analyses assume that the income elasticity of WTP is unit elastic (i.e., a 10

percent higher real income level implies a 10 percent higher WTP to reduce risk changes),

empirical evidence suggests that income elasticity is substantially less than one and thus

relatively inelastic. As real income rises, the WTP value also rises but at a slower rate than

real income.

The effects of real income changes on WTP estimates can influence benefits

estimates in two different ways: through real income growth between the year a WTP study

was conducted and the year for which benefits are estimated, and through differences in

income between study populations and the affected populations at a particular time. 

Empirical evidence of the effect of real income on WTP gathered to date is based on studies

examining the former. The Environmental Economics Advisory Committee (EEAC) of the

Science Advisory Board (SAB) advised EPA to adjust WTP for increases in real income over

time but not to adjust WTP to account for cross-sectional income differences “because of the

sensitivity of making such distinctions, and because of insufficient evidence available at

present” (EPA-SAB-EEAC-00-013). A recent advisory by another committee associated

with the SAB, the Advisory Council on Clean Air Compliance Analysis, has provided

conflicting advice. While agreeing with “the general principle that the willingness to pay to

reduce mortality risks is likely to increase with growth in real income. The same increase
4-17

should be assumed for the WTP for serious nonfatal health effects

(EPA-SAB-COUNCIL-ADV-04-004, p. 52),” they note that “given the limitations and

uncertainties in the available empirical evidence, the Council does not support the use of the

proposed adjustments for aggregate income growth as part of the primary analysis

(EPA-SAB-COUNCIL-ADV-04-004, p. 53).” Until these conflicting advisories have been

reconciled, EPA will continue to adjust valuation estimates to reflect income growth using

the methods described below. 

Based on a review of the available income elasticity literature, we adjusted the

valuation of human health benefits upward to account for projected growth in real U.S.

income. Faced with a dearth of estimates of income elasticities derived from time-series

studies, we applied estimates derived from cross-sectional studies in our analysis. Details of

the procedure can be found in Kleckner and Neumann (1999). An abbreviated description of

the procedure we used to account for WTP for real income growth between 1990 and 2010

and 2015 is presented below. 

Reported income elasticities suggest that the severity of a health effect is a primary

determinant of the strength of the relationship between changes in real income and WTP. As

such, we use different elasticity estimates to adjust the WTP for minor health effects, severe

and chronic health effects, and premature mortality. Note that because of the variety of

empirical sources used in deriving the income elasticities, there may appear to be

inconsistencies in the magnitudes of the income elasticities relative to the severity of the

effects (apriori one might expect that more severe outcomes would show less income

elasticity of WTP). We have not imposed any additional restrictions on the empirical

estimates of income elasticity. We also expect that the WTP for improved visibility in Class I

areas would increase with growth in real income. The relative magnitude of the income

elasticity of WTP for visibility compared with those for health effects suggests that visibility

is not as much of a necessity as health, thus, WTP is more elastic with respect to income. 

The elasticity values used to adjust estimates of benefits in 2010 and 2015 are presented in

Table 4-3.

In addition to elasticity estimates, projections of real gross domestic product (GDP)

and populations from 1990 to 2010 and 2015 are needed to adjust benefits to reflect real per

capita income growth. For consistency with the emissions and benefits modeling, we used

national population estimates for the years 1990 to 1999 based on U.S. Census Bureau

estimates (Hollman et al., 2000). These population estimates are based on application of a 
8

U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at http://www.bea.doc.gov/bea/dn/0897nip2/

tab2a.htm.) and U.S. Bureau of Economic Analysis, Economics and Budget Outlook. Note that projections

for 2007 to 2010 are based on average GDP growth rates between 1999 and 2007.

9

In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the

relative growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP

based on the Bureau of Economic Analysis projections.

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Table 4-3. Elasticity Values Used to Account for Projected Real Income Growtha

Benefit Category Central Elasticity Estimate

Minor Health Effect 0.14

Severe and Chronic Health Effects 0.45

Premature mortality 0.40

Visibility 0.90

a Derivation of estimates can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI estimates

are assigned an adjustment factor of 1.0. 

cohort-component model applied to 1990 U.S. Census data projections (U.S. Bureau of

Census, 2000). For the years between 2000 and 2015, we applied growth rates based on the

U.S. Census Bureau projections to the U.S. Census estimate of national population in 2000. 

We used projections of real GDP provided in Kleckner and Neumann (1999) for the years

1990 to 2010.8

 We used projections of real GDP (in chained 1996 dollars) provided by

Standard and Poor’s (2000) for the years 2010 to 2015.9

Using the method outlined in Kleckner and Neumann (1999) and the population and

income data described above, we calculated WTP adjustment factors for each of the

elasticity estimates listed in Table 4-4. Benefits for each of the categories (minor health

effects, severe and chronic health effects, premature mortality, and visibility) are adjusted by

multiplying the unadjusted benefits by the appropriate adjustment factor. Table 4-4 lists the

estimated adjustment factors. Note that, for premature mortality, we applied the income

adjustment factor to the present discounted value of the stream of avoided mortalities

occurring over the lag period. Also note that because of a lack of data on the dependence of

COI and income, and a lack of data on projected growth in average wages, no adjustments

are made to benefits based on the COI approach or to work loss days and worker

productivity. This assumption leads us to underpredict benefits in future years because it is 
4-19

Table 4-4. Adjustment Factors Used to Account for Projected Real Income Growtha

Benefit Category 2010 2015

Minor Health Effect 1.034 1.073

Severe and Chronic Health Effects 1.113 1.254

Premature Mortality 1.100 1.222

Visibility 1.239 1.581

a Based on elasticity values reported in Table 4-3, U.S. Census population projections, and projections of real

GDP per capita.

likely that increases in real U.S. income would also result in increased COI (due, for

example, to increases in wages paid to medical workers) and increased cost of work loss days

and lost worker productivity (reflecting that if worker incomes are higher, the losses

resulting from reduced worker production would also be higher). 

4.1.3 Methods for Describing Uncertainty

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. As

outlined both in this and preceding chapters, many inputs were used to derive the final

estimate of benefits, including emission inventories, air quality models (with their associated

parameters and inputs), epidemiological health effect estimates, estimates of values (both

from WTP and COI studies), population estimates, income estimates, and estimates of the

future state of the world (i.e., regulations, technology, and human behavior). Each of these

inputs may be uncertain and, depending on its role in the benefits analysis, may have a

disproportionately large impact on final estimates of total benefits. For example, emissions

estimates are used in the first stage of the analysis. As such, any uncertainty in emissions

estimates will be propagated through the entire analysis. When compounded with

uncertainty in later stages, small uncertainties in emission levels can lead to large impacts on

total benefits. 

Some key sources of uncertainty in each stage of the benefits analysis are the

following:

C gaps in scientific data and inquiry;

C variability in estimated relationships, such as epidemiological effect estimates,

introduced through differences in study design and statistical modeling;
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C errors in measurement and projection for variables such as population growth

rates;

C errors due to misspecification of model structures, including the use of surrogate

variables, such as using PM10 when PM2.5 is not available, excluded variables, and

simplification of complex functions; and

C biases due to omissions or other research limitations.

Some of the key uncertainties in the benefits analysis are presented in Table 4-5. 

The NRC report on EPA’s benefits analysis methodology highlighted the need for

EPA to conduct rigorous quantitative analysis of uncertainty in its benefits estimates. In

response to these comments, EPA has initiated the development of a comprehensive

methodology for characterizing the aggregate impact of uncertainty in key modeling

elements on both health incidence and benefits estimates. For this analysis of the final

CAIR, EPA has developed a limited probabilistic simulation approach based on Monte Carlo

methods to propagate the impact of a limited set of sources of uncertainty through the

modeling framework. Issues such as correlation between input parameters and the

identification of reasonable upper and lower bounds for input distributions characterizing

uncertainty in additional model elements will be addressed in future versions of the

uncertainty framework. 

One component of EPA’s uncertainty analysis methodology that is partially reflected

in the CAIR analysis is our work using the results of an expert elicitation to characterize

uncertainty in the effect estimates used to estimate premature mortality resulting from both

short-term and longer-term exposures to PM. This expert elicitation was aimed at evaluating

uncertainty in both the form of the mortality impact function (e.g., threshold versus linear

models) and the fit of a specific model to the data (e.g., confidence bounds for specific

percentiles of the mortality effect estimates). Additional issues, such as the ability of longterm cohort studies to capture premature mortality resulting from short-term peak PM

exposures, were also addressed in the expert elicitation. In collaboration with OMB, EPA

completed a pilot expert elicitation which is used in the ancillary uncertainty analysis for

CAIR (as discussed in Section 4.3). Based on our experience during the pilot, EPA plans to

conduct a full-scale expert elicitation in 2005 that will provide a more robust characterization

of the uncertainty in the premature mortality function. 
4-21

Table 4-5. Primary Sources of Uncertainty in the Benefits Analysis

1. Uncertainties Associated with Impact Functions

S The value of the ozone or PM effect estimate in each impact function.

S Application of a single impact function to pollutant changes and populations in all locations.

S Similarity of future-year impact functions to current impact functions. 

S Correct functional form of each impact function. 

S Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source epidemiological study. 

S Application of impact functions only to those subpopulations matching the original study population.

2. Uncertainties Associated with Ozone and PM Concentrations 

S Responsiveness of the models to changes in precursor emissions resulting from the control policy.

S Projections of future levels of precursor emissions, especially ammonia and crustal materials.

S Model chemistry for the formation of ambient nitrate concentrations.

S Lack of ozone monitors in rural areas requires extrapolation of observed ozone data from urban to rural areas.

S Use of separate air quality models for ozone and PM does not allow for a fully integrated analysis of pollutants and their interactions.

S Full ozone season air quality distributions are extrapolated from a limited number of simulation days.

S Comparison of model predictions of particulate nitrate with observed rural monitored nitrate levels indicates that REMSAD

overpredicts nitrate in some parts of the Eastern United States

3. Uncertainties Associated with PM Mortality Risk

S Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.

S Direct causal agents within the complex mixture of PM have not been identified.

S The extent to which adverse health effects are associated with low-level exposures that occur many times in the year versus peak

exposures.

S The extent to which effects reported in the long-term exposure studies are associated with historically higher levels of PM rather than

the levels occurring during the period of study.

S Reliability of the limited ambient PM2.5 monitoring data in reflecting actual PM2.5 exposures.

4. Uncertainties Associated with Possible Lagged Effects

S The portion of the PM-related long-term exposure mortality effects associated with changes in annual PM levels that would occur in a

single year is uncertain as well as the portion that might occur in subsequent years.

5. Uncertainties Associated with Baseline Incidence Rates

S Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore may not accurately represent the

actual location-specific rates.

S Current baseline incidence rates may not approximate well baseline incidence rates in 2015.

S Projected population and demographics may not represent well future-year population and demographics.

6. Uncertainties Associated with Economic Valuation

S Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and therefore have uncertainty

surrounding them.

S Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates because of differences in income or

other factors.

7. Uncertainties Associated with Aggregation of Monetized Benefits

S Health and welfare benefits estimates are limited to the available impact functions. Thus, unquantified or unmonetized benefits are

not included.
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For the final CAIR, EPA addressed key sources of uncertainty through Monte Carlo

propagation of uncertainty in the C-R functions and economic valuation functions and

through a series of sensitivity analyses examining the impact of alternate assumptions on the

benefits estimates that are generated. It should be noted that the Monte Carlo-generated

distributions of benefits reflect only some of the uncertainties in the input parameters. 

Uncertainties associated with emissions, air quality modeling, populations, and baseline

health effect incidence rates are not represented in the distributions of benefits for CAIR. 

Our point estimate of total benefits is uncertain because of the uncertainty in model

elements discussed above (see Table 4-5). Uncertainty about specific aspects of the health

and welfare estimation models is discussed in greater detail in the following sections. The

total benefits estimate may understate or overstate actual benefits of the rule.

In considering the monetized benefits estimates, the reader should remain aware of

the many limitations of conducting the analyses mentioned throughout this RIA. One

significant limitation of both the health and welfare benefits analyses is the inability to

quantify many of the effects listed in Table 4-1. For many health and welfare effects, such as

changes in ecosystem functions and PM-related materials damage, reliable impact functions

and/or valuation functions are not currently available. In general, if it were possible to

monetize these benefit categories, the benefits estimates presented in this analysis would

increase, although the magnitude of such an increase is highly uncertain. Unquantified

benefits are qualitatively discussed in the health and welfare effects sections. Furthermore,

EPA explores the implication of the potential relationship between O3 and premature

mortality in its sensitivity analysis. In addition to unquantified benefits, there may also be

environmental costs (disbenefits) that we are unable to quantify. These endpoints are

qualitatively discussed in the health and welfare effects sections as well. The net effect of

excluding benefit and disbenefit categories from the estimate of total benefits depends on the

relative magnitude of the effects. 

Although we are not currently able to estimate the magnitude of these unquantified

and unmonetized benefits, specific categories merit further discussion. The EPA believes

there is considerable value to the public for the benefit categories that could not be

monetized. With regard to unmonetized PM-related health benefit categories listed in

Table 4-2, we feel these benefits may be small relative to those categories we were able to

quantify and monetize.

However, there is one category where new studies suggest the possibility of

significant additional economic benefits. Over the past several years, EPA’s SAB has
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expressed the view that there were not sufficient data to show a separate ozone mortality

effect, in essence, saying that any ozone benefits are captured in the PM-related mortality

benefit estimates. However, in their most recent advice, the SAB recommended that EPA

reconsider the evidence on ozone-related mortality based on the publication of several recent

analyses that found statistically significant associations between ozone and mortality. Based

on these studies and the recommendations from the SAB, EPA has sponsored three

independent meta-analyses of the ozone-mortality epidemiology literature to inform a

determination on including this important health endpoint. The studies are complete and

have been accepted for publication in the journal Epidemiology in July 2005 [see Bell et al.,

in press; Ito et al., in press; Levy et al., in press].

The Agency believes that publication of these meta-analyses will significantly

enhance the scientific defensibility of benefits estimates for ozone, that include the benefits

of premature mortality reductions. In addition, a study published in JAMA in November

2004 also confirmed that ozone mortality impacts can be calculated separately from PM

mortality impacts (Bell et al., 2004). EPA’s believes that there is sufficient evidence to

return to the SAB to confirm that these studies address their previous concerns. Using effect

estimates similar to those found in these new studies, EPA estimates the monetary value of

the ozone-related premature mortality benefits could be substantial. We estimate ozone

mortality benefits may yield roughly 500 reduced premature mortalities per year and may

increase the benefits of CAIR by approximately $3 billion annually.

In addition to unquantified and unmonetized health benefit categories, Table 4-2

shows a number of welfare benefit categories that are omitted from the monetized benefit

estimates for this rule. Only a subset of the expected visibility benefits-those for Class I

areas in the southeastern United States are included in the monetary benefits estimates we

project for this rule. We believe the benefits associated with these non-health benefit

categories are likely significant. For example, we are able to quantify significant visibility

improvements in Class I areas in the Northeast and Midwest, but are unable at present to

place a monetary value on these improvement. Similarly, we anticipate improvement in

visibility in residential areas within the CAIR region for which we are currently unable to

monetize benefits. For the Class I areas in the southeastern U.S., we estimate annual benefits

of $1.78 billion beginning in 2015 for visibility improvements. The value of visibility

benefits in areas where we were unable to monetize benefits could also be substantial. 
4-24

We conduct supplemental analyses related to visibility and household cleaning costs

later in this chapter. Based on these analyses, expanded coverage of these benefit categories

could increase total benefits by over $500 million. (See Appendix C for more details.)

In a recent study, Resources for the Future (RFF) estimates total benefits (i.e., the

sum of use and nonuse values) of natural resource improvements for the Adirondacks

resulting from a program that would reduce acidification in 40 percent of the lakes in the

Adirondacks of concern for acidification (Banzhaf, 2004). While the study requires further

evaluation, the RFF study does suggests that the benefits of acid deposition reductions for

CAIR could be substantial in terms of the total monetized value for ecological endpoints.

Another area of potential benefits not monetized relates to potential reductions in

nitrogen deposition from CAIR for estuaries and coastal waters within the CAIR region. 

Nitrogen deposition contributes to eutrophication and water quality degradation in estuaries

and coastal waters. While we are unable to monetize the benefits of such reductions, the

Chesapeake Bay Program estimated the reduced mass of delivered nitrogen loads likely to

result from CAIR, based upon the CAIR proposal deposition estimates published in January

2004. Atmospheric deposition of nitrogen accounts for a significant portion of the nitrogen

loads to the Chesapeake with 28 percent of the nitrogen loads to the watershed coming from

air deposition. Based upon the CAIR proposal nitrogen deposition rates published in the

January 2004 proposal, the Chesapeake Bay Program finds that CAIR will likely reduce the

nitrogen loads to the Bay by 10 million pounds per year by 2010 (Sweeney, 2004). These

substantial nitrogen load reductions more than fulfill the EPA’s commitment to reduce

atmospheric deposition delivered to the Chesapeake Bay by 8 million pounds annually. The

benefits of these atmospheric deposition reductions for the Bay are likely to be substantial. 

4.1.4 Demographic Projections

Quantified and monetized human health impacts depend on the demographic

characteristics of the population, including age, location, and income. We use projections

based on economic forecasting models developed by Woods and Poole, Inc. The Woods and

Poole (WP) database contains county-level projections of population by age, sex, and race

out to 2025. Projections in each county are determined simultaneously with every other

county in the United States to take into account patterns of economic growth and migration. 

The sum of growth in county-level populations is constrained to equal a previously

determined national population growth, based on Bureau of Census estimates (Hollman et

al., 2000). According to WP, linking county-level growth projections together and

constraining to a national-level total growth avoids potential errors introduced by forecasting
4-25

each county independently. County projections are developed in a four-stage process. First,

national-level variables such as income, employment, and populations are forecasted. 

Second, employment projections are made for 172 economic areas defined by the Bureau of

Economic Analysis, using an “export-base” approach, which relies on linking industrialsector production of nonlocally consumed production items, such as outputs from mining,

agriculture, and manufacturing with the national economy. The export-based approach

requires estimation of demand equations or calculation of historical growth rates for output

and employment by sector. Third, population is projected for each economic area based on

net migration rates derived from employment opportunities and following a cohortcomponent method based on fertility and mortality in each area. Fourth, employment and

population projections are repeated for counties, using the economic region totals as bounds. 

The age, sex, and race distributions for each region or county are determined by aging the

population by single year of age by sex and race for each year through 2015 based on

historical rates of mortality, fertility, and migration.

The WP projections of county-level population are based on historical population

data from 1969 through 1999 and do not include the 2000 Census results. Given the

availability of detailed 2000 Census data, we constructed adjusted county-level population

projections for each future year using a two-stage process. First, we constructed ratios of the

projected WP populations in a future year to the projected WP population in 2000 for each

future year by age, sex, and race. Second, we multiplied the block-level 2000 Census

population data by the appropriate age-, sex-, and race-specific WP ratio for the county

containing the census block for each future year. This results in a set of future population

projections that is consistent with the most recent detailed Census data. 

As noted above, values for environmental quality improvements are expected to

increase with growth in real per capita income. Accounting for real income growth over

time requires projections of both real GDP and total U.S. populations. For consistency with

the emissions and benefits modeling, we used national population estimates based on the

U.S. Census Bureau projections.

4.1.5 Health Benefits Assessment Methods

The largest monetized benefits of reducing ambient concentrations of PM and ozone

are attributable to reductions in health risks associated with air pollution. EPA’s Criteria

Documents for ozone and PM list numerous health effects known to be linked to ambient

concentrations of these pollutants (EPA, 1996a; 1996b). As illustrated in Figure 4-1,

quantification of health impacts requires several inputs, including epidemiological effect
4-26

estimates (concentration-response functions), baseline incidence and prevalence rates,

potentially affected populations, and estimates of changes in ambient concentrations of air

pollution. Previous sections have described the population and air quality inputs. This

section describes the effect estimates and baseline incidence and prevalence inputs and the

methods used to quantify and monetize changes in the expected number of incidences of

various health effects.

4.1.5.1 Selecting Health Endpoints and Epidemiological Effect Estimates

Certain quantified health benefits of the rule may be related to ozone only, PM only,

or both pollutants. Based on the available epidemiological data, we quantified decreased

worker productivity, respiratory hospital admissions for children under two years of age, and

school absences related to ozone but not PM. The PM-only health effects we quantified

include premature mortality, nonfatal heart attacks, CB, acute bronchitis, upper and lower

respiratory symptoms, asthma exacerbations, and days of work lost. The health effects that

we quantified relate to both PM and ozone include hospital admissions, emergency room

visits for asthma, and MRADs. Although recent epidemiological evidence points to an

association between short term exposures to ozone and premature mortality, EPA is not

prepared to quantify this impact in the primary analysis for the CAIR due to the need for

additional review of the issue by the Health Effects Subcommittee of the SAB. 

We relied on the published scientific literature to ascertain the relationship between

PM and ozone exposure and adverse human health effects. We evaluated studies using the

selection criteria summarized in Table 4-6. These criteria include consideration of whether

the study was peer reviewed, the match between the pollutant studied and the pollutant of

interest, the study design and location, and characteristics of the study population, among

other considerations. The selection of C-R functions for the benefits analysis is guided by

the goal of achieving a balance between comprehensiveness and scientific defensibility.

Some health effects are excluded from this analysis for three reasons: the possibility

of double-counting (such as hospital admissions for specific respiratory diseases),

uncertainties in applying effect relationships based on clinical studies to the affected

population, or a lack of an established relationship between the health effect and pollutant in

the published epidemiological literature. An improvement in ambient PM and ozone air

quality may reduce the number of incidences within each unquantified effect category that

the U.S. population would experience. Although these health effects are believed to be PM

or ozone induced, effect estimates are not available for quantifying the benefits associated 
4-27

Table 4-6. Summary of Considerations Used in Selecting C-R Functions

Consideration Comments

Peer-Reviewed

Research

Peer-reviewed research is preferred to research that has not undergone the peer-review

process.

Study Type Among studies that consider chronic exposure (e.g., over a year or longer), prospective

cohort studies are preferred over cross-sectional studies because they control for

important individual-level confounding variables that cannot be controlled for in

cross-sectional studies. 

Study Period Studies examining a relatively longer period of time (and therefore having more data) are

preferred, because they have greater statistical power to detect effects. More recent

studies are also preferred because of possible changes in pollution mixes, medical care,

and lifestyle over time. However, when there are only a few studies available, studies

from all years will be included.

Population

Attributes

The most technically appropriate measures of benefits would be based on impact

functions that cover the entire sensitive population but allow for heterogeneity across age

or other relevant demographic factors. In the absence of effect estimates specific to age,

sex, preexisting condition status, or other relevant factors, it may be appropriate to select

effect estimates that cover the broadest population to match with the desired outcome of

the analysis, which is total national-level health impacts.

Study Size Studies examining a relatively large sample are preferred because they generally have

more power to detect small magnitude effects. A large sample can be obtained in several

ways, either through a large population or through repeated observations on a smaller

population (e.g., through a symptom diary recorded for a panel of asthmatic children).

Study Location U.S. studies are more desirable than non-U.S. studies because of potential differences in

pollution characteristics, exposure patterns, medical care system, population behavior,

and lifestyle.

Pollutants

Included in

Model

When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it

is important to use properly specified impact functions that include both pollutants. 

Using single-pollutant models in cases where both pollutants are expected to affect a

health outcome can lead to double-counting when pollutants are correlated.

Measure of PM For this analysis, impact functions based on PM2.5 are preferred to PM10 because CAIR

will regulate emissions of PM2.5 precursors, and air quality modeling was conducted for

this size fraction of PM. Where PM2.5 functions are not available, PM10 functions are

used as surrogates, recognizing that there will be potential downward (upward) biases if

the fine fraction of PM10 is more (less) toxic than the coarse fraction. 

Economically

Valuable Health

Effects

Some health effects, such as forced expiratory volume and other technical measurements

of lung function, are difficult to value in monetary terms. These health effects are not

quantified in this analysis.

Nonoverlapping

Endpoints

Although the benefits associated with each individual health endpoint may be analyzed

separately, care must be exercised in selecting health endpoints to include in the overall

benefits analysis because of the possibility of double-counting of benefits. 
10There has been a great deal of research recently on the potential effect of ozone on premature mortality

(Anderson et al, 2004; Bell et al, 2004; Thurston and Ito, 2001). Although the air pollutant most clearly

associated with premature mortality is PM, with dozens of studies reporting such an association, repeated

ozone exposure is a likely contributing factor for premature mortality, causing an inflammatory response in

the lungs that may predispose elderly and other sensitive individuals to become more susceptible. Appendix

C presents a sensitivity analysis showing the potential impacts of CAIR on ozone-related mortality.

11In this analysis, the fixed effects model assumes that there is only one pollutant coefficient for the entire

modeled area. The random effects model assumes that studies conducted in different locations are

estimating different parameters; therefore, there may be a number of different underlying pollutant

coefficients. 

4-28

with reducing these effects.10 The inability to quantify these effects lends a downward bias

to the monetized benefits presented in this analysis.

In general, the use of results from more than a single study can provide a more robust

estimate of the relationship between a pollutant and a given health effect. However, there are

often differences between studies examining the same endpoint, making it difficult to pool

the results in a consistent manner. For example, studies may examine different pollutants or

different age groups. For this reason, we consider very carefully the set of studies available

examining each endpoint and select a consistent subset that provides a good balance of

population coverage and match with the pollutant of interest. In many cases, either because

of a lack of multiple studies, consistency problems, or clear superiority in the quality or

comprehensiveness of one study over others, a single published study is selected as the basis

of the effect estimate.

When several effect estimates for a pollutant and a given health endpoint have been

selected, they are quantitatively combined or pooled to derive a more robust estimate of the

relationship. The Benefits TSD completed for the nonroad diesel rulemaking provides

details of the procedures used to combine multiple impact functions (Abt Associates, 2003). 

In general, we used fixed or random effects models to pool estimates from different studies

of the same endpoint. Fixed effects pooling simply weights each study’s estimate by the

inverse variance, giving more weight to studies with greater statistical power (lower

variance). Random effects pooling accounts for both within-study variance and betweenstudy variability, due, for example, to differences in population susceptibility. We used the

fixed effects model as our null hypothesis and then determined whether the data suggest that

we should reject this null hypothesis, in which case we would use the random effects

model.11 Pooled impact functions are used to estimate hospital admissions (PM), school

absence days (ozone), lower respiratory symptoms (PM), asthma exacerbations (PM), and

asthma-related emergency room visits (ozone). For more details on methods used to pool
4-29

incidence estimates, see the Benefits TSD for the nonroad diesel rulemaking (Abt

Associates, 2003). 

Effect estimates for a pollutant and a given health endpoint were applied consistently

across all locations nationwide. This applies to both impact functions defined by a single

effect estimate and those defined by a pooling of multiple effect estimates. Although the

effect estimate may, in fact, vary from one location to another (e.g., because of differences in

population susceptibilities or differences in the composition of PM), location-specific effect

estimates are generally not available. 

The specific studies from which effect estimates for the primary analysis are drawn

are included in Table 4-7. 

Premature Mortality. Both long- and short-term exposures to ambient levels of air

pollution have been associated with increased risk of premature mortality. The size of the

mortality risk 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. 

Although a number of uncertainties remain to be addressed by continued research

(NRC, 1998), a substantial body of published scientific literature documents the correlation

between elevated PM concentrations and increased mortality rates. Time-series methods

relate short-term (often day-to-day) changes in PM concentrations and changes in daily

mortality rates up to several days after a period of elevated PM concentrations. Cohort

methods examine the potential relationship between community-level PM exposures over

multiple years (i.e., long-term exposures) and community-level annual mortality rates.

Researchers have found statistically significant associations between PM and premature

mortality using both types of studies. In general, the risk estimates based on the cohort

studies are larger than those derived from time-series studies. Cohort analyses are thought to

better capture the full public health impact of exposure to air pollution over time, because

they capture the effects of long-term exposures and possibly some component of short-term

exposures (Kunzli et al., 2001; NRC, 2002). This section discusses some of the issues

surrounding the estimation of premature mortality.

Over a dozen studies have found significant associations between various measures

of long-term exposure to PM and elevated rates of annual mortality, beginning with Lave and

Seskin (1977). Most of the published studies found positive (but not always statistically 
4-30

Table 4-7. Endpoints and Studies Used to Calculate Total Monetized Health Benefits

Endpoint Pollutant Study

Study

Population

Premature Mortality

Premature mortality

—cohort study, allcause

PM2.5

(annual

mean)

Pope et al. (2002) >29 years

Premature mortality

— all-cause

PM2.5

(annual

mean)

Woodruff et al. (1997) Infant (<1 year)

Chronic Illness

Chronic bronchitis PM2.5 Abbey et al. (1995) >26 years

Nonfatal heart

attacks

PM2.5 Peters et al. (2001) Adults

Hospital Admissions 

Respiratory Ozone Pooled estimate:

Schwartz (1995)—ICD 460-519 (all resp)

Schwartz (1994a, 1994b)—ICD 480-486

(pneumonia)

Moolgavkar et al. (1997)—ICD 480-487

(pneumonia)

Schwartz (1994b)—ICD 491-492, 494-496 (COPD)

Moolgavkar et al. (1997)—ICD 490-496 (COPD) 

>64 years

Ozone Burnett et al. (2001) <2 years

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD)

Ito (2003)—ICD 490-496 (COPD)

>64 years

PM2.5 Moolgavkar (2000)—ICD 490-496 (COPD) 20–64 years

PM2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years

PM2.5 Sheppard (2003)—ICD 493 (asthma) <65 years

Cardiovascular PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 390-429 (all

cardiovascular)

Ito (2003)—ICD 410-414, 427-428 (ischemic heart

disease, dysrhythmia, heart failure)

>64 years

PM2.5 Moolgavkar (2000)—ICD 390-429 (all

cardiovascular)

20–64 years

Asthma-related ER

visits

Ozone Pooled estimate: 

Weisel et al. (1995), Cody et al. (1992), Stieb et al.

(1996)

All ages

PM2.5 Norris et al. (1999) 0–18 years

(continued)
4-31

Table 4-7. Endpoints and Studies Used to Calculate Total Monetized Health Benefits

(continued)

Endpoint Pollutant Study

Study

Population

Other Health Endpoints

Acute bronchitis PM2.5 Dockery et al. (1996) 8–12 years

Upper respiratory

symptoms

PM10 Pope et al. (1991) Asthmatics, 

9–11 years

Lower respiratory

symptoms

PM2.5 Schwartz and Neas (2000) 7–14 years

Asthma

exacerbations

PM2.5 Pooled estimate:

Ostro et al. (2001) (cough, wheeze and shortness of

breath)

Vedal et al. (1998) (cough)

6–18 yearsa

Work loss days PM2.5 Ostro (1987) 18–65 years

School absence days Ozone Pooled estimate:

Gilliland et al. (2001)

Chen et al. (2000)

9–10 years

6–11 years

Worker productivity Ozone Crocker and Horst (1981) Outdoor workers,

18–65

MRADs PM2.5,

Ozone

Ostro and Rothschild (1989) 18–65 years

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al.

(1998) study. Based on advice from the SAB-HES, we extended the applied population to 6 to 18, reflecting

the common biological basis for the effect in children in the broader age group.

significant) associations with available PM indices such as total suspended particles (TSP). 

However, exploration of alternative model specifications sometimes raised questions about

causal relationships (e.g., Lipfert, Morris, and Wyzga [1989]). These early “cross-sectional”

studies (e.g., Lave and Seskin [1977]; Ozkaynak and Thurston [1987]) were criticized for a

number of methodological limitations, particularly for inadequate control at the individual

level for variables that are potentially important in causing mortality, such as wealth,

smoking, and diet. More recently, several studies have been published that use improved

approaches and appear to be consistent with the earlier body of literature. These new

“prospective cohort” studies reflect a significant improvement over the earlier work because

they include individual-level information with respect to health status and residence. The

most extensive analyses have been based on data from two prospective cohort groups, often
12EPA recognizes that the ACS cohort also is not representative of the demographic mix in the general

population. The ACS cohort is almost entirely white and has higher income and education levels relative to

the general population. EPA’s approach to this problem is to match populations based on the potential for

demographic characteristics to modify the effect of air pollution on mortality risk. Thus, for the various

ACS-based models, we are careful to apply the effect estimate only to ages matching those in the original

studies, because age has a potentially large modifying impact on the effect estimate, especially when

younger individuals are excluded from the study population. For the Lipfert analysis, the applied population

should be limited to that matching the sample used in the analysis. This sample was all male, veterans, and

diagnosed hypertensive. There are also a number of differences between the composition of the sample and

the general population, including a higher percentage of African Americans (35 percent) and a much higher

percentage of smokers (81 percent former smokers, 57 percent current smokers) than the general population

(12 percent African American, 24 percent current smokers). 

4-32

referred to as the Harvard “Six-Cities Study” (Dockery et al., 1993) and the “American

Cancer Society or ACS study” (Pope et al., 1995); these studies have found consistent

relationships between fine particle indicators and premature mortality across multiple

locations in the United States. A third major data set comes from the California-based 7th

Day Adventist Study (e.g., Abbey et al., 1999), which reported associations between

long-term PM exposure and mortality in men. Results from this cohort, however, have been

inconsistent, and the air quality results are not geographically representative of most of the

United States. More recently, a cohort of adult male veterans diagnosed with hypertension

has been examined (Lipfert et al., 2000). The characteristics of this group differ from the

cohorts in the Six-Cities, ACS, and 7th Day Adventist studies with respect to income, race,

health status, and smoking status. Unlike previous long-term analyses, this study found

some associations between mortality and ozone but found inconsistent results for PM

indicators. Because of the selective nature of the population in the veteran’s cohort, we have

chosen not to include any effect estimates from the Lipfert et al. (2000) study in our benefits

assessment.12

Given their consistent results and broad geographic coverage, the Six-Cities and ACS

data have been particularly important in benefits analyses. The credibility of these two

studies is further enhanced by the fact that they were subject to extensive reexamination and

reanalysis by an independent team of scientific experts commissioned by HEI (Krewski

et al., 2000). The final results of the reanalysis were then independently peer reviewed by a

Special Panel of the HEI Health Review Committee. The results of these reanalyses

confirmed and expanded those of the original investigators. This intensive independent

reanalysis effort was occasioned both by the importance of the original findings and concerns

that the underlying individual health effects information has never been made publicly

available. 
4-33

While the HEI reexamination lends credibility to the original studies, it also

highlights sensitivities concerning the relative impact of various pollutants, the potential role

of education in mediating the association between pollution and mortality, and the influence

of spatial correlation modeling. Further confirmation and extension of the overall findings

using more recent air quality and a longer follow-up period for the ACS cohort was recently

published (Pope et al., 2002).

In developing and improving the methods for estimating and valuing the potential

reductions in mortality risk over the years, EPA consulted with the SAB-HES. That panel

recommended using long-term prospective cohort studies in estimating mortality risk

reduction (EPA-SAB-COUNCIL-ADV-99-005, 1999). This recommendation has been

confirmed by a recent report from the National Research Council, which 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). More specifically, the SAB recommended

emphasis on the ACS study because it includes a much larger sample size and longer

exposure interval and covers more locations (e.g., 50 cities compared to the Six-Cities Study)

than other studies of its kind. As explained in the regulatory impact analysis for the

Heavy-Duty Engine/Diesel Fuel rule (EPA, 2000d), more recent EPA benefits analyses have

relied on an improved specification of the ACS cohort data that was developed in the HEI

reanalysis (Krewski et al., 2000). The latest reanalysis of the ACS cohort data (Pope et al.,

2002) provides additional refinements to the analysis of PM-related mortality by

a) extending the follow-up period for the ACS study subjects to 16 years, which triples the

size of the mortality data set; b) substantially increasing exposure data, including

consideration for cohort exposure to PM2.5 following implementation of the PM2.5 standard in

1999; c) controlling for a variety of personal risk factors including occupational exposure

and diet; and d) using advanced statistical methods to evaluate specific issues that can

adversely affect risk estimates including the possibility of spatial autocorrelation of survival

times in communities located near each other. Because of these refinements, the SAB-HES

recommends using the Pope et al. (2002) study as the basis for the primary mortality estimate

for adults and suggests that alternate estimates of mortality generated using other cohort and

time-series studies could be included as part of the sensitivity analysis (SAB-HES, 2004).

The SAB-HES also recommended using the estimated relative risks from the Pope et

al. (2002) study based on the average exposure to PM2.5, measured by the average of two

PM2.5 measurements, over the periods 1979–1983 and 1999–2000. In addition to relative

risks for all-cause mortality, the Pope et al. (2002) study provides relative risks for

cardiopulmonary, lung cancer, and all-other cause mortality. Because of concerns regarding
4-34

the statistical reliability of the all-other cause mortality relative risk estimates, we calculated

mortality impacts for the primary analysis based on the all-cause relative risk. However, we

provide separate estimates of cardiopulmonary and lung cancer deaths to show how these

important causes of death are affected by reductions in PM2.5.

Recently published studies have strengthened the case for an association between PM

exposure and respiratory inflamation and infection leading to premature mortality in children

under 5 years of age. Specifically, 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 (SAB-HES, 2003). 

The SAB-HES also cites the study by Belanger et al. (2003) as corroborating findings linking

PM exposure to increased respiratory inflamation and infections in children. Recently, a

study by Chay and Greenstone (2003) found that reductions in TSP caused by the recession

of 1981–1982 were related to reductions in infant mortality at the county level. With regard

to the cohort study conducted by Woodruff et al. (1997), the SAB-HES notes 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 recommends that 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 (SAB-HES,

2004).

Chronic Bronchitis. CB is characterized by mucus in the lungs and a persistent wet

cough for at least 3 months a year for several years in a row. CB affects an estimated 5

percent of the U.S. population (American Lung Association, 1999). A limited number of

studies have estimated the impact of air pollution on new incidences of CB. Schwartz (1993)

and Abbey et al. (1995) provide evidence that long-term PM exposure gives rise to the

development of CB in the United States. Because the CAIR is expected to reduce primarily

PM2.5, this analysis uses only the Abbey et al. (1995) study, because it is the only study

focusing on the relationship between PM2.5 and new incidences of CB. 

Nonfatal Myocardial Infarctions (heart attacks). Nonfatal heart attacks have been

linked with short-term exposures to PM2.5 in the United States (Peters et al., 2001) and other

countries (Poloniecki et al., 1997). We used a recent study by Peters et al. (2001) as the

basis for the impact function estimating the relationship between PM2.5 and nonfatal heart

attacks. Peters et al. is the only available U.S. study to provide a specific estimate for heart
4-35

attacks. Other studies, such as Samet et al. (2000) and Moolgavkar (2000), show a consistent

relationship between all cardiovascular hospital admissions, including those for nonfatal

heart attacks, and PM. Given the lasting impact of a heart attack on long-term health costs

and earnings, we provide a separate estimate for nonfatal heart attacks. The estimate used in

the CAIR analysis is based on the single available U.S. effect estimate. The finding of a

specific impact on heart attacks is consistent with hospital admission and other studies

showing relationships between fine particles and cardiovascular effects both within and

outside the United States. Several epidemiologic studies (Liao et al., 1999; Gold et al., 2000;

Magari et al., 2001) have shown that heart rate variability (an indicator of how much the

heart is able to speed up or slow down in response to momentary stresses) is negatively

related to PM levels. Heart rate variability is a risk factor for heart attacks and other

coronary heart diseases (Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997; Tsuji

et al., 1996). As such, significant impacts of PM on heart rate variability are consistent with

an increased risk of heart attacks.

Hospital and Emergency Room Admissions. Because of the availability of detailed

hospital admission and discharge records, there is an extensive body of literature examining

the relationship between hospital admissions and air pollution. Because of this, many of the

hospital admission endpoints use pooled impact functions based on the results of a number of

studies. In addition, some studies have examined the relationship between air pollution and

emergency room visits. Since most emergency room visits do not result in an admission to

the hospital (the majority of people going to the emergency room are treated and return

home), we treat hospital admissions and emergency room visits separately, taking account of

the fraction of emergency room visits that are admitted to the hospital.

The two main groups of hospital admissions estimated in this analysis are respiratory

admissions and cardiovascular admissions. There is not much evidence linking ozone or PM

with other types of hospital admissions. The only type of emergency room visits that have

been consistently linked to ozone and PM in the United States are asthma-related visits. 

To estimate avoided incidences of cardiovascular hospital admissions associated with

PM2.5, we used studies by Moolgavkar (2003) and Ito (2003). Additional published studies

show a statistically significant relationship between PM10 and cardiovascular hospital

admissions. However, given that the control options we are analyzing are expected to reduce

primarily PM2.5, we focus on the two studies that examine PM2.5. Both of these studies

provide an effect estimate for populations over 65, allowing us to pool the impact functions

for this age group. Only Moolgavkar (2000) provided a separate effect estimate for
13Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence

criteria. However, given that no other estimates are available for this age group, we chose to use the existing

study. Given the very small (<5 percent) difference in the effect estimates for people 65 and older with

cardiovascular hospital admissions between the original and reanalyzed results, we do not expect this choice

to introduce much bias.

4-36

populations 20 to 64.13 Total cardiovascular hospital admissions are thus the sum of the

pooled estimate for populations over 65 and the single study estimate for populations 20 to

64. Cardiovascular hospital admissions include admissions for myocardial infarctions. To

avoid double-counting benefits from reductions in myocardial infarctions when applying the

impact function for cardiovascular hospital admissions, we first adjusted the baseline

cardiovascular hospital admissions to remove admissions for myocardial infarctions. 

To estimate total avoided incidences of respiratory hospital admissions, we used

impact functions for several respiratory causes, including chronic obstructive pulmonary

disease (COPD), pneumonia, and asthma. As with cardiovascular admissions, additional

published studies show a statistically significant relationship between PM10 and respiratory

hospital admissions. We used only those focusing on PM2.5. Both Moolgavkar (2000) and

Ito (2003) provide effect estimates for COPD in populations over 65, allowing us to pool the

impact functions for this group. Only Moolgavkar (2000) provides a separate effect estimate

for populations 20 to 64. Total COPD hospital admissions are thus the sum of the pooled

estimate for populations over 65 and the single study estimate for populations 20 to 64. Only

Ito (2003) estimated pneumonia and only for the population 65 and older. In addition,

Sheppard (2003) provided an effect estimate for asthma hospital admissions for populations

under age 65. Total avoided incidences of PM-related respiratory-related hospital

admissions is the sum of COPD, pneumonia, and asthma admissions.

To estimate the effects of PM air pollution reductions on asthma-related ER visits, we

use the effect estimate from a study of children 18 and under by Norris et al. (1999). As

noted earlier, there is another study by Schwartz examining a broader age group (less than

65), but the Schwartz study focused on PM10 rather than PM2.5. We selected the Norris et al.

(1999) effect estimate because it better matched the pollutant of interest. Because children

tend to have higher rates of hospitalization for asthma relative to adults under 65, we will

likely capture the majority of the impact of PM2.5 on asthma emergency room visits in

populations under 65, although there may still be significant impacts in the adult population

under 65. 
14See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002. 

4-37

To estimate avoided incidences of respiratory hospital admissions associated with

ozone, we used a number of studies examining hospital admissions for a range of respiratory

illnesses, including pneumonia and COPD. Two age groups, adults over 65 and children

under 2, were examined. For adults over 65, Schwartz (1995) provides effect estimates for

two different cities relating ozone and hospital admissions for all respiratory causes (defined

as ICD codes 460–519). Impact functions based on these studies were pooled first before

being pooled with other studies. Two studies (Moolgavkar et al., 1997; Schwartz, 1994a)

examine ozone and pneumonia hospital admissions in Minneapolis. One additional study

(Schwartz, 1994b) examines ozone and pneumonia hospital admissions in Detroit. The

impact functions for Minneapolis were pooled together first, and the resulting impact

function was then pooled with the impact function for Detroit. This avoids assigning too

much weight to the information coming from one city. For COPD hospital admissions, two

studies are available: Moolgavkar et al. (1997), conducted in Minneapolis, and Schwartz

(1994b), conducted in Detroit. These two studies were pooled together. To estimate total

respiratory hospital admissions for adults over 65, COPD admissions were added to

pneumonia admissions, and the result was pooled with the Schwartz (1995) estimate of total

respiratory admissions. Burnett et al. (2001) is the only study providing an effect estimate

for respiratory hospital admissions in children under 2.

Acute Health Events and School/Work Loss Days. As indicated in Table 4-1, in

addition to mortality, chronic illness, and hospital admissions, a number of acute health

effects not requiring hospitalization are associated with exposure to ambient levels of ozone

and PM. The sources for the effect estimates used to quantify these effects are described

below. 

Around 4 percent of U.S. children between the ages of 5 and 17 experience episodes

of acute bronchitis annually (American Lung Association, 2002c). Acute bronchitis is

characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a

number of days. According to the MedlinePlus medical encyclopedia,14 with the exception

of cough, most acute bronchitis symptoms abate within 7 to 10 days. Incidence of episodes

of acute bronchitis in children between the ages of 5 and 17 were estimated using an effect

estimate developed from Dockery et al. (1996). 

Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children

aged 7 to 14 were estimated using an effect estimate from Schwartz and Neas (2000). 
15Estimating asthma exacerbations associated with air pollution exposures is difficult, due to concerns about

double-counting of benefits. Concerns over double-counting stem from the fact that studies of the general

population also include asthmatics, so estimates based solely on the asthmatic population cannot be directly

4-38

Because asthmatics have greater sensitivity to stimuli (including air pollution),

children with asthma can be more susceptible to a variety of upper respiratory symptoms

(e.g., runny or stuffy nose; wet cough; and burning, aching, or red eyes). Research on the

effects of air pollution on upper respiratory symptoms has thus focused on effects in

asthmatics. Incidences of upper respiratory symptoms in asthmatic children aged 9 to 11 are

estimated using an effect estimate developed from Pope et al. (1991). 

Health effects from air pollution can also result in missed days of work (either from

personal symptoms or from caring for a sick family member). Days of work lost due to

PM2.5 were estimated using an effect estimate developed from Ostro (1987). Children may

also be absent from school because of respiratory or other diseases caused by exposure to air

pollution. Most studies examining school absence rates have found little or no association

with PM2.5, but several studies have found a significant association between ozone levels and

school absence rates. We used two recent studies, Gilliland et al. (2001) and Chen et al.

(2000), to estimate changes in absences (school loss days) due to changes in ozone levels. 

The Gilliland et al. study estimated the incidence of new periods of absence, while the Chen

et al. study examined absence on a given day. We converted the Gilliland estimate to days of

absence by multiplying the absence periods by the average duration of an absence. We

estimated an average duration of school absence of 1.6 days by dividing the average daily

school absence rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic

absence rate from Gilliland et al. (2001). This provides estimates from Chen et al. (2000)

and Gilliland et al. (2001), which can be pooled to provide an overall estimate.

MRAD result when individuals reduce most usual daily activities and replace them

with less strenuous activities or rest, yet not to the point of missing work or school. For

example, a mechanic who would usually be doing physical work most of the day will instead

spend the day at a desk doing paper and phone work because of difficulty breathing or chest

pain. The effect of PM2.5 and ozone on MRAD was estimated using an effect estimate

derived from Ostro and Rothschild (1989).

For CAIR, we have followed the SAB-HES recommendations regarding asthma

exacerbations in developing the primary estimate. To prevent double-counting, we focused

the estimation on asthma exacerbations occurring in children and excluded adults from the

calculation.15 Asthma exacerbations occurring in adults are assumed to be captured in the
added to the general population numbers without double-counting. In one specific case (upper respiratory

symptoms in children), the only study available is limited to asthmatic children, so this endpoint can be

readily included in the calculation of total benefits. However, other endpoints, such as lower respiratory

symptoms and MRADs, are estimated for the total population that includes asthmatics. Therefore, to simply

add predictions of asthma-related symptoms generated for the population of asthmatics to these total

population-based estimates could result in double-counting, especially if they evaluate similar endpoints. 

The SAB-HES, in commenting on the analytical blueprint for 812, acknowledged these challenges in

evaluating asthmatic symptoms and appropriately adding them into the primary analysis (SAB-HES, 2004). 

However, despite these challenges, the SAB-HES recommends the addition of asthma-related symptoms

(i.e., asthma exacerbations) to the primary analysis, provided that the studies use the panel study approach

and that they have comparable design and baseline frequencies in both asthma prevalence and exacerbation

rates. Note also, that the SAB-HES, while supporting the incorporation of asthma exacerbation estimates,

does not believe that the association between ambient air pollution, including ozone and PM, and the new

onset of asthma is sufficiently strong to support inclusion of this asthma-related endpoint in the primary

estimate. 

4-39

general population endpoints such as work loss days and MRADs. Consequently, if we had

included an adult-specific asthma exacerbation estimate, we would likely double-count

incidence for this endpoint. However, because the general population endpoints do not cover

children (with regard to asthmatic effects), an analysis focused specifically on asthma

exacerbations for children (6 to 18 years of age) could be conducted without concern for

double-counting. 

To characterize asthma exacerbations in children, we selected two studies (Ostro et

al., 2001; Vedal et al., 1998) that followed panels of asthmatic children. Ostro et al. (2001)

followed a group of 138 African-American children in Los Angeles for 13 weeks, recording

daily occurrences of respiratory symptoms associated with asthma exacerbations (e.g.,

shortness of breath, wheeze, and cough). This study found a statistically significant

association between PM2.5, measured as a 12-hour average, and the daily prevalence of

shortness of breath and wheeze endpoints. Although the association was not statistically

significant for cough, the results were still positive and close to significance; consequently,

we decided to include this endpoint, along with shortness of breath and wheeze, in generating

incidence estimates (see below). Vedal et al. (1998) followed a group of elementary school

children, including 74 asthmatics, located on the west coast of Vancouver Island for 18

months including measurements of daily peak expiratory flow (PEF) and the tracking of

respiratory symptoms (e.g., cough, phlegm, wheeze, chest tightness) through the use of daily

diaries. Association between PM10 and respiratory symptoms for the asthmatic population

was only reported for two endpoints: cough and PEF. Because it is difficult to translate PEF

measures into clearly defined health endpoints that can be monetized, we only included the

cough-related effect estimate from this study in quantifying asthma exacerbations. We
4-40

employed the following pooling approach in combining estimates generated using effect

estimates from the two studies to produce a single asthma exacerbation incidence estimate. 

First, we pooled the separate incidence estimates for shortness of breath, wheeze, and cough

generated using effect estimates from the Ostro et al. study, because each of these endpoints

is aimed at capturing the same overall endpoint (asthma exacerbations) and there could be

overlap in their predictions. The pooled estimate from the Ostro et al. study is then pooled

with the cough-related estimate generated using the Vedal study. The rationale for this

second pooling step is similar to the first; both studies are attempting to quantify the same

overall endpoint (asthma exacerbations). 

Additional epidemiological studies are available for characterizing asthma-related

health endpoints (the full list of epidemiological studies considered for modeling asthmarelated incidence is presented in Table 4-8). However, based on recommendations from the

SAB-HES, we decided not to use these additional studies in generating the primary estimate. 

In particular, the Yu et al. (2000) estimates show a much higher baseline incidence rate than

other studies, which may lead to an overstatement of the expected impacts in the overall

asthmatic population. The Whittemore and Korn (1980) study did not use a well-defined

endpoint, instead focusing on a respondent-defined “asthma attack.” Other studies looked at

respiratory symptoms in asthmatics but did not focus on specific exacerbations of asthma. 

4.1.5.2 Uncertainties Associated with Health Impact Functions

Within-Study Variation. Within-study variation refers to the precision with which a

given study estimates the relationship between air quality changes and health effects. Health

effects studies provide both a “best estimate” of this relationship plus a measure of the

statistical uncertainty of the relationship. The size of this uncertainty depends on factors

such as the number of subjects studied and the size of the effect being measured. The results

of even the most well-designed epidemiological studies are characterized by this type of

uncertainty, though well-designed studies typically report narrower uncertainty bounds

around the best estimate than do studies of lesser quality. In selecting health endpoints, we

generally focus on endpoints where a statistically significant relationship has been observed

in at least some studies, although we may pool together results from studies with both

statistically significant and insignificant estimates to avoid selection bias.

Across-Study Variation. Across-study variation refers to the fact that different

published studies of the same pollutant/health effect relationship typically do not report

identical findings; in some instances the differences are substantial. These differences can

exist even between equally reputable studies and may result in health effect estimates that 
4-41

Table 4-8. Studies Examining Health Impacts in the Asthmatic Population Evaluated

for Use in the Benefits Analysis

Endpoint Definition Pollutant Study Study Population

Asthma Attack Indicators

Shortness of

breath

Prevalence of shortness of

breath; incidence of

shortness of breath

PM2.5 Ostro et al. (2001) African-American

asthmatics, 8–13

Cough Prevalence of cough;

incidence of cough

PM2.5 Ostro et al. (2001) African-American

asthmatics, 8–13

Wheeze Prevalence of wheeze;

incidence of wheeze

PM2.5 Ostro et al. (2001) African-American

asthmatics, 8–13

Asthma

exacerbation

$1 mild asthma symptom: 

wheeze, cough, chest

tightness, shortness of breath

PM10,

PM1.0

Yu et al. (2000) Asthmatics, 5–13

Cough Prevalence of cough PM10 Vedal et al. (1998) Asthmatics, 6–13

Other Symptoms/Illness Endpoints

Upper

respiratory

symptoms

$1 of the following: runny

or stuffy nose; wet cough;

burning, aching, or red eyes 

PM10 Pope et al. (1991) Asthmatics, 9–11

Moderate or

worse asthma

Probability of moderate (or

worse) rating of overall

asthma status

PM2.5 Ostro et al. (1991) Asthmatics, all ages

Acute bronchitis $1 episodes of bronchitis in

the past 12 months

PM2.5 McConnell et al.

(1999)

Asthmatics, 9–15

Phlegm “Other than with colds, does

this child usually seem

congested in the chest or

bring up phlegm?”

PM2.5 McConnell et al.

(1999)

Asthmatics, 9–15

Asthma attacks Respondent-defined asthma

attack

PM2.5,

ozone

Whittemore and

Korn (1980)

Asthmatics, all ages

vary considerably. Across-study variation can result from two possible causes. One

possibility is that studies report different estimates of the single true relationship between a

given pollutant and a health effect because of differences in study design, random chance, or

other factors. For example, a hypothetical study conducted in New York and one conducted

in Seattle may report different C-R functions for the relationship between PM and mortality,

in part because of differences between these two study populations (e.g., demographics,

activity patterns). Alternatively, study results may differ because these two studies are in

fact estimating different relationships; that is, the same reduction in PM in New York and
16Although we are not able to use region-specific effect estimates, we use region-specific baseline incidence

rates where available. This allows us to take into account regional differences in health status, which can

have a significant impact on estimated health benefits.

4-42

Seattle may result in different reductions in premature mortality. This may result from a

number of factors, such as differences in the relative sensitivity of these two populations to

PM pollution and differences in the composition of PM in these two locations. In either

case, where we identified multiple studies that are appropriate for estimating a given health

effect, we generated a pooled estimate of results from each of those studies.

Application of C-R Relationship Nationwide. Regardless of the use of impact

functions based on effect estimates from a single epidemiological study or multiple studies,

each impact function was applied uniformly throughout the United States to generate health

benefit estimates. However, to the extent that pollutant/health effect relationships are region

specific, applying a location-specific impact function at all locations in the United States

may result in overestimates of health effect changes in some locations and underestimates of

health effect changes in other locations. It is not possible, however, to know the extent or

direction of the overall effect on health benefit estimates introduced by applying a single

impact function to the entire United States. This may be a significant uncertainty in the

analysis, but the current state of the scientific literature does not allow for a region-specific

estimation of health benefits.16

Extrapolation of Impact Functions Across Populations. Epidemiological studies

often focus on specific age ranges, either due to data availability limitations (e.g., most

hospital admission data come from Medicare records, which are limited to populations 65

and older), or to simplify data collection (e.g., some asthma symptom studies focus on

children at summer camps, which usually have a limited age range). We have assumed for

the primary analysis that most impact functions should be applied only to those populations

with ages that strictly match the populations in the underlying epidemiological studies. 

However, in many cases, there is no biological reason why the observed health effect would

not also occur in other populations within a reasonable range of the studied population. For

example, Dockery et al. (1996) examined acute bronchitis in children aged 8 to 12. There is

no biological reason to expect a very different response in children aged 6 or 14. By

excluding populations outside the range in the studies, we may be underestimating the health

impact in the overall population. In response to recommendations from the SAB-HES,

where there appears to be a reasonable physiological basis for expanding the age group

associated with a specific effect estimate beyond the study population to cover the full age
4-43

group (e.g., expanding from a study population of 7 to 11 year olds to the full 6- to 18-year

child age group), we have done so and used those expanded incidence estimates in the

primary analysis.

Uncertainties in the PM Mortality Relationship. A substantial body of published

scientific literature demonstrates a correlation between elevated PM concentrations and

increased premature mortality. However, much about this relationship is still uncertain. 

These uncertainties include the following:

Causality: Epidemiological studies are not designed to definitively prove causation. 

For the analysis of the CAIR, we assumed a causal relationship between exposure to elevated

PM and premature mortality, based on the consistent evidence of a correlation between PM

and mortality reported in the substantial body of published scientific literature. 

Other Pollutants: PM concentrations are correlated with the concentrations of other

criteria pollutants, such as ozone and CO. To the extent that there is correlation, this analysis

may be assigning mortality effects to PM exposure that are actually the result of exposure to

other pollutants. Recent studies (see Thurston and Ito [2001] and Bell et al [2004]) have

explored whether ozone may have mortality effects independent of PM. EPA is currently

evaluating the epidemiological literature on the relationship between ozone and mortality.

Shape of the C-R Function: The shape of the true PM mortality C-R function is

uncertain, but this analysis assumes the C-R function has a non-threshold log-linear form

throughout the relevant range of exposures. If this is not the correct form of the C-R

function, or if certain scenarios predict concentrations well above the range of values for

which the C-R function was fitted, avoided mortality may be misestimated. Although not

included in the primary analysis, the potential impact of a health effects threshold on avoided

incidences of PM-related premature mortality is explored as a key sensitivity analysis.

The possible existence of an effect threshold is a very important scientific question

and issue for policy analyses such as this one. In 1999, the EPA SAB Advisory Council for

Clean Air Compliance advised EPA that there was currently no scientific basis for selecting

a threshold of 15 :g/m3

 or any other specific threshold for the PM-related health effects

considered in typical benefits analyses (EPA-SAB-Council-ADV-99-012, 1999). In 2000, as

a part of their review of benefits methods, the National Research Council concluded that

there is no evidence for any departure from linearity in the observed range of exposure to

PM10 or PM2.5, nor any indication of a threshold (NRC, 2002). They cite the weight of

evidence available from both short- and long-term exposure models and the similar effects
4-44

found in cities with low and high ambient concentrations of PM. Most recently, EPA’s

updated (2004) Criteria Document states, “In summary, the available evidence does not

either support or refute the existence of thresholds for effects of PM on mortality across the

range of uncertainties in the studies.” The PM criteria document identifies the general shape

of exposure-response relationship(s) between PM and/or other pollutants and observed health

effects (e.g., potential indications of thresholds), as an important issue and uncertainty in

interpreting the overall PM epidemiology database. 

These recommendations are supported by the recent literature on health effects of

short and longer term PM exposures (Daniels et al., 2000; Pope, 2000; Pope et al, 2002;

Rossi et al., 1999; Schwartz and Zanobetti, 2000; Schwartz, Laden, and Zanobetti, 2002;

Smith et al, 2000) that finds in most cases no evidence of a nonlinear relationship between

PM and health effects and certainly does not find a distinct threshold. Recent cohort

analyses by HEI (Krewski et al., 2000) and Pope et al. (2002) provide additional evidence of

a quasi-linear relationship between long-term exposures to PM2.5 and mortality. According

to the latest draft PM criteria document, Krewski et al. (2000) found a “found a visually

near-linear relationship between all-cause and cardiopulmonary mortality residuals and mean

sulfate concentrations, near-linear between cardiopulmonary mortality and mean PM2.5, but

a somewhat nonlinear relationship between all-cause mortality residuals and mean PM2.5

concentrations that flattens above ~20 :g/m3. The confidence bands around the fitted curves

are very wide, however, neither requiring a linear relationship nor precluding a nonlinear

relationship if suggested by reanalyses” (Krewski et al. (2000), page 8-138). The Pope et al.

(2002) analysis, which represented an extension to the Krewski et al. analysis, found that the

functions relating PM2.5 and mortality are not significantly different from linear associations.

Based on the recent literature and advice from the SAB, we assume there are no

thresholds for modeling health effects. Although not included in the primary analysis, the

potential impact of a health effects threshold on avoided incidences of PM-related premature

mortality is explored as a key sensitivity analysis.

Regional Differences: As discussed above, significant variability exists in the results

of different PM/mortality studies. This variability may reflect regionally specific C-R

functions resulting from regional differences in factors such as the physical and chemical

composition of PM. If true regional differences exist, applying the PM/mortality C-R

function to regions outside the study location could result in misestimation of effects in these

regions.
4-45

Exposure/Mortality Lags: There is a time lag between changes in PM exposures and

the total realization of changes in annual mortality rates. For the chronic PM/mortality

relationship, the length of the lag is unknown and may be dependent on the kind of exposure. 

The existence of such a lag is important for the valuation of premature mortality incidence

because economic theory suggests that benefits occurring in the future should be discounted. 

There is no specific scientific evidence of the existence or structure of a PM effects lag. 

However, current scientific literature on adverse health effects similar to those associated

with PM (e.g., smoking-related disease) and the difference in the effect size between chronic

exposure studies and daily mortality studies suggests that all incidences of premature

mortality reduction associated with a given incremental change in PM exposure probably

would not occur in the same year as the exposure reduction. The smoking-related literature

also implies that lags of up to a few years or longer are plausible. The SAB-HES suggests

that appropriate lag structures may be developed based on the distribution of cause-specific

deaths within the overall all-cause estimate. Diseases with longer progressions should be

characterized by long-term lag structures, while impacts occurring in populations with

existing disease may be characterized by short-term lags. 

A key question is the distribution of causes of death within the relatively broad

categories analyzed in the cohort studies used. While we may be more certain about the

appropriate length of cessation lag for lung cancer deaths, it is not clear what the appropriate

lag structure should be for different types of cardiopulmonary deaths, which include both

respiratory and cardiovascular causes. Some respiratory diseases may have a long period of

progression, while others, such as pneumonia, have a very short duration. In the case of

cardiovascular disease, there is an important question of whether air pollution is causing the

disease, which would imply a relatively long cessation lag, or whether air pollution is

causing premature death in individuals with preexisting heart disease, which would imply

very short cessation lags. 

The SAB-HES provides several recommendations for future research that could

support the development of defensible lag structures, including the use of disease-specific lag

models, and the construction of a segmented lag distribution to combine differential lags

across causes of death. The SAB-HES recommended that until additional research has been

completed, EPA should assume a segmented lag structure characterized by 30 percent of

mortality reductions occurring in the first year, 50 percent occurring evenly over years 2 to 5

after the reduction in PM2.5, and 20 percent occurring evenly over the years 6 to 20 after the

reduction in PM2.5. The distribution of deaths over the latency period is intended to reflect

the contribution of short-term exposures in the first year, cardiopulmonary deaths in the 2- to
4-46

5-year period, and long-term lung disease and lung cancer in the 6- to 20-year period. For

future analyses, the specific distribution of deaths over time will need to be determined

through research on causes of death and progression of diseases associated with air pollution. 

It is important to keep in mind that changes in the lag assumptions do not change the total

number of estimated deaths but rather the timing of those deaths. 

Cumulative Effects: We attribute the PM/mortality relationship in the underlying

epidemiological studies to cumulative exposure to PM. However, the relative roles of PM

exposure duration and PM exposure level in inducing premature mortality remain unknown

at this time. 

4.1.5.3 Baseline Health Effect Incidence Rates

The epidemiological studies of the association between pollution levels and adverse

health effects generally provide a direct estimate of the relationship of air quality changes to

the relative risk of a health effect, rather than an estimate of the absolute number of avoided

cases. For example, a typical result might be that a 10 :g/m3

 decrease in daily PM2.5 levels

might decrease hospital admissions by 3 percent. The baseline incidence of the health effect

is necessary to convert this relative change into a number of cases. The baseline incidence

rate provides an estimate of the incidence rate (number of cases of the health effect per year,

usually per 10,000 or 100,000 general population) in the assessment location corresponding

to baseline pollutant levels in that location. To derive the total baseline incidence per year,

this rate must be multiplied by the corresponding population number (e.g., if the baseline

incidence rate is number of cases per year per 100,000 population, it must be multiplied by

the number of 100,000s in the population).

Some epidemiological studies examine the association between pollution levels and

adverse health effects in a specific subpopulation, such as asthmatics or diabetics. In these

cases, it is necessary to develop not only baseline incidence rates, but also prevalence rates

for the defining condition (e.g., asthma). For both baseline incidence and prevalence data,

we use age-specific rates where available. Impact functions are applied to individual age

groups and then summed over the relevant age range to provide an estimate of total

population benefits.

In most cases, because of a lack of data or methods, we have not attempted to project

incidence rates to future years, instead assuming that the most recent data on incidence rates

is the best prediction of future incidence rates. In recent years, better data on trends in

incidence and prevalence rates for some endpoints, such as asthma, have become available. 
4-47

We are working to develop methods to use these data to project future incidence rates. 

However, for our primary benefits analysis of the final CAIR, we continue to use current

incidence rates. 

Table 4-9 summarizes the baseline incidence data and sources used in the benefits

analysis. We use the most geographically disaggregated data available. For premature

mortality, county-level data are available. For hospital admissions, regional rates are

available. However, for all other endpoints, a single national incidence rate is used, due to a

lack of more spatially disaggregated data. In these cases, we used national incidence rates

whenever possible, because these data are most applicable to a national assessment of

benefits. However, for some studies, the only available incidence information comes from

the studies themselves; in these cases, incidence in the study population is assumed to

represent typical incidence at the national level. 

Age, cause, and county-specific mortality rates were obtained from the U.S. Centers

for Disease Control and Prevention (CDC) for the years 1996 through 1998. CDC maintains

an online data repository of health statistics, CDC Wonder, accessible at

http://wonder.cdc.gov/. The mortality rates provided are derived from U.S. death records

and U.S. Census Bureau postcensal population estimates. Mortality rates were averaged

across 3 years (1996 through 1998) to provide more stable estimates. When estimating rates

for age groups that differed from the CDC Wonder groupings, we assumed that rates were

uniform across all ages in the reported age group. For example, to estimate mortality rates

for individuals ages 30 and up, we scaled the 25- to 34-year-old death count and population

by one-half and then generated a population-weighted mortality rate using data for the older

age groups. Note that we have not projected any changes in mortality rates over time. We

are aware that the U.S. Census projections of total and age-specific mortality rates used in

our population projections are based on projections of declines in mortality rates for younger

populations and increases in mortality rates for older populations over time. We are

evaluating the most appropriate way to incorporate these projections into our database of

county-level cause-specific mortality rates. In the interim, we have not attempted to adjust

future mortality rates. This will lead to an overestimate of mortality benefits in future years,

with the overestimation bias increasing as benefits are projected into the future. We do not at

this time have a quantified estimate of the magnitude of the potential bias in the years

analyzed for this rule (2010 and 2015).
4-48

Table 4-9. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact

Functions, General Population

Endpoint Parameter

Rates

Value Sourcea

Mortality Daily or annual mortality

rate

Age-, cause-, and

county-specific

rate

CDC Wonder (1996–1998)

Hospitalizations Daily hospitalization rate Age-, region-, and

cause-specific rate

1999 NHDS public use data filesb

Asthma ER

Visits

Daily asthma ER visit

rate Age- and regionspecific visit rate

2000 NHAMCS public use data

filesc; 1999 NHDS public use data

filesb

Chronic

Bronchitis

Annual prevalence rate

per person

C Aged 18–44

C Aged 45–64

C Aged 65 and older

0.0367

0.0505

0.0587

1999 NHIS (American Lung

Association, 2002b, Table 4) 

Annual incidence rate per

person

0.00378 Abbey et al. (1993b, Table 3)

Nonfatal

Myocardial

Infarction (heart

attacks)

Daily nonfatal myocardial

infarction incidence rate

per person, 18+

C Northeast

C Midwest

C South

C West

0.0000159

0.0000135

0.0000111

0.0000100

1999 NHDS public use data filesb

;

adjusted by 0.93 for probability of

surviving after 28 days (Rosamond et

al., 1999)

Asthma

Exacerbations

Incidence (and

prevalence) among

asthmatic AfricanAmerican children

C daily wheeze

C daily cough

C daily dyspnea

0.076 (0.173)

0.067 (0.145)

0.037 (0.074)

Ostro et al. (2001)

Prevalence among

asthmatic children

C daily wheeze

C daily cough

C daily dyspnea

0.038

0.086

0.045

Vedal et al. (1998)

Acute Bronchitis Annual bronchitis

incidence rate, children

0.043 American Lung Association (2002c,

Table 11)

(continued)
4-49

Table 4-9. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact

Functions, General Population (continued)

Endpoint Parameter

Rates

Value Sourcea

Lower

Respiratory

Symptoms

Daily lower respiratory

symptom incidence

among childrend

0.0012 Schwartz et al. (1994, Table

2)

Upper

Respiratory

Symptoms

Daily upper respiratory

symptom incidence

among asthmatic children

0.3419 Pope et al. (1991, Table 2)

Work Loss Days Daily WLD incidence

rate per person (18–65)

C Aged 18–24

C Aged 25–44

C Aged 45–64

0.00540

0.00678

0.00492

1996 HIS (Adams et al., 1999,

Table 41); U.S. Bureau of the

Census (2000)

Minor

RestrictedActivity Days

Daily MRAD incidence

rate per person

0.02137 Ostro and Rothschild (1989,

p. 243)

School Loss

Dayse

Daily school absence rate

per person

0.055 National Center for Education

Statistics (1996)

Daily illness-related

school absence rate per

persone

C Northeast

C Midwest

C South

C Southwest

0.0136

0.0146

0.0142

0.0206

1996 HIS (Adams et al., 1999,

Table 47); estimate of 180

school days per year

Daily respiratory illnessrelated school absence

rate per person

C Northeast

C Midwest

C South

C West

0.0073

0.0092

0.0061

0.0124

1996 HIS (Adams et al., 1999,

Table 47); estimate of 180

school days per year

a The following abbreviations are used to describe the national surveys conducted by the National Center for Health Statistics: HIS refers

to the National Health Interview Survey; NHDS—National Hospital Discharge Survey; NHAMCS—National Hospital Ambulatory

Medical Care Survey.

b See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/.

c See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/.

d Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze.

e The estimate of daily illness-related school absences excludes school loss days associated with injuries to match the definition in the

Gilliland et al. (2001) study.
4-50

For the set of endpoints affecting the asthmatic population, in addition to baseline

incidence rates, prevalence rates of asthma in the population are needed to define the

applicable population. Table 4-9 lists the baseline incidence rates and their sources for

asthma symptom endpoints. Table 4-10 lists the prevalence rates used to determine the

applicable population for asthma symptom endpoints. Note that these reflect current asthma

prevalence and assume no change in prevalence rates in future years. As noted above, we are

investigating methods for projecting asthma prevalence rates in future years. 

Table 4-10. Asthma Prevalence Rates Used to Estimate Asthmatic Populations in

Impact Functions

Population Group

Asthma Prevalence Rates

Value Source

All Ages 0.0386 American Lung Association (2002a, Table 7)—based on 1999 HIS

< 18 0.0527 American Lung Association (2002a, Table 7)—based on 1999 HIS

5–17 0.0567 American Lung Association (2002a, Table 7)—based on 1999 HIS

18–44 0.0371 American Lung Association (2002a, Table 7)—based on 1999 HIS

45–64 0.0333 American Lung Association (2002a, Table 7)—based on 1999 HIS

65+ 0.0221 American Lung Association (2002a, Table 7)—based on 1999 HIS

Male, 27+ 0.021 2000 HIS public use data filesa

African American, 5 to 17 0.0726 American Lung Association (2002a, Table 9)—based on 1999 HIS

African American, <18 0.0735 American Lung Association (2002a, Table 9)—based on 1999 HIS

a See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/.

4.1.5.4 Selecting Unit Values for Monetizing Health Endpoints

The appropriate economic value for a change in a health effect depends on whether

the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect

has occurred). Reductions in ambient concentrations of air pollution generally lower the risk

of future adverse health affects by a small amount for a large population. The appropriate

economic measure is therefore ex ante WTP for changes in risk. However, epidemiological

studies generally provide estimates of the relative risks of a particular health effect avoided

due to a reduction in air pollution. A convenient way to use this data in a consistent

framework is to convert probabilities to units of avoided statistical incidences. This measure

is calculated by dividing individual WTP for a risk reduction by the related observed change
4-51

in risk. For example, suppose a measure is able to reduce the risk of premature mortality

from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk

reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1

million ($100/0.0001 change in risk). Using this approach, the size of the affected

population is automatically taken into account by the number of incidences predicted by

epidemiological studies applied to the relevant population. The same type of calculation can

produce values for statistical incidences of other health endpoints.

For some health effects, such as hospital admissions, WTP estimates are generally

not available. In these cases, we use the cost of treating or mitigating the effect as a primary

estimate. For example, for the valuation of hospital admissions we use the avoided medical

costs as an estimate of the value of avoiding the health effects causing the admission. These

COI estimates generally 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. Table 4-11 summarizes the value estimates per

health effect that we used in this analysis. Values are presented both for a 1990 base income

level and adjusted for income growth in the two future analysis years, 2010 and 2015. Note

that the unit values for hospital admissions are the weighted averages of the ICD-9 codespecific values for the group of ICD-9 codes included in the hospital admission categories. 

A discussion of the valuation methods for premature mortality and CB is provided here

because of the relative importance of these effects. Discussions of the methods used to value

nonfatal myocardial infarctions (heart attacks) and school absence days are provided because

these endpoints have only recently been added to the analysis and the valuation methods are

still under development. In the following discussions, unit values are presented at 1990

levels of income for consistency with previous analyses. Equivalent future-year values can

be obtained from Table 4-11. COI estimates are converted to constant 1999 dollar

equivalents using the medical CPI.

4.1.5.4.1 Valuing Reductions in Premature Mortality Risk. We estimate the

monetary benefit of reducing premature mortality risk using the VSL approach, which is a

summary measure for the value of small changes in mortality risk experienced by a large

number of people. The mean value of avoiding one statistical death is assumed to be $5.5

million in 1999 dollars. This represents a central value consistent with the range of values

suggested by recent meta-analyses of the wage-risk VSL literature. The distribution of VSL

is characterized by a confidence interval from $1 to $10 million, based on two meta-analyses

of the wage-risk VSL literature. The $1 million lower confidence limit represents the lower 
4-52

Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$)

Health Endpoint

Central Estimate of Value Per Statistical Incidence 

Derivation of Estima

1990 Income

Level

2010 Income

Level

2015 Income

Level

Premature Mortality (Value of aStatistical Life)

$5,500,000 $6,000,000 $6,400,000 Point estimate is the mean of a normal distributi

confidence interval between $1 and $10 millionbased on two meta-analyses of the wage-risk VSrepresents the lower end of the interquartile rangTaylor (2002) meta-analysis and $10 million repinterquartile range from the Viscusi and Aldy (2VSL represents the value of a small change in mthe affected population.

Chronic Bronchitis (CB) $340,000 $380,000 $400,000 Point estimate is the mean of a generated distrib

case of pollution-related CB. WTP to avoid a cais derived by adjusting WTP (as described in Via severe case of CB for the difference in severity elasticity of WTP with respect to severity of CB

Nonfatal Myocardial Infarction(heart attack)

3% discount rate

Age 0–24Age 25–44Age 45–54Age 55–65Age 66 and over7% discount rateAge 0–24Age 25–44Age 45–54Age 55–65Age 66 and over

$66,902$74,676$78,834

$140,649

$66,902$65,293$73,149$76,871

$132,214

$65,293

$66,902$74,676$78,834

$140,649

$66,902$65,293$73,149$76,871

$132,214

$65,293

$66,902$74,676$78,834

$140,649

$66,902$65,293$73,149$76,871

$132,214

$65,293

Age-specific cost-of-illness values reflect lost eacosts over a 5-year period following a nonfatal Mare based on Cropper and Krupnick (1990). Diron simple average of estimates from Russell et a(1990).Lost earnings:

Cropper and Krupnick (1990). Present discountearnings:age of onset: at 3% at 7%

25-44 $8,774 $7,85545-54 $12,932 $11,57855-65 $74,746 $66,920Direct medical expenses: An average of: 

1. Wittels et al. (1990) ($102,658—no discount2. Russell et al. (1998), 5-year period ($22,331 at 7% discount rate)
4-53

Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)

Health Endpoint

Central Estimate of Value Per Statistical

Incidence 

Derivation of Estima

1990 Income

Level

2010 Income

Level

2015 Income

Level

Hospital AdmissionsChronic Obstructive PulmonaryDisease (COPD)(ICD codes 490-492, 494-496)

$12,378 $12,378 $12,378 The COI estimates (lost earnings plus direct med

ICD-9 code-level information (e.g., average hoslength of hospital stay, and weighted share of toillnesses) reported in Agency for Healthcare Res(www.ahrq.gov). 

Pneumonia(ICD codes 480-487)

$14,693 $14,693 $14,693 The COI estimates (lost earnings plus direct med

ICD-9 code-level information (e.g., average hoslength of hospital stay, and weighted share of toillnesses) reported in Agency for Healthcare Res(www.ahrq.gov). 

Asthma Admissions $6,634 $6,634 $6,634 The COI estimates (lost earnings plus direct med

ICD-9 code-level information (e.g., average hoslength of hospital stay, and weighted share of toillnesses) reported in Agency for Healthcare Res(www.ahrq.gov). 

All Cardiovascular(ICD codes 390-429)

$18,387 $18,387 $18,387 The COI estimates (lost earnings plus direct med

ICD-9 code-level information (e.g., average hoslength of hospital stay, and weighted share of toillnesses) reported in Agency for Healthcare Res(www.ahrq.gov). 

Emergency Room Visits for Asthma $286 $286 $286 Simple average of two unit COI values: 

(1) $311.55, from Smith et al. (1997) and (2) $260.67, from Stanford et al. (1999).
4-54

Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)

Health Endpoint

Central Estimate of Value Per Statistical

Incidence 

Derivation of Estima

1990 Income

Level

2010 Income

Level

2015 Income

Level

Respiratory Ailments Not Requiring HospitalizationUpper Respiratory Symptoms (URS) $25 $26 $26 Combinations of the three symptoms for which W

that closely match those listed by Pope et al. resu“symptom clusters,” each describing a “type” of derived for each type of URS, using mid-range e1994) to avoid each symptom in the cluster and WTPs. The dollar value for URS is the average seven different types of URS.

Lower Respiratory Symptoms (LRS) $16 $17 $17 Combinations of the four symptoms for which W

that closely match those listed by Schwartz et al“symptom clusters,” each describing a “type” of derived for each type of LRS, using mid-range e1994) to avoid each symptom in the cluster and WTPs. The dollar value for LRS is the average 11 different types of LRS.

Asthma Exacerbations $42 $43 $44 Asthma exacerbations are valued at $42 per inci

average WTP estimates for the four severity defday,” described in Rowe and Chestnut (1986). Tasthmatics to estimate WTP for avoidance of a “by the subjects. For purposes of valuation, an asbe equivalent to a day in which asthma is moderthe Rowe and Chestnut (1986) study.

Acute Bronchitis $360 $370 $380 Assumes a 6-day episode, with daily value equa

high values for related respiratory symptoms recal. (1994).
4-55

Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence 

Derivation of Estima

1990 Income

Level

2010 Income

Level

2015 Income

Level

Restricted Activity and Work/School Loss DaysWork Loss Days (WLDs) Variable

(national

median = )

County-specific median annual wages divided bvacation) and then by 5—to get median daily wCensus, compiled by Geolytics, Inc.

School Absence Days $75 $75 $75 Based on expected lost wages from parent stayi

Estimated daily lost wage (if a mother must stais based on the median weekly wage among wo2000 (U.S. Census Bureau, Statistical Abstract Section 12: Labor Force, Employment, and EaThis median wage is $551. Dividing by 5 givewage of $103.The expected loss in wages due to a day of schomother would have to stay home with her child probability that the mother is in the workforce twould lose if she missed a day = 72.85% of $10

Worker Productivity $0.95 per

worker per 10%

change in ozone

per day

$0.95 per

worker per

10% change in

ozone per day

$0.95 per

worker per 10%

change in ozone

per day

Based on $68—median daily earnings of workefishing—from Table 621, Statistical Abstract oTime Wage and Salary Workers—Number and (Source of data in table: U.S. Bureau of Labor Employment and Earnings, monthly).

Minor Restricted Activity Days(MRADs)

$51 $53 $54 Median WTP estimate to avoid one MRAD fro
17The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the

federal government. EPA adopted a 3 percent discount rate for its base estimate in this case to reflect

reliance on a “social rate of time preference” discounting concept. We have also calculated benefits and

costs using a 7 percent rate consistent with an “opportunity cost of capital” concept to reflect the time value

of resources directed to meet regulatory requirements. In this case, the benefit and cost estimates were not

significantly affected by the choice of discount rate. Further discussion of this topic appears in EPA’s

Guidelines for Preparing Economic Analyses (EPA, 2000b).

4-56

end of the interquartile range from the Mrozek and Taylor (2002) meta-analysis. The $10

million upper confidence limit represents the upper end of the interquartile range from the

Viscusi and Aldy (2003) meta-analysis. Because the majority of the studies in these metaanalyses are based on datasets from the early 1990s or previous decades, we continue to

assume that the VSL estimates provided by those meta-analyses are in 1990 income

equivalents. Future research might provide income-adjusted VSL values for individual

studies that can be incorporated into the meta-analyses. This would allow for a more reliable

base-year estimate for use in adjusting VSL for aggregate changes in income over time. 

As indicated in the previous section on quantification of premature mortality benefits,

we assumed for this analysis that some of the incidences of premature mortality related to

PM exposures occur in a distributed fashion over the 20 years following exposure. To take

this into account in the valuation of reductions in premature mortality, we applied an annual

3 percent discount rate to the value of premature mortality occurring in future years.17

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. Regardless of the theoretical economic

considerations, EPA prefers not to draw distinctions in the monetary value assigned to the

lives saved even if they differ in age, health status, socioeconomic status, gender, or other

characteristic of the adult population.

Following the advice of the EEAC of the SAB, EPA currently uses the VSL approach

in calculating the primary estimate 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 (EPA-SAB-EEAC-00-013, 2000). Although there are

several differences between the labor market studies EPA uses to derive a VSL estimate and

the PM air pollution context addressed here, those differences in the affected populations and

the nature of the risks imply both upward and downward adjustments. Table 4-12 lists some

of these differences and the expected effect on the VSL estimate for air pollution-related 
18This conclusion was based on a assessment of uncertainty based on statistical error in epidemiological effect

estimates and economic valuation estimates. Additional sources of model error such as those examined in

the pilot PM mortality expert elicitation may result in different conclusions about the relative contribution of

sources of uncertainty.

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Table 4-12. Expected Impact on Estimated Benefits of Premature Mortality Reductions

of Differences Between Factors Used in Developing Applied VSL and Theoretically

Appropriate VSL

Attribute Expected Direction of Bias

Age Uncertain, perhaps overestimate

Life Expectancy/Health Status Uncertain, perhaps overestimate

Attitudes Toward Risk Underestimate

Income Uncertain

Voluntary vs. Involuntary Uncertain, perhaps underestimate

Catastrophic vs. Protracted Death Uncertain, perhaps underestimate

mortality. In the absence of a comprehensive and balanced set of adjustment factors, EPA

believes it is reasonable to continue to use the $5.5 million value while acknowledging the

significant limitations and uncertainties in the available literature.

The SAB-EEAC has reviewed many potential VSL adjustments and the state of the

economics literature. The SAB-EEAC advised EPA to “continue to use a wage-risk-based

VSL as its primary estimate, including appropriate sensitivity analyses to reflect the

uncertainty of these estimates,” and that “the only risk characteristic for which adjustments

to the VSL can be made is the timing of the risk” (EPA-SAB-EEAC-00-013, EPA, 2000). In

developing our primary estimate of the benefits of premature mortality reductions, we have

followed this advice and discounted over the lag period between exposure and premature

mortality. 

Uncertainties Specific to Premature Mortality Valuation. The economic benefits

associated with premature mortality are the largest category of monetized benefits of the

final CAIR. In addition, in prior analyses, EPA has identified valuation of mortality benefits

as the largest contributor to the range of uncertainty in monetized benefits (see EPA

[1999a]).18 Because of the uncertainty in estimates of the value of premature mortality

avoidance, it is important to adequately characterize and understand the various types of
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economic approaches available for mortality valuation. Such an assessment also requires an

understanding of how alternative valuation approaches reflect that some individuals may be

more susceptible to air pollution-induced mortality or reflect differences in the nature of the

risk presented by air pollution relative to the risks studied in the relevant economics

literature.

The health science literature on air pollution indicates that several human

characteristics affect the degree to which mortality risk affects an individual. For example,

some age groups appear to be more susceptible to air pollution than others (e.g., the elderly

and children). Health status prior to exposure also affects susceptibility. An ideal benefits

estimate of mortality risk reduction would reflect these human characteristics, in addition to

an individual’s WTP to improve one’s own chances of survival plus WTP to improve other

individuals’ survival rates. The ideal measure would also take into account the specific

nature of the risk reduction commodity that is provided to individuals, as well as the context

in which risk is reduced. To measure this value, it is important to assess how reductions in

air pollution reduce the risk of dying from the time that reductions take effect onward and

how individuals value these changes. Each individual’s survival curve, or the probability of

surviving beyond a given age, should shift as a result of an environmental quality

improvement. For example, changing the current probability of survival for an individual

also shifts future probabilities of that individual’s survival. This probability shift will differ

across individuals because survival curves depend on such characteristics as age, health state,

and the current age to which the individual is likely to survive.

Although a survival curve approach provides a theoretically preferred method for

valuing the benefits of reduced risk of premature mortality associated with reducing air

pollution, the approach requires a great deal of data to implement. The economic valuation

literature does not yet include good estimates of the value of this risk reduction commodity. 

As a result, in this study we value avoided premature mortality risk using the VSL approach.

Other uncertainties specific to premature mortality valuation include the following:

C Across-study variation: There is considerable uncertainty as to whether the

available literature on VSL provides adequate estimates of the VSL saved by air

pollution reduction. Although there is considerable variation in the analytical

designs and data used in the existing literature, the majority of the studies involve

the value of risks to a middle-aged working population. Most of the studies

examine differences in wages of risky occupations, using a wage-hedonic

approach. Certain characteristics of both the population affected and the

mortality risk facing that population are believed to affect the average WTP to
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reduce the risk. The appropriateness of a distribution of WTP based on the

current VSL literature for valuing the mortality-related benefits of reductions in

air pollution concentrations therefore depends not only on the quality of the

studies (i.e., how well they measure what they are trying to measure), but also on

the extent to which the risks being valued are similar and the extent to which the

subjects in the studies are similar to the population affected by changes in

pollution concentrations. 

C Level of risk reduction: The transferability of estimates of the VSL from the

wage-risk studies to the context of the CAIR analysis rests on the assumption that,

within a reasonable range, WTP for reductions in mortality risk is linear in risk

reduction. For example, suppose a study estimates that the average WTP for a

reduction in mortality risk of 1/100,000 is $50, but that the actual mortality risk

reduction resulting from a given pollutant reduction is 1/10,000. If WTP for

reductions in mortality risk is linear in risk reduction, then a WTP of $50 for a

reduction of 1/100,000 implies a WTP of $500 for a risk reduction of 1/10,000

(which is 10 times the risk reduction valued in the study). Under the assumption

of linearity, the estimate of the VSL does not depend on the particular amount of

risk reduction being valued. This assumption has been shown to be reasonable

provided the change in the risk being valued is within the range of risks evaluated

in the underlying studies (Rowlatt et al., 1998).

C Voluntariness of risks evaluated: Although job-related mortality risks may differ

in several ways from air pollution-related mortality risks, the most important

difference may be that job-related risks are incurred voluntarily, or generally

assumed to be, whereas air pollution-related risks are incurred involuntarily. 

Some evidence suggests that people will pay more to reduce involuntarily

incurred risks than risks incurred voluntarily. If this is the case, WTP estimates

based on wage-risk studies may understate WTP to reduce involuntarily incurred

air pollution-related mortality risks.

C Sudden versus protracted death: A final important difference related to the nature

of the risk may be that some workplace mortality risks tend to involve sudden,

catastrophic events, whereas air pollution-related risks tend to involve longer

periods of disease and suffering prior to death. Some evidence suggests that WTP

to avoid a risk of a protracted death involving prolonged suffering and loss of

dignity and personal control is greater than the WTP to avoid a risk (of identical

magnitude) of sudden death. To the extent that the mortality risks addressed in

this assessment are associated with longer periods of illness or greater pain and

suffering than are the risks addressed in the valuation literature, the WTP

measurements employed in the present analysis would reflect a downward bias.

C Self-selection and skill in avoiding risk: Recent research (Shogren and Stamland,

2002) suggests that VSL estimates based on hedonic wage studies may overstate
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the average value of a risk reduction. This is based on the fact that the risk-wage

trade-off revealed in hedonic studies reflects the preferences of the marginal

worker (i.e., that worker who demands the highest compensation for his risk

reduction). This worker must have either higher risk, lower risk tolerance, or

both. However, the risk estimate used in hedonic studies is generally based on

average risk, so the VSL may be upwardly biased because the wage differential

and risk measures do not match.

4.1.5.4.2 Valuing Reductions in the Risk of Chronic Bronchitis. The best available

estimate of WTP to avoid a case of CB comes from Viscusi et al. (1991). The Viscusi et al.

study, however, describes a severe case of CB to the survey respondents. We therefore

employ an estimate of WTP to avoid a pollution-related case of CB, based on adjusting the

Viscusi et al. (1991) estimate of the WTP to avoid a severe case. This is done to account for

the likelihood that an average case of pollution-related CB is not as severe. The adjustment

is made by applying the elasticity of WTP with respect to severity reported in the Krupnick

and Cropper (1992) study. Details of this adjustment procedure are provided in the Benefits

TSD for the Nonroad Diesel rulemaking (Abt Associates, 2003).

We use the mean of a distribution of WTP estimates as the central tendency estimate

of WTP to avoid a pollution-related case of CB in this analysis. The distribution

incorporates uncertainty from three sources: the WTP to avoid a case of severe CB, as

described by Viscusi et al.; the severity level of an average pollution-related case of CB

(relative to that of the case described by Viscusi et al.); and the elasticity of WTP with

respect to severity of the illness. Based on assumptions about the distributions of each of

these three uncertain components, we derive a distribution of WTP to avoid a pollutionrelated case of CB by statistical uncertainty analysis techniques. The expected value (i.e.,

mean) of this distribution, which is about $331,000 (2000$), is taken as the central tendency

estimate of WTP to avoid a PM-related case of CB.

4.1.5.4.3 Valuing Reductions in Nonfatal Myocardial Infarctions (Heart Attacks). 

The Agency has recently incorporated into its analyses the impact of air pollution on the

expected number of nonfatal heart attacks, although it has examined the impact of reductions

in other related cardiovascular endpoints. We were not able to identify a suitable WTP value

for reductions in the risk of nonfatal heart attacks. Instead, we use a COI unit value with two

components: the direct medical costs and the opportunity cost (lost earnings) associated with

the illness event. Because the costs associated with a myocardial infarction extend beyond

the initial event itself, we consider costs incurred over several years. Using age-specific

annual lost earnings estimated by Cropper and Krupnick (1990) and a 3 percent discount
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rate, we estimated a present discounted value in lost earnings (in 2000$) over 5 years due to

a myocardial infarction of $8,774 for someone between the ages of 25 and 44, $12,932 for

someone between the ages of 45 and 54, and $74,746 for someone between the ages of 55

and 65. The corresponding age-specific estimates of lost earnings (in 2000$) using a 7

percent discount rate are $7,855, $11,578, and $66,920, respectively. Cropper and Krupnick

(1990) do not provide lost earnings estimates for populations under 25 or over 65. As such,

we do not include lost earnings in the cost estimates for these age groups.

We found three possible sources in the literature of estimates of the direct medical

costs of myocardial infarction:

C Wittels et al. (1990) estimated expected total medical costs of myocardial

infarction over 5 years to be $51,211 (in 1986$) for people who were admitted to

the hospital and survived hospitalization. (There does not appear to be any

discounting used.) Wittels et al. was used to value coronary heart disease in the

812 Retrospective Analysis of the Clean Air Act. Using the CPI-U for medical

care, the Wittels estimate is $109,474 in year 2000$. This estimated cost is based

on a medical cost model, which incorporated therapeutic options, projected

outcomes, and prices (using “knowledgeable cardiologists” as consultants). The

model used medical data and medical decision algorithms to estimate the

probabilities of certain events and/or medical procedures being used. The authors

note that the average length of hospitalization for acute myocardial infarction has

decreased over time (from an average of 12.9 days in 1980 to an average of 11

days in 1983). Wittels et al. used 10 days as the average in their study. It is

unclear how much further the length of stay for myocardial infarction may have

decreased from 1983 to the present. The average length of stay for ICD code 410

(myocardial infarction) in the year-2000 Agency for Healthcare Research and

Quality (AHRQ) HCUP database is 5.5 days. However, this may include patients

who died in the hospital (not included among our nonfatal myocardial infarction

cases), whose length of stay was therefore substantially shorter than it would be if

they had not died.

C Eisenstein et al. (2001) estimated 10-year costs of $44,663 in 1997$, or $49,651

in 2000$ for myocardial infarction patients, using statistical prediction

(regression) models to estimate inpatient costs. Only inpatient costs (physician

fees and hospital costs) were included.

C Russell et al. (1998) estimated first-year direct medical costs of treating nonfatal

myocardial infarction of $15,540 (in 1995$) and $1,051 annually thereafter. 

Converting to year 2000$, that would be $23,353 for a 5-year period (without

discounting) or $29,568 for a 10-year period.
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In summary, the three different studies provided significantly different values (see

Table 4-13).

Table 4-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart

Attacks

Study Direct Medical Costs (2000$) Over an x-Year Period, for x =

Wittels et al. (1990) $109,474a 5

Russell et al. (1998) $22,331b 5

Eisenstein et al. (2001) $49,651b 10

Russell et al. (1998) $27,242b 10

a Wittels et al. did not appear to discount costs incurred in future years.

b Using a 3 percent discount rate.

As noted above, the estimates from these three studies are substantially different, and

we have not adequately resolved the sources of differences in the estimates. Because the

wage-related opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year

period, we used estimates for medical costs that similarly cover a 5-year period (i.e.,

estimates from Wittels et al. (1990) and Russell et al. (1998). We used a simple average of

the two 5-year estimates, or $65,902, and added it to the 5-year opportunity cost estimate. 

The resulting estimates are given in Table 4-14.

Table 4-14. Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial

Infarction

Age Group Opportunity Cost Medical Costa Total Cost

0–24 $0 $65,902 $65,902

25–44 $8,774b $65,902 $74,676

45–54 $12,253b $65,902 $78,834

55–65 $70,619b $65,902 $140,649

> 65 $0 $65,902 $65,902

a An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

b From Cropper and Krupnick (1990), using a 3 percent discount rate.
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4.1.5.4.4 Valuing Reductions in School Absence Days. School absences associated

with exposure to ozone are likely to be due to respiratory-related symptoms and illnesses. 

Because the respiratory symptom and illness endpoints we are including are all PM-related

rather than ozone-related, we do not have to be concerned about double-counting of benefits

if we aggregate the benefits of avoiding ozone-related school absences with the benefits of

avoiding PM-related respiratory symptoms and illnesses. 

One possible approach to valuing a school absence is using a parental opportunity

cost approach. This method requires two steps: estimate the probability that, if a schoolaged child stays home from school, a parent will have to stay home from work to care for the

child, and value the lost productivity at the person’s wage. Using this method, we would

estimate the proportion of families with school-age children in which both parents work and

value a school loss day as the probability of a work loss day resulting from a school loss day

(i.e., the proportion of households with school-age children in which both parents work)

times some measure of lost wages (whatever measure we use to value work loss days). 

There are two significant problems with this method, however. First, it omits WTP to avoid

the symptoms/illness that resulted in the school absence. Second, it effectively gives zero

value to school absences that do not result in a work loss day (unless we derive an alternative

estimate of the value of the parent’s time for those cases in which the parent is not in the

labor force). We are investigating approaches using WTP for avoid the symptoms/illnesses

causing the absence. In the interim, we use the parental opportunity cost approach.

For the opportunity-cost approach, we make an explicit, conservative assumption that

in married households with two working parents, the female parent will stay home with a

sick child. From the U.S. Census Bureau, Statistical Abstract of the United States: 2001, we

obtained 1) the numbers of single, married, and “other” (i.e., widowed, divorced, or

separated) women with children in the workforce and 2) the rates of participation in the

workforce of single, married, and “other” women with children. From these two sets of

statistics, we inferred the numbers of single, married, and “other” women with children and

the corresponding percentages. These percentages were used to calculate a weighted-average

participation rate, as shown in Table 4-15.

Our estimated daily lost wage (if a mother must stay at home with a sick child) is

based on the median weekly wage among women age 25 and older in 2000 (U.S. Census

Bureau, Statistical Abstract of the United States: 2001, Section 12: Labor Force,

Employment, and Earnings, Table No. 621). This median wage is $551. Dividing by 5 gives

an estimated median daily wage of $103.
19In a recent article, Hall et al. (2003) use a similar methodology to derive a mid-estimate value per school

absence day for California of between $70 and $81, depending on differences in incomes between three

counties in California. Our national average estimate of $75 per absence is consistent with these published

values.

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Table 4-15. Women with Children: Number and Percentage in the Labor Force, 2000,

and Weighted Average Participation Ratea

Number (in

millions) in

Labor Force

(1)

Participation

Rate

(2)

Implied Total

Number in

Population (in

millions)

(3) = (1)/(2)

Implied

Percent in

Population

(4)

Weighted

Average

Participation

Rate [=sum

(2)*(4) over

rows] 

Single 3.1 73.9% 4.19 11.84%

Married 18.2 70.6% 25.78 72.79%

Otherb 4.5 82.7% 5.44 15.36%

Total: 35.42

72.85%

a Data in columns (1) and (2) are from U.S. Census Bureau, Statistical Abstract of the United States: 2001,

Section 12: Labor Force, Employment, and Earnings, Table No. 577.

b Widowed, divorced, or separated.

The expected loss in wages due to a day of school absence in which the mother would

have to stay home with her child is estimated as the probability that the mother is in the

workforce times the daily wage she would lose if she missed a day = 72.85 percent of $103,

or $75.19 Of course, non-working parent time also has value. Determining that value is not

straightforward. In a world with perfect labor markets, economic theory suggests that a

non-working parent’s time is worth at least (and generally exactly) that of a working parent. 

Otherwise, they would choose to work. Imperfect labor markets could imply a lower value

of time, but still one above zero. Compounding the uncertainty about the value of time is

uncertainty about the amount of time sacrificed by a non-working parent to care for a sick

child. Since the value of a non-working parent’s time is a function of the usual activities

they perform, and caring for a child is presumably one of those activities, the crucial question

is how much additional time is spent caring for a sick child during a school absence and what

other activities are foregone as a result. Assuming a negligible reallocation of these activities
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and thus zero costs of a school absence to a household with a non-working parent provides a

lower-bound on the benefits estimate for averting these impacts.

4.1.6 Human Welfare Impact Assessment

PM and ozone have numerous documented effects on environmental quality that

affect human welfare. These welfare effects include direct damages to property, either

through impacts on material structures or by soiling of surfaces, direct economic damages in

the form of lost productivity of crops and trees, indirect damages through alteration of

ecosystem functions, and indirect economic damages through the loss in value of recreational

experiences or the existence value of important resources. EPA’s Criteria Documents for

PM and ozone list numerous physical and ecological effects known to be linked to ambient

concentrations of these pollutants (EPA, 1996a; 1996b). This section describes individual

effects and how we quantify and monetize them. These effects include changes in

commercial crop and forest yields, visibility, and nitrogen deposition to estuaries.

4.1.6.1 Visibility Benefits

Changes in the level of ambient PM caused by the reduction in emissions from CAIR

will change the level of visibility in much of the Eastern United States. Visibility directly

affects people’s enjoyment of a variety of daily activities. Individuals value visibility both in

the places they live and work, in the places they travel to for recreational purposes, and at

sites of unique public value, such as the Great Smokey Mountains National Park. This

section discusses the measurement of the economic benefits of improved visibility.

It is difficult to quantitatively define a visibility endpoint that can be used for

valuation. Increases in PM concentrations cause increases in light extinction, a measure of

how much the components of the atmosphere absorb light. More light absorption means that

the clarity of visual images and visual range is reduced, ceteris paribus. Light absorption is

a variable that can be accurately measured. Sisler (1996) created a unitless measure of

visibility, the deciview, based directly on the degree of measured light absorption. 

Deciviews are standardized for a reference distance in such a way that one deciview

corresponds to a change of about 10 percent in available light. Sisler characterized a change

in light extinction of one deciview as “a small but perceptible scenic change under many
20A change of less than 10 percent in the light extinction budget represents a measurable improvement in

visibility but may not be perceptible to the eye in many cases. Some of the average regional changes in

visibility are less than one deciview (i.e., less than 10 percent of the light extinction budget) and thus less

than perceptible. However, this does not mean that these changes are not real or significant. Our

assumption is then that individuals can place values on changes in visibility that may not be perceptible. 

This is quite plausible if individuals are aware that many regulations lead to small improvements in visibility

that, when considered together, amount to perceptible changes in visibility.

21The Clean Air Act designates 156 national parks and wilderness areas as Class I areas for visibility protection.

22For details of the visibility estimates discussed in this chapter, please refer to the Benefits TSD for the

Nonroad Diesel rulemaking (Abt Associates, 2003).

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circumstances.” Air quality models were used to predict the change in visibility, measured

in deciviews, of the areas affected by the control options.20

EPA considers benefits from two categories of visibility changes: residential

visibility and recreational visibility. In both cases economic benefits are believed to consist

of use values and nonuse values. Use values include the aesthetic benefits of better visibility,

improved road and air safety, and enhanced recreation in activities like hunting and

birdwatching. Nonuse values are based on people’s beliefs that the environment ought to

exist free of human-induced haze. Nonuse values may be more important for recreational

areas, particularly national parks and monuments.

Residential visibility benefits are those that occur from visibility changes in urban,

suburban, and rural areas and also in recreational areas not listed as federal Class I areas.21

For the purposes of this analysis, recreational visibility improvements are defined as those

that occur specifically in federal Class I areas. A key distinction between recreational and

residential benefits is that only those people living in residential areas are assumed to receive

benefits from residential visibility, while all households in the United States are assumed to

derive some benefit from improvements in Class I areas. Values are assumed to be higher if

the Class I area is located close to their home.22

Only two existing studies provide defensible monetary estimates of the value of

visibility changes. One is a study on residential visibility conducted in 1990 (McClelland et

al., 1993) and the other is a 1988 survey on recreational visibility value (Chestnut and Rowe,

1990a; 1990b). Although there are a number of other studies in the literature, they were

conducted in the early 1980s and did not use methods that are considered defensible by

current standards. Both the Chestnut and Rowe and McClelland et al. studies use the CV

method. There has been a great deal of controversy and significant development of both
23An SAB advisory letter indicates that “many members of the Council believe that the Chestnut and Rowe

study is the best available” (EPA-SAB-COUNCIL-ADV-00-002, 1999, p. 13). However, the committee did

not formally approve use of these estimates because of concerns about the peer-reviewed status of the study. 

EPA believes the study has received adequate review and has been cited in numerous peer-reviewed

publications (Chestnut and Dennis, 1997).

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theoretical and empirical knowledge about how to conduct CV surveys in the past decade. In

EPA’s judgment, the Chestnut and Rowe study contains many of the elements of a valid CV

study and is sufficiently reliable to serve as the basis for monetary estimates of the benefits

of visibility changes in recreational areas.23 This study serves as an essential input to our

estimates of the benefits of recreational visibility improvements in the primary benefits

estimates. Consistent with SAB advice, EPA has designated the McClelland et al. study as

significantly less reliable for regulatory benefit-cost analysis, although it does provide useful

estimates on the order of magnitude of residential visibility benefits (EPA-SAB-COUNCILADV-00-002, 1999). Residential visibility benefits are not calculated for this analysis.

The Chestnut and Rowe study measured the demand for visibility in Class I areas

managed by the National Park Service (NPS) in three broad regions of the country: 

California, the Southwest, and the Southeast. Respondents in five states were asked about

their WTP to protect national parks or NPS-managed wilderness areas within a particular

region. The survey used photographs reflecting different visibility levels in the specified

recreational areas. The visibility levels in these photographs were later converted to

deciviews for the current analysis. The survey data collected were used to estimate a WTP

equation for improved visibility. In addition to the visibility change variable, the estimating

equation also included household income as an explanatory variable.

The Chestnut and Rowe study did not measure values for visibility improvement in

Class I areas outside the three regions. Their study covered 86 of the 156 Class I areas in the

United States. We can infer the value of visibility changes in the other Class I areas by

transferring values of visibility changes at Class I areas in the study regions. A complete

description of the benefits transfer method used to infer values for visibility changes in Class

I areas outside the study regions is provided in the Benefits TSD for the Nonroad Diesel

rulemaking (Abt Associates, 2003).

The Chestnut and Rowe study (Chestnut and Rowe, 1990a; 1990b), although

representing the best available estimates, has a number of limitations. These include the

following:
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C The age of the study (late 1980s) will increase the uncertainty about the

correspondence of the estimated values to those that might be provided by current

or future populations.

C The survey focused only on populations in five states, so the application of the

estimated values to populations outside those states requires that preferences of

populations in the five surveyed states be similar to those of nonsurveyed states.

C There is an inherent difficulty in separating values expressed for visibility

improvements from an overall value for improved air quality. The Chestnut and

Rowe study attempted to control for this by informing respondents that “other

households are being asked about visibility, human health, and vegetation

protections in urban areas and at national parks in other regions.” However, most

of the respondents did not feel that they were able to segregate visibility at

national parks entirely from residential visibility and health effects.

C It is not clear exactly what visibility improvements the respondents to the

Chestnut and Rowe survey were valuing. For the purpose of the benefits analysis

for this rule, EPA assumed that respondents provided values for changes in

annual average visibility. Because most policies will result in a shift in the

distribution of visibility (usually affecting the worst days more than the best

days), the annual average may not be the most relevant metric for policy analysis.

C The WTP question asked about changes in average visibility. However, the

survey respondents were shown photographs of only summertime conditions,

when visibility is generally at its worst. It is possible that the respondents

believed those visibility conditions held year-round, in which case they would

have been valuing much larger overall improvements in visibility than what

otherwise would be the case.

C The survey did not include reminders of possible substitutes (e.g., visibility at

other parks) or budget constraints. These reminders are considered to be best

practice for stated preference surveys.

C The Chestnut and Rowe survey focused on visibility improvements in and around

national parks and wilderness areas. The survey also focused on visibility

improvements of national parks in the southwest United States. Given that

national parks and wilderness areas exhibit unique characteristics, it is not clear

whether the WTP estimate obtained from Chestnut and Rowe can be transferred

to other national parks and wilderness areas, without introducing additional

uncertainty.

In general, the survey design and implementation reflect the period in which the survey was

conducted. Since that time, many improvements to the stated preference methodology have
4-69

been developed. As future survey efforts are completed, EPA will incorporate values for

visibility improvements reflecting the improved survey designs. 

The estimated relationship from the Chestnut and Rowe study is only directly

applicable to the populations represented by survey respondents. EPA used benefits transfer

methodology to extrapolate these results to the population affected by the final CAIR. A

general WTP equation for improved visibility (measured in deciviews) was developed as a

function of the baseline level of visibility, the magnitude of the visibility improvement, and

household income. The behavioral parameters of this equation were taken from analysis of

the Chestnut and Rowe data. These parameters were used to calibrate WTP for the visibility

changes resulting from CAIR. The method for developing calibrated WTP functions is based

on the approach developed by Smith et al. (2002). Available evidence indicates that

households are willing to pay more for a given visibility improvement as their income

increases (Chestnut, 1997). The benefits estimates here incorporate Chestnut’s estimate that

a 1 percent increase in income is associated with a 0.9 percent increase in WTP for a given

change in visibility. 

Using the methodology outlined above, EPA estimates that the total WTP for the

visibility improvements in Southeastern Class I areas brought about by CAIR is $1.14 billion

in 2010 and $1.78 billion in 2015. This value includes the value to households living in the

same state as the Class I area as well as values for all households in the United States living

outside the state containing the Class I area, and the value accounts for growth in real

income. 

The benefits resulting from visibility improvements in Southeastern Class I areas

under the final CAIR are presented in Figure 4-2. This figure presents these benefits both in

terms of the total benefits modeled for each of the Class I areas (i.e., the “Park Benefits”

map) and the benefits realized by the populations in each of the 48 contiguous states (i.e., the

“State Benefits” map). The latter results reflect the WTP of state residents for visibility

improvements occurring in Class I areas in the Southeastern United States.

One major source of uncertainty for the visibility benefits estimate is the benefits

transfer process used. Judgments used to choose the functional form and key parameters of

the estimating equation for WTP for the affected population could have significant effects on

the size of the estimates. Assumptions about how individuals respond to changes in visibility

that are either very small or outside the range covered in the Chestnut and Rowe study could

also affect the results.
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Figure 4-2. CAIR Final Rule Visibility Improvements in Class I Areas in the Southeast 

4.1.6.2 Agricultural, Forestry, and Other Vegetation-Related Benefits

The Ozone Criteria Document notes that ozone affects vegetation throughout the

United States, impairing crops, native vegetation, and ecosystems more than any other air

pollutant (EPA, 1996a, page 5-11). Changes in ground-level ozone resulting from the

control options are expected to affect crop and forest yields throughout the affected area.

Well-developed techniques exist to provide monetary estimates of these benefits to

agricultural producers and to consumers. These techniques use models of planting decisions,

yield response functions, and the supply of and demand for agricultural products. The

resulting welfare measures are based on predicted changes in market prices and production

costs. Models also exist to measure benefits to silvicultural producers and consumers. 

However, these models have not been adapted for use in analyzing ozone-related forest

impacts. Because of resource limitations, we are unable to provide agricultural or benefits

estimates for the final CAIR rule.

4.1.6.2.1 Agricultural Benefits. Laboratory and field experiments have shown

reductions in yields for agronomic crops exposed to ozone, including vegetables (e.g.,
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lettuce) and field crops (e.g., cotton and wheat). The most extensive field experiments,

conducted under the National Crop Loss Assessment Network (NCLAN), examined 15

species and numerous cultivars. The NCLAN results show that several economically

important crop species are sensitive to ozone levels typical of those found in the United

States (EPA, 1996a). In addition, economic studies have shown a relationship between

observed ozone levels and crop yields (Garcia et al., 1986). 

4.1.6.2.2 Forestry Benefits. Ozone also has been shown conclusively to cause

discernible injury to forest trees (EPA, 1996a; Fox and Mickler, 1996). In our previous

analysis of the HD Engine/Diesel Fuel rule, we were able to quantify the effects of changes

in ozone concentrations on tree growth for a limited set of species. Because of resource

limitations, we were not able to quantify such impacts for this analysis. 

4.1.6.2.3 Other Vegetation Effects. An additional welfare benefit expected to accrue

as a result of reductions in ambient ozone concentrations in the United States is the economic

value the public receives from reduced aesthetic injury to forests. There is sufficient

scientific information available to reliably establish that ambient ozone levels cause visible

injury to foliage and impair the growth of some sensitive plant species (EPA, 1996a). 

However, present analytic tools and resources preclude EPA from quantifying the benefits of

improved forest aesthetics.

Urban ornamentals (floriculture and nursery crops) represent an additional vegetation

category likely to experience some degree of negative effects associated with exposure to

ambient ozone levels and likely to affect large economic sectors. In the absence of adequate

exposure-response functions and economic damage functions for the potential range of

effects relevant to these types of vegetation, no direct quantitative economic benefits analysis

has been conducted. The farm production value of ornamental crops was estimated at over

$14 billion in 2003 (USDA, 2004). This is therefore a potentially important welfare effects

category. However, information and valuation methods are not available to allow for

plausible estimates of the percentage of these expenditures that may be related to impacts

associated with ozone exposure.

The EGU program, by reducing NOx emissions, will also reduce nitrogen deposition

on agricultural land and forests. There is some evidence that nitrogen deposition may have

positive effects on agricultural output through passive fertilization. Holding all other factors

constant, farmers’ use of purchased fertilizers or manure may increase as deposited nitrogen

is reduced. Estimates of the potential value of this possible increase in the use of purchased

fertilizers are not available, but it is likely that the overall value is very small relative to other
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health and welfare effects. The share of nitrogen requirements provided by this deposition is

small, and the marginal cost of providing this nitrogen from alternative sources is quite low. 

In some areas, agricultural lands suffer from nitrogen oversaturation due to an abundance of

on-farm nitrogen production, primarily from animal manure. In these areas, reductions in

atmospheric deposition of nitrogen represent additional agricultural benefits.

Information on the effects of changes in passive nitrogen deposition on forests and

other terrestrial ecosystems is very limited. The multiplicity of factors affecting forests,

including other potential stressors such as ozone, and limiting factors such as moisture and

other nutrients, confound assessments of marginal changes in any one stressor or nutrient in

forest ecosystems. However, reductions in the deposition of nitrogen could have negative

effects on forest and vegetation growth in ecosystems where nitrogen is a limiting factor

(EPA, 1993).

On the other hand, there is evidence that forest ecosystems in some areas of the

United States are nitrogen saturated (EPA, 1993). Once saturation is reached, adverse effects

of additional nitrogen begin to occur such as soil acidification, which can lead to leaching of

nutrients needed for plant growth and mobilization of harmful elements such as aluminum. 

Increased soil acidification is also linked to higher amounts of acidic runoff to streams and

lakes and leaching of harmful elements into aquatic ecosystems.

4.1.6.3 Benefits from Reductions in Materials Damage

The control options that we modeled are expected to produce economic benefits in

the form of reduced materials damage. There are two important categories of these benefits. 

Household soiling refers to the accumulation of dirt, dust, and ash on exposed surfaces. 

Criteria pollutants also have corrosive effects on commercial/industrial buildings and

structures of cultural and historical significance. The effects on historic buildings and

outdoor works of art are of particular concern because of the uniqueness and irreplaceability

of many of these objects.

Previous EPA benefits analyses have been able to provide quantitative estimates of

household soiling damage. Consistent with SAB advice, we determined that the existing

data (based on consumer expenditures from the early 1970s) are too out of date to provide a

reliable estimate of current household soiling damages (EPA-SAB-COUNCIL-ADV-98-003,

1998). 

EPA is unable to estimate any benefits to commercial and industrial entities from

reduced materials damage. Nor is EPA able to estimate the benefits of reductions in PM-
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related damage to historic buildings and outdoor works of art. Existing studies of damage to

this latter category in Sweden (Grosclaude and Soguel, 1994) indicate that these benefits

could be an order of magnitude larger than household soiling benefits.

4.1.6.4 Benefits from Reduced Ecosystem Damage

The effects of air pollution on the health and stability of ecosystems are potentially

very important but are at present poorly understood and difficult to measure. The reductions

in NOx caused by the final rule could produce significant benefits. Excess nutrient loads,

especially of nitrogen, cause a variety of adverse consequences to the health of estuarine and

coastal waters. These effects include toxic and/or noxious algal blooms such as brown and

red tides, low (hypoxic) or zero (anoxic) concentrations of dissolved oxygen in bottom

waters, the loss of submerged aquatic vegetation due to the light-filtering effect of thick algal

mats, and fundamental shifts in phytoplankton community structure (Bricker et al., 1999). 

Direct functions relating changes in nitrogen loadings to changes in estuarine benefits

are not available. The preferred WTP-based measure of benefits depends on the availability

of these functions and on estimates of the value of environmental responses. Because neither

appropriate functions nor sufficient information to estimate the marginal value of changes in

water quality exist at present, calculation of a WTP measure is not possible. 

If better models of ecological effects can be defined, EPA believes that progress can

be made in estimating WTP measures for ecosystem functions. These estimates would be

superior to avoided cost estimates in placing economic values on the welfare changes

associated with air pollution damage to ecosystem health. For example, if nitrogen or sulfate

loadings can be linked to measurable and definable changes in fish populations or definable

indexes of biodiversity, then CV studies can be designed to elicit individuals’ WTP for

changes in these effects. This is an important area for further research and analysis and will

require close collaboration among air quality modelers, natural scientists, and economists.

4.2 Benefits Analysis—Results

Applying the impact and valuation functions described previously in this chapter to

the estimated changes in ozone and PM yields estimates of the changes in physical damages

(e.g., premature mortalities, cases, admissions, change in light extinction) and the associated

monetary values for those changes. Estimates of physical health impacts are presented in

Table 4-16. Monetized values for both health and welfare endpoints are presented in

Table 4-17, along with total aggregate monetized benefits. All of the monetary benefits are

in constant-year 1999 dollars.
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Table 4-16. Clean Air Interstate Rule: Estimated Reduction in Incidence of Adverse

Health Effectsa

2010 2015

Health Effect Incidence Reduction

PM-Related Endpoints

Premature Mortalityb,c

 Adult, age 30 and over

 Infant, age <1 year

13,000

29

17,000

36

Chronic bronchitis (adult, age 26 and over) 6,900 8,700

Nonfatal myocardial infarction (adults, age 18 and older) 17,000 22,000

Hospital admissions—respiratory (all ages)d 4,300 5,500

Hospital admissions—cardiovascular (adults, age >18)e 3,800 5,000

Emergency room visits for asthma (age 18 years and younger) 10,000 13,000

Acute bronchitis (children, age 8–12) 16,000 19,000

Lower respiratory symptoms (children, age 7–14) 190,000 230,000

Upper respiratory symptoms (asthmatic children, age 9–18) 150,000 180,000

Asthma exacerbation (asthmatic children, age 6–18) 240,000 290,000

Work loss days (adults, age 18–65) 1,400,000 1,700,000

Minor restricted-activity days (adults, age 18–65) 8,100,000 9,900,000

Ozone-Related Endpoints

Hospital admissions—respiratory causes (adult, 65 and older)f 610 1,700

Hospital admissions—respiratory causes (children, under 2) 380 1,100

Emergency room visit for asthma (all ages) 100 280

Minor restricted-activity days (adults, age 18–65) 280,000 690,000

School absence days 180,000 510,000

a Incidences are rounded to two significant digits. These estimates represent benefits for CAIR Nationwide

for the final CAIR program inclusive of the proposal to include SO2 and annual NOx controls for New Jersey

and Delaware. Note these estimates may be slightly overstated due to the inclusion of SO2 and annual NOx

controls for Arkansas. b Premature mortality benefits associated with ozone are not addressed in the primary analysis. c PM premature mortality impacts for adults are based on application of the effect estimate derived from the

Pope et al (2002) cohort study. Infant premature mortality based upon studies by Woodruff, et al 1997. d Respiratory hospital admissions for PM include admissions for COPD, pneumonia, and asthma. e Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart

disease, dysrhythmias, and heart failure. f Respiratory hospital admissions for ozone include admissions for total respiratory and subcategories for

COPD and pneumonia.
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Table 4-17. Estimated Monetary Value in Reductions in Incidence of Health and

Welfare Effects (in millions of 1999$)a,b

2010 2015

Health Effect Pollutant Estimated Value of Reductions

Premature mortalityc,d

 Adult, age 30 and over

 3% discount rate

 7% discount rate

 Infant, < 1 year

PM2.5 $67,300

$56,600

$168

$92,800

$78,100

$222

Chronic bronchitis (adults, 26 and over) PM2.5 $2,520 $3,340

Non-fatal acute myocardial infarctions 

 3% discount rate

 7% discount rate

PM2.5 $1,420

$1,370

$1,850

$1,790

Hospital admissions for respiratory causes PM2.5, O3 $45.2 $78.9

Hospital admissions for cardiovascular causes PM2.5 $80.7 $105

Emergency room visits for asthma PM2.5,O3 $2.84 $3.56

Acute bronchitis (children, age 8–12) PM2.5 $5.63 $7.06

Lower respiratory symptoms (children, 7–14) PM2.5 $2.98 $3.74

Upper respiratory symptoms (asthma, 9–11) PM2.5 $3.80 $4.77

Asthma exacerbations PM2.5 $10.3 $12.7

Work loss days PM2.5, $180 $219

Minor restricted-activity days (MRADs) PM2.5,O3 $422 $543

School absence days O3 $12.9 $36.4

Worker productivity (outdoor workers, 18–65) O3 $7.66 $19.9

Recreational visibility, 81 Class I areas PM2.5 $1,140 $1,780

Monetized Total e 

 Base Estimate:

 3% discount rate

 7% discount rate 

PM2.5,O3

$73,300 + B

$62,600 + B

$101,000 + B

$86,300 + B

a Monetary benefits are rounded to three significant digits for ease of presentation and computation. Benefits in

this table are nationwide (with the exception of visibility and ozone) and are associated with NOx and SO2

reductions at EGU sources. Ozone benefits relate to the eastern United States. Visibility benefits relate to Class

I areas in the southeastern United States. These estimates relate to the final CAIR program inclusive of the

proposal to include SO2 and annual NOx controls for New Jersey and Delaware. Note that these estimates may

be slightly overstated due to the inclusion of SO2 and annual NOx controls for Arkansas in the modeling. b Monetary benefits adjusted to account for growth in real GDP per capita between 1990and the analysis year

(2010 or 2015) c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure described earlier. 

Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for

preparing economic analyses (EPA, 2000b; OMB, 2003). d Adult premature mortality estimates based upon studies by Pope, et al 2002. Infant premature mortality based

upon Woodruff et al 1997. e B represents the monetary value of health and welfare benefits and disbenefits not monetized. A detailed listing

is provided in Table 4-2.
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Not all known PM- and ozone-related health and welfare effects could be quantified or

monetized. The monetized value of these unquantified effects is represented by adding an

unknown “B” to the aggregate total. The estimate of total monetized health benefits is thus

equal to the subset of monetized PM- and ozone-related health and welfare benefits plus B,

the sum of the nonmonetized health and welfare benefits.

Total monetized benefits are dominated by benefits of mortality risk reductions. The

primary analysis estimate projects that the final rule will result in 13,000 avoided premature

deaths annually in 2010 and 17,000 avoided premature deaths annually in 2015. The

increase in annual benefits from 2010 to 2015 reflects additional emission reductions from

the standards, as well as increases in total population and the average age (and thus baseline

mortality risk) of the population. Note that unaccounted for changes in baseline mortality

rates over time may lead to reductions in the estimated number of avoided premature

mortalities.

Our estimate of total monetized benefits in 2010 for the final rule is $73.3 billion

using a 3 percent discount rate and $62.6 billion using a 7 percent discount rate. In 2015, the

monetized benefits are estimated at $101 billion using a 3 percent discount rate and $86.3

billion using a 7 percent discount rate. Health benefits account for 98 percent of total

benefits, in part because we are unable to quantify most of the nonhealth benefits. These

unquantified benefits may be substantial and could exceed the costs of the rule, although the

magnitude of these benefits is highly uncertain. The monetized benefit associated with

reductions in the risk of premature mortality, which accounts for $67.3 billion in 2010 and

$92.8 billion in 2015, is over 90 percent of total monetized health benefits. The next largest

benefit is for reductions in chronic illness (CB and nonfatal heart attacks), although this

value is more than an order of magnitude lower than for premature mortality. Hospital

admissions for respiratory and cardiovascular causes, visibility, MRADs, work loss days,

school absence days, and worker productivity account for the majority of the remaining

benefits. The remaining categories each account for a small percentage of total benefit;

however, they represent a large number of avoided incidences affecting many individuals. A

comparison of the incidence table to the monetary benefits table reveals that there is not

always a close correspondence between the number of incidences avoided for a given

endpoint and the monetary value associated with that endpoint. For example, there are

almost 100 times more work loss days than premature mortalities, yet work loss days account

for only a very small fraction of total monetized benefits. This reflects the fact that many of

the less severe health effects, while more common, are valued at a lower level than the more

severe health effects. Also, some effects, such as hospital admissions, are valued using a
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proxy measure of WTP. As such, the true value of these effects may be higher than that

reported in Table 4-16. 

Ozone benefits are in aggregate positive for the nation. However, because ozone

increases occur during certain hours of the day in some urban areas, there is a dampening of

overall ozone benefits in both 2010 and 2015, although the net incidence and benefits

estimates for all health effects categories are net positive. Overall, ozone benefits are low

relative to PM benefits for similar endpoint categories because of the increases in ozone

concentrations during some hours of some days in certain urban areas. 

4.2.1 Potential Benefits of the New Jersey and Delaware Proposal

The benefit estimate results presented reflect benefits for the final CAIR program

inclusive of the New Jersey and Delaware proposal. Air quality modeling was not conducted

for the New Jersey and Delaware proposal. For this reason, an analysis of the potential

benefits for the New Jersey and Delaware proposal could not be completed with any degree

of specificity. However based on the air quality modeling results for the CAIR, we make

rough estimates of the benefits that might occur with this proposal. Including New Jersey

and Delaware in the CAIR program would result in additional reductions of SO2 and NOx

emissions. We estimate that approximately $630 million of the total annual CAIR program

benefits previously discussed could be attributable to annual SO2 and NOx controls for New

Jersey and Delaware in 2010 and approximately $1.1 billion could be attributable to annual

SO2 and NOx controls for New Jersey and Delaware in 2015.

4.3 Probabilistic Analysis of Uncertainty in the Benefits Estimates

The recent NRC report on estimating public health benefits of air pollution regulations

recommended that EPA begin to move the assessment of uncertainties from its ancillary

analyses into its primary analyses by conducting probabilistic, multiple-source uncertainty

analyses (NRC, 2002). The probability distributions required for these analyses should be

based on available data and expert judgment. The NRC also recommended that EPA use

both internal and external experts as needed, in each case identifying those experts whose

judgments are used and the rationales and empirical bases for their judgments. 

As part of an overall program to improve the Agency’s characterization of

uncertainties in health benefits analyses, this section describes EPA’s initial efforts to

address uncertainties associated with the PM mortality C-R relationship and valuation. 

Similar to our approach in the Nonroad Diesel RIA, for the CAIR, we present two types of

probabilistic approaches. The first approach generates a distribution of benefits based on the
24In each iteration of the Monte Carlo procedure, a value is randomly drawn from the incidence distribution, and

a value is randomly drawn from the unit dollar value distribution. The total dollar benefit for that iteration is

the product of the two. If this is repeated for many (e.g., thousands of) iterations, the distribution of total

dollar benefits associated with the endpoint is generated. For details on the specific Monte Carlo approach

we used, see Appendix B.

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sampling error and variability in the underlying health and economic valuation studies used

in the benefits modeling framework. The second approach uses the results from a pilot

expert elicitation project designed to characterize key aspects of uncertainty in the ambient

PM2.5/mortality relationship. Both approaches provide insights into the likelihood of

different outcomes and about the state of knowledge regarding the benefits estimates. Both

approaches have different strengths and weaknesses, that are summarized below.

We provide likelihood distributions both for the total dollar benefits estimate and for

the incidence of premature mortality to show the uncertainty described by each expert’s

judgment relative to the range of uncertainty associated with the standard error in the Pope et

al. (2002) study. The uncertainty about the total dollar benefit associated with any single

endpoint combines the uncertainties from two sources—the C-R relationship and the

valuation—and is estimated with a Monte Carlo method.24 Our estimates of the likelihood

distributions for total benefits should be viewed within the context of the wide range of

sources of uncertainty that we have not incorporated, including uncertainty in emissions, air

quality, and baseline health effect incidence rates.

In benefit analyses of air pollution regulations conducted to date, the estimated impact

of reductions in premature mortality has accounted for 85 to 95 percent of total benefits. 

Therefore, in characterizing the uncertainty related to the estimates of total benefits it is

particularly important to attempt to characterize the uncertainties associated with this

endpoint. We conducted two different Monte Carlo analyses, one based on the distribution

of reductions in premature mortality characterized by the mean effect estimate and standard

error from the Pope et al. (2002) study (our primary estimate), and one based on the results

from a pilot expert elicitation project (IEc, 2004). The Pope et al. study is described earlier

in this chapter. A detailed discussion of the pilot expert elicitation project is provided in

Appendix B. We summarize several key points about the project below.

As a first step in addressing the NRC recommendations regarding expert elicitation,

EPA, in collaboration with OMB, conducted a pilot expert elicitation to characterize

uncertainties in the relationship between ambient PM2.5 and mortality. This pilot was

designed to provide EPA with an opportunity to improve its understanding of the design and
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application of expert elicitation methods to economic benefits analysis, to lay the

groundwork for a more comprehensive elicitation, and to provide more information about the

uncertainty in the PM2.5-mortality relationship in the context of the Nonroad Diesel RIA and

similar analyses conducted in the near term. 

The pilot project elicited the judgments of five experts in the PM health sciences, all

members of at least one of two recent National Academy of Sciences scientific committees

focused on particulate matter. The specific process used to select experts is summarized in

Appendix B and detailed in the technical report describing the elicitation (IEc, 2004) along

with additional information about the experts’ affiliations and fields of expertise. The

responses of each expert to questions enumerated in the elicitation protocol provide the

inputs for developing distributions of mortality benefits. 

The elicitation approach tested in the pilot was peer reviewed by four experts

(Mansfield, 2004) and generally received favorable comments, including

C the pilot project followed “best practices” for expert elicitation and was well

documented,

C the elicitation was well conducted and is an appropriate technique for

characterizing uncertainty,

C experts chosen are well known and respected and represent a range of views, and

C the expert selection process/protocol was very good.

The peer-review report also provides recommendations on how to improve the process

for a more comprehensive expert elicitation addressing the PM mortality C-R relationship. 

Specifically, the peer-review recommendations included

C holding a pre-elicitation workshop to ensure that all experts are properly

motivated and conditioned (i.e., on the same footing) before the interviews and to

allow for information sharing prior to the elicitation; 

C allowing for some form of communication following the individual interviews to

allow review of the experts’ responses and allow them to adjust their estimates if

necessary—one way to accomplish this is through a post-elicitation workshop;

and

C changing the encoding process to ensure that extreme values (upper and lower

ranges) are collected prior to judgments on central tendency. This sequencing

would avoid anchoring or adjustment heuristics associated with biased estimates

of uncertainty.
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In addition, although not listed explicitly as a criticism, some of the peer reviewers

noted the small number of experts participating in the pilot and suggested a larger number of

experts should be used in EPA’s next elicitation. 

The peer reviewers also offered varying comments on the methods for combining the

results of the pilot elicitation. Several of the reviewers preferred that the expert opinions not

be combined or stated that they knew of no agreed-upon method for combining results from

expert elicitations. They stated that presenting each expert’s response independently allows

for differences in the individual distributions to be recognized. Two of the reviewers

indicated that they were reasonably comfortable with the method used in this study to

combine the results, while the other two reviewers offered comments on the combined result

of the elicitation. One reviewer stated that the combined distributions do not adequately

capture the opinions of individual experts but rather average them out. He states that it is

possible in such cases that the combined judgments may generate results that none of the

experts would agree on. Another reviewer stated that expert elicitation studies typically do

not combine judgments, but if one were to combine them, he recommended that the response

of each be maintained independently from the other experts and run through the benefits

model completely prior to combining the results.

For more details regarding the peer-review comments, see Appendix B. The full peerreview report is also available at www.epa.gov/ttn/ecas/benefits.html.

The distributions of all other, nonmortality health endpoints are characterized by the

reported mean and standard deviations from the epidemiology literature. Details on the

distributions used for individual health endpoints are provided in Appendix B. We are

unable at this time to characterize the uncertainty in the estimate of benefits of improvements

in visibility at Class I areas. As such, we treat the visibility benefits as fixed and add them to

all percentiles of the health benefits distribution. Given this unequal treatment of endpoints,

it is likely that these distributions do not capture the full range of benefits, and in fact are

likely to understate the uncertainty, especially on the high end of the range due to omission

of potentially significant benefit categories. We include them here primarily as an

illustration of the impacts of using probabilistic (expert elicitation and statistical error-based)

distributions for premature mortality associated with PM2.5 compared with EPA’s traditional

approach. 

Figure 4-3 presents box plots of the distributions of the reduction in PM2.5-related

premature mortality based on the C-R distributions provided by each expert, as well as that

for our primary estimate, based on the statistical error associated with Pope et al. (2002). 
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11,600

2,800

7,100

13,700

16,700

0

10,00020,00030,00040,00050,00060,000

Pope et al (2002)

Statistical Error

Expert A Expert B Expert C Expert D 

Reduction in Premature Mortality in 2015

Note: Distributions labeled Expert A - Expert E are based on individual expert responses. The distribution labeled Pope et al. (2002) Statistical Erro

and standard error of the C-R function from the study.

Distribution of 

Primary 

Estimate

Distributions Based on Application of Pilot Expert Elicitat

Figure 4-3. Results of Illustrative Application of Pilot Expert Elicitation: Annual Reductions in Prem2015 Associated with the Clean Air Interstate Rule 
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The distributions are depicted as box plots with the diamond symbol (—) showing the mean,

the dash (–) showing the median (50th percentile), the box defining the interquartile range

(bounded by the 25th and 75th percentiles), and the whiskers defining the 90 percent

confidence interval (bounded by the 5th and 95th percentiles of the distribution). Our

primary estimate based on the Pope et al. (2002) study shows that the average number of

premature deaths avoided in 2015 is 16,700. This is higher than four of the experts and

lower than one expert and falls within the uncertainty bounds of all but one expert. The

figure shows that the average annual number of premature deaths avoided in 2015 ranges

from approximately 2,800 (based on the judgments of Expert C) to 22,800 (based on the

judgments of Expert E). The medians span zero to 20,000, with the zero value due to the

high threshold associated with one of the expert’s distributions. The statistical uncertainty

bounds of all of the estimates, including the Pope et al.-based distribution, overlap. 

Although the uncertainty bounds for each expert include zero, and some distributions have

significant percentiles at zero, all of the distributions have a positive mean estimate. 

Figure 4-4 presents box plots of the distributions of monetized benefits of reductions

in premature mortality associated with use of the Pope et al. (2002) and expert-based

mortality incidence distributions. Our primary estimate based on the Pope et al. (2002) study

shows that the mean annual benefit is roughly $93 billion. Mean annual benefits for each

expert elicited during the pilot expert elicitation range from approximately $16 billion (based

on judgments of Expert C) to $130 billion (based on the judgments of Expert E). Impacts on

the distribution of total benefits (including visibility and non-mortality health benefits) are

discussed in Appendix B.

The uncertainty estimates based on statistical error have the strength of presenting a

statistical measure of the uncertainty in the underlying studies serving as the basis for the

estimates used in the analysis. However, this approach captures only a limited portion of the

uncertainty about the parameters. The 5th and 95th percentile points of the distributions are

based on statistical error and cross-study variability and provide some insight into how

uncertain our estimate is with regard to those sources of uncertainty. However, it does not

capture other sources of uncertainty regarding the model specification and other inputs to the

model, including emissions, air quality, and aspects of the health science not captured in the

studies, such as the likelihood that PM is causally related to premature mortality and other

serious health effects.
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$64,0

$16,000

$40,000

$76,000

$93,000

$0

$100,000$200,000$300,000$400,000

Pope et al (2002) Statistical

Error

Expert A Expert B Expert C Expert D 

Millions of $1999

Note: Mortality distributions labeled Expert A - Expert E are based on individual expert responses. The mortality distribution labeled Pope et al. (2002) statistand standard error of the C-R function from the study. Mortality valuation is based on a normally distributed VSL with a mean of $5.5 million and a 95% CI beVSL distribution has then been adjsuted for income growth out to 2015 using an adjustment factor of 1.15.

Distributions Based on Application of Pilot Expert Elicitation 

Results

Distribution of 

Primary 

Estimate

Figure 4-4. Results of Illustrative Application of Pilot Expert Elicitation: Dollar Value of Annual ReduMortality in 2015 Associated with the Clean Air Interstate Rule
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4.4 Discussion

This analysis has estimated the health and welfare benefits of reductions in ambient

concentrations of particulate matter and ozone resulting from reduced emissions of NOx and

SO2 from affected EGUs. The result suggests there will be significant health and welfare

benefits arising from regulating emissions from EGUs in the United States. Our estimate

that 17,000 premature mortalities would be avoided when the emissions reductions from the

regulation are fully realized provides additional evidence of the important role that pollution

from the EGU sector plays in the public health impacts of air pollution.

Other uncertainties that we could not quantify include the importance of unquantified

effects and uncertainties in the modeling of ambient air quality. Inherent in any analysis of

future regulatory programs are uncertainties in projecting atmospheric conditions and sourcelevel emissions, as well as population, health baselines, incomes, technology, and other

factors. The assumptions used to capture these elements are reasonable based on the

available evidence. However, data limitations prevent an overall quantitative estimate of the

uncertainty associated with estimates of total economic benefits. If one is mindful of these

limitations, the magnitude of the benefits estimates presented here can be useful information

in expanding the understanding of the public health impacts of reducing air pollution from

EGUs.

EPA will continue to evaluate new methods and models and select those most

appropriate for estimating the health benefits of reductions in air pollution. It is important to

continue improving benefits transfer methods in terms of transferring economic values and

transferring estimated impact functions. The development of both better models of current

health outcomes and new models for additional health effects such as asthma, high blood

pressure, and adverse birth outcomes (such as low birth weight) will be essential to future

improvements in the accuracy and reliability of benefits analyses (Guo et al., 1999; IbaldMulli et al., 2001). Enhanced collaboration between air quality modelers, epidemiologists,

toxicologists, and economists should result in a more tightly integrated analytical framework

for measuring health benefits of air pollution policies.
4-85

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CHAPTER 5

QUALITATIVE ASSESSMENT OF NONMONETIZED BENEFITS

5.1 Introduction

In addition to the enumerated human health and welfare benefits resulting from

reductions in ambient levels of PM and ozone, CAIR will result in benefits that we are not

able to monetize. This chapter discusses welfare benefits associated with reduced sulfur and

nitrogen deposition that affects acidification of ecosystems and eutrophication in water

bodies. Other welfare benefits including potential visibility improvements, agricultural yield

increases, forestry production increases, reductions in soiling and materials damage, mercury

health and welfare benefits, and other welfare categories are discussed in Chapter 4 of this

report.

5.2 Atmospheric Deposition of Sulfur and Nitrogen—Quantification of Impacts for

the Rule

The benefits associated with reduced sulfur and nitrogen deposition to aquatic, forest,

and coastal ecosystems are qualitatively discussed in this section. We also quantify the

reductions in acid deposition for the nation’s lakes in the northeast, including the

Adirondacks, and in southeast streams that are expected to occur as a result of this rule. 

Figures 5-1 and 5-2 present the reductions in sulfur and nitrogen deposition anticipated to

occur in 2010 and 2015 in the Eastern United States as a result of emission reductions

anticipated under the final CAIR. As shown in Figure 5-1, CAIR is predicted to result in

sulfur deposition reductions ranging from slightly less than 1 to 59 percent in specific areas

of the CAIR region and other eastern states with reductions of 16.6 percent on average for

the areas east of the 100th parallel in 2015. As Figure 5-2 depicts, changes in all forms of

nitrogen deposition in the region are expected to range from small areas of slight increases

that do not exceed 1.5 percent to reductions up to 19 percent and average reductions of 4.6

percent for areas east of the 100th parallel in 2015.

Figure 5-3 reports average nitrogen and sulfur deposition reductions anticipated for

the Adirondacks, New England, the Blue Ridge in 2015 under CAIR. As shown in

Figure 5-3, sulfur depositions reductions from baseline conditions are predicted in 2015 of 
5-2

Figure 5-1. Percentage Reduction of All Forms of Sulfur Deposition for the Years 2010

and 2015

Figure 5-2. Percentage Reduction of All Forms of Nitrogen Deposition for the Years

2010 and 2015
5-3

Figure 5-3. CAIR Nitrogen and Sulfur Deposition Reductions in the Adirondacks, New

England, and the Blue Ridge 

approximately 30 percent in the Adirondacks, 24 percent in New England, and 34 percent in

the Blue Ridge. In 2015, these same regions are expected to experience nitrogen deposition

reductions ranging from 6 to 8 percent from baseline conditions.

5.3 Atmospheric Deposition of Sulfur and Nitrogen—Impacts on Aquatic, Forest,

and Coastal Ecosystems

Atmospheric deposition of sulfur and nitrogen, more commonly known as acid rain,

occurs when emissions of SO2 and NOx react in the atmosphere (with water, oxygen, and

oxidants) to form various acidic compounds. These acidic compounds fall to Earth in either

a wet form (rain, snow, and fog) or a dry form (gases and particles). Prevailing winds

transport the acidic compounds hundreds of miles, often across state and national borders. 

Acidic compounds (including small particles such as sulfates and nitrates) cause many

negative environmental effects. These pollutants
5-4

C acidify lakes and streams,

C harm sensitive forests, and

C harm sensitive coastal ecosystems.

The effect of atmospheric deposition of acids on freshwater and forest ecosystems depends

largely on the ecosystem’s ability to neutralize the acid (Driscoll et al., 2001). This is

referred to as an ecosystem’s acid neutralizing capacity (ANC). Acid neutralization occurs

when positively charged ions such as calcium, potassium, sodium, and magnesium,

collectively known as base cations, are released. As water moves through a watershed, two

important chemical processes act to neutralize acids. The first involves cation exchange in

soils, a process by which hydrogen ions from the acid deposition displace other cations from

the surface of soil particles, releasing these cations to soil and surface water. The second

process is mineral weathering, where base cations bound in the mineral structure of rocks are

released as the minerals gradually break down over long time periods. As the base cations

are released by weathering, they neutralize acidity and increase the pH level in soil water and

surface waters. Acid deposition, because it consists of acid anions (e.g., sulfate, nitrate),

leaches some of the accumulated base cation reserves from the soils into drainage waters. 

The leaching rate of these base cations may accelerate to the point where it significantly

exceeds the resupply via weathering (Driscoll et al., 2001). CAIR is expected to reduce

atmospheric deposition of nitrogen and sulfur and to reduce the total nitrogen and sulfur load

in areas of the East.

Soils, forests, surface waters and aquatic biota (fish, algae, and the rest), and coastal

ecosystems share water, nutrients, and other essential ecosystem components and are

inextricably linked by the chemical processes described above. For example, the same base

cations that help to neutralize acidity in lakes and streams are also essential nutrients in

forest soils, meaning that cation depletion both increases freshwater acidification and

decreases forest productivity. Similarly, the same nitrogen atom that contributes to stream

acidification can ultimately contribute to coastal eutrophication as it travels downstream to

an estuarine environment. Therefore, to understand the full effects of atmospheric

deposition, it is necessary to recognize the interactions between all of these systems.

5.3.1 Freshwater Acidification

Acid deposition causes acidification of surface waters. In the 1980s, acid rain was

found to be the dominant cause of acidification in 75 percent of acidic lakes and 50 percent

of acidic streams. Areas especially sensitive to acidification include portions of the
5-5

Northeast (particularly the Adirondack and Catskill Mountains, portions of New England,

and streams in the mid-Appalachian highlands) and Southeastern streams. Some highelevation Western lakes, particularly in the Rocky Mountains, have become acidic,

especially during snowmelt. However, although many Western lakes and streams are

sensitive to acidification, they are not subject to continuously high levels of acid deposition

and so have not become chronically acidified (NAPAP, 1990).

ANC, a key indicator of the ability of the water and watershed soil to neutralize the

acid deposition it receives, depends largely on the watershed’s physical characteristics: 

geology, soils, and size. Waters that are sensitive to acidification tend to be located in small

watersheds that have few alkaline minerals and shallow soils. Conversely, watersheds that

contain alkaline minerals, such as limestone, tend to have waters with a high ANC.

As acidity increases, aluminum leached from the soil flows into lakes and streams

and can be toxic to aquatic species. The lower pH levels and higher aluminum levels that

result from acidification make it difficult for some fish and other aquatic species to survive,

grow, and reproduce. In some waters, the number of species of fish able to survive has been

directly correlated to water acidity. Acidification can also decrease fish population density

and individual fish size (U.S. Department of the Interior, 2003). 

Recent watershed mass balance studies in the Northeast reveal that loss of sulfate

from the watershed exceeds atmospheric sulfur deposition (Driscoll et al., 2001). This

suggests that these soils have become saturated with sulfur, meaning that the supply of sulfur

from deposition exceeds the sulfur demands of the ecosystem. As a result, sulfur is gradually

being released or leached from the watershed into the surface waters as sulfate. Scientists

now expect that the release of sulfate that previously accumulated in watersheds will delay

the recovery of surface waters in the Northeast that is anticipated in response to the recent

SO2 emission controls (Driscoll et al., 2001).

A major study of the ecological response to acidification is taking place in the Bear

Brook Watershed in Maine. Established in 1986 as part of the EPA’s Watershed

Manipulation Project, the project has found that experimental additions of sulfur and

nitrogen to the watershed increased the concentrations of both sulfate and nitrate in the West

Bear Brook stream. Stream water concentrations of several other ions, including base

cations, aluminum, and ANC, changed substantially as well (Norton et al., 1999). During the

first year of treatment, 94 percent of the nitrogen added experimentally to the Bear Brook

watershed was retained, while the remainder leached into streams as nitrate. Nitrogen

retention decreased to about 82 percent in subsequent years (Kahl et al., 1993; 1999). 
5-6

Although the forest ecosystem continued to accumulate nitrogen, nitrate leaching into the

stream continued at elevated levels throughout the length of the experiment. This nitrate

contributed to both episodic and chronic acidification of the stream. This and other similar

studies have allowed scientists to quantify acidification and recovery relationships in eastern

watersheds in much more detail than was possible in 1990. 

The Appalachian Mountain region receives some of the highest rates of acid

deposition in the United States (Herlihy et al., 1993). The acid-base status of stream waters

in forested upland watersheds in the Appalachian Mountains was extensively investigated in

the early 1990s (e.g., Church et al. [1992]; Herlihy et al. [1993]; Webb et al. [1994]; van

Sickle and Church [1995]). A more recent assessment of the southern Appalachian region

from West Virginia to Alabama identified watersheds that are sensitive to acid deposition

using geologic bedrock and the associated buffering capacity of soils to neutralize acid. The

assessment found that approximately 59 percent of all trout stream length in the region is in

areas that are highly vulnerable to acidification and that 27 percent is in areas that are

moderately vulnerable (SAMAB, 1996). Another study estimated that 18 percent of

potential brook trout streams in the mid-Appalachian Mountains are too acidic for brook

trout survival (Herlihy et al., 1996). Perhaps the most important study of acid-base

chemistry of streams in the Appalachian region in recent years has been the Virginia Trout

Stream Sensitivity Study (Webb et al., 1994). Trend analyses of these streams indicate that

few long-term sampling sites are recovering from acidification, most are continuing to

acidify, and the continuing acidification is at levels that are biologically significant for brook

trout populations (Webb et al., 2000).

5.3.1.1 Water/Watershed Modeling

Researchers have used models to help them understand and predict atmospheric,

environmental, and human health responses to acid deposition for well over 20 years. Since

1990, watershed modeling capabilities have also improved as researchers are continuing to

refine and expand models that project acidification of waterbodies. Unlike the response of

air quality and deposition to changes in emissions, lakes and streams take years to decades to

fully reflect reductions in acid deposition. In some cases, soil chemistry has been

significantly altered, and ions must either build up or be leached out before the chemistry can

return to its pre-acidification status. Therefore, lake and stream conditions are presented for

2030. These results may still not reflect the full scope of ecosystem response to CAIR

emissions reductions.
5-7

5.3.1.2 Description of the MAGIC Model and Methods

A number of mathematical models of soil and surface water acidification in response

to atmospheric deposition were developed in the early 1980s (e.g., Christopherson and

Wright [1981]; Christopherson et al. [1982]; Schnoor et al. [1984]; Booty and Kramer

[1984]; Goldstein et al. [1984]; Cosby et al. [1985a,b,c]). These models were based on

process-level information about the acidification process and were built for a variety of

purposes ranging from estimating transient water quality responses for individual storm

events to estimating chronic acidification of soils and base flow surface water. One of these

models (MAGIC—the Model of Acidification of Groundwater In Catchments; Cosby et al.

[1985a,b,c]) has been in use now for more than 15 years. MAGIC has been applied

extensively in North America and Europe to both individual sites and regional networks of

sites and has also been used in Asia, Africa, and South America. The utility of MAGIC for

simulating a variety of water and soil acidification responses at the laboratory, plot, hillslope,

and catchment scales has been tested using long-term monitoring data and experimental

manipulation data. MAGIC has been widely used in policy and assessment activities in the

United States and in several countries in Europe. 

5.3.1.3 Model Structure

MAGIC is a lumped-parameter model of intermediate complexity, developed to

predict the long-term effects of acidic deposition on surface water chemistry. The model

simulates soil solution chemistry and surface water chemistry to predict the monthly and

annual average concentrations of the major ions in these waters. MAGIC consists of the

following: (1) a section in which the concentrations of major ions are assumed to be

governed by simultaneous reactions involving sulfate adsorption, cation exchange,

dissolution-precipitation-speciation of aluminum, and dissolution-speciation of inorganic

carbon; and (2) a mass balance section in which the flux of major ions to and from the soil is

assumed to be controlled by atmospheric inputs, chemical weathering, net uptake, and loss in

biomass and losses to runoff. At the heart of MAGIC is the size of the pool of exchangeable

base cations in the soil. As the fluxes to and from this pool change over time owing to

changes in atmospheric deposition, the chemical equilibria between soil and soil solution

shift to give changes in surface water chemistry. The degree and rate of change of surface

water acidity thus depend both on flux factors and the inherent characteristics of the affected

soils.

Cation exchange is modeled using equilibrium (Gaines-Thomas) equations with

selectivity coefficients for each base cation and aluminum. Sulfate adsorption is represented
5-8

by a Langmuir isotherm. Aluminum dissolution and precipitation are assumed to be

controlled by equilibrium with a solid phase of aluminum trihydroxide. Aluminum

speciation is calculated by considering hydrolysis reactions as well as complexation with

sulfate, fluoride, and dissolved organic compounds. Effects of carbon dioxide on pH and on

the speciation of inorganic carbon are computed from equilibrium equations. Organic acids

are represented in the model as tri-protic analogues. Weathering rates are assumed to be

constant. Two alternative mechanisms are offered for simulating nitrate and ammonium in

soils: either (1) first order equations representing net uptake and retention or (2) a set of

equations and compartments describing process-based nitrogen dynamics in soils controlled

by soil nitrogen pools. Input-output mass balance equations are provided for base cations

and strong acid anions, and charge balance is required for all ions in each compartment. 

Given a description of the historical, current, and expected future deposition at a site, the

model equations are solved numerically to give long-term reconstructions of surface water

chemistry (for complete details of the model see Cosby et al. [1985 a,b,c]; [2001]).

MAGIC has been used to reconstruct the history of acidification, to examine current

patterns of recovery, and to simulate the future trends in stream water acidity in both

individual catchment and regional applications at a large number of sites across North

America and Europe (e.g., Beier et al. [1995]; Cosby et al. [1985b,1990, 1995, 1996]; Cosby

and Wright, 1998; Ferrier et al. [2001]; Hornberger et al. [1989]; Jenkins et al. [1990];

Moldan et al. [1998]; Norton et al. [1992]; Whitehead et al. [1988, 1997]; Wright et al.

[1990, 1994, 1998]).

5.3.1.4 Model Implementation

Atmospheric deposition and net uptake-release fluxes for the base cations and strong

acid anions are required as inputs to the model. These inputs are generally assumed to be

uniform over the catchment. Atmospheric fluxes are calculated from concentrations of the

ions in precipitation and the rainfall volume into the catchment. The atmospheric fluxes of

the ions must be corrected for dry deposition of gas, particulates, and aerosols and for inputs

in cloud/fog water. The volume discharge for the catchment must also be provided to the

model. In general, the model is implemented using average hydrologic conditions and

meteorological conditions in annual or seasonal simulations (i.e., mean annual or mean

monthly deposition); precipitation and lake discharge are used to drive the model. Values for

soil and surface water temperature, partial pressure of carbon dioxide, and organic acid

concentrations must also be provided at the appropriate temporal resolution.
5-9

In this application, deposition input to MAGIC was estimated from the policy

scenario emissions data based on previous known relationships between emissions of acid

deposition precursors and acid deposition. As implemented in this project, the model is a

two-compartment representation of a catchment. Atmospheric deposition enters the soil

compartment, and the equilibrium equations are used to calculate soil water chemistry. The

water is then routed to the stream compartment, and the appropriate equilibrium equations

are reapplied to calculate runoff chemistry. 

Once initial conditions (initial values of variables in the equilibrium equations) have

been established, the equilibrium equations are solved for soil water and surface water

concentrations of the remaining variables. These concentrations are used to calculate the

lake discharge output fluxes of the model for the first time step. The mass balance equations

are (numerically) integrated over the time step, providing new values for the total amounts of

base cations and strong acid anions in the system. These in turn are used to calculate new

values of the remaining variables, new lake discharge fluxes, and so forth. The output from

MAGIC is thus a time trace for all major chemical constituents for the period of time chosen

for the integration. 

5.3.1.5 Calibration Procedure

The aggregated nature of the model requires that it be calibrated to observed data

from a system before it can be used to examine potential system response. Calibration is

achieved by setting the values of certain parameters within the model that can be directly

measured or observed in the system of interest (called “fixed” parameters). The model is

then run (using observed atmospheric and hydrologic inputs) and the simulated values of

surface water and soil chemical variables (called “criterion” variables) are compared to

observed values of these variables. If the observed and simulated values differ, the values of

another set of parameters in the model (called “optimized” parameters) are adjusted to

improve the fit. After a number of iterations, the simulated-minus-observed values of the

criterion variables usually converge to zero (within some specified tolerance). The model is

then considered calibrated. If new assumptions (or values) for any of the fixed variables or

inputs to the model are subsequently adopted, the model must be recalibrated by readjusting

the optimized parameters until the simulated-minus-observed values of the criterion variables

again fall within the specified tolerance.

Calibrations are based on volume-weighted mean annual or seasonal fluxes for a

given period of observation. The length of the period of observation used for calibration is

not arbitrary. Model output will be more reliable if the annual flux estimates used in
5-10

calibration are based on a number of years rather than just 1 year. There is a lot of year-toyear variability in atmospheric deposition and catchment runoff. Averaging over a number

of years reduces the likelihood that an “outlier” year (very dry, etc.) is the primary data on

which model forecasts are based. On the other hand, averaging over too long a period may

remove important trends in the data that the model needs to simulate. 

The calibration procedure requires that stream water quality, soil chemical and

physical characteristics, and atmospheric deposition data be available for each catchment. 

The water quality data needed for calibration are the concentrations of the individual base

cations (Ca, Mg, Na, and K) and acid anions (Cl, SO4, and NO3) and the pH. The soil data

used in the model include soil depth and bulk density, soil pH, soil cation-exchange capacity,

and exchangeable bases on the soil (Ca, Mg, Na, and K). The atmospheric deposition inputs

to the model must be estimates of total deposition, not just wet deposition. In some

instances, direct measurements of either atmospheric deposition or soil properties may not be

available for a given site with stream water data. In these cases, the required data can often

be estimated by assigning soil properties based on some landscape classification of the

catchment and assigning deposition using model extrapolations from some national or

regional atmospheric deposition monitoring network. 

Soil Physical and Chemical Properties. Soils data for model calibration are usually

derived as averaged values of soil parameters within a catchment. If soils data for a given

location are vertically stratified, the soils data for the individual soil horizons at that

sampling site can be aggregated based on horizon, depth, and bulk density to obtain single

vertically aggregated values for the site, or the stratified data can be used directly in the

model.

Total Atmospheric Deposition. Total atmospheric deposition consists of three

components: wet deposition, the flux of ions occurring in precipitation; dry deposition,

resulting from gaseous and particulate fluxes; and cloud/fog deposition (which can be

particularly important in mountainous inland areas or moderate highlands in areas adjacent to

oceans or seas). Estimates of precipitation volume and ionic concentrations in precipitation

can be used to calculate wet deposition for a site. Observations of dry deposition or

cloud/fog deposition are very infrequent. The approach usually used to quantify these

components relies on some estimate of the ratio of estimated total deposition to the observed

wet deposition for important ions (e.g., sulphate, nitrate, and ammonium ions). These ratios

(called dry deposition factors) are then used to calculate total deposition from the observed

wet deposition data.
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Historical Loading. Calibration of the model (and estimation of the historical

changes at the sites) requires a temporal sequence of historical anthropogenic deposition. 

Our current understanding of ecosystem responses to acidic deposition suggests that future

ecosystem responses can be strongly conditioned by historical acidic loadings. Thus, as part

of the model calibration process, the model should be constrained by some measure of

historical deposition to the site. However, such long-term, continuous historical deposition

data do not exist. The usual approach is to use historical emissions data as a surrogate for

deposition. The emissions for each year in the historical period can be normalized to

emissions in a reference year (a year for which observed deposition data are available). 

Using this scaled sequence of emissions, historical deposition can be estimated by

multiplying the total deposition estimated for each site in reference year by the emissions

scale factor for any year in the past to obtain deposition for that year. 

5.3.1.6 MAGIC Modeling Results

Watershed modeling was undertaken for the CAIR proposal. It was determined that

the watershed modeling conducted for the proposal is essentially unchanged for the final

rule. This modeling projects that 1 percent of northeastern lakes would be chronically acidic

in 2030 as a result of CAIR. In contrast, the same model used to analyze existing control

programs projects 6 percent of northeastern lakes would be chronically acidic in 2030. The

modeling projects that, under CAIR, 28 percent of northeastern lakes would be episodically

acidic in 2030, compared to 25 percent in 2030 under existing control programs. For

Adirondack lakes, a subset of northeastern lakes, the signals of surface water chemical

recovery are much stronger. Under CAIR, no Adirondack lakes would be chronically acidic,

and 64 percent would be episodically acidic in 2030, as opposed to 12 percent chronically

acidic and 52 percent episodically acidic in 2030 under current control programs. 

Because of the age and types of soils in many high-elevation areas of the southeast,

streams in that region are more frequently characterized by a delayed response to changes in

deposition. For the ecosystems modeled in this region, 17 percent of streams are currently

chronically acidic, and this level stays the same under CAIR in 2030; the proportion of

episodically acidic streams increases from 19 percent under current conditions to 23 percent

under CAIR, which reflects a decrease in the proportion of nonacidic streams from 64

percent under current conditions to 60 percent under CAIR in 2030. It is important to note

that, under the base case, the proportion of nonacidic streams decreases even further,

dropping from 64 percent under current conditions to 58 percent in 2030. Thus, in the

southeast, CAIR would slow the deterioration of stream health (episodically acidic) expected
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under the base case and would prevent additional streams from becoming chronically acidic. 

Results of the MAGIC modeling are summarized in Table 5-1. 

Table 5-1. Acidification Changes in Water Bodies as a Result of CAIR

Current

Base Case

(2030)

Transport

Rule (2030)

Northeastern Lakes

chronically acidic 10% 6% 1%

episodically acidic 21% 25% 28%

nonacidic 69% 69% 71%

Adirondack Lakes

chronically acidic 21% 12% 0%

episodically acidic 43% 52% 64%

nonacidic 36% 36% 36%

Southeastern Streams

chronically acidic 17% 17% 17%

episodically acidic 19% 25% 23%

nonacidic 64% 58% 60%

5.3.1.7 Study of Benefits of Natural Resource Improvements in the Adirondacks

The EPA conducted MAGIC modeling to quantify acidification improvements likely

to occur as a result of CAIR in lakes in the northeastern U.S., the Adirondack Lakes, and

streams in the southeastern U.S. However, we were unable to estimate the monetary benefits

associated with the improvements predicted by the MAGIC modeling. A study conducted by

RFF estimates the monetary benefits associated with natural resource improvements due to

acidification reductions in the Adirondacks (Banzhaf, 2004). Since CAIR results in

acidification improvements in the Adirondack Lakes, the RFF study may be relevant to this

rulemaking. The RFF study estimates the total benefits (i.e., the sum of use and nonuse

values) of natural resource improvements for the Adirondacks resulting from a program that
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would reduce acidification in 40 percent of the lakes in the Adirondacks of concern for

acidification. RFF estimates that there are significant benefits for the hypothesized

improvements in lake health for New York State households. The study is undergoing peer

review with an intent to publish in the near future. Based on the peer review, the Agency

will determine whether and how it should be used to quantify the benefits associated with

reductions in acidification. As the MAGIC modeling indicates CAIR is also expected to

result in significant acid deposition reductions for the Adirondacks, leading to reduced

acidification and improved lake health. The benefits of these improvements in lake health

relative to the RFF estimates will depend upon the exact magnitude of the acid deposition

reductions, the number of lakes with improvements, and the magnitude of acidification

improvements. The RFF study suggests that the benefits of acid deposition reductions for

CAIR could be substantial in terms of the total monetized value for ecological endpoints.

5.3.2 Forest Ecosystems

Reductions in sulfur and nitrogen deposition under CAIR are expected to reduce the

effects of acid deposition on forests. Our current understanding of the effects of acid

deposition on forest ecosystems has come to focus increasingly on the effects of

biogeochemical processes that affect plant uptake, retention, and cycling of nutrients within

forested ecosystems. Research results from the 1990s indicate that documented decreases in

base cations (calcium, magnesium, potassium, and others) from soils in the northeastern and

southeastern United States are at least partially attributable to acid deposition (Lawrence et

al., 1997; Huntington et al., 2000). Base cation depletion is a cause for concern because of

the role these ions play in acid neutralization and, in the case of calcium, magnesium, and

potassium, their importance as essential nutrients for tree growth. It has been known for

some time that depletion of base cations from the soil interferes with the uptake of calcium

by roots in forest soils (Shortle and Smith, 1988). Recent research indicates it also leads to

aluminum mobilization (Lawrence et al., 1995), which can have harmful effects on fish

(Dept. of Interior, 2003).

The plant physiological processes affected by reduced calcium availability include

cell wall structure and growth, carbohydrate metabolism, stomatal regulation, resistance to

plant pathogens, and tolerance of low temperatures (DeHayes et al., 1999). Soil structure,

macro and micro fauna, decomposition rates, and nitrogen metabolism are also important

processes that are significantly influenced by calcium levels in soils. The importance of

calcium as an indicator of forest ecosystem function is due to its diverse physiological roles,

coupled with the fact that calcium mobility in plants is very limited and can be further
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reduced by tree age, competition, and reduced soil water supply (McLaughlin and Wimmer,

1999).

A clear link has now been established in red spruce stands between acid deposition,

calcium supply, and sensitivity to abiotic stress. Red spruce uptake and retention of calcium

is affected by acid deposition in two main ways: leaching of important stores of calcium

from needles (DeHayes et al., 1999) and decreased root uptake of calcium due to calcium

depletion from the soil and aluminum mobilization (Smith and Shortle, 2001; Shortle et al.,

1997; Lawrence et al., 1997). Acid deposition leaches calcium from mesophyll cells of

1-year-old red spruce needles (Schaberg et al., 2000), which in turn reduces freezing

tolerance (DeHayes et al., 1999). These changes increase the sensitivity of red spruce to

winter injuries under normal winter conditions in the Northeast, result in the loss of needles,

and impair the overall health of forest ecosystems (DeHayes et al., 1999). Red spruce must

also expend more metabolic energy to acquire calcium from soils in areas with low

calcium/aluminum ratios, resulting in slower tree growth (Smith and Shortle, 2001).

Losses of calcium from forest soils and forested watersheds have now been

documented as a sensitive early indicator of the soil response to acid deposition for a wide

range of forest soils in the United States (Lawrence et al., 1999; Huntington et al., 2000). 

There is a strong relationship between acid deposition and leaching of base cations from

hardwood forest (e.g., maple, oak) soils, as indicated by long-term data on watershed mass

balances (Likens et al., 1996; Mitchell et al., 1996), plot- and watershed-scale acidification

experiments in the Adirondacks (Mitchell et al., 1994) and in Maine (Norton et al., 1994;

Rustad et al., 1996), and studies of soil solution chemistry along an acid deposition gradient

from Minnesota to Ohio (MacDonald et al., 1992). 

Although sulfate is the primary cause of base cation leaching, nitrate is a significant

contributor in watersheds that are nearly nitrogen saturated (Adams et al., 1997). Recent

studies of the decline of sugar maples in the Northeast demonstrate a link between low base

cation availability, high levels of aluminum and manganese in the soil, and increased levels

of tree mortality due to native defoliating insects (Horsley et al., 2000). The chemical

composition of leaves and needles may also be altered by acid deposition, resulting in

changes in organic matter turnover and nutrient cycling.
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5.3.3 Coastal Ecosystems

Since 1990, a large amount of research has been conducted on the impact of nitrogen

deposition to coastal waters. It is now known that nitrogen deposition is a significant source

of nitrogen to many estuaries (Valigura et al., 2001; Howarth, 1998). The amount of

nitrogen entering estuaries due to atmospheric deposition varies widely, depending on the

size and location of the estuarine watershed and other sources of nitrogen in the watershed. 

For a handful of estuaries, atmospheric deposition of nitrogen contributes well over 40

percent of the total nitrogen load; however, in most estuaries for which estimates exist, the

contribution from atmospheric deposition ranges from 15 to 30 percent. The area with the

highest deposition rates (30 percent deposition rates) stretches from Massachusetts to the

Chesapeake Bay and along the central Gulf of Mexico coast.

Nitrogen is often the limiting nutrient in coastal ecosystems. Increasing the levels of

nitrogen in coastal waters can cause significant changes to those ecosystems. Approximately

60 percent of estuaries in the United States (65 percent of the estuarine surface area) suffer

from overenrichment of nitrogen, a condition known as eutrophication (Bricker et al., 1999). 

Symptoms of eutrophication include changes in the dominant species of plankton (the

primary food source for many kinds of marine life) that can cause algal blooms, low levels of

oxygen in the water column, fish and shellfish kills, and cascading population changes up the

food chain. Many of the most highly eutrophic estuaries are along the Gulf and mid-Atlantic

coasts, overlapping many of the areas with the highest nitrogen deposition, but there are

eutrophic estuaries in every region of the coterminous U.S. coastline.

Figure 5-4 shows the percent reductions in nitrogen deposition to coastal hydrologic

regions expected to result from CAIR within the CAIR region. As Figure 5-4 depicts,

changes in all forms of nitrogen deposition in the hydrologic regions within the CAIR region

are expected to range from small areas of slight increases that do not exceed 1.5 percent to

reductions up to 19 percent and average reductions of 4.6 percent for areas east of the 100th

parallel in 2015. 

The Chesapeake Bay Program estimated the reduced mass of delivered nitrogen loads

likely to result from CAIR, based upon the CAIR proposal deposition estimates published in

January 2004 (Sweeney, 2004). Atmospheric deposition of nitrogen accounts for a

significant portion of the nitrogen loads to the Chesapeake with 28 percent of the nitrogen

loads from the watershed coming from air deposition. Based upon the CAIR proposal

nitrogen deposition rates published in the January 2004 proposal, the Chesapeake Bay 
5-16

Figure 5-4. CAIR Nitrogen Deposition Reductions in Hydrologic Regions
5-17

Program finds that CAIR will likely reduce the nitrogen loads to the Bay by 10 million

pounds per year by 2010. Nitrogen deposition reductions for the final CAIR are anticipated

to be comparable to the proposed rule in this region. These substantial nitrogen load

reductions more than fulfill the EPA’s commitment to reduce atmospheric deposition

delivered to the Chesapeake Bay by 8 million pounds annually. The monetized value of such

reductions for the Bay are likely to be substantial, but we are unable to estimate the monetary

value of these reductions at this time.

5.3.4 Potential Other Impacts

This rule is expected to result in many categories of benefits that we are currently

unable to quantify or monetize. It is possible that reductions in nitrogen deposition resulting

from this rule may lessen the benefits of passive fertilization for forests and terrestrial

ecosystems where nutrients are a limiting factor and for some croplands.

The effects of ozone and particulate matter on radiative transfer in the atmosphere

can also lead to effects of uncertain magnitude and direction on the penetration of ultraviolet

light and climate. Ground level ozone makes up a small percentage of total atmospheric

ozone (including the stratospheric layer) that attenuates penetration of UVb radiation to the

ground. EPA’s past evaluation of the information indicates that potential disbenefits would

be small, variable, and with too many uncertainties to attempt quantification of relatively

small changes in average ozone levels over the course of a year (EPA, 2005a). EPA’s most

recent provisional assessment of the currently available information indicates that potential

but unquantifiable benefits may also arise from reducing ozone-related attenuation of UVb

radiation (EPA, 2005b). Sulfate and nitrate particles also scatter UVb, which can decrease

exposure of horizontal surfaces to UVb, but increase exposure of vertical surfaces. In this

case as well, both the magnitude and direction of the effect of reductions in sulfate and

nitrate particles are too uncertain to quantify (EPA, 2004). Ozone is a greenhouse gas, and

sulfates and nitrates can reduce the amount of solar radiation reaching the earth, but EPA

believes that we are unable to quantify any net climate-related disbenefit or benefit

associated with the combined ozone and PM reductions in this rule.

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Webb, J.R., F.A. Deviney, Jr., B.J. Cosby, A.J. Bulger, and J.N. Galloway. 2000. Change in

Acid- Base Status in Streams in the Shenandoah National Park and the Mountains

of Virginia. American Geophysical Union, Biochemical Studies of the

Shenandoah National Park. http://www.nps.gov/shen/air_quality.htm.
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Webb, J.R., F.A. Deviney, J.N. Galloway, C.A. Rinehart, P.A Thompson, and S. Wilson. 

1994. The Acid-Base Status of Native Brook Trout Streams in the Mountains of

Virginia. A Regional Assessment Based on the Virginia Trout Stream Sensitivity

Study. Charlottesville, VA: Univ. of Virginia. http://www.nps.gov/shen/

air_quality.htm.

Whitehead, P.G., B. Reynolds, G.M. Hornberger, C. Neal, B.J. Cosby, and P. Paricos. 1988. 

“Modelling Long-Term Stream Acidification Trends in Upland Wales at

Plynlimon.” Hydrological Processes 2:357-368.

Whitehead, P.G., J. Barlow, E.Y. Haworth, and J.K. Adamson. 1997. “Acidification in

Three Lake District Tarns: Historical Long Term Trends and Modelled Future

Behaviour under Changing Sulphate and Nitrate Deposition.” Hydrology and

Earth System Sciences 1:197-204.

Wright, R.F., B.J. Cosby, M.B. Flaten, and J.O. Reuss. 1990. “Evaluation of an

Acidification Model with Data from Manipulated Catchments in Norway.” Nature

343:53-55.

Wright, R.F., B.J. Cosby, R.C. Ferrier, A Jenkins, A.J. Bulger, and R. Harriman. 1994.

“Changes in the Acidification of Lochs in Galloway, Southwestern Scotland, 1979-

1988: The MAGIC Model Used to Evaluate the Role of Afforestation, Calculate

Critical Loads, and Predict Fish Status.” Journal of Hydrology 161:257-285.

Wright, R.F., B.A. Emmett, and A. Jenkins. 1998. “Acid Deposition, Land-Use Change and

Global Change: MAGIC7 Model Applied to Risdalsheia, Norway (RAIN and

CLIMEX projects) and Aber, UK (NITREX project).” Hydrology and Earth

System Sciences 2:385-397.
6-1

CHAPTER 6

ELECTRIC POWER SECTOR PROFILE

This chapter discusses important aspects of the power sector as they relate to CAIR,

including the types of power-sector sources affected by CAIR, and provides background on

the power sector and EGUs. In addition, this chapter provides some historical background

on EPA regulation of and future projections for the power sector. 

6.1 Power-Sector Overview

The functions of the power sector can be separated into three distinct operating

activities: generation, transmission, and distribution. 

6.1.1 Generation

Electricity generation is the first process in the delivery of electricity to consumers. 

The process of generating electricity, in most cases, involves creating heat to rotate turbines

which, in turn, create electricity. The power sector consists of over 16,000 generating units,

consisting of fossil-fuel fired units, nuclear units, and hydroelectric and renewable sources

dispersed throughout the country (see Table 6-1).

Table 6-1. Existing Electricity Generating Capacity by Energy Source, 2002

Energy Source Number of Generators Generator Nameplate Capacity (MW)

Coal 1,566 338,199

Petroleum 3,076 43,206

Natural Gas 2,890 194,968

Dual Fired 2,974 180,174

Other Gases 104 2,210

Nuclear 104 104,933

Hydroelectric 4,157 96,343

Other Renewables 1,501 18,797

Other 41 756

Total 16,413 979,585

Source: EIA
6-2

These electric-generating sources provide electricity for commercial, industrial, and

residential uses, each of which consumes roughly one-third of the total electricity produced

(see Table 6-2). 

Table 6-2. Total U.S. Electric Power Industry Retail Sales in 2003 (Billion kWh)

N %

Residential 1,280 37%

Commercial 1,119 32%

Industrial 991 28%

Other 109 3%

All Sectors 3,500 100%

Source: EIA

In 2003, electric-generating sources produced 3,848 billion kWh to meet electricity

demand. Roughly 70 percent of this electricity was produced through the combustion of

fossil fuels, primarily coal and natural gas, with coal accounting for more than half of the

total (see Table 6-3).

Table 6-3. Electricity Net Generation in 2003 (Billion kWh)

N %

Coal 1,970 51%

Petroleum 118 3%

Natural Gas 629 16%

Other Gases 11 0.3%

Nuclear 764 20%

Hydroelectric 275 7%

Other 81 2%

Total 3,848 100%

Source: EIA

Note: Retail sales and net generation may not correspond exactly because net generation data may include

net exported electricity and loss of electricity.
6-3

Coal-fired generating units typically supply “base-load” electricity, which means

these units operate continuously throughout the day. Coal-fired generation, along with

nuclear generation, meet the part of demand that is relatively constant. Gas-fired generation,

however, typically supplies “peak” power, when there is increased demand for electricity

(e.g., 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 morning

when demand for electricity is reduced).

6.1.2 Transmission

Transmission is the term used to describe the movement of electricity, through use of

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

local distribution. Transmission systems have been traditionally characterized as a collection

of independently operated networks or grids interconnected by bulk transmission interfaces.

Within a well-defined service territory, the regulated utility has historically had

responsibility for all aspects of developing, maintaining, and operating transmission of

electricity. These responsibilities typically included system planning and expanding,

maintaining power quality and stability, and responding to failures.

6.1.3 Distribution

Distribution of electricity involves networks of smaller wires and substations that

take the higher voltage from the transmission system and step it down to lower levels to

match the needs of customers. The transmission and distribution system is the classic

example of a natural monopoly because it is not practical to have more than one set of lines

running from the electricity-generating sources to neighborhoods or from the curb to the

house.

Transmission and distribution have been considered differently than generation in

current efforts to restructure the industry. Transmission has generally been developed by the

larger vertically integrated utilities that typically operate generation and distribution

networks. Distribution is handled by a large number of utilities that often only sell

electricity. Electricity restructuring has focused primarily on converting the industry to fully

compete the sale of electricity production or generation and not the transmission or

distribution of electricity. The restructuring of the industry is, in large part, the separating of

generation assets from the transmission and distribution assets into separate economic
6-4

entities in many state efforts. Transmissions and distribution remain price regulated

throughout the country based on the cost of service.

6.2 Deregulation and Restructuring

The ongoing process of deregulation of wholesale and retail electric markets is

changing the structure of the electric power industry. In addition to reorganizing asset

management between companies, deregulation is aimed at the functional unbundling of

generation, transmission, distribution, and ancillary services the power sector has historically

provided to 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, communications, and energy, which were all thought to be natural

monopolies (prior to 1970) that warranted governmental control of pricing. Some of the

primary drivers for deregulation of electric power included the desire for more efficient

investment choices, the possibility of lower electric rates, reduced costs of combustion

turbine technology that opened the door for more companies to sell power, and complexity of

monitoring utilities’ cost of service and establishing cost-based rates for various customer

classes (see Figure 6-1). The pace of restructuring in the electric power industry slowed

significantly in response to market volatility and financial turmoil associated with

bankruptcy filings of key energy companies in California. 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. Another 18 other states that had seriously

explored the possibility of deregulation in 2000 reported no legislative or regulatory activity

in 2001 (DOE, EIA, 2003a). Currently, there are 17 states where price deregulation of

generation (restructuring) has occurred. Fifteen of these states are in the CAIR region. The

effort is more or less at a standstill; however, at the federal level, there are efforts in the form

of proposed legislation and proposed Federal Energy Regulatory Commission (FERC)

actions aimed at reviving restructuring. For states that have not begun restructuring efforts,

it is unclear when and at what pace these efforts will proceed.
6-5

( y)

Figure 6-1. Status of State Electricity Industry Restructuring Activities (as of February

2003)

6.3 Pollution and EPA Regulation of Emissions

The burning of fossil fuels, which generates about 70 percent of our electricity

nationwide, results in air emissions of SO2 and NOx, important precursors in the formation of

fine particles and ozone (NOx only). The power sector is a major contributor of both these

pollutants, and reductions of SO2 and NOx emissions are critical to EPA’s efforts to bring

about attainment with the fine particle and ozone NAAQS through programs like CAIR. In

2003, the power sector accounted for 67 percent of total nationwide SO2 emissions and 22

percent of total nationwide NOx emissions (see Figure 6-2).

Different types of fossil fuel-fired units vary widely in their air emissions levels for

SO2 and NOx, particularly when uncontrolled. For coal-fired units, NOx emission rates can

vary from under 0.05 lbs/mmBtu (for a unit with selective catalytic reduction for NOx

removal) to over 1 lb/mmBtu for an uncontrolled cyclone boiler. NOx emissions from

coal-fired power plants are formed during combustion and are a result of both nitrogen in

coal and nitrogen in the air. SO2 emission rates can vary from under 0.1 lbs/mmBtu (for

some units with flue gas desulfurization for SO2 removal) to over 5 lbs/mmBtu for units
6-6

Figure 6-2. Emissions of SO2 and NOx from the Power Sector (2003)

burning higher sulfur coal. For an uncontrolled coal plant, SO2 emissions are directly related

to the amount of sulfur in the coal. 

Oil and gas-fired units also have a wide range of NOx emissions depending on both

the plant type and the controls installed. Units with selective catalytic reduction (SCR) can

have emission rates under 0.01 lbs/mmBtu, while completely uncontrolled units can have

emission rates in excess of 0.5 lbs/mmBtu. Gas-fired units have very little SO2 emissions. 

NOx emission rates on oil-fired units can range from under 0.1 lbs/mmBtu (for units with

new combustion controls) to over 0.6 lbs/mmBtu for units without combustion controls. SO2

emissions for oil-fired units can range from under 0.1 lbs/mmBtu for units burning low sulfur

distillate oil to over 2 lbs/mmBtu for units burning high sulfur residual oil.

6.4 Pollution Control Technologies

There are two primary options for reducing SO2 emissions from coal-burning power

plants. Units may switch from higher to lower sulfur coal, or they may use flue gas

desulfurization (FGD, commonly referred to as scrubbers). According to data submitted to

EPA for compliance with the Title IV Acid Rain Program, the SO2 emission rates for

coal-fired units varied from under 0.4 lbs/mmBtu to over 5 lbs/mmBtu depending on the type

of coal combusted. 
6-7

It is generally easier to switch to a coal within the same rank (e.g., bituminous or

sub-bituminous) because these coals will have similar heat contents and other characteristics. 

Switching completely to sub-bituminous coal (which typically has a lower sulfur content)

from bituminous coal is likely to require some modifications to the unit. Limited blending of

sub-bituminous coal with bituminous coal can often be done with much more limited

modifications. 

The two most commonly used scrubber types include wet scrubbers and spray dryers. 

Wet scrubbers can use a variety of sorbents to capture SO2 including limestone and

magnesium enhanced lime. The choice of sorbent can affect the performance, size, and

capital and operating costs of the scrubber. New wet scrubbers typically achieve at least

95 percent SO2 removal. Spray dryers can achieve over 90 percent removal.

One method of reducing NOx emissions is through the use of combustion controls

(such as low NOx burners and over-fired air). Combustion controls reduce NOx, by ensuring

that the combustion of coal occurs under conditions under which less formation of NOx

occurs. Post-combustion controls reduce NOx by removing the NOx after it has been formed. 

The most common post-combustion control is SCR. SCR systems inject ammonia (NH3),

which combines with the NOx in the flue gas, to form nitrogen and water and uses a catalyst

to enhance the reaction. These systems can reduce NOx by 90 percent and achieve emission

rates of around 0.06 lbs/mmBtu. Selective noncatalytic reduction also removes NOx by

injecting ammonia, but no catalyst is used. These systems can reduce NOx by up to 40

percent.

For more detail on the cost and performance assumptions of pollution controls, see

the documentation for the Integrated Planning Model (IPM), a dynamic linear programming

model that EPA uses to examine air pollution control policies for SO2 and NOx throughout

the contiguous United States for the entire power system. Documentation for IPM can be

found at www.epa.gov/airmarkets/epa-ipm.

6.5 Regulation of the Power Sector

At the federal level, efforts to reduce emissions of SO2 and NOx have been occurring

since 1970. Policy makers have recognized the need to address these harmful emissions, and

incremental steps have been taken to ensure that the country meets air quality standards. 

CAIR is the next step towards realizing attainment of the standards.
6-8

Federal regulation of SO2 and NOx emissions at power plants began with the 1970

Clean Air Act. The Act required the Agency to develop performance standards for a number

of source categories including coal-fired power plants. The first New Source Performance

Standards (NSPS) for power plants (subpart D) required new units to limit SO2 emissions

either by using scrubbers or by using low sulfur coal. NOx was required to be limited

through the use of low NOx burners. A new NSPS (subpart Da), promulgated in 1978,

tightened the standards for SO2 requiring scrubbers on all new units.

The 1990 Clean Air Act Amendments (CAAA) placed a number of new requirements

on power plants. The Acid Rain Program, established under Title IV of the 1990 CAAA,

requires major reductions of SO2 and NOx emissions. The SO2 program sets a permanent cap

on the total amount of SO2 that can be emitted by electric power plants in the contiguous

United States at about one-half of the amount of SO2 these sources emitted in 1980. Using a

market-based cap-and-trade mechanism allows flexibility for individual combustion units to

select their own methods of compliance. The program uses a more traditional approach to

NOx emission limitations for certain coal-fired electric utility boilers, with the objective of

achieving a 2 million ton reduction from projected NOx emission levels that would have been

emitted in 2000 without implementation of Title IV. 

The Acid Rain Program comprises two phases for SO2 and NOx. Phase I applied

primarily to the largest coal-fired electric generation sources from 1995 through 1999 for

SO2 and from 1996 through 1999 for NOx. Phase II for both pollutants began in 2000. For

SO2, it applies to thousands of combustion units generating electricity nationwide; for NOx it

generally applies to affected units that burned coal during 1990 through 1995. The Acid

Rain Program has led to the installation of a number of scrubbers on existing coal-fired units

as well as significant fuel switching to lower sulfur coals. Under the NOx provisions of Title

IV, most existing coal-fired units were required to install low NOx burners. 

The CAAA also placed much greater emphasis on control of NOx to reduce ozone

nonattainment. This has led to the formation of several regional NOx trading programs as

well as an intrastate NOx trading program in Texas. The Ozone Transport Commission (a

group of northeast states) created an interstate NOx trading program that began in 1999. In

1998, EPA promulgated regulations (the NOx SIP Call) that required 21 states in the eastern

United States and the District of Columbia to reduce NOx emissions that contributed to

nonattainment in downwind states using the cap-and-trade approach. This program began in
6-9

the summer of 2004 and has resulted in the installation of significant amounts of selective

catalytic reduction.

In addition to federal programs to reduce emissions of SO2 and NOx, several states

have also taken action. Several states, like North Carolina, New York, Connecticut, and

Massachusetts, have moved to control these emissions to address nonattainment.

6.6 Cap and Trade

The cap-and-trade system under CAIR, which is largely based on the Acid Rain

Trading Program and the NOx SIP Call, provides the power sector with considerable

flexibility in meeting the emission reduction requirements. Cap-and-trade regulation is an

extremely efficient tool that allows for environmental goals to be met in the most costeffective manner, because firms have economic incentives to achieve emissions reductions

where they are cheapest. The system allows for various compliance options, with each firm

determining what option works best given certain costs, such as fuel costs or costs of

pollution controls.

In addition to the pollution control options discussed above, companies can comply

with cap-and-trade programs through more efficient use of the generating fleet to take

advantage of generating sources that emit less and run more efficiently, commonly referred

to as dispatch changes. By shifting generation to these more efficient units, the power sector

is reducing the cost of compliance because there is a cost to pollute under a cap. Another

option is purchasing additional allowances to cover emissions.

6.7 Clean Air Interstate Rule

To address air quality problems and improve public health and the environment, EPA

is finalizing CAIR. The final CAIR requires annual SO2 and NOx reductions in 23 States and

the District of Columbia, and also requires ozone season NOx reductions in 25 States and the

District of Columbia. Many of the CAIR States are affected by both the annual SO2 and NOx

reduction requirements and the ozone season NOx requirements. CAIR allows affected states

to adopt a two-phased cap-and-trade program to meet emissions reduction requirements of

roughly 73 percent for SO2 and 61 percent for NOx from 2003 levels.

The rule would affect roughly 3,000 fossil fuel-fired units with a nameplate capacity

greater than 25 MW. These sources accounted for roughly 89 percent of nationwide SO2

emissions and 79 percent of nationwide NOx emissions in 2003 (see Table 6-4).
1

EPA uses the IPM to make power-sector forecasts about emissions, costs, and other key factors of the power

sector. Industry projections presented here are from EPA’s base case scenario. For more information about

IPM, see http://www.epa.gov/airmarkets/epa-ipm/index.html.

6-10

Table 6-4. Emissions of SO2 and NOx in 2003 and Percentage of Emissions in the CAIR

Affected Region (tons)

SO2 NOx

CAIR Region 9,407,406 3,222,636

Nationwide 10,595,069 4,165,026

CAIR Emissions as % of Nationwide Emissions 89% 79%

Source: EPA.

Note: Region includes states covered for the annual SO2 and NOx trading programs (Alabama, District of

Columbia, Florida, Georgia, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maryland, Michigan, Minnesota,

Mississippi, Missouri, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas,

Virginia, West Virginia, and Wisconsin).

EPA modeling1 shows that coal-fired and oil/gas-fired generation will continue to

play an important part of the electricity generating portfolio in the United States. Electricity

demand is anticipated to grow by 1.6 percent a year, and total electricity demand is projected

to be 4,198 billion kWh by 2010. Table 6-5 shows current electricity generation and

projected levels in 2010 and 2015 using EPA modeling.

Table 6-5. Current Electricity Net Generation and EPA Projections for 2010 and 2015

(Billion kWh)

2003 2010 2015

Coal 1,970 2,198 2,242

Oil/Gas 758 777 1,026

Other 1,119 1,223 1,235

Total 3,848 4,198 4,503

Source: 2003 data is from EIA. Projections are from the Integrated Planning Model run by EPA.
6-11

6.8 Price Elasticity of Electricity

Electricity performs a vital and high-value function in the economy; as a result,

electricity consumers are generally unable or unwilling to alter consumption as the price

increases. Demand for electricity, especially in the short run, is not very sensitive to changes

in prices and is considered relatively price inelastic although some demand reduction does

occur. With that in mind, EPA modeling does not incorporate a “demand response” to any

increases in electricity prices because of the reasons mentioned. Electricity demand is

considered to be constant in EPA modeling applications and the reduction in production

costs that would result from lower demand is not considered. This leads to some

overstatement in the private compliance costs that EPA estimates.
7-1

States controlled for both SO2 and NOx

States controlled for ozone season NOx only

States not covered by CAIR

Figure 7-1. CAIR Modeled Region

CHAPTER 7

COST, ECONOMIC, AND ENERGY IMPACTS

This chapter reports the cost, economic, and energy impact analysis performed for CAIR. 

EPA used the IPM, developed by ICF Consulting, to conduct its analysis. IPM is a dynamic

linear programming model that can be used to examine air pollution control policies for SO2 and

NOx throughout the contiguous United States for the entire power system. Documentation for

IPM can be found at www.epa.gov/airmarkets/epa-ipm.

7.1 Modeling Background

The analysis presented here covers the electric power sector, a major source of SO2 and

NOx emissions nationwide and the industry that EPA assumes that States will control in setting

State emissions reduction requirements. EPA has also assumed that States implement those

reductions through a cap-and-trade program. For SO2 and NOx, EPA modeled an annual, twophased control strategy for 26 eastern States and the District of Columbia (see Figure 7-1). For

NOx, separate ozone season caps were applied to Connecticut and Massachusetts. See Table 7-1

for total annual emissions caps under CAIR. 
7-2

Table 7-1. CAIR Annual Emissions Caps (Million Tons)

2010–2014 (‘09-‘14 for NOx) 2015–Thereafter

SO2 3.6 2.5

NOx 1.5 1.3

The final CAIR requires annual SO2 and NOx reductions in 23 States and the District of

Columbia, and also requires ozone season NOx reductions in 25 States and the District of

Columbia. Many of the CAIR States are affected by both the annual SO2 and NOx reduction

requirements and the ozone season (May-September) NOx requirements. Using IPM, EPA

modeled the cost and emissions impacts of CAIR. For further discussion about the scope and

requirements of CAIR, see the Final CAIR preamble.

EPA initially conducted IPM modeling for today’s final action using a control strategy

that is similar, but not identical to, the final CAIR requirements. The control strategy that EPA

initially modeled included three additional States (Arkansas, Delaware and New Jersey) within

the region and required these States to make annual SO2 and NOx reductions. However, these

three States are not required to make annual reductions under the final CAIR. In the “Proposed

Rules” section of today’s Federal Register publication, EPA is publishing a proposal to include

Delaware and New Jersey in the CAIR region for annual SO2 and NOx reductions. This RIA is

to serve as EPA’s analytical assessment of both today’s Final CAIR and the proposed rule for

incorporating Delaware and New Jersey into the annual SO2 and NOx requirements of CAIR. 

The addition of Arkansas, Delaware, and New Jersey brought the total number of affected States

for annual SO2 and NOx to 26 plus the District of Columbia for the initial model run. Arkansas

will not be included in the annual SO2 and NOx requirements either as part of today’s Final

CAIR or the “Proposed Rule,” but is included for the ozone season CAIR requirement as part of

today’s Final Rule. The initial model run also included individual State ozone season NOx caps

for Connecticut and Massachusetts, and did not include ozone season NOx caps for any other

CAIR States.

The Agency conducted revised final IPM modeling that reflects the final CAIR control

strategy. The final IPM modeling includes regionwide annual SO2 and NOx caps on the 23

States and the District of Columbia for States required to make annual reductions, and includes a

regionwide ozone season NOx cap on the 25 States and the District of Columbia required to

make ozone season reductions. EPA modeled the final CAIR NOx strategy as an annual NOx cap

with a nested, separate ozone season NOx cap.
7-3

In this chapter of the RIA, the projected CAIR costs and emissions are derived from the

IPM run reflecting CAIR with Arkansas, Delaware, and New Jersey included for the annual

requirements, and without a separate ozone season NOx cap for ozone season States. However,

where IPM results differ significantly between the scenario with Arkansas, Delaware, and New

Jersey, and the scenario with these three States included for NOx summer season only, EPA has

highlighted these differences and included them in this chapter as well. The air quality and

benefits analyses done in support of CAIR are based on emission projections from the initial

IPM run with Arkansas, Delaware, and New Jersey included for annual SO2 and NOx.

EPA believes that the differences between the initial IPM run and the final IPM run have

very little impact on projected control costs, emissions, and other impacts. Modeling the CAIR

region without Arkansas, Delaware, and New Jersey do not change the results presented here in

any significant way, and in any event, this chapter generally reflects the geographic scope of the

CAIR program as EPA intends it to be ultimately. IPM output files for the model runs used in

CAIR analyses are available in the CAIR docket.

CAIR was designed to achieve significant emissions reductions in a highly cost-effective

manner to reduce the transport of fine particles that have been found to contribute to

nonattainment. EPA analysis has found that the most efficient method to achieve the emissions

reduction targets is through a cap-and-trade system on the power sector that States have the

option of adopting. The power sector accounted for 67 percent of nationwide SO2 emissions and

22 percent of nationwide NOx emissions in 2002. States, in fact, can choose not to participate in

the optional cap-and-trade program and can choose to obtain equivalent emissions reductions

from other sectors. However, EPA believes that a region-wide cap-and-trade system for the

power sector is the best approach for reducing emissions. The modeling done with IPM assumes

a region-wide cap and trade system on the power sector for the States covered. However, EPA

recognizes that States may choose to cover other sources and may use a different approach for

reducing emissions.

IPM has been used for evaluating the economic and emission impacts of environmental

policies for over a decade. The model’s base case incorporates title IV of the Clean Air Act (the

Acid Rain Program), the NOx SIP Call, various New Source Review (NSR) settlements, and

several State rules affecting emissions of SO2 and NOx that were finalized prior to April of 2004. 

The NSR settlements include agreements between EPA and Southern Indiana Gas and Electric

Company (Vectren), Public Service Enterprise Group, Tampa Electric Company, We Energies

(WEPCO), Virginia Electric & Power Company (Dominion), and Santee Cooper. IPM also

includes various current and future State programs in Connecticut, Illinois, Maine,

Massachusetts, Minnesota, New Hampshire, North Carolina, New York, Oregon, Texas, and
7-4

Wisconsin. IPM includes State rules that have been finalized and/or approved by a State’s

legislature or environmental agency. The base case is used to provide a reference point to

compare environmental policies and assess their impacts and does not reflect a future scenario

that EPA predicts will occur. 

The economic modeling presented in this chapter has been developed for specific

analyses of the power sector. Thus, the model has been designed to reflect the industry as

accurately as possible. As a result, EPA has used discount rates in IPM that are appropriate for

the various types of investments and other costs that the power sector incurs. The discount rates

used in IPM may differ from discount rates used in other EPA analyses done for CAIR,

particularly the discount rates used in the benefits analysis that are assumed to be social discount

rates. EPA uses the best available information from utilities, financial institutions, debt rating

agencies, and government statistics as the basis for the discount rates used for power sector

modeling. These discount rates have undergone review by the power sector and the Energy

Information Administration. EPA’s discount rate approach has not been challenged in court.

EPA’s modeling is based on its best judgment for various input assumptions that are

uncertain, particularly assumptions for future fuel prices and electricity demand growth. To

some degree, EPA addresses the uncertainty surrounding these two assumptions through its

sensitivity analysis, which is discussed in Appendix D. 

More detail on IPM can be found in the model documentation, which provides additional

information on the assumptions discussed here as well as all other assumptions and inputs to the

model (www.epa.gov/airmarkets/epa-ipm).

7.2 Projected SO2 and NOx Emissions and Reductions

Because of the existence of a bank of allowances under the title IV Acid Rain Program

that sources will be allowed to use under the requirements of CAIR, emissions of SO2 in 2010

and 2015 will be higher than the caps that are required for CAIR. Table 7-2 provides projected

emissions levels.

As shown in Figure 7-2, the results of EPA modeling of CAIR show that substantial SO2

emissions reductions occur in the Midwest and Mid-Atlantic regions of the country. Significant

NOx emissions reductions occur across the CAIR region (see Figure 7-3), and with CAIR, ozone

season NOx emissions reductions are lower than they would have been with the NOx SIP Call

(base case) (see Figure 7-4). For NOx, the annual CAIR cap achieves greater emission

reductions during the ozone season than the NOx SIP Call summer requirement.
7-5

Table 7-2. Projected Emissions of SO2 and NOx with the Base Casea (No Further Controls)

and with CAIR (Million Tons)

Coverage

2010 2015 2020

Base CaseCAIREmission ReductionsBase CaseCAIREmission Reductions Base CaseCAIREmission Reductions

SO2

(annual)

Nationwide 9.7 6.1 3.6 8.9 4.9 4.0 8.6 4.2 4.5

CAIR Region 8.8 5.2 3.6 8.0 4.1 4.0 7.9 3.4 4.5

NOx

(annual)

Nationwide 3.6 2.4 1.2 3.7 2.1 1.5 3.7 2.1 1.6

CAIR Region 2.8 1.5 1.3 2.8 1.3 1.5 2.9 1.3 1.6

NOx

(summer)

Nationwide 1.2 1.0 0.2 1.2 0.9 0.3 1.2 1.0 0.3

CAIR Region 0.8 0.7 0.2 0.8 0.6 0.3 0.8 0.6 0.3

Note: Numbers may not add due to rounding. The emissions data presented here are EPA modeling results and

the CAIR region includes States modeled for the annual SO2 and NOx requirements. “Summer” is from

May 1–September 30, which is the ozone season.

a Base case includes title IV Acid Rain Program, NOx SIP Call, and State rules finalized before March 2004.

Source: Integrated Planning Model run by EPA.

7.3 Projected Costs

For the modeled region, EPA projects that the annual incremental costs of CAIR are $2.4

billion in 2010 and $3.6 billion in 2015 (see Table 7-3). In 2020, the annual costs are $4.4

billion. The cost of electricity generation represents roughly one-third to one-half of total

electricity costs, with transmission and distribution costs representing the remaining portion. A

better impact measure is the impact on electricity pricing, which is shown in a later table. The

marginal costs of CAIR for SO2 and NOx can also be found in Table 7-3.

7.4 Projected Control Technology Retrofits

CAIR is projected to result in the installation of an additional 64 GW of flue gas

desulfurization (scrubbers) on existing coal-fired generation capacity for SO2 control and an

additional 34 GW of selective catalytic reduction technology (SCR) on existing coal-fired

generation capacity for NOx control by 2015 (see Table 7-4). The first phase of CAIR will result

in 37 GW of additional scrubbers and 14 GW of SCR by 2010. Much of the NOx reductions 
7-6

Base Case 2010

CAIR 2010

Base Case 2015

CAIR 2015 Scale: 1,373,038 tons in Ohio, Base Case 2010

Figure 7-2. SO2 Emissions from the Power Sector in 2010 and 2015 with and without

CAIR

Source: Integrated Planning Model run by EPA.

Note: Arkansas, Delaware, and New Jersey are not included in the annual SO2 and NOx requirements of the

Final CAIR. These States are included in the ozone season requirement only. Modeling presented here did not

include an ozone season requirement and covered a region that is inclusive of these three States for the annual

requirements. Please see earlier modeling background discussion for more detail.
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Base Case 2010

CAIR 2010

Base Case 2015

CAIR 2015

Scale: 274,372 tons in Ohio, Base Case 2015

Figure 7-3. NOx Emissions from the Power Sector in 2010 and 2015 With and Without

CAIR.

Source: Integrated Planning Model run by EPA.

Note: Arkansas, Delaware, and New Jersey are not included in the annual SO2 and NOx requirement of the Final

CAIR. These States are included in the ozone season requirement only. Modeling presented here did not include an

ozone season requirement and covered a region that is inclusive of these three States for the annual requirements. 

Please see earlier modeling background discussion for more detail.
7-8

Base Case 2010

CAIR 2010

Base Case 2015

CAIR 2015 Scale: 93,685 tons in Texas, Base Case 2010

Figure 7-4. Ozone Season NOX Emissions from the Power Sector in 2010 and 2015 with and

without CAIR

Source: Integrated Planning Model run by EPA.

Note: Arkansas, Delaware, and New Jersey are not included in the annual SO2 and NOx requirements of the Final CAIR. 

These States are included in the ozone season requirements only. Modeling presented here did not included an ozone season

requirement and covered a region that is inclusive of these three States for the annual requirements. Please see earlier

modeling background discussion for more detail.
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Table 7-3. Annualized Regional Cost of CAIR and Marginal Cost of SO2 and NOx

Reductions with CAIR ($1999)

2010 2015 2020

Annualized Cost (billions) $2.4 $3.6 $4.4

Marginal Cost ($/ton)

SO2 $700 $1,000 $1,400

NOx $1,300 $1,600 $1,600

Note: Numbers rounded to the nearest hundred million for annualized cost and nearest hundred for marginal cost.

Source: Integrated Planning Model run by EPA.

Table 7-4. Pollution Controls by Technology with the Base Case (No Further Controls)

and with CAIR (GW)

Base Case Total

(Cumulative) Incremental with CAIR Total with CAIR(Cumulative)

Technology 2010 2015 2020 2010 2015 2020 2010 2015 2020

Scrubbers 110 116 117 37 64 82 147 180 199

SCR 111 119 121 14 34 33 126 152 154

Note: Numbers may not add due to rounding. Base case retrofits include existing scrubbers and SCR as well as

additional retrofits for the Title IV Acid Rain Program, the NOx SIP call, NSR settlements, and various

State rules.

Source: Integrated Planning Model run by EPA.

achieved in the first phase of the rule can be attributed to the large pool of existing SCR that are

used during the ozone season in the NOx SIP call region that, for relatively little cost, run the

SCRs year-round. A small number of coal-fired units also install selective noncatalytic

reduction technology (SNCR) for NOx control under CAIR. Emission reductions are achieved

through a combination of compliance options, such as additional pollution control installations,

generation shifts towards more efficient electricity producing units, and fuel and coal switching.

7.5 Projected Generation Mix

Table 7-5 and Figure 7-5 show the generation mix with CAIR. Coal-fired generation and

natural gas-fired generation are projected to remain relatively unchanged because of the phasedin nature of CAIR, which allows industry the appropriate amount of time to install the necessary

pollution controls.
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0

1,000

2,000

3,000

4,000

5,000

6,000

Base

Case

CAIR Base

Case

CAIR Base

Case

CAIR

Thousand GWhs

2010

2015 2020

Coal

Gas/Oil

Nuclear

Hydro

Renewables

Other

Figure 7-5. Generation Mix with and without CAIR

Source: Integrated Planning Model run by EPA.

Table 7-5. Generation Mix with the Base Case (No Further Controls) and with CAIR

(Thousand GWhs)

2003

2010 2015 2020

Generating

Fuel Use

Base

Case CAIR

 Change

from

Base

Case

Base

Case CAIR

 Change

from

Base

Case

Base

Case

CAIR

Percent

Change

 Change

from

Base

Case

Coal 1,970 2,198 2,163 –1.6% 2,242 2,195 –2.1% 2,410 2,381 –1.2%

Oil/Natural Gas 758 777 809 4.1% 1,026 1,072 4.5% 1,221 1,250 2.3%

Other 1,120 1,223 1,218 –0.4% 1,235 1,233 –0.2% 1,218 1,217 –0.1%

Total 3,848 4,198 4,190 –0.2% 4,503 4,499 –0.1% 4,850 4,847 –0.1%

Note: Numbers may not add due to rounding.

Source: 2003 data are from EIA and projections are from the Integrated Planning Model run by EPA.
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Relative to the base case, about 5.3 GW of coal-fired capacity is projected to be

uneconomic to maintain (about 1.7 percent of all coal-fired capacity and 0.5 percent of all

generating capacity), and about 1 GW of coal-fired capacity is projected to repower to natural

gas or Integrated Gasification Combined Cycle (IGCC). Uneconomic units, for the most part,

are small and infrequently used generating units that are dispersed throughout the CAIR region.

In practice, units projected to be uneconomic to maintain may be “mothballed,” retired, or kept

in service to ensure transmission reliability in certain parts of the grid. EPA modeling is unable

to distinguish between these potential outcomes. IPM can only predict that specific generating

units are uneconomic to maintain, based on their fuel, operating and fixed costs, and whether

they are needed to meet both demand and reliability reserve requirements. “Repowering”

converts units to combined-cycle natural gas or IGCC.

7.6 Projected Capacity Additions

In addition, EPA projects that future growth in electric demand will be met with a

combination of new natural gas- and coal-fired capacity (see Table 7-6).

Table 7-6. Total Coal and Natural Oil/Gas-Fired Capacity by 2020 (GW)

Current Base Case CAIR

Pulverized Coal 305 318 314

IGCC 0.6 8 9

Oil/Gas 395 467 469

Source: Current data are from EPA’s NEEDS 2004. Projections are from the Integrated Planning Model run by

EPA.

7.7 Projected Coal Production for the Electric Power Sector

Coal production for electricity generation is expected to increase relative to current

levels, with or without CAIR (see Table 7-7 and Figure 7-6). The reductions in emissions from

the power sector will be met through the installation of pollution controls for SO2 and NOx

removal. The pollution controls can achieve up to a 95 percent SO2 removal rate, which allows

industry to rely more heavily on local bituminous coal in the eastern and central parts of the

country that has a higher sulfur content and is less expensive to transport than western

subbituminous coal.

7.8 Projected Retail Electricity Prices

Retail electricity prices for the CAIR region are projected to increase a small amount

with CAIR (see Table 7-8 and Figure 7-7). The cap-and-trade approach allows industry to meet 
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West

Interior

Appalachia

2000

2003201020152020

2000

2003201020152020

2000

2003201020152020

Scale: Appalachia 2000 = 299 million tons

Figure 7-6. Current Coal Production Levels and Projected Production with

CAIR

Source: Integrated Planning Model run by EPA.

Table 7-7. Coal Production for the Electric Power Sector with the Base Case (No Further

Controls) and with CAIR (Million Tons)

2000 2003

Base Case CAIR

Supply Area 2010 2015 2020 2010 2015 2020

Appalachia 299 275 325 315 301 306 310 331

Interior 131 135 161 162 173 164 193 219

West 475 526 603 631 714 607 579 607

National 905 936 1,089 1,109 1,188 1,077 1,082 1,156

Source: 2000 and 2003 data are derived from EIA data. All projections are from the Integrated Planning Model run

by EPA.
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0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

2000 2010 2015

Mills/KWh

Base Case CAIR

Figure 7-7. Regional Electricity Prices with and without CAIR

Source: Integrated Planning Model run by EPA.

Table 7-8. Projected Regional Retail Electricity Prices with the Base Case (No Further

Controls) and with CAIR (Mills/kWh)

Year Base Case CAIR Percent Change

2010 58 59 2.0%

2015 61 62 2.7%

2020 61 62 1.8%

Source: EPA’s Retail Electricity Price Model.

the requirements of CAIR in the most cost-effective manner, thereby minimizing the costs

passed on to consumers. Regional retail electricity prices are projected to be 2 to 3 percent

higher with CAIR. Retail electricity prices by NERC region are provided in Table 7-9

(Figure 7-8). These results show small increases in retail prices for the NERC regions in the

eastern part of the country. By 2020, CAIR region retail electricity prices are projected to be

roughly 1.8 percent higher with CAIR (Table 7-8 and Figure 7-7).
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Table 7-9. Retail Electricity Prices by NERC Region with the Base Case (No Further

Controls) and with CAIR (Mills/kWh)

Power

Region Primary States Included 2000

Base Case CAIR Percentage Change

2010 2015 2020 2010 2015 2020 2010 2015 2020

ECAR (1) OH, MI, IN, KY, WV, PA 57.4 51.7 55.2 56.1 53.8 58.5 58.0 4.0% 5.9% 3.4%

ERCOT (2) TX 65.1 57.9 64.4 62.6 59.3 64.6 63.3 2.5% 0.2% 1.2%

MAAC (3) PA, NJ, MD, DC, DE 80.4 59.3 69.4 72.2 61.2 71.7 72.8 3.2% 3.4% 0.8%

MAIN (4) IL, MO, WI 61.2 52.6 57.8 61.0 54.0 60.3 62.0 2.6% 4.3% 1.7%

MAPP (5) MN, IA, SD, ND, NE 57.4 52.8 49.3 47.6 52.9 49.6 48.0 0.2% 0.7% 0.8%

NY (6) NY 104.3 82.8 87.9 88.1 83.3 88.8 88.4 0.5% 1.0% 0.3%

NE (7) VT, NH, ME, MA, CT, RI 89.9 77.4 83.9 82.8 77.5 84.7 83.0 0.1% 1.0% 0.2%

FRCC (8) FL 67.9 71.2 71.3 69.5 71.7 72.3 70.5 0.8% 1.3% 1.5%

STV (9) VA, NC, SC, GA, AL, MS,

TN, AR, LA

59.3 56.2 55.1 55.3 57.0 56.2 56.6 1.5% 2.1% 2.3%

SPP (10) KS, OK, MO 59.3 54.2 57.0 56.7 54.6 57.5 57.0 0.7% 0.9% 0.6%

Regionwide 66.0 58.0 60.8 61.0 59.2 62.4 62.1 2.0% 2.7% 1.8%

Source: EPA’s Retail Electricity Price Model. 2000 prices are from EIA’s AEO 2003.

Figure 7-8. NERC Power Regions
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7.9 Projected Fuel Price Impacts

The impacts of CAIR on coal prices and natural gas prices before shipment are shown

below (Table 7-10). 

Table 7-10. Henry Hub Natural Gas Prices and Average Delivered Coal Prices with the

Base Case (No Further Controls) and with CAIR ($1999)

Fuel 2000

Base Case CAIR Percentage Change

2010 2015 2020 2010 2015 2020 2010 2015 2020

Natural Gas 4.15 3.20 3.25 3.16 3.25 3.30 3.20 1.6% 1.5% 1.3%

Coal 1.25 1.05 1.01 0.96 1.05 0.98 0.93 0.0% –3.0% –3.1%

Source: Integrated Planning Model run by EPA. 2000 natural gas data are from Platts GASdat. 2000 coal prices 

are from EIA.

Note: Coal price changes largely result from changes in the mix of coal types used. Delivered coal prices vary

widely, but large changes in the cost of each type of coal are not projected.

7.10 Key Differences in EPA Model Runs for Final CAIR Modeling

As previously stated, the emissions, cost, air quality, and benefits analyses done for the

final CAIR are from a modeling scenario that requires annual SO2 and NOx reductions in 26

States and the District of Columbia and ozone season NOx requirements in Connecticut and

Massachusetts (See Figure 7-1). This modeling differs from today’s final CAIR, in that

Arkansas, Delaware, and New Jersey are not included in the annual SO2 and NOx requirements,

and various States are required to meet an ozone season NOx requirement (See Figure 7-9). 

Modeling was done based upon the Final CAIR region, and in large part, results under this

scenario are not significantly different in scope or magnitude.

Coal production, minemouth coal and wellhead natural gas prices, generation and

generating capacity, and electricity prices do not differ significantly between the scenarios (all

less than a 1 percent difference in modeling results). Nationwide emissions of SO2 and NOx are

roughly 1 to 3 percent higher when Arkansas, Delaware, and New Jersey are not included in the

annual programs. The only other notable difference in the modeling results is the difference in

retrofits; retrofits of SCR and FGD are roughly 2-4 percent lower without these three States. All

IPM runs done in support of CAIR and used as part of the final CAIR package are in the final

CAIR Docket and can be found on EPA’s website: (http://www.epa.gov/airmarkets/

epa-ipm/iaqr.html). A complete list of IPM runs can be found in Appendix D of this RIA.
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States controlled for both SO2 and NOx

States not covered by CAIR

States controlled for Ozone Season NOx

Figure 7-9. Final CAIR Region

Note: Delaware and New Jersey are not included in the Final CAIR for the annual SO2 and NOx

requirements. However, EPA intends on incorporating these two States in the annual CAIR program

through a separate rulemaking. See earlier discussion for more detail.

In addition, EPA did an analysis to determine the incremental impact of including

Delaware and New Jersey in the annual SO2 and NOx requirements from inclusion in the ozone

season only NOx requirement. Inclusion of these two States leads to roughly an additional fifty

thousand tons of SO2 reduction and ten thousand tons of NOx reduction in 2015. The

incremental cost of including these two States is estimated to be roughly $40 million in 2015. 

Please see “Proposed Rules” section of today’s Federal Register publication for more detail on

the inclusion of Delaware and New Jersey for the annual CAIR requirements.

7.11 Projected Primary PM Emissions from Power Plants

IPM does not project primary PM emissions from power plants. These emissions are

projected using a combination of IPM outputs and emission factors. Separate methodologies are

used to project filterable PM emissions and condensible PM emissions. The sum is the total

projected primary PM emissions.

For filterable PM emissions, emission factors were developed for each unit based on

historical information regarding installed emissions controls and types of fuel burned. This

methodology tends to underpredict reductions in filterable PM emissions between the base case

and the control case because no changes are assumed in the emission factors even if a unit is

projected to install a control such as an FGD, which could lead to a decrease in filterable PM

emissions. 

For condensible PM emissions, emission factors were changed between the base case and

the control case to reflect SO2 controls projected to be installed in the control case. Although
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EPA used the best emission factors available for its analysis, these emission factors did not

account for the potential changes in condensible PM emissions due to the installations of SCRs. 

The formation of additional condensible PM (in the form of SO3 and H2SO4) in units with SCRs

depends on a number of factors, including coal sulfur content, combustion conditions and

characteristics of the catalyst used in the SCR, and is likely to vary widely from unit to unit. 

SCRs are generally designed and operated so that they minimize increases in condensible PM. 

This limitation leads to an overprediction of reductions in condensible PM emissions for units

with SCRs. For a more complete description of the methodologies used to project PM emissions

see “Document for the 2001 Electrical Generating Unit (EGU) Trends Procedures Report,”

Section 4.2, September 25, 2003.

7.12 Limitations of Analysis

EPA’s modeling is based on its best judgment for various input assumptions that are

uncertain. Assumptions for future fuel prices and electricity demand growth deserve particular

attention because of the importance of these two key model inputs to the power sector. To some

degree, EPA addresses the uncertainty surrounding these two assumptions through its sensitivity

analysis, which is discussed in Appendix D. As a general matter, the Agency selects the best

available information from available engineering studies of air pollution controls and has set up

what it believes is the most reasonable modeling framework for analyzing the cost, emission

changes, and other impacts of regulatory controls. 

The annualized cost estimates of the private compliance costs that are provided in this

analysis are meant to show the increase in production (engineering) costs of CAIR to the power

sector. In simple terms, the private compliance costs that are presented are the annual increase

in revenues required for the industry to be as well off after CAIR is implemented as before. To

estimate these annualized costs, EPA uses a conventional and widely-accepted approach that is

commonplace in economic analysis of power sector costs for estimating engineering costs in

annual terms. For estimating annualized costs, EPA has applied a capital recovery factor (CRF)

multiplier to capital investments and added that to the annual incremental operating expenses. 

The CRF is derived from estimates of the cost of capital (private discount rate), the amount of

insurance coverage required, local property taxes, and the life of capital. The private compliance

costs presented earlier are EPA’s best estimate of the direct private compliance costs of CAIR. 

The annualization factor used for pure social cost calculations (for annualized costs)

normally includes the life of capital and the social discount rate. For purposes of benefit-cost

analysis of this rule, EPA has calculated the annualized social costs using the discount rates from

the benefits analysis for CAIR (3 percent and 7 percent and a 30 year life of capital. The cost of

added insurance necessary because of CAIR was included in the calculations, but local taxes
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were not included because they are considered to be transfer payments, and not a social cost). 

Using these discount rates, the social costs of CAIR are $1.91 billion in 2010 and $2.56 billion in

2015 using a discount rate of 3 percent, and $2.14 billion in 2010 and $3.07 billion in 2015 using

a discount rate of 7 percent. 

The annualized regional cost of CAIR, as quantified here, is EPA’s best assessment of

the cost of implementing CAIR, assuming that States adopt the model cap and trade program. 

These costs are generated from rigorous economic modeling of changes in the power sector due

to CAIR. This type of analysis using IPM has undergone peer review and federal courts have

upheld regulations covering the power sector that have relied on IPM’s cost analysis.

The direct private compliance cost includes, but is not limited to, capital investments in

pollution controls, operating expenses of the pollution controls, investments in new generating

sources, and additional fuel expenditures. EPA believes that the cost assumptions used for CAIR 

reflect, as closely as possible, the best information available to the Agency today. The relatively

small cost associated with monitoring emissions, reporting, and record keeping for affected

sources is not included in these annualized cost estimates, but EPA has done a separate analysis

and estimated the cost to be less than $42 million (see Section X. B. Paperwork Reduction Act). 

Furthermore, there are some unquantified costs that EPA wants to identify as limits to its

analysis. These costs include the costs of federal and State administration of the program, which

we believe are modest given our experience with the Acid Rain Program and the NOx Budget

Trading Program and likely to be less than the alternative of States developing approvable SIPs,

securing EPA approval of those SIPs, and Federal/State enforcement to deal with the air

pollution transport problem that CAIR addresses. There also may be unquantified costs of

transitioning to CAIR, such as the costs associated with the retirement of smaller or less efficient

electricity generating units, and employment shifts as workers are retrained at the same company

or re-employed elsewhere in the economy. There are certain relatively small permitting costs

associated with Title IV that new program entrants face (we believe there are far less than 1,000

new entrants who may require one day of additional work for trading permits). In a separate

analysis explained later in this RIA, the EPA estimates the indirect costs and impacts of higher

electricity prices on the entire economy (see Regulatory Impact Analysis for the Final Clean Air

Interstate Rule, Appendix E [March 2005]). 

Cost estimates for CAIR are based on results from ICF’s Integrated Planning Model. 

The model minimizes the costs of producing electricity (including abatement costs) while

meeting load demand and other constraints (full documentation for IPM can be found at

www.epa.gov/airmarkets/epa-ipm). The structure of the model assumes that the electric utility

industry will be able to meet the environmental emission caps at least cost. Montgomery (1972)
1

 2010 Phase II cost estimate in $1995.

2

 See recent regulatory impact analysis for the Tier 2 Regulations for passenger vehicles (1999) and Heavy-Duty

Diesel Vehicle Rules (2000). There is also evidence that scrubber costs will decrease in the future because of the

learning-by-doing phenomenon, as more scrubbers are installed (see Manson, Nelson, and Neumann, 2002).

3

 There is also evidence that scrubber costs will decrease in the future because of the learning-by-doing phenomenon,

as more scrubbers are installed (see Manson, Nelson, and Neumann, 2002).

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has shown that this least cost solution corresponds to the equilibrium of an emission permit

system. See also Atkinson and Tietenburg (1982), Krupnick et al. (1980), and McGartland and

Oates (1985). However, to the extent that transaction and/or search costs, combined with 

institutional barriers, restrict the ability of utilities to exhaust all the gains from emissions

trading, costs are underestimated by the model. Utilities in the IPM model also have “perfect

foresight.” To the extent that utilities misjudge future conditions affecting the economics of

pollution control, costs may be understated as well. 

From another vantage point, this modeling analysis does not take into account the

potential for advancements in the capabilities of pollution control technologies for SO2 and NOx

removal as well as reductions in their costs over time. Market-based cap and trade regulation

serves to promote innovation and the development of new and cheaper technologies. As an

example, recent cost estimates of the Acid Rain SO2 trading program by Resources for the

Future (RFF) and MIT’s Center for Energy and Environmental Policy Research (CEEPR) have

been as much as 83 percent lower than originally projected by the EPA (see Carlson et al., 2000;

Ellerman, 2003). It is important to note that the original analysis for the Acid Rain Program

done by EPA also relied on an optimization model like IPM. Ex ante, EPA cost estimates of

roughly $2.7 to $6.2 billion1 in 1989 were an overestimate of the costs of the program in part

because of the limitation of economic modeling to predict technological improvement of

pollution controls and other compliance options such as fuel switching. Ex post estimates of the

annual cost of the Acid Rain SO2 trading program range from $1.0 to $1.4 billion. Harrington et

al. have examined cost analyses of EPA programs and found a tendency for predicted costs to

overstate actual implementation costs in market-based programs (Harrington, Morgenstern, and

Nelson, 2000). In recognition of this, EPA’s mobile source program uses adjusted engineering

cost estimates of pollution control equipment and installation costs to account for this fact, which

EPA has not done in this case.2

It is also important to note that the capital cost assumptions for scrubbers used in EPA

modeling applications are highly conservative. These are a substantial part of the compliance

costs. Data available from recent published sources show the reported FGD costs from recent

installations to be below the levels projected by IPM.3

 In addition, EPA also conducted a survey
4

 The degree of substitution/curtailment depends on the price elasticity of demand for electricity.

5

 See “Analysis of S. 485, the Clear Skies Act of 2003, and S. 843, the Clean Air Planning Act of 2003.” Energy

Information Administration. September, 2003. EIA modeling indicated that the Clear Skies Act of 2003 (a

nationwide cap and trade program for SO2, NOx, and mercury), demand response could lower present value costs

by as much as 47% below what it would have been without an emission constraint similar to CAIR.

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of recent FGD installations and compared the costs of these installations to the costs used in

IPM. This survey included small, mid-size, and large units. Examples of the comparison of

these referenced published data with the FGD capital cost estimates obtained from IPM are

provided in the Final CAIR docket. 

EPA’s latest update of IPM incorporates State rules or regulations adopted before March

2004 and various NSR settlements. Documentation for IPM can be found at

www.epa.gov/airmarkets/epa-ipm. Any State or settlement action since that time has not been

accounted for in our analysis in this chapter.

As configured in this application, the IPM model does not take into account demand

response (i.e., consumer reaction to electricity prices). The increased retail electricity prices

shown in Tables 7-8 and 7-9 would prompt end users to curtail (to some extent) their use of

electricity and encourage them to use substitutes.4

 The response would lessen the demand for

electricity, resulting in electricity price increases slightly lower than IPM predicts, which would

also reduce generation and emissions. Because of demand response, certain unquantified

negative costs (i.e., savings) result from the reduced resource costs of producing less electricity

because of the lower quantity demanded. To some degree, these saved resource costs will offset

the additional costs of pollution controls and fuel switching that we would anticipate with CAIR. 

Although the reduction in electricity use is likely to be small, the cost savings from such a large

industry ($250 billion in revenues in 2003) is likely to be substantial. EIA analysis examining

multi-pollutant legislation under consideration in 2003 indicates that the annualized costs of

CAIR may be overstated substantially by not considering demand response, depending on the

magnitude and coverage of the price increases.5

Recent research suggests that the total social costs of a new regulation may be affected

by interactions between the new regulation and pre-existing distortions in the economy, such as

taxes. In particular, if cost increases due to a regulation are reflected in a general increase in the

price level, the real wage received by workers may be reduced, leading to a small fall in the total

amount of labor supplied. This “tax interaction effect” may result in an increase in deadweight

loss in the labor market and an increase in total social costs. Although there is a good case for

the existence of the tax interaction effect, recent research also argues for caution in making prior

assumptions about its magnitude. However, there are currently no government-wide economic
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analytical guidelines which discuss the tax interaction effect and its potential relevance for

estimation of federal program costs and benefits. The limited empirical data available to support

quantification of any such effect leads to this qualitative identification of the costs.

On balance, after consideration of various unquantified costs (and savings that are

possible), EPA believes that the annual private compliance costs that we have estimated are

more likely to overstate the future annual compliance costs that industry will incur, rather than

understate those costs.

7.13 Significant Energy Impact

According to E.O. 13211: Actions that Significantly Affect Energy Supply, Distribution,

or Use, this rule is significant because it has a greater than 1 percent impact on the cost of

natural gas and electricity production and it results in the retirement of greater than 500 MW of

coal-fired generation.

Several aspects of CAIR are designed to minimize the impact on energy production. 

First, EPA recommends a trading program rather than the use of command-and-control

regulations. Second, compliance deadlines are set cognizant of the impact that those deadlines

have on electricity production. Both of these aspects of CAIR reduce the impact of the proposal

on the electricity sector.

7.14 Industry-Sector Impacts

EPA estimates the direct costs of implementing CAIR at $3.6 billion in 2015 in the CAIR

region. Given the impact of this rule on electricity generators, we believe it is important to

gauge the extent to which the rule might affect other industry sectors. To do so, we conducted a

limited analysis of the economy-wide effects of implementing CAIR.

EPA was particularly interested in learning how anticipated changes in electricity prices

might affect industry sectors that are large electricity users. The models we employed indicated

those impacts would be small, even without incorporating the beneficial economic effects of

CAIR-related air quality improvements such as improved worker health and productivity. 

Rather, our analyses continue to show that the value of even the limited subset of CAIR benefits

we were able to quantify substantially outweigh implementation costs. 

By focusing only on cost-side spillover effects on the economy, the industry-sector

impacts projected by our macroeconomic models are likely overstated, primarily because the

positive market impacts of CAIR on labor availability and productivity are excluded. In this

regard, an independent panel of experts has encouraged EPA to work toward incorporating both
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beneficial and costly effects when modeling the economy-wide consequences of regulation. 

EPA is working to develop this capability.

Although neither model has yet been configured to include the indirect economic benefits

of air quality improvements, EPA employed two distinct computable general equilibrium models

to gauge the potential magnitude of the economy-wide effects of CAIR implementation costs. 

The first model, known as IGEM, has a long track record and was used by the Agency for the

first of the two Clean Air Act Section 812 studies. The other model, called EMPAX-CGE, is

currently in peer review and has the advantage of disaggregating the United States into multiple

regions. As with all models, these tools have their respective strengths and weaknesses, and

differences in data and choice of functional form imply that the models are likely to show

slightly different results. Despite the differences between the models, the results of the

respective analyses show similarly small impacts of CAIR on energy-intensive industries. For

example, production changes for the chemical manufacturing industry are estimated at –0.01

percent to –0.04 percent in 2010. Furthermore, if labor productivity improvements and other

benefits of improved air quality were included, the small production output decreases projected

by both models might be partially or entirely offset. Please see Appendix E for more details.

7.15 References

Atkinson, S., and T. Tietenberg. 1982. “The Empirical Properties of Two Classes of Design for

Transferable Discharge Permit Markets.” Journal of Environmental Economics and

Management 9:101-121 

Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “Sulfur

Dioxide Control by Electric Utilities: What Are the Gains from Trade?” Journal of

Political Economy 108(#6):1292-1326.

Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2

Cap-and-Trade Program. Massachusetts Institute of Technology Center for Energy and

Environmental Policy Research.

Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory Cost

Estimates.” Journal of Policy Analysis and Management 19(2):297-322.

Krupnick, A., W. Oates, and E. Van De Verg. 1980. “On Marketable Air Pollution Permits: 

The Case for a System of Pollution Offsets.” Journal of Environmental Economics and 

Management 10:233-47.

Manson, Nelson, and Neumann. 2002. “Assessing the Impact of Progress and Learning Curves

on Clean Air Act Compliance Costs.” Industrial Economics Incorporated.
7-23

McGartland, A., and W. Oates. 1985. “Marketable Permits for the Prevention of Environmental

Deterioration.” Journal of Environmental Economics and Management 12:207-228.

Montgomery, W. David. 1972. “Markets in Licenses and Efficient Pollution Control

Programs.” Journal of Economic Theory 5(3):395-418.
8-1

CHAPTER 8

STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSES

This chapter presents discussion and analyses relating to relevant Executive Orders

and statutory requirements relevant for CAIR. We discuss potential impacts to affected

small entities as required by the Regulatory Flexibility Act (RFA), as amended by the Small

Business Regulatory Enforcement Fairness Act (SBREFA). We also describe the analysis

conducted to meet the requirements of the Unfunded Mandates Reform Act of 1995 

(UMRA) that assess the impact of CAIR for state, local and Tribal governments and the

private sector. Analyses conducted to comply with the Paperwork Reduction Act (PRA) are

also discussed. In addition, we address the requirements of Executive Order (EO) 13045: 

Protection of Children from Environmental Health and Safety Risks; EO 13175: 

Consultation and Coordination with Indian Tribal Governments; and EO 12898: Federal

Actions to Address Environmental Justice in Minority Populations and Low-Income

Populations. Discussion of Executive Order 13211: Actions that Significantly Affect

Energy Supply, Distribution or Use is provided in Chapter 7 of this report. 

8.1 Small Entity Impacts

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. 

For the purposes of assessing the impacts of CAIR on small entities, a small entity is

defined as:

(1) A small business according to the Small Business Administration size

standards by the North American Industry Classification System (NAICS)

category of the owning entity. The range of small business size standards for

electric utilities is 4 billion kilowatt-hours of production or less;
1

 See Michigan v. EPA, 213 F.3d 663, 668-69 (D.C. Cir. 2000), cert. den. 121 S.Ct. 225, 149 L.Ed.2d 135

(2001). An agency’s certification need consider the rule’s impact only on entities subject to the rule.

8-2

(2) a small government jurisdiction that is a government of a city, county, town,

district, or special district with a population of less than 50,000; and

(3) a small organization that is any not-for-profit enterprise that is independently

owned and operated and is not dominant in its field. 

Table 8-1 lists entities potentially affected by this proposed rule with applicable

NAICS code. It is important to note that the proposed rule leaves states to decide which

sources to control, such that states may choose to regulate source categories in addition to

those listed in Table 8-1.

Table 8-1. Potentially Regulated Categories and Entitiesa

Category

NAICS

Codeb Examples of Potentially Regulated Entities

Industry 221112 Fossil fuel-fired electric utility steam generating units.

Federal

Government

221112c Fossil fuel-fired electric utility steam generating units owned by

the federal government.

State/Local/

Tribal

Government

221112c Fossil fuel-fired electric utility steam generating units owned by

municipalities.

921150 Fossil fuel-fired electric utility steam generating units in Indian

Country.

a Include NAICS categories for source categories that own and operate electric generating units only. b North American Industry Classification System. c Federal, state, or local government-owned and operated establishments are classified according to the

activity in which they are engaged.

Courts have interpreted the RFA to require a regulatory flexibility analysis only when

small entities will be subject to the requirements of the rule.1 This rule would not establish

specific requirements applicable to small entities. Instead, it would require states to develop,

adopt, and submit SIP revisions that would achieve the necessary SO2 and NOx reductions,

leaving to states the task of determining how and by which entities these reductions will be

obtained. Because affected states would decide which sources to control and the extent of

emissions reductions each selected source would have to achieve, EPA cannot definitively
2

 eGRID is available at http://www.epa.gov/cleanenergy/egrid/download.htm.

8-3

project the effect of the proposed rule on small entities. Although not required, EPA

conducts a general analysis of the potential impact of CAIR on small entities.

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 NOx and SO2 budgets. This analysis assumes that all affected states choose to

meet their budgets by controlling EGUs only. This analysis does not examine potential

indirect economic impacts associated with CAIR, such as employment effects in industries

providing fuel and pollution control equipment, or the potential effects of electricity price

increases on industries and households. 

As is noted in Chapter 7, EPA initially conducted IPM modeling for today’s final

action using a control strategy that is similar, but not identical to, the final CAIR

requirements. The control strategy that EPA initially modeled included three additional

States (Arkansas, Delaware and New Jersey) within the region and required these States to

make annual SO2 and NOx reductions. While these three States are included in this analysis,

they are not required to make annual reductions under the final CAIR (in the “Proposed

Rules” section of today’s Federal Register publication, EPA is publishing a proposal to

include Delaware and New Jersey in the CAIR region for annual SO2 and NOx reductions). 

The implication of this is that total impacts on small entities are somewhat overstated in this

analysis. 

8.1.1 Identification of Small Entities

EPA used EGRID data as a basis for compiling the list of potentially affected small

entities. EGRID is EPA’s Emissions & Generation Resource Integrated Database, which

contains emissions and resource mix data for virtually every power plant and company that

generates electricity in the United States.2 The data set contains detailed ownership and

corporate affiliation information. For plants burning fossil fuel as the primary fuel,

plant-level boiler and generator capacity, heat input, generation, and emissions data were

aggregated by owner and then parent company. Entities with more than 4 billion kWh of

annual electricity generation were removed from the list, as were municipal-owned entities

serving a population greater than 50,000. Finally, for cooperatives, investor-owned utilities,

and subdivisions that generate less than 4 billion kWh of electricity annually but may be part

of a large entity, additional research on power sales, operating revenues, and other business

activities was performed to make a final determination regarding size. Because the rule does
8-4

not affect units with a generating capacity of 25 MW or less, small entities that do not own at

least one generating unit with a capacity greater than 25 MW were dropped from the data set. 

According to EPA’s analysis, approximately 185 small entities were exempted by this

provision. Finally, small entities for which IPM does not project generation in 2010 or 2015

in the base case were omitted from the analysis because they are not projected to be

operating and thus will not face the costs of compliance with CAIR. Four small municipal

entities are omitted for this reason. After omitting entities for the reasons above, EPA

identified a total of 75 potentially affected small entities, out of a possible 264. The number

of potentially affected small entities by ownership type is listed in Table 8-2. 

Table 8-2. Projected Impact of CAIR on Small Entities

EGU

Ownership

Type

Number of

Potentially

Affected

Entities

Total Net

Compliance Cost

($1999 millions)

Number of Small

Entities with

Compliance Costs

>1% of Generation

Revenues

Number of Small

Entities with

Compliance Costs

>3% of Generation

Revenues

2010 2015 2010 2015 2010 2015

Cooperative 17 13.2 22.3 8 11 5 9

InvestorOwned Utility2 3.1 2.8 1 2 0 0

Municipal 49 –28.2 ! 9.1 15 29 15 19

Subdivision 5 –15.2 –1.7 3301

Other 2 0.1 0.1 1 1 0 1

Total 75 –27.0 14.4 28 46 20 30

Note: The total number of potentially affected entities in this table excludes the 189 entities that have been

dropped because they will not be affected by CAIR. Also, 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 CAIR. 

Source: IPM and TRUM analysis

8.1.2 Overview of Analysis and Results

This section presents the methodology and results for estimating the impact on CAIR

to small entities in 2010 and 2015 based on the following endpoints:

C annual economic impacts of CAIR on small entities and 

C ratio of small entity impacts to revenues from electricity generation.
8-5

8.1.2.1 Methodology for Estimating Impacts of CAIR on Small Entities

An entity can comply with CAIR through some combination of the following:

installing retrofit technologies, purchasing allowances, switching to a cleaner fuel, or

reducing emissions through a reduction in generation. 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, EPA estimates compliance

costs as follows:

CCompliance = ) COperating+Retrofit + ) CFuel + ) CAllowances + ) CTransaction – ) R (8.1)

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 CAIR,

for example, EPA projects that the price of low-sulfur coal will fall as many units install

scrubbers and switch away from low-sulfur coal to cheaper bituminous coal, such that many

entities actually experience a reduction in fuel costs as a result of lower prices due to the

demand shift. Similarly, although some units will forgo some level of electricity generation

(and thus revenues) to comply, this impact will be lessened on these entities by the projected

increase in electricity prices under CAIR as well as reductions in fuel costs, and those not

reducing generation levels will see an increase in electricity revenues. Elsewhere, units

burning high or medium sulfur coal might decide to pay relatively more for low-sulfur coal

under CAIR and sell allowances on the market, in the hopes of negating some or all of their

compliance cost. Because this analysis evaluates the total costs along each of the four

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, 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 CAIR relative to the base case. These individual components of

compliance cost were estimated as follows:
3

 A similar approach was used in regulatory impact analyses for the 126 FIP and NOx SIP Call.

8-6

(1) Operating and retrofit costs: Using the IPM-parsed output for the base case

and CAIR (http://www.epa.gov/airmarkets/epa-ipm/iaqr.html), EPA identified

units that install control technology under CAIR and the technology installed. 

The equations for calculating retrofit costs were adopted from EPA’s

Technology Retrofit and Updating Model (TRUM). 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 annualized

retrofit cost for units projected to install FGD, SCR, or SNCR. 

(2) Sale or purchase of allowances: EPA estimated the value of initial SO2 and

NOx allowance holdings. For SO2, units were assumed to retain their Phase II

allowance allocations as determined under EPA’s 1998 reallocation of Acid

Rain allowances, adjusted to reflect the 50 percent reduction in 2010 and

65 percent reduction in 2015 under CAIR. Because of the resources involved

in compiling allowance-holding data, the value of banked SO2 allowances was

not considered in this analysis. The implication of this is that the annual net

purchase of allowances may be overstated for some units. For NOx, the state

emission budgets were assumed to be apportioned to units on a heat-input

basis. Each unit was assumed to receive a share of the state NOx emission

budget equal to its share of the total state heat input for that year in the base

case. This is a simplification of what is included in the model rule, which

proposes allocating NOx allowances based on heat input from 1999-2002.3

However, states can ultimately decide how to allocate NOx allowances.

To estimate the value of allowances holdings, allocated allowances were

subtracted from projected emissions, and the difference was then multiplied

by the allowance prices projected by IPM for 2010 and 2015. Units were

assumed to purchase or sell allowances to exactly cover their projected

emissions under CAIR. 

(3) Fuel costs: Fuel costs were estimated by multiplying fuel input (MMBtu) by

region and fuel-type-adjusted fuel prices ($/MMBtu) from TRUM. The

change in fuel expenditures under CAIR was then estimated by taking the

difference in fuel costs between CAIR and the base case. 
4

 Neither the costs nor the revenues of units that retire under CAIR are included in the impact estimates. 

Because these units are better off retiring under CAIR than continuing operation, the true cost of the rule on

these units is not represented by our modeling. The true cost of CAIR for these units is the differential

between their costs in the base case and the costs of meeting their customers’ demand under the rule. 

8-7

(4) Value of electricity generated: EPA estimated electricity generation by first

estimating unit capacity factor and maximum fuel capacity. Unit capacity

factor is estimated by dividing fuel input (MMBtu) by maximum fuel capacity

(MMBtu). The maximum fuel capacity was estimated by multiplying

capacity (MW) * 8,760 operating hours * heat rate (MMBtu/MWh). The

value of electricity generated is then estimated by multiplying capacity

(MW)*capacity factor*8,760*regional-adjusted retail electricity price

($/MWh). 

As discussed later in this analysis, the small entities projected to be affected

by CAIR do not have to operate in a competitive market environment and thus

should be able to pass compliance costs on to consumers. To somewhat

account for this, we incorporated the projected regional-adjusted retail

electricity price calculated under CAIR in our estimation of generation

revenue under CAIR. 

(5) Administrative costs: Because most affected units are already monitored as

a result of other regulatory requirements, EPA considered the primary

administrative cost to be transaction costs related to purchasing or selling

allowances. EPA assumed that transaction costs were equal to 1.5 percent of

the total absolute value of a unit’s allowances. This assumption is based on

market research by ICF Consulting. 

8.1.2.2 Results

The potential impacts of CAIR on small entities are summarized in Table 8-2. All

costs are presented in $1999. EPA estimated the annualized net compliance cost to small

entities to be approximately –$27.0 million in 2010 and $14.4 million in 2015.4

 As discussed

below, these negative net compliance costs in 2010 are largely due to higher electricity prices

(and therefore higher generation revenue) and reduced low-sulfur coal prices under CAIR. 

Based on EPA analysis, small entities experiencing the greatest impact under CAIR in terms

of cost as a share of revenue are those projected to both reduce output and purchase

allowances. 
8-8

Furthermore, approximately 180 MW of small cooperative capacity (5 units of 61 in

this analysis) are projected by IPM to be uneconomic to maintain under CAIR relative to the

base case, as are approximately 265 MW of small municipality-owned capacity (6 units of

101 in this analysis). One plant owned by a cooperative is projected to be uneconomic, as is

one plant owned by a municipality. Overall, about 445 MW of total small entity capacity, or

1.0 percent of total small entity capacity in the CAIR region, is projected to be uneconomic

to maintain under CAIR relative to the base case. To put these numbers in context, of all

affected capacity under CAIR, about 5.3 GW (1.7 percent) of coal-fired capacity is projected

to be uneconomic to maintain relative to the base case. This comparison suggests that small

entities should not be disproportionately affected by CAIR. In practice, units projected to be

uneconomic to maintain may be “mothballed,” retired, or kept in service to ensure

transmission reliability in certain parts of the grid. Our IPM modeling is unable to

distinguish between these potential outcomes. Notably, none of the units affected are likely

to be in a competitive market environment and thus should be able to pass compliance costs

on to consumers.

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, focusing in particular

on entities for which this measure is greater than 1 percent. Although this metric is

commonly used in 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,

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 75 small entities considered in this analysis, and 264 total small entities in the

CAIR region, 28 entities may experience compliance costs greater than 1 percent of

generation revenues in 2010, while 46 may in 2015. Entities that experience negative net

costs under CAIR are excluded from these totals. These results do not fully account for the

reality that none of these entities operate in a competitive market and thus should be able to

recover all of their costs of complying with CAIR. It should also be emphasized that under

CAIR, states, through their choice of NOx allowance allocation methodologies, can

potentially mitigate adverse affects of CAIR on small entities. The number of entities with

compliance costs exceeding 3 percent of generation revenues is also included in Table 8-2. 
8-9

The distribution across entities of economic impacts as a share of base case revenue

is summarized in Table 8-3. Although the distributions of economic impacts on each

ownership type are in general fairly tight, there are a few outliers for which the percentage of

economic impacts as a share of revenue is either very low or very high relative to the

capacity-weighted average. In the cases where entities are projected to experience negative

net impacts that are a high percentage of revenues, these entities have units that are able to

switch to a cheaper, lower-sulfur coal to comply with CAIR and are able to maintain or

increase generation levels, thus increasing revenues. Additionally, entities in regions for

which we project large electricity price increases relative to other regions tend to be among

those at the lower end of the distribution. In the cases where entities are projected to

experience positive net impacts that are a high percentage of revenues, these entities do not

find it economic to retrofit and are unable to switch to a lower sulfur coal. Thus, these

entities comply primarily by purchasing allowances and reducing generation. 

Table 8-3. Summary of Distribution of Economic Impacts of CAIR on Small Entities 

EGU Ownership

Type

Capacity-Weighted

Average Economic

Impacts as a % of

Generation Revenues Min Max

2010 2015 2010 2015 2010 2015

Cooperative 1.0% 1.8% !20.9% !13.1% 11.5% 8.4%

Investor-owned utility 1.6% 1.5% 0.4% 1.5% 2.0% 1.5%

Municipal –3.8% !1.3% !13.8% !20.4% 17.2% 43.4%

Subdivision –0.1% 0.0% ! 80.0% ! 27.6% 1.9% 3.1%

Other –0.3% !0.2% !0.7% !0.8% 1.9% 3.1%

All –0.2% 0.0% !80.0% !27.6% 17.2% 43.4%

Source: IPM and TRUM analysis
8-10

The separate components of annualized costs to small entities under CAIR are

summarized in Table 8-4. The most significant components of incremental cost to these

entities under CAIR are retrofit and operating cost and allowance purchases. Fuel costs fall

over all ownership groups, because of the combination of switching to bituminous coal,

reduced overall fuel use, and lower prices for low-sulfur coal. Additionally, increases in

electricity generation revenue are experienced over municipal, subdivision, and other, in

2010, and over all ownership types in 2015. This is due largely to the projected increase in

electricity prices under CAIR. 

Table 8-4. Incremental Annualized Costs under CAIR Summarized by Ownership

Group and Cost Category ($1999 millions)

EGU Ownership

Type

Retrofit +

Operating

Cost

Net Purchase of

Allowances Fuel Cost

Lost Electricity

Revenue

Administrative

Cost

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Cooperative 4.1 15.7 35.2 54.0 –34.0 –34.8 7.5 –13.1 0.3 0.5

Investor-Owned

Utility

–0.1 0.0 6.5 11.1 –6.0 –6.9 2.6 –1.5 0.0 0.1

Municipal 2.2 5.6 17.7 30.7 –18.7 –27.7 –29.6 –18.0 0.2 0.3

Subdivision 10.4 9.4 –1.6 1.7 –3.2 –8.4 –20.9 –4.5 0.05 0.06

Other 0.374 0.375 –0.146 –0.106 –0.138 –0.117 !0.007 –0.018 0.001 0.001

Note: Numbers may not add to totals in Table 8-2 due to rounding. 

Source: IPM and TRUM analysis.

8.1.3 Summary of Small Entity Impacts

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. While EPA concludes that the RFA as amended by SBREFA does

not apply to CAIR, these impacts have been calculated to provide additional understanding

of the nature of potential impacts, and additional information to the states as they revise SIPs

to meet the emissions budgets set by this rulemaking. 

Overall, about 445 MW of total small entity capacity, or 1.0 percent of total small

entity capacity in the CAIR region, is projected to be uneconomic to maintain under CAIR

relative to the base case. In practice, units projected to be uneconomic to maintain may be
8-11

“mothballed,” retired, or kept in service to ensure transmission reliability in certain parts of

the grid. Our IPM modeling is unable to distinguish between these potential outcomes. 

Furthermore, of the 75 small entities potentially affected, and the 264 small entities in

the CAIR region that are included in EPA’s modeling, 28 may experience compliance costs

in excess of 1 percent of revenues in 2010, and 46 may in 2015, based on our 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 1 percent of revenues have some potential for significant impact resulting from

implementation of CAIR. However, as noted above, it is EPA’s position that because none

of the affected entities currently operate in a competitive market environment, they should be

able to pass the costs of complying with CAIR on to rate-payers. Furthermore, the decision

to include only units greater than 25 MW in size exempts 185 small entities that would

otherwise be potentially affected by CAIR. 

Two other points should be considered when evaluating the impact of CAIR,

specifically, and cap-and-trade programs more generally, on small entities. First, under

CAIR, the cap-and-trade program is designed such that states determine how NOx allowances

are to be allocated across units. A state that wishes to mitigate the impact of the rule on

small entities might choose to allocate NOx allowances in a manner that is favorable to small

entities. . 

8.2 Unfunded Mandates Reform Act (UMRA) Analysis

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, 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).
8-12

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

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.

EPA prepared a written statement for the Supplemental Notice of Proposed

Rulemaking (SNPR) consistent with the requirements of Section 202 of the UMRA. 

Furthermore, as EPA stated in the proposal, EPA is not directly establishing any regulatory

requirements that may significantly or uniquely affect small governments, including Tribal

governments. Thus, under CAIR, EPA is not obligated to develop under Section 203 of the

UMRA a small government agency plan. Furthermore, in a manner consistent with the

intergovernmental consultation provisions of Section 204 of the UMRA, EPA carried out

consultations with the governmental entities affected by this rule. 

For several reasons, EPA is not concluding that the requirements of the UMRA apply

to CAIR. First, it is questionable whether a requirement to submit a SIP revision would

constitute a federal mandate in any case. The obligation for a state to revise its SIP that

arises out of Section 110(a) of the CAA is not legally enforceable by a court of law and at

most is a condition for continued receipt of highway funds. Therefore, it is possible to view

an action requiring such a submittal as not creating any enforceable duty within the meaning

of Section 421(5)(9a)(I) of UMRA (2 U.S.C. 658 (a)(I)). Even if it did, the duty could be

viewed as falling within the exception for a condition of federal assistance under Section

421(5)(a)(i)(I) of UMRA (2 U.S.C. 658(5)(a)(i)(I)).

As noted earlier, however, notwithstanding these issues, EPA prepared for the SNPR

the statement that would be required by the UMRA if its statutory provisions applied, and

EPA has consulted with governmental entities as would be required by the UMRA. While

not required for CAIR, EPA analyzed the economic impacts of CAIR on government entities

for informational purposes. This analysis does not examine potential indirect economic

impacts associated with CAIR, such as employment effects in industries providing fuel and

pollution control equipment, or the potential effects of electricity price increases on

industries and households.

As is noted in Chapter 7, EPA initially conducted IPM modeling for today’s final

action using a control strategy that is similar, but not identical to, the final CAIR

requirements. The control strategy that EPA initially modeled included three additional

States (Arkansas, Delaware and New Jersey) within the region and required these States to
8-13

make annual SO2 and NOx reductions. While these three States are included in this analysis,

they are not required to make annual reductions under the final CAIR (in the “Proposed

Rules” section of today’s Federal Register publication, EPA is publishing a proposal to

include Delaware and New Jersey in the CAIR region for annual SO2 and NOx reductions). 

The implication of this is that total impacts on government-owned entities are somewhat

overstated in this analysis. 

8.2.1 Identification of Government-Owned Entities

Using eGRID data, EPA identified state- and municipality-owned utilities and

subdivisions in the CAIR region. EPA then used IPM-parsed output to associate these plants

with individual generating units. Entities that did not own at least one unit with a generating

capacity of greater than 25 MW were omitted from the analysis because of their exemption

from the rule. This exempts 179 entities owned by state or local governments. Additionally,

government-owned entities for which IPM does not project generation in either 2010 or 2015

under the base case or CAIR were exempted from this analysis, because they are not

projected to be operating and thus will not face the costs of compliance with CAIR. Five

municipal entities were dropped from the analysis for this reason. Thus, EPA identified 81

state and municipality-owned utilities that are potentially affected by CAIR, out of a possible

265, which are summarized in Table 8-5. 

Table 8-5. Summary of Potential Impacts on Government Entities under CAIR 

EGU

Ownership

Type

Potentially

Affected

Entities

Projected

Annualized Costs

($1,000,000)

Number of

Government Entities

with Compliance

Costs >1% of

Generation Revenues

Number of

Government Entities

with Compliance

Costs >3% of

Generation Revenues

2010 2015 2010 2015 2010 2015

Subdivision 5 !$15.2 !$1.7 3 3 0 1

State 7 !$134.2 !$110.5 0 2 0 0

Municipal 69 !$162.5 !$97.9 17 34 17 22

Total 81 !$311.9 –$210.2 20 39 17 23

Note: The total number of potentially affected entities in this table excludes the 184 entities that have been

dropped because they will not be affected by CAIR. Also, 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 CAIR. 

Source: IPM and TRUM analysis
8-14

8.2.2 Overview of Analysis and Results

After identifying potentially affected government entities, EPA estimated the impact

of CAIR in 2010 and 2015 based on the following:

C total impacts of compliance on government entities and

C ratio of small entity impacts to revenues from electricity generation.

The financial burden to owners of EGUs under CAIR is composed of compliance and

administrative costs. This section outlines the compliance and administrative costs for the 81

potentially affected government-owned units in the CAIR region. 

8.2.2.1 Methodology for Estimating Impacts of CAIR on Government Entities

The primary burden on state and municipal governments that operate utilities under

CAIR is the cost of installing control technology on units to meet SO2 and NOx emission

limits or the cost of purchasing allowances. However, an entity can comply with CAIR

through any combination of the following: installing retrofit technologies, purchasing

allowances, switching to a cleaner fuel, or reducing emissions through a reduction in

generation. 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, EPA estimates compliance

costs as follows:

CCompliance = ) COperating+Retrofit + ) CFuel + ) CAllowances + ) CTransaction – ) R (8.2)

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 CAIR,

for example, EPA projects that the price of low-sulfur coal will fall as many units install

scrubbers and switch away from low-sulfur coal to cheaper bituminous coal, such that many

entities burning low-sulfur coal actually experience a reduction in fuel costs as a result of the

demand shift. Similarly, although some units will forgo some level of electricity generation

(and thus revenues) to comply, this impact will be lessened on these entities by the projected

increase in electricity prices under CAIR as well as reductions in fuel costs, while those not

reducing generation levels will see an increase in electricity revenues. Elsewhere, units
8-15

burning high- or medium-sulfur coal might decide to pay relatively more for low-sulfur coal

under CAIR and sell allowances on the market, in the hopes of negating some or all of their

compliance cost. Because this analysis evaluates the total costs along each of the four

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, EPA used IPM-parsed output for the base case and CAIR

(http://www.epa.gov/airmarkets/epa-ipm/iaqr.html) 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

CAIR relative to the base case. These components of compliance cost were estimated as

follows:

(1) Retrofit and operating costs: Using the IPM-parsed output for the base case

and CAIR, EPA identified units that install control technology under CAIR

and the technology installed. The equations for calculating retrofit costs for

SCR, SNCR, and FGD were adopted from EPA’s TRUM. The model

calculates the capital cost (in $/MW), the fixed O&M cost (in $/MW-year),

the variable O&M cost (in $/MWh), and the total annualized retrofit and

operating cost by unit. 

(2) Sale or purchase of allowances: EPA estimated the value of initial SO2 and

NOx allowance holdings. For SO2, units were assumed to retain their Phase II

allowance allocations as determined under EPA’s 1998 reallocation of Acid

Rain allowances, adjusted to reflect the 50 percent reduction in 2010 and

65 percent reduction in 2015 under CAIR. The value of banked SO2

allowances was not considered in this analysis. Because the use of banked

allowances is expected to be a significant compliance strategy, this analysis

most likely overstates annualized compliance costs. For NOx, the state

emission budgets were assumed to be apportioned to units on a heat-input

basis. Each unit was assumed to receive a share of the state NOx emission

budget equal to its share of the total state heat input for that year in the base

case. This is a simplification of what is included in the model rule, which

proposes allocating NOx allowances based on heat input from 1999 through
5

A similar approach was used in impact analyses for the 126 FIP and NOx SIP Call.

6

All costs are reported in 1999 dollars. 

8-16

1992.5 However, states can ultimately decide how to allocate NOx

allowances. 

To estimate the value of allowances holdings, allocated allowances were

subtracted from projected emissions, and the difference was then multiplied

by the allowance price projected by IPM. Units were assumed to purchase or

sell allowances to exactly cover their projected emissions under CAIR. 

(3) Fuel costs: Fuel costs were estimated by multiplying fuel input (MMBtu) by

region and fuel type-adjusted fuel prices ($/MMBtu) from TRUM. The

change in fuel expenditures under CAIR was then estimated by taking the

difference in fuel costs between CAIR and the base case. 

(4) Value of electricity generated: EPA estimated electricity generation by first

estimating the unit capacity factor and maximum fuel capacity. The unit

capacity factor is estimated by dividing fuel input (MMBtu) by maximum fuel

capacity (MMBtu). The maximum fuel capacity was estimated by

multiplying capacity (MW) * 8,760 operating hours * heat rate

(MMBtu/MWh). The value of electricity generated was then estimated by

multiplying capacity (MW)*capacity factor*8,760*regional-adjusted retail

electricity price ($/MWh). 

(5) Administrative costs: Because most affected units are already monitored as

a result of other regulatory requirements, EPA considered the primary

administrative cost to be transaction costs related to purchasing or selling

allowances. EPA assumed that transaction costs were equal to 1.5 percent of

the total absolute value of a unit’s allowances. This assumption is based on

market research by ICF Consulting. 

8.2.2.2 Results

A summary of economic impacts on government-owned entities is presented in

Table 8-5. According to EPA’s analysis, the total net economic impact on each category of

government-owned entity (state- and municipality-owned utilities and subdivisions) is

expected to be negative in both 2010 and 2015.6

 IPM modeling of CAIR projects that

approximately 340 MW (8 units of 219 in this analysis) of municipality-owned capacity
7

Neither the costs nor the revenues of units that retire under CAIR are included in this portion of the analysis. 

Because these units are better off retiring under CAIR than continuing operation, the true cost of the rule on

these units is not represented by our modeling. The true cost of CAIR for these units is the differential

between their costs in the base case and the costs of meeting their customers’ demand under the rule. 

8-17

would be uneconomic to maintain under CAIR, beyond what is projected in the base case. 

This represents about 0.4 percent of all subdivision, state, and municipality capacity in the

CAIR region. For comparison, overall affected capacity under CAIR, about 5.3GW, or

1.7 percent of all coal-fired capacity is projected to be uneconomic to maintain relative to the

base case. This comparison suggests that government entities should not face a

disproportionate burden under CAIR. In practice, units projected to be uneconomic to

maintain may be “mothballed,” retired, or kept in service to ensure transmission reliability in

certain parts of the grid. Our IPM modeling is unable to distinguish between these potential

outcomes. 

As was done for the small entities analysis, 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.7 EPA projects that 20 government entities will have

compliance costs greater than 1 percent of revenues from electricity generation in 2010, and

39 will in 2015. Entities that are projected to experience negative compliance costs under

CAIR are not included in those totals. This approach is more indicative of a 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 CAIR through rate

increases. Given this, 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. 

The distribution across entities of economic impacts as a share of base case revenue

is summarized in Table 8-6. For state-owned entities and subdivisions, the maximum

economic impact as a share of base case revenues is approximately 3 percent. A few

municipality-owned entities experience economic impacts that are significantly higher than

the capacity-weighted average for this group. In the cases where entities are projected to

experience positive net costs that are a high percentage of revenues, these entities do not find

it economic to retrofit and are unable to switch to a lower-sulfur coal. Thus, these entities

comply primarily through the purchase of allowances and reductions in generation. 
8-18

Table 8-6. Distribution of Economic Impacts on Government Entities under CAIR 

EGU Ownership Type

Capacity-Weighted

Average Economic

Impacts as a % of

Generation Revenues Min Max

2010 2015 2010 2015 2010 2015

Sub-division –3.6% –2.0% –80.0% –27.6% 1.9% 3.1%

State –5.2% –3.9% –11.4% –10.2% 0.2% 2.8%

Municipal –5.9% –0.3% –13.8% –20.4% 17.2% 43.5%

All –4.2% –2.3% !80.0% –27.6% 17.2% 43.5%

Source: IPM and TRUM analysis

Additionally, a few entities are projected to experience negative net costs that are a

high percentage of base case revenues. These entities have units that are able to switch to a

cheaper, lower-sulfur coal to comply with CAIR and are able to maintain or increase

generation levels, thus increasing revenues. Additionally, entities in regions for which we

project large electricity price increases relative to other regions tend to be among those at the

lower end of the distribution.

The various components of annualized incremental cost under CAIR to each group of

government entities are summarized in Table 8-7. Overall, with the exceptions of

subdivisions in 2010, each group is a net purchaser of allowances. Additionally, each group

experiences both a reduction in fuel expenditures and an increase in electricity revenue under

CAIR. Incremental fuel costs are negative because of the combination of a reduction in total

coal use, switching to bituminous coal, and reduced low-sulfur coal prices under CAIR. 

Additionally, although total electricity generation by government entities falls slightly under

CAIR, the total loss in revenues is more than exceeded by the revenue gains projected as a

result of retail electricity prices rising under CAIR. 

8.2.3 Summary of Government Entity Impacts

EPA examined the potential economic impacts on state and municipality-owned

entities associated with this rulemaking based on assumptions of how the affected states will

implement control measures to meet their emissions. Although EPA does not conclude that

the requirements of the UMRA apply to CAIR, these impacts have been calculated to 
8-19

Table 8-7. Incremental Annualized Costs under CAIR Summarized by Ownership

Group and Cost Category ($1,000,000)

EGU Ownership

Type

Retrofit + 

Operating Cost

Net Purchase of

Allowances Fuel Cost

Lost Electricity

Revenue

Administrative

Cost

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Subdivision 10.4 9.4 –1.6 1.7 –3.2 –8.4 –20.9 –4.5 0.0 0.1

State 20.1 25.9 29.2 52.5 –116.8 –143.1 –67.0 –46.3 0.3 0.5

Municipal 21.3 26.7 39.3 94.8 –120.0 –156.8 –103.7 –63.7 0.7 1.0

Source: IPM and TRUM analysis

provide additional understanding of the nature of potential impacts and additional

information to the states as they revise SIPs to meet the emissions budgets set by this

rulemaking. 

According to EPA’s analysis, the total net economic impact on government-owned

entities is expected to be negative in both 2010 and 2015. However, IPM modeling projects

that about 340 MW of municipality-owned capacity (about 0.4 percent of all subdivision,

state, and municipality capacity in the CAIR region) would be uneconomic to maintain under

CAIR, beyond what is projected in the base case. In practice, units projected to be

uneconomic to maintain may be “mothballed,” retired, or kept in service to ensure

transmission reliability in certain parts of the grid. Our IPM modeling is unable to

distinguish between these potential outcomes.

Of the 81 government entities considered in this analysis and the 265 government

entities in the CAIR region that are included in EPA’s modeling, 20 may experience

compliance costs in excess of 1 percent of revenues in 2010, and 39 may in 2015, 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

CAIR. However, as noted above, it is EPA’s position that because these government entities

can pass on their costs of compliance to rate-payers, they will not be significantly affected. 

Furthermore, the decision to include only units greater than 25 MW in size exempts 179

government entities that would otherwise be potentially affected by CAIR. 
8-20

The above points aside, potential adverse impacts of CAIR on state- and

municipality-owned entities could be limited by the fact that the cap-and-trade program is

designed such that states determine how NOx allowances are to be allocated across units. A

state that wishes to mitigate the impact of the rule on state- or municipality-owned entities 

might choose to allocate NOx allowances in a manner that is favorable to these entities. 

Finally, in general, the use of cap-and-trade programs in general will limit impacts on entities

owned by small governments relative to a less flexible command-and-control program. 

8.3 Paperwork Reduction Act

In compliance with the Paperwork Reduction Act (44 U.S.C. 3501 et seq.), EPA

submitted a proposed Information Collection Request (ICR) (EPA ICR number 2512.01) to

the Office of Management and Budget (OMB) for review and approval on July 19, 2004

(FR 42720-42722). The ICR describes the nature of the information collection and its

estimated burden and cost associated with the final rule. In cases where information is

already collected by a related program, the ICR takes into account only the additional

burden. This situation arises in states that are also subject to requirements of the

Consolidated Emissions Reporting Rule (EPA ICR number 0916.10; OMB control number

2060-0088) or for sources that are subject to the Acid Rain Program (EPA ICR number

1633.13; OMB control number 2060-0258) or NOx SIP Call (EPA ICR number 1857.03;

OMB number 2060-0445) requirements. 

EPA solicited comments on specific aspects of the information collection. The

purpose of the ICR is to estimate the anticipated monitoring, reporting, and record-keeping

burden estimates and associated costs for states, local governments, and sources that are

expected to result from CAIR. 

The record-keeping and reporting burden to sources resulting from states choosing to

participate in a regional cap-and-trade program is approximately $42 million annually. This

estimate includes the annualized cost of installing and operating appropriate SO2 and NOx

emissions monitoring equipment to measure and report the total emissions of these pollutants

from affected EGUs (serving generators greater than 25 megawatt electrical). The burden to

state and local air agencies includes any necessary SIP revisions, performance of monitoring

certification, and fulfilling of audit responsibilities. More information on the ICR analysis is

included in the official CAIR docket. 

In accordance with the Paperwork Reduction Act on July 19, 2004, an ICR was made

available to the public for comment. The 60-day comment period expired September 19,

2004, with no public comments received specific to the ICR.
8-21

8.4 Children’s Health

E.O. 13045, “Protection of Children from Environmental Health Risks and Safety

Risks” (62 FR 19885, April 23, 1997), applies to any rule that (1) is determined to be

“economically significant” as defined under E.O. 12866 and (2) concerns an environmental

health or safety risk that EPA has reason to believe may have a disproportionate effect on

children. If the regulatory action meets both criteria, Section 5–501 of the Order directs the

Agency to evaluate the environmental health or safety effects of the planned rule on children

and explain why the planned regulation is preferable to other potentially effective and

reasonably feasible alternatives considered by the Agency. 

This final rule is not subject to this E.O., because it does not involve decisions on

environmental health or safety risks that may disproportionately affect children. EPA

believes that the emissions reductions from the strategies in this rule will further improve air

quality and will further improve children’s health. Chapter 4 of the RIA outlines benefits

such as reduced incidences of respiratory illness, acute bronchitis, and asthma attacks for

children that are anticipated to occur as a result of this rule.

8.5 Tribal Impacts

E.O. 13175, entitled “Consultation and Coordination with Indian Tribal

Governments” (65 FR 67249, November 9, 2000), requires EPA to develop an accountable

process to ensure “meaningful and timely input by Tribal officials in the development of

regulatory policies that have Tribal implications.” This rule does not have “Tribal

implications” as specified in E.O. 13175. 

This rule addresses transport of pollutants that are precursor for ozone and PM2.5. 

The CAA provides for states and Tribes to develop plans to regulate emissions of air

pollutants within their jurisdictions. The regulations clarify the statutory obligations of states

and Tribes that develop plans to implement this rule. The Tribal Authority Rule (TAR) gives

Tribes the opportunity to develop and implement CAA programs, but it leaves to the

discretion of the Tribe whether to develop these programs and which programs, or

appropriate elements of a program, the Tribe will adopt.

This rule does not have Tribal implications as defined by E.O. 13175. It does not

have a substantial direct effect on one or more Indian Tribes, because no Tribe has

implemented an air quality management program at this time. Furthermore, this rule does

not affect the relationship or distribution of power and responsibilities between the federal

government and Indian Tribes. The CAA and the TAR establish the relationship of the
8-22

federal government and Tribes in developing plans to attain the NAAQS, and this rule does

nothing to modify that relationship. Because this rule does not have Tribal implications,

E.O. 13175 does not apply.

If one assumes a Tribe is implementing a Tribal Implementation Plan, this final rule

would have implications for that Tribe, but it would neither impose substantial direct costs

on the Tribe nor preempt Tribal law. As provided above, EPA has estimated that the total

annual private costs for the rule for the CAIR region as implemented by state, local, and

Tribal governments is approximately $2.4 billion in 2010 and $3.6 billion in 2015 (1999$). 

There are currently very few emissions sources in Indian country that could be affected by

this rule and the percentage of Tribal land that will be impacted is very small. For Tribes

that choose to regulate sources in Indian country, the costs would be attributed to inspecting

regulated facilities and enforcing adopted regulations.

Although E.O. 13175 does not apply to this rule, EPA consulted with Tribal officials

in developing this rule. EPA has encouraged Tribal input at an early stage. Also, EPA held

periodic meetings with the states and the Tribes during the technical development of this

rule. Three meetings were held with the Crow Tribe, where the Tribe expressed concerns

about potential impacts of the rule on the coal mine operations. In addition, EPA held three

calls with Tribal environmental professionals to address concerns specific to the Tribes. 

These discussions have given EPA valuable information about Tribal concerns regarding the

development of this rule. EPA has provided briefings for Tribal representatives, and the

newly formed National Tribal Air Association (NTAA), and other national Tribal forums. 

Input from Tribal representatives has been taken into consideration in developing this rule. 

8.6 Environmental Justice

E.O. 12898, “Federal Actions to Address Environmental Justice in Minority

Populations and Low-Income Populations,” requires federal agencies to consider the impact

of programs, policies, and activities on minority populations and low-income populations. 

According to EPA guidance, agencies are to assess whether minority or low-income

populations face risks or a rate of exposure to hazards that are significant and that

“appreciably exceed or is likely to appreciably exceed the risk or rate to the general

population or to the appropriate comparison group” (EPA, 1998).

In accordance with E.O. 12898, the Agency has considered whether this rule may

have disproportionate negative impacts on minority or low income populations. The Agency

expects this rule to lead to reductions in air pollution and exposures generally. For this
8-23

reason, negative impacts to these subpopulations that appreciably exceed similar impacts to

the general population are not expected. 

8.7 Reference

U.S. Environmental Protection Agency (EPA). April 1998. Guidance for Incorporating

Environmental Justice Concerns in EPA’s NEPA Compliance Analyses. Washington,

DC: Office of Federal Activities.
9-1

SECTION 9

COMPARISON OF BENEFITS AND COSTS

The estimated social costs to implement the final CAIR program, as described in this

document, are approximately $1.91 or $2.14 billion annually for 2010 and $2.56 or $3.07

billion annually for 2015 (1999 dollars, 3 percent and 7 percent discount rate, respectively). 

Thus, the net benefits (social benefits minus social costs) of the program in 2010 are

approximately $71.4 + B billion or $60.4 + B billion annually and in 2015 are $98.5 + B

billion or $83.2 + B billion annually (1999 dollars, based on a discount rate of 3 percent and

7 percent, respectively). (B represents the sum of all unquantified benefits and disbenefits of

the regulation.) Therefore, implementation of this rule is expected, based purely on

economic efficiency criteria, to provide society with a significant net gain in social welfare,

even given the limited set of health and environmental effects we were able to quantify. 

Addition of ozone-, directly emitted PM2.5-, mercury-, acidification-, and eutrophicationrelated impacts would likely increase the net benefits of the rule. Table 9-1 presents a

summary of the benefits, costs, and net benefits of the final rule. 

The benefits and costs reported represent estimates for a complete CAIR program

that includes the CAIR promulgated rule and the proposal to include annual SO2 and NOx

controls for New Jersey and Delaware. Annual SO2 and NOx controls for Arkansas are

included in the modeling used to develop these estimates resulting in a minimal

overstatement of the reported benefits and costs for the complete CAIR program.

Air quality modeling was not conducted for the New Jersey and Delaware proposal. 

For this reason, an analysis of the potential benefits for the New Jersey and Delaware

proposal could not be completed with any degree of specificity. However based on the air

quality modeling results for the CAIR, we make rough estimates of the benefits and net

benefits that might occur with this proposal. Including New Jersey and Delaware in the

CAIR program would result in additional reductions of SO2 and NOx emissions. We

estimate that approximately $630 million of the total annual CAIR program benefits

previously discussed may be attributable to annual SO2 and NOx controls for New Jersey and

Delaware in 2010. This estimate increases to approximately $1.1 billion annually in 2015. 

The full CAIR analysis including New Jersey and Delaware showed a benefit-cost ratio of 
9-2

Table 9-1. Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air

Interstate Rulea

 (billions of 1999 dollars)

Description 2010 2015 

Social costsb

3 percent discount rate $1.91 $2.56

7 percent discount rate $2.14 $3.07

Social benefitsc,d,e

3 percent discount rate

7 percent discount rate

73.3 + B

62.6 + B

101 + B

86.3 + B

Health-related benefits:

3 percent discount rate

7 percent discount rate

72.1

61.4

99.3

84.5

Visibility benefits 1.14 1.78

Net benefits (benefits-costs)

e,f

3 percent discount rate

7 percent discount rate

$71.4 + B

$60.4 + B

$98.5 + B

$83.2 + B

a All estimates are rounded to three significant digits for ease of presentation and computation. Estimates

represent a complete CAIR program that includes the CAIR promulgated rule and the proposal to include

annual SO2 and NOx controls for New Jersey and Delaware. Annual SO2 and NOx controls for Arkansas are

included in the modeling used to develop these estimates resulting in a minimal overstatement of the benefits

and costs for the complete CAIR program.

b Note that costs are the annualized total costs of reducing pollutants including NOx and SO2 for the EGU

source category in the CAIR region in the years 2010 and 2015. 

c As this table indicates, total benefits are driven primarily by PM-related health benefits. The reduction in

premature fatalities each year accounts for over 90 percent of total monetized benefits. Benefits in this table

are nationwide (with the exception of ozone and visibility) and are associated with NOx and SO2 reductions. 

Ozone benefits represent benefits in the eastern United States. Visibility benefits represent benefits in Class I

areas in the southeastern United States.

d Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all

unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and

monetized are listed in Table 1-4.

e Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure described in

Chapter 4. Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB

guidelines for preparing economic analyses (EPA, 2000; OMB, 2003).

f Net benefits are rounded to the nearest $100 million. Columnar totals may not sum due to rounding.
9-3

around 39:1 in 2015. Based on the relatively low estimated private costs of including New

Jersey and Delaware of $30 million in 2010 and $40 million in 2015, it is highly unlikely

that costs of including New Jersey and Delaware would exceed benefits even if benefits of

controlling SO2 and NOx for New Jersey and Delaware were substantially lower than the

average benefit we used to estimate the benefits. It is highly unlikely that benefits are much

lower than average given the urban nature of much of New Jersey, and the proximity of New

Jersey and Delaware to many heavily populated urban areas.

As with any complex analysis of this scope, there are several uncertainties inherent in

the final estimate of benefits and costs, that are described fully in Chapters 4 and 7. In

addition to the uncertainty characterization provided in these chapters, we also present two

types of probabilistic approaches to characterize uncertainty in the benefit estimate of the

CAIR program. The first approach generates a distribution of benefits based on the classical

statistical error expressed in the underlying health and economic valuation studies used in the

benefits modeling framework. The second approach uses the results from a pilot expert

elicitation project designed to characterize key aspects of uncertainty in the ambient

PM2.5/mortality relationship, and augments the uncertainties in the mortality estimate with

the statistical error reported for other endpoints in the benefit analysis. 

9.1 References

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine

Particulate Air Pollution.” Journal of the American Medical Association

287:1132-1141. 

U.S. Environmental Protection Agency (EPA). September 2000. Guidelines for Preparing

Economic Analyses. EPA 240-R-00-003. 

U.S. Office of Management and Budget (OMB). 2003. Circular A-4 Guidance to Federal

Agencies on Preparation of Regulatory Analysis.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected

Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and

Particulate Air Pollution in the United States.” Environmental Health Perspectives

105(6):608-612.
1

 The modeling for the rule includes annual SO2 and NOx controls for Arkansas and results in a minimal

overstatement of the benefits and costs of the CAIR program (CAIR final plus the New Jersey and Delaware

proposal).

2

Note that the net benefits reported in this appendix are estimated using the private costs of the respective rules

rather than social costs. Thus the net benefits shown for the CAIR program in this appendix differ

somewhat from the estimates presented in the body of this report.

A-1

APPENDIX A

BENEFITS AND COSTS OF THE CLEAN AIR INTERSTATE RULE, THE CLEAN

AIR VISIBILITY RULE, AND THE CLEAN AIR INTERSTATE RULE PLUS THE

CLEAN AIR VISIBILITY RULE

This appendix presents the benefits and costs for the CAIR program (CAIR final rule

plus the New Jersey and Delaware proposal),1

 the EGU requirements for Best Available

Retrofit Technology (BART) Guidelines for the Regional Haze Rule, and the CAIR program

in the CAIR region plus BART control for EGUs elsewhere in the country (CAIR Plus

BART in the Non-CAIR Region). It is important to note that the CAIR, CAIR Plus BART in

the Non-CAIR Region, and BART Nationwide benefit and costs estimates reflect controls

for the EGU source category only, while the BART regulation will potentially affect 26

source categories. The analysis presented in this Appendix was conducted to show the

benefits and costs of the alternative programs addressing the EGU sector.2

As Table A-1 shows, annual net benefits for the CAIR program are $97.4 billion in

2015. This estimate compares to annual net benefits of $44.3 billion for BART nationwide

program and $100 billion for CAIR Plus BART in the Non-CAIR Region (assuming a 3

percent discount rate). These estimates become $82.7 billion for CAIR, $84.9 billion for

CAIR Plus BART in the Non-CAIR Region, and $37.0 billion for the BART nationwide

program assuming a 7 percent discount rate. The analysis shows that if one assumes the

CAIR program exists, the incremental benefits of requiring BART controls for the EGU

source category only in areas outside the CAIR region are approximately $3.2 to $4 billion

(7 percent and 3 percent discount rate, respectively). Related incremental costs are

approximately $1 billion. All estimates are shown in 1999 dollars. Table A-2 lists the 
A-2

Table A-1. Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air

Interstate Rule, 2015 (billions of 1999 dollars)a

Description CAIR Programg

CAIR Plus BART

in the Non-CAIR

Regiong BART Nationwide

Private costsb $3.57 $4.55 $5.19

Social benefitsc,d,e

3 percent discount rate

7 percent discount rate

$101 + B

$86.3 + B

$105 + B

$89.5 + B

$49.5+ B

$42.2 + B

Health-related benefits:

3 percent discount rate

7 percent discount rate

99.3

84.5

103

87.6

48.8

41.5

Visibility benefits 1.78 1.89 0.699

Net benefits (benefits-costs)e,f

3 percent discount rate

7 percent discount rate

$97.4 + B

$82.7 + B

$100 + B

$84.9+ B

$44.3 + B

$37.0 + B

a All estimates are rounded to three significant digits for ease of presentation and computation. These annual

estimates represent the benefits and costs of these regulatory programs expected to occur in 2015. BART

estimates reflect benefits and costs for controls for the EGU source category only. b Note that costs presented are the annual total private costs to the power sector of reducing pollutants

including NOx and SO2. The costs are estimated using the IPM and assume affected firms face cost of

capital ranging from 5.34 to 6.74 percent. CAIR costs reflect costs for the CAIR region. Costs for CAIR

Plus BART in the Non-CAIR Region and BART nationwide are national cost estimates. c Total benefits are driven primarily by PM-related health benefits. The reduction in premature fatalities each

year accounts for over 90 percent of total monetized benefits in 2015. Benefits in this table are nationwide

(with the exception of ozone and visibility) and are associated with NOx and SO2 reductions. Ozone benefits

relate to the eastern United States. Visibility benefits relate to Class I areas in the southeastern United

States. While ozone benefits are expected for each of these programs, ozone benefits are included in the

CAIR program benefits estimates only. The benefit estimates for CAIR Plus in the Non-CAIR Region

BART and BART nationwide do not include ozone benefits. Inclusion of ozone benefits would not likely

alter the conclusions reached on the magnitude of the difference between the options. d Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all

unquantified benefits and disbenefits. Potential effects categories that have not been quantified and

monetized are listed in Table 1-4 and Table 1-5. e Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure described in

Chapter 4. Results reflect 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines

for preparing economic analyses (EPA, 2000; OMB, 2003). f Net benefits are rounded to the nearest $100 million. Columnar totals may not sum due to rounding. g CAIR costs and benefits are the estimates for the CAIR program that includes the promulgated CAIR and the

proposal to include annual SO2 and NOx controls for New Jersey and Delaware. Modeling for CAIR

assumes annual SO2 and NOx controls for Arkansas that is not a part of the CAIR program. Thus the

benefits and costs reported are slightly overstated.
A-3

Table A-2. Clean Air Interstate Rule: Estimated Reduction in Health Effects

(Incidence)—2015

Health Effect

CAIR

Programf

CAIR Plus

BART in the

Non-CAIR

Regionf

BART

Nationwide

PM-Related Endpoints:

Premature mortalityb

Adult, age 30 and over

Infant, age <1 year 

17,000

36

17,000

38

8,200

19

Chronic bronchitis (adult, age 26 and over) 8,700 9,100 4,400

Nonfatal myocardial infarction (adults, age 18 and older) 22,000 23,000 11,000

Hospital admissions—respiratory (all ages)c 5,500 5,700 2,700

Hospital admissions—cardiovascular (adults, age >18)d 5,000 5,200 2,500

Emergency room visits for asthma (age 18 years and

younger)

13,000 13,000 6,500

Acute bronchitis (children, aged 8–12) 19,000 20,000 10,000

Lower respiratory symptoms (children, aged 7–14) 230,000 240,000 120,000

Upper respiratory symptoms (asthmatic children, aged 9–18) 180,000 190,000 92,000

Asthma exacerbation (asthmatic children, aged 6–18) 290,000 310,000 150,000

Work loss days (adults, aged 18–65) 1,700,000 1,700,000 830,000

Minor restricted-activity days (adults, aged 18–65) 9,900,000 10,300,000 5,000,000

Ozone-Related Endpoints e

Hospital admissions—respiratory causes (adult, 65 and older) 1,700 NE NE

Hospital admissions—respiratory causes (children, under 2) 1,100 NE NE

Emergency room visit for asthma (all ages) 280 NE NE

Minor restricted-activity days (adults, aged 18–65) 690,000 NE NE

School absence days 510,000 NE NE

a Incidences are rounded to two significant digits.. BART estimates reflect incidences for controls for the EGU

source category only. b Premature mortality benefits associated with ozone are not quantified in the primary analysis. Adult

premature mortality estimates are based upon studies by Pope et al., 2002. Infant premature mortality

estimates are based upon studies by Woodruff, Grillo, and Schoendorf, 1997. c Respiratory hospital admissions for PM include admissions for COPD, pneumonia, and asthma. d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart

disease, dysrhythmias, and heart failure. e Although ozone benefits are expected to occur for CAIR Plus BART in the Non-CAIR Region and BART

nationwide, ozone benefits are estimated for the CAIR program only. f These health effects incidences reflect estimates for the CAIR program (the promulgated CAIR and the

proposal to include annual SO2 and NOx controls for New Jersey and Delaware in CAIR). Modeling for

CAIR assumes annual SO2 and NOx controls for Arkansas that is not a part of the CAIR program. Thus the

incidence estimates reported for CAIR are slightly overstated.

NE = Not estimated
A-4

reduction in health incidence resulting from the CAIR program, CAIR Plus BART in the

Non-CAIR Region, and BART nationwide. Table A-3 depicts the monetary value of the

benefit categories listed on Table A-2. We were unable to estimate all of the benefits and

disbenefits associated with these rulemakings as summarized in Table 1-4. These

unquantified effects are represented by the letter B.

Table A-3. Estimated Monetary Value in Reductions in Incidence of Health and

Welfare Effects (in millions of 1999$)—2015a,b,c

Health Effect Pollutant

CAIR

Programf

CAIR Plus

BART in the

Non-CAIR

Regionf

BART

Nationwide

Premature mortalityd

 Adult >30 years

 3% discount rate

 7% discount rate

 Child <1 year

PM2.5 $92,800

78,100

222

$96,300

81,100

232

$45,700

38,400

116

Chronic bronchitis (adults, 26 and over) PM2.5 3,340 3,510 1,690

Nonfatal acute myocardial infarctions 

 3% discount rate

 7% discount rate

PM2.5 1,850

1,790

1,920

1,860

905

876

Hospital admissions for respiratory causes PM2.5, O3 78.9 43.6 20.6

Hospital admissions for cardiovascular causes PM2.5 105 82.6 39.3

Emergency room visits for asthma PM2.5, O3 3.56 3.62 1.79

Acute bronchitis (children, aged 8–12) PM2.5 7.06 7.41 3.68

Lower respiratory symptoms (children, 7–14) PM2.5 3.74 3.87 1.91

Upper respiratory symptoms (asthma, 9–11) PM2.5 4.77 4.69 2.30

Asthma exacerbations PM2.5 12.7 13.4 6.56

Work loss days PM2.5 219 209 101

Minor restricted-activity days (MRADs) PM2.5, O3 543 528 256

School absence days O3 36.4 NE NE

Worker productivity (outdoor workers, 18–65) O3 19.9 NE NE

Recreational visibility, 81 Class I areas PM2.5 1,780 1,890 699

Monetized Total e 

 Base estimate:

 3% discount rate

 7% discount rate 

$101+B

$86.3+B

$105+B

$89.5+B

$49.5+

B

$42.2+

B a Monetary benefits are rounded to three significant digits for ease of presentation and computation. Benefit

estimates relate to emissions reductions for the EGU source category only. Estimates represent nationwide

benefits (with the exception of ozone and visibility) and are associated with NOx and SO2 reductions. Ozone

benefits represent benefits for the eastern United States. Visibility estimates relate to Class I areas in the

southeastern United States. BART estimates reflect benefits for controls for the EGU source category only.

(continued)
A-5

Table A-3. Estimated Monetary Value in Reductions in Incidence of Health and

Welfare Effects (in millions of 1999$)—2015a,b,c (continued)

b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and 2015. c Ozone benefits are estimated for the final CAIR. While ozone benefits are anticipated for CAIR plus BART

in the Non-CAIR Region and BART nationwide, these ozone benefits were not estimated. d Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure described

earlier. Results show 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for

preparing economic analyses (EPA, 2000; OMB, 2003). Adult premature mortality estimates are based upon

studies by Pope et al., 2002. Infant premature mortality estimates are based upon studies by Woodruff,

Grillo, and Schoendorf, 1997. e B represents the monetary value of health and welfare benefits not monetized. A detailed listing of

unquantified benefits is provided in Table 1-4. Columnar totals may not add due to rounding. f These benefits reflect estimates for the CAIR program (the promulgated CAIR and the proposal to include

annual SO2 and NOx controls for New Jersey and Delaware in CAIR). Modeling for CAIR assumes annual

SO2 and NOx controls for Arkansas that is not a part of the CAIR program. Thus the benefit estimates

reported for CAIR are slightly overstated.

NE = Not estimated

A.1 References

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine 

Particulate Air Pollution.” Journal of the American Medical Association

287:1132-1141. 

U.S. Environmental Protection Agency (EPA). September 2000. Guidelines for Preparing

Economic Analyses. EPA 240-R-00-003. 

U.S. Office of Management and Budget (OMB). 2003. Circular A-4 Guidance to Federal

Agencies on Preparation of Regulatory Analysis.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected 

Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and

Particulate Air Pollution in the United States.” Environmental Health Perspectives

105(6):608-612.
B-1

APPENDIX B

SUPPLEMENTAL ANALYSES ADDRESSING UNCERTAINTIES IN THE

BENEFITS ANALYSES

B.1 Introduction

The recent NAS report on estimating public health benefits of air pollution

regulations recommended that EPA begin to move the assessment of uncertainties from its

ancillary analyses into its primary analyses by conducting probabilistic, multiple-source

uncertainty analyses. In this appendix, we describe our progress toward improving our

approach of characterizing the uncertainties in our economic benefits estimates, with

particular emphasis on the C-R function relating premature mortality to exposures to ambient

PM2.5. We present two approaches to generating probabilistic distributions designed to

illustrate the potential influence of some aspects of the uncertainty in the C-R function in a

PM benefits analysis. The first approach generates a probabilistic estimate of statistical

uncertainty based on standard errors reported in the underlying studies used in the benefit

modeling framework. The second approach uses the results from a pilot expert elicitation

designed to characterize certain aspects of uncertainty in the ambient PM2.5/mortality

relationship. 

In recent benefit analyses of air pollution regulations, estimation of the reduction in

premature mortality from the control of particles accounts for 85 to 95 percent of total

benefits. Therefore, it is an endpoint that will be an important focus for characterizing the

uncertainty related to the estimates of total benefits. As part of a collaboration between

EPA’s Office of Air and Radiation (OAR) and the Office of Management and Budget

(OMB) on the Nonroad Diesel Rule, we conducted a pilot expert elicitation intended to more

fully characterize uncertainty in the estimate of mortality resulting from exposure to PM.

It should be recognized that in addition to uncertainty, the annual benefits estimates

for the final CAIR also are inherently variable, due to the truly random processes that govern

pollutant emissions and ambient air quality in a given year. Factors such as hourly rate of

emissions and daily weather display constant variability regardless of our ability to

accurately measure them. 
1

 Because this is a national analysis in which, for each endpoint, a single C-R function is applied everywhere,

there are two sources of uncertainty about incidence: statistical uncertainty (due to sampling error) about the

true value of the pollutant coefficient in the location where the C-R function was estimated and uncertainty

about how well any given pollutant coefficient approximates $*.

B-2

B.1.1 General Approach

In addition to the two approaches to characterize uncertainty for PM mortality, we

also wanted to incorporate information on uncertainties of other endpoints in the benefits

model. For this rule we did not attempt to assign probabilities to all of the uncertain

parameters in the model because of a lack of resources and reliable methods. At this time,

we simply generate estimates of the distributions of dollar benefits for PM health effects and

for total dollar benefits including visibility. For non-mortality endpoints, we provide a

likelihood distribution for the total benefits estimate, based solely on the statistical

uncertainty surrounding the estimated C-R functions and the assumed distributions around

the unit values. Visibility benefits are also included in the estimate of total benefits, but

because of data limitations, are characterized as a constant value rather than a distribution. 

This effectively shifts the distribution of total benefits upwards at all percentiles by the same

amount. 

Our estimate of the likelihood distribution for total benefits should be viewed as

incomplete because of the wide range of sources of uncertainty that we have not

incorporated. The 5th and 95th percentile points of our estimate are based on statistical error,

and cross-study variability provides some insight into how uncertain our estimate is with

regard to those sources of uncertainty. However, it does not capture other sources of

uncertainty regarding other inputs to the model, including emissions, air quality, baseline

population incidence, projected exposures, or the model itself, including aspects of the health

science not captured in the studies, such as the likelihood that PM is causally related to

premature mortality and other serious health effects and the likelihood that ozone has an

independent effect on mortality. Thus, a likelihood description based on the standard error

would provide a misleading picture about the overall uncertainty in the estimates. 

Both the uncertainty about the incidence changes1

 and uncertainty about unit dollar

values can be characterized by distributions. Each “likelihood distribution” characterizes our

beliefs about what the true value of an unknown variable (e.g., the true change in incidence

of a given health effect in relation to PM exposure) is likely to be, based on the available
2

 Although such a “likelihood distribution” is not formally a Bayesian posterior distribution, it is very similar in

concept and function (see, for example, the discussion of the Bayesian approach in Kennedy (1990),

pp. 168-172).

3

 This method assumes that the incidence change and the unit dollar value for an endpoint are stochastically

independent.

B-3

information from relevant studies.2 Unlike a sampling distribution (which describes the

possible values that an estimator of an unknown variable might take on), this likelihood

distribution describes our beliefs about what values the unknown variable itself might be. 

Such likelihood distributions can be constructed for each underlying unknown variable (such

as a particular pollutant coefficient for a particular location) or for a function of several

underlying unknown variables (such as the total dollar benefit of a regulation). In either

case, a likelihood distribution is a characterization of our beliefs about what the unknown

variable (or the function of unknown variables) is likely to be, based on all the available

relevant information. A likelihood description based on such distributions is typically

expressed as the interval from the 5th percentile point of the likelihood distribution to the 95th

percentile point. If all uncertainty had been included, this range would be the “credible

range” within which we believe the true value is likely to lie with 90 percent probability.

B.2 Monte-Carlo Based Uncertainty Analysis

The uncertainty about the total dollar benefit associated with any single endpoint

combines the uncertainties from these two sources (the C-R relationship and the valuation)

and is estimated with a Monte Carlo method. In each iteration of the Monte Carlo procedure,

a value is randomly drawn from the incidence distribution, another value is randomly drawn

from the unit dollar value distribution; the total dollar benefit for that iteration is the product

of the two.3

 When this is repeated for many (e.g., thousands of) iterations, the distribution of

total dollar benefits associated with the endpoint is generated. 

Using this Monte Carlo procedure, a distribution of dollar benefits can be generated

for each endpoint. As the number of Monte Carlo draws gets larger and larger, the Monte

Carlo-generated distribution becomes a better and better approximation of a joint likelihood

distribution (for the considered parameters) making up the total monetary benefits for the

endpoint. 

After endpoint-specific distributions are generated, the same Monte Carlo procedure

can then be used to combine the dollar benefits from different (nonoverlapping) endpoints to

generate a distribution of total dollar benefits. 
B-4

The estimate of total benefits may be thought of as the end result of a sequential

process in which, at each step, the estimate of benefits from an additional source is added. 

Each time an estimate of dollar benefits from a new source (e.g., a new health endpoint) is

added to the previous estimate of total dollar benefits, the estimated total dollar benefits

increases. However, our bounding or likelihood description of where the true total value lies

also increases as we add more sources. 

As an example, consider the benefits from reductions in PM-related hospital

admissions for cardiovascular disease. Because the actual dollar value is unknown, it may be 

described using a variable, with a distribution describing the possible values it might have. If

this variable is denoted as X1, then the mean of the distribution, E(X1) and the variance of X1,

denoted Var(X1), and the 5th and 95th percentile points of the distribution (related to Var(X1)),

are ways to describe the likelihood for the true but unknown value for the benefits reduction. 

Now suppose the benefits from reductions in PM-related hospital admissions for

respiratory diseases are added. Like the benefits from reductions in PM-related hospital

admissions for cardiovascular disease, the likelihood distribution for where we expect the

true value to be may be considered a variable, with a distribution. Denoting this variable as

X2, the benefits from reductions in the incidence of both types of hospital admissions is X1 +

X2. This variable has a distribution with mean E(X1 + X2) = E(X1) + E(X2), and a variance of

Var(X1 + X2) = Var(X1) + Var(X2) + 2Cov(X1,X2); if X1 and X2 are stochastically

independent, then it has a variance of Var(X1 + X2) = Var(X1) + Var(X2), and the covariance

term is zero.

The benefits from reductions in all nonoverlapping PM-related health and welfare

endpoints are (Xm+1, ..., Xn) is X = X1 + ... + Xn. The mean of the distribution of total

benefits, X, is

E(X) = E(X1) + E(X2) + ... + E(Xn) (B.1)

and the variance of the distribution of total benefits—assuming that the components are

stochastically independent of each other (i.e., no covariance between variables)—is

Var(X) = Var(X1) + Var(X2) + ... + Var(Xn) (B.2)

If all the means are positive, then each additional source of benefits increases the

point estimate (mean) of total benefits. However, with the addition of each new source of

benefits, the variance of the estimate of total benefits also increases. That is,

E(X1) < E(X1 + X2) < E(X1 + X2 + X3) < ... < E(X1 + ... + Xn) = E(X) (B.3)
B-5

Var(X1) < Var(X1 + X2) < Var(X1 + X2 + X3) < ... < Var(X1 + ... + Xn) = Var(X). (B.4)

That is, the addition of each new source of benefits results in a larger mean estimate of total

benefits (as more and more sources of benefits are included in the total) about which there is

less certainty. This phenomenon occurs whenever estimates of benefits are added.

Calculated with a Monte Carlo procedure, the distribution of X is composed of

random draws from the components of X. In the first draw, a value is drawn from each of

the distributions, X1, X2, through Xn; these values are summed; and the procedure is repeated

again, with the number of repetitions set at a high enough value (e.g., 5,000) to reasonably

trace out the distribution of X. The 5th percentile point of the distribution of X will be

composed of points pulled from all points along the distributions of the individual

components and not simply from the 5th percentile. Although the sum of the 5th percentiles

of the components would be represented in the distribution of X generated by the Monte

Carlo, it is likely that this value would occur at a significantly lower percentile. For a similar

reason, the 95th percentile of X will be less than the sum of the 95th percentiles of the

components, and instead the 95th percentile of X will be composed of component values that

are significantly lower than the 95th percentiles.

The physical effects estimated in this analysis are assumed to occur independently. It

is possible that, for any given pollution level, there is some correlation between the

occurrence of physical effects, due to say avoidance behavior or common causal pathways

and treatments (e.g., stroke, some kidney disease, and heart attack are related to treatable

blood pressure). Estimating accurately any such correlation, however, is beyond the scope of

this analysis, and instead it is simply assumed that the physical effects occur independently.

We conducted two different Monte Carlo analyses, one based on the distribution of

reductions in premature mortality characterized by the mean effect estimate and standard

error from the epidemiology study of PM-associated mortality associated with the primary

estimate in Chapter 4 (Pope et al., 2002), and one based on the results from a pilot expert

elicitation project (IEc, 2004). In both analyses, the distributions of all other health

endpoints are characterized by the reported mean and standard deviations from the

epidemiology literature. Distributions for unit dollar values are based on reported ranges or

distributions of values in the economics literature and are summarized in Table B-1. We are

unable at this time to characterize the uncertainty in the estimate of benefits of improvements

in visibility at Class I areas. As such, we treat the visibility benefits as fixed and add them to 
B-6

Table B-1. Distributions for Unit Values of Health Endpoints

Health Endpoint

Mean Value,

Adjusted for

Income Growth to

2030 Derivation of Distribution

Premature Mortality

(Value of a Statistical

Life)

$5,500,000

Normal distribution with mean of $5.5 million and standard

deviation of $2.3 million, anchored at 2.5th and 97.5th percentiles of

$1 and $10 million, respectively. Confidence interval is based on

two meta-analyses of the wage-risk VSL literature. $1 million

represents the lower end of the interquartile range from the Mrozek

and Taylor (2000) meta-analysis. $10 million represents the upper

end of the interquartile range from the Viscusi and Aldy (2003)

meta-analysis. The VSL represents the value of a small change in

mortality risk aggregated over the affected population. Normal

distribution chosen through best professional judgment.

Chronic Bronchitis (CB) $430,000

The WTP to avoid a case of pollution-related CB is calculated as 

WTP WTP e , where x is the severity of an average CB x

x = − −

13

13 * β*( )

case, WTP13 is the WTP for a severe case of CB, and $ is the

parameter relating WTP to severity, based on the regression results

reported in Krupnick and Cropper (1992). The distribution of

WTP for an average severity-level case of CB was generated by

Monte Carlo methods, drawing from each of three distributions: 

(1) WTP to avoid a severe case of CB is assigned a 1/9 probability

of being each of the first nine deciles of the distribution of WTP

responses in Viscusi et al. (1991); (2) the severity of a pollutionrelated case of CB (relative to the case described in the Viscusi

study) is assumed to have a triangular distribution, with the most

likely value at severity level 6.5 and endpoints at 1.0 and 12.0; and

(3) the constant in the elasticity of WTP with respect to severity is

normally distributed with mean = 0.18 and standard deviation =

0.0669 (from Krupnick and Cropper [1992]). This process and the

rationale for choosing it is described in detail in the Costs and

Benefits of the Clean Air Act, 1990 to 2010 (EPA, 1999).

(continued)
B-7

Table B-1. Distributions for Unit Values of Health Endpoints (continued)

Health Endpoint

Mean Value,

Adjusted for

Income Growth to

2030 Derivation of Distribution

Nonfatal Myocardial

Infarction (heart attack)

3% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

$66,902

$74,676

$78,834

$140,649

$66,902

$65,293

$73,149

$76,871

$132,214

$65,293

No distribution available. Age-specific COI values reflecting lost

earnings and direct medical costs over a 5-year period following a

nonfatal MI. Lost earnings estimates based on Cropper and

Krupnick (1990). Direct medical costs based on simple average of

estimates from Russell et al. (1998) and Wittels et al. (1990).

Lost earnings:

Cropper and Krupnick (1990). Present discounted value of 5 years

of lost earnings:

age of onset at 3% at 7%

25–44 $8,774 $7,855

45–54 $12,932 $11,578

55–65 $74,746 $66,920

Direct medical expenses: An average of: 

1. Wittels et al. (1990) ($102,658—no discounting)

2. Russell et al. (1998), 5-year period. ($22,331 at 3% discount

rate; $21,113 at 7% discount rate)

Hospital Admissions

All Respiratory

(ICD codes 480-487,

490-492, 494-496)

$12,378

No distributions available. The COI point estimates (lost earnings

plus direct medical costs) are based on ICD-9 code-level

information (e.g., average hospital care costs, average length of

hospital stay, and weighted share of total COPD category illnesses)

reported in Agency for Healthcare Research and Quality (2000)

(www.ahrq.gov).

All Cardiovascular

(ICD codes 390-429)

$18,387

No distribution available. The COI point estimates (lost earnings

plus direct medical costs) are based on ICD-9 code-level

information (e.g., average hospital care costs, average length of

hospital stay, and weighted share of total cardiovascular category

illnesses) reported in Agency for Healthcare Research and Quality

(2000) (www.ahrq.gov). 

(continued)
B-8

Table B-1. Distributions for Unit Values of Health Endpoints (continued)

Health Endpoint

Mean Value,

Adjusted for

Income Growth to

2030 Derivation of Distribution

Emergency Room Visits

for Asthma $286

No distribution available. The COI point estimate is the simple 

average of two unit COI values: 

(1) $311.55, from Smith et al. (1997) and 

(2) $260.67, from Stanford et al. (1999).

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory

Symptoms (URS) $27

Combinations of the three symptoms for which WTP estimates are

available that closely match those listed by Pope et al. result in

seven different “symptom clusters,” each describing a “type” of

URS. A dollar value was derived for each type of URS, using

mid-range estimates of WTP (IEc, 1994) to avoid each symptom in

the cluster and assuming additivity of WTPs. In the absence of

information surrounding the frequency with which each of the

seven types of URS occurs within the URS symptom complex, we

assumed a uniform distribution between $10 and $45.

Lower Respiratory

Symptoms (LRS) $17

Combinations of the four symptoms for which WTP estimates are

available that closely match those listed by Schwartz et al. result in

11 different “symptom clusters,” each describing a “type” of LRS. 

A dollar value was derived for each type of LRS, using mid-range

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster

and assuming additivity of WTPs. The dollar value for LRS is the

average of the dollar values for the 11 different types of LRS. In

the absence of information surrounding the frequency with which

each of the 11 types of LRS occurs within the LRS symptom

complex, we assumed a uniform distribution between $8 and $25.

Asthma Exacerbations $45

Asthma exacerbations are valued at $45 per incidence, based on

the mean of average WTP estimates for the four severity

definitions of a “bad asthma day,” described in Rowe and Chestnut

(1986). This study surveyed asthmatics to estimate WTP for

avoidance of a “bad asthma day,” as defined by the subjects. For

purposes of valuation, an asthma exacerbation is assumed to be

equivalent to a day in which asthma is moderate or worse as

reported in the Rowe and Chestnut (1986) study. The value is

assumed have a uniform distribution between $17 and $73.

(continued)
B-9

Table B-1. Distributions for Unit Values of Health Endpoints (continued)

Health Endpoint

Mean Value,

Adjusted for

Income Growth to

2030 Derivation of Distribution

Acute Bronchitis $390

Assumes a 6-day episode, with the distribution of the daily value

specified as uniform with the low and high values based on those

recommended for related respiratory symptoms in Neumann et al.

(1994). The low daily estimate of $10 is the sum of the mid-range

values recommended by IEc (1994) for two symptoms believed to

be associated with acute bronchitis: coughing and chest tightness. 

The high daily estimate was taken to be twice the value of a minor

respiratory restricted-activity day, or $110. 

Restricted Activity, Work Loss, and School Absence Days

School Absences $75

No distribution available. Point estimate is based on (1) the

probability that if a school child stays home from school, a

working mother will have to stay home from work to care for the

child (0.73); and (2) lost productivity at the female parent’s wage

(average of $103).

Work Loss Days

(WLDs) Variable 

No distribution available. Point estimate is based on countyspecific median annual wages divided by 50 (assuming 2 weeks of

vacation) and then by 5—to get median daily wage. U.S. Year

2000 Census, compiled by Geolytics, Inc.

Minor RestrictedActivity Days (MRADs) $55

Median WTP estimate to avoid one MRAD from Tolley et al.

(1986). Distribution is assumed to be triangular with a minimum

of $22 and a maximum of $83, with a most likely value of $55. 

Range is based on assumption that value should exceed WTP for a

single mild symptom (the highest estimate for a single

symptom—for eye irritation—is $16.00) and be less than that for a

WLD. The triangular distribution acknowledges that the actual

value is likely to be closer to the point estimate than either

extreme.

all percentiles of the PM health benefits distribution. Results of the Monte Carlo analysis

based on the Pope et al. (2002) distribution are presented in the next section. Results of the

Monte Carlo analysis based on the pilot expert elicitation are presented in Section B.3.

B.2.1 Monte Carlo Analysis Using Classical Statistical Sources of Uncertainty

Based on the Monte Carlo techniques described earlier, we generated likelihood

distributions for the dollar value of total annual benefits including PM health, ozone health,

and visibility benefits for the final CAIR. For this analysis, the likelihood descriptions for

the true value for each of the health endpoint incidence measures, including premature
B-10

mortality, were based on classical statistical uncertainty measures, including the mean and

standard deviation for the C-R relationships in the epidemiological literature, and assumption

of particular likelihood distribution shapes for the valuation for each health endpoint value

based on reported values in the economic literature. Table B-1 summarizes the chosen

parameters for likelihood distributions for unit values for each of the PM health effects

included in the Monte Carlo simulation. The distributions for the value used to represent

incidence of a health effect in the total benefits valuation represent both the simple statistical

uncertainty surrounding individual effect estimates and, for those health endpoints with

multiple effects from different epidemiology studies, interstudy variability. Visibility

benefits are also included in the distribution of total benefits; however, we were unable to

characterize a distribution for visibility benefits. As such, they are simply added to each

percentile of the distribution of health benefits. 

Results of the Monte Carlo simulations are presented in Table B-2. The table

provides the estimated means of the distributions and the estimated 5th and 95th percentiles of

the distributions. The contribution of mortality to the mean benefits and to both the 5th and

95th percentiles of total benefits is substantial, with mortality accounting for 93 percent of the

mean estimate, and even the 5th percentile of mortality benefits dominating the 95th percentile

of all other benefit categories. Thus, the choice of value and the shape for likelihood

distribution for VSL should be examined closely and is key information to provide to

decision makers for any decision involving this variable. The 95th percentile of total benefits

is approximately twice the mean, while the 5 th percentile is approximately one-fourth of the

mean. The overall range from 5th to 95th represents about one order of magnitude. 

B.3 Pilot Expert Elicitation of PM Mortality Uncertainty

Expert elicitation is a formal, highly structured and well-documented process

whereby expert judgments, usually of multiple experts, are obtained. Formal expert

elicitation usually involves experts with training and expertise in statistics, decision analysis,

and probability encoding who work with subject matter experts to structure questions about

uncertain relationships or parameters and who design and implement the process used to

obtain probability and other judgments from subject matter experts. Several academic

traditions—judgment and decision-making, human factors, cognitive sciences, expert

systems, management science, to name a few—have sought to understand how to

successfully elicit probabilistic judgments from both lay people and experts (Morgan and

Henrion, 1990; Cooke, 1991; Wright and Ayton, 1994; Ayyub, 2002). Over the past 2

decades, an increasing number of studies have used expert judgment techniques to

characterize uncertainty in quantities of interest to environmental risk analysis and decision 
B-11

Table B-2. Distribution of Value of Annual Human Health and Welfare Benefits in

2015 for the Final CAIR Rulea

Endpoint

Monetary Benefitsb, c

(Millions 2000$, Adjusted for Income Growth)

Pollutant

5th

Percentile Mean

95th

Percentile

Premature mortalityc

Long-term exposure, (adults, >30yrs) PM $22,000 $93,000 $190,000

Long-term exposure (child <1yr) PM $56 $220 $440

Chronic bronchitis (adults, 26 and over) PM $160 $3,300 $11,000

Nonfatal myocardial infarctions PM $440 $1,800 $4,100

Hospital admissions from respiratory causes O3 and PM $30 $79 $140

Hospital admissions from cardiovascular causes PM $64 $110 $150

Emergency room visits for asthma O3 and PM $2.0 $3.6 $5.3

Acute bronchitis (children, aged 8–12) PM ($0.2) $7.1 $17

Lower respiratory symptoms (children, aged 7–14) PM $1.4 $3.7 $6.9

Upper respiratory symptoms (asthmatic children,

aged 9–11)

PM $1.1 $4.8 $10

Asthma exacerbations PM $0.1 $13 $34

Work loss days (adults, aged 18–65) PM $190 $220 $250

Minor restricted-activity days (adults, aged 18–65) O3 and PM $320 $550 $790

School absence days (children, aged 6–11) O3 $12 $36 $75

Worker productivity (outdoor workers, aged 18–65) O3 $20 $20 $20

Recreational visibility (81 Class I areas) PM $1,800 $1,800 $1,800

Monetized Totald O3 and PM $26,000+B $100,000+B $210,000+B

a Monetary benefits are rounded to two significant digits.

b Monetary benefits are adjusted to account for growth in real GDP per capita between 1990 and 2015.

c Results show 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses (EPA,

2000; OMB, 2003).

d B represents the monetary value of the nonmonetized health and welfare benefits. A detailed listing of unquantified PM-, ozone-, and

mercury-related health effects is provided in Table 4-2. 
B-12

making. North and Merkhofer (1976) considered using expert judgment in evaluating

emission control strategies. As referred to by the NAS, EPA’s Office of Air Quality

Planning and Standards (OAQPS) successfully used expert judgment to characterize

uncertainty in the health effects of exposure to lead (McCurdy and Richmond, 1983;

Whitfield and Wallsten, 1989) and to ozone (Whitfield et al., 1991; Winkler et al., 1995). 

Amaral (1983) and Morgan et al. (1984) used expert judgment to evaluate the transport and

impacts of sulfur air pollution. Several studies have been done in the area of climate change

(Manne and Richels, 1994; Nordhaus, 1994; Morgan and Keith, 1995; Reilly et al., 2001). 

Hawkins and Evans (1989) used industrial hygienists to predict toluene exposures to workers

involved in a batch chemical process. In a more recent use of expert judgment in exposure

analysis, Walker et al. (2001, 2003) asked experts to estimate ambient, indoor and personal

air concentrations of benzene. A few studies have used expert judgment to characterize

uncertainty in chemical dose-response: Hawkins and Graham (1988) and Evans et al.

(1994a) for formaldehyde and Evans et al. (1994b) for risk of exposure to chloroform in

drinking water. Expert judgment has also been used to characterize residential radon risks

(Krewski et al., 1999). The Non-Road Diesel Rule (Mansfield, 2004) was the first

illustration of an application of the results of an expert elicitation study to a regulatory policy

analysis. 

In its 2002 report, the NAS provides a number of recommendations for how EPA

might improve the characterization of uncertainty in its benefits analyses. One

recommendation was that:

“EPA should begin to move the assessment of uncertainties from its ancillary

analyses into its primary analyses by conducting probabilistic, multiple-source

uncertainty analyses. This shift will require specification of probability distributions

for major sources of uncertainty. These distributions should be based on available

data and expert judgment” (NAS, 2002, p. 14). The NAS elaborated on this

recommendation by saying “although the specific methods for selection and

elicitation of experts may need to be modified somewhat, the protocols that have

been developed and tested by OAQPS [in prior EPA projects—see below] provide a

solid foundation for future work in the area. EPA may also consider having its

approaches reviewed by decision analysts, biostatisticians, and psychologists from

other fields where expert judgment is applied” (NAS, 2002, p. 140). 

They recommended the use of formally elicited expert judgments but noted that a number of

issues must be addressed and that sensitivity analyses would be needed for distributions that

are based on expert judgment. They also recommended that EPA clearly distinguish between
4

 The Council is an advisory committee with an independent statutory charter that is organized and supported

under EPA’s SAB.

5

 Council/HES report: “...in view of the fact that the pilot project is well-underway, the experts have already

been selected, and many (if not all) of the interviews have been conducted, the HES sees little potential

benefit in providing detailed suggestions about the design or conduct of the pilot study” (EPA-SABCOUNCIL-ADV-04-002, March 2004, page 34).

B-13

data-derived components of an uncertainty assessment and those based on expert opinions. 

As a first step in addressing the NAS recommendations regarding expert elicitation, EPA, in

collaboration with OMB, conducted a pilot expert elicitation to characterize uncertainties in

the relationship between ambient PM2.5 and mortality. 

B.3.1 Elicitation Method

This pilot was designed to provide EPA with an opportunity to improve its

understanding of the design and application of expert elicitation methods to economic

benefits analysis and lay the groundwork for a more comprehensive elicitation. The scope of

the pilot was limited to a one-year period to allow for inclusion in the Non-Road Diesel Rule. 

As such, we focused the elicitation on the C-R function of PM mass rather than on individual

issues surrounding an estimate of the change in mortality due to PM exposure. We selected

experts for participation from two previously established expert panels of the NAS. Due to

time constraints, we chose not to conduct a workshop with the experts prior to the elicitation,

which is often included in the protocol of elicitations to help condition and prepare the

experts for the elicitation. A full description of the pilot is contained in a report titled “An

Expert Judgment Assessment of the Concentration-Response Relationship between PM2.5

Exposure and Mortality” (IEc, 2004) available in the public docket of the Non-Road Diesel

Rule.

The analytic plan for the pilot was developed based on established elicitation

methods as suggested by the NAS and published in the peer-reviewed literature. The plan

and protocol were reviewed in three separate contexts. It was internally reviewed by EPA

and OMB scientists with experience using expert elicitation methods. The project team that

implemented the pilot consisted of individuals with experience in expert elicitation and

individuals with expertise in PM health effects and health benefits. Second, as part of a

review of the analytical blueprint of EPA’s Second Prospective Analysis of the Costs and

Benefits of the Clean Air Act under Section 812 of the Act, a panel of outside experts—the

Health Effects Subcommittee (HES) of the Advisory Council on Clean Air Compliance

Analysis (Council)4

—provided a limited5 and preliminary review of the methodology and
6

 A full report of the peer review is available at www.epa.gov/ttn/ecas/benefits.html.

B-14

design of the expert elicitation (EPA-SAB-COUNCIL-ADV-04-002, March 2004), and

provided the following comments: 

C “We applaud the Agency’s interest in exploring the use of formal expert judgment

as a tool for improving uncertainty analysis and believe that the proposed pilot

study has great potential to yield important insights. The pilot is well designed to

inform subsequent and more comprehensive expert elicitation projects, but relies

on the opinions of a relatively small group of experts. It may provide preliminary

information about the general magnitude of the mortality effects, and may yield a

sense of both the uncertainty inherent in these estimates and the factors largely

responsible for such uncertainty. However, until the pilot study methods and

results have been subjected to peer review, it may be unwise for the Agency to

rely directly on these preliminary results in key policy decisions.” 

C In presenting results of the pilot elicitation, “the HES advises EPA to present the

entire collection of individual judgments; to carefully examine the collection of

individual judgments noting the extent of agreement or disagreement; to

thoughtfully assess the reasons for any disagreement; and to consider formal

combinations of judgments only after such deliberation and with full awareness of

the context ...” 

C “The HES recognizes that in order to make the pilot tractable it was necessary to

limit participation, and is aware of the many factors which must be balanced in

the selection of expert panels (Hawkins and Graham, 1988), but is concerned

about whether the judgments of such a limited group can reasonably be

interpreted as representing a fair and balanced view of the current state of

knowledge.” 

The Council, however, did not provide a peer review of the final report, or the interpretation

and application of results. The protocol was then tested on PM scientists from within EPA

and external to the Agency, who would not be part of the final elicitation process. 

Upon completion of the final report, the pilot was reviewed in a third context. The

EPA commissioned a peer review of the pilot from a panel of four experts on the topics of

expert elicitation, decision analysis, and uncertainty characterization6

 (Mansfield, 2004). 

The review was generally positive. Overall, the reviewers indicated approval of the

procedures EPA followed to conduct the expert elicitation, commenting that the procedures

“were well documented and followed the standard elicitation protocol.” Reviewers cited the

approach for selecting experts as a strength of the assessment. They also provided a variety

of specific suggestions, including: 
B-15

• Some commented that the number of experts included in the elicitation was

small and could possibly be expanded in the future; however, there is not an

established method to determine the appropriate number of experts. 

• Several reviewers discussed was the lack of a pre- and post-elicitation

workshop with the participating experts, which is typically conducted in other

elicitations. They urged EPA to allow sufficient time to include these steps in

future elicitations but recognized the need to exclude these steps in the pilot.

Specifically, the reviewers stated that the experts should have communicated

with each other before and/or after the individual interviews. Group

communication prior to the individual interviews would have aided in the

motivation and conditioning steps of the elicitation, while communication,

either in person or through a summary document, would have allowed an

expert to adjust his response based on the responses of the other experts. 

• The encoding process of elicitation could be improved. In the elicitation

process, the reviewers interpreted that some of the experts provided

judgments based on a central tendency before providing judgments on

extreme values (upper and lower ranges). This type of sequencing may

introduce anchoring or adjustment heuristics, which are associated with

biased estimates of uncertainty. Although the authors of the elicitation report

introduced the topic of heuristics for the experts, reviewers felt that a more

substantive discussion on how the study addressed known sources and any

other potential forms of bias was necessary. 

• Reviewers also provided considerable comment on whether results of the pilot

should be combined into a single estimate. Several of the reviewers preferred

that the expert opinions not be combined or stated that they knew of no

agreed-upon method for combining results from expert elicitations. This

allows for the differences in the individual distributions to be recognized. 

Two of the reviewers indicated that they were reasonably comfortable with

the method used in this study to combine the results, while the other two

reviewers offered comments on the combined result of the elicitation. One

reviewer stated that the combined distributions do not adequately capture the

opinions of individual experts but rather average them out. It is possible in

such cases that the combined judgments may generate results that none of the

experts could agree on. Another reviewer stated that expert elicitation studies

typically do not combine judgments, but if one were to combine them, he
B-16

recommended that the response of each be maintained independently from the

other experts and run through the benefits model completely prior to

combining the results. 

B.3.2 Elicitation Results

Figure B-1 displays the responses of the experts to the quantitative elicitation

question for the mortality effects of changes in long-term PM2.5 exposures. The distributions

provided by each expert, identified by the letters A through E, are depicted as box plots with

the diamond symbol showing the median (50th percentile), a circle symbol showing the mean

estimate, the box defining the interquartile range (bounded by the 25th and 75th percentiles),

and the whiskers defining each expert’s 90 percent confidence interval (bounded by the 5th

and 95th percentiles of the distribution).

As illustrated by the figures, the experts exhibited considerable variation in both the

median values they reported and in the spread of uncertainty about the median. In response

to the question concerning the effects of changes in long-term exposures to PM2.5, the median

value ranged from values at or near zero to a 0.7 percent increase in annual nonaccidental

mortality per 1 :g/m3 increase in annual mean PM2.5 concentration (within a range of PM2.5

concentrations from 8 to 20 :g/m3). The variation in the responses for the effects of longterm exposures largely reflects differences of opinion among the experts on a number of

factors such as the interpretation of key epidemiological results from long-term cohort

studies, the likelihood of a causal relationship, and the shape of the C-R function. Some

observations concerning the outcome of the individual expert judgments are provided below:

Key Cohort Studies. The experts’ nonzero responses for the percentage change in

annual mortality were mostly influenced by the Krewski et al. (2000) reanalysis of the

original American Cancer Society (ACS) cohort study and by the later Pope et al. (2002)

update of the ACS study that included additional years of follow-up. In the characterization

of uncertainty upper bounds, none of the experts placed substantial weight on the mortality

estimates from the Six-Cities study (Dockery et al., 1993) in composing their quantitative

responses, despite citing numerous strengths of that analysis. Concern about sample size and

representativeness of the Six-Cities study for the entire United States appeared to be major

reasons for de-emphasizing those results. In addition, all of the experts gave a value of zero

at the 5th percentile, and thus the C-R functions are bounded by zero. We use a normal

distribution to characterize the pilot results, but the distribution could potentially be skewed

because of the bounding at zero.
B-17

0

0.5

1

1.5

2

A B* C--8 ug/m3 C--10 ug/m3 C--15 ug/m3 C--20 ug/m3 D E

Expert

% Increase in Mortality per 1 ug/m3 PM2

Figure B-1. Summary of Experts’ Judgments About the Percentage Increase in Annual

Average Nonaccidental Mortality Associated with a 1 :g/m3

 Increase in Annual

Average Exposures to PM2.5

* Expert B specified this distribution for the PM/mortality coefficient above an uncertain threshold that he

characterized as ranging between 4 and 15 with a modal value of 12 :g/m3

. As illustrated here, considerable

variation exists in both the median values and the spread of uncertainty provided by the experts. The median

value of the percentage change in annual nonaccidental mortality per unit change in annual PM2.5

concentration (within a range of PM2.5 concentrations from 8 to 20 :g/m3) ranged from values at or near zero

to a value of 0.7 percent. The variation in the responses largely reflects differences in the amount of

uncertainty each expert considered inherent in the key epidemiological results from long-term cohort studies,

the likelihood of a causal relationship, and the shape of the C-R function. The technical report (IEc, 2004)

provides detailed descriptions of the experts’ judgments about these factors, but we present a few brief

observations relative to their responses below.

** Expert C specified a nonlinear model and provided distributions for the slope of the curve at four discrete

concentrations within the range. 
B-18

Causality for Long-Term Effects. Three of the five experts gave distributions more

heavily weighted towards zero. Those experts were also the ones who expressed the lowest

probability of a causal effect of long-term exposure to PM2.5 in response to the preliminary

questions. All of the experts placed at least a 5 percent probability on the possibility that

there is no causal relationship between fine PM exposure and mortality; as a result, all

experts gave a fifth percentile value for the C-R coefficient of zero. For most of the experts,

this was based primarily on residual concerns about the strength of the mechanistic link

between the exposures and mortality.

Shape of the C-R Function for Long-Term Effects. The other key determinant of

each expert’s responses for long-term effects was his assumption about the nature of the C-R

function across the range of baseline annual average PM2.5 concentrations assumed in the

pilot (8 to 20 :g/m3). Three experts (A, D, and E) assumed that the function relating

mortality with PM concentrations would be log-linear with constant slope over the specified

range. They therefore gave a single estimate of the distribution of the slope describing that

log-linear function. The other two experts provided more complex responses.

Expert B assumed a population threshold in his model, below which there would be

no effect of increased PM2.5 exposure and above which the relationship would be log-linear. 

He characterized his estimate of a possible threshold as uncertain, ranging between 4 :g/m3

and 15 :g/m3, with a modal value of 12 :g/m3. He then described a distribution for the slope

for the log-linear function that might exist above the threshold; this distribution is depicted in

Figure B-2. The effect of incorporating the uncertain threshold is essentially to shift his

entire distribution downward.

Expert C believed that the increased relative risks for mortality observed in the cohort

studies were likely to be the result of exposures at the higher end of the exposure range, and

he expected there to be a declining effect on mortality with decreasing levels of PM2.5. He

also argued that some practical concentration threshold was likely to exist below which we

would not observe any increase in mortality. He reflected these beliefs by developing a

nonlinear model within the range from 8 to 20 :g/m3

; he described the model by providing

distributions for the slope of the curve at four discrete concentrations within the range. 
B-19

0

0.5

1

1.5

2

Above threshold Incorporating

threshold**

 Expert B

% Increase in Mortality per 1 ug/m3 PM 2.5

Figure B-2. Expert B’s Distributions for the Percentage Increase in Annual

Nonaccidental Mortality Associated with a 1 :g/m3

 Increase in Long-Term Exposures

to PM2.5: Comparison of His Distribution Above a Threshold to His Expected

Distribution* for the Range 8-20 :g/m3

* Expert B specified the threshold as uncertain between 4 and 15 :g/m3 with a modal value at 12 :g/m3. He

assumed the percentage increase in mortality to increase linearly with concentration above the threshold. 

His effective distribution was simulated using Monte Carlo techniques assuming an underlying distribution

of population-weighted annual average PM2.5 concentrations for the United States generated from the

BenMAP model (see the technical report [IEc, 2004] for details).
B-20

B.3.3 Experts’ Views of Sources of Uncertainty

The experts were asked at several points during the interview to discuss the key

sources of potential bias and uncertainty in current evidence on which they relied for their

judgments. In the context of the quantitative discussion they were asked to list the top five

issues. They were encouraged to think about how these issues would affect the uncertainty

surrounding their best estimate of the potential impact on total mortality of a small change in

long-term exposure to PM2.5. The tables summarizing the factors identified by each expert

may be found in Appendix E of the technical report (IEc, 2004).

Many of the same factors appeared in the list of the five experts. However, the

experts often differed on whether a particular factor was a source of potential bias or

uncertainty. Some of the common concerns raised as either sources of bias or uncertainty

were

C residual confounding by smoking, 

C residual confounding by “lifestyle” or other personal factors or “stressors,”

C exposure errors/misclassification,

C the role of co-pollutants as confounders or effect modifiers, 

C impact of the relative toxicity of PM components, 

C representativeness of the cohort populations with respect to the general U.S.

population, and

C investigator/publication biases.

Despite the many qualitative discussions about sources of uncertainty, because the

pilot study did not elicit quantitative judgments about the size and nature of impacts of each

source of uncertainty and bias, we were unable to systematically evaluate the nature of the

influence of these factors on the quantitative results provided by each expert unless an expert

explicitly adjusted his estimates by a particular factor. 

B.3.5 Limitations in Pilot Elicitation Design

The pilot elicitation has afforded many opportunities for learning about expert

elicitation in the context of economic benefits analysis. During the process of developing the

protocol, the project team noted limitations inherent in the design of the pilot, some of which

were also commented on by the SAB/HES in their review of the protocol prior to conducting
B-21

the elicitation. Additional detail on the strengths and weaknesses of the pilot are provided in

the technical report (IEc, 2004).

The limited scope of the pilot meant that a full expert elicitation process was

truncated, and many aspects of the uncertainty surrounding the PM2.5/mortality relationship

could not be characterized quantitatively. Recognizing this, the results of the pilot are only

used in this benefits estimation for illustrative purposes.

C Small panel of experts—Because of resource constraints we limited the pilot to a

panel of five experts. As noted above, the SAB-HES (in Section B.3.1) brings

into question whether the judgments of such a limited group can reasonably be

interpreted as representing a fair and balanced view of the current state of

knowledge. 

Little analytical research has been conducted on the more difficult question of

how to determine the ideal number of experts for a particular application. We

have not found any analyses of the effect of expert panel size based on

comparisons of empirical results of expert judgment studies. A theoretical

analysis by Clemen and Winkler (1985) suggests that where data sources are

moderately positively dependent there are diminishing marginal returns to the

value of information associated with each additional data source. In the context

of expert judgment studies, such a result implies that when dealing with experts

of similar backgrounds who rely on the same models and studies, a larger expert

panel may not provide significantly higher quality results than a smaller one. 

However, the addition of an expert expected to provide a more independent

assessment, such as an expert from a different but pertinent field, would be

expected to exhibit a much greater value of information. Clemen and Winkler

(1999) note that “heterogeneity among experts is highly desirable.” These

findings would appear to support addressing complex issues using a panel

comprising relatively small subgroups (perhaps three to five experts each) from

multiple disciplines. Although the decision analysis field tends to use relatively

small sample sizes (i.e., typically 5 to 10 experts), some are not comfortable with

obtaining a combined distribution from such small numbers in the absence of an

a priori assessment of the degree to which the expert panel is likely to be

representative of the overall population of relevant experts on the question of

interest. 

C Use of an aggregate elicitation question—The expert judgment literature

discusses two broad approaches to elicitation of judgments: an aggregated and a

disaggregated approach. As the term implies, an aggregated approach asks the

expert to estimate the quantity of interest directly, for example, the numbers of

newspapers sold in the United States in a particular year. In a disaggregated

approach, the expert (or group of experts) is asked to construct a model for

estimating the quantity of interest and is asked directly about the inputs to that
B-22

model (e.g., population in each state, percentage of the population that reads

newspapers). The intuition is that it is easier for experts to answer questions

about the intermediate quantities than about the total quantity. 

The project team carefully considered the relative advantages and disadvantages

of the two approaches, however, the time and resources necessary to develop a

disaggregated model structure drove the decision to undertake an aggregate

approach to elicit the C-R coefficient for the PM2.5/mortality relationship. Based

on advice from the SAB-HES, the project team felt that separate questions to

address effects of short- and long-term exposures, though still at a high level of

aggregation, would prove to be easier for experts to address than a question that

“rolled up” all the effects into a single estimate. This level of disaggregation also

enabled the elicitation team to explore with experts possible overlap in reported

mortality effects detected using short-term and long-term epidemiological studies. 

Nonetheless, a major goal of the preliminary and follow-up questions in the

protocol was to identify critical issues that could be addressed by developing a

more disaggregated approach in a future assessment. 

The aggregated design limits our ability to determine the influence of any one key

factor over others in a large list of issues that the experts were to consider prior to

answering the quantitative question. It also limited the ability of the experts to

express their views about the difference in the C-R function based on the location

in the United States (i.e., the demographics of the exposed population, the air

concentration of PM and/or PM mixture). 

C No workshop was conducted—It is customary to conduct a workshop prior to the

elicitation interview with the experts. This allows the experts to become familiar

with the protocol and to discuss methods to limit bias during the interview. 

Because of time constraints for the pilot, we did not conduct a pre-elicitation

workshop.

C No calibration of experts—In some elicitation studies, the authors use a

calibration measure to weight the experts appropriately for the purposes of

combining the results of the elicitation. We do not have calibration measures that

could be used to assess the results of this pilot. At this point, we can only assess

the process—did the pilot assessment employ a structure, supporting materials,

and a process that enabled experts to make judgments that would be likely to be

well calibrated? Without calibration measures, we cannot weight experts based

on their performance on calibration and thus, we present only an equal weighting

of the responses. The peer reviewers agree that if a combination is provided, then

the equal weighting of judgments is preferred.
B-23

B.3.6 Combining the Expert Judgments for Application to Economic Benefit Analyses

Many methods are available to combine the responses from the experts. Each method

has advantages and disadvantages from a statistical viewpoint. The project team is not aware

of any rule of thumb in statistics that would provide guidance for combining linear and

nonlinear functions. Therefore, we considered four alternative methods for combining the

results as an illustration of potential combinations of the results. The peer review provides

extensive comments on whether a combination is necessary and on the methods employed

here. 

Analysts must give careful thought to whether and how to combine the results of

individual expert judgments into a single distribution. When dealing with a limited number

of experts, the analyst must be particularly careful to identify the influence of each expert’s

response on the combined distribution. Therefore, we considered four alternative methods

for combining the pilot results, each of which had limitations. The peer review also provides

a lengthy discussion of issues in combining expert judgments. In this section, we discuss the

issues we considered in combining the results of the pilot and how we came to the conclusion

that for the illustrative benefits analysis presented in Section B.5 below, we would present

both the individual quantitative distributions of the C-R coefficient elicited from the five

experts interviewed and results based on a probabilistic estimate that represents the

combined results of the pilot based on an equal weighting of the calculated change in

mortality incidence based on the individual judgments. 

B.3.6.1 Background

Some investigators (e.g., Hawkins and Graham [1990]; Winkler and Wallsten [1995];

and Morgan et al. [1984]) have preferred to keep expert opinions separate to preserve the

diversity of opinion on the issues of interest. In such situations, presenting a range of values

expressed by the experts can help decision makers to understand the sensitivity of their

analyses to the analytical model chosen, thereby bounding possible outcomes. Individual

judgments also can illustrate varying opinions arising from different disciplinary

perspectives or from the rational selection of alternative theoretical models or data sets

(Morgan and Henrion, 1990). Nonetheless, analysts are often interested in developing a

single distribution of values that reflects a synthesis of the judgments elicited from a group of

experts. 

On the other hand, there are advantages to combining the results across experts. An

extensive literature describes methods for combining expert judgments. These methods can

be broadly classified as either mathematical or behavioral (Clemen and Winkler, 1999). 
7

 “Motivational bias” refers to the willful distortion of an expert’s true judgments. The origins of this bias can

vary but could include, for example, a reluctance to contradict views expressed by one’s employer or a

deliberate attempt to skew the outcome of the study for political gain.

B-24

Mathematical approaches range from simple averaging of responses to much more complex

models incorporating information about the quality of expert responses, potential dependence

among expert judgments, or (in the case of Bayesian methods) prior probability distributions

about the variable of interest. Behavioral approaches require the interaction of experts in an

effort to encourage them to achieve consensus, either through face-to-face meetings or

through the exchange of information about judgments among experts. As noted in the

technical report (IEc, 2004),analysts have raised both methodological and practical concerns

with respect to the behavioral approach. Therefore, we used a mathematical combination

process to derive a single distribution.

One advantage of mathematical combination over behavioral approaches is the ability

to be completely transparent about how weights have been assigned to the judgments of

specific experts and about what assumptions have been made concerning the degree of

correlation between experts. Several approaches can be used to assign weights to individual

experts. Weights can be assigned based on the analyst’s opinion of the relative expertise of

each expert; on a quantitative assessment of the calibration and informativeness (i.e.,

precision) of each expert as determined from their responses to a set of calibration questions

(as described in Cooke [1991]); or on weights assigned by each expert, either to him or

herself or to the other experts on the panel (see Evans et al. [1994b] for an example of this

approach). Ideally, such a weighting system would address problems of uneven calibration

and informativeness across experts, as well as potential motivational biases (Cooke, 1991).7 

At the design stages of the pilot, we decided that the resulting expert judgments

would be combined using equal weights, essentially calculating the arithmetic mean of the

expert responses, for simplicity and transparency. Some empirical evidence suggests that the

simple combination rules, like equal weighting, perform equally well when compared to

more complex methods in terms of calibration scores for the combined results (Clemen and

Winkler, 1999).

B.3.6.2 Alternative Combination Methods

Although a combination method using equal weights for the results of each expert is

straightforward in principle, applying it in this context of the results of the pilot was

complicated by the fact that the elicitation protocol gave the experts freedom to specify

different forms for the C-R function. If all the experts had chosen the same form of the C-R
B-25

function (e.g., if each expert had specified a log-linear C-R function with a constant, but

uncertain, C-R coefficient [i.e., slope] over the PM range specified in the protocol), the

combination of their distributions for the C-R coefficient would require a simple averaging

across experts at each elicited percentile. However, in this assessment, three experts

specified log-linear functions with constant C-R coefficients over the specified range of

PM2.5 concentrations, and two of the experts specified the C-R coefficient was likely to vary

over the range of specified PM2.5 concentrations (as discussed in Section B.4.2 above). 

These more complex C-R functions necessitated some additional steps in calculating the

combined results.

As discussed in the technical report for the pilot (IEc, 2004), individual responses

either can be combined before application of the benefits model or during the application of

the model, allowing each expert’s C-R function to be estimated in the benefits model

independently. We considered three alternative approaches to combining the expert

judgments before application to the benefits model each of which differs in how the

combined estimate accounts for the underlying particulate air pollution levels. We

considered the use of (1) a uniform distribution and equal weighting, which involves taking a

simple average of the responses across experts for each percentile, (2) a normal distribution

describing the population-weighted annual average PM2.5 concentration data, and (3) a

combined distribution specified at four intervals of PM2.5 concentrations that coincide with

those specified by Expert B. 

Overall, the combination methods considered result in fairly similar results at the

median and mean relative risk estimate. However, slight differences occur in the tails of the

distribution in their characterization of uncertainty. In particular, when combining the upper

bound is averaged out to a lower value that may skew the results (in comparison to the views

expressed by the experts). Thus, the resulting estimates from the combined distribution may

not be estimates to which the experts would agree (i.e, Expert E may not agree with a

lowering of the upper bound estimate due to averaging across experts). In Figure B-3, the

C-R function for the population-weighted combination method was compared to the existing

cohort epidemiological studies of the long-term PM2.5/mortality relationship. We observe

that the results of the pilot elicitation are generally within the range of findings from these

epidemiological studies. However, as expected, the elicitation results in a larger spread of

uncertainty than is given by the standard errors of the individual studies.
B-26

0

0.5

1

1.5

2

Combined

Expert

Distribution*

Pope et al., 1995 Pope et al.,

Reanalysis, 63

cities, Krewski,

2000

Pope et al., 2002 Dockery et al.,

1993 Reanalysis,

Krewski, 2000

% Increase in Mortality per 1 ug/m3 PM2.5

Figure B-3. Comparison of A Combination of the Distributions of Expert Judgment to

Selected Published Studies

For the purposes of providing a supplemental analysis of the CAIR, we decided to

combine the results of the pilot after applying each experts’ distribution independently

through BenMAP, the model EPA currently uses for economic benefits analyses of air

quality regulations affecting PM and other criteria pollutants. Specifically, we derived the

total mortality incidence for each expert and combined (or pooled) the estimates into an

aggregate value before taking an average of the mortality incidence. This is referred to as a

“pooled” approach and is used in our modeling framework for other benefits endpoints that

have multiple C-R functions (due to multiple studies). We prefer the pooled approach

because it seems to reduce the amount of alteration of the actual step-function responses
8

 Expert B specified a distribution for the C-R coefficient for PM2.5 concentrations above a threshold and

assigned the coefficient a value of zero for all PM concentrations below the threshold. He then specified a

probability distribution to describe the uncertainty about the threshold value. Expert C specified separate

distributions for the C-R coefficient at four discrete points within the concentration ranges defined in the

protocol to represent a continuous C-R function whose slope varied with the PM2.5 concentration. Expert C

indicated that the coefficient value between these points was best modeled as a continuous function, rather

than a step function. Both experts assumed the same functional forms in responding to elicitation question.

B-27

provided by Experts B and C (although some adjustments must still be made).8 Details of the

illustration are provided in Section B.6.

B.4 Illustrative Application of Pilot Expert Elicitation Results

In this section, we illustrate how expert judgments can be applied in a benefits

analysis. We estimated avoided incidence of mortality associated with the CAIR rule for

each expert and also applied the pooled approach for combining results across participants to

develop a combined distribution of avoided incidence of premature mortality. We then used

Monte Carlo simulations to combine the expert judgment-based distributions with the

distribution for VSL and with the values for other health and welfare endpoints. We present

the resulting distributions of total dollar benefits to demonstrate how using expert elicitation

can improve the characterization of the overall uncertainty in benefits estimates. The values

generated below are not intended to replace the primary estimate of benefits of CAIR. They

are included solely as an illustration of expert elicitation-based distributions that

characterize the uncertainty of the estimate of premature mortality associated with long-term

exposure to PM2.5 rather than a data-derived distribution.

B.4.1 Method 

B.4.1.1 C-R Distribution Based on Combined Results Across Experts 

As discussed in Section B.4.5, we converted each expert’s percentile responses about

mortality associated with long-term exposure into a custom distribution such that each

percentile is correctly represented and percentiles in between are represented as continuous

functions (custom distributions were generated using Crystal Ball and are represented as

15,000 equally probable points).

For Experts A, D, and E, we used a standard log-linear functional form:

(B.5) ∆ ∆

yy e x =⋅ − ⋅

0 ( ), 1 β
B-28

where we set $ equal to ln(1+B/100), where B is the percentage change in all-cause mortality

associated with a 1 :g reduction in PM2.5. BenMAP then represents the distribution of )y

based on the custom distribution of $.

The conditional functions of Experts B and C required us to estimate some values on

the C-R function between the points that were elicited, which requires an extrapolation from

the response provided in the pilot to create continuous distributions. This can alter the true

response given by these experts to the elicitation. This component of the combination was

not included in the peer-review material.

Specifically, Expert C provided a set of conditional C-R functions for different

baseline levels of PM2.5. Expert C provided four conditional responses, one for 8 :g/m3

, one

for 10 :g/m3, one for 15 :g/m3, and one for 20 :g/m3. To “fill in” the C-R function for

intermediate baseline PM2.5 values, we linearly interpolated between the responses for each

pair of points (e.g., 10 to 15 or 15 to 20). We calculated interpolated values for 13 points,

ranging from 8 :g to 20 :g. For baseline values less than 8 :g, we assigned a value of zero

(essentially assuming a threshold at 8 :g). For baseline values greater than 20 :g, we

assigned the values provided by Expert C for 20 :g. This may result in an underestimate of

the incidence of mortality for Expert C. For each of the conditional functions, we used a loglinear specification, similar to A, D, and E. Total incidence of mortality for Expert C is the

sum of the conditional estimates over the range of baseline air concentrations.

Expert B provided a log-linear C-R function, conditional on an unknown threshold

characterized by a triangular distribution bounded by 4 :g and 15 :g, with a mode at 12 :g. 

We discretized the triangular distribution into 12 ranges of unit length (e.g., 4 to 5, 5 to 6)

and calculated the expected value of the response at each population grid cell based on the

observed baseline PM2.5 and the probability of that baseline value exceeding the potential

threshold. We assumed that if a grid cell has a baseline value above the threshold, then the

full value of the reduction in PM2.5 at that grid cell is associated with a reduction in mortality. 

This may result in an overestimate of the mortality impact for Expert B because for grid cells

where the baseline level is only marginally above the threshold, a benefit might only accrue

to the change in PM2.5 down to the threshold. The rest of the change would not result in any

mortality reduction. Because most of the changes in air quality are relatively small

(population-weighted change in annual mean PM2.5 is around 1 :g), and larger changes tend

to occur where there are higher baseline PM2.5 levels, this should not be a large issue. 

To provide context for the estimates based on the experts providing conditional and

threshold specifications, it is useful to summarize the CAIR baseline PM2.5 levels in 2015. 

Table B-5 lists the population distribution of baseline concentrations of PM2.5 in 2015.
9

 As discussed above, the elicitation results were combined assuming equal weight for each expert’s

distribution. We assumed complete dependence of the expert’s distributions for this illustrative analysis, so

that each percentile of the pooled distribution is simply the average of the corresponding percentiles of the

five experts.

B-29

Table B-5. Population Distribution of Baseline Ambient PM2.5

Baseline PM2.5 (:g/m3) 2015 Population (millions) Percent of Total 2015 Population

PM2.5<5 4.3 1.3%

5#PM2.5<10 81.2 25.6%

10#PM2.5<15 190.5 60.1%

15#PM2.5<20 29.2 9.2%

20#PM2.5<25 11.0 3.5%

25#PM2.5<30 0.7 0.2%

B.4.1.2 Estimated Reduction in Premature Morality and Valuation

Based on the air quality modeling conducted for the final CAIR, we calculated the

reduction in incidence of premature mortality associated with PM2.5 and the value of that

reduction. We used Monte Carlo simulations to derive the distributions of the dollar values

of estimated reductions in premature mortality. For each expert, the Monte Carlo simulation

generates a dollar value by randomly sampling from the distribution of the reduction in

mortality incidence and the distribution of VSL (normally distributed with a mean of $5.5

and a 95 percent confidence interval between $1 and $10 million) and multiplying the values

together. This yields an estimate of the dollar value of the mortality reductions. This

process is repeated 5,000 times to generate a distribution of dollar values. The Monte Carlo

process was conducted using the estimated distribution for each expert individually and for

the combined (pooled) distribution, as well as for the distribution derived from the Pope et al.

(2002) study.

B.4.2 Results

Figure B-4 presents box plots that display the distribution of the reduction in PM2.5-

related premature mortality based on the C-R distributions provided by each expert, as well

as a pooled distribution across the experts.9 The estimated distributions of the reduction in

PM2.5-related premature mortality corresponding to each individual expert’s reported 

distribution (labeled A through E) of percent risk are presented in Figure B-4, along with a 
B-30

11,5

22,800

11,600

2,800

7,100

13,700

0

10,00020,00030,00040,00050,00060,000

Expert A Expert B Expert C Expert D Expert E Combined Experts 

Reduction in Premature Mortality in 2015

Note: Distributions labeled Expert A - Expert E are based on individual expert responses. The distribution labeled Combined Experts is based on Monte Carlo sampling from the individual expert distributions assuming equal weights. The distribution labeled Pope et al. (2002) Statistical Error is based on the mean and standard error of the C-R function from the study.

Figure B-4. Results of Illustrative Application of Pilot Expert Elicitation: Annual Reductions in Prem2015 Associated with the Clean Air Interstate Rule
B-31

pooled estimate assuming equal weight for each expert’s distribution and the Pope et al.

(2002) results for comparison. Corresponding distributions of the dollar value of the

reductions in premature mortality are presented in Figure B-6. The distributions are depicted

as box plots with the diamond symbol (—) showing the mean, the dash (–) showing the

median (50th percentile), the box defining the interquartile range (bounded by the 25th and

75th percentiles), and the whiskers defining the 90 percent confidence interval (bounded by

the 5th and 95th percentiles of the distribution). For comparison, the figure also displays the

distribution derived from the statistical error associated with Pope et al. (2002). 

The figure shows that the average annual number of premature deaths avoided

because of CAIR in 2015 ranges from approximately 3,000 to 23,000, depending on the C-R

function used. The medians span zero to 20,000, with the zero value due to the low threshold

associated with one of the expert’s distributions. At the means of the distributions,

45 percent more premature deaths are predicted to be avoided by the estimate based on Pope

et al. (2002) than based on the estimate pooled across the five experts. Specifically, because

less than a quarter of the population is expected to live in areas with PM2.5 levels above the

threshold specified by Expert C, and much of the decrease in PM2.5 associated with CAIR

occurs below that threshold, a much smaller decrease in premature morality is predicted for

Expert C than those experts who provided continuous C-R functions down to zero (PM2.5) as

well as for Expert B who provided an uncertain threshold. Furthermore, note that above the

50th percentile, the C-R functions provided by all of the experts predict positive benefits from

the modeled control option.

The boxplots displayed in Figure B-4 are derived by applying the C-R distributions

specified by each expert (as presented in Figure B-1) to the change in air quality predicted by

the final CAIR. Although the Figures B-4 and B-1 show similar patterns, there are important

differences. Specifically, the ratio of 75th percentiles of the C-R functions specified by

Experts A and B (as denoted in Figure B-1) is 0.4, whereas the ratio of the predicted change

in incidence of premature mortality associated with the modeled preliminary control option

is 0.5. This 25 percent increase (from 0.4 to 0.5) in the ratio highlights the impact of the

extent of the predicted air quality change on the choice of C-R function used in the benefits

analysis.

The combined expert distribution depicted in Figure B-4 provides additional insights. 

The combined (average) distribution has a 90 percent credible interval between zero and

37,000. When compared with results derived from the Pope et al. (2002) study, it is clear

that the combined expert distribution reflects greater uncertainty about the estimated

reduction in premature mortality, which is expected given that the elicitation exercise was

designed to encompass more sources of uncertainty than were addressed in the Pope et al.

study, including fundamental model uncertainty. In addition, the expert judgments place

more weight on the lower end of the distribution than the Pope et al. study. The mean

estimate from the combined expert distribution is over 30 percent lower than the mean
B-32

derived from the Pope et al. (2002) distribution. However, the 90 percent confidence interval

based on the standard error from Pope et al. (2002) is completely contained within the 90

percent credible interval of the combined expert distribution. 

Figure B-5 shows the same data using cumulative distribution functions (CDFs). 

This figure is valuable for demonstrating differences in degree of certainty in achieving

specific reductions in premature mortality. For instance, the Pope et al. (2002) C-R

distribution predicts a 90 percent chance that there will be more than 10,000 fewer premature

deaths, whereas the pooled distribution from the pilot predicts less than a 50 percent chance

of more than 10,000 fewer premature deaths resulting from CAIR. The probabilities

associated with the individual experts for avoiding 10,000 or more premature deaths range

from about 5 percent to 80 percent, demonstrating once again the sensitivity of the estimate

to assumptions regarding the C-R function. The CDFs of the estimated reductions in

premature mortality show that for several experts there is a small probability of a

substantially more reduction in pre-mature mortality. For example, the 75th percentile of the

distribution based on Expert B’s responses is at 11,000 fewer deaths, while the 98th percentile

for that distribution is over three times higher, at 35,000. The CDF also shows that, although

most of the experts provided fairly wide distributions reflecting incorporation of information

beyond what is demonstrated in any one empirical study, the CDF based on Expert C’s

responses is much narrower, reflecting the high degree of confidence he placed on the

existence of a threshold below 15 :g. 

Figures B-6 and B-7 use box plots and CDFs to display the estimated dollar value of

these annual reductions in premature mortality. Whereas the average based on the Pope et al.

(2002) distribution is $93 billion, the average based on the pooled estimate from the pilot is

$64 billion, a difference of approximately one-third. Once the C-R distributions are

combined with the VSL distributions, not only are the mean values closer to one another, but

the distributions show considerably more overlap.

Because these distributions are the result of a Monte Carlo simulation combining the

non-normal distributions for reductions in mortality with a normal distribution for VSL, the

resulting distributions will also be non-normal, but the shape depends on the skewness of

each of the input distribution of mortality reductions. For example, the ratio of the 95th to

75th percentile of mortality reductions for Expert B is 2.9, while the same ratio for the value

of mortality reductions is 3.4, indicating the value distribution is more skewed than the

reductions distribution. In general, combining normal or left-skewed distributions in a

mulitplicative fashion will result in left-skewed distributions with greater skewness than the

input distributions. So even for the normally distributed estimates based on Pope et al.

(2002), the value distribution is somewhat skewed, because it is the result of multiplying two

normally distributed random variables. 
B-33

0

0.10.20.30.40.50.60.70.80.9

1

0 10,000 20,000 30,000 40,000 50,000 

Annual Reductions in Incidence of Premature Mortality in 2015

Probability

Expert A Expert B Expert C Expert D Expert E Combined Experts Pope e

Note: Distributions labeled Expert A - Expert E are basresponses. The distribution labeled Combined Experts sampling from the individual expert distributions assumdistribution labeled Pope et al. (2002) Statistical Error standard error of the C-R function from the study.

Figure B-5. Cumulative Distribution Functions for Annual Reductions in Premature Mortality in 2015 Clean Air Interstate Rule
B-34

$130,000

$64,000

$16,000

$40,000

$76,000

$0

$50,000

$100,000$150,000$200,000$250,000$300,000$350,000$400,000

Expert A Expert B Expert C Expert D Expert E Combined 

Millions of $1999

Note: Mortality distributions labeled Expert A - Expert E are based on individual expert responses. The distribution labeled Combined Experts is based on Monte Carlo sampling from the individual expert distributions assuming equal weights. The mortality distribution labeled Pope et al. (2002) statistical error is based on the mean and standard error of the C-R function from the study. Mortality valuation is based on a normally distributed VSL with a mean of $5.5 million and a 95% CI between $1 and $10 million. The VSL distribution has then been adjsuted for income growth out to 2015 using an adjustment factor of 1.15.

Figure B-6. Results of Illustrative Application of Pilot Expert Elicitation: Dollar Value of Annual RedPremature Mortality in 2015 Associated with the Clean Air Interstate Rule
B-35

0

0.10.20.30.40.50.60.70.80.9

1

$0 $100,000 $200,000 $300,000 $400,000 $5

Million ($1999)

Cumulative Probability

Expert A Expert B Expert C Expert D Expert E Combined Experts Pop

Note: Mortality distributions labeled Expert A - Expert E aexpert responses. The distribution labeled Combined ECarlo sampling from the individual expert distributions asThe mortality distribution labeled Pope et al. (2002) statimean and standard error of the C-R function from the stubased on a normally distributed VSL with a mean of $5.5 between $1 and $10 million. The VSL distribution has thincome growth out to 2015 using an adjustment factor of 

Figure B-7. Cumulative Distribution Functions for Dollar Value of Annual Reductions in Premature MAssociated with the Clean Air Interstate Rule
10Note that visibility benefits are treated as fixed for this illustrative analysis. We are working on methods to

characterize the uncertainty in visibility and other nonhealth benefits.

B-36

The shapes of the two distributions are more similar in this case because both reflect

the same additional information in the VSL distribution. This demonstrates that, as

additional sources of uncertainty are added to the analysis, the influence of any single source

of uncertainty will fall. Because VSL is a large source of uncertainty, the influence on

overall uncertainty relative to the distribution of the mortality reduction is also large. All of

the distributions of the value of mortality reductions have a small negative tail, this time

because of propagation of the normally distributed VSL, which has a small amount of the

distribution below zero. Again, we interpret this as a statistical artifact rather than a true

probability that the value of a statistical life is negative (implying that individuals would pay

to increase the risk of death). 

We used additional Monte Carlo simulations to combine the expert-based

distributions for the dollar benefits of mortality with the distributions of dollar benefits for

the remaining health and welfare endpoints to derive estimates of the overall distribution of

total dollar benefits.10 The box plots for these distributions of overall dollar benefits

associated with CAIR in 2015 are presented in Figure B-8. Because mortality accounts for

over 90 percent of the benefits, the addition of other endpoints has little impact on the overall

distributions. The overall mean annual total dollar benefits in 2015 for the distribution

incorporating the combined expert distribution for reductions in premature mortality is $74

billion, compared to $100 billion for the results derived from the Pope et al. (2002) study.

The CDFs for total dollar benefits are provided in Figure B-9. These again suggest

that using the pilot expert elicitation-based representation of uncertainty in the relationship

between PM2.5 and premature mortality has a large impact on the shape and range of the

distribution of total benefits. The Pope et al. (2002) derived results have an approximately

Weibull-shaped distribution with a range from 5th to 95th percentiles of $26 billion to $210

billion, or about one order of magnitude. The distribution of total dollar benefits

incorporating the combined expert distribution for reductions in premature mortality has a

much more skewed shape with an elongated positive tail above the 75th percentile with a

range from 5th to 95th percentiles of $3 billion to $240 billion, or about two orders of

magnitude, with significant probability mass at the lower end of the range. 
B-37

$140,000

$74,000

$24,000

$48,000

$85,000

0

100000200000300000400000

Expert A Expert B Expert C Expert D Expert E Combined Ex

Millions of $1999

Note: All non-mortality distributions are based on classical statistical error derived from the standard errors reported in epidemiology studies and distributions of unit values based on empirical data. Visibility benefits are included as a constant. Mortality distributions labeled Expert A - Expert E are based on individual expert responses. The distribution labeled Combined Experts is based on Monte Carlo sampling from the individual expert distributions assuming equal weights. The mortality distribution labeled Pope et al. (2002) statistical error is based on the mean and standard error of the C-R function from the study. Dollar benefits have been adjusted upwards to account for growth in real income out to 2015.

Figure B-8. Results of Illustrative Application of Pilot Expert Elicitation: Dollar Value of Total Annu

Associated with the Clean Air Interstate Rule
B-38

0

0.10.20.30.40.50.60.70.80.9

1

$0 $100,000 $200,000 $300,000 $400,0

Million ($1999)

Cumulative Probability

Expert A Expert B Expert C Expert D Expert E Combined Experts Pop

Note: All non-mortality distributions are based on cderived from the standard errors reported in epidemdistributions of unit values based on empirical data. included as a constant. The distribution labeled ComMonte Carlo sampling from the individual expert disweights. The mortality distribution labeled Pope et abased on the mean and standard error of the C-R fubenefits have been adjusted upwards to account fo2015.

Figure B-9. Cumulative Distribution Functions of Dollar Value of Total Annual PM-Related Health anin 2015 Associated with the Clean Air Interstate Rule
B-39

B.5.3 Limitations of the Application of the Pilot Elicitation Results to the CAIR Scenario

The results presented in this section should be viewed cautiously given the limited

scope of the pilot and the limitations of the elicitation design and methods used to combine

the expert judgments discussed above. Therefore, the results presented above should be

considered “illustrative” until the methods used to interpret and apply the results of the pilot

have been peer reviewed by EPA’s SAB. 

Specific limitations of the illustrative application include the following:

C Extrapolation of percentile responses provided by individual experts. Each expert

provided minimum and maximum values, as well as the 5th, 25th, 50th, 75th, and

95th percentiles. To generate the continuous distributions of mortality impacts, we

had to make assumptions about the continuity of the distributions between the

reported percentiles. The use of assumptions adds uncertainty to the results. 

C Interpolation of C-R relationship across PM2.5 levels. Expert C provided a set of

conditional distributions of the C-R relationship conditioned on the baseline level

of PM2.5. Because he only provided functions for a limited number of baseline

levels, we had to interpolate the values between levels, introducing additional

uncertainty. In addition, Expert C provided no information on the C-R function

for baseline PM2.5 levels below 8 :g/m3 or above 20 :g/m3. We assumed no

mortality impacts for baseline levels lower than 8 and no increase in the C-R

function above 20. If the interpolation method was incorrect, it likely biased our

results downward.

C Interpretation of Expert B’s results. Expert B provided a conditional distribution

for the C-R function, conditioned on an uncertain threshold. Expert B provided

additional information about the shape of the distribution for the threshold. To

develop an applied function, we assumed that the uncertain threshold could be

incorporated into the C-R function by constructing an expected value function. If

our interpretation was flawed, the specific functions introduced may lead to a

slight overestimate of mortality impacts.

C Ranges based on individual experts should be viewed with caution because they

represent only a single individual’s interpretation of the state of knowledge about

PM and mortality. Results for individual experts should not be extracted and

presented without reference to the full range of results across the five experts. 

C Any range of results presented based on the application of the pilot results in the

CAIR scenario should be presented along with their relative likelihood of

occurring (i.e., the percentile represented in the distribution).
B-40

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C-1

APPENDIX C

SENSITIVITY ANALYSES OF THE KEY PARAMETERS IN THE BENEFITS

ANALYSIS

The primary analysis presented in Chapter 4 is based on our current interpretation of

the scientific and economic literature. That interpretation requires judgments regarding the

best available data, models, and modeling methodologies and the assumptions that are most

appropriate to adopt in the face of important uncertainties. The majority of the analytical

assumptions used to develop the primary estimates of benefits have been reviewed and

approved by EPA’s SAB. Both EPA and the SAB recognize that data and modeling

limitations as well as simplifying assumptions can introduce significant uncertainty into the

benefit results and that alternative choices exist for some inputs to the analysis, such as the

mortality C-R functions. 

This appendix supplements our primary estimates of benefits with a series of

sensitivity calculations that use other sources of health effect estimates and valuation data for

key benefits categories. These supplemental estimates examine sensitivity to both valuation

issues (e.g., the appropriate income elasticity) and for physical effects issues (e.g., possible

recovery from chronic illnesses). These supplemental estimates are not meant to be

comprehensive. Rather, they reflect some of the key issues identified by EPA or

commentors as likely to have a significant impact on total benefits. The individual

adjustments in the tables should not simply be added together because 1) there may be

overlap among the alternative assumptions and 2) the joint probability among certain sets of

alternative assumptions may be low. 

C.1 Premature Mortality—Long-Term Exposure

Reduction in the risk of premature mortality is the most important PM-related health

outcome in terms of contribution to dollar benefits in the analysis for this rule. There are at

least three important analytical assumptions that may significantly impact the estimates of

the number and valuation of avoided premature mortalities. These include selection of the

C-R function, structure of the lag between reduced exposure and reduced mortality risk, and

effect thresholds. Results of this set of sensitivity analyses are presented in Table C-1.
C-2

Table C-1. Sensitivity of Benefits of Premature Mortality Reductions to Alternative

Assumptions (Relative to Primary Estimate Benefits of the Final CAIR)

Description of Sensitivity Analysis

Avoided Incidences Value (million 1999$)b

2010 2015 2010 2015

Alternative Concentration-Response Functions for PM-Related Premature Mortality

Pope/ACS Study (2002)c

Lung Cancer 2,000 2,700 $11,000 $14,000

Cardiopulmonary 9,700 13,000 $51,000 $67,000

Krewski/Harvard Six-Cities Study 29,000 38,000 $150,000 $200,000

Alternative Lag Structures for PM-Related Premature Mortality 

None Incidences all occur in the first year 13,000 17,000 $74,000 $102,000

8-year Incidences all occur in the 8th year

3% discount rate 13,000 17,000 $60,000 $83,000

7% discount rate 13,000 17,000 $46,000 $64,000

15-year Incidences all occur in the 15th year

3% discount rate 13,000 17,000 $49,000 $68,000

7% discount rate 13,000 17,000 $29,000 $40,000

Alternative

Segmented

20 percent of incidences occur in 1st year, 50

percent in years 2 to 5, and 30 percent in

years 6 to 20

3% discount rate 13,000 17,000 $65,000 $90,000

7% discount rate 13,000 17,000 $52,000 $71,000

5-Year

Distributed

50 percent of incidences occur in years 1

and 2 and 50 percent in years 2 to 5

3% discount rate 13,000 17,000 $71,000 $97,000

7% discount rate 13,000 17,000 $66,000 $91,000

Exponential Incidences occur at an exponentially

declining rate following year of change in

exposure

3% discount rate 13,000 17,000 $71,000 $75,000

7% discount rate 13,000 17,000 $65,000 $90,000

Alternative Thresholds

No Threshold (base estimate) 13,000 17,000 $67,000 $93,000

5 :g/m3 13,000 17,000 $67,000 $93,000

10 :g/m3 11,000 14,000 $59,000 $78,000

15 :g/m3 780 690 $4,100 $3,800

20 :g/m3 0 0 $0 $0

25 :g/m3 0 0 $0 $0 a Incidences rounded to two significant digits.

b Dollar values rounded to two significant digits.

c Note that the sum of lung cancer and cardiopulmonary deaths will not be equal to the total all-cause death estimate. Some residual

mortality is associated with long-term exposures to PM2.5 that is not captured by the cardiopulmonary and lung cancer categories.
C-3

C.1.1 Alternative C-R Functions

Following the advice of the most recent EPA SAB-HES, we used the Pope et al.

(2002) all-cause mortality model to derive our primary estimate of avoided premature

mortality (EPA-SAB-COUNCIL-ADV-04-002, 2004). While the SAB-HES “recommends

that the base case rely on the Pope et al. (2002) study and that EPA use total mortality

concentration-response functions (C-R), rather than separate cause-specific C-R functions, to

calculate total PM mortality cases,” (EPA-SAB-COUNCIL-ADV-04-002, 2004, p.2) they

also suggested that “the cause-specific estimates can be used to communicate the relative

contribution of the main air pollution related causes of death.” (EPA-SAB-COUNCIL-ADV04-002, 2004, p.18) As such, in Table C-1 we provide the estimates of cardiopulmonary and

lung cancer deaths based on the Pope et al. (2002) study.

In addition, the SAB-HES noted that the ACS cohort used in Pope et al. (2002) “has

some inherent deficiencies, in particular the imprecise exposure data, and the

nonrepresentative (albeit very large) population” (EPA-SAB-COUNCIL-ADV-04-002, 2004,

p.18). The SAB-HES suggests that while not necessarily a better study, the ACS is a prudent

choice for the primary estimate. They go on to note that “the Harvard Six-Cities C-R

functions are valid estimates on a more representative, although geographically selected,

population, and its updated analysis has not yet been published. The Six-Cities estimates

may be used in a sensitivity analysis to demonstrate that, with different but also plausible

selection criteria for C-R functions, benefits may be considerably larger than suggested by

the ACS study” (EPA-SAB-COUNCIL-ADV-04-002, 2004, p.18). In previous advice, the

SAB has noted that “the [Harvard Six-Cities] study had better monitoring with less

measurement error than did most other studies” (EPA-SAB-COUNCIL-ADV-99-012, 1999). 

The demographics of the ACS study population (i.e., largely white and middle to upper

middle-class) may also produce a downward bias in the estimated PM mortality coefficient,

because a variety of analyses indicate that the effects of PM tend to be significantly greater

among groups of lower socioeconomic status (Krewski et al., 2000), although the cause of

this difference has not been identified. The Harvard Six-Cities study also covered a broader

age category (25 and older compared to 30 and older in the ACS study). We emphasize that,

based on our understanding of the relative merits of the two datasets, the Pope et al. (2002)

ACS model based on mean PM2.5 levels in 63 cities is the most appropriate model for

analyzing the premature mortality impacts of CAIR. Thus it is used for our primary estimate

of this important health effect. 
C-4

C.1.2 Alternative Lag Structures

Over the last ten years, there has been a continuing discussion and evolving advice

regarding the timing of changes in health effects following changes in ambient air pollution. 

It has been hypothesized that some reductions in premature mortality from exposure to

ambient PM2.5 will occur over short periods of time in individuals with compromised health

status, but other effects are likely to occur among individuals who, at baseline, have

reasonably good health that will deteriorate because of continued exposure. No animal

models have yet been developed to quantify these cumulative effects, nor are there

epidemiologic studies bearing on this question. The SAB-HES has recognized this lack of

direct evidence. However, in early advice, they also note that “although there is substantial

evidence that a portion of the mortality effect of PM is manifest within a short period of

time, i.e., less than one year, it can be argued that, if no lag assumption is made, the entire

mortality excess observed in the cohort studies will be analyzed as immediate effects, and

this will result in an overestimate of the health benefits of improved air quality. Thus some

time lag is appropriate for distributing the cumulative mortality effect of PM in the

population” (EPA-SAB-COUNCIL-ADV-00-001, 1999, p. 9). In recent advice, the SABHES suggests that appropriate lag structures may be developed based on the distribution of

cause-specific deaths within the overall all-cause estimate (EPA-SAB-COUNCIL-ADV-04-

002, 2004). They suggest that diseases with longer progressions should be characterized by

longer-term lag structures, while air pollution impacts occurring in populations with existing

disease may be characterized by shorter-term lags. 

A key question is the distribution of causes of death within the relatively broad

categories analyzed in the long-term cohort studies. Although it may be reasonable to

assume the cessation lag for lung cancer deaths mirrors the long latency of the disease, it is

not at all clear what the appropriate lag structure should be for cardiopulmonary deaths,

which include both respiratory and cardiovascular causes. Some respiratory diseases may

have a long period of progression, while others, such as pneumonia, have a very short

duration. In the case of cardiovascular disease, there is an important question of whether air

pollution is causing the disease, which would imply a relatively long cessation lag, or

whether air pollution is causing premature death in individuals with preexisting heart disease,

which would imply very short cessation lags. The SAB-HES provides several

recommendations for future research that could support the development of defensible lag

structures, including using disease-specific lag models and constructing a segmented lag

distribution to combine differential lags across causes of death (EPA-SAB-COUNCIL-ADV04-002, 2004). The SAB-HES indicated support for using “a Weibull distribution or a
1

Although these studies were conducted for 8 and 15 years, respectively, the choice of the duration of the study

by the authors was not likely due to observations of a lag in effects but is more likely due to the expense of

conducting long-term exposure studies or the amount of satisfactory data that could be collected during this

time period.

C-5

simpler distributional form made up of several segments to cover the response mechanisms

outlined above, given our lack of knowledge on the specific form of the distributions” (EPASAB-COUNCIL-ADV-04-002, 2004, p. 24). However, they noted that “an important

question to be resolved is what the relative magnitudes of these segments should be, and how

many of the acute effects are assumed to be included in the cohort effect estimate” (EPASAB-COUNCIL-ADV-04-002, 2004, p. 24-25). Since the publication of that report in

March 2004, EPA has sought additional clarification from this committee. In its followup

advice provided in December 2004, this SAB suggested that until additional research has

been completed, EPA should assume a segmented lag structure characterized by 30 percent

of mortality reductions occurring in the first year, 50 percent occurring evenly over years 2

to 5 after the reduction in PM2.5, and 20 percent occurring evenly over the years 6 to 20 after

the reduction in PM2.5 (EPA-COUNCIL-LTR-05-001, 2004). The distribution of deaths over

the latency period is intended to reflect the contribution of short-term exposures in the first

year, cardiopulmonary deaths in the 2- to 5-year period, and long-term lung disease and lung

cancer in the 6- to 20-year period. Furthermore, in their advisory letter, the SAB-HES

recommended that EPA include sensitivity analyses on other possible lag structures. In this

appendix, we investigate the sensitivity of premature mortality-reduction related benefits to

alternative cessation lag structures, noting that ongoing and future research may result in

changes to the lag structure used for the primary analysis. 

In previous advice from the SAB-HES, they recommended an analysis of 0-, 8-, and

15-year lags, as well as variations on the proportions of mortality allocated to each segment

in the segmented lag structure (EPA-SAB-COUNCIL-ADV-00-001, 1999,

(EPA-COUNCIL-LTR-05-001, 2004). The 0-year lag is representative of EPA’s assumption

in previous RIAs. The 8- and 15-year lags are based on the study periods from the Pope et

al. (1995) and Dockery et al. (1993) studies, respectively.1

 However, neither the Pope et al.

nor Dockery et al. studies assumed any lag structure when estimating the relative risks from

PM exposure. In fact, the Pope et al. and Dockery et al. analyses do not supporting or refute

the existence of a lag. Therefore, any lag structure applied to the avoided incidences

estimated from either of these studies will be an assumed structure. The 8- and 15-year lags

implicitly assume that all premature mortalities occur at the end of the study periods (i.e., at

8 and 15 years). 
C-6

In addition to the simple 8- and 15-year lags, we have added three additional

sensitivity analyses examining the impact of assuming different allocations of mortality to

the segmented lag of the type suggested by the SAB-HES. The first sensitivity analysis

assumes that more of the mortality impact is associated with chronic lung diseases or lung

cancer and less with acute cardiopulmonary causes. This illustrative lag structure is

characterized by 20 percent of mortality reductions occurring in the first year, 50 percent

occurring evenly over years 2 to 5 after the reduction in PM2.5, and 30 percent occurring

evenly over the years 6 to 20 after the reduction in PM2.5. The second sensitivity analysis

assumes the 5-year distributed lag structure used in previous analyses, which is equivalent to

a three-segment lag structure with 50 percent in the first 2-year segment, 50 percent in the

second 3-year segment, and 0 percent in the 6- to 20-year segment. The third sensitivity

analysis assumes a negative exponential relationship between reduction in exposure and

reduction in mortality risk. This structure is based on an analysis by Röösli et al. (2004),

which estimates the percentage of total mortality impact in each period t as

. (C.1) [ ] ( )

[ ] ( )

%

.

.

Mortality Reduction (t) = − +−

− +−

−

−

=

∞

∑

RR e

RR e

t

t

t

1 11

1 11

0 5

0 5

1

The Röösli et al. (2004) analysis derives the lag structure by calculating the rate constant 

(–0.5) for the exponential lag structure that is consistent with both the relative risk from the

cohort studies and the change in mortality observed in intervention type studies (e.g., Pope et

al. [1992] and Clancy et al. [2002]). This is the only lag structure examined that is based on

empirical data on the relationship between changes in exposure and changes in mortality. 

However, the analysis has not yet been peer reviewed and is thus not yet appropriate for

adoption in the primary analysis.

The estimated impacts of alternative lag structures on the monetary benefits

associated with reductions in PM-related premature mortality (estimated with the Pope et al.

ACS impact function) are presented in Table C-1. These estimates are based on the value of

statistical lives saved approach (i.e., $5.5 million per incidence) and are presented for both a

3 and 7 percent discount rate over the lag period. 
2

The advice from the 2004 SAB-HES (EPA-SAB-COUNCIL-ADV-04-002) is characterized by the following:

“For the studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted the most

careful work on this issue. They report that the associations between PM2.5 and both all-cause and

cardiopulmonary mortality were near linear within the relevant ranges, with no apparent threshold. 

Graphical analyses of these studies (Dockery et al., 1993, Figure 3, and Krewski et al., 2000, page 162) also

suggest a continuum of effects down to lower levels. Therefore, it is reasonable for EPA to assume a no

threshold model down to, at least, the low end of the concentrations reported in the studies.”

3

The illustrative example in Appendix B presents the potential implications of assuming some probability of a

threshold on the benefits estimate.

C-7

C.1.3 Thresholds

Although the consistent advice from EPA’s SAB2 has been to model premature

mortality associated with PM exposure as a nonthreshold effect, that is, with harmful effects

to exposed populations regardless of the absolute level of ambient PM concentrations. 

EPA’s most recent PM2.5 Criteria Document concludes that “the available evidence does not

either support or refute the existence of thresholds for the effects of PM on mortality across

the range of concentrations in the studies” (U.S. EPA, 2004, p. 9-44). Some researchers have

hypothesized the presence of a threshold relationship. The nature of the hypothesized

relationship is that there might exist a PM concentration level below which further reductions

no longer yield premature mortality reduction benefits.3

We constructed a sensitivity analysis by assigning different cutpoints below which

changes in PM2.5 are assumed to have no impact on premature mortality. The sensitivity

analysis illustrates how our estimates of the number of premature mortalities in the primary

estimate might change under a range of alternative assumptions for a PM mortality threshold. 

If, for example, there were no benefits of reducing PM concentrations below the PM2.5

standard of 15 :g/m3, our estimate of the total number of avoided PM-related premature

mortalities in 2015 from the primary analysis would be reduced by approximately 96

percent, from approximately 17,000 annually to approximately 700 annually. The recent

NRC report stated that “for pollutants such as PM10 and PM2.5, there is no evidence for any

departure of linearity in the observed range of exposure, nor any indication of a threshold”

(NRC, 2002, p. 109). At a threshold of 10 :g, approximately the 20th percentile of observed

concentrations in the Pope et al. (2002) study, mortality impacts would be reduced by only

16 percent to approximately 14,000 annually. Another possible sensitivity analysis that we
4

The pilot expert elicitation discussed in Appendix B provides some information on the impact of applying

nonlinear and threshold-based C-R functions. 

C-8

have not conducted at this time might examine the potential for a nonlinear relationship at

lower exposure levels.4

One important assumption that we adopted for the threshold sensitivity analysis is

that no adjustments are made to the shape of the C-R function above the assumed threshold. 

Instead, thresholds were applied by simply assuming that any changes in ambient

concentrations below the assumed threshold have no impacts on the incidence of premature

mortality. If there were actually a threshold, then the shape of the C-R function would likely

change and there would be no health benefits to reductions in PM below the threshold. 

However, as noted by the NRC, “the assumption of a zero slope over a portion of the curve

will force the slope in the remaining segment of the positively sloped concentration-response

function to be greater than was indicated in the original study” and that “the generation of the

steeper slope in the remaining portion of the concentration-response function may fully

offset the effect of assuming a threshold.” The NRC suggested that the treatment of

thresholds should be evaluated in a formal uncertainty analysis. 

C.1.3 Summary of Results

The results of these sensitivity analyses demonstrate that choice of effect estimate can

have a large impact on benefits, potentially doubling benefits if the effect estimate is derived

from the HEI reanalysis of the Harvard Six-Cities data (Krewski et al., 2000). Because of

discounting of delayed benefits, the lag structure may also have a large impact on monetized

benefits, reducing benefits by 30 percent if an extreme assumption that no effects occur until

after 15 years is applied. However, for most reasonable distributed lag structures,

differences in the specific shape of the lag function have relatively small impacts on overall

benefits. For example, the overall impact of moving from the previous 5-year distributed lag

to the segmented lag recommended by the SAB-HES in 2004 in the primary estimate is

relatively modest, reducing benefits by approximately 5 percent when a 3 percent discount

rate is used and 15 percent when a 7 percent discount rate is used. If no lag is assumed,

benefits are increased by around 10 percent relative to the segmented lag with a 3 percent

discount rate and 30 percent with a 7 percent discount rate. Benefits are more sensitive to

assumptions regarding the potential for a threshold. The threshold sensitivity analysis

indicates that over 84 percent of the premature mortality-related benefits are due to changes
C-9

in PM2.5 concentrations occurring above 10 :g/m3, and around 5 percent are due to changes

above 15 :g/m3, the current PM2.5 standard.

C.2 Other Health Endpoint Sensitivity Analyses

C.2.1 Ozone-Related Mortality

To provide the reader with a fuller understanding of the health effects associated with

reductions in air pollution associated with the final CAIR, this set of sensitivity estimates

examines the potential impact of reductions in ozone on incidence of premature mortality. 

Although PM is the air pollutant most clearly associated with premature mortality, recent

research suggests that short-term ozone exposure likely contributes to premature death. 

Although the CAIR analysis uses cohort studies to characterize the effect of PM2.5 on

premature mortality, such cohort studies have for the most part not documented an

association between ozone exposure and premature mortality. Time series studies, which

look specifically at the effects of short-term exposures, have documented effects on

premature mortality from both PM2.5 and ozone, although results have been mixed. Several

recent analyses have found consistent statistical associations between short-term ozone

exposure and increased mortality (Bell et al., 2004; Fairly et al., 2003; Thurston and Ito,

2001; Toulomi et al., 1997). The most recent of these, Bell et al. (2004), used the extensive

National Morbidity, Mortality, and Air Pollution Study database to examine associations

between ozone and premature mortality in 95 U.S. urban communities. They found that on

average, short-term changes in ozone are significantly associated with premature mortality,

and that the association is robust to adjustment for particulate matter, weather, and

seasonality.

Although they do not constitute a database as extensive as that for PM, these recent

studies provide supporting evidence for including mortality in ozone health benefits

analyses. In a 2001 analysis, Thurston and Ito reviewed previously published time-series

studies examining the effect of daily ozone levels on daily mortality. Thurston and Ito

hypothesized that much of the variability in published estimates of the ozone/mortality effect

could be explained by how well each model controlled for the influence of weather, an

important confounder and that earlier studies, which used less-sophisticated approaches to

controlling for weather, consistently underpredicted the ozone/mortality effect. 

Thurston and Ito (2001) also found that models incorporating a nonlinear temperature

specification appropriate for the “U-shaped” nature of the temperature/mortality relationship

(i.e., increased deaths at both very low and very high temperatures) produced ozone/
C-10

mortality effect estimates that were both more strongly positive (an increase in relative risk

over the pooled estimate for all studies evaluated, from 1.036 to 1.056) and consistently

statistically significant. Further accounting for the interaction effects between temperature

and relative humidity strengthened the positive effect. Including a PM index to control for

PM/mortality effects had little effect on these results, suggesting a relationship between

ozone and mortality independent of that for PM. However, most of the studies Thurston and

Ito examined controlled only for PM10 or broader measures of particles and did not directly

control for PM2.5. As such, there still may be potential for confounding of PM2.5 and ozone

mortality effects, given that ozone and PM2.5 are highly correlated during summer months in

some areas. 

Two recent World Health Organization reports concluded that recent epidemiological

studies have strengthened the evidence that there are short-term O3 effects on mortality and

respiratory morbidity (Anderson et al. 2004; WHO, 2003). In addition, Levy et al. (2001)

assessed the epidemiological evidence regarding the link between short-term exposures to

ozone and premature mortality. Based on four U.S. studies (Kellsall et al., 1997;

Moolgavkar et al., 1995; Ito and Thurston, 1996; Moolgavkar, 2000), they concluded that an

appropriate pooled effect estimate is a 0.5 percent increase in premature deaths per 10 :g/m3

increase in 24-hour average ozone concentrations, with a 95 percent confidence interval

between 0.3 percent and 0.7 percent. 

In its September 2001 advisory on the draft analytical blueprint for the second

Section 812 prospective analysis, the SAB HES cited the Thurston and Ito study as a

significant advance in understanding the effects of ozone on daily mortality and

recommended re-evaluation of the ozone mortality endpoint for inclusion in the next

prospective study (EPA-SAB-COUNCIL-ADV-01-004, 2001). Based on these new analyses

and recommendations, EPA sponsored three independent meta-analyses of the

ozone-mortality epidemiology literature to inform a determination on including this

important health endpoint. Publication of these meta-analyses will significantly enhance the

scientific defensibility of benefits estimates for ozone, which include the benefits of

premature mortality reductions. 

We estimate ozone mortality for this sensitivity analysis with the recognition that the

exact magnitude of the effects estimate is subject to continuing uncertainty. As we have

done in the sensitivity analyses for prior RIAs, such as for the non-road diesel rule, we used

results from three U.S. studies to calculate the base-case ozone mortality estimate

(Table C-2). We selected these studies (Ito and Thurston, 1996; Moolgavkar et al., 1995; 
C-11

Table C-2. Sensitivity Estimates for Ozone-Related Premature Mortality

Avoided Incidences

Monetized Value

(Million 1999$)

Description of Sensitivity Analysis 2010 2015 2010 2015

Mortality from short-term ozone exposureb

Ito and Thurston (1996) 290 850 $1,700 $5,200

Moolgavkar et al. (1995) 150 410 $900 $2,500

Samet et al. (1997) 230 630 $1,400 $3,800

Pooled estimate (random effects weights) 190 520 $1,100 $3,200

a All estimates rounded to two significant digits. b Mortality valued using base estimate of $5.5 million per premature statistical death, adjusted for income

growth.

Samet et al., 1997) based on the logic that the demographic and environmental conditions

existing when these studies were conducted would, on average, be most similar (relative to

international studies) to the conditions prevailing when the CAIR would be implemented. 

We excluded a fourth U.S. study by Kinney et al. (1995) because, as Levy et al. (2001)

noted, that study included only a linear term for temperature. Because Kinney et al. (1995)

found no significant ozone effect, including this study would lead to an underestimate of true

mortality impacts and increase the uncertainty surrounding the estimated mortality

reductions. 

We then estimated the change in mortality incidence resulting from applying the

effect estimate from each study and combined the results using a random-effects weighting

procedure that accounts for both the precision of the individual effect estimates and betweenstudy variability (see Appendix D for more details on this method for combining results). 

However, it is important to note that this procedure only captures the uncertainty in the

underlying epidemiological work and does not capture other sources of uncertainty, such as

that in estimating air pollution exposure (Levy et al., 2001).

Table C-2 shows that if it is assumed that ozone independently affects premature

mortality, then in 2010, an additional 190 premature deaths might be avoided from

reductions in ozone concentrations due to CAIR, while in 2015, an additional 520 deaths

might be avoided. This would add an additional $1.1 billion in monetized benefits in 2010,

and an additional $3.2 billion in monetized benefits in 2015. 
C-12

C.2.2 Alternative and Supplementary Estimates

We also examined how the value for individual endpoints or total benefits would

change if we were to make a different assumption about specific elements of the benefits

analysis. Specifically, in Table C-3, we show the impact of alternative assumptions about

other parameters, including treatment of reversals in CB, alternative impact functions for PM

hospital and emergency room admissions, valuation of residential visibility, valuation of

recreational visibility at Class I areas outside of the study regions examined in the Chestnut

and Rowe (1990a, 1990b) study, and valuation of household soiling damages.

Table C-3. Additional Parameter Sensitivity Analyses 

Alternative

Calculation Description of Estimate

Impact on Primary Benefit

Estimate (million 2000$)

2010 2015

1

Treatment of

reversals in

CB 

Instead of omitting cases of CB that reverse

after a period of time, they are treated as

being cases with the lowest severity rating. 

The number of avoided chronic CB in 2010

increases from 6,900 to 13,000 (88%). The

increase in 2015 is from 8,700 to 16,000

(84%).

+$880 +$1,100

2

Value of

visibility

changes in all

Class I areas

Values of visibility changes at Class I areas in

California, the Southwest, and the Southeast

are transferred to visibility changes in Class I

areas in other regions of the country.

+$200 +$200

3

Household

soiling

damage

Value of decreases in expenditures on

cleaning are estimated using values derived

from Manuel et al. (1983).

+$280 +$340

An important assumption related to chronic conditions is the possible reversal in CB

incidences (row 1 of Table C-3). Reversals are defined as those cases where an individual

reported having CB at the beginning of the study period but reported not having CB in

follow-up interviews at a later point in the study period. Because chronic diseases are longlasting or permanent by definition, if the disease abates in a shorter period of time it is not

chronic. However, we have not captured the benefits of reducing incidences of bronchitis

that are somewhere in between acute and chronic. Since chronic bronchitis may be assigned

a range of severities, one way to address this is to treat reversals as cases of CB that are at the
C-13

lowest severity level. These reversals of CB thus are assigned the lowest value for CB in this

sensitivity analysis, rather than omitting reversals as is the case in the primary analysis.

The alternative calculation for recreational visibility (row 2 of Table C-3) is an

estimate of the full value of visibility in the entire region affected by the CAIR emission

reductions. The Chestnut and Rowe (1990a) study from which the primary valuation

estimates are derived only examined WTP for visibility changes in the southeastern portion

of the affected region. To obtain estimates of WTP for visibility changes in the northeastern

and central portion of the affected region, we have to transfer the southeastern WTP values. 

This introduces additional uncertainty into the estimates. However, we have taken steps to

adjust the WTP values to account for the possibility that a visibility improvement in parks in

one region is not necessarily the same environmental quality good as the same visibility

improvement at parks in a different region. This may be due to differences in the scenic

vistas at different parks, uniqueness of the parks, or other factors, such as public familiarity

with the park resource. To take this potential difference into account, we adjusted the WTP

being transferred by the ratio of visitor days in the two regions.

The alternative calculation for household soiling (row 5 of Table C-3) is based on the

Manuel et al. (1983) study of consumer expenditures on cleaning and household

maintenance. This study has been cited as being “the only study that measures welfare

benefits in a manner consistent with economic principals” (Desvousges et al., 1998). 

However, the data used to estimate household soiling damages in the Manuel et al. study are

from a 1972 consumer expenditure survey and as such may not accurately represent

consumer preferences in 2015. EPA recognizes this limitation, but believes the Manuel et al.

estimates are still useful in providing an estimate of the likely magnitude of the benefits of

reduced PM household soiling.

C.3 Income Elasticity of Willingness to Pay

As discussed in Chapter 4, our estimates of monetized benefits account for growth in

real GDP per capita by adjusting the WTP for individual endpoints based on the central

estimate of the adjustment factor for each of the categories (minor health effects, severe and

chronic health effects, premature mortality, and visibility). We examined how sensitive the

estimate of total benefits is to alternative estimates of the income elasticities. Table C-4 lists

the ranges of elasticity values used to calculate the income adjustment factors, while

Table C-5 lists the ranges of corresponding adjustment factors. The results of this sensitivity

analysis, giving the monetized benefit subtotals for the four benefit categories, are presented

in Table C-6.
C-14

Table C-4. Ranges of Elasticity Values Used to Account for Projected Real Income

Growtha

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effect 0.04 0.30

Severe and Chronic Health Effects 0.25 0.60

Premature Mortality 0.08 1.00

Visibilityb — —

a Derivation of these ranges can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI estimates are

assigned an adjustment factor of 1.0. 

b No range was applied for visibility because no ranges were available in the current published literature.

Table C-5. Ranges of Adjustment Factors Used to Account for Projected Real Income

Growtha

Benefit Category

Lower Sensitivity Bound Upper Sensitivity Bound

2010 2015 2010 2015

Minor Health Effect 1.010 1.015 1.074 1.114

Severe and Chronic

Health Effects 1.061 1.094 1.153 1.241

Premature Mortality 1.019 1.029 1.269 1.437

Visibilityb ————

a Based on elasticity values reported in Table C-4, U.S. Census population projections, and projections of real GDP per

capita.

b No range was applied for visibility because no ranges were available in the current published literature.
C-15

Table C-6. Sensitivity Analysis of Alternative Income Elasticitiesa

Benefit Category

Benefits in Millions of 1999$

Lower Sensitivity Bound Upper Sensitivity Bound

2010 2015 2010 2015

Minor Health Effect $640 $830 $670 $880

Severe and Chronic Health

Effects $3,900 $5,100 $4,200 $5,600

Premature Mortality $63,000 $83,000 $78,000 $120,000

Visibility and Other Welfare

Effectsb $1,100 $1,800 $1,100 $1,800

Total Benefits $68,000 $91,000 $84,000 $120,000

a All estimates rounded to two significant digits.

b No range was applied for visibility because no ranges were available in the current published literature.

Consistent with the impact of mortality on total benefits, the adjustment factor for

mortality has the largest impact on total benefits. The value of mortality in 2015 ranges from

90 percent to 130 percent of the primary estimate based on the lower and upper sensitivity

bounds on the income adjustment factor. The effect on the value of minor and chronic health

effects is much less pronounced, ranging from 98 percent to 105 percent of the primary

estimate for minor effects and from 93 percent to 106 percent for chronic effects.

C.4 References

Anderson, H.R., R.W. Atkinson, J.L. Peacock, L. Marston, and K. Konstantinou. 2004.

Meta-analysis of Time-series Studies and Panel Studies of Particulate Matter (PM)

and Ozone (O3): Report of a WHO Task Group. Copenhagen: World Health

Organization.

Bell, M.L., A. McDermott, S.L. Zeger, J.M. Samet, and F. Dominici. 2004. “Ozone and

Short-term Mortality in 95 U.S. Urban Communities, 1987-2000.” Journal of the

American Medical Association 292:2372-2378. 

Chestnut, L.G. 1997. “Draft Memorandum: Methodology for Estimating Values for

Changes in Visibility at National Parks.” April 15.
C-16

Chestnut, L.G., and R.D. Rowe. 1990a. Preservation Values for Visibility Protection at the

National Parks: Draft Final Report. Prepared for Office of Air Quality Planning and

Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC and

Air Quality Management Division, National Park Service, Denver, CO.

Chestnut, L.G., and R.D. Rowe. 1990b. “A New National Park Visibility Value Estimates.” 

In Visibility and Fine Particles, Transactions of an AWMA/EPA International

Specialty Conference, C.V. Mathai, ed. Air and Waste Management Association,

Pittsburgh.

Clancy, L., P. Goodman, H. Sinclair, and D.W. Dockery. 2002. “Effect of Air-pollution

Control on Death Rates in Dublin, Ireland: An Intervention Study.” Lancet Oct

19;360(9341):1210-4.

Desvousges, W.H., F.R. Johnson, and H.S. Banzhaf. 1998. Environmental Policy Analysis

With Limited Information: Principles and Applications of the Transfer Method (New

Horizons in Environmental Economics.) Edward Elgar Pub: London. 

Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, and

F.E. Speizer. 1993. “An Association between Air Pollution and Mortality in Six

U.S. Cities.” New England Journal of Medicine 329(24):1753-1759.

EPA-SAB-COUNCIL-ADV-00-001. October 1999. The Clean Air Act Amendments

(CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the

Health and Ecological Effects Subcommittee on Initial Assessments of Health and

Ecological Effects. Part 2. 

EPA-SAB-COUNCIL-ADV-99-012. July 1999. The Clean Air Act Amendments (CAAA)

Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health

and Ecological Effects Subcommittee on Initial Assessments of Health and Ecological

Effects. Part 1. 

EPA-SAB-COUNCIL-ADV-01-004. September 2001. Review of the Draft Analytical Plan

for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act

1990-2020: An Advisory by a Special Panel of the Advisory Council on Clean Air

Compliance Analysis. 
C-17

EPA-SAB-COUNCIL-ADV-04-002. March 2004. Advisory on Plans for Health Effects

Analysis in the Analytical Plan for EPA’s Second Prospective Analysis—Benefits and

Costs of the Clean Air Act, 1990-2020: Advisory by the Health Effects Subcommittee

of the Advisory Council on Clean Air Compliance Analysis.

Fairley, D. 2003. “Mortality and Air Pollution for Santa Clara County, California, 1989-

1996.” In: Revised Analysis of Time Series Studies of Air Pollution and Health. 

Special Report. Boston: Health Effects Institute, 97-106.

Ito, K., and G.D. Thurston. 1996. “Daily PM10/Mortality Associations: An Investigations of

At-Risk Subpopulations.” Journal of Exposure Analysis and Environmental

Epidemiology 6(1):79-95.

Kellsall, J., J.M. Samet, and S.L. Zeger. 1997. “Air Pollution and Mortality in Philadelphia,

1974-1988.” American Journal of Epidemiology 146:750-762.

Kinney, P.L., K. Ito, and G.D. Thurston. 1995. “A Sensitivity Analysis of Mortality PM-10

Associations in Los Angeles.” Inhalation Toxicology 7(1):59-69.

Kleckner, N., and J. Neumann. June 3, 1999. “Recommended Approach to Adjusting WTP

Estimates to Reflect Changes in Real Income.” Memorandum to Jim Democker, US

EPA/OPAR.

Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M.

Abrahamowicz, and W.H. White. July 2000. Reanalysis of the Harvard Six Cities

Study and the American Cancer Society Study of Particulate Air Pollution and

Mortality. Special Report to the Health Effects Institute, Cambridge MA.

Levy, J.L., Carrothers, T.J., J.T. Tuomisto, J.K. Hammitt, and J.S. Evans. 2001. “Assessing

the Public Health Benefits of Reduced Ozone Concentrations.” Environmental

Health Perspectives 109:1215-1226.

Manuel, E.H., R.L. Horst, K.M. Brennan, W.N. Lanen, M.C. Duff, and J.K. Tapiero. 1983. 

Benefits Analysis of Alternative Secondary National Ambient Air Quality Standards

for Sulfur Dioxide and Total Suspended Particulates, Volumes I-IV. Prepared for

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

Standards. Research Triangle Park, NC. 

Moolgavkar, S.H., E.G. Luebeck, T.A. Hall, and E.L. Anderson. 1995. “Air Pollution and

Daily Mortality in Philadelphia.” Epidemiology 6(5):476-484.
C-18

Moolgavkar, S.H. 2000. “Air Pollution and Hospital Admissions for Diseases of the

Circulatory System in Three U.S. Metropolitan Areas.” Journal of the Air and Waste

Management Association 50:1199-1206. 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of

Proposed Air Pollution Regulations. The National Academies Press: Washington,

D.C.

Pope, C.A. III, J. Schwartz, and M.R. Ransom. 1992. “Daily Mortality and PM10 Pollution

in Utah Valley.” Arch Environ Health 47(3):211-217. 

Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and

C.W. Heath, Jr. 1995. “Particulate Air Pollution as a Predictor of Mortality in a

Prospective Study of U.S. Adults.” American Journal of Respiratory Critical Care

Medicine 151:669-674. 

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine

Particulate Air Pollution.” Journal of the American Medical Association

287:1132-1141. 

Röösli, M., N. Künzli, and C. Braun-Fahrländer. August 1-4, 2004. “Use of Air Pollution

‘Intervention-Type’ Studies in Health Risk Assessment.” 16th Conference of the

International Society for Environmental Epidemiology, New York.

Samet, J.M., S.L. Zeger, J.E. Kelsall, J. Xu, and L.S. Kalkstein. March 1997. Air Pollution,

Weather, and Mortality in Philadelphia 1973-1988. Cambridge, MA: Health Effects

Institute. 

Thurston, G.D., and K. Ito. 2001. “Epidemiological Studies of Acute Ozone Exposures and

Mortality.” J Expo Anal Environ Epidemiol. 11(4):286-94.

Toulomi, G., K. Katsouyanni, D. Zmirou, and J. Schwartz. 1997. “Short-term Effects of

Ambient Oxidant Exposure on Mortality: A Combined Analysis within the APHEA

Project.” American Journal of Epidemiology 146:177-183.

U.S. Environmental Protection Agency (EPA). 2004. Air Quality Criteria for Particulate

Matter, Volume II. Office of Research and Development. EPA/600/P-99/002bF,

October 2004.
C-19

World Health Organization (WHO). 2003. Health Aspects of Air Pollution with Particulate

Matter, Ozone, and Nitrogen Dioxide: Report on a WHO Working Group. 

EUR/03/5042688. Bonn, Germany: World Health Organization.
D-1

APPENDIX D

SENSITIVITY ANALYSES OF KEY PARAMETERS IN THE COST AND

ECONOMIC IMPACT ANALYSIS AND A LISTING OF IPM RUNS IN SUPPORT

OF CAIR

This appendix presents results of sensitivity analyses using the IPM with alternative

assumptions for the price of natural gas and the growth rate of electricity demand. In

addition, a list of the IPM runs that were used in the various analyses done in support of the

final CAIR is provided. Model output from each of the IPM runs listed in this memo is

available in the CAIR docket and also on EPA’s Web site at www.epa.gov/airmarkets/

epa-ipm.

EPA uses IPM to estimate costs and, more broadly, analyze the projected impact of

air emission control policies on the electric power sector in the 48 contiguous states and the

District of Columbia. IPM is a multiregional, dynamic, deterministic linear programming

model of the U.S. electric power sector. IPM documentation is available in the CAIR docket

and also on EPA’s Web site at www.epa.gov/airmarkets/epa-ipm/.

Modeling applications of IPM produce forecasts for model plants (i.e., clusters of

real-life EGUs with similar characteristics). The model plant projections can be used to

produce parsed results, which are unit-level results derived from the model plant projections. 

Projections for individual plants are based on data currently available and modeling

parameters that are simplifications of the real world. It is likely that some future actions

regarding individual plants could differ from model projections of actions. However, the

aggregate impacts are expected to be appropriately characterized by the model. Where

appropriate, EPA produced parsed results from IPM runs for use in analyzing the air quality

impacts of CAIR.

D.1 Effects of Change in Assumptions for Natural Gas Prices and Electricity Growth

Sensitivity analyses were performed using projections from the 2004 Annual Energy

Outlook produced by the Energy Information Administration (EIA). EPA used EIA

estimates for the difference between natural gas prices and coal prices, which we have shorthanded as “EIA natural gas prices,” as well as EIA’s projection of electricity growth. These
D-2

particular assumptions involve considering the higher differential between minemouth coal

and wellhead natural gas prices. For the years 2010, 2015, and 2020, there was a higher

differential of $0.25 mmBtu, $0.42 mmBtu, and $0.38 mmBtu, respectively. The electricity

growth was changed to match EIA’s growth of 1.8 percent a year rather than EPA’s growth

of 1.6 percent. 

Total annual regional costs of CAIR with EIA assumptions are in Table D-1. The

costs of CAIR with EIA assumptions for natural gas prices and electricity growth in 2010

and 2015 are only slightly different from costs of CAIR without those assumptions and can

be attributed to the building of new and cleaner coal-fired capacity that leads to lower overall

costs (see Tables D-1 and D-2). As demand continues to grow, coal-fired generation

continues to increase and requires the use of additional scrubbers. Although more pollution

controls are installed using EIA assumptions, dispatch changes lead to the use of more

efficient generation. The power sector is less inclined to use gas as a compliance option in

the region because of the higher operating cost. Once the power sector passes the point

where there is no longer excess gas capacity in the marketplace (as currently exists), new

coal-fired capacity is the logical choice to meet demand. This new capacity would be built

inside and outside the CAIR region. 

The annualized regional cost of CAIR, as presented in Table D-1, is EPA’s best

assessment of the cost of implementing CAIR, assuming that States adopt the model cap and

trade program. These costs are generated from rigorous economic modeling of changes in

the power sector due to CAIR. This type of analysis using IPM has undergone peer review

and been upheld in Federal courts. The direct cost includes, but is not limited to, capital

investments in pollution controls, operating expenses of the pollution controls, investments

in new generating sources, and additional fuel expenditures. EPA believes that these costs

reflect, as closely as possible, the additional costs of CAIR to industry. The relatively small

cost associated with monitoring emissions for affected sources is not included in the

annualized cost, but EPA has done a separate analysis and estimated the cost to be less than

$42 million (see Section X. B. Paperwork Reduction Act). However, there may exist certain

costs that EPA has not quantified in these estimates. These costs may include costs of

transitioning to CAIR, such as the costs associated with the retirement of smaller or less

efficient electricity generating units, employment shifts as workers are retrained at the same

company or re-employed elsewhere in the economy, and certain relatively small permitting

costs associated with Title IV that new program entrants face. Although EPA has not

quantified these costs, the Agency believes that they are small compared to the quantified

costs of the program on the power sector. The annualized cost estimates are the best and 
D-3

Table D-1. Annual Regional Costs of CAIR with EPA and EIA Assumptions for

Natural Gas Prices and Electric Growth (Billion $1999)

Year EPA Assumptions EIA Assumptions

2010 $2.4 $2.6

2015 $3.6 $3.4

2020 $4.4 $4.1

Source: Integrated Planning Model run by EPA.

Table D-2. Incremental Pollution Controls under CAIR with EPA and EIA

Assumptions for Natural Gas and Electricity Growth (Incremental GWs)

EPA Assumptions EIA Assumptions

Technology 2010 2015 2020 2010 2015 2020

FGD 37 64 82 45 69 92

SCR 14 34 33 18 39 40

Source: Integrated Planning Model run by EPA.

most accurate based upon available information. At the macroeconomic level, the indirect

costs and impacts of higher electricity prices on the entire economy are presented in

Appendix E of the Regulatory Impact Analysis.

The marginal cost to remove additional tons of SO2 and NOx increases slightly with

EIA assumptions for natural gas and electricity growth, as more controls are installed on

coal-fired units that are slightly more expensive to control. Table D-3 compares the result of

sensitivity analysis to the CAIR case with EPA assumptions. 

Table D-4 shows nationwide emissions of SO2 and NOx using EIA assumptions. 

Coal-fired generation under CAIR increases using EIA assumptions for natural gas prices

and electricity growth. Table D-5 shows the generation mix with EIA assumptions.
D-4

Table D-3. Marginal Cost of SO2 and NOx Reductions under CAIR with EPA and EIA

Assumptions for Natural Gas Prices and Electric Growth ($/ton, in $1999)

2010 2015 2020

SO2

EPA Assumptions $700 $1,000 $1,400

EIA Assumptions $800 $1,200 $1,500

NOx

EPA Assumptions $1,300 $1,600 $1,600

EIA Assumptions $1,400 $1,700 $1,700

Source: Integrated Planning Model run by EPA.

Table D-4. Projected Nationwide Emissions of SO2 and NOx under CAIR with EPA

and EIA Assumptions for Natural Gas and Electric Growth (Million Tons)

SO2 NOx

2010 2015 2020 2010 2015 2020

Base Case with EPA Assumptions 9.7 8.9 8.6 3.6 3.7 3.7

CAIR with EPA Assumptions 6.1 4.9 4.2 2.4 2.1 2.1

Base Case with EIA Assumptions 9.7 8.8 8.6 3.7 3.7 3.8

CAIR with EIA Assumptions 6.1 5.0 4.0 2.4 2.1 2.2

Source: Integrated Planning Model run by EPA.
D-5

Table D-5. Generation Mix under CAIR with EPA and EIA Assumptions for Natural

Gas and Electric Growth (Thousand GWhs)

EPA Assumptions EIA Assumptions

Fuel 2010 2015 2020 2010 2015 2020

Base

Case

Coal 2,198 2,242 2,410 2,243 2,638 3,048

Oil/Natural Gas 777 1,026 1,221 902 867 873

Other 1,223 1,235 1,218 1,224 1,235 1,224

Total 4,198 4,503 4,850 4,369 4,739 5,145

CAIR

Coal 2,163 2,195 2,381 2,228 2,632 3,045

Oil/Natural Gas 809 1,072 1,250 916 871 874

Other 1,218 1,233 1,217 1,223 1,234 1,221

Total 4,190 4,499 4,847 4,367 4,738 5,141

Note: Numbers may not add due to rounding.

Source: Integrated Planning Model run by EPA.

Coal production patterns change slightly and production for all three major coalproducing regions is higher, because coal-fired generation is a cheaper source of electricity

than natural gas in most parts of the country with the higher EIA prices, even as more

pollution controls are added to coal-fired generation and used to meet the additional

electricity demand (see Table D-6). 

Electricity prices are not greatly altered with EIA assumptions for natural gas and

electricity growth (see Tables D-7 and D-8). For the CAIR region, average electricity prices

are projected to be lower than current levels (2000) using both EPA and EIA assumptions for

natural gas and electricity growth.

The IPM sensitivities listed in this appendix (Table D-9) that are not discussed in this

document were also used in support of CAIR, and relevant discussion can be found in the

appropriate section of the CAIR preamble.
D-6

Table D-6. Coal Production for the Electric Power Sector under CAIR with EPA and

EIA Assumptions for Natural Gas and Electricity Growth (Million Tons)

Supply

Area 2000 2003

EPA Assumptions EIA Assumptions

2010 2015 2020 2010 2015 2020

Base

Case

Appalachia 299 275 325 315 301 328 341 340

Interior 131 135 161 162 173 161 182 247

West 475 526 603 631 714 626 748 840

National 905 936 1,089 1,109 1,188 1,115 1,271 1,428

CAIR

Appalachia 299 275 306 310 331 320 367 390

Interior 131 135 164 193 219 174 207 260

West 475 526 607 579 607 614 676 765

National 905 936 1,077 1,082 1,156 1,109 1,250 1,415

Source: 2000 and 2003 data are from EIA. All projections are from the Integrated Planning Model run by EPA.

Table D-7. Retail Electricity Prices by NERC Region with the Base Case (No Further

Controls) and with CAIR Using EPA Assumptions for Natural Gas and Electricity

Growth (Mills/kWh)

Power

Region

Base Case CAIR Percent Change

Primary States Included 2000 2010 2015 2020 2010 2015 2020 2010 2015 2020

ECAR OH, MI, IN, KY, WV, PA 57.4 51.7 55.2 56.1 53.8 58.5 58.0 4.0% 5.9% 3.4%

ERCOT TX 65.1 57.9 64.4 62.6 59.3 64.6 63.3 2.5% 0.2% 1.2%

MAAC PA, NJ, MD, DC, DE 80.4 59.3 69.4 72.2 61.2 71.7 72.8 3.2% 3.4% 0.8%

MAIN IL, MO, WI 61.2 52.6 57.8 61.0 54.0 60.3 62.0 2.6% 4.3% 1.7%

MAPP MN, IA, SD, ND, NE 57.4 52.8 49.3 47.6 52.9 49.6 48.0 0.2% 0.7% 0.8%

NY NY 104.3 82.8 87.9 88.1 83.3 88.8 88.4 0.5% 1.0% 0.3%

NE VT, NH, ME, MA, CT, RI 89.9 77.4 83.9 82.8 77.5 84.7 83.0 0.1% 1.0% 0.2%

FRCC FL 67.9 71.2 71.3 69.5 71.7 72.3 70.5 0.8% 1.3% 1.5%

STV VA, NC, SC, GA, AL, MS, TN, AR, LA 59.3 56.2 55.1 55.3 57.0 56.2 56.6 1.5% 2.1% 2.3%

SPP KS, OK, MO 59.3 54.2 57.0 56.7 54.6 57.5 57.0 0.7% 0.9% 0.6%

Regionwide 66.0 58.0 60.8 61.0 59.2 62.4 62.1 2.0% 2.7% 1.8%

Source: Retail Electricity Price Model run by EPA. 2000 prices are from EIA’s AEO 2003.
D-7

Table D-8. Retail Electricity Prices by NERC Region with the Base Case (No Further

Controls) and with CAIR Using EIA Assumptions for Natural Gas and Electricity

Growth (Mills/kWh)

Power

Region

Base Case CAIR Percent Change

Primary States Included 2000 2010 2015 2020 2010 2015 2020 2010 2015 2020

ECAR OH, MI, IN, KY, WV, PA 57.4 53.5 59.8 57.1 55.3 61.5 58.8 3.5% 3.0% 3.0%

ERCOT TX 65.1 63.3 66.0 64.4 63.6 66.6 65.0 0.5% 0.8% 0.9%

MAAC PA, NJ, MD, DC, DE 80.4 63.1 74.7 72.8 64.0 75.4 73.7 1.4% 1.0% 1.3%

MAIN IL, MO, WI 61.2 54.9 63.8 62.4 55.9 65.2 63.3 1.7% 2.2% 1.5%

MAPP MN, IA, SD, ND, NE 57.4 52.9 49.6 48.1 53.1 49.9 48.6 0.4% 0.6% 0.9%

NY NY 104.3 89.0 91.3 87.8 89.1 91.9 88.8 0.1% 0.6% 1.1%

NE VT, NH, ME, MA, CT, RI 89.9 85.1 85.5 81.2 84.7 85.9 81.8 -0.4% 0.5% 0.9%

FRCC FL 67.9 72.5 74.6 73.7 73.3 75.3 74.3 1.1% 0.9% 0.9%

STV VA, NC, SC, GA, AL, MS, TN, AR, LA 59.3 57.1 57.1 57.1 57.8 58.3 58.6 1.1% 2.0% 2.6%

SPP KS, OK, MO 59.3 56.2 59.5 57.9 56.7 59.7 58.1 0.9% 0.3% 0.3%

Regionwide 66.0 60.5 64.0 62.5 61.3 65.1 63.6 1.4% 1.6% 1.8%

Source: Retail Electricity Price Model run by EPA. 2000 prices are from EIA’s AEO 2003.
D-8

Table D-9. Listing of Runs from the Integrated Planning Model Used in Analyses Done

in Support of the CAIR Final Rule Analyses

Run Name Run Description

Base Case 2004 Base case model run, which includes the national Title IV

SO2 cap-and-trade program; NOx SIP Call regional ozone

season cap-and-trade program; and state-specific programs

in Connecticut, Illinois, Maine, Massachusetts, Minnesota,

Missouri, New Hampshire, New York, North Carolina,

Oregon, Texas, and Wisconsin. This run represents

conditions without the proposed CAIR.

CAIR 2004_Analysis CAIR control strategy used for much of the analytical work

for the final CAIR (includes AR/DE/NJ for annual controls

and no ozone season cap and is the IPM run used for air

quality modeling)

CAIR 2004_Final Final CAIR policy (includes annual and ozone season caps

for the States who contribute to PM2.5 and/or ozone

nonattainment) 

CAIR 2004_Final_DE and NJ CAIR Final policy with DE and NJ included in the annual

program (based off CAIR 2004_Final)

BART 2004_Nationwide Nationwide BART control strategy

CAIR + BART 2004 CAIR Analysis control strategy, with BART requirements in

non-CAIR states

CAIR 2004_CSP CAIR Final policy with annual NOx compliance supplement

pool

Base Case 2004_EIA Base Case run with EIA assumptions for the difference

between natural gas prices and coal prices, as well as EIA’s

projection of electricity growth

CAIR 2004_EIA CAIR Analysis run with EIA assumptions for the difference

between natural gas prices and coal prices, as well as EIA’s

projection of electricity growth

CAIR 2004_EIA_One Phase CAIR Analysis run with EIA assumptions for the difference

between natural gas prices and coal prices, EIA’s projection

of electricity growth, and Phase II caps pushed to 2010

CAIR 2004_EIA_SCR Costs CAIR Analysis run with EIA assumptions for the difference

between natural gas prices and coal prices, EIA’s projection

of electricity growth, and SCR capital costs and fixed O&M

costs scaled up 30 percent

CAIR 2004_SCR Bypass_NOx SIP Call CAIR Analysis run with additional SCR bypass cost for

existing NOx SIP Call units only

(Continued)
D-9

Table D-9. Listing of Runs from the Integrated Planning Model Used in Analyses Done

in Support of the CAIR Final Rule Analyses (continued)

Run Name Run Description

CAIR 2004_No NOx CAIR Analysis SO2 policy, with base case NOx

CAIR 2004_No SO2 CAIR Analysis NOx policy, with base case SO2

CAIR 2004_No SO2_Summer NOx_1 CAIR NOx control during ozone season only in all CAIR

states, with base case SO2

CAIR 2004_No SO2_ Summer NOx_2 CAIR NOx control during ozone season in 8-hour ozone

states, with base case SO2

BART 2004_No NOx Nationwide BART SO2 limits, with base case NOx

BART 2004_No SO2 Nationwide BART NOx limits, with base case SO2

CAIR 2004_No Retirement Ratios CAIR Analysis run but with an SO2 cap rather than use of

Title IV allowance retirement ratios for use in CAIR

Parsed Files

EPA base case parsed for year 2010

EPA base case parsed for year 2015

EPA base case parsed for year 2020

EPA CAIR parsed for year 2010

EPA CAIR parsed for year 2015

EPA CAIR parsed for year 2020

EPA BART parsed for year 2015

EPA CAIR + BART parsed for year 2015
E-1

APPENDIX E

CAIR INDUSTRY-SECTOR IMPACTS 

EPA estimates the direct costs of implementing CAIR at $3.6 billion in 2015 in the

CAIR region. Given the impact of this rule on electricity generators, we believe it is

important to gauge the extent to which the rule might affect other industry sectors. To do so,

we conducted a limited analysis of the economy-wide effects of implementing CAIR.

We were particularly interested in learning how anticipated changes in electricity

prices might affect industry sectors that are large electricity users. The models we employed

indicated those impacts would be small, even without incorporating the beneficial economic

effects of CAIR-related air quality improvements such as improved worker health and

productivity. Rather, our analyses continue to show that the value of even the limited subset

of CAIR benefits we were able to quantify substantially outweigh implementation costs. The

degree to which projected benefits exceed projected costs would be even greater if we were

able to include a number of other beneficial effects, such as a reduction in acid rain damage

and lowering of nitrogen deposition.

By focusing only on cost-side spillover effects on the economy, the industry-sector

impacts projected by our macroeconomic models are likely overstated, primarily because the

positive market impacts of the CAIR on labor availability and productivity are excluded. In

this regard, an independent panel of experts has encouraged EPA to work toward

incorporating both beneficial and costly effects when modeling the economy-wide

consequences of regulation. EPA is actively working to develop this capability. 

Although neither model has yet been configured to include the indirect economic

benefits of air quality improvements, EPA employed two distinct computable general

equilibrium models to gauge the potential magnitude of the economy-wide effects of CAIR

implementation costs. The first model, known as Intertemporal General Equilibrium Model

(IGEM), has a long track record and was used by the Agency for the first of the two Clean

Air Act Section 812 studies. The other model, called EMPAX-CGE, is currently in peer

review and has the advantage of disaggregating the U.S. into multiple regions. As with all

models, these tools have their respective strengths and weaknesses, and differences in data

and choice of functional form imply that the models are likely to show slightly different
E-2

results. Despite the differences between the models, the results of the respective analyses

show similarly small impacts of CAIR on energy intensive industries. For example,

production changes for the chemical manufacturing industry are estimated at –0.01 percent to

–0.04 percent in 2010. Furthermore, neither model was configured to capture the beneficial

economic consequences of the increased labor availability and productivity expected to result

from CAIR-related air quality improvements. If these labor productivity improvements were

included, the small production output decreases projected by both models might be partially

or entirely offset. EPA continues to investigate the feasibility of incorporating labor

productivity gains and other beneficial effects of air quality improvements in computable

general equilibrium models. The individual analyses of CAIR by the two general

equilibrium models follow.

E.1 IGEM Economy-wide Analysis of CAIR: Analysis of Electric-Sector Impacts

CAIR is designed to improve air quality in nonattainment areas by achieving

reductions in SO2 and/or NOX emissions from the electric power industry in 29 eastern states

and the District of Columbia. 

EPA has modeled the macroeconomic impacts of the electric-sector changes expected

from this rule. This analysis used the IGEM developed by Dale Jorgenson, Peter Wilcoxen,

and Mun Sing Ho and maintained by Dale Jorgenson Associates. This appendix discusses

the economy-wide model and the approach used to analyze the rule, along with the results of

this modeling. 

IGEM is a dynamic computable general equilibrium model of the U.S. economy. The

model is a stylized representation of the entire economy, in which supply, demand, prices,

and quantities for goods and services reach equilibria for each year of the simulation’s time

horizon. IGEM represents the U.S. economy as 35 distinct sectors, roughly corresponding to

the two-digit levels of the North American Industry Classification System (NAICS). This

level of disaggregation allows for insight into the economy-wide effects of policies that

directly affect only a limited number of sectors.

The model has been used in peer-reviewed academic studies and in government,

private-sector, and nonprofit policy analyses, including the first study of the Clean Air Act

under Section 812. In the 812 process, the model was subjected to peer review by the EPA’s

SAB.
E-3

E.1.1 Modeling Approach

EPA believes that the best tool currently available to analyze rules and policies

affecting the electric power sector is IPM developed by ICF Consulting. (For information on

this model, see www.epa.gov/airmarkets/epa-ipm.) IPM is a highly disaggregated model of

the electric utility industry that provides a much more detailed view of changes in the sector

than would be possible in the framework of an economy-wide model such as IGEM. 

However, IPM currently has no representation of other sectors and cannot analyze the

economy-wide impacts of the rule. 

EPA has chosen to use the results of IPM regarding electric sector costs and fuel

quantity changes and introduce them as inputs into IGEM. Introducing these changes as

inputs causes IGEM to find new equilibria, as the relative prices of factor inputs adjust and

economic agents change their demand for goods and services. A change of this nature in an

economy-wide model shows that, compared to a reference case (base case), some sectors

experience increased demand for their outputs, while other sectors face reduced demand. 

The model accounts for the overall changes in economic activity, reporting the respective

impacts on the output of each of the 35 individual producing industries, as well as on

consumer prices, labor supply and demand, and GDP. IGEM, therefore, complements the

sector-specific IPM analysis by showing how the changes in the electric sector affect the

other sectors of the economy in some detail.

Please note that this analysis only accounts for CAIR impacts to the electric power

sector itself as modeled by IPM. It does not account for other economic effects, including

the substantial economic benefits of reduced emissions of atmospheric pollutants. The

annual benefits to which EPA can assign a dollar valuation are estimated to be $73.3 billion

in 2010 and $60.4 billion (3 percent and 7 percent discount rates, respectively) in 2010 and

$101 billion or $86.3 billion (3 percent and 7 percent discount rates, respectively) in 2015. 

There are additional benefits, including environmental benefits and some health

improvements for which EPA could not estimate dollar values. See Chapter 4 for more

information on benefits analyses. The dollar figures cited above and throughout this

appendix are in 1999 dollars.

E.1.2 Modeling Methodology

Economic models have different embedded assumptions and model structures; thus, it

is always challenging to link two models together. Because IGEM is an aggregate,

econometrically derived model of the entire U.S. economy and IPM is a detailed, technology
E-4

rich model of a single sector, these challenges are certainly present. EPA developed the

methodology of linking the IPM outputs as IGEM inputs by working closely with Dale

Jorgenson Associates.

The approach used in the present analysis consists of two steps. The first is to use

IPM to calculate the total incremental resource costs of CAIR to the electric sector compared

to a reference case. These costs include changes in capital costs, operating and maintenance

(O&M) costs, and fuel costs associated with electricity generation. The incremental costs

then are added to the resource cost of inputs for the IGEM electricity sector. The simulation

shows that these additional costs result in changes in productivity and, hence, lower supply

of and demand for electricity in the rest of the economy. For businesses, governments, and

households, electricity becomes relatively more expensive, and these “consumers” adjust

their purchasing behaviors, substituting away from electricity.

The second step is to account for the changes IPM projects for the coal sector. More

than 90 percent of coal produced in the United States is consumed in the electric sector

(DOE, 2004). EPA considers the IPM projections of coal production for the power sector to

be sufficiently accurate to incorporate them into the economy-wide representation of IGEM. 

These impacts are introduced into IGEM by adjusting the productivity of the coal sector to

account for the IPM projection of the quantity change of coal consumed. The specific

percentage change to the IGEM coal-sector productivity is calculated as the percentage

reduction in coal consumption projected by IPM, multiplied by the fraction of total coal

output consumed by the electric sector. This adjustment changes the price of coal, and all

sectors then adjust, demanding relatively less coal.

Because of the differences in underlying data, model structures, and estimated

parameters between IGEM and IPM, it is not possible to match both the price and quantity

changes arising in each methodology. EPA and Dale Jorgenson Associates believe that more

meaningful estimates for the economy arise by explicitly matching the coal quantity impacts

projected by IPM rather than IPM’s projected price impacts. Furthermore, although the

majority of coal produced in the United States is consumed in the electric sector, this is not

the case for natural gas. Therefore, IPM projections of gas usage for the electric sector are

not necessarily indicative of the economy-wide demand for this fuel, and EPA has chosen

not to model the impacts of CAIR on natural gas using the same approach as that used for

coal. Rather, IGEM is allowed to solve for all changes in natural gas markets as the

economy responds to the rule’s impacts on electricity and coal markets.
E-5

E.1.3 Projected Impacts on Specific Industries

The productivity changes incurred by the electric sector and coal sector contribute to

changes in output for virtually all sectors. This happens because, as noted above, relative

price levels for electricity and for energy sources which may substitute for electricity change

under CAIR. In this simulation, consumers demand for energy intensive goods tends, in

general, to fall slightly, while demand for other goods tends to rise. 

For the vast majority of the 35 IGEM sectors, the impact is less than 0.05 percent

(five one hundredths of 1 percent) of forecasted domestic output measured in sales and may

be positive, representing increased production, or negative, representing loss of output. 

For some sectors, however, the results are more pronounced. IGEM shows increases

in natural gas output, because natural gas is substituted for coal and electricity; this increase

is 0.16 percent in 2010 and 0.20 percent in 2015. Other sectors benefit as well, as consumers

substitute away from energy consumption to other products, particularly agriculture and

food.

Sectors that face relatively larger output changes also include the electric and coal

sectors. The loss in the coal sector is very similar to that predicted by IPM, at 1.27 percent 

in 2010 and 1.63 percent in 2015. The electric sector itself faces a loss of output of 1.01

percent in 2010 and 1.27 percent in 2015. Other energy-intensive industries also lose

output, ranging from 0.01 percent for the chemical industry to 0.10 percent for petroleum

refining. Figure E-1 details the 2015 results. (The complete results for 2010 and 2015 for

each of the 35 sectors represented in IGEM are included later in this appendix in

Section E.1.9.) 

E.1.4 Projected Impacts on Consumer Prices 

Indices of consumer prices are well-accepted measures of price levels within an

economy. The indices increase when consumers face higher prices for the goods they

purchase. These indices are calculated as weighted-average prices for particular “baskets” of

goods commonly purchased by households. Within IGEM, “the basket” is based on

household purchases of the 35 commodities available in its various markets for goods and

services. The electric sector impacts of the CAIR have effects on IGEM’s aggregate

consumer price level. There is a direct effect as electricity becomes relatively more

expensive and an indirect effect as industries that consume electricity face a higher price for 
E-6

-5.0% -4.0% -3.0% -2.0% -1.0% 0.0% 1.0%

Agriculture, forestry, fisheries

Metal mining

Coal mining

Crude oil and gas extraction

Non-metallic mineral mining

Construction

Food and kindred products

Tobacco manufactures

Textile mill products

Apparel and other textile products

Lumber and wood products

Furniture and fixtures

Paper and allied products

Printing and publishing

Chemicals and allied products

Petroleum refining

Rubber and plastic products

Leather and leather products

Stone, clay and glass products

Primary metals

Fabricated metal products

Non-electrical machinery

Electrical machinery

Motor vehicles

Other transportation equipment

Instruments

Miscellaneous manufacturing

Transportation and warehousing

Communications

Electric utilities (services)

Gas utilities (services)

Wholesale and retail trade

Finance, insurance and real estate

Personal and business services

Government enterprises

Percentage Change from Reference Case

Source: Intertemporal General Equilibrium Model

Figure E-1. Impact on Domestic Output, by Sector, 2015
E-7

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

2010 2015

Percent Change From Reference Case 

Source: Intertemporal General Equilibrium Model

a factor input, and then pass on that price increase by charging higher prices for goods and

services. Figure E-2 shows the percentage change in consumer prices (approximately 0.032

percent in 2010 and 0.036 percent 2015) over the reference case.

Figure E-2. Change in Consumer Prices Compared to Reference Case

E.1.5 Projected Impacts on Labor Markets

IGEM projects changes in labor utilization under the CAIR scenario (see Figure E-3). 

Note that this is not an explicit statement about employment or jobs, as the IGEM model

does not calculate estimates of employment or job creation. Rather, the model accounts for

labor supply and demand in terms of “hours.” An increase in “labor input” associated with

the CAIR implies an increase in the number of quality-adjusted hours supplied by

households and demanded by employers. In the IGEM framework, as energy prices rise,

firms substitute other inputs, which include capital, materials, and labor. For details on the

representation of labor within IGEM, and information on interpreting the results of the

model, please see Section E.1.8. The change in labor input is less than 0.01 percent in 2010

and 2015.
E-8

-0.05%

-0.04%

-0.03%

-0.02%

-0.01%

0.00%

0.01%

0.02%

0.03%

0.04%

0.05%

2010 2015

Percent Change From Reference Case 

Source: Intertem poral G eneral Equ ilibrium M odel

Figure E-3. Change in Labor Input Compared to Reference Case

E.1.6 Projected Impacts on GDP

In this analysis, the costs incurred by the electric sector contribute to a change in

GDP. EPA believes that these GDP results are incomplete, as they do not represent an

economy-wide modeling of the substantial economic benefits expected to accrue due to

decreased health impacts and other effects of reduced emissions. Because these results are

incomplete, the impacts on GDP should not be construed as the costs of the rule. The results

here show only the impact of resource cost changes to the electricity and coal sectors. These

changes cause the other IGEM sectors and households to adjust to accommodate higher

energy prices, and the economy incurs net losses. Almost all general equilibrium models can

be expected to show that a change in resource costs to one sector tends to be transmitted to

other sectors. IGEM is no exception. As shown in Figure E-4, IGEM projects the overall

cost as a result of the direct effects on coal and electricity to be approximately 0.03 percent

of GDP (three one hundredths of one percent) in 2010 and 2015.
E-9

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

2010 2015

Percent Change From Reference Case 

Source: Intertemporal General Equilibrium Model

Figure E-4. Change in Gross Domestic Product (GDP) Compared to Reference

However, these effects must be viewed in the context of a growing economy; IGEM

projects overall reference case GDP growth to be similar to that projected by the Energy

Information Administration’s 2004 Annual Energy Outlook (AEO). According to IGEM,

U.S. GDP is projected to grow to $13.9 trillion in 2010 and to $16.2 trillion in 2015. 

Figure E-5 shows the projected growth of GDP in the base case compared to the growth of

GDP including the costs (but not the benefits) of CAIR. The dotted line representing growth

in the economy in the scenario which includes CAIR impacts is slightly lower than the solid

line representing the reference case. (Note that in Figure E-5, the apparent difference is

smaller than thickness of the lines. The policy line appears to lie nearly on top of the

reference line).
E-10

$16,153 - Reference

$13,893 - Reference $13,889 - CAIR

$16,147 - CAIR

$12,500

$13,000

$13,500

$14,000

$14,500

$15,000

$15,500

$16,000

$16,500

2010 2015

Gross Domestic Product (billion 1999$)

IGEM Reference Case

CAIR Policy Case

Source: IntertemporalGeneral Equilibrium Model 

Figure E-5. U.S. Gross Domestic Product (GDP): Reference Case vs. CAIR

E.1.7 IGEM Model Description

As noted above, IGEM is a dynamic, multiperiod, multisector computable general

equilibrium model. IGEM solves for supply and demand equilibria in the markets for 35

commodities produced by 35 domestic industries as well as imported from overseas. These

commodities are demanded by producers as intermediate inputs to production or by

households, businesses, government, or exporters as finished goods. Each producing sector

is represented by equations that specify the sector’s production (outputs) and factor demands

(inputs) and the degree to which these factor inputs might be substituted for one another. 

IGEM is unique among models of this type in that the important economic parameters used

in the model were derived entirely from econometric analyses of historical U.S. economic

data. These parameters include measures of the substitutability of inputs to production,
1

See <http://www.epa.gov/cleanair2004/> for details.

2

See <http://www.epa.gov/airmarkt/epa-ipm/> for complete IPM documentation.

E-11

consumers’ relative demands for goods and services, and trends in productivity

improvement.

IGEM is characterized by “perfect foresight,” in which economic agents act

rationally with complete knowledge of the future to maximize the long-term net benefits

arising from their decisions. It is also important to note that IGEM represents capital as

“perfectly mobile,” so that infrastructure is easily converted to other uses as dictated by

demand considerations within the context of overall availability (supply).

E.1.8 Labor Representation in IGEM

IGEM is a “full employment” model, in which all inputs are fully used, including the

labor market. This assumption follows from IGEM’s focus on market equilibria (supply and

demand balances) in all markets, including labor, and from the fact that labor is represented

by quality-adjusted hours and not by persons. Labor supply is endogenously determined in

the model, so that the amount of labor supply and demand and the real (i.e., inflationadjusted) wage rate adjust in response to policy changes. Labor use, the labor variable

reported by IGEM, is a proxy measurement for “employment,” but there are distinct

differences. In the full employment framework, households decide the quantity of labor

services to provide. In IGEM, workers have the option to enjoy leisure or provide labor and

do so based on the prevailing real wage rate. An increase in the labor utilization rate implies

that individuals are choosing to provide more labor to the market in response to firms’

demand for labor as a substitute for electricity and coal.

E.1.9 Sector Detail

Thirty-five sectors comprise the U.S. economy and are represented in IGEM. The list

of these sectors, along with the response of the sectors to the costs of CAIR, are presented in

Table E-1. The table shows the percentage change in output from each sector (the change is

in “constant – dollar sales” as a measure of output).

E.2 EMPAX-CGE Regional Macroeconomic Analysis of CAIR

CAIR reduces emissions of SO2 and NOx from electricity generation to improve air

quality.1 To complement the analysis of CAIR effects on electricity generation conducted by

the EPA using the IPM,2 the macroeconomic implications of this rule have been estimated 
E-12

Sector Percent Change from Reference

2010 2015

Agriculture, forestry, fisheries 0.07% 0.10%

Metal mining -0.04% -0.06%

Coal mining -1.27% -1.63%

Crude oil and gas extraction -0.04% -0.06%

Non-metallic mineral mining -0.03% -0.04%

Construction -0.01% -0.01%

Food and kindred products 0.11% 0.16%

Tobacco manufactures 0.08% 0.12%

Textile mill products -0.01% 0.01%

Apparel and other textile products -0.05% -0.06%

Lumber and wood products 0.00% 0.01%

Furniture and fixtures -0.02% -0.03%

Paper and allied products 0.01% 0.01%

Printing and publishing 0.01% 0.02%

Chemicals and allied products -0.01% -0.01%

Petroleum refining -0.07% -0.10%

Rubber and plastic products -0.02% -0.02%

Leather and leather products -0.05% -0.06%

Stone, clay and glass products 0.00% 0.01%

Primary metals -0.07% -0.09%

Fabricated metal products -0.01% -0.01%

Non-electrical machinery -0.02% -0.02%

Electrical machinery -0.02% -0.02%

Motor vehicles -0.03% -0.05%

Other transportation equipment -0.01% -0.02%

Instruments -0.03% -0.02%

Miscellaneous manufacturing -0.03% -0.04%

Transportation and warehousing 0.02% 0.03%

Communications 0.02% 0.02%

Electric utilities (services) -1.01% -1.27%

Gas utilities (services) 0.16% 0.20%

Wholesale and retail trade 0.01% 0.01%

Finance, insurance and real estate 0.03% 0.03%

Personal and business services 0.02% 0.02%

Government enterprises 0.00% -0.01%

Source: Intertemporal General Equilbrium Model

Table E-1. Change in Sector Output in Response to CAIR
3

See Section E.2.8 for additional details on the EMPAX-CGE model.

4

See <http://www.epa.gov/interstateairquality/technical.html>.

E-13

using EPA’s EMPAX-CGE model. EMPAX-CGE is a macroeconomic simulation model

developed by RTI International (RTI) for EPA’s Office of Air Quality Planning and

Standards (OAQPS). The focus of this analysis of CAIR is examining the sectoral and

regional distribution of economic effects across the U.S. economy. This appendix section

discusses the EMPAX model, the approach used to incorporate electricity-sector results from

IPM, and the results of the macroeconomic analysis. Please note that this analysis focuses on

electricity-sector impacts of CAIR as estimated by IPM. It does not account for other

economic and noneconomic effects, especially the substantial economic and health benefits

associated with reduced emissions. 

E.2.1 Background and Summary of EMPAX-CGE Model3

EMPAX was first developed in 2000 to support the economic analysis of EPA’s

maximum achievable control technology (MACT) rules controlling emissions from three

categories of combustion sources (reciprocating internal combustion engines, boilers, and

turbines). The initial framework consisted of a national multimarket partial-equilibrium

model with linkages between manufacturing industries and the energy sector. Effects of

combustion rules on these industries were estimated through their influence on energy prices

and output. Modified versions of EMPAX were subsequently used to analyze economic

impacts of strategies for improving air quality in the Southern Appalachian mountain region. 

Recent work on EMPAX has extended its scope to cover all aspects of the U.S.

economy at a regional level in either static or dynamic modes (the dynamic version of

EMPAX is used in this analysis). Although major regulations directly affect a large number

of industries, substantial indirect impacts can also result from changes in production, input

use, income, and household consumption patterns. Consequently, EMPAX now includes

economic linkages among all industrial and energy sectors as well as households that supply

factors of production and purchase goods (i.e., a computable general equilibrium, or CGE,

framework). This gives the version of EMPAX called EMPAX-CGE the ability to trace

economic impacts as they are transmitted throughout the economy and allows it to provide

critical insights to policy makers evaluating the magnitude and distribution of costs

associated with environmental policies. The EMPAX-CGE model was used to investigate

macroeconomic impacts of the Interstate Air Quality Rule, predecessor to CAIR.4
5

Static versions of EMPAX-CGE have more industries and households because they do not have to solve for

multiple time periods simultaneously and, consequently, have few computational constraints on the number

of industries and households.

6

EIA forecasts from the Annual Energy Outlook 2003 (AEO) are used in this analysis.

E-14

The dynamic version of EMPAX-CGE employed in this analysis of CAIR is an

intertemporally optimizing model. Agents have perfect foresight and maximize utility across

all time periods subject to budget constraints, while firms maximize profits subject to

technology constraints. Nested constant elasticity of substitution (CES) functions are used to

portray substitution possibilities available to producers and consumers. Along with the

underlying data, the nesting structures and associated substitution elasticities define current

production technologies and possible alternatives. Most industries have constant returns to

scale with the exception of fossil-fuel and agriculture industries that have decreasing returns

to scale due to use of factors in fixed supply (land and inputs of primary fuels, respectively). 

The current tax structure of the United States is included in EMPAX-CGE to account for

“tax interaction” effects, where interactions between tax distortions and environmental

policies can affect macroeconomic costs of policies.

The economic data in this CGE model come from state-level information provided by

the Minnesota IMPLAN Group, and the energy data come from the DOE’s Energy

Information Agency (EIA). In the dynamic version of EMPAX-CGE, these data are used to

define five regions within the United States, each containing 17 industries and four types of

households classified by income.5 The five regions have been selected to preserve important

regional differences in electricity generation technologies, and 17 industries are included that

cover five important types of energy (coal, crude oil, electricity from fossil and nonfossil

generation, natural gas, and refined petroleum), the energy-intensive industries most likely to

be affected by environmental policies, and the remaining sectors of the economy. 

Four sources of economic growth are included: technological change from

improvements in energy efficiency, growth in the available labor supply from population

growth and changes in labor productivity, increases in stocks of natural resources, and capital

accumulation. Changes in energy use per unit of output are modeled through exogenous

autonomous energy efficiency improvements (AEEI). The baseline solution in

EMPAX-CGE matches, as closely as possible, EIA forecasts for energy production by fuel

type, energy prices, fuel consumption by industry, industrial output, and regional economic

growth through 2025.6
7

See, for example, the analyses of energy/climate using CGE models organized by the Stanford University

Energy Modeling Forum (http://www.stanford.edu/group/EMF/home/index.htm).

E-15

Distortions associated with the existing tax structure in the United States have been

included in EMPAX-CGE. A wide range of theoretical and empirical literature has

examined “tax interactions” and found that they can substantially alter costs of

environmental (and other) policies. The IMPLAN economic database used by EMPAX-CGE

includes information on some types of taxes, which have been combined with other sources

to cover important distortions from capital and income taxes. 

E.2.2 Modeling Approach for Electricity Policies

EMPAX-CGE can be used to analyze a wide array of policy issues and is capable of

estimating how a change in a single part (or multiple parts) of the economy will influence

producers and consumers across the United States. However, although CGE models have

been used extensively to analyze climate policies that limit carbon emissions from electricity

production,7

 some other types of utility emissions policies are more difficult to consider. 

Unlike carbon dioxide, emissions of pollutants such as SO2, NOx, and mercury are not

necessarily proportional to fuel use.

These types of emissions can be lowered by a variety of methods: fuel switching

from high- to low-sulfur coal, moving from coal- to gas-fired generation, and/or installing

retrofit equipment designed to reduce emissions. However, the boiler-specific nature of

these decisions, and their costs and effects, cannot be adequately captured by the more

general structure of a CGE model. In addition, because of the ways that retrofits and

construction of new generating units affect electricity prices and fuel use, a detailed

characterization of electricity markets is preferable when estimating implications of policies

like CAIR. For these reasons, we developed an interface that allows a linkage between

EMPAX-CGE and the IPM model.

IPM is a comprehensive model of electricity generation and transmission in the

United States. The model contains data on all generating units available to dispatch

electricity to the national grid, their existing equipment configurations and fuel consumption,

transmission constraints, and generating costs. It includes characteristics of new units and

retrofits that can be built and/or installed. IPM is capable of estimating how electric utilities

will respond to policies by determining the least-cost methods of generating sufficient

electricity to meet demands, while meeting emissions reduction (and other) objectives. 
E-16

However, IPM does not fully consider how changes in the electricity sector, or

electricity prices, will affect the rest of the U.S. economy. Combining the strengths of IPM

(disaggregated unit-level analyses of electricity policies) with the strengths of CGE models

(macroeconomic effects of environmental policies) allows investigation of economy-wide

implications of policies that would normally be hard to estimate consistently and effectively. 

For electricity-generation regulations like CAIR that require a very disaggregated level of

analysis, IPM can determine for EMPAX-CGE a number of electricity market outcomes

needed to evaluate macroeconomic implications of policies. The linkage with IPM then

allows EMPAX-CGE to take these findings and use them in “counterfactual” policy

evaluations. 

Among the many results provided by IPM, several can potentially have significant

implications for the rest of the economy including changes in electricity prices, fuel

consumption by utilities, fuel prices, and changes in electricity production expenditures. 

EMPAX-CGE is capable of simultaneously incorporating some or all of these IPM findings,

depending on the desired type and degree of linkage between the two models. At the

regional level, EMPAX-CGE can match changes estimated by IPM for the following

variables:

C electricity prices (percentage change in retail prices)

C coal and gas consumption for electricity (percentage changes in Btus)

C coal and gas prices (percentage changes in prices)

C coal and gas expenditures ($ changes—Btus of energy input times $/MMBtu)

C capital costs ($ changes)

C fixed operating costs ($ changes)

C variable operating costs ($ changes)

For EMPAX-CGE to effectively incorporate IPM data on changes in costs, they have

to be expressed in terms of the productive inputs used in CGE models (i.e., capital, labor, and

material inputs produced by other industries). Rather than assume these costs represent a

proportional scaling up of all inputs to the electricity industry in EMPAX-CGE, we use

Nestor and Pasurka (1995) data on purchases made by industries for environmental

protection reasons to allocate these additional expenditures across inputs within 
E-17

EMPAX-CGE. Once these expenditures are specified, the incremental costs from IPM can

be used to adjust the production technologies and input purchases by electricity generation in

the CGE model.

E.2.3 Modeling Methodology for CAIR

The macroeconomic impacts of CAIR, as simulated by CGE models, will be a

function of the methodology used to link IPM to EMPAX-CGE and the economic

interactions accounted for by the CGE model. Initial effects will revolve around how using

additional resources in electricity generation draws some capital, labor, and materials from

other sectors of the economy. This, in turn, may affect prices in markets supplying these

inputs to utilities. Similarly, changes in coal and gas use in electricity and associated impacts

on their prices will have implications for the rest of the economy (although any spillovers in

coal markets will have limited effects outside of electricity because most is used for

generation). In addition, any electricity price increases associated with these initial effects

will encourage improvements in energy efficiency, switching to alternate forms of energy

(increases in natural gas prices may mitigate this effect), and lower consumption of

electricity in general (these demand decreases would lead to lower production levels with

associated benefits for the environment). The magnitude of these adjustments will be a

function of the structure of the CGE model and the elasticities in it that control the ease of

these substitutions.

The macroeconomic effects beyond energy production and consumption decisions

also depend on the theoretical structure of the CGE model used in the analysis. Similar to

the perfect-foresight nature of IPM, CGE models like EMPAX-CGE assume that firms and

consumers will observe and anticipate policies to be enacted in the future. This causes them

to adjust their behavior and investment decisions in all time periods in the model (including

the starting year of the model). As a result, anticipation of changes in production and

consumption costs in the future will cause shifts in behavior in all model years as people

prepare ahead of policy enactment. The aggregate implications of these changes will also be

influenced by income effects (how people will alter consumption levels in response to having

more or less money) and substitution effects (how people will alter their patterns of

consumption purchases in response to changes in relative prices of goods). For example, an

anticipated decrease in labor productivity in the future (leading to lower wages) may cause

an increase in work effort today, while labor is more productive. Alternatively, it may lead 
8

See Section E.2.15 for EMPAX-CGE results using other linkages to IPM.

E-18

to a decrease in work effort today because, in part, labor today is used to generate capital

goods for tomorrow, which will be used to augment less-productive labor in the future.

The methodology used to link IPM and EMPAX-CGE for this CAIR analysis focuses

on CAIR resource costs and implications for coal use by utilities.8 IPM estimates of

additional resources used by electric utilities (the capital, fixed, and variable costs) are used

to adjust generation technologies in EMPAX-CGE. The same procedure is used to account

for incremental increases in natural gas purchases by utilities (although all these costs are

applied exclusively to natural gas inputs to electricity generation in EMPAX-CGE, rather

than being apportioned using the Nestor and Pasurka data). Given that approximately 90

percent of all coal is consumed in generation, we use IPM estimates of changes in coal use

directly within EMPAX-CGE, expressed as percentage changes in Btus, rather than adopting

a less direct linkage. However, for both coal and natural gas, EMPAX-CGE is allowed to

estimate the impacts on commodity prices faced by the rest of the economy. 

E.2.4 Projected Impacts on Specific Industries

Impacts of CAIR on electricity-generation costs and their subsequent effects on

electricity prices, along with coal market changes, will affect output and prices of all

industries in EMPAX-CGE. These effects may increase or decrease output and/or revenue,

depending on their implications for production costs and technologies, and shifts in

household demands. However, as shown in Figure E-6, estimates for output changes from

CAIR (outside of electricity and coal) are generally around 0.05 percent and may be positive

or negative.

Some industries are affected more than others and others may increase production. 

Natural gas output increases as electric utilities switch out of coal generation and as higher

electricity prices cause other businesses and consumers to move to alternate energy sources. 

Energy-intensive sectors of the economy are relatively more affected than other firms

because they rely more heavily on electricity and other fuels. However, the largest of these

declines in output (aluminum) is approximately two-tenths of 1 percent. 
E-19

-5.0% -4.0% -3.0% -2.0% -1.0% 0.0% 1.0%

Transportation

Services

Other Manufacturing

EIS: Aluminum

EIS: Iron and Steel

EIS: Cement

EIS: Glass

EIS: Chemicals

EIS: Paper and Allied

EIS: Food and Kindred

Agriculture

Petroleum

Natural Gas

Electricity

Crude Oil

Coal 

Percent Change from Reference

Figure E-6. CAIR Impacts on U.S. Domestic Output, 2015

Source: EMPAX-CGE.

Regional effects tend to show variation that does not appear at the national level. 

Figure E-7 shows these regional results for energy markets and highlights the aggregate U.S.

results with a solid bar. The largest differences are in electricity generation because the

western part of the United States is relatively unaffected by the policy, other than through

spillover effects reflected in both IPM and EMPAX-CGE. Increases in natural gas output

and declines in coal production are also distributed unevenly across the country.
9

Results for the seven components of energy-intensive sectors are aggregated in Figure E-8 for presentation

purposes, although they are all included simultaneously in the EMPAX-CGE model run.

E-20

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Coal Crude Oil Electricity Natural Gas Petroleum

Percent Change from Reference

Figure E-7. CAIR Impacts on Regional Energy Output, 2015

Source: EMPAX-CGE.

Figure E-8 shows regional results for nonenergy industries in EMPAX-CGE.9

Although these sectors show considerable regional variation based on differences in

production methods and energy price effects, the majority of the impacts are less than five

one-hundredths of 1 percent. Energy-intensive firms in the West experience a comparative

advantage relative to other parts of the country. Impacts on the services industry are

uniformly small, however; given the size of this sector, the effect on total revenue is larger

than for all other industries combined.

Although the average effect on energy-intensive sectors industries is negative

because of increased energy input costs, industries in some parts of the United States are

estimated to be raising their output. Even though energy prices have risen slightly, they 
E-21

-0.25%

-0.20%

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Agriculture Energy-Intensive

Manufacturing

Other

Manufacturing

Services Transportation

Percent Change from Reference 

Figure E-8. CAIR Impacts on Regional Industrial Output, 2015

Source: EMPAX-CGE.

experience an advantage over similar firms in other regions that face proportionately larger

price increases. Although the West sees the greatest improvement in comparative advantage,

output also rises in other regions (see Figure E-9).

E.2.5 Projected Impacts on Consumer Prices

Changes in consumer price levels are used to measure price effects of policies and

any resulting implications for average purchase prices paid by households. EMPAX-CGE

calculates an overall price level across the “basket” of goods and services bought by

consumers. For a policy like CAIR, consumer price levels will be affected directly by

changes in electricity prices faced by households and indirectly by changes in goods prices

that have been produced using electricity. Figure E-10 shows percentage changes in average

consumer prices on average ranging from 0.02 to 0.04 percent. 
E-22

-1.0%

-0.8%

-0.6%

-0.4%

-0.2%

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Food and

Kindred

Paper and

Allied

Chemicals Glass Cement Iron and Steel Aluminum

Percent Change from Reference

Figure E-9. CAIR Impacts on Regional Energy-Intensive Output, 2015

E.2.6 Projected Impacts on Labor Markets

CGE models like EMPAX-CGE typically consider how policies may influence labor

markets through how they alter the number of productivity-adjusted hours of labor supplied

by households (this is not the same as estimating jobs or employment). Empirical estimates

of labor-supply elasticities are used by EMPAX-CGE to simulate how demands by firms and

supply decisions by households are made, along with resulting implications for real wages. 

EMPAX-CGE, similar to IGEM, is a full-employment model in which households choose

between labor and leisure time, based on both income and substitution effects.
E-23

0.00%

0.01%

0.02%

0.03%

0.04%

0.05%

0.06%

0.07%

0.08%

0.09%

0.10%

2010 2015

Percent Change from Reference

Figure E-10. Change in Consumer Prices Compared to Reference Case

Source: EMPAX-CGE.

Figure E-11 gives EMPAX-CGE’s projected impacts of CAIR on labor markets. The

results indicate that people are choosing to work slightly fewer hours in response to small

declines in real wage rates, rather than work more hours to offset additional costs of

purchasing goods. All of these effects are extremely small, however, on the order of two

one-thousandths of 1 percent. 

E.2.7 Projected Impacts on GDP

The combination of all economic interactions as described earlier will be reflected in

the changes in GDP estimated by a CGE model. Given that this cost-based approach to

analyzing CAIR does not reflect its benefits to the environment, public health, and labor

productivity, CGE models (including EMPAX-CGE) will tend to estimate declines in total 
E-24

-0.010%

-0.008%

-0.006%

-0.004%

-0.002%

0.000%

0.002%

0.004%

0.006%

0.008%

0.010%

2010 2015

Percent Change from Reference

Figure E-11. Change in Labor Inputs Compared to Reference Case

Source: EMPAX-CGE

production in the United States, as shown in Figure E-12. Because these results are

incomplete and do not reflect potential benefits of CAIR, the impacts on GDP should not be

construed as the costs of the rule. EMPAX-CGE projects decreases in GDP of between 0.03

percent and 0.05 percent (five one-hundredths of 1 percent). 

Overall, it should be noted that the estimated implications of CAIR for U.S. GDP are

extremely small relative to the total size of the economy. Figure E-13 illustrates GDP in the

model baseline and CAIR policy cases. As shown, the GDP impact is negligible and, in fact,

it is not possible to adjust the scale of the graph to the point where the two lines do not

overlap. Even these small costs could be reversed if the CGE analyses were extended to

include benefits associated with CAIR such as improvements in labor productivity from

environmental improvements.
E-25

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

2010 2015

Change in GDP (Percentage Change) .

Figure E-12. Change in GDP Compared to Reference Case

Source: EMPAX-CGE

National GDP effects like those in Figure E-13 may tend to obscure variation at a

regional or local level. Several potential sources of divergences in regional impacts exist:

C differences in IPM regional results based on regional mixes of generation

technologies (coal, gas, oil, and nonfossil use), which may be averaged out at a

national level;

C differences in regional production and consumption patterns for electricity and

nonelectricity energy goods;

C differences in industrial composition of regional economies;

C differences in household consumption patterns; and

C differences in regional growth forecasts.
E-26

$14,435 - Reference

$12,403 - Reference $12,399 - CAIR

$14,427 - CAIR

$11,000

$11,500

$12,000

$12,500

$13,000

$13,500

$14,000

$14,500

$15,000

2010 2015

Gross Domestic Product (Billion 1999$) .

EMPAX Reference Case

CAIR Policy Case

Figure E-13. U.S. Gross Domestic Product (GDP): Reference Case vs. CAIR

Source: EMPAX-CGE.

Figure E-14 presents the regional GDP changes estimated by EMPAX-CGE that

underlie the national U.S. results above. As with other types of results, western states are

relatively unaffected by CAIR for several reasons: most of the resource costs are

experienced by generators on the East Coast and in the Midwest, the industrial composition

of the West (especially California) tends to lean towards less energy-intensive industries like

services, and production patterns for energy-intensive sectors shift towards the West as it

experiences an improvement in its comparative advantage in their production. Other parts of

the United States, like the South and Midwest, depend more on coal-fired generation than the

West and, as a result, have slightly larger relative GDP declines than the U.S. average. In the

Northeast, there is a relatively equal reliance on coal versus natural gas in electricity

production, so its estimated impacts are in line with national averages.
E-27

-0.25%

-0.20%

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

2010 2015

Change in GDP (%) 

Northeast South Midwest Plains West US

Figure E-14. Change in Regional GDP Compared to Reference Case

Source: EMPAX-CGE.

Tables E-2 and E-3 show EMPAX-CGE estimates of changes in revenue and output

quantities for 2010 and 2015.

E.2.8 EMPAX-CGE Model Description: General Model Structure

This section provides additional details on the EMPAX-CGE model structure, data

sources, and assumptions. The version of EMPAX-CGE used in this CAIR analysis is a

dynamic, intertemporally optimizing model that solves in 5-year intervals from 2005 to

2050. It uses the classical Arrow-Debreu general equilibrium framework wherein

households maximize utility subject to budget constraints, and firms maximize profits

subject to technology constraints. The model structure, in which agents are assumed to have

perfect foresight and maximize utility across all time periods, allows agents to modify

behavior in anticipation of future policy changes, unlike dynamic recursive models that

assume agents do not react until a policy has been implemented. 
E-28

Table E-2. U.S. Domestic Output Changes

Model Run Industry 2010 2015

Percentage Change in Revenue (%) Coal –2.73% –3.13%

Crude oil –0.04% –0.08%

Electricity 0.60% 0.93%

Natural gas 0.38% 0.16%

Petroleum –0.03% –0.06%

Agriculture –0.04% –0.07%

Energy-intensive manufacturing –0.04% –0.07%

Other manufacturing –0.03% –0.07%

Services –0.04% –0.06%

Transportation –0.04% –0.09%

Percentage Change in Quantity (%) Coal –0.81% –1.17%

Crude oil –0.02% –0.04%

Electricity –1.33% –2.01%

Natural gas 0.08% 0.05%

Petroleum –0.02% –0.04%

Agriculture –0.03% –0.05%

Energy-intensive manufacturing –0.04% –0.07%

Other manufacturing 0.00% –0.04%

Services –0.01% –0.02%

Transportation –0.01% –0.04%

Source: EMPAX-CGE.
10Although it is not illustrated in Figure E-15, some differences across industries exist in their handling of

energy inputs. In addition, the agriculture and fossil-fuel sectors in EMPAX-CGE contain equations that

account for the presence of fixed inputs to production (land and fossil-fuel resources, respectively).

E-29

Table E-3. U.S. Domestic Energy-Intensive Sector Output Changes

Model Run Industry 2010 2015

Percentage Change in Revenue (%) Food and Kindred –0.04% –0.06%

Paper and Allied –0.03% –0.07%

Chemicals –0.04% –0.08%

Glass –0.04% –0.09%

Cement –0.01% –0.02%

Iron and Steel –0.02% –0.06%

Aluminum 0.02% –0.02%

Percentage Change in Quantity (%) Food and Kindred –0.03% –0.04%

Paper and Allied –0.03% –0.07%

Chemicals –0.04% –0.08%

Glass –0.05% –0.11%

Cement –0.02% –0.09%

Iron and Steel –0.02% –0.08%

Aluminum –0.12% –0.20%

Source: EMPAX-CGE.

Nested CES functions are used to portray substitution possibilities available to

producers and consumers. Figure E-15 illustrates this general framework and gives a broad

characterization of the model.10 Along with the underlying data, these nesting structures and

associated substitution elasticities determine the effects that will be estimated for policies. 

These nesting structures and elasticities used in EMPAX-CGE are generally based on the

Emissions Prediction and Policy Analysis (EPPA) Model developed at the Massachusetts

Institute of Technology (Babiker et al., 2001). Although the two models are quite different

(EPPA is a recursive-dynamic, international model focused on national-level climate-change

policies), both are intended to simulate how agents will respond to environmental policies.
11See Brooke et al. (1998) for a description of GAMS (http://www.gams.com/).

12Solving EMPAX-CGE as a MCP problem implies that complementary slackness is a feature of the equilibrium

solution. In other words, any firm in operation will earn zero economic profits and any unprofitable firms

will cease operations. Similarly, for any commodity with a positive price, supply will equal demand, or

conversely any good in excess supply will have a zero price.

13See Rutherford (1999) for MPSGE documentation (http://debreu.colorado.edu).

E-30

Utility 

Leisure

Goods (16 types) 

Consumption 

Household utility is a CES function 

of consumption and leisure. 

Consumption is a Cobb-Douglas 

composite of the 16 types of goods. 

Each consumption good is a CES 

composite of foreign and 

domestically produced goods. 

Domestic Foreign 

Domestic goods are a CES composite 

of locally produced goods and goods 

Local from other regions. 

Output 

Regional 

Output 

KLE Intermediates 

M ost producer goods use fixed proportions of intermediate inputs and a 

capital-labor-energy (KLE) composite. 

Intermediate materials inputs are the 11 types of nonenergy goods, in fixed proportion for each industry. 

E nergy V alue A dded 

Capital Labor 

Energy 

(5 Types) 

The K LE composite is a CES function 

of energy and value-added (KL). 

Value added is a Cobb-Douglas 

composite of capital and labor. 

Energy is a CES composite of 5 types of fuel. The 

structure of this function varies across industries. 

Figure E-15. General Production and Consumption Nesting Structure in EMPAXCGE

Given this basic similarity, EMPAX-CGE has adopted a comparable structure. 

EMPAX-CGE is programmed in the GAMS11 language (Generalized Algebraic Modeling

System) and solved as a mixed complementarity problem (MCP)12 using MPSGE software

(Mathematical Programming Subsystem for General Equilibrium).13 The PATH solver from

GAMS is used to solve the MCP equations generated by MPSGE.
14See <http://www.implan.com/index.html> for a description of the Minnesota IMPLAN Group and its data.

15These EIA sources include AEO 2003, the Manufacturing Energy Consumption Survey, State Energy Data

Report, State Energy Price and Expenditure Report, and various annual industry profiles.

E-31

E.2.9 Data Sources

The economic data come from state-level information provided by the Minnesota

IMPLAN Group14 and energy data come from EIA.15 Although IMPLAN data contain

information on the value of energy production and consumption in dollars, these data are

replaced with EIA data for several reasons. First, the policies being investigated typically

focus on energy markets, making it essential to include the best possible characterization of

these markets in the model. Although the IMPLAN data are developed from a variety of

government data sources at the U.S. Bureau of Economic Analysis and U.S. Bureau of Labor

Statistics, these data do not always agree with energy information collected by EIA directly

from manufacturers and electric utilities. Second, it is necessary to have physical quantities

for energy consumption in the model to portray effects of environmental policies. EIA

reports physical quantities, while IMPLAN does not. Finally, although the IMPLAN data

reflect the year 2000, the initial baseline year for the model is 2005. Thus, AEO energy

production and consumption, output, and economic growth forecasts for 2005 are used to

adjust the year 2000 IMPLAN data. 

EMPAX-CGE combines these economic and energy data to create a balanced social

accounting matrix (SAM) that provides a baseline characterization of the economy. The

SAM contains data on the value of output in each sector, payments for factors of production

and intermediate inputs by each sector, household income and consumption, government

purchases, investment, and trade flows. A balanced SAM for the year 2005 consistent with

the desired sectoral and regional aggregation is produced using procedures developed by

Babiker and Rutherford (1997) and described in Rutherford and Paltsev (2000). The

methodology relies on standard optimization techniques to maintain the calculated energy

statistics while minimizing the changes needed in the economic data to create a new

balanced SAM that matches AEO forecasts for the baseline model year of 2005. 

These data are used to define 10 regions within the United States, each containing 40

industries. Regions have been selected to capture important differences across the country in

electricity-generation technologies, while industry aggregations are controlled by available

energy consumption data. Prior to solving EMPAX-CGE, these regions and industries are

aggregated up to the categories to be included in the analysis.
16Energy-intensive sectors industry categories are based on EIA definitions of energy-intensive manufacturers in

the Assumptions for the Annual Energy Outlook 2003.

E-32

Table E-4 presents the industry categories included in EMPAX-CGE for the CAIR

analysis. Their focus is on maintaining as much detail in the energy-intensive sectors16 as is

allowed by available energy consumption data and computational limits of dynamic CGE

models. In addition, the electricity industry is separated into fossil-fuel generation and

nonfossil generation, which is necessary because many electricity policies like CAIR affect

only fossil-fired electricity.

Table E-4. EMPAX-CGE Industries

EMPAX Industry NAICS Classifications

Coal 2121

Crude Oila 211111

Electricity (fossil and nonfossil) 2211

Natural Gas 211112, 2212, 4862

Petroleum Refining 324

Agriculture 11

Energy-Intensive Sector: Food 311

Energy-Intensive Sector: Paper and Allied 322

Energy-Intensive Sector: Chemicals 325

Energy-Intensive Sector: Glass 3272

Energy-Intensive Sector: Cement 3273

Energy-Intensive Sector: Iron and Steel 3311

Energy-Intensive Sector: Aluminum 3313

Other Manufacturing 312-316, 321, 323, 326-327, 331-339

Services All Others

Transportationb 481-488 a Although NAICS 211111 covers crude oil and gas extraction, the gas component of this sector is moved to

the Natural Gas industry. b Transportation does not include NAICS 4862 (natural gas distribution), which is part of the natural gas

industry.
17Economic data and information on nonelectricity energy markets are generally available only at the state level,

which necessitates an approximation of the NERC regions that follows state boundaries. For the CAIR

analysis, these approximations include Northeast = NPCC + MAAC, Southeast = SERC + FERC, Midwest

= ECAR + MAIN, Plains = MAPP + SPP + ERCOT, and West = WSCC. See <http://www.nerc.com/> for

further discussion of these regions.

E-33

Figure E-16 shows the five regions used in this analysis, which have been defined

based on the expected regional distribution of policy impacts, availability of economic and

energy data, and computational limits on model size. These regions have been constructed

from the underlying 10-region database designed to follow, as closely as possible, the

electricity market regions defined by the North American Electric Reliability Council

(NERC).17 

Figure E-16. Regions Defined in EMPAX-CGE for the CAIR Analysis
E-34

E.2.10 Production Functions

All productive markets are assumed to be perfectly competitive and have production

technologies that exhibit constant returns to scale, except for the agriculture and natural

resource extracting sectors, which have decreasing returns to scale because they use factors

in fixed supply (land and fossil fuels, respectively). The electricity industry is separated into

two distinct sectors: fossil-fuel generation and nonfossil generation. This allows tracking of

variables such as heat rates for fossil-fired utilities (Btus of energy input per kilowatt hour of

electricity output).

All markets must clear (i.e., supply must equal demand in every sector) in every

period, and the income of each agent in the model must equal their factor endowments plus

any net transfers. Along with the underlying data, the nesting structures shown in

Figure E-15 and associated substitution elasticities define current production technologies

and possible alternatives. 

E.2.11 Utility Functions

Each region in the dynamic version of EMPAX-CGE contains four representative

households, classified by income, that maximize intertemporal utility over all time periods in

the model subject to budget constraints, where the income groups are 

C $0 to $14,999, 

C $15,000 to $29,999, 

C $30,000 to $49,999, and 

C $50,000 and above. 

These representative households are endowed with factors of production including

labor, capital, natural resources, and land inputs to agricultural production. Factor prices are

equal to the marginal revenue received by firms from employing an additional unit of labor

or capital. The value of factors owned by each representative household depends on factor

use implied by production within each region. Income from sales of these productive factors

is allocated to purchases of consumption goods to maximize welfare. 

Within each time period, intratemporal utility received by a household is formed from

consumption of goods and leisure. All consumption goods are combined using a

Cobb-Douglas structure to form an aggregate consumption good. This composite good is

then combined with leisure time to produce household utility. The elasticity of substitution
E-35

between consumption goods and leisure depends on empirical estimates of labor-supply

elasticities and indicates how willing households are to trade off leisure time for

consumption. Over time, households consider the discounted present value of utility

received from all periods’ consumption of goods and leisure. 

Following standard conventions of CGE models, factors of production are assumed to

be intersectorally mobile within regions, but migration of productive factors is not allowed

across regions. This assumption is necessary to calculate welfare changes for the

representative household located in each region in EMPAX-CGE. EMPAX-CGE also

assumes that ownership of natural resources and capital embodied in nonfossil electricity

generation is spread across the United States through capital markets. 

E.2.12 Trade

In EMPAX-CGE, all goods and services are assumed to be composite, differentiated

“Armington” goods made up of locally manufactured commodities and imported goods. 

Output of local industries is initially separated into output destined for local consumption by

producers or households and output destined for export. This local output is then combined

with goods from other regions in the United States using Armington-trade elasticities that

indicate agents make relatively little distinction between output from firms located within

their region and output from firms in other regions within the United States. Finally, the

domestic composite goods are aggregated with imports from foreign sources using lower

trade elasticities to capture the fact that foreign imports are more differentiated from

domestic output than are imports from other regional suppliers in the United States. 

E.2.13 Tax Rates and Distortions

Taxes and associated distortions in economic behavior have been included in

EMPAX-CGE because theoretical and empirical literature found that taxes can substantially

alter estimated policy costs. The IMPLAN economic database used by EMPAX-CGE

includes information on taxes such as indirect business taxes (all sales and excise taxes) and

Social Security taxes. However, IMPLAN reports factor payments for labor and capital at

their gross-of-tax values, which necessitates use of additional data sources to determine

personal income and capital tax rates. Information from the TAXSIM model at the National

Bureau of Economic Research (Feenberg and Coutts, 1993), along with user-cost-of-capital

calculations from Fullerton and Rogers (1993), are used to establish tax rates. 

Along with these rates, distortions associated with taxes are a function of labor

supply decisions of households. As with other CGE models focused on interactions between
18See Babiker et al. (2001) for a discussion of how this methodology was used in the EPPA model (EPPA

assumes that AEEI parameters are the same across all industries in a country, while AEEI values in

EMPAX-CGE are industry specific).

E-36

tax and environmental policies (e.g., Bovenberg and Goulder [1996]; Goulder and Williams

[2003]), an important feature of EMPAX-CGE is its inclusion of a labor-leisure

choice—how people decide between working and leisure time. Labor supply elasticities

related to this choice determine, to a large extent, how distortionary taxes are in a CGE

model. Elasticities based on the relevant literature have been included in EMPAX-CGE (i.e.,

0.4 for the compensated labor supply elasticity and 0.15 for the uncompensated labor supply

elasticity). These elasticity values give an overall marginal excess burden associated with

the existing tax structure of approximately 0.3.

E.2.14 Intertemporal Dynamics and Economic Growth

There are four sources of economic growth in EMPAX-CGE: technological change

from improvements in energy efficiency, growth in the available labor supply (from both

population growth and changes in labor productivity), increases in stocks of natural

resources, and capital accumulation. Energy consumption per unit of output tends to decline

over time because of improvements in production technologies and energy conservation. 

These changes in energy use per unit of output are modeled as AEEIs, which are used to

replicate energy consumption forecasts by industry and fuel from EIA.18 The AEEI values

provide the means for matching expected trends in energy consumption that have been taken

from the AEO forecasts. They alter the amount of energy needed to produce a given quantity

of output by incorporating improvements in energy efficiency and conservation. Labor force

and regional economic growth, electricity generation, changes in available natural resources,

and resource prices are also based on the AEO forecasts.

Savings provide the basis for capital formation and are motivated through people’s

expectations about future needs for capital. Savings and investment decisions made by

households determine aggregate capital stocks in EMPAX-CGE. The IMPLAN dataset

provides details on the types of goods and services used to produce the investment goods

underlying each region’s capital stocks. Adjustment dynamics associated with formation of

capital are controlled by using quadratic adjustment costs experienced when installing new

capital, which imply that real costs are experienced to build and install new capital

equipment.
19A steady-state growth path requires all variables in the model to grow at a constant rate over time, including

labor, output, inputs to production, and consumption. If the model has been properly specified, the

steady-state replication check will show that the economy remains in equilibrium in each year along this

path.

E-37

Prior to investigating policy scenarios, it is necessary to establish a baseline path for

the economy that incorporates economic growth and technology changes that are expected to

occur in the absence of the policy actions. Beginning from the initial balanced SAM dataset,

a “steady-state” growth path is first specified for the economy to ensure that the model

remains in equilibrium in future years.19 Once the model is able to replicate a steady-state

growth path, the assumption of a constant growth rate is replaced by actual forecasts from

AEO. After incorporating these forecasts, EMPAX-CGE is solved to generate a baseline

consistent with them through 2025. Once this baseline is established, it is possible to run

“counterfactual” policy experiments.

E.2.15 Alternative IPM-to-EMPAX Linkages

As discussed in Section E.2.2, EMPAX-CGE is capable of incorporating a variety of

results from IPM, depending on the desired type of linkage between the two models. This

section presents macroeconomic impacts of CAIR, as shown by changes in GDP, from two

alternative methods of using IPM results. These findings are contrasted to the “Central

Case” (i.e., the results presented above) to demonstrate how the methodology used to link the

two models can influence results. One alternative, referred to as the “IPM Price & Fuel

Case,” places a higher degree of reliance on IPM results than the “Central Case.” In the

other alternative, referred to as the “Unconstrained Case,” EMPAX-CGE is allowed to

determine more market outcomes than in the “Central Case.” These scenarios provide a

range of results that illustrate the macroeconomic implications of different methods for

linking macroeconomic models with the IPM results.

Specifically, the three alternative approaches are as follows:

C “Central Case”—This case from the main analysis incorporates IPM estimates of

resource costs (capital costs, and fixed and variable operating costs), along with

percentage changes in coal use (expressed in Btus), in EMPAX-CGE. The IPM

resource costs are used to adjust electricity generation costs within EMPAX-CGE

by requiring additional purchases of capital, labor, and material inputs. Natural

gas expenditures are also adjusted based on IPM findings by requiring additional

purchases of gas by utilities.
20IPM’s estimated wholesale prices are converted into changes in retail prices for EMPAX-CGE using EIA

estimates of transmission and distribution costs of approximately $27 per megawatt hour.

E-38

C “Unconstrained Case”—This case incorporates IPM estimates of both resource

costs and fuel expenditures for coal and gas into EMPAX-CGE. Unlike the

“Central Case,” this case allows changes in the quantity of coal use in the

electricity sector to be estimated by EMPAX-CGE, once declines in the dollar

value of coal purchases from the IPM model have been incorporated into the

model. These declines in coal purchases are integrated using the same

methodology applied to the capital, labor, and material inputs needed to generate

electricity—in this case, by requiring fewer purchases of coal to produce

electricity.

C “IPM Price & Fuel Case”—This case places the most reliance on IPM findings. 

Rather than allowing EMPAX-CGE to determine electricity price outcomes based

on IPM resource costs, it replicates the IPM price results in EMPAX-CGE and

concentrates on examining their implications for the rest of the economy.20

Similarly, this case uses IPM data on changes in coal and gas use (in physical

units) by electricity sector instead of allowing the CGE model to make these

decisions. Market prices for coal and gas are still determined by EMPAX-CGE. 

This case takes into consideration the fact that, although most resource costs of

electricity policies are borne by coal-fired generation, electricity prices are

typically determined by the marginal unit in operation. Because of this, there

may not be a direct correlation between policy costs and implications for

electricity prices, although the economy outside of the electricity industry will

respond to both electricity prices and any effects from drawing additional

resources into electricity production.

Figure E-17 illustrates the implications of these alternative linkages between IPM and

EMPAX-CGE for estimates of CAIR GDP effects. The “Central Case” from the main

analysis and “Unconstrained Case” follow similar paths; however, the “Unconstrained Case”

is uniformly less expensive. It provides more degrees of freedom to adjust coal consumption

by utilities in response to demand changes estimated by EMPAX-CGE and also has added

flexibility to shift among production inputs. This results in GDP impacts to around 20

percent lower than in the “Central Case.” 
E-39

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

2010 2015

Change in GDP (%) 

EMPAX - Central Case EMPAX - Unconstrained EMPAX - IPM Price & Fuel

Figure E-17. GDP Impacts of Alternative Linkages (in %)

The “IPM Price & Fuel Case” shows changes in GDP that follow the same pattern as

the “Central Case,” but are generally lower. The methodologies of these two cases are

substantially different: many of the effects in the “IPM Price & Fuel Case” are driven by

IPM’s estimated changes in electricity prices (and to a lesser degree by impacts on gas prices

from increased demand by generators), while in the “Central Case” electricity prices

predicted by EMPAX-CGE are controlled by how many additional resource costs are

entering electricity production. IPM results show moderate increases in electricity prices in

2010 and 2015, leading to smaller GDP effects in the “IPM Price & Fuel Case” than in the

“Central Case.” 
E-40

E.3 References

Babiker, M.H., and T.F. Rutherford. 1997. “Input-Output and General Equilibrium

Estimates of Embodied CO2: A Data Set and Static Framework for Assessment.” 

University of Colorado at Boulder, Working Paper 97-2. Available at

http://debreu.colorado.edu/papers/gtaptext.html.

Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I.S. Wing, and R.C. Hyman. 2001. 

“The MIT Emissions Prediction and CO2 Policy Analysis (EPPA) Model: Revisions,

Sensitivities, and Comparisons of Results.” MIT Joint Program on the Science and

Policy of Global Change, Report No. 71. Available at

http://web.mit.edu/globalchange/www/eppa.html.

Bovenberg, L.A., and L.H. Goulder. 1996. “Optimal Environmental Taxation in the

Presence of Other Taxes: General-Equilibrium Analysis.” American Economic

Review 86(4):985-1000. 

Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User’s Guide. 

GAMS Development Corporation. Available at http://www.gams.com.

Feenberg, D., and E. Coutts. 1993. “An Introduction to the TAXSIM Model.” Journal of

Policy Analysis and Management 12(1):189-194. Available at

http://www.nber.org/~taxsim/.

Fullerton, D., and D. Rogers. 1993. “Who Bears the Lifetime Tax Burden?” Washington,

DC: The Brookings Institute. Available at http://bookstore.brookings.edu/

book_details.asp?product%5Fid=10403.

Goulder, L.H., and R.C. Williams. 2003. “The Substantial Bias from Ignoring General

Equilibrium Effects in Estimating Excess Burden, and a Practical Solution.” Journal

of Political Economy 111:898-927.

Minnesota IMPLAN Group. 2003. State-Level Data for 2000. Available from

http://www.implan.com/index.html.

Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmental Protection Industry: A

Proposed Framework for Assessment. U.S. Environmental Protection Agency, Office

of Policy, Planning, and Evaluation. EPA 230-R-95-001. Available at

http://yosemite.epa.gov/ee/epa/eermfile.nsf/11f680ff78df42f585256b45007e6235/41

b8b642ab9371df852564500004b543/$FILE/EE-0217A-1.pdf. 
E-41

Rutherford, T.F. 1999. “Applied General Equilibrium Modeling with MPSGE as a GAMS

Subsystem: An Overview of the Modeling Framework and Syntax.” Computational

Economics 14(1):1-46. Also available at

http://www.gams.com/solvers/mpsge/syntax.htm.

Rutherford, T.F., and S.V. Paltsev. 2000. “GTAP-Energy in GAMS: The Dataset and Static

Model.” University of Colorado at Boulder, Working Paper 00-2. Available at

http://debreu.colorado.edu/papers/gtaptext.html.

U.S. Department of Energy, Energy Information Administration. State Energy Data Report.

Washington DC. Available at http://www.eia.doe.gov/emeu/states/

_use_multistate.html.

U.S. Department of Energy, Energy Information Administration. State Energy Price and

Expenditure Report. Washington DC. Available at

http://www.eia.doe.gov/emeu/states/price_multistate.html. 

U.S. Department of Energy, Energy Information Administration. 2001. Manufacturing

Energy Consumption Survey 1998. Washington DC. Available at

http://www.eia.doe.gov/emeu/mecs/. 

U.S. Department of Energy, Energy Information Administration. January 2003. Annual

Energy Outlook 2003. DOE/EIA-0383(2003). Washington DC. Available at

http://www.eia.doe.gov/oiaf/archive/aeo03/pdf/0383(2003).pdf.

U.S. Department of Energy, Energy Information Administration. January 2004. Annual

Energy Outlook 2004. DOE/EIA-0383(2003). Washington, DC. Available at

http://www.EOA.DOE.GOV/OIAF/AOE/PDF/0383 (2001).
F-1

*

*

*

#

# = Center Grid-Cell “E”

* = Air Pollution Monitor

*

*

*

*

APPENDIX F

ADDITIONAL TECHNICAL INFORMATION SUPPORTING 

THE BENEFITS ANALYSIS

This appendix provides additional technical details about several important elements

of the benefits analysis, including the spatial interpolation method and health effect pooling

methods. Additional details on benefits methods can be found in the BenMAP User’s

Manual, available in the docket and online at http://www.epa.gov/ttn/ecas/benmodels.html. 

F.1 Voronoi Neighbor Averaging

In calculating the base year concentrations of PM species and ozone at model grid

cells prior to scaling with model outputs, we used a spatial interpolation method known as

Voronoi Neighbor Averaging (VNA).

The first step in VNA is to identify the set of neighboring monitors for each of the

grid cells in the continental United States. The figure below presents nine grid cells and

seven monitors, with the focus on identifying the set of neighboring monitors for grid cell E.
F-2

di, . . 1

1

20

1

20

1

16

1

14

= 0 27

+ +





 





=

In particular, BenMAP identifies the nearest monitors, or “neighbors,” by drawing a

polygon, or Voronoi cell, around the center of each grid cell. The polygons have the special

property that the boundaries are the same distance from the two closest points.

We then chose those monitors that share a boundary with the center of grid cell E. These are

the nearest neighbors, and we used these monitors to estimate the air pollution level for this

grid cell.

To estimate the air pollution level in each grid cell, BenMAP calculates the air

pollution metrics for each of the neighboring monitors and then calculates an

inverse-distance weighted average of the metrics. The further the monitor is from the grid

cell center, the smaller the weight. 

The weight for the monitor 20 kilometers from the center of grid cell E is calculated as

follows:
F-3

The weights for the other monitors would be calculated in a similar fashion. 

F.2 The Random/Fixed Effect Pooling Procedure

Often more than one study has estimated a C-R function for a given pollutant-health

endpoint combination. Each study provides an estimate of the pollutant coefficient, $, in the

C-R function, along with a measure of the uncertainty of the estimate. Because uncertainty

decreases as sample size increases, combining data sets is expected to yield more reliable

estimates of $ and therefore more reliable estimates of the incidence change predicted using

$. Combining data from several comparable studies to analyze them together is often

referred to as meta-analysis.

For a number of reasons, including data confidentiality, it is often impractical or

impossible to combine the original data sets. Combining the results of studies to produce

better estimates of $ provides a second-best but still valuable way to synthesize information

(DerSimonian and Laird, 1986). This is referred to as pooling. Pooling $s requires that all

of the studies contributing estimates of $ use the same functional form for the C-R function. 

That is, the $s must be measuring the same thing.

It is also possible to pool the study-specific estimates of incidence change derived

from the C-R functions, instead of pooling the underlying $s themselves. For a variety of

reasons, this is often possible when it is not feasible to pool the underlying $s. For example,
F-4

if one study is log-linear and another is linear, we could not pool the $s because they are not

different estimates of a coefficient in the same C-R function but are instead estimates of

coefficients in different C-R functions. We can, however, calculate the incidence change

predicted by each C-R function (for a given change in pollutant concentration and, for the

log-linear function, a given baseline incidence rate) and pool these incidence changes. 

BenMAP allows the pooling of incidence changes predicted by several studies for the same

pollutant-health endpoint group combination. It also allows the pooling of the corresponding

study-specific estimates of monetary benefits.

As with estimates based on only a single study, BenMAP allows you to characterize

the uncertainty surrounding pooled estimates of incidence change and/or monetary benefit. 

To do this, BenMAP pools the study-specific distributions of incidence changes (or monetary

benefit) to derive a pooled distribution. This pooled distribution incorporates information

from all the studies used in the pooling procedure.

F.2.1 Weights Used for Pooling

The relative contribution of any one study in the pooling process depends on the

weight assigned to that study. A key component of the pooling process, then, is the

determination of the weight given to each study. Various methods can be used to assign

weights to studies. Below we discuss the possible weighting schemes that are available in

BenMAP. 

Subjective (User-specified) Weights

BenMAP allows the user the option of specifying the weights to be used. Suppose,

for example, the user wants to simply average all study-specific results. He would then

assign a weight of 1/N to each of the N study-specific distributions that are to be pooled. 

Note that subjective weights are limited to two decimal places and are normalized if they do

not sum to one.

Automatically Generated Weights

A simple average has the advantage of simplicity but the disadvantage of not taking

into account the uncertainty of each of the estimates. Estimates with great uncertainty

surrounding them are given the same weight as estimates with very little uncertainty. A

common method for weighting estimates involves using their variances. Variance takes into

account both the consistency of data and the sample size used to obtain the estimate, two key
F-5

factors that influence the reliability of results. BenMAP has two methods of automatically

generating pooling weights using the variances of the input distributions—fixed effects

pooling and random/fixed effects pooling.

The discussion of these two weighting schemes is first presented in terms of pooling

the pollutant coefficients (the $s), because that most closely matches the discussion of the

method for pooling study results as it was originally presented by DerSimonian and Laird

(1986). We then give an overview of the analogous weighting process used within BenMAP

to generate weights for incidence changes rather than $s.

F.3 Fixed Effects Weights

The fixed effects model assumes that there is a single true C-R relationship and

therefore a single true value for the parameter $ that applies everywhere. Differences among

$s reported by different studies are therefore simply the result of sampling error. That is,

each reported $ is an estimate of the same underlying parameter. The certainty of an

estimate is reflected in its variance (the larger the variance, the less certain the estimate). 

Fixed effects pooling therefore weights each estimate under consideration in proportion to

the inverse of its variance.

Suppose there are n studies, with the ith study providing an estimate $i

 with variance

vi (I = 1, ..., n). Let

denote the sum of the inverse variances. Then the weight, wi

, given to the ith estimate, $i, is

This means that estimates with small variances (i.e., estimates with relatively little

uncertainty surrounding them) receive large weights and those with large variances receive

small weights.
F-6

The estimate produced by pooling based on a fixed effects model, then, is just a

weighted average of the estimates from the studies being considered, with the weights as

defined above. That is,

The variance associated with this pooled estimate is the inverse of the sum of the inverse

variances:

Table F-1 shows the relevant calculations for this pooling for three sample studies.

Table F-1. Example of Fixed Effects Model Calculations

Study $i vi 1/vi wi wi*$i

1 0.75 0.1225 8.16 0.016 0.012

2 1.25 0.0025 400 0.787 0.984

3 1.00 0.0100 100 0.197 0.197

Sum 3 = 508.16 3 = 1.000 3 = 1.193

The sum of weighted contributions in the last column is the pooled estimate of $

based on the fixed effects model. This estimate (1.193) is considerably closer to the estimate

from study 2 (1.25) than is the estimate (1.0) that simply averages the study estimates. This

reflects the fact that the estimate from study 2 has a much smaller variance than the estimates

from the other two studies and is therefore more heavily weighted in the pooling.

The variance of the pooled estimate, vfe, is the inverse of the sum of the variances, or

0.00197. (The sums of the $i and vi are not shown, because they are of no importance. The

sum of the 1/vi is S, used to calculate the weights. The sum of the weights, wi, i=1, ..., n, is

1.0, as expected).
F-7

F.4 Random/Fixed Effects Weights

An alternative to the fixed effects model is the random effects model, which allows

the possibility that the estimates $i from the different studies may in fact be estimates of

different parameters, rather than just different estimates of a single underlying parameter. In

studies of the effects of PM10 on mortality, for example, if the composition of PM10 varies

among study locations the underlying relationship between mortality and PM10 may be

different from one study location to another. For example, fine particles make up a greater

fraction of PM10 in Philadelphia than in El Paso. If fine particles are disproportionately

responsible for mortality relative to coarse particles, then one would expect the true value of

$ in Philadelphia to be greater than the true value of $ in El Paso. This would violate the

assumption of the fixed effects model.

The following procedure can test whether it is appropriate to base the pooling on the

random effects model (vs. the fixed effects model). A test statistic, Qw, the weighted sum of

squared differences of the separate study estimates from the pooled estimate based on the

fixed effects model, is calculated as

Under the null hypothesis that there is a single underlying parameter, $, of which all the $is

are estimates, Qw has a chi-squared distribution with n–1 degrees of freedom. (Recall that n

is the number of studies in the meta-analysis.) If Qw is greater than the critical value

corresponding to the desired confidence level, the null hypothesis is rejected. That is, in this

case the evidence does not support the fixed effects model, and the random effects model is

assumed, allowing the possibility that each study is estimating a different $. (BenMAP uses

a 5 percent one-tailed test.)

The weights used in a pooling based on the random effects model must take into

account not only the within-study variances (used in a meta-analysis based on the fixed

effects model) but the between-study variances as well. These weights are calculated as

follows:

Using Qw, the between-study variance, 02

, is
F-8

It can be shown that the denominator is always positive. Therefore, if the numerator

is negative (i.e., if Qw < n–1), then 02 is a negative number, and it is not possible to calculate

a random effects estimate. In this case, however, the small value of Qw would presumably

have led to accepting the null hypothesis described above, and the meta-analysis would be

based on the fixed effects model. The remaining discussion therefore assumes that 02 is

positive.

Given a value for 02, the random effects estimate is calculated in almost the same

way as the fixed effects estimate. However, the weights now incorporate both the withinstudy variance (vi

) and the between-study variance (02). Whereas the weights implied by the

fixed effects model used only vi, the within-study variance, the weights implied by the

random effects model use vi +02.

Let vi

* = vi +02. Then

and 

The estimate produced by pooling based on the random effects model, then, is just a

weighted average of the estimates from the studies being considered, with the weights as

defined above. That is, 
F-9

The variance associated with this random effects pooled estimate is, as it was for the

fixed effects pooled estimate, the inverse of the sum of the inverse variances:

The weighting scheme used in a pooling based on the random effects model is

basically the same as that used if a fixed effects model is assumed, but the variances used in

the calculations are different. This is because a fixed effects model assumes that the

variability among the estimates from different studies is due only to sampling error (i.e., each

study is thought of as representing just another sample from the same underlying

population), while the random effects model assumes that there is not only sampling error

associated with each study, but that there is also between-study variability—each study is

estimating a different underlying $. Therefore, the sum of the within-study variance and the

between-study variance yields an overall variance estimate.

F.5 Fixed Effects and Random/Fixed Effects Weighting to Pool Incidence Change

Distributions and Dollar Benefit Distributions 

Weights can be derived for pooling incidence changes predicted by different studies,

using either the fixed effects or the fixed/random effects model, in a way that is analogous to

the derivation of weights for pooling the $s in the C-R functions. As described above,

BenMAP generates a Latin hypercube representation of the distribution of incidence change

corresponding to each C-R function selected. The means of those study-specific Latin

hypercube distributions of incidence change are used in exactly the same way as the reported

$s are used to calculate fixed effects and random effects weights described above. The

variances of incidence change are used in the same way as the variances of the $s. The

formulas above for calculating fixed effects weights, for testing the fixed effects hypothesis,

and for calculating random effects weights can all be used by substituting the mean incidence
1

 There may be a problem with transferring the fixed effects hypothesis test to “incidence change space.” The

test statistic to test the fixed effects model is a chi-squared random variable. In the original paper on this

pooling method, DerSimonian and Laird (1986) were discussing the pooling of estimates of parameters,

which are generally normally distributed. The incidence changes predicted from a C-R function will not be

normally distributed if the C-R function is not a linear function of the pollutant coefficient, which, in most

cases it is not. (Most C-R functions are log-linear.) In that case, the test statistic may not be chi-square

distributed. However, most log-linear C-R functions are nearly linear because their coefficients are very

small. In that case the test statistic is likely to be nearly chi-square distributed. 

F-10

change for the ith C-R function for $i and the variance of incidence change for the ith C-R

function for vi.

1

Similarly, weights can be derived for dollar benefit distributions. As described

above, BenMAP generates a Latin hypercube representation of the distribution of dollar

benefits. The means of those Latin hypercube distributions are used in exactly the same way

as the reported $s are used to calculate the fixed effects and random effects weights

described above. The variances of dollar benefits are used in the same way as the variances

of the $s. The formulas above for calculating fixed effects weights, for testing the fixed

effects hypothesis, and for calculating random effects weights can all be used by substituting

the mean dollar benefit change for the ith valuation for $i and the variance of dollar benefits

for the ith valuation for vi.

BenMAP always derives fixed effects and random/fixed effects weights using

nationally aggregated results, and uses those weights for pooling at each grid cell (or county,

etc., if the user chooses to aggregate results prior to pooling). This is done because BenMAP

does not include any regionally based uncertainty—that is, all uncertainty is at the national

level in BenMAP, and all regional differences (e.g., population) are treated as certain.

F.6 Reference

DerSimonian, R. and N. Laird, “Meta-analysis in Clinical Trials,” Controlled Clinical Trials

7:3 (1986), 177-188.
G-1

APPENDIX G

HEALTH-BASED COST-EFFECTIVENESS OF REDUCTIONS IN AMBIENT PM2.5

ASSOCIATED WITH CAIR

G.1 Summary

Health-based cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) have

been used to analyze numerous health interventions but have not been widely adopted as

tools to analyze environmental policies. The Office of Management and Budget (OMB)

recently issued Circular A-4 guidance on regulatory analyses, requiring federal agencies to

“prepare a CEA for all major rulemakings for which the primary benefits are improved

public health and safety to the extent that a valid effectiveness measure can be developed to

represent expected health and safety outcomes.” Environmental quality improvements may

have multiple health and ecological benefits, making application of CEA more difficult and

less straightforward. For CAIR CEA may provide a useful framework for evaluation: nonhealth benefits are substantial, but the majority of quantified benefits come from health

effects. Therefore, EPA is including in the CAIR RIA a preliminary and experimental

application of one type of CEA—a modified quality-adjusted life-years (QALYs) approach. 

In this instance, the direct usefulness of cost-effectiveness analysis is mitigated by the lack of

rule alternatives to compare relative effectiveness, but one can still make some comparisons

to other benchmarks bearing in mind methodological differences.

QALYs were developed to evaluate the effectiveness of individual medical

treatments, and EPA is still evaluating the appropriate methods for CEA for environmental

regulations. Agency concerns with the standard QALY methodology include the treatment

of people with fewer years to live (the elderly); fairness to people with preexisting conditions

that may lead to reduced life expectancy and reduced quality of life; and how the analysis

should best account for nonhealth benefits, such as improved visibility. 

The Institute of Medicine (a member institution of the National Academies of

Science) has established the Committee to Evaluate Measures of Health Benefits for

Environmental, Health, and Safety Regulation to assess the scientific validity, ethical

implications, and practical utility of a wide range of effectiveness measures used or proposed

in CEA. This committee is expected to produce a report by the end of 2005. In the interim,
1

This metric is also referred to as a “fair QALY” in Hubbell (2004b). The metric is also being considered by the

Institute of Medicine Committee to Evaluate Measures of Health Benefits for Environmental, Health, and

Safety Regulation under the term “fair QALY.”

G-2

however, agencies are expected to provide CEAs for rules covered by Circular A-4

requirements.

The methodology presented in this appendix is not intended to stand as precedent

either for future air pollution regulations or for other EPA regulations where it may be

inappropriate. It is intended solely to demonstrate one particular approach to estimating the

cost-effectiveness of reductions in ambient PM2.5 in achieving improvements in public

health. Reductions in ambient PM2.5 likely will have other health and environmental benefits

that will not be reflected in this CEA. Other EPA regulations affecting other aspects of

environmental quality and public health may require additional data and models that may

preclude the development of similar health-based CEAs. A number of additional

methodological issues must be considered when conducting CEAs for environmental

policies, including treatment of nonhealth effects, aggregation of acute and long-term health

impacts, and aggregation of life extensions and quality-of-life improvements in different

populations. The appropriateness of health-based CEA should be evaluated on a case-bycase basis subject to the availability of appropriate data and models, among other factors.

CAIR is expected to result in substantial reductions in potential population exposure

to ambient concentrations of PM. The benefit-cost analysis presented in Chapter 4 shows

that CAIR achieves substantial health benefits whose monetized value far exceeds costs (net

benefits are over $100 billion in 2015). Despite the risk of oversimplifying benefits,

cautiously-interpreted cost-effectiveness calculations may provide further evidence of

whether the costs associated with CAIR are a reasonable health investment for the nation. 

This analysis provides estimates of commonly used health-based effectiveness

measures, including lives saved, life years saved (from reductions in mortality risk), and

QALYs saved (from reductions in morbidity risk) associated with the reduction of ambient

PM2.5 due to CAIR. In addition, a new aggregate effectiveness metric, Morbidity Inclusive

Life Years (MILY) is introduced to address some of the concerns about aggregation of life

extension and quality-of-life impacts.1

 It represents the sum of life years gained due to

reductions in premature mortality and the QALY gained due to reductions in chronic

morbidity. This measure may be preferred to existing QALY aggregation approaches

because it does not devalue life extensions in individuals with preexisting illnesses that

reduce quality of life. However, the MILY measure is still based on life years and thus still
G-3

inherently gives more weight to interventions that reduce mortality and morbidity impacts

for younger populations with higher remaining life expectancy. This analysis focuses on life

extensions and improvements in quality of life through reductions in two diseases with

chronic impacts: chronic bronchitis (CB) and nonfatal acute myocardial infarctions. Monte

Carlo simulations are used to propagate uncertainty in several analytical parameters and

characterize the distribution of estimated impacts. 

Presented in three different metrics, the analysis suggests the following:

C In 2010 CAIR will result in:

– 13,000 (95% CI: 5,400 – 20,000) premature deaths avoided, or

– 140,000 (95% CI: 104,000 – 184,000) life years gained (discounted at 3

percent), or

– 190,000 (95% CI: 140,000 – 250,000) MILYs gained (discounted at 3

percent). 

C In 2015, CAIR will result in:

– 17,000 (95% CI: 7,200 – 26,000) premature deaths avoided, or

– 190,000 (95% CI: 140,000 – 240,000) life years gained (discounted at 3

percent), or 

– 250,000 (95% CI: 180,000 – 330,000) MILYs gained (discounted at 3

percent). 

C Using a 7 percent discount rate, mean discounted life years gained are 110,000 in

2010 and 140,000 in 2015; mean MILYs gained are 140,000 in 2010 and 180,000

in 2015. (The estimates of premature deaths avoided are not affected by the

discount rate.) 

C The associated reductions in CB and nonfatal acute myocardial infarctions will

reduce medical costs by approximately $2.1 billion in 2010 and $2.7 billion in

2015 based on a 3 percent discount rate, or $1.8 billion in 2010 and $2.3 billion in

2015 based on a 7 percent discount rate. 

C Other health and nonhealth benefits are valued at $1.9 billion in 2010 and $2.8

billion in 2015. 

Direct private compliance costs of the CAIR rule are $2.4 billion in 2010 and $3.6

billion in 2015. Therefore, the net costs (costs minus avoided cost of illness minus other

benefits) are negative, indicating that CAIR results in cost savings. As such, traditional cost-
G-4

effectiveness ratios are not informative. However, it is possible to calculate the maximum

costs for the rule that would still result in cost-effective improvements in public health

compared with standard benchmarks of $50,000 and $100,000 per QALY: 

1. Taking into account avoided medical costs and other benefits, annual costs of

CAIR would need to exceed $14 billion (95% CI: $9.8 billion – $18 billion)

in 2010 and $18 billion (95% CI: $13 billion – $23 billion) in 2015 to have a

cost per MILY that exceeds a benchmark of $50,000, based on a 3 percent

discount rate. 

2. Annual costs of CAIR would need to exceed $23 billion (95% CI: $17 billion

– $31 billion) in 2010 and $30 billion (95% CI: $22 billion – $40 billion) in

2015 to have a cost per MILY that exceeds a benchmark of $100,000, based

on a 3 percent discount rate. 

3. Using a 7 percent discount rate, annual costs of CAIR would need to exceed

$11 billion in 2010 and $14 billion in 2015 to have a cost per MILY that

exceeds a benchmark of $50,000, and would need to exceed $18 billion in

2010 and $23 billion in 2015 to have a cost per MILY that exceeds a

benchmark of $100,000. 

Given costs of $2.4 billion and $3.6 billion in 2010 and 2015, respectively, CAIR is

clearly a very cost-effective way to achieve improvements in public health.

G.2 Introduction

Analyses of environmental regulations have typically used benefit-cost analysis to

characterize impacts on social welfare. Benefit-cost analyses allow for aggregation of the

benefits of reducing mortality risks with other monetized benefits of reducing air pollution,

including acute and chronic morbidity, and nonhealth benefits such as improved visibility. 

One of the great advantages of the benefit-cost paradigm is that a wide range of quantifiable

benefits can be compared to costs to evaluate the economic efficiency of particular actions. 

However, alternative paradigms such as CEA and CUA analyses may also provide useful

insights. CEA involves estimation of the costs per unit of benefit (e.g., lives or life years

saved). CUA is a special type of CEA using preference-based measures of effectiveness,

such as QALYs.

CEA and CUA are most useful for comparing programs that have similar goals, for

example, alternative medical interventions or treatments that can save a life or cure a disease. 
G-5

They are less readily applicable to programs with multiple categories of benefits, such as

those reducing ambient air pollution, because the cost-effectiveness calculation is based on

the quantity of a single benefit category. In other words, we cannot readily convert

improvements in nonhealth benefits such as visibility to a health metric such as life years

saved. For these reasons, environmental economists prefer to present results in terms of

monetary benefits and net benefits.

However, QALY-based CUA has been widely adopted within the health economics

literature (Neumann, 2003; Gold et al., 1996) and in the analysis of public health

interventions (US FDA, 2004). QALY-based analyses have not been as accepted in the

environmental economics literature because of concerns about the theoretical consistency of

QALYs with individual preferences (Hammitt, 2002), treatment of nonhuman health

benefits, and a number of other factors (Freeman, Hammitt, and De Civita, 2002). For

environmental regulations, benefit-cost analysis has been the preferred method of choosing

among regulatory alternatives in terms of economic efficiency. Recently several academic

analyses have proposed the use of life years-based benefit-cost or CEAs of air pollution

regulations (Cohen, Hammitt, and Levy, 2003; Coyle et al., 2003; Rabl, 2003; Carrothers,

Evans, and Graham, 2002). In addition, the World Health Organization has adopted the use

of disability-adjusted life years, a variant on QALYs, to assess the global burden of disease

due to different causes, including environmental pollution (Murray et al., 2002; de Hollander

et al., 1999). 

Recently, the U.S. OMB (Circular A-4, 2003) issued new guidance requiring federal

agencies to provide both CEA and benefit-cost analyses for major regulations. The OMB

Circular A-4 directs agencies to “prepare a CEA for all major rulemakings for which the

primary benefits are improved public health and safety to the extent that a valid effectiveness

measure can be developed to represent expected health and safety outcomes.” We are

including a CEA for CAIR to illustrate one potential approach for conducting a CEA. This is

an experimental application, and EPA is still evaluating the appropriate methods for CEA for

environmental regulations with multiple outcomes. The Institute of Medicine (a member

institution of the National Academies of Science) has empaneled the Committee to Evaluate

Measures of Health Benefits for Environmental, Health, and Safety Regulation to assess the

scientific validity, ethical implications, and practical utility of a wide range of effectiveness

measures used or proposed in CEA. This committee is expected to produce a report by the

end of 2005. However, in the interim, are required to provide CEAs to comply with Circular

A-4. As such, EPA has begun developing methodologies for estimating the costeffectiveness of air pollution regulations. The methodology presented in this appendix is not
G-6

intended to stand as precedent either for future air pollution regulations or for other EPA

regulations governing water, solid waste, or other regulatory objectives. It is intended solely

to demonstrate one particular approach to estimating the effectiveness of reductions in

ambient PM2.5 in achieving improvements in public health. This analysis focuses on

effectiveness measured by improvements in life expectancy and reductions in the incidence

of two diseases with chronic impacts on quality of life: CB and nonfatal acute myocardial

infarctions. Other EPA regulations affecting other aspects of environmental quality and

public health may require additional data and models that may preclude the development of

similar QALY-based analyses. The appropriateness of QALY-based CEA should be

evaluated on a case-by-case basis subject to the availability of appropriate data and models.

Preparation of a CEA requires identification of an appropriate measure of rule

effectiveness. Given the significant impact of reductions in ambient PM2.5 on reductions in

the risk of mortality, lives saved is an important measure of effectiveness. However, one of

the ongoing controversies in health impact assessment regards whether reductions in

mortality risk should be reported and valued in terms of statistical lives saved or in terms of

statistical life years saved. Life years saved measures differentiate among premature

mortalities based on the remaining life expectancy of affected individuals. In general, under

the life years approach, older individuals will gain fewer life years than younger individuals

for the same reduction in mortality risk during a given time period, making interventions that

benefit older individuals seem less beneficial relative to similar interventions benefitting

younger individuals. A further complication in the debate is whether to apply quality

adjustments to life years lost. Under this approach, individuals with preexisting health

conditions would have fewer QALYs lost relative to healthy individuals for the same loss in

life expectancy, making interventions that primarily benefit individuals with poor health

seem less beneficial to similar interventions affecting primarily healthy individuals.

In addition to substantial mortality risk reduction benefits, CAIR will also result in

significant reductions in chronic and acute morbidity. Several approaches have been

developed to incorporate both morbidity and mortality into a single effectiveness metric. 

The most common of these is the QALY approach, which expresses all morbidity and

mortality impacts in terms of quality of life multiplied by the duration of time with that

quality of life. The QALY approach has some appealing characteristics. For example, it can

account for morbidity effects as well as losses in life expectancy without requiring the

assignment of dollar values to calculate total benefits. By doing so it provides an alternative

framework to benefit-cost analysis for aggregating quantitative measures of health impacts. 
G-7

While used extensively in the economic evaluation of medical interventions (Gold et

al., 1996), QALYs have not been widely used in evaluating environmental health regulations. 

A number of specific issues arise with the use of QALYs in evaluating environmental

programs that affect a broad and heterogeneous population and that provide both health and

nonhealth benefits. The U.S. Public Health Service report on cost-effectiveness in health and

medicine notes the following: 

For decisions that involve greater diversity in interventions and the people to whom

they apply, cost-effectiveness ratios continue to provide essential information, but

that information must, to a greater degree, be evaluated in light of circumstances and

values that cannot be included in the analysis. Individuals in the population will

differ widely in their health and disability before the intervention, or in age, wealth,

or other characteristics, raising questions about how society values gains for the more

and less health, for young and old, for rich and poor, and so on. The assumption that

all QALYs are of equal value is less likely to be reasonable in this context. (Gold et

al., 1996, p. 11)

Use of QALYs as a measure of effectiveness for environmental regulations is still

developing, and while this analysis provides one framework for using QALYs to evaluate

environmental regulations, there are clearly many issues, both scientific and ethical, that

need to be addressed with additional research. The Institute of Medicine panel evaluating

QALYs and other effectiveness measures will develop criteria for choosing among the

measures that potentially are useful for regulatory impact analysis and will make

recommendations regarding measures appropriate for assessing the health benefits of

regulatory interventions and propose criteria for identifying regulations for which CEA is

appropriate and informative.

This appendix presents cost-effectiveness methodologies for evaluating programs

such as CAIR that are intended to reduce ambient PM2.5 starting from the standard QALY

literature and seeking a parallel structure to benefit-cost analysis in the use of air quality and

health inputs (see Hubbell [2004a] for a discussion of some of the issues that arise in

comparing QALY and benefit-cost frameworks in analyzing air pollution impacts). For the

purposes of this analysis, we calculate effectiveness using several different metrics, including

lives prolonged, life years gained, and modified QALYs. For the life years and QALY-type

approaches, we use life table methods to calculate the change in life expectancy expected to

result from changes in mortality risk from PM. We use existing estimates of preferences for

different health states to obtain QALY weights for morbidity endpoints associated with air
G-8

pollution. In general, consistent with the Gold et al. (1996) recommendations, we use

weights obtained from a societal perspective when available. We explore several different

sources for these weights to characterize some of the potential uncertainty in the QALY

estimates. We follow many of the principles of the reference case analysis as defined in

Gold et al. (1996), although in some cases we depart from the reference case approach when

data limitations require us to do so (primarily in the selection of quality-of-life weights for

morbidity endpoints). We also depart from the reference case in the method of combining

life expectancy and quality-of-life gains. 

Results in most tables are presented only at a discount rate of 3 percent, rather than at

both 3 percent and 7 percent as recommended in EPA and OMB guidance. This is strictly

for ease of presentation in EPA’s first demonstration of this approach to cost-effectiveness. 

Aggregate results at 7 percent are presented in the summary, and the impact of using a 7

percent discount rate instead of 3 percent rate is summarized in a sensitivity analysis.

Monte Carlo simulation methods are used to propagate uncertainty in several of the

model parameters throughout the analysis. We characterize overall uncertainty in the results

with 95 percent confidence intervals based on the Monte Carlo simulations. In addition, we

examine the impacts of changing key parameters, such as the discount rate, on the

effectiveness measures and the cost-effectiveness metrics. 

The remainder of this appendix provides an overview of the key issues involved in

life year- and QALY-based approaches for evaluating the health impacts of air pollution

regulations, provides detailed discussions of the steps required for each type of effectiveness

calculation, and presents the CEA for CAIR. Section G.3 introduces the various

effectiveness measures and discusses some of the assumptions required for each. 

Section G.4 details the methodology used to calculate changes in life years and quality

adjustments for mortality and morbidity endpoints. Section G.5 provides the results for

CAIR and discusses their implications for cost-effectiveness of CAIR.

G.3 Effectiveness Measures

Three major classes of benefits are associated with reductions in air pollution: 

mortality, morbidity, and nonhealth (welfare). For the purposes of benefit-cost analysis,

EPA has presented mortality-related benefits using estimates of avoided premature

mortalities, representing the cumulative result of reducing the risk of premature mortality

from long-term exposure to PM2.5 for a large portion of the U.S. population. Morbidity

benefits have been characterized by numbers of new incidences avoided for chronic diseases
2

Life expectancy is an ex ante concept, indicating the impact on an entire population’s expectation of the

number of life years they have remaining, before knowing which individuals will be affected. Life

expectancy thus incorporates both the probability of an effect and the impact of the effect if realized. Life

years is an ex post concept, indicating the impact on individuals who actually die from exposure to air

pollution. Changes in population life expectancy will always be substantially smaller than changes in life

years per premature mortality avoided, although the total life years gained in the population will be the

same. This is because life expectancy gains average expected life years gained over the entire population,

while life years gained measures life years gained only for those experiencing the life extension. 

G-9

such as CB, avoided admissions for hospitalizations associated with acute and chronic

conditions, and avoided days with symptoms for minor illnesses. Nonhealth benefits are

characterized by the monetary value of reducing the impact (e.g., the dollar value of

improvements in visibility at national parks).

For the purposes of CEA, we focus the effectiveness measure on the quantifiable

health impacts of the reduction in PM2.5. Treatment of nonhealth benefits is important and is

discussed in some detail later in this section. If the main impact of interest is reductions in

mortality risk from air pollution, the effectiveness measures are relatively straightforward to

develop. Mortality impacts can be characterized similar to the benefits analysis, by counting

the number of premature mortalities avoided, or can be characterized in terms of increases in

life expectancy or life years.2 Estimates of premature mortality have the benefit of being

relatively simple to calculate, are consistent with the benefit-cost analysis, and do not impose

additional assumptions on the degree of life shortening. However, some have argued that

counts of premature mortalities avoided are problematic because a gain in life of only a few

months would be considered equivalent to a gain of a many life years, and the true

effectiveness of an intervention is the gain in life expectancy or life years (Rabl, 2003; Miller

and Hurley, 2003). 

Calculations of changes in life years and life expectancy can be accomplished using

standard life table methods (Miller and Hurley, 2003). However, the calculations require

assumptions about the baseline mortality risks for each age cohort affected by air pollution. 

A general assumption may be that air pollution mortality risks affect the general mortality

risk of the population in a proportional manner. However, some concerns have been raised

that air pollution affects mainly those individuals with preexisting cardiovascular and

respiratory disease, who may have reduced life expectancy relative to the general population. 

This issue is explored in more detail below.

Air pollution is also associated with a number of significant chronic and acute

morbidity endpoints. Failure to consider these morbidity effects may understate the cost-
G-10

effectiveness of air pollution regulations or give too little weight to reductions in particular

pollutants that have large morbidity impacts but no effect on life expectancy. The QALY

approach explicitly incorporates morbidity impacts into measures of life years gained and is

often used in health economics to assess the cost-effectiveness of medical spending programs

(Gold et al., 1996). Using a QALY rating system, health quality ranges from 0 to 1, where 1

may represent full health, 0 death, and some number in between (e.g., 0.8) an impaired

condition. QALYs thus measure morbidity as a reduction in quality of life over a period of

life. QALYs assume that duration and quality of life are equivalent, so that 1 year spent in

perfect health is equivalent to 2 years spent with quality of life half that of perfect health. 

QALYs can be used to evaluate environmental rules under certain circumstances, although

some very strong assumptions (detailed below) are associated with QALYs. The U.S. Public

Health Service Panel on Cost Effectiveness in Health and Medicine recommended using

QALYs when evaluating medical and public health programs that primarily reduce both

mortality and morbidity (Gold et al., 1996). Although there are significant nonhealth

benefits associated with air pollution regulations, over 90 percent of quantifiable monetized

benefits are health-related, as is the case with CAIR. Thus, it can be argued that QALYs are

more applicable for these types of regulations than for other environmental policies. 

However, the value of nonhealth benefits should not be ignored. As discussed below, we

have chosen to subtract the value of nonhealth benefits from the costs in the numerator of the

cost-effectiveness ratio. 

In the following sections, we lay out a phased approach to describing effectiveness. 

We begin by discussing how the life-extending benefits of air pollution reductions are

calculated, and then we incorporate morbidity effects using the QALY approach. We also

introduce an alternative aggregated health metric, Morbidity Inclusive Life Years (MILY) to

address some of the ethical concerns about aggregating life extension impacts in populations

with preexisting disabling conditions. 

The use of QALYs is predicated on the assumptions embedded in the QALY

analytical framework. As noted in the QALY literature, QALYs are consistent with the

utility theory that underlies most of economics only if one imposes several restrictive

assumptions, including independence between longevity and quality of life in the utility

function, risk neutrality with respect to years of life (which implies that the utility function is

linear), and constant proportionality in trade-offs between quality and quantity of life

(Pliskin, Shepard, and Weinstein, 1980; Bleichrodt, Wakker, and Johannesson, 1996). To

the extent that these assumptions do not represent actual preferences, the QALY approach

will not provide results that are consistent with a benefit-cost analysis based on the Kaldor-
3

The Kaldor-Hicks efficiency criterion requires that the “winners” in a particular case be potentially able to

compensate the “losers” such that total societal welfare improves. In this case, it is sufficient that total

benefits exceed total costs of the regulation. This is also known as a potential Pareto improvement, because

gains could be allocated such that at least one person in society would be better off while no one would be

worse off.

G-11

Hicks criterion.3 Even if the assumptions are reasonably consistent with reality, because

QALYs represent an average valuation of health states rather than the sum of societal WTP,

there are no guarantees that the option with the highest QALY per dollar of cost will satisfy

the Kaldor-Hicks criterion (i.e., generate a potential Pareto improvement [Garber and Phelps,

1997]).

Benefit-cost analysis based on WTP is not without potentially troubling underlying

structures as well, incorporating ability to pay (and thus the potential for equity concerns)

and the notion of consumer sovereignty (which emphasizes wealth effects). Table G-1

compares the two approaches across a number of parameters. For the most part, WTP allows

parameters to be determined empirically, while the QALY approach imposes some

conditions a priori. 

Table G-1. Comparison of QALY and WTP Approaches

Parameter QALY WTP

Risk aversion Risk neutral Empirically determined

Relation of duration and quality Independent Empirically determined

Proportionality of duration/

quality trade-off

Constant Variable

Treatment of time/age in utility

function

Utility linear in time Empirically determined

Preferences Community/Individual Individual

Source of preference data Stated Revealed and stated

Treatment of income and prices Not explicitly considered Constrains choices
G-12

G.4 Changes in Premature Death, Life Years, and Quality of Life

To generate health outcomes, we used the same framework as for the benefit-cost

analysis described in Chapter 4. For convenience, we summarize the basic methodologies

here. For more details, see Chapter 4 and the BenMAP user’s manual

(http://www.epa.gov/ttn/ecas/benmodels.html). 

BenMAP uses health impact functions to generate changes in the incidence of health

effects. Health impact functions are derived from the epidemiology literature. A standard

health impact function has four components: an effect estimate from a particular

epidemiological study, a baseline incidence rate for the health effect (obtained from either

the epidemiology study or a source of public health statistics like CDC), the affected

population, and the estimated change in the relevant PM summary measure.

A typical health impact function might look like this:

∆ ∆yy e x =⋅ − ⋅

0 ( ), 1 β

where y0 is the baseline incidence, equal to the baseline incidence rate times the potentially

affected population; $ is the effect estimate; and )x is the estimated change in PM2.5. There

are other functional forms, but the basic elements remain the same.

G.4.1 Calculating Reductions in Premature Deaths

As in several recent air pollution health impact assessments (e.g., Kunzli et al., 2000; 

EPA, 2004), we focus on the prospective cohort long-term exposure studies in deriving the

health impact function for the estimate of premature mortality. Cohort analyses are better

able to capture the full public health impact of exposure to air pollution over time (Kunzli et

al., 2001; NRC, 2002). We selected an effect estimate from the extended analysis of the

ACS cohort (Pope et al., 2002). This latest re-analysis of the ACS cohort data provides

additional refinements to the analysis of PM-related mortality by (a) extending the follow-up

period for the ACS study subjects to 16 years, which triples the size of the mortality data set;

(b) substantially increasing exposure data, including consideration for cohort exposure to

PM2.5 following implementation of PM2.5 standard in 1999; (c) controlling for a variety of

personal risk factors including occupational exposure and diet; and (d) using advanced

statistical methods to evaluate specific issues that can adversely affect risk estimates,

including the possibility of spatial autocorrelation of survival times in communities located

near each other. The effect estimate from Pope et al. (2002) quantifies the relationship

between annual mean PM2.5 levels and all-cause mortality in adults 30 and older. We
G-13

selected the effect estimate estimated using the measure of PM representing average

exposure over the follow-up period, calculated as the average of 1979–1984 and 1999–2000

PM2.5 levels. The effect estimate from this study is 0.0058, which is equivalent to a relative

risk of 1.06 for a 10 :g change in PM2.5. Although there are other cohort-based studies of the

relationship between PM2.5 and mortality, none provide the same level of population and

geographic coverage as the ACS study. 

Age, cause, and county-specific mortality rates were obtained from CDC for the years

1996 through 1998. CDC maintains an online data repository of health statistics, CDC

Wonder, accessible at http://wonder.cdc.gov/. The mortality rates provided are derived from

U.S. death records and U.S. Census Bureau postcensal population estimates. Mortality rates

were averaged across 3 years (1996 through 1998) to provide more stable estimates. When

estimating rates for age groups that differed from the CDC Wonder groupings, we assumed

that rates were uniform across all ages in the reported age group. For example, to estimate

mortality rates for individuals ages 30 and up, we scaled the 25- to 34-year old death count

and population by one-half and then generated a population-weighted mortality rate using

data for the older age groups. 

The reductions in incidence of premature mortality within each age group associated

with the CAIR reductions in PM2.5 in 2010 and 2015 are summarized in Table G-2.

G.4.2 Calculating Changes in Life Years from Direct Reductions in PM2.5-Related

Mortality Risk

To calculate changes in life years associated with a given change in air pollution, we

used a life table approach coupled with age-specific estimates of reductions in premature

mortality. We began with the complete unabridged life table for the United States in 2000,

obtained from CDC (CDC, 2002). For each 1-year age interval (e.g., zero to one, one to two)

the life table provides estimates of the baseline probability of dying during the interval,

person years lived in the interval, and remaining life expectancy. From this unabridged life

table, we constructed an abridged life table to match the age intervals for which we have

predictions of changes in incidence of premature mortality. We used the abridgement

method described in CDC (2002). Table G-3 presents the abridged life table for 10-year age

intervals for adults over 30 (to match the Pope et al. [2002] study population). Note that the

abridgement actually includes one 5-year interval, covering adults 30 to 34, with the

remaining age intervals covering 10 years each. This is to provide conformity with the age

intervals available for mortality rates.
G-14

Table G-2. Estimated Reduction in Incidence of All-cause Premature Mortality

Associated with the Clean Air Interstate Rule

 Reduction in All-Cause Premature Mortality 

(95% CI)

Age Interval 2010 2015

30 – 34 96 

(33 – 160)

120

(42 – 200)

35 – 44 370 

(120 – 610)

420 

(140 – 700)

45 – 54 870 

(300 – 1,400)

1,000 

(340 – 1,700)

55 – 64 1,700 

(580 – 2,800)

2,300 

(790 – 3,800)

65 – 74 2,400 

(830 – 4,000)

3,600 

(1,200 – 6,000)

75 – 84 3,400 

(1,100 – 5,600)

4,100 

(1,400 – 6,800)

85+ 3,800 

(1,300 – 6,400)

5,100 

(1,700 – 8,400)

Total 12,700

(4,300 – 21,000)

17,000

(5,700 – 28,000)

From the abridged life table (Table G-3), we obtained the remaining life expectancy

for each age cohort, conditional on surviving to that age. This is then the number of life

years lost for an individual in the general population dying during that age interval. This

information can then be combined with the estimated number of premature deaths in each

age interval calculated with BenMAP (see previous subsection). Total life years gained will

then be the sum of life years gained in each age interval:

Total LifeYears LE M i i

i

N

= × =

∑

1

,

where LEi is the remaining life expectancy for age interval i, Mi is the change in incidence of

mortality in age interval i, and N is the number of age intervals.
G-15

Table G-3. Abridged Life Table for the Total Population, United States, 2000

Age Interval

Probability of

Dying

Between Ages

x to x+1

Number

Surviving to

Age x

Number

Dying

Between Ages

x to x+1

Person Years

Lived

Between Ages

x to x+1

Total Number

of Person

Years Lived

Above Age x

Expectation

of Life at

Age x

Start Age End Age qx Ix dx Lx Tx ex

30 35 0.00577 97,696 564 487,130 4,723,539 48.3

35 45 0.01979 97,132 1,922 962,882 4,236,409 43.6

45 55 0.04303 95,210 4,097 934,026 3,273,527 34.4

55 65 0.09858 91,113 8,982 872,003 2,339,501 25.7

65 75 0.21779 82,131 17,887 740,927 1,467,498 17.9

75 85 0.45584 64,244 29,285 505,278 726,571 11.3

85 95 0.79256 34,959 27,707 196,269 221,293 6.3

95 100 0.75441 7,252 5,471 20,388 25,024 3.5

100+ 1.00000 1,781 1,781 4,636 4,636 2.6

For the purposes of determining cost-effectiveness, it is also necessary to consider the

time-dependent nature of the gains in life years. Standard economic theory suggests that

benefits occurring in future years should be discounted relative to benefits occurring in the

present. OMB and EPA guidance suggest discount rates of three and seven percent. As

noted earlier, we present gains in future life years discounted at 3 percent. Results based on

7 percent are included in the summary and the overall impact of a 7 percent rate is

summarized in Table G-16. Selection of a 3 percent discount rate is also consistent with

recommendations from the U.S. Public Health Service Panel on Cost Effectiveness in Health

and Medicine (Gold et al., 1996). 

Discounted total life years gained is calculated as follows:

Discounted LY e dt rt LE= −

∫ , 0

where r is the discount rate, equal to 0.03 in this case, t indicates time, and LE is the life

expectancy at the time when the premature death would have occurred. Life years are further

discounted to account for the lag between the reduction in ambient PM2.5 and the reduction in

mortality risk. We use the same 20-year segmented lag structure that is used in the benefitcost analysis (see Chapter 4).
G-16

The most complete estimate of the impacts of PM2.5 on life years is calculated using

the Pope et al. (2002) C-R function relating all-cause mortality in adults 30 and over with

ambient PM2.5 concentrations averaged over the periods 1979–1983 and 1999–2000. Use of

all-cause mortality is appropriate if there are no differences in the life expectancy of

individuals dying from air pollution-related causes and those dying from other causes. The

argument that long-term exposure to PM2.5 may affect mainly individuals with serious

preexisting illnesses is not supported by current empirical studies. For example, the Krewski

et al. (2000) ACS reanalysis suggests that the mortality risk is no greater for those with

preexisting illness at time of enrollment in the study. Life expectancy for the general

population in fact includes individuals with serious chronic illness. Mortality rates for the

general population then reflect prevalence of chronic disease, and as populations age the

prevalence of chronic disease increases. 

The only reason one might use a lower life expectancy is if the population at risk

from air pollution was limited solely to those with preexisting disease. Also, note that the

OMB Circular A-4 notes that “if QALYs are used to evaluate a lifesaving rule aimed at a

population that happens to experience a high rate of disability (i.e., where the rule is not

designed to affect the disability), the number of life years saved should not necessarily be

diminished simply because the rule saves lives of people with life-shortening disabilities. 

Both analytic simplicity and fairness suggest that the estimate number of life years saved for

the disabled population should be based on average life expectancy information for the

relevant age cohorts.” As such, use of a general population life expectancy is preferred over

disability-specific life expectancies. Our primary life years calculations are thus consistent

with the concept of not penalizing individuals with disabling chronic health conditions by

assessing them reduced benefits of mortality risk reductions.

For this analysis, direct impacts on life expectancy are measured only through the

estimated change in mortality risk based on the Pope et al. (2002) C-R function. The SABHES has advised against including additional gains in life expectancy due to reductions in

incidence of chronic disease or nonfatal heart attacks (EPA-SAB-COUNCIL-ADV-04-002). 

Although reductions in these endpoints are likely to result in increased life expectancy, the

HES has suggested that the cohort design and relatively long follow-up period in the Pope et

al. study should capture any life-prolonging impacts associated with those endpoints. 

Impacts of CB and nonfatal heart attacks on quality of life will be captured separately in the

QALY calculation as years lived with improved quality of life. The methods for calculating

this benefit are discussed below. 
G-17

G.4.2.1 Should Life Years Gained Be Adjusted for Initial Health Status?

The methods outlined above provide estimates of the total number of life years

gained in a population, regardless of the quality of those life years, or equivalently, assuming

that all life years gained are in perfect health. In some CEAs (Cohen, Hammitt, and Levy,

2003; Coyle et al., 2003), analysts have adjusted the number of life years gained to reflect the

fact that 1) the general public is not in perfect health and thus “healthy” life years are less

than total life years gained and 2) those affected by air pollution may be in a worse health

state than the general population and therefore will not gain as many “healthy” life years

adjusted for quality, from an air pollution reduction. This adjustment, which converts life

years gained into QALYs, raises a number of serious ethical issues. Proponents of QALYs

have promoted the nondiscriminatory nature of QALYs in evaluating improvements in

quality of life (e.g., an improvement from a score of 0.2 to 0.4 is equivalent to an

improvement from 0.8 to 1.0), so the starting health status does not affect the evaluation of

interventions that improve quality of life. However, for life-extending interventions, the

gains in QALY will be directly proportional to the baseline health state (e.g., an individual

with a 30-year life expectancy and a starting health status of 0.5 will gain exactly half the

QALYs of an individual with the same life expectancy and a starting health status of 1.0 for a

similar life-extending intervention). This is troubling because it imposes an additional

penalty for those already suffering from disabling conditions. Brock (2002) notes that “the

problem of disability discrimination represents a deep and unresolved problem for resource

prioritization.” 

OMB (2003) has recognized this issue in their Circular A-4 guidance, which includes

the following statement:

When CEA is performed in specific rulemaking contexts, you should be prepared to

make appropriate adjustments to ensure fair treatment of all segments of the

population. Fairness is important in the choice and execution of effectiveness

measures. For example, if QALYs are used to evaluate a lifesaving rule aimed at a

population that happens to experience a high rate of disability (i.e., where the rule is

not designed to affect the disability), the number of life years saved should not

necessarily be diminished simply because the rule saves the lives of people with

life-shortening disabilities. Both analytic simplicity and fairness suggest that the

estimated number of life years saved for the disabled population should be based on

average life expectancy information for the relevant age cohorts. More generally,

when numeric adjustments are made for life expectancy or quality of life, analysts
G-18

should prefer use of population averages rather than information derived from

subgroups dominated by a particular demographic or income group. (p. 13) 

This suggests two adjustments to the standard QALY methodology: one adjusting the

relevant life expectancy of the affected population, and the other affecting the baseline

quality of life for the affected population. 

In addition to the issue of fairness, potential measurement issues are specific to the air

pollution context that might argue for caution in applying quality-of-life adjustments to life

years gained due to air pollution reductions. A number of epidemiological and toxicological

studies link exposure to air pollution with chronic diseases, such as CB and atherosclerosis

(Abbey et al., 1995; Schwartz, 1993; Suwa et al., 2002). If these same individuals with

chronic disease caused by exposure to air pollution are then at increased risk of premature

death from air pollution, there is an important dimension of “double jeopardy” involved in

determining the correct baseline for assessing QALYs lost to air pollution (see Singer et al.

[1995] for a broader discussion of the double-jeopardy argument). 

Analyses estimating mortality from acute exposures that ignore the effects of longterm exposure on morbidity may understate the health impacts of reducing air pollution. 

Individuals exposed to chronically elevated levels of air pollution may realize an increased

risk of death and chronic disease throughout life. If at some age they contract heart (or some

other chronic) disease as a result of the exposure to air pollution, they will from that point

forward have both reduced life expectancy and reduced quality of life. The benefit to that

individual from reducing lifetime exposure to air pollution would be the increase in life

expectancy plus the increase in quality of life over the full period of increased life

expectancy. If the QALY loss is determined based on the underlying chronic condition and

life expectancy without regard to the fact that the person would never have been in that state

without long-term exposure to elevated air pollution, then the person is placed in double

jeopardy. In other words, air pollution has placed more people in the susceptible pool, but

then we penalize those people in evaluating policies by treating their subsequent deaths as

less valuable, adding insult to injury, and potentially downplaying the importance of life

expectancy losses due to air pollution. If the risk of chronic disease and risk of death are

considered together, then there is no conceptual problem with measuring QALYs, but this

has not been the case in recent applications of QALYs to air pollution (Carrothers, Evans,

and Graham, 2002; Coyle et al., 2003). The use of QALYs thus highlights the need for a

better understanding of the relationship between chronic disease and long-term exposure and
G-19

suggests that analyses need to consider morbidity and mortality jointly, rather than treating

each as a separate endpoint (this is an issue for current benefit-cost approaches as well).

Because of the fairness and measurement concerns discussed above, for the purposes

of this analysis, we do not reduce the number of life years gained to reflect any differences in

underlying health status that might reduce quality of life in remaining years. Thus, we

maintain the assumption that all direct gains in life years resulting from mortality risk

reductions will be assigned a weight of 1.0. The U.S. Public Health Service Panel on Cost

Effectiveness in Health and Medicine recommends that “since lives saved or extended by an

intervention will not be in perfect health, a saved life year will count as less than 1 full

QALY” (Gold et al., 1996). However, for the purposes of this analysis, we propose an

alternative to the traditional aggregate QALY metric that keeps separate quality adjustments

to life expectancy and gains in life expectancy. As such, we do not make any adjustments to

life years gained to reflect the less than perfect health of the general population. Gains in

quality of life will be addressed as they accrue because of reductions in the incidence of

chronic diseases. This is an explicit equity choice in the treatment of issues associated with

quality-of-life adjustments for increases in life expectancy that still capitalizes on the ability

of QALYs to capture both morbidity and mortality impacts in a single effectiveness measure.

G.5 Calculating Changes in the Quality of Life Years (Morbidity)

In addition to directly measuring the quantity of life gained, measured by life years, it

may also be informative to measure gains in the quality of life. Reducing air pollution also

leads to reductions in serious illnesses that affect quality of life. These include CB and

cardiovascular disease, for which we are able to quantify changes in the incidence of nonfatal

heart attacks. To capture these important benefits in the measure of effectiveness, they must

first be converted into a life-year equivalent so that they can be combined with the direct

gains in life expectancy.

For this analysis, we developed estimates of the QALYs gained from reductions in

the incidence of CB and nonfatal heart attacks associated with reductions in ambient PM2.5. 

In general, QALY calculations require four elements:

1. the estimated change in incidence of the health condition,

2. the duration of the health condition,

3. the quality-of-life weight with the health condition, and
G-20

4. the quality-of-life weight without the health condition (i.e., the baseline health

state).

The first element is derived using the health impact function approach. The second element

is based on the medical literature for each health condition. The third and fourth elements

are derived from the medical cost-effectiveness and cost-utility literature. In the following

two subsections, we discuss the choices of elements for CB and nonfatal heart attacks.

The preferred source of quality-of-life weights are those based on community

preferences, rather than patient or clinician ratings (Gold et al., 1996). Several methods are

used to estimate quality-of-life weights. These include rating scale, standard gamble, time

trade-off, and person trade-off approaches (Gold, Stevenson, and Fryback, 2002). Only the

standard gamble approach is completely consistent with utility theory. However, the time

trade-off method has also been widely applied in eliciting community preferences (Gold,

Stevenson, and Fryback, 2002). 

Quality-of-life weights can be directly elicited for individual specific health states or

for a more general set of activity restrictions and health states that can then be used to

construct QALY weights for specific conditions (Horsman et al., 2003; Kind, 1996). For this

analysis, we used weights based on community-based preferences, using time trade-off or

standard gamble when available. In some cases, we used patient or clinician ratings when no

community preference-based weights were available. Sources for weights are discussed in

more detail below. Table G-4 summarizes the key inputs for calculating QALYs associated

with chronic health endpoints.

G.5.1 Calculating QALYs Associated with Reductions in the Incidence of Chronic

Bronchitis

CB is characterized by mucus in the lungs and a persistent wet cough for at least 3

months a year for several years in a row. CB affects an estimated 5 percent of the U.S.

population (American Lung Association, 1999). For gains in quality of life resulting from

reduced incidences of PM-induced CB, discounted QALYs are calculated as

DISCOUNTEDQALY GAINED CB D w w ( ) , ii ii

CB

i

= ×× − ∑ ∆ *
G-21

Table G-4. Summary of Key Parameters Used in QALY Calculations for Chronic

Disease Endpoints

Parameter Value(s) Source(s)

Discount rate 0.03 (0.07

sensitivity

analysis)

Gold et al. (1996), U.S. EPA (2000), U.S. OMB (2003)

Quality of life

preference score for

chronic bronchitis

0.5 – 0.7 Triangular distribution centered at 0.7 with upper bound at 0.9

(Vos, 1999a) (slightly better than a mild/moderate case) and a

lower bound at 0.5 (average weight for a severe case based on

Vos [1999a] and Smith and Peske [1994])

Duration of acute phase

of acute myocardial

infarction (AMI)

5.5 days – 22

days

Uniform distribution with lower bound based on average

length of stay for an AMI (AHRQ, 2000) and upper bound

based on Vos (1999b).

Probability of CHF post

AMI

0.2 Vos, 1999a (WHO Burden of Disease Study, based on Cowie

et al., 1997)

Probability of angina

post AMI

0.51 American Heart Association, 2003

(Calculated as the population with angina divided by the total

population with heart disease)

Quality-of-life

preference score for

post-AMI with CHF (no

angina)

0.80 – 0.89 Uniform distribution with lower bound at 0.80 (Stinnett et al.,

1996) and upper bound at 0.89 (Kuntz et al., 1996). Both

studies used the time trade-off elicitation method.

Quality-of-life

preference score for

post-AMI with CHF and

angina

0.76 – 0.85 Uniform distribution with lower bound at 0.76 (Stinnett et al.,

1996, adjusted for severity) and upper bound at 0.85 (Kuntz et

al., 1996). Both studies used the time trade-off elicitation

method.

Quality-of-life

preference score for

post-AMI with angina

(no CHF)

0.7 – 0.89 Uniform distribution with lower bound at 0.7, based on the

standard gamble elicitation method (Pliskin, Stason, and

Weinstein, 1981) and upper bound at 0.89, based on the time

trade-off method (Kuntz et al., 1996).

Quality-of-life

preference score for

post-AMI (no angina, no

CHF)

0.93 Only one value available from the literature. Thus, no

distribution is specified. Source of value is Kuntz et al.

(1996).
4

Prevalence rates for CB were obtained from the 1999 National Health Interview Survey (American Lung

Association, 2002). Prevalence rates were available for three age groups: 18–44, 45–64, and 65 and older. 

Prevalence rates per person for these groups were 0.0367 for 18–44, 0.0505 for 45–64, and 0.0587 for 65

and older. The incidence rate for new cases of CB (0.00378 per person) was taken directly from Abbey et

al. (1995).

G-22

where )CBi is the number of incidences of CB avoided in age interval i, wi is the average

QALY weight for age interval i, is the QALY weight associated with CB, is the wi

CB Di

*

discounted duration of life with CB for individuals with onset of disease in age interval i,

equal to , where Di e dt is the duration of life with CB for individuals with onset of rt

t

Di −

= ∫ 1

disease in age interval i.

A limited number of studies have estimated the impact of air pollution on new

incidences of CB. Schwartz (1993) and Abbey et al. (1995) provide evidence that long-term

PM exposure gives rise to the development of CB in the United States. Because this analysis

focuses on the impacts of reducing ambient PM2.5, only the Abbey et al. (1995) study is used,

because it is the only study focusing on the relationship between PM2.5 and new incidences of

CB. The number of cases of CB in each age interval is derived from applying the impact

function from Abbey et al. (1995), to the population in each age interval with the appropriate

baseline incidence rate.4 The effect estimate from the Abbey et al. (1995) study is 0.0137,

which, based on the logistic specification of the model, is equivalent to a relative risk of 1.15

for a 10 :g change in PM2.5. Table G-5 presents the estimated reduction in new incidences of

CB associated with CAIR in 2010 and 2015.

CB is assumed to persist for the remainder of an affected individual’s lifespan. 

Duration of CB will thus equal life expectancy conditioned on having CB. CDC has

estimated that COPD (of which CB is one element) results in an average loss of life years

equal to 4.26 per COPD death, relative to a reference life expectancy of 75 years (CDC,

2003). Thus, we subtract 4.26 from the remaining life expectancy for each age group, up to

age 75. For age groups over 75, we apply the ratio of 4.26 to the life expectancy for the 65 to

74 year group (0.237) to the life expectancy for the 75 to 84 and 85 and up age groups to

estimate potential life years lost and then subtract that value from the base life expectancy.

Quality of life with chronic lung diseases has been examined in several studies. In an

analysis of the impacts of environmental exposures to contaminants, de Hollander et al.

(1999) assigned a weight of 0.69 to years lived with CB. This weight was based on 
G-23

Table G-5. Estimated Reduction in Incidence of Chronic Bronchitis Associated with

the Clean Air Interstate Rule

Reduction in Incidence (95% Confidence Interval)

Age Interval 2010 2015

25 – 34 1,100 (31 – 2,200) 1,400 (40 – 2,800)

35 – 44 1,400 (41 – 2,800) 1,700 (47 – 3,300)

45 – 54 1,600 (46 – 3,200) 1,900 (52 – 3,700)

55 – 64 1,300 (36 – 2,500) 1,800 (49 – 3,400)

65 – 74 770 (21 – 1,500) 1,100 (32 – 2,200)

75 – 84 470 (13 – 910) 570 (16 – 1,100)

85+ 210 (6 – 400) 270 (8 – 530)

Total 6,900 (190 – 14,000) 8,700 (240 – 17,000)

physicians’ evaluations of health states similar to CB. Salomon and Murray (2003)

estimated a pooled weight of 0.77 based on visual analogue scale, time trade-off, standard

gamble, and person trade-off techniques applied to a convenience sample of health

professionals. The Harvard Center for Risk Analysis catalog of preference scores reports a

weight of 0.40 for severe COPD, with a range from 0.2 to 0.8, based on the judgments of the

study’s authors (Bell et al., 2001). The Victoria Burden of Disease (BoD) study used a

weight of 0.47 for severe COPD and 0.83 for mild to moderate COPD, based on an analysis

by Stouthard et al. (1997) of chronic diseases in Dutch populations (Vos, 1999a). Based on

the recommendations of Gold et al. (1996), quality-of-life weights based on community

preferences are preferred for CEA of interventions affecting broad populations. Use of

weights based on health professionals is not recommended. It is not clear from the Victoria

BoD study whether the weights used for COPD are based on community preferences or

judgments of health professionals. The Harvard catalog score is clearly identified as based

on author judgment. Given the lack of a clear preferred weight, we select a triangular

distribution centered at 0.7 with an upper bound at 0.9 (slightly better than a mild/moderate

case defined by the Victoria BoD study) and a lower bound at 0.5 based on the Victoria BoD

study. We will need additional empirical data on quality of life with chronic respiratory

diseases based on community preferences to improve our estimates.
5

Monte Carlo simulation uses random sampling from distributions of parameters to characterize the effects of

uncertainty on output variables. For more details, see Gentile (1998).

G-24

Selection of a reference weight for the general population without CB is somewhat

uncertain. It is clear that the general population is not in perfect health; however, there is

some uncertainty as to whether individuals’ ratings of health states are in reference to a

perfect health state or to a generally achievable “normal” health state given age and general

health status. The U.S. Public Health Service Panel on Cost Effectiveness in Health and

Medicine recommends that “since lives saved or extended by an intervention will not be in

perfect health, a saved life year will count as less than 1 full QALY” (Gold et al., 1996). 

Following Carrothers, Evans, and Graham (2002), we assumed that the reference weight for

the general population without CB is 0.95. To allow for uncertainty in this parameter, we

assigned a triangular distribution around this weight, bounded by 0.9 and 1.0. Note that the

reference weight for the general population is used solely to determine the incremental

quality-of-life improvement applied to the duration of life that would have been lived with

the chronic disease. For example, if CB has a quality-of-life weight of 0.7 relative to a

reference quality-of-life weight of 0.9, then the incremental quality-of-life improvement in

0.2. If the reference quality-of-life weight is 0.95, then the incremental quality-of-life

improvement is 0.25. As noted above, the population is assumed to have a reference weight

of 1.0 for all life years gained due to mortality risk reductions.

We present discounted QALYs over the duration of the lifespan with CB using a 3

percent discount rate. Based on the assumptions defined above, we used Monte Carlo

simulation methods as implemented in the Crystal BallTM software program to develop the

distribution of QALYs gained per incidence of CB for each age interval.5 Based on the

assumptions defined above, the mean 3 percent discounted QALY gained per incidence of

CB for each age interval along with the 95 percent confidence interval resulting from the

Monte Carlo simulation is presented in Table G-6. Table G-6 presents both the undiscounted

and discounted QALYs gained per incidence.

G.5.2 Calculating QALYs Associated with Reductions in the Incidence of Nonfatal

Myocardial Infarctions

Nonfatal heart attacks, or acute myocardial infarctions, require more complicated

calculations to derive estimates of QALY impacts. The actual heart attack, which results

when an area of the heart muscle dies or is permanently damaged because of oxygen

deprivation, and subsequent emergency care are of relatively short duration. Many heart 
G-25

Table G-6. QALYs Gained per Avoided Incidence of CB

Age Interval QALYs Gained per Incidence

Start Age End Age Undiscounted Discounted (3%)

25 34 12.15

(4.40-19.95)

6.52

(2.36-10.71) 

35 44 9.91

(3.54-16.10)

5.94

(2.12-9.66)

45 54 7.49

(2.71-12.34)

5.03

(1.82-8.29)

55 64 5.36

(1.95-8.80)

4.03

(1.47-6.61)

65 74 3.40

(1.22-5.64)

2.84

(1.02-4.71)

75 84 2.15

(0.77-3.49)

1.92

(0.69-3.13)

85+ 0.79

(0.27-1.29)

0.77

(0.26-1.25)

attacks result in sudden death. However, for survivors, the long-term impacts of advanced

CHD are potentially of long duration and can result in significant losses in quality of life and

life expectancy.

In this phase of the analysis, we did not independently estimate the gains in life

expectancy associated with reductions in nonfatal heart attacks. Based on recommendations

from the SAB-HES, we assumed that all gains in life expectancy are captured in the

estimates of reduced mortality risk provided by the Pope et al. (2002) analysis. We only

estimate the change in quality of life over the period of life affected by the occurrence of a

heart attack. This may understate the QALY impacts of nonfatal heart attacks but ensures

that the overall QALY impact estimates across endpoints do not double-count potential lifeyear gains.

Our approach adapts a CHD model developed for the Victoria Burden of Disease

study (Vos, 1999b). This model accounts for the lost quality of life during the heart attack

and the possible health states following the heart attack. Figure G-1 shows the heart attack

QALY model in diagrammatic form. The total gain in QALYs is calculated as:
G-26

Nonfatal AMI

Congestive Heart Failure

Yes

No

Angina

Angina

Yes

No

No

Yes

Post AMI QALY with Angina and CHF

Post AMI QALY with CHF without Angina

Post AMI QALY with Angina without CHF

Post AMI QALY without Angina or CHF

Acute Treatment Stage Chronic Post-AMI Follow up Stage

Figure G-1. Decision Tree Used in Modeling Gains in QALYs from Reduced Incidence

of Nonfatal Acute Myocardial Infarctions

( ) ( )

DISCOUNTED AMI QALY GAINED

AMI D w w AMI p D w w i i

AMI

i i

AMI

i j

i j ij

PostAMI

i ij

postAMI

i

=

∑ ∑ × ×− + × ×− ∑ =

∆ ∆ * *

1

4

where )AMIi is the number of nonfatal acute myocardial infarctions avoided in age interval

i, is the QALY weight associated with the acute phase of the AMI, pj w is the probability i

AMI

of being in the jth post-AMI status, is the QALY weight associated with post-AMI wij

postAMI
6

Daily nonfatal myocardial infarction incidence rates per person were obtained from the 1999 National Hospital

Discharge Survey (assuming all diagnosed nonfatal AMI visit the hospital). Age-specific rates for four

regions are used in the analysis. Regional averages for populations 18 and older are 0.0000159 for the

Northeast, 0.0000135 for the Midwest, 0.0000111 for the South, and 0.0000100 for the West.

G-27

health status j, wi is the average QALY weight for age interval i, , the D e dt i

AMI rt

t

Di

AMI

* = −

= ∫ 1

discounted value of , the duration of the acute phase of the AMI, and Di

AMI

D e dt , is the discounted value of , the duration of post-AMI i

postAMI rt

t

Di

postAMI

* = −

= ∫ 1

Dij

PostAMI

health status j.

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the

United States (Peters et al., 2001) and other countries (Poloniecki et al., 1997). We used a

recent study by Peters et al. (2001) as the basis for the impact function estimating the

relationship between PM2.5 and nonfatal heart attacks. Peters et al. is the only available U.S.

study to provide a specific estimate for heart attacks. Other studies, such as Samet et al.

(2000) and Moolgavkar (2000), show a consistent relationship between all cardiovascular

hospital admissions, including for nonfatal heart attacks, and PM. Given the lasting impact

of a heart attack on longer-term health costs and earnings, we chose to provide a separate

estimate for nonfatal heart attacks based on the single available U.S. effect estimate. The

finding of a specific impact on heart attacks is consistent with hospital admission and other

studies showing relationships between fine particles and cardiovascular effects both within

and outside the United States. These studies provide a weight of evidence for this type of

effect. Several epidemiologic studies (Liao et al., 1999; Gold et al., 2000; Magari et al.,

2001) have shown that heart rate variability (an indicator of how much the heart is able to

speed up or slow down in response to momentary stresses) is negatively related to PM levels. 

Heart rate variability is a risk factor for heart attacks and other CHDs (Carthenon et al.,

2002; Dekker et al., 2000; Liao et al., 1997, Tsuji et al., 1996). As such, significant impacts

of PM on heart rate variability are consistent with an increased risk of heart attacks. 

The number of avoided nonfatal AMI in each age interval is derived from applying

the impact function from Peters et al. (2001) to the population in each age interval with the

appropriate baseline incidence rate.6

 The effect estimate from the Peters et al. (2001) study

is 0.0241, which, based on the logistic specification of the model, is equivalent to a relative
7

Average length of stay estimated from the HCUP data includes all discharges, including those due to death. As

such, the 5.5-day average length of stay is likely an underestimate of the average length of stay for AMI

admissions where the patient is discharged alive.

G-28

risk of 1.27 for a 10 :g change in PM2.5. Table G-7 presents the estimated reduction in

nonfatal AMI associated with CAIR in 2010 and 2015.

Table G-7. Estimated Reduction in Nonfatal Acute Myocardial Infarctions Associated

with the Clean Air Interstate Rule

Reduction in Incidence*(95% Confidence Interval)

Age Interval 2010 2015

18 – 24 9 (2 – 16) 11 (3 – 18)

25 – 34 92 (23 – 160) 120 (29 – 200)

35 – 44 630 (160 – 1,100) 700 (180 – 1,200)

45 – 54 2,400 (600 – 4,200) 2,800 (690 – 4,800)

55 – 64 4,000 (990 – 6,900) 5,300 (1,300 – 9,200)

65 – 74 4,000 (990 – 6,900) 5,800 (1,400 – 10,000)

75 – 84 3,800 (940 – 6,500) 4,600 (1,100 – 7,900)

85+ 2,300 (570 – 4,000) 3,000 (750 – 5,200)

Total 17,000 (4,300 – 30,000) 22,000 (5,600 – 39,000)

Acute myocardial infarction results in significant loss of quality of life for a relatively

short duration. The WHO Global Burden of Disease study, as reported in Vos (1999b),

assumes that the acute phase of an acute myocardial infarction lasts for 0.06 years, or around

22 days. An alternative assumption is the acute phase is characterized by the average length

of hospital stay for an AMI in the United States, which is 5.5 days, based on data from the

Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project

(HCUP).7

 We assumed a distribution of acute phase duration characterized by a uniform

distribution between 5.5 and 22 days, noting that due to earlier discharges and in-home

therapy available in the United States, duration of reduced quality of life may continue after

discharge from the hospital. In the period during and directly following an AMI (the acute

phase), we assigned a quality of life weight equal to 0.605, consistent with the weight for the

period in treatment during and immediately after an attack (Vos, 1999b). 
G-29

During the post-AMI period, a number of different health states can determine the

loss in quality of life. We chose to classify post-AMI health status into four states defined by

the presence or absence of angina and congestive heart failure (CHF). This makes a very

explicit assumption that without the occurrence of an AMI, individuals would not experience

either angina or CHF. If in fact individuals already have CHF or angina, then the quality of

life gained will be overstated. We do not have information about the percentage of the

population have been diagnosed with angina or CHF with no occurrence of an AMI. Nor do

we have information on what proportion of the heart attacks occurring due to PM exposure

are first heart attacks versus repeat attacks. Probabilities for the four post-AMI health states

sum to one.

Given the occurrence of a nonfatal AMI, the probability of congestive heart failure is

set at 0.2, following the heart disease model developed by Vos (1999b). The probability is

based on a study by Cowie et al. (1997), which estimated that 20 percent of those surviving

AMI develop heart failure, based on an analysis of the results of the Framingham Heart

Study. 

The probability of angina is based on the prevalence rate of angina in the U.S.

population. Using data from the American Heart Association, we calculated the prevalence

rate for angina by dividing the estimated number of people with angina (6.6 million) by the

estimated number of people with CHD of all types (12.9 million). We then assumed that the

prevalence of angina in the population surviving an AMI is similar to the prevalence of

angina in the total population with CHD. The estimated prevalence rate is 51 percent, so the

probability of angina is 0.51. 

Combining these factors leads to the probabilities for each of the four health states as

follows:

I. Post AMI with CHF and angina = 0.102

II. Post AMI with CHF without angina = 0.098

III. Post AMI with angina without CHF = 0.408

IV. Post AMI without angina or CHF = 0.392

Duration of post-AMI health states varies, based in part on assumptions regarding life

expectancy with post-AMI complicating health conditions. Based on the model used for

established market economies (EME) in the WHO Global Burden of Disease study, as

reported in Vos (1999b), we assumed that individuals with CHF have a relatively short
G-30

remaining life expectancy and thus a relatively short period with reduced quality of life

(recall that gains in life expectancy are assumed to be captured by the cohort estimates of

reduced mortality risk). Table G-8 provides the duration (both discounted and undiscounted)

of CHF assumed for post-AMI cases by age interval.

Table G-8. Assumed Duration of Congestive Heart Failure

Age Interval Duration of Heart Failure

Start Age End Age Undiscounted Discounted (3%)

18 24 7.11 6.51

25 34 6.98 6.40

35 44 6.49 6.00

45 54 5.31 4.99

55 64 1.96 1.93

65 74 1.71 1.69

75 84 1.52 1.50

85+ 1.52 1.50

Duration of health states without CHF is assumed to be equal to the life expectancy

of individuals conditional on surviving an AMI. Ganz et al. (2000) note that “Because

patients with a history of myocardial infarction have a higher chance of dying of CHD that is

unrelated to recurrent myocardial infarction (for example, arrhythmia), this cohort has a

higher risk for death from causes other than myocardial infarction or stroke than does an

unselected population.” They go on to specify a mortality risk ratio of 1.52 for mortality

from other causes for the cohort of individuals with a previous (nonfatal) AMI. The risk

ratio is relative to all-cause mortality for an age-matched unselected population (i.e., general

population). We adopted the same ratios and applied them to each age-specific all-cause

mortality rate to derive life expectancies (both discounted and undiscounted) for each age

group after an AMI, presented in Table G-9. These life expectancies are then used to

represent the duration of non-CHF post-AMI health states (III and IV). 

For the four post-AMI health states, we used QALY weights based on preferences for

the combined conditions characterizing each health state. A number of estimates of QALY

weights are available for post-AMI health conditions. 
G-31

Table G-9. Assumed Duration of Non-CHF Post-AMI Health States

Age Interval Post-AMI Life Expectancy (non-CHF)

Start Age End Age Undiscounted Discounted (3%)

18 24 55.5 27.68

25 34 46.1 25.54

35 44 36.8 22.76

45 54 27.9 19.28

55 64 19.8 15.21

65 74 12.8 10.82

75 84 7.4 6.75

85+ 3.6 3.47

The first two health states are characterized by the presence of CHF, with or without

angina. The Harvard Center for Risk Analysis catalog of preference scores provides several

specific weights for CHF with and without mild or severe angina and one set specific to

post-AMI CHF. Following the Victoria Burden of Disease model, we assumed that most

cases of angina will be treated and thus kept at a mild to moderate state. We thus focused

our selection on QALY weights for mild to moderate angina. The Harvard database includes

two sets of community preference-based scores for CHF (Stinnett et al., 1996; Kuntz et al.,

1996). The scores for CHF with angina range from 0.736 to 0.85. The lower of the two

scores is based on angina in general with no delineation by severity. Based on the range of

the scores for mild to severe cases of angina in the second study, one can infer that an

average case of angina has a score around 0.96 of the score for a mild case. Applying this

adjustment raises the lower end of the range of preference scores for a mild case of angina to

0.76. We selected a uniform distribution over the range 0.76 to 0.85 for CHF with mild

angina, with a midpoint of 0.81. The same two studies in the Harvard catalog also provide

weights for CHF without angina. These scores range from 0.801 to 0.89. We selected a

uniform distribution over this range, with a midpoint of 0.85.

The third health state is characterized by angina, without the presence of CHF. The

Harvard catalog includes five sets of community preference-based scores for angina, one that

specifies scores for both mild and severe angina (Kuntz et al., 1996), one that specifies mild

angina only (Pliskin, Stason, and Weinstein, 1981), one that specifies severe angina only

(Cohen, Breall, and Ho, 1994), and two that specify angina with no severity classification
G-32

(Salkeld, Phongsavan, and Oldenburg, 1997; Stinnett et al., 1996). With the exception of the

Pliskin, Stason, and Weinstein score, all of the angina scores are based on the time trade-off

method of elicitation. The Pliskin, Stason, and Weinstein score is based on the standard

gamble elicitation method. The scores for the nonspecific severity angina fall within the

range of the two scores for mild angina specifically. Thus, we used the range of mild angina

scores as the endpoints of a uniform distribution. The range of mild angina scores is from

0.7 to 0.89, with a midpoint of 0.80.

For the fourth health state, characterized by the absence of CHF and/or angina, there

is only one relevant community preference score available from the Harvard catalog. This

score is 0.93, derived from a time trade-off elicitation (Kuntz et al., 1996). Insufficient

information is available to provide a distribution for this weight; therefore, it is treated as a

fixed value. 

Similar to CB, we assumed that the reference weight for the general population

without AMI is 0.95. To allow for uncertainty in this parameter, we assigned a triangular

distribution around this weight, bounded by 0.9 and 1.0.

Based on the assumptions defined above, we used Monte Carlo simulation methods

as implemented in the Crystal BallTM software program to develop the distribution of

QALYs gained per incidence of nonfatal AMI for each age interval. For the Monte Carlo

simulation, all distributions were assumed to be independent. The mean QALYs gained per

incidence of nonfatal AMI for each age interval is presented in Table G-10, along with the 95

percent confidence interval resulting from the Monte Carlo simulation. Table G-10 presents

both the undiscounted and discounted QALYs gained per incidence.

G.6 Cost-Effectiveness Analysis

Given the estimates of changes in life expectancy and quality of life, the next step is

to aggregate life expectancy and quality-of-life gains to form an effectiveness measure that

can be compared to costs to develop cost-effectiveness ratios. This section discusses the

proper characterization of the combined effectiveness measure and the appropriate

calculation of the numerator of the cost-effectiveness ratio. 
G-33

Table G-10. QALYs Gained per Avoided Nonfatal Myocardial Infarction

Age Interval QALYs Gained per Incidencea

Start Age End Age Undiscounted Discounted (3%)

18 24 4.18

(1.24-7.09)

2.17

(0.70-3.62)

25 34 3.48

(1.09-5.87)

2.00

(0.68-3.33)

35 44 2.81

(0.88-4.74)

1.79

(0.60-2.99)

45 54 2.14

(0.67-3.61)

1.52

(0.51-2.53)

55 64 1.49

(0.42-2.52)

1.16

(0.34-1.95)

65 74 0.97

(0.30-1.64)

0.83

(0.26-1.39)

75 84 0.59

(0.20-0.97)

0.54

(0.19-0.89)

85+ 0.32

(0.13-0.50)

0.31

(0.13-0.49)

a Mean of Monte Carlo generated distribution; 95% confidence interval presented in parentheses.

G.6.1 Aggregating Life Expectancy and Quality-of-Life Gains

To develop an integrated measure of changes in health, we simply sum together the

gains in life years from reduced mortality risk in each age interval with the gains in QALYs

from reductions in incidence of CB and acute myocardial infarctions. The resulting measure

of effectiveness then forms the denominator in the cost-effectiveness ratio. What is this

combined measure of effectiveness? It is not a QALY measure in a strict sense, because we

have not adjusted life-expectancy gains for preexisting health status (quality of life). It is

however, an effectiveness measure that adds to the standard life years calculation a scaled

morbidity equivalent. Thus, we term the aggregate measure morbidity inclusive life years, or

MILYs. Alternatively, the combined measure could be considered as QALYs with an

assumption that the community preference weight for all life-expectancy gains is 1.0. If one

considers that this weight might be considered to be a “fair” treatment of those with
G-34

preexisting disabilities, the effectiveness measure might be termed “fair QALY” gained. 

However, this implies that all aspects of fairness have been addressed, and there are clearly

other issues with the fairness of QALYs (or other effectiveness measures) that are not

addressed in this simple adjustment. The MILY measure violates some of the properties

used in deriving QALY weights, such as linear substitution between quality of life and

quantity of life. However, in aggregating life expectancy and quality-of-life gains, it merely

represents an alternative social weighting that is consistent with the spirit of the recent OMB

guidance on CEA. The guidance notes that “fairness is important in the choice and execution

of effectiveness measures” (OMB, 2003). The resulting aggregate measure of effectiveness

will not be consistent with a strict utility interpretation of QALYs; however, it may still be a

useful index of effectiveness.

Applying the life expectancies and distributions of QALYs per incidence for CB and

AMI to estimated distributions of incidences yields distributions of life expectancy and

QALYs gained due to CAIR. These distributions reflect both the quantified uncertainty in

incidence estimates and the quantified uncertainty in QALYs gained per incidence.

For the CAIR 2010 analysis year, Table G-11 presents the mean 3 percent discounted

MILYs gained for each age interval, broken out by life expectancy and quality-of-life

categories. Note that quality-of-life gains occur from age 18 and up, while life expectancy

gains accrue only after age 29. This is based on the ages of the study populations in the

underlying epidemiological studies. It is unlikely that such discontinuities exist in reality,

but to avoid overstating effectiveness, we chose to limit the life-expectancy gains to those

occurring in the population 30 and over and the morbidity gains to the specific adult

populations examined in the studies. Table G-12 provides the same information for the 2015

analysis year. 

It is worth noting that around a third of mortality-related benefits are due to

reductions in premature deaths among those 75 and older, while only 7 percent of morbidity

benefits occur in this age group. This is due to two factors: (1) the relatively low baseline

mortality rates in populations under 75, and (2) the relatively constant baseline rates of

chronic disease coupled with the relatively long period of life that is lived with increased

quality of life without CB and advanced heart disease.
G-35

Table G-11. Estimated Gains in 3 Percent Discounted MILYs Associated with the

Clean Air Interstate Rule in 2010a

Age 

Life Years Gained

from Mortality Risk 

Reductions

(95% CI)

QALY Gained from

Reductions in

Chronic Bronchitis

(95% CI)

QALY Gained from

Reductions in Acute

Myocardial Infarctions

(95% CI)

Total Gain in 

MILYs

(95% CI)

18–24 — — 20

(4 – 42)

20

(4 – 42)

25–34 2,500

(880 – 4,100)

7,300

(430 – 18,000)

180

(34 – 410)

10,000

(3,000 – 20,000)

35–44 9,100

(3,100 – 15,000)

8,500

(330 – 21,000)

1,100

(200 – 2,500)

19,000

(8,200 – 32,000)

45–54 19,000

(6,600 – 31,000)

8,200

(410 – 20,000)

3,600

(640 – 8,100)

31,000

(15,000 – 48,000)

55–64 31,000

(11,000 – 51,000)

5,200

(310 – 13,000)

4,500

(760 – 10,000)

41,000

(19,000 – 63,000)

65–74 34,000

(12,000 – 57,000)

2,200

(110 – 5,100)

3,200

(590 – 7,300)

40,000

(17,000 – 63,000)

75–84 33,000

(11,000 – 54,000)

900

(43 – 2,100)

2,000

(380 – 4,400)

36,000

(14,000 – 57,000)

85+ 15,000

(5,200 – 25,000)

160

(7 – 370)

680

(130 – 1,500)

16,000

(6,000 – 26,000)

Total 140,000

(100,000 – 180,000)

33,000

(1,700 – 78,000)

15,000

(2,900 – 34,000)

190,000

(140,000 – 250,000)

a Note that all estimates have been rounded to two significant digits.

The relationship between age and the distribution of MILYs gained from mortality

and morbidity is shown in Figure G-2. Because the baseline mortality rate is increasing in

age at a much faster rate than the prevalence rate for CB, the share of MILYs gained

accounted for by mortality is proportional to age. At the oldest age interval, avoiding

incidences of CB leads to only a few MILYs gained, due to the lower number of years lived

with CB. MILYs gained from avoided premature mortality is low in the youngest age

intervals because of the low overall mortality rates in these intervals, although the number of

MILYs per incidence is high. In later years, even though the MILYs gained per incidence

avoided is low, the number of cases is very high due to higher baseline mortality rates. 
G-36

Table G-12. Estimated Gains in 3 Percent Discounted MILYs Associated with the

Clean Air Interstate Rule in 2015a

Age 

Life Years Gained

from Mortality Risk 

Reductions

(95% CI)

QALY Gained from

Reductions in

Chronic Bronchitis

(95% CI)

QALY Gained from

Reductions in Acute

Myocardial Infarctions

(95% CI)

Total Gain in 

MILYs

(95% CI)

18–24 — — 22 (4 – 52) 22 (4 – 52)

25–34 3,200

(1,100 – 5,300)

9,400

(510 – 22,000)

230

(42 – 520)

13,000

(3,800 – 26,000)

35–44 11,000

(3,800 – 17,000)

9,800

(670 – 24,000)

1,250

(230 – 2,800)

22,000

(9,600 – 37,000)

45–54 22,000

(8,200 – 36,000)

9,400

(430 – 23,000)

4,100

(780 – 9,300)

36,000

(18,000 – 55,000)

55–64 42,000

(15,000 – 69,000)

7,100

(440 – 17,000)

5,900

(1,000 – 14,000)

55,000

(27,000 – 84,000)

65–74 51,000

(18,000 – 84,000)

3,200

(230 – 7,800)

4,700

(870 – 11,000)

59,000

(26,000 – 92,000)

75–84 40,000

(15,000 – 65,000)

1,100

(68 – 2,700)

2,400

(430 -5,400)

44,000

(18,000 – 69,000)

85+ 20,000

(7,400 – 33,000)

210

(14 – 500)

890

(180 – 1,900)

21,000

(8,400 – 34,000)

Total 190,000

(140,000 – 240,000)

40,000

(2,400 – 96,000)

20,000

(3,600 – 44,000)

250,000

(180,000 – 330,000)

a Note that all estimates have been rounded to two significant digits.

Summing over the age intervals provides estimates of total MILYs gained for CAIR

in 2010 and 2015. The total number of discounted (3 percent) MILYs gained in 2010 is

190,000 (95% CI: 140,000 – 250,000) and in 2015 is 250,000 (95% CI: 180,000 – 330,000). 

G.6.2 Dealing with Acute Health Effects and Nonhealth Effects

Health effects from exposure to particulate air pollution encompass a wide array of

chronic and acute conditions in addition to premature mortality (EPA, 1996). Although

chronic conditions and premature mortality generally account for the majority of monetized

benefits, acute symptoms can affect a broad population or sensitive populations (e.g., asthma

exacerbations in asthmatic children. In addition, reductions in air pollution may result in a
G-37

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

25-34 35-44 45-54 55-64 65-74 75-84 85+

Age Group

Percent of Total MILY

Morbidity

Mortality

Figure G-2. Distribution of Mortality and Morbidity Related MILY Across Age

Groups for the CAIR in 2015 (3 percent Discount Rate)

broad set of nonhealth environmental benefits, including improved visibility in national

parks, increased agricultural and forestry yields, reduced acid damage to buildings, and a

host of other impacts. QALYs address only health impacts, and the OMB guidance notes

that “where regulation may yield several different beneficial outcomes, a cost-effectiveness

comparison becomes more difficult to interpret because there is more than one measure of

effectiveness to incorporate in the analysis.” 

With regard to acute health impacts, Bala and Zarkin (2000) suggest that QALYs are

not appropriate for valuing acute symptoms, because of problems with both measuring utility

for acute health states and applying QALYs in a linear fashion to very short duration health

states. Johnson and Lievense (2000) suggest using conjoint analysis to get healthy-utility

time equivalences that can be compared across acute effects, but it is not clear how these can
G-38

be combined with QALYs for chronic effects and loss of life expectancy. There is also a

class of effects that EPA has traditionally treated as acute, such as hospital admissions,

which may also result in a loss of quality of life for a period of time following the effect. For

example, life after asthma hospitalization has been estimated with a utility weight of 0.93

(Bell et al., 2001; Kerridge, Glasziou, and Hillman, 1995). 

How should these effects be combined with QALYs for chronic and mortality

effects? One method would be to convert the acute effects to QALYs; however, as noted

above, there are problems with the linearity assumption (i.e., if a year with asthma symptoms

is equivalent to 0.7 year without asthma symptoms, then 1 day without asthma symptoms is

equivalent to 0.0019 QALY gained). This is troubling from both a conceptual basis and a

presentation basis. An alternative approach is simply to treat acute health effects like

nonhealth benefits and subtract the dollar value (based on WTP or COI) from compliance

costs in the CEA.

To address the issues of incorporating acute morbidity and nonhealth benefits, OMB

suggests that agencies “subtract the monetary estimate of the ancillary benefits from the

gross cost estimate to yield an estimated net cost.” As with benefit-cost analysis, any

unquantified benefits and/or costs should be noted and an indication of how they might affect

the cost-effectiveness ratio should be described. We will follow this recommended “net

cost” approach in the illustrative exercise, specifically in netting out the benefits of health

improvements other than reduced mortality and chronic morbidity, and the benefits of

improvements in visibility at national parks (see Chapter 4 for more details on these benefit

categories). 

G.6.3 Cost-Effectiveness Ratios

Construction of cost-effectiveness ratios requires estimates of effectiveness (in this

case measured by lives saved, life years gained, or MILYs gained) in the denominator and

estimates of costs in the numerator. The estimate of costs in the numerator should include

both the direct costs of the controls necessary to achieve the reduction in ambient PM2.5 and

the avoided costs (cost savings) associated with the reductions in morbidity (Gold et al.,

1996). In general, because reductions in air pollution do not require direct actions by the

affected populations, there are no specific costs to affected individuals (aside from the

overall increases in prices that might be expected to occur as control costs are passed on by

affected industries). Likewise, because individuals do not engage in any specific actions to

realize the health benefit of the pollution reduction, there are no decreases in utility (as might

occur from a medical intervention) that need to be adjusted for in the denominator. Thus, the
8

Gold et al. (1996) recommend not including lost earnings in the cost-of-illness estimates, suggesting that in

some cases, they may be already be counted in the effectiveness measures. However, this requires that

individuals fully incorporate the value of lost earnings and reduced labor force participation opportunities

into their responses to time-tradeoff or standard-gamble questions. For the purposes of this analysis and for

consistency with the way costs-of-illness are calculated for the benefit-cost analysis, we have assumed that

individuals do not incorporate lost earnings in responses to these questions. This assumption can be relaxed

in future analyses with improved understanding of how lost earnings are treated in preference elicitations.

G-39

elements of the numerator are direct costs of controls minus the avoided COI associated with

CB and nonfatal AMI. In addition, to account for the value of reductions in acute health

impacts and nonhealth benefits, we net out the monetized value of these benefits from the

numerator to yield a “net cost” estimate. For the MILY aggregate effectiveness measure, the

denominator is simply the sum of life years gained from increased life expectancy and the

sum of QALYs gained from the reductions in CB and nonfatal AMI.

Avoided costs for CB and nonfatal AMI are based on estimates of lost earnings and

medical costs.8 Using age-specific annual lost earnings and medical costs estimated by

Cropper and Krupnick (1990) and a 3 percent discount rate, we estimated a lifetime present

discounted value (in 2000$) due to CB of $150,542 for someone between the ages of 27 and

44; $97,610 for someone between the ages of 45 and 64; and $11,088 for someone over 65. 

The corresponding age-specific estimates of lifetime present discounted value (in 2000$)

using a 7 percent discount rate are $86,026, $72,261, and $9,030, respectively. These

estimates assumed that 1) lost earnings continue only until age 65, 2) medical expenditures

are incurred until death, and 3) life expectancy is unchanged by CB.

Because the costs associated with a myocardial infarction extend beyond the initial

event itself, we consider costs incurred over several years. Using age-specific annual lost

earnings estimated by Cropper and Krupnick (1990) and a 3 percent discount rate, we

estimated a present discounted value in lost earnings (in 2000$) over 5 years due to a

myocardial infarction of $8,774 for someone between the ages of 25 and 44, $12,932 for

someone between the ages of 45 and 54, and $74,746 for someone between the ages of 55

and 65. The corresponding age-specific estimates of lost earnings (in 2000$) using a 7

percent discount rate are $7,855, $11,578, and $66,920, respectively. Cropper and Krupnick

(1990) do not provide lost earnings estimates for populations under 25 or over 65. Thus, we

do not include lost earnings in the cost estimates for these age groups.

Two estimates of the direct medical costs of myocardial infarction are used. The first

estimate is from Wittels, Hay, and Gotto (1990), which estimated expected total medical

costs of MI over 5 years to be $51,211 (in 1986$) for people who were admitted to the
G-40

hospital and survived hospitalization (there does not appear to be any discounting used). 

Using the CPI-U for medical care, the Wittels estimate is $109,474 in year 2000$. This

estimated cost is based on a medical cost model, which incorporated therapeutic options,

projected outcomes, and prices (using “knowledgeable cardiologists” as consultants). The

model used medical data and medical decision algorithms to estimate the probabilities of

certain events and/or medical procedures being used. The second estimate is from Russell et

al. (1998), which estimated first-year direct medical costs of treating nonfatal myocardial

infarction of $15,540 (in 1995$), and $1,051 annually thereafter. Converting to year 2000$,

that would be $23,353 for a 5-year period (without discounting).

The two estimates from these studies are substantially different, and we have not

adequately resolved the sources of differences in the estimates. Because the wage-related

opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year period, we used

estimates for medical costs that similarly cover a 5-year period. We used a simple average of

the two 5-year estimates, or $65,902, and add it to the 5-year opportunity cost estimate. The

resulting estimates are given in Table G-13.

Table G-13. Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial

Infarction

Age Group Opportunity Cost Medical Costa Total Cost

0 – 24 $0 $65,902 $65,902

25-44 $8,774b $65,902 $74,676

45 – 54 $12,253b $65,902 $78,834

55 – 65 $70,619b $65,902 $140,649

>65 $0 $65,902 $65,902

a An average of the 5-year costs estimated by Wittels, Hay, and Gotto (1990) and Russell et al. (1998). b From Cropper and Krupnick (1990), using a 3 percent discount rate.

The total avoided COI by age group associated with the reductions in CB and

nonfatal acute myocardial infarctions is provided in Table G-14. Note that the total avoided

COI associated with CAIR is $2.1 billion in 2010 and $2.7 billion in 2015. Note that this

does not include any direct avoided medical costs associated with premature mortality. Nor

does it include any medical costs that occur more than 5 years from the onset of a nonfatal

AMI. Therefore, this is likely an underestimate of the true avoided COI associated with

CAIR. 
G-41

Table G-14. Avoided Costs of Illness Associated with Reductions in Chronic Bronchitis

and Nonfatal Acute Myocardial Infarctions Associated with CAIR in 2010 and 2015

Avoided Cost of Illness (in millions of 2000$) (95% confidence interval)a

Chronic Bronchitis Nonfatal Acute Myocardial Infarction

Age Range 2010 2015 2010 2015

18-24 — — $1

($0 – $2)

$1

($0 – $2)

25-34 $166

($5 – $325)

$212

($6 – $415)

$7

($1 – $17)

$8

($1 – $22)

35-44 $215

($6 – $421)

$247

($7 – $484)

$46

($7 – $118)

$52

($8 – $134)

45-54 $156

($4 – $305)

$179

($5 – $350)

$184

($29 – $467)

$211

($33 – $535)

55-64 $125

($3 – $244)

$168

($5 – $328)

$547

($133 – $1,171)

$728

($177 – $1,558)

65-74 $8

($0 – $16)

$12

($0 – $24)

$251

($30 – $684)

$367

($44 – $999)

75-84 $5

($0 – $10)

$6

($0 – $12)

$238

($28 – $649)

$289

($34 – $788)

85+ $2

($0 – $4)

$3

($0 – $6)

$145

($17 – $396)

$190

($23 – $516)

Total $677

($360 – $991)

$827

($447 – $1,213)

$1,418

($680 – $2,310)

$1,846

($870 – $3,007)

a Note that the confidence intervals for avoided COI include both the uncertainty in the unit values for each

health effect and the uncertainty in the estimated change in incidence for each health effect. Uncertainties

are combined using Monte Carlo simulation methods.

In a traditional cost-effectiveness analysis, net costs of the intervention would be

divided by the effectiveness measure to calculate a cost per life year or cost per QALY. 

However, for both of the years of analysis, net costs of CAIR are negative, implying that

CAIR is a cost-saving rule. However, it is possible to calculate the costs that would be

necessary for the cost-effectiveness of CAIR to exceed various thresholds. Costeffectiveness ratios are usually interpreted in a relative sense, because there is no universally

agreed on cost-effectiveness cutoff for environmental health interventions. Although the

U.S. Public Health Service Panel on Cost Effectiveness in Health and Medicine did not

recommend a cost-effectiveness threshold for generalized use, it may be useful to identify

cost thresholds that would make CAIR cost-ineffective relative to other life-saving or quality
G-42

of life-improving interventions. The Harvard Cost Utility Analysis database suggests a

median cost-utility ratio of $31,000 per QALY (2002$) for respiratory and cardiovascular

interventions, while Teng et al. (1995) report a median cost per life-year saved for livesaving interventions of $48,000 (1993$). The health economics literature often uses either

$50,000 or $100,000 per QALY as a de facto cut point with ratios less than these values

considered cost-effective. For the purposes of this analysis, we computed the costs necessary

to exceed the $50,000 and $100,000 cost-effectiveness thresholds, without endorsing

$50,000 or $100,000 as an absolute threshold beyond which interventions should not be

implemented. Decisions as to whether a specific control strategy is justified should be based

on a complete comparison of benefits and costs.

Table G-15 summarizes the effectiveness measures and avoided costs associated with

CAIR in 2010 and 2015 and presents the implicit costs of CAIR that would be necessary for

the cost-effectiveness ratio to exceed the $50,000 and $100,000 threshold. 

G.7 Discount Rate Sensitivity Analysis

A large number of parameters and assumptions are necessary in conducting a CEA. 

Where appropriate and supported by data, we have included distributions of parameter values

that were used in generating the reported confidence intervals. For the assumed discount

rate, we felt it more appropriate to examine the impact of the assumption using a sensitivity

analysis rather than through the integrated probabilistic uncertainty analysis.

The choice of a discount rate, and its associated conceptual basis, is a topic of

ongoing discussion within the academic community. OMB and EPA guidance require using

both a 7 percent rate and a 3 percent rate. In the most recent benefit-cost analyses of air

pollution regulations, a 3 and 7 percent discount rate have been adopted in the primary

analysis. A 3 percent discount rate reflects a “social rate of time preference” discounting

concept. A 3 percent discount rate is also consistent with the recommendations of the NAS

panel on CEA (Gold et al., 1996), which suggests that “a real annual (riskless) rate of 3

percent should be used in the Reference Case analysis.” We have also calculated MILYs and

the implicit cost thresholds using a 7 percent rate consistent with an “opportunity cost of

capital” concept to reflect the time value of resources directed to meet regulatory

requirements. Further discussion of this topic appears in Chapter 7 of Gold et al. (1996), in

Chapter 6 of the EPA Guidelines for Economic Analysis, and in OMB Circular A-4.
G-43

Table G-15. Summary of Results for the Clean Air Interstate Rulea

Result Using 3% Discount Rate (95% Confidence Interval)

2010 2015

Life years gained from mortality

risk reductions

140,000

(100,000 – 180,000)

190,000

(140,000 – 240,000)

QALY gained from reductions in

chronic bronchitis

33,000

(1,700 – 78,000)

40,000

(2,400 – 96,000)

QALY gained from reductions in

acute myocardial infarctions

15,000

(2,900 – 34,000)

20,000

(3,600 – 44,000)

Total gain in MILYs 190,000

(140,000 – 250,000)

250,000

(180,000 – 330,000)

Avoided cost of illness

Chronic bronchitis $680 million

($360 million – $990 million)

$830 million

($450 million – $1,200 million)

Nonfatal AMI $1,400 million

($680 million – $2,300 million)

$1,800 million

($870 million – $3,000 million)

CAIR rule costsb $2.4 billion $3.6 billion

Net cost per MILYs Cost saving Cost saving

Implied annual cost necessary to

exceed $50,000/QALY threshold

$13 billion

($10 billion – $18 billion)

$18 billion

($13 billion – $23 billion)

Implied annual cost necessary to

exceed $100,000/QALY threshold

$23 billion

($17 billion – $31 billion)

$30 billion

($22 billion – $40 billion)

a Consistent with recommendations of Gold et al. (1996), all summary results are reported at a precision level

of two significant digits to reflect limits in the precision of the underlying elements. b Costs are the private firm costs of compliance derived from the Integrated Planning Model, as discussed in

Chapter 2, and reflect discounting using firm specific costs of capital.

Table G-16 presents a summary of results using the 7 percent discount rate and the

percentage difference between the 7 percent results and the base case 3 percent results. 

Adoption of a 7 percent discount rate decreases the estimated life years and QALYs gained

from implementing the CAIR. Adopting a discount rate of 7 percent results in a 26 percent

reduction in the estimated total MILYs gained in each year, while the implicit cost necessary

to exceed the $50,000 cost-effectiveness threshold is reduced by 20 percent in each year.
G-44

Table G-16. Impacts of Using a 7 Percent Discount Rate on Cost Effectiveness Analysis

for the Clean Air Interstate Rule in 2015

Result Using 7 Percent

Discount Rate

Percentage Change

Relative to Result Using 3

Percent Discount Rate

Life years gained from mortality

risk reductions

140,000 –25

QALY gained from reductions in

chronic bronchitis

26,000 –35

QALY gained from reductions in

acute myocardial infarctions

15,000 –22

Total gain in MILYs 180,000 –26

Avoided cost of illness

Chronic bronchitis $540 million –35

Nonfatal AMI $1,800 million –3a

Implied cost necessary to exceed

$50,000/QALY threshold

$14 billion –20

Implied cost necessary to exceed

$100,000/QALY threshold

$24 billion –23

a There is a 3 percent difference in estimated avoided costs of nonfatal AMI; however, because of rounding,

the reported cost for both 3 and 7 percent discount rates is $1,800 million.

G.8 Conclusions

We calculated the effectiveness of CAIR based on reductions in premature deaths and

incidence of chronic disease. We measured effectiveness using several different metrics,

including lives saved, life years saved, and QALYs (for improvements in quality of life due

to reductions in incidence of chronic disease). We suggested a new metric for aggregating

life years saved and improvements in quality of life, morbidity inclusive life years (MILY)

which assumes that society assigns a weight of one to years of life extended regardless of

preexisting disabilities or chronic health conditions.

Using the MILYs metric, we estimated that CAIR could cost up to $14 billion

annually in 2010 and up to $18 billion annually in 2015 and would still likely be cost-
G-45

effective relative to other health interventions for cardiovascular and respiratory disease. 

Given costs of $2.4 billion and $3.6 billion in 2010 and 2015, respectively, CAIR is clearly a

very cost-effective way to achieve improvements in public health.

CEA of environmental regulations that have substantial public health impacts may be

informative in identifying programs that have achieved cost-effective reductions in health

impacts and can suggest areas where additional controls may be justified. However, the

overall efficiency of a regulatory action can only be judged through a complete benefit-cost

analysis that takes into account all benefits and costs, including both health and nonhealth

effects. The benefit-cost analysis for CAIR, provided in Chapter 4, shows that CAIR has

large net benefits, indicating that CAIR will likely result in improvements in overall public

welfare and will provide health benefits in a highly cost-effective manner.

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