Regulatory Impact Analysis for the 

Final Clean Air Visibility Rule or the

Guidelines for Best Available Retrofit

Technology (BART) Determinations

Under the Regional Haze Regulations
EPA-452/R-05-004

June 2005

Regulatory Impact Analysis for the 

Final Clean Air Visibility Rule or the Guidelines for

Best Available Retrofit Technology (BART)

Determinations Under the Regional Haze Regulations

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 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2

1.2.1 Health Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

1.2.2 Welfare Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-7

1.2.3 Uncertainty in the Benefits Estimates . . . . . . . . . . . . . . . . . . . . 1-7

1.3 Not All Benefits Quantified . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-8

1.4 Costs and Economic Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-10

1.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11

1.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-12

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

2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.2 Regulated Source Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.3 Control Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

2.3.1 Electric Generating Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5

2.3.2 Sources Other than Electric Generating Units . . . . . . . . . . . . . . 2-6

2.4 Baseline and Years of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7

2.5 Organization of this Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8

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

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

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

3.2.1 PM Air Quality Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

3.2.1.1 Modeling Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8

3.2.1.2 Simulation Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9

3.2.1.3 Model Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9

3.2.1.4 CMAQ Model Evaluation . . . . . . . . . . . . . . . . . . . . . . 3-9

3.2.1.5 Converting CMAQ Outputs to Benefits Inputs . . . . . 3-13

3.2.1.6 PM Air Quality Results . . . . . . . . . . . . . . . . . . . . . . . 3-15

3.2.2 Visibility Degradation Estimates . . . . . . . . . . . . . . . . . . . . . . . 3-15

3.2.2.1 Procedures for Estimating Visibility Degradation . . 3-17

3.3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-23

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

4.1 Benefit Analysis—Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . 4-11

4.1.1 Valuation Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12

4.1.2 Growth in WTP Reflecting National Income Growth 

Over Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-14

4.1.3 Methods for Describing Uncertainty . . . . . . . . . . . . . . . . . . . . 4-18

4.1.4 Demographic Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-22

4.1.5 Health Benefits Assessment Methods . . . . . . . . . . . . . . . . . . . 4-23

4.1.5.1 Selecting Health Endpoints and Epidemiological 

Effect Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-23

4.1.5.2 Uncertainties Associated with Health Impact 

Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-36

4.1.5.3 Baseline Health Effect Incidence Rates . . . . . . . . . . 4-42

4.1.5.4 Selecting Unit Values for Monetizing Health 

Endpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-46

4.1.6 Human Welfare Impact Assessment . . . . . . . . . . . . . . . . . . . . 4-58

4.1.6.1 Visibility Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-58

4.1.6.2 Agricultural, Forestry, and Other VegetationRelated Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-63

4.1.6.3 Benefits from Reductions in Materials Damage . . . . 4-65

4.1.6.4 Benefits from Reduced Ecosystem Damage . . . . . . . 4-66

4.2 Benefits Analysis—Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-67

4.3 Uncertainty in the Benefits Estimates . . . . . . . . . . . . . . . . . . . . . . . . . 4-70

4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-73

4.5 Cost Effectiveness Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-74

4.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-76
v

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

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

5.2 Atmospheric Deposition of Sulfur and Nitrogen—Impacts on 

Aquatic, Forest, and Coastal Ecosystems . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.2.1 Freshwater Acidification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-2

5.2.2 Forest Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4

5.2.3 Coastal Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6

5.2.4 Potential Other Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6

5.3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-7

6. PROFILE OF POTENTIALLY AFFECTED INDUSTRY SECTORS . . . . . . 6-1

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

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

6.1.2 Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5

6.1.3 Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5

6.1.4 Deregulation and Restructuring . . . . . . . . . . . . . . . . . . . . . . . . . 6-6

6.1.5 Pollution and EPA Regulation of Emissions . . . . . . . . . . . . . . . 6-7

6.1.6 Pollution Control Technologies . . . . . . . . . . . . . . . . . . . . . . . . . 6-8

6.1.7 Regulation of the Power Sector . . . . . . . . . . . . . . . . . . . . . . . . . 6-9

6.2 Cement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-11

6.2.1 The Supply Side: Production and Costs . . . . . . . . . . . . . . . . . 6-11

6.2.1.1 Production Process . . . . . . . . . . . . . . . . . . . . . . . . . . 6-11

6.2.1.2 Types of Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12

6.2.1.3 Production Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12

6.2.2 The Demand Side . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-13

6.2.3 Industry Organization: Market Structure, Plants, and Firms . 6-13

6.2.4 Markets and Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15

6.3 Industrial Organic Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-17

6.3.1 The Supply Side: Production and Costs . . . . . . . . . . . . . . . . . 6-17

6.3.1.1 Production Processes . . . . . . . . . . . . . . . . . . . . . . . . 6-17

6.3.1.2 Types of Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-18

6.3.1.3 Major By-Products and Co-Products . . . . . . . . . . . . 6-18

6.3.1.4 Production Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-19

6.3.2 The Demand Side . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-19

6.3.3 Organization of the Industry: Market Concentration, 

Plants, and Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-20

6.3.3.1 Capacity Utilization . . . . . . . . . . . . . . . . . . . . . . . . . 6-21

6.3.4 Markets and Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-22

6.4 Crude Petroleum and Natural Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-23

6.4.1 The Supply Side: Production and Costs . . . . . . . . . . . . . . . . . 6-23
vi

6.4.1.1 Production Processes . . . . . . . . . . . . . . . . . . . . . . . . 6-23

6.4.1.2 Types of Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-23

6.4.1.3 Major By-products . . . . . . . . . . . . . . . . . . . . . . . . . . 6-24

6.4.1.4 Production Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-24

6.4.2 Demand-Side Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 6-24

6.4.3 Organization of the Industry: Market Concentration, 

Plants, and Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-26

6.4.4 Markets and Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-27

6.5 Paper and Allied Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-28

6.5.1 The Supply Side: Production and Costs . . . . . . . . . . . . . . . . . 6-28

6.5.1.1 Production Process . . . . . . . . . . . . . . . . . . . . . . . . . . 6-28

6.5.1.2 Types of Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-29

6.5.1.3 Major By-Products and Co-Products . . . . . . . . . . . . 6-29

6.5.1.4 Production Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-29

6.5.2 The Demand Side . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-29

6.5.3 Organization of the Industry: Market Concentration, 

Plants, and Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-30

6.5.4 Markets and Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-32

6.6 Petroleum Refining Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-33

6.6.1 The Supply Side: Production and Costs . . . . . . . . . . . . . . . . . 6-33

6.6.1.1 Refinery Production Processes/Technology . . . . . . . 6-33

6.6.1.2 Potential Changes in Refining Technology Due 

to EPA Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . 6-34

6.6.1.3 Types of Products . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-35

6.6.1.4 Production Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-35

6.6.2 The Demand Side . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-37

6.6.2.1 Uses and Consumers . . . . . . . . . . . . . . . . . . . . . . . . . 6-38

6.6.2.2 Substitution Possibilities in Consumption . . . . . . . . 6-39

6.6.3 Industry Organization: Market Concentration, Plants, 

and Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-40

6.6.3.1 Market Structure—Concentration . . . . . . . . . . . . . . . 6-40

6.6.3.2 Plants and Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-40

6.6.3.3 Firm Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . 6-41

6.6.4 Markets and Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-42

6.7 Primary Metal Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-42

6.7.1 The Supply Side: Production and Costs . . . . . . . . . . . . . . . . . 6-42

6.7.1.1 Production Processes . . . . . . . . . . . . . . . . . . . . . . . . 6-42

6.7.1.2 Types of Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-46

6.7.1.3 Major By-Products and Co-Products . . . . . . . . . . . . 6-47

6.7.1.4 Production Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-48

6.7.2 The Demand Side . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-48

6.7.3 Organization of the Industry: Market Concentration, 

Plants, and Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-49

6.7.4 Markets and Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-51
vii

6.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-52

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

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

7.2 Projected SO2 and NOx Emissions and Reductions . . . . . . . . . . . . . . . . 7-6

7.3 Projected Costs, Control Technology, and Fuel Costs . . . . . . . . . . . . . . 7-7

7.4 Projected Retail Electricity Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-8

7.5 Key Differences in EPA Model Runs for Final BART Modeling . . . . 7-10

7.6 Limitations of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10

7.7 IPM Runs for CAIR Better-than-BART Determination . . . . . . . . . . . 7-14

7.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14

8. RESULTS OF COST, EMISSIONS REDUCTIONS, AND ECONOMIC

IMPACT ANALYSES FOR NONELECTRICITY GENERATING UNITS . . 8-1

8.1 Results in Brief . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

8.2 Summary of Results for Nonelectricity Generating Sources . . . . . . . . . 8-2

8.3 Costs and Analysis Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-14

8.4 Types of Emissions Control Technologies Employed in These Analyses8-16

8.4.1 SO2 Emissions Control Technologies . . . . . . . . . . . . . . . . . . . 8-16

8.4.1.1 SO2 Control Technology for Non-EGU Sources . . . 8-16

8.4.2 NOx Emissions Control Technologies . . . . . . . . . . . . . . . . . . . 8-17

8.4.2.1 NOx Control Technology for Non-EGU Sources . . . 8-17

8.4.2.2 NOx Control Technology for Other Non-EGU 

BART Source Categories . . . . . . . . . . . . . . . . . . . . . 8-18

8.5 Listing of Affected Source Categories and Results for Each . . . . . . . . 8-19

8.5.1 Results for Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . 8-21

8.5.2 Results for Petroleum Refineries . . . . . . . . . . . . . . . . . . . . . . . 8-23

8.5.3 Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-27

8.5.4 Results for Portland Cement Plants . . . . . . . . . . . . . . . . . . . . . 8-30

8.5.5 Results for Hydrofluoric, Sulfuric, and Nitric Acid Plants . . . 8-34

8.5.6 Results for Chemical Process Plants . . . . . . . . . . . . . . . . . . . . 8-37

8.5.7 Results for Iron and Steel Mills . . . . . . . . . . . . . . . . . . . . . . . . 8-40

8.5.8 Results for Coke Oven Batteries . . . . . . . . . . . . . . . . . . . . . . . 8-44
viii

8.5.9 Results for Sulfur Recovery Plants . . . . . . . . . . . . . . . . . . . . . 8-47

8.5.10 Results for Primary Aluminum Ore Reduction Plants . . . . . . . 8-49

8.5.11 Results for Lime Kilns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-53

8.5.12 Results for Glass Fiber Processing Plants . . . . . . . . . . . . . . . . 8-55

8.5.13 Results for Municipal Incinerators . . . . . . . . . . . . . . . . . . . . . . 8-57

8.5.14 Results for Coal Cleaning Plants . . . . . . . . . . . . . . . . . . . . . . . 8-58

8.5.15 Results for Carbon Black Plants . . . . . . . . . . . . . . . . . . . . . . . 8-59

8.5.16 Results for Phosphate Rock Processing Plants . . . . . . . . . . . . 8-61

8.5.17 Results for Secondary Metal Production Facilities . . . . . . . . . 8-62

8.6 Caveats and Limitations of the Analyses . . . . . . . . . . . . . . . . . . . . . . . 8-63

8.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-65

9. STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSES . . . . . . . 9-1

9.1 Small Entity Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1

9.1.1 EGU Sector Small Business Impacts . . . . . . . . . . . . . . . . . . . . . 9-4

9.1.2 Non-EGU Sector Small Business Impacts . . . . . . . . . . . . . . . . . 9-5

9.2 Unfunded Mandates Reform Act (UMRA) . . . . . . . . . . . . . . . . . . . . . . 9-5

9.2.1 EGU UMRA Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-6

9.2.2 Non-EGU UMRA Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-6

9.3 Paperwork Reduction Act . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7

9.4 Executive Order 13132: Federalism . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7

9.5 Executive Order 13175: Consultation and Coordination with 

Indian Tribal Governments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-8

9.6 Executive Order 13045: Protection of Children from Environmental

Health and Safety Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-8

9.7 Executive Order 13211: Actions Concerning Regulations That

Significantly Affect Energy Supply, Distribution, or Use . . . . . . . . . . . 9-9

9.8 National Technology Transfer and Advancement Act . . . . . . . . . . . . . 9-10

9.9 Executive Order 12898: Federal Actions to Address Environmental 

Justice in Minority Populations and Low-Income Populations . . . . . . 9-10

9.10 Congressional Review Act . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-11

9.11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-11
ix

10. COMPARISON OF BENEFITS AND COSTS . . . . . . . . . . . . . . . . . . . . . . . . 10-1

10.1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-3

Appendix A: BART Industry-Sector Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

Appendix B: Cost and Economic Impact Supplemental Information and Sensitivity 

Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

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

Appendix D: Visibility Benefits Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1

Appendix E: 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 . . . . E-1

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

Appendix G: Results for Two Additional Scenarios Applied to BART Non-EGU Source

Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G-1
x

LIST OF FIGURES

Number Page

3-1 CMAQ Modeling Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8

4-1 Key Steps in Air Quality Modeling-Based Benefits Analysis . . . . . . . . . . . . . . 4-8

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

Southeast and Southwest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-63

6-1 Status of State Electricity Industry Restructuring Activities 

(as of February 2003) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

6-2 Emissions of SO2 and NOx from the Power Sector (2003) . . . . . . . . . . . . . . . . 6-8

6-3 PADD Districts of the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-37

7-1 CAIR Modeled Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2

7-2 NERC Power Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

7-3 Final CAIR Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10
xi

LIST OF TABLES

Number Page

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

Air Visibility Rule—2015 (billions of 1999 dollars) . . . . . . . . . . . . . . . . . . . . . 1-3

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

Adverse Health Effects in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

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

Welfare Effects for the Clean Air Visibility Rule in 2015 

(in millions of 1999$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6

1-4 Unquantified and Nonmonetized Effects of the Clean Air Visibility Rule . . . . 1-9

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

3-2 Summary of Modeled Baseline Emissions, CAIR Control Case, and BART

Control Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

3-3 Summary of Modeled Emissions Changes for the BART Rule: 2015 . . . . . . . . 3-6

3-4 Model Performance Statistics for BART CMAQ 2001 . . . . . . . . . . . . . . . . . . 3-12

3-5 Selected Performance Evaluation Statistics from the CMAQ 2001 

Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13

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

Air Visibility Rule in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-16

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

to Clean Air Visibility Rule in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-16

3-8 Summary of Deciview Visibility Impacts at Class I Areas in the Nation 

Scenario 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19

4-1 Estimated Monetized Benefits of the Final CAVR . . . . . . . . . . . . . . . . . . . . . . 4-3

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

CAVR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4

4-3 Elasticity Values Used to Account for Projected Real Income Growth . . . . . 4-16

4-4 Adjustment Factors Used to Account for Projected Real Income Growth . . . 4-17

4-5 Primary Sources of Uncertainty in the Benefits Analysis . . . . . . . . . . . . . . . . 4-19

4-6 Summary of Considerations Used in Selecting C-R Functions . . . . . . . . . . . . 4-25

4-7 Endpoints and Studies Used to Calculate Total Monetized Health Benefits . . 4-27

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

for Use in the Benefits Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-37

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

Impact Functions, General Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-44

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

Impact Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-46
xii

4-11 Unit Values Used for Economic Valuation of Health Endpoints (1999$) . . . . 4-48

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

of Differences Between Factors Used in Developing Applied VSL and

Theoretically Appropriate VSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-52

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

Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-57

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

Infarction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-57

4-15 Clean Air Visibility Rule: Estimated Reduction in Incidence of Adverse 

Health Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-68

4-16 Estimated Monetary Value of Reductions in Incidence of Health and 

Welfare Effects Associated with the CAVR (millions of 1999$) . . . . . . . . . . 4-69

4-17 Results of Illustrative Application of Pilot Expert Elicitation: Annual 

Reductions in Premature Mortality in 2015 Associated with the Clean Air

Visibility Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-72

6-1 Examples of Affected Source Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

6-2 Existing Electricity Generating Capacity by Energy Source, 2002 . . . . . . . . . . 6-3

6-3 Total U.S. Electric Power Industry Retail Sales in 2003 (Billion kWh) . . . . . . 6-4

6-4 Electricity Net Generation in 2003 (Billion kWh) . . . . . . . . . . . . . . . . . . . . . . . 6-4

6-5 Costs of Production for the Cement Industry: 1997–2001 . . . . . . . . . . . . . . . 6-13

6-6 Capacity Utilization Rates: 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15

6-7 Cement Market Statistics 1998–2002 (103

 Metric Tons, Unless 

Otherwise Noted) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-16

6-8 Inputs for the Industrial Organic Chemicals Industry (NAICS 3251), 

1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-19

6-9 Historical Size of Establishments and Value of Shipments for the Industrial

Organic Chemicals Industry (SIC 2869/NAICS 3251) . . . . . . . . . . . . . . . . . . 6-20

6-10 1997 Size of Establishments, Value of Shipments, and Payroll for the 

Industrial Organic Chemicals Industry (NAICS 3251) . . . . . . . . . . . . . . . . . . 6-21

6-11 Capacity Utilization Ratios for the Industrial Organic Chemicals Industry

(NAICS 3251), 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-22

6-12 Value of Shipments for the Industrial Organic Chemicals, N.E.C. 

Industry (SIC 2869/NAICS 3251), 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . 6-22

6-13 Costs of Production for the Crude Petroleum and Natural Gas Industry: 

1997. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-25

6-14 Crude Petroleum and Natural Gas Establishment and Company Statistics: 

1997 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-26

6-15 Estimated U.S. Oil and Gas Reserves, Annual Production, and Imports, 

2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-27

6-16 Inputs for the Paper and Allied Products Industry (NAICS 322), 

1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-30

6-17 Measures of Market Concentration for Paper and Allied Products 

Markets, 1997 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-30
xiii

6-18 Size of Establishments and Value of Shipments for the Paper and 

Allied Products Industry (NAICS 322) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-31

6-19 Capacity Utilization Ratios for the Paper and Allied Products Industry,

1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-32

6-20 Value of Shipments for the Paper and Allied Products Industry 

(NAICS 322), 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-32

6-21 Types of Petroleum Products Produced by U.S. Refineries . . . . . . . . . . . . . . . 6-36

6-22 Petroleum Refinery Costs of Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-36

6-23 Adjusted Sales of Distillate Fuel Oil by End Use (2000) . . . . . . . . . . . . . . . . 6-38

6-24 Adjusted Sales of Distillate Fuel Oil by End Use and by PADD . . . . . . . . . . 6-39

6-25 Number of Petroleum Refineries by PADD, 2003 . . . . . . . . . . . . . . . . . . . . . . 6-41

6-26 Sales of Distillate Fuel Oils to End Users 1984–1999 

(thousands of barrels per day) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-43

6-27 Inputs for the Primary Metal Manufacturing Industry (NAICS 331), 

1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-48

6-28 1997 Measures of Market Concentration for the Primary Metal 

Manufacturing Industry (NAICS 331) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-50

6-29 Size of Establishments and Value of Shipments for the Primary Metal

Manufacturing Industry (NAICS 331) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-50

6-30 Capacity Utilization Ratios for the Primary Metal Manufacturing Industry,

1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-51

6-31 Value of Shipments for the Primary Metal Manufacturing Industry 

(NAICS 331), 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-51

7-1 BART Scenarios Modeled in IPM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2

7-2 CAIR Annual Emissions Caps (Million Tons) . . . . . . . . . . . . . . . . . . . . . . . . . 7-3

7-3 Number of Coal-Fired EGU BART-Eligible Units . . . . . . . . . . . . . . . . . . . . . . 7-3

7-4 Projected Emissions of SO2 and NOx with CAIR and with BART 

(thousand tons from units greater than 25MW) . . . . . . . . . . . . . . . . . . . . . . . . . 7-7

7-5 Annualized Cost of BART . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7

7-6 National Pollution Controls by Technology under BART (GW) . . . . . . . . . . . 7-8

7-7 Retail Electricity Prices by NERC Region with CAIR and with BART

(Mills/kWh) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

8-1 SO2 Emissions and Emission Reductions for BART Source Categories 

in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-3

8-2 NOx Emissions and Emission Reductions for BART Source Categories 

in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-5

8-3 Total Annualized Costs of Control for BART Source Categories in 2015 

(million 1999$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-7

8-4 SO2 Emissions and Emission Reductions for BART Source Categories 

in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-9

8-5 NOx Emissions and Emission Reductions for BART Source Categories 

in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-11
xiv

8-6 Total Annualized Costs of Control for BART Source Categories in 2015 

(million 1999$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-13

8-7 Available SO2 Control Technologies for Industrial Boilers and Other 

Non-EGU Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17

8-8 Available NOx Control Technologies for Industrial Boilers . . . . . . . . . . . . . . 8-18

8-9 Available NOx Control Technologies for Other Non-EGU Source 

Categories Other than Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-19

8-10 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-21

8-11 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-22

8-12 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-22

8-13 2015 Cost and Cost-Effectiveness Results for NOx Control at BART-Eligible

Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-23

8-14 2015 Cost Results for SO2 and NOx Control at BART-Eligible 

Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-24

8-15 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Petroleum Refineries . . . . . . . . . . . . . . . . 8-24

8-16 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Petroleum Refineries . . . . . . . . . . . . . . . . 8-25

8-17 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Units at Petroleum Refineries . . . . . . . . . . . . . . . . . . . . . . . . . 8-26

8-18 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU 

BART-Eligible Units at Petroleum Refineries . . . . . . . . . . . . . . . . . . . . . . . . . 8-26

8-19 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible 

Units at Petroleum Refineries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-27

8-20 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . 8-28

8-21 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . 8-28

8-22 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Units at Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-29

8-23. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU 

BART-Eligible Units at Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-30

8-24 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible 

Units at Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-30

8-25 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Portland Cement Plants . . . . . . . . . . . . . . 8-31

8-26 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Portland Cement Plants . . . . . . . . . . . . . . 8-31

8-27 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Units at Portland Cement Plants . . . . . . . . . . . . . . . . . . . . . . . 8-32

8-28 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU 

BART-Eligible Units at Portland Cement Plants . . . . . . . . . . . . . . . . . . . . . . . 8-33
xv

8-29 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible 

Units at Portland Cement Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-33

8-30 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric 

Acid Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-34

8-31 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric 

Acid Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-35

8-32 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants . . . . . 8-35

8-33 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU 

BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants . . . . . 8-36

8-34 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible 

Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants . . . . . . . . . . . . . . . . . . 8-37

8-35 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Chemical Process Plants . . . . . . . . . . . . . 8-37

8-36 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Chemical Process Plants . . . . . . . . . . . . . 8-38

8-37 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Units at Chemical Process Plants . . . . . . . . . . . . . . . . . . . . . . 8-39

8-38 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU 

BART-Eligible Units at Chemical Process Plants . . . . . . . . . . . . . . . . . . . . . . 8-40

8-39 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible 

Units at Chemical Process Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-40

8-40 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Iron and Steel Mills . . . . . . . . . . . . . . . . . 8-41

8-41 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Iron and Steel Mills . . . . . . . . . . . . . . . . . 8-41

8-42 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Units at Iron and Steel Mills . . . . . . . . . . . . . . . . . . . . . . . . . . 8-42

8-43 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU 

BART-Eligible Units at Iron and Steel Mills . . . . . . . . . . . . . . . . . . . . . . . . . . 8-43

8-44 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible 

Units at Iron and Steel Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-43

8-45 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Coke Oven Batteries . . . . . . . . . . . . . . . . 8-44

8-46 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Coke Oven Batteries . . . . . . . . . . . . . . . . 8-45

8-47 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Units at Coke Oven Batteries . . . . . . . . . . . . . . . . . . . . . . . . . 8-45

8-48 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU 

BART-Eligible Units at Coke Oven Batteries . . . . . . . . . . . . . . . . . . . . . . . . . 8-46

8-49 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible 

Units at Coke Oven Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-47
xvi

8-50 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Sulfur Recovery Plants . . . . . . . . . . . . . . 8-47

8-51 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Sulfur Recovery Plants . . . . . . . . . . . . . . 8-48

8-52 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Units at Sulfur Recovery Plants . . . . . . . . . . . . . . . . . . . . . . . 8-49

8-53 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU 

BART-Eligible Units at Sulfur Recovery Plants . . . . . . . . . . . . . . . . . . . . . . . 8-50

8-54 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible 

Units at Sulfur Recovery Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-50

8-55 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction 

Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-51

8-56 2015 NOx Baseline Emissions and Emission Reductions (in tons) for 

Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction 

Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-51

8-57 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU 

BART-Eligible Units at Primary Aluminum Ore Reduction Plants . . . . . . . . 8-52

8-58 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU 

BART-Eligible Units at Primary Aluminum Ore Reduction Plants . . . . . . . . 8-53

8-59 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible 

Units at Primary Aluminum Ore Reduction Plants . . . . . . . . . . . . . . . . . . . . . 8-54

8-60 2015 NOx Emission Reductions (in tons) for BART-Eligible Lime Kilns . . . 8-54

8-61 2015 Cost and Cost-Effectiveness Results for BART-Eligible Lime Kilns . . 8-55

8-62 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at 

Glass Fiber Processing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-56

8-63 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at 

Glass Fiber Processing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-56

8-64 2015 NOx Emission Reductions (in tons) for BART-Eligible Municipal

Incinerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-57

8-65 2007 Cost and Cost-Effectiveness Results for BART-Eligible Municipal

Incinerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-58

8-66 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at Coal

Cleaning Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-58

8-67 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at Coal

Cleaning Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-59

8-68 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at Carbon

Black Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-60

8-69 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at 

Carbon Black Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-60

8-70 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at 

Phosphate Rock Processing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-61

8-71 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at 

Phosphate Rock Processing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-62
xvii

8-72 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at Secondary

Metal Production Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-62

9-1 Potentially Regulated EGU Categories and Entities . . . . . . . . . . . . . . . . . . . . . 9-2

9-2 Examples of Potentially Regulated Non-EGU Categories and Entities . . . . . . . 9-2

10-1 Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air

Visibility Rule—2015 (billions of 1999 dollars) . . . . . . . . . . . . . . . . . . . . . . . 10-2
1-1

CHAPTER 1

EXECUTIVE SUMMARY

Synopsis

EPA has estimated the benefits and costs of the Clean Air Visibility Rule or BART

rule and finds that the rule results in estimated annual net benefits ranging from $1.9 to

$12.0 billion in 2015. These alternate net benefit estimates reflect differing assumptions

about State actions that may result from BART guidelines and different social discount

rates of 3 and 7 percent used to estimate the social benefits and costs of the rule. In 2015,

the total annual quantified benefits range from $2.2 to $14.3 billion and the annual social

costs range from $300 million to $2.9 billion depending on the scenario analyzed and the

social discount rate—benefits outweigh social costs in all scenarios analyzed. Visibility

benefits in the Class I areas in the southeastern and southwestern United States, a subset of

expected visibility benefits expected from the rule, range from $80 million to $420 million

per year for the scenarios analyzed. 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 the Clean Air Visibility Rule are substantial

and far outweigh the costs.

1.1 Background

On July 20, 2001 (66 FR 38108), the U.S. Environmental Protection Agency (EPA)

proposed guidelines for implementing the best available retrofit technology (BART)

requirements under the Regional Haze Rule. The proposed guidelines were intended to ease

implementation of the Regional Haze Rule that was published on July 1, 1999 (64 FR

35714). We received numerous comments on the proposal. In addition, on May 24, 2002,

the U.S. Court of Appeals for the D.C. Circuit issued a ruling striking down the Regional

Haze Rule in part and upholding it in part. The court vacated the process for determining

both (1) the sources to which BART must apply and (2) how a State should determine the

level of control for each source subject to BART. To fully respond to the court’s ruling, we

reproposed the BART guidelines and the section of the Regional Haze Rule relevant to the

BART guidelines on March 5, 2004. The reproposal reflects both our review of the public

comments and our response to the court ruling. 
1-2

This document presents estimates of the health and welfare benefits and the estimated

costs of the BART program referred to officially as the Clean Air Visibility Rule (CAVR) in

2015. CAVR and BART are used interchangeably in this document to refer to this rule. This

document recognizes that the recently signed Clean Air Interstate Rule (CAIR) will

accomplish the requirements of BART for the power sector in the CAIR region where annual

sulfur dioxide (SO2) and nitrogen oxide (NOx) controls are required. CAIR, as promulgated,

will affect a 28-State and the District of Columbia (DC) region in the eastern United States,

and the BART rule is applicable nationwide. This rule is applicable to the electricgenerating sector (EGU) and 25 other source categories (non-EGU) sector.

1.2 Results

A comparison of the benefits and costs of the rule in 2015 is shown in Table 1-1. The

benefits and costs reported for CAVR in Table 1-1 represent estimates assuming CAIR in the

baseline that includes the CAIR promulgated rule and the concurrent proposal to include

annual SO2 and NOx controls for New Jersey and Delaware. The modeling used to provide

CAIR baseline estimates also assumes annual SO2 and NOx controls for Arkansas that are not

a part of the complete CAIR program resulting in an understatement of the reported benefits

and costs for CAVR. The recently promulgated Clean Air Mercury Rule (CAMR) is not

considered in the baseline for CAVR.

In this RIA, we have provided analyses for three different regulatory scenarios that

provide information about the actions States may require to meet potential BART

requirements for affected BART-eligible EGU and non-EGU sources. These scenarios

should be viewed as illustrative, because States will make the ultimate decisions on the

BART-eligible sources to control and levels of control based on the guideline criteria, other

than for those sources where presumptive limits are applicable. The alternative scenarios

analyzed provide a range of benefits, costs, and net benefits that may result from this rule. 

For more details of the alternative control scenarios assumptions see Chapters 2, 7, and 8 of

this document. We believe State actions for BART for non-EGU sources are likely to fall

somewhere within the range of alternatives presented in the analysis.

Additional 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. 
1-3

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

Visibility Rule—2015a (billions of 1999 dollars)

Description Scenario 1 Scenario 2 Scenario 3

Social costsb

3 percent discount rate $0.4 $1.4 $2.3

7 percent discount rate $0.3 $1.5 $2.9

Social benefitsc,d,e

3 percent discount rate $2.6 + B $10.1 + B $14.3 + B

7 percent discount rate $2.2 + B $8.6 + B $12.2 + B

Health-related benefits:

3 percent discount rate $2.5 $9.8 $13.9

7 percent discount rate $2.1 $8.4 $11.8

Visibility benefits $0.08 $0.24 $0.42

Net benefits (benefits-costs)

e,f

3 percent discount rate $2.2 + B $8.7 + B $12.0 + B

7 percent discount rate $1.9 + B $7.1 + B $9.3 + B a All estimates are rounded to two significant digits for ease of presentation and computation. 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 is assumed to because implemented in the baseline for the BART

analysis. 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 and

potentially a minimal understatement of the benefits and costs for BART. The impact of the recently

promulgated CAMR was not been considered in the baseline for BART. 

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

non-EGU source categories nationwide in 2015. The discount rate used to conduct the analysis impacts the

control strategies chosen for the non-EGU source category resulting in greater level of controls under the 3

percent discount rate for Scenario 1. 

c As this table indicates, total benefits are driven primarily by particulate matter (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 visibility) and are associated with NOx and SO2

reductions. Visibility benefits represent benefits in Class I areas in the southeastern and southwestern United

States. Ozone benefits are likely to occur with BART but are not estimated in this analysis. 

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 and disbenefit 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.
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 (GDP) per capita between the

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

EPA and the Office of Management and Budget (OMB) guidelines for preparing economic analyses (EPA,

2000; OMB, 2003). 

1-4

1.2.1 Health Benefits

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

key contributors to fine particle and ozone formation. SO2 contributes to the formation of

fine particle pollution (PM2.5), and NOx contributes to the formation of both PM2.5 and

ground-level ozone.1

Our analyses suggest CAVR would yield total benefits in 2015 ranging from $2.6 to

$14.3 billion (based on a 3 percent discount rate) and from $2.2 to $12.2 billion (based on a 7

percent discount rate). For Scenario 2, these benefits include the value of avoiding

approximately 1,600 premature deaths, 2,200 nonfatal heart attacks, 960 hospitalizations for

respiratory and cardiovascular diseases, 170,000 lost work days, and 1 million days when

adults restrict normal activities because of respiratory symptoms exacerbated by PM2.5

pollution.2 

Because of schedule and resource limitations, EPA did not conduct a quantitative

analysis of benefits from reductions (and potential disbenefits from increases) in groundlevel ozone as a result of precursor emissions reductions projected for BART. However, it is

unlikely that net benefits resulting from ozone reductions would have a significant impact on

any conclusions reached regarding the overall benefits for this rulemaking.

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 2015 and Table 1-3

for the estimated value of those reductions.
1-5

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

Health Effects in 2015a,b

Incidence Reduction

Health Effect Scenario 1 Scenario 2 Scenario 3

PM-Related Endpoints:

Premature mortalityc

 Adult, age 30 and over

 Infant, age <1 year 

400

1

1,600

4

2,300

5

Chronic bronchitis (adult, age 26 and

over)

230 890 1,300

Nonfatal myocardial infarction (adults,

age 18 and older)

570 2,200 3,000

Hospital admissions—respiratory (all

ages)d

140 510 720

Hospital admissions—cardiovascular

(adults, age >18)e

120 450 640

Emergency room visits for asthma (age

18 years and younger)

370 1,300 1,800

Acute bronchitis (children, age 8–12) 550 2,100 3,000

Lower respiratory symptoms (children,

age 7–14) 

6,600 25,000 36,000

Upper respiratory symptoms (asthmatic

children, age 9–18)

5,000 19,000 27,000

Asthma exacerbation (asthmatic

children, age 6–18)

8,100 31,000 44,000

Work loss days (adults, age 18–65) 44,000 170,000 240,000

Minor restricted-activity days (MRADs)

(adults, age 18–65)

260,000 1,000,000 1,400,000

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

modeling used to derive these incidence estimates assumes the final CAIR program in the baseline 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 for CAIR resulting in a slight

understatement of the reported benefits and costs for the CAVR. The recently promulgated CAMR has not been

considered in the baseline for CAVR. b Ozone benefits are expected for CAVR but are not estimated for this analysis. c Adult premature mortality based on studies by Pope et al. (2002). Infant premature mortality is based on 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.
1-6

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

Welfare Effects for the Clean Air Visibility Rule in 2015 (in millions of 1999$)a,b

Effect Scenario 1 Scenario 2 Scenario 3

Health Effects:

Premature mortalityc,d

 Adult >30 years

 3% discount rate

 7% discount rate

 Child <1 year

$2,330

$1,960

$6.12

$9,180

$7,730

$23.8

$13,000

$10,900

$34.2

Chronic bronchitis (adults, 26 and over) $90.5 $353 $498

Nonfatal acute myocardial infarctions 

 3% discount rate

 7% discount rate

$49.3

$45.8

$189

$176

$264

$245

Hospital admissions for respiratory causes $1.07 $4.03 $5.65

Hospital admissions for cardiovascular causes $2.6 $10.0 $14.1

Emergency room visits for asthma $0.106 $0.362 $0.51

Acute bronchitis (children, age 8–12) $0.207 $0.79 $1.12

Lower respiratory symptoms (children, 7–14) $0.109 $0.415 $0.587

Upper respiratory symptoms (asthma, 9–11) $0.137 $0.523 $0.74

Asthma exacerbations $0.367 $1.4 $1.98

Work loss days $5.56 $22.4 $31.5

Minor restricted-activity days (MRADs) $13.8 $54.1 $76.3

Welfare Effects:

Recreational visibility, southeastern and

southwestern Class I areas

$84 $239 $416

Monetized Totale 

 Base Estimate:

 3% discount rate

 7% discount rate 

$2,600 + B

$2,200 + B

$10,100 + B

$8,600 + B

$14,300 + B

$12,200 + B a Monetary benefits are rounded to three significant digits. These estimates are nationwide with the exception of visibility

benefits. Ozone benefits are expected for CAVR but have not been estimated for this analysis. Visibility benefits relate

to Class I areas in the southeastern and southwestern United States. The benefit estimates assume the final CAIR

program in the baseline 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 the CAIR baseline estimates assumes annual SO2 and

NOx controls for Arkansas resulting in a slight understatement of the reported benefits and costs for CAVR. The

recently promulgated CAMR is not considered in the baseline for CAVR. b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis year of 2015. c Valuation assumes discounting over the Science Advisory Board (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 (EPA, 2000; OMB, 2003). d Adult premature mortality based on studies by Pope et al. (2002). Infant premature mortality based on studies by

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

provided in Table 1-4. Totals are rounded to the nearest 100 million, and totals may not sum due to rounding.
1-7

1.2.2 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

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

CAVR will yield welfare benefits of approximately $240 million in 2015 (1999$) for

visibility improvements in southeastern and southwestern (including California) Class I

(national park) areas for Scenario 2. 

1.2.3 Uncertainty in the Benefits Estimates

Characterization of health-related benefits associated with PM reductions is a

complex process that is subject to a variety of potential sources of uncertainty. Key

assumptions underlying the estimate of avoided premature mortality include the following: 

C 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 established, the

weight of the available epidemiological and experimental evidence supports an

assumption of causality.

C 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. 

C The concentration-response (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 standards and those that do not meet the standard. 

C 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. 

Use of the Pope et al. (2002)-derived mortality function to support this analysis is

associated with uncertainty resulting from (a) potential of the study to incompletely capture
1-8

short-term exposure-related mortality effects, (b) potential mismatch between study and

analysis populations, which introduces various forms of bias into the results, and (c) failure

to identify all key confounders and effects modifiers that could result in incorrect effects

estimates relating morality to PM2.5 exposure. EPA is researching methods to characterize all

elements of uncertainty in the dose-response function for mortality. As is discussed in detail

in the CAIR RIA (EPA, 2005), EPA has used two methods to quantify uncertainties in the

mortality function, including the statistical uncertainty derived from the standard errors

reported in the Pope et al. (2002) study and the use of results of a pilot expert elicitation

conducted in 2004 to investigate other uncertainties in the mortality estimate. Because this

analysis is an illustrative analysis, we do not quantify uncertainty with these two methods in

this report. In the CAIR benefit analysis, the statistical uncertainty from the standard error of

the Pope et al. (2002) study was twice the mean benefit estimate at the 95th percentile and

one-fourth of the mean at the 5th percentile, while the expert elicitation provided mean

estimates that ranged in value from less than one-third of the mean estimate from the Pope et

al. (2002) study-based estimate to two-and-one-half times the Pope et al. (2002)-based

estimate. The confidence intervals from the pilot elicitation applied to the CAIR benefit

analysis ranged in value from zero at the 5th percentile to a value at the 95th percentile that is

seven times higher than the Pope et al. (2002)-based estimate. These results are highly

dependent on the air quality scenarios applied to the C-R functions of the Pope et al. (2002)

study and the pilot expert elicitation. Thus, the characterization of uncertainty discussed in

the CAIR RIA could differ greatly from what would be observed for CAVR because of

differences in population-weighted changes in concentrations of PM2.5 (i.e., the location of

populations’ exposure relative to the changes in air quality) and may be especially sensitive

to the differences in baseline PM2.5 air quality experienced by populations prior to

implementation of the CAVR. EPA is continuing its research of methods to characterize

uncertainty in total benefits estimates and is conducting a full-scale expert elicitation. The

full-scale expert elicitation is scheduled to be completed by the end of 2005. 

1.3 Not All Benefits Quantified

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

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

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

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

ecosystem functions. Table 1-4 provides a list of these benefits. 
1-9

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

• Hospital admissions: respiratory

• Emergency room visits for asthma

• Minor restricted-activity days

• School-loss days

• Asthma attacks

• Cardiovascular emergency room visits

• Acute respiratory symptoms

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

(continued)
1-10

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

(continued)

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

Mercury Healthg • 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 Depositiong

Welfare

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

• Impacts to commercial, subsistence, and recreational fishing a In addition to primary economic endpoints, a number of biological responses 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, a number of biological responses 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 on 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 because the literature is insufficient. g Mercury emission reductions are not anticipated for BART from the EGU source category because the cap-and-trade

program promulgated for the CAMR (March 2005); however, the geographic location of mercury reductions may change

as a result of this rule. EPA believes any such effects for these sources would likely be minimal. Mercury reductions are

expected for the non-EGU source categories. The mercury reductions for BART from the non-EGU source categories are

expected to be small compared to reductions resulting from the recently promulgated CAIR and CAMR (March 2005).

1.4 Costs and Economic Impacts

The control strategies analyzed in this report represent EPA’s best approximation of

the emission controls for BART-eligible sources that States may require. Ultimately, States

will make the determination of those BART-eligible sources to control and the level of costeffective controls. We recognize the uncertainty in these estimates and present benefit and

cost estimates for three scenarios.

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

the power industry range from $253 to $896 million in 2015. These costs represent the total
1-11

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

BART requirements set out in the rule assuming alternative control scenarios. Estimates are

in 1999 dollars. Costs of the rule are estimated using the Integrated Planning Model (IPM)

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. Under

Scenario 2, the social costs of this rule for the EGU sector are estimated to range from $119

to $688 million in 2015 and assuming a 3 or 7 percent discount rate. 

Average retail electricity prices are projected to increase roughly 0.1 percent with

CAVR in the 2015 time frame for Scenario 2. Coal-fired generation as well as coal

production and natural gas-fired generation under CAVR are projected to remain essentially

unchanged relative to CAIR baseline levels. It is also not expected that CAVR will change

the composition of new generation built to meet growth in electricity demand. CAVR is also

not expected to impact coal or natural gas prices.

For the non-EGU sectors, we estimate that emission control costs will range from 

approximately $151 million to $2.2 billion in 2015 for the alternative regulatory scenarios

and at a 3 or 7 percent discount rate. These estimates are based on an analysis conducted that

assumes States will implement controls with a maximum cost ranging from $1,000 to

$10,000 per ton of SO2 or NOx emission reductions. 

1.5 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 CAVR in future years.

A major source of uncertainty in the analysis conducted for this RIA is the

uncertainty surrounding the actions States may take to comply with BART. We have

conducted a range of scenarios that represent increasing levels of stringency of controls
1-12

States may require to implement BART. It is likely that the benefits and costs of BART as

required by States will fall within the range of estimates presented in this document.

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 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 consider transactional costs and/or savings 

from BART on the labor supply. 

1.6 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. Environmental Protection Agency (EPA). March 2005. Regulatory Impact Analysis for

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

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

Agencies on Preparation of Regulatory Analysis.
1-13

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.
2-1

SECTION 2

INTRODUCTION AND BACKGROUND

For this rulemaking, we are developing guidelines for BART determinations under

the regional haze regulations. Specifically, we are addressing the issues of: 1) the sources

that must apply BART, and 2) guidance as to how a State should determine the level of

control for each source subject to BART in response to the court’s ruling. We are finalizing

a reproposal of the BART guidelines and the section of the regional haze rule relevant to the

BART guidelines. This final rule reflects both our review of the public comments, and our

response to the court ruling.

This document presents the health and welfare benefits of the Clean Air Visibility

Rule (CAVR) or BART rule and compares the benefits of this rule to the estimated costs of

implementing the rule in 2015. This section provides background information including a

discussion of the need for the proposed regulation, a brief discussion of the potentially

regulated source categories, and control scenarios analyzed in the RIA.

2.1 Background

In 1999, the EPA published a final rule to address a type of visibility impairment

known as regional haze 64 FR 35714, July 1, 1999. The regional haze rule requires state

implementation plans (SIPs) to address regional haze visibility impairment in 156 Federallyprotected parks and wilderness areas. These 156 scenic areas are called “Class I areas” in the

Clean Air Act (CAA). This rule fulfilled a long-standing EPA commitment to address

regional haze under the authority and requirements of sections 169A and 169B of the CAA. 

As required by section 169A(b)(2)(A) and 169A(g) of the CAA, we included in the

final regional haze rule a requirement for BART for certain large stationary sources that were

put in place between 1962 and 1977. We discussed these requirements in detail in the

preamble to the final rule. (See 64 FR 35737-35743). The regulatory requirements for

BART are codified at 40 CFR 51.308(e), and in the definitions that appear in 40 CFR 51.301. 

The CAA, in 169A(b)(2)(A) and in 169A(g)(7), uses the term “major stationary

source” to describe those sources that are the focus of the BART requirement. To avoid

confusion with other CAA requirements that also use the term “major stationary source” to
2-2

refer to a somewhat different population of sources, the regional haze rule uses the term

“BART-eligible source” to describe these sources. BART-eligible sources are those sources

that have the potential to emit 250 tons or more of a visibility-impairing air pollutant, were

put in place or under construction between August 7, 1962 and August 7, 1977, and whose

operations fall within one or more of 26 specifically listed source categories. Under CAA

section 169A(b)(2)(A), BART is required for any BART-eligible source which “emits any

air pollutant that may reasonably be anticipated to cause or contribute to any impairment of

visibility in any such area.” Accordingly, for stationary sources meeting these criteria, States

must address the BART requirement when they develop their regional haze SIPs.

Section 169A(g)(7) of the Clean Air Act requires that States must consider the

following factors in making BART determinations:

(1) the costs of compliance,

(2) the energy and nonair quality environmental impacts of compliance,

(3) any existing pollution control technology in use at the source,

(4) the remaining useful life of the source, and 

(5) the degree of improvement in visibility which may reasonably be anticipated to

result from the use of such technology.

These statutory factors for BART appear in the regional haze rule in 40 CFR 51.308(e)(1)(ii). 

The regional haze rule provides States with two alternative ways to approach the

requirement for BART in the CAA. Under the first approach, contained in 51.308(e)(1) of

the regional haze rule, SIPs would contain source specific emission limits for each source

subject to BART. Under the second approach, States may elect to adoptive alternative

measures, such as a regional emissions trading program, in lieu of BART so long as the

alternative measures achieve “more reasonable progress” than would application of sourcespecific BART emission limits. In the preamble to the 1999 regional haze rule, we discuss a

number of issues related to both approaches. 

In addition, in the preamble to the regional haze rule (64 FR 35741, July 1, 1999) we

committed to issuing further guidelines to clarify the requirements of the BART provision. 

The purpose of this rule is to fulfill this commitment by providing guidelines for States to

use in identifying their BART eligible sources, in identifying those sources that must

undergo a detailed BART analysis (i.e., “sources subject to BART”), and in conducting the
2-3

technical analysis of possible controls in light of the statutory factors listed above (“the

BART determination”).

Finally, the rule discusses proposed changes to the regional haze rule, and a

reproposal of the BART guidelines, in response to the May 24, 2002 ruling from the US

Court of Appeals for the D.C. Circuit, that struck down the regional haze rule in part (and

upheld it in part). This rulemaking finalizes guidelines for States to use in identifying their

BART eligible sources, in identifying those sources that must undergo a detailed BART

analysis.

2.2 Regulated Source Categories

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

and Tribes with BART-eligible stationary sources to revise their implementation plans to

meet the BART requirements. However, States have the flexibility to choose what sources to

control. The CAA uses the following 26 source category titles to describe the types of

stationary sources that are BART-eligible: 

(1) Fossil-fuel fired steam electric plants of more than 250 million British thermal

units (Btu) per hour heat input,

(2) Coal cleaning plants (thermal dryers),

(3) Kraft pulp mills,

(4) Portland cement plants,

(5) Primary zinc smelters,

(6) Iron and steel mill plants,

(7) Primary aluminum ore reduction plants,

(8) Primary copper smelters,

(9) Municipal incinerators capable of charging more than 250 tons of refuse per

day,

(10) Hydrofluoric, sulfuric, and nitric acid plants,

(11) Petroleum refineries,

(12) Lime plants,
2-4

(13) Phosphate rock processing plants,

(14) Coke oven batteries,

(15) Sulfur recovery plants,

(16) Carbon black plants (furnace process),

(17) Primary lead smelters,

(18) Fuel conversion plants,

(19) Sintering plants,

(20) Secondary metal production facilities,

(21) Chemical process plants,

(22) Fossil-fuel boilers of more than 250 million BTUs per hour heat input,

(23) Petroleum storage and transfer facilities with a capacity exceeding 300,000

barrels,

(24) Taconite ore processing facilities,

(25) Glass fiber processing plants, and

(26) Charcoal production facilities.

Most of the source category titles are general descriptors that are inclusive of all the

operations at a given plant. Some plant sites may have more than one of the categories

present. Examples of this would include plants with both “petroleum refineries” and “sulfur

recovery plants,” or with both “iron and steel mill plants” and “sintering plants.” On the

other hand, some plant sites may include some emissions units meeting one of these 26

descriptions, but other emissions units that do not.

2.3 Control Scenarios

The source-specific BART guidelines require emissions reductions from sources

emitting sulfur dioxides (SO2) and nitrous oxides (NOx). The analyses conducted for this

RIA include three regulatory alternative scenarios that States may choose to follow to

comply with BART. The alternatives include three scenarios of increasing stringency -

Scenario 1, Scenario 2, and Scenario 3. A brief discussion of the these alternatives for the

electric generating units (EGUs) and all other sources follows. More details of the
1

 These levels are commonly achievable by flue gas desulfurization controls (“scrubbers”). 

2-5

alternative control scenarios and associated control costs are discussed in chapters 7 and 8 of

this report.

2.3.1 Electric Generating Units

In the revised BART guidelines, we have included presumptive control levels for SO2

and NOx emissions from coal-fired electric generating units greater than 200 megawatts

(MW) in capacity at plants greater than 750 MW in capacity. Given the similarities of these

units to other BART-eligible coal-fired units greater than 200 MW at plants 750 MW or less,

EPA’s guidance suggests that states control such units at similar levels for BART. The

guidelines would require 750 MW power plants to meet specific control levels of either 95

percent control or controls of 0.15 lbs/MMBtu, for each EGU greater than 200 MW, unless

the State determines that an alternative control level is justified based on a careful

consideration of the statutory factors.1 Thus, for example, if the source convincingly

demonstrates unique circumstances affecting its ability to cost-effectively reduce its

emissions, the State may take that into account in determining whether the presumptive

levels of control are appropriate for the facility. For an EGU greater than 200 MW in size,

but located at a power plant smaller than 750 MW in size, States may also find that such

controls are cost-effective when taking into consideration the costs of compliance in the

BART analysis in applying the five factor test for the BART determination. In our analysis

we have assumed that no additional controls will occur where units have existing scrubbers

and that no controls will occur for oil-fired units. While these levels may represent current

control capabilities, we expect that scrubber technology will continue to improve and control

costs will continue to decline. 

For NOx, for those large EGUs that have already installed selective catalytic

reduction (SCR) or selective non-catalytic reduction (SNCR) during the ozone season, States

should require the same controls for BART. However, those controls should be required to

operate year-round for BART. For sources currently using SCR or SNCR for part of the

year, States should presume that the use of those same controls year-round is highly costeffective. For other sources, the guidelines establish presumptive emission levels that vary

depending largely upon boiler type and fuel burned. For coal-fired cyclone units with a size

greater than 200 MW, our analysis assumes these units will install SCR. For all other coalfired units, our analysis assumed these units will install current combustion control
2-6

technology. In addition, we assume no additional controls for oil and/or gas-fired steam

units. 

We present alternative regulatory scenarios. Scenario 2 represents our application of

the presumptive limits described above to all BART eligibility EGUs greater than 200 MW. 

For Scenario 1, we assume that only 200 MW BART-eligible EGUs located at facilities

above 750 MW capacity will comply with the SO2 requirements and NOx controls. In this

scenario, no facilities less than 750 MW capacity are assumed to install BART controls. For

Scenario 1, we assume that units with existing SCRs will operate those SCR units year round

annually. In contrast in Scenario 3, we analyzed SO2 controls equivalent to 95 percent

reductions or 0.1 lbs per MMBtu on all previously uncontrolled units. NOx controls for this

most stringent scenario presume SCRs will be installed on all units greater than 100 MW

capacity and combustion controls will be installed on units greater than 25 MW but less than

100 MW capacity. The EPA analyzed the costs of each BART scenario using the Integrated

Planning Model (IPM). The EPA has used this model extensively in past rulemakings to

analyze the impacts of regulations on the power sector. 

The analysis presented assumes that BART-eligible EGUs affected by the Clean Air

Interstate Rule (CAIR) (March 2005) have met the requirements of this rule. Thus, no

additional controls for EGUs beyond CAIR are anticipated or modeled for the 28 State plus

District of Columbia CAIR region. In addition, we are assuming no additional SO2 controls

for sources located in States of Arizona, Utah, Oregon, Wyoming, and New Mexico or Tribal

lands located in these States due to agreements made with the Western Regional Air

Partnership (WRAP). See Chapter 7 for a more detailed discussion of the emission controls

scenarios assumed for the EGU sector. An analysis of EGU controls under CAIR and a more

conservative approach to BART controls for EGU sources is included in Appendix E of this

report. This analysis provides information as to the possible incremental benefits and costs

associated with requiring EGU controls for BART sources in the non-CAIR region. It

should be noted that a more strict interpretation of BART than in the BART guidelines

finalized was assumed for this analysis and that the costs and benefits for BART reported in

Appendix E differ from those estimated for the three regulatory scenarios analyzed for this

rulemaking. 

2.3.2 Sources Other than Electric Generating Units

As previously discussed there are 25 source categories potentially subject to BART in

addition to EGUs (referred to as non-EGU source categories) as defined by the CAA. The

EPA evaluated a set of SO2 and NOx emission control technologies available for these source
2-7

categories and estimated the associated costs of control using AirControlNET. The control

scenarios evaluated assume maximum control measure cost caps of $1,000 per ton (Scenario

1), $4,000 per ton (Scenario 2), and $10,000 per ton (Scenario 3). The EPA also conducted a

cost analysis for $2,000 per ton and $3,000 per ton, and the results of this analysis are

presented in Appendix G of this document. The analysis consists of applying SO2 and NOx

controls to each non-EGU source category up to the specified cost per ton cap in each

scenario. These cost per ton caps are specified in average cost terms. As control stringency

is increased, the marginal costs are also estimated for each non-EGU source category. The

scenarios examined are based on the costs of technologies such as scrubbers for SO2 control,

and varying types of technologies for NOx control. Scrubbers are the most common type of

SO2 control for most non-EGU sources for each scenario, while combustion controls such as

low NOx burners (LNB) and post-combustion controls such as selective noncatalytic

reduction (SNCR) and selective catalytic reduction (SCR) are commonly applicable to most

of the non-EGU source category. Combustion controls are commonly applied as part of

Scenario 1, while SNCR and SCR are more commonly applied either by themselves or in

combination with combustion controls as part of Scenarios 2 and 3. Analyses are not

available for 8 of the 25 non-EGU source categories, because there are no available control

measures for these sources or there are no sources in these categories included in the nonEGU emissions data utilized in these analyses. The marginal costs of these alternative

regulatory scenarios are presented along with the results of these analyses in Chapter 8 of

this report. All of these results are estimated using a nationwide database of BART-eligible

non-EGU sources that is based on information collected from Regional Planning

Organizations (RPOs) in the fall of 2004. This database became part of the baseline for this

analysis. More information on this non-EGU source database is available in Chapter 3 of

this report. Just as for affected EGUs, all impacts to non-EGUs are estimated for the year

2015.

2.4 Baseline and Years of Analysis

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

and Tribes to meet the BART guidelines of the Regional Haze Rule. To comply with the

BART guidelines, 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 including the

recently promulgated Clean Air Interstate Rule. However, EPA did not consider actions

States may take to implement the ozone and PM2.5 NAAQS standards nor the recently

promulgated Clean Air Mercury Rule in the baseline for this analysis. 
2-8

In the analysis, the controls and reductions are assumed to be required in 2015, a date

that is generally consistent with the expected timing of the rule. States must submit SIPs

relevant to the BART requirements in January 2008. After approval of the SIP, there is a 5

year compliance date. Thus, controls are likely to be installed by the end of 2013 or the

beginning of 2014 to comply with the rule. In addition, EPA had existing inventories,

modeling, and base case runs for 2015 to use for the analysis. The year 2015 is used in this

analysis. All estimates presented in this report represent annualized estimates of the benefits

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

in these particular years of analysis.

2.5 Organization of this Report

This document describes the health and welfare benefits of the proposed rule. The

document is organized as follows:

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, Profile of Potentially Affected Industries, describes the major

industries that may be affected by this rule.

C Chapter 7, Cost and Economic Impacts for the EGU Sector, describes the costs of

the rule to the power section and related economic impacts.

C Chapter 8, Results of Cost, Emission Reductions, and Economic Impact Analysis

for the Non-Electricity Generating Sectors, describes the costs of the rule to

affected industry sectors and related economic impacts.

C Chapter 9, Executive Order and Statutory Requirements, describes impact

analyses conducted to meet executive order and statutory requirements.

C Chapter 10, Comparison of Benefits and Costs, provides a comparison of the

monetized benefits and estimated annual costs of the proposed rule.

C Appendix A, BART Industry-Sector Impacts

C Appendix B, Cost and Economic Impact Supplemental Information and

Sensitivity Analyses
2-9

C Appendix C, Additional Technical Information Supporting 

the Benefits Analysis

C Appendix D, Visibility Benefits Methodology

C Appendix E, 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

C Appendix F, Sensitivity Analyses of Some Key Parameters in the Benefits

Analysis

C Appendix G, Additional Control Scenarios for Non-EGU Source Categories
3-1

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 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 summarizes the baseline emissions inventories and the

emissions reductions that were modeled for this rule. Section 3.2 summarizes the methods

for and results of estimating air quality for the 2015 base case and control scenarios for the

purposes of the benefits analysis. There are separate sections for PM and visibility.

3.1 Emissions Inventories and Estimated Emissions Reductions

The emission sources and the basis for current and future-year emission inventories

for BART are listed in Table 3-1. The data source for the EGU source category is the 2001

base year data from the Acid Rain Trading Program. Modeling of the potential BART

emission controls for the EGU source category including potential emission reductions from

the program was completed with the IPM. The data source for the non-EGU BART eligible

source categories is the 2001 National Emission Inventory (NEI). Data necessary to identify

potentially affected BART eligible non-EGU sources were provided by the RPOs in response

to an Information Collection Request. More details concerning the development of the

emissions inventory data may be found in the Emissions Inventory Technical Support

Document (Emissions Inventory TSD) available in the docket for this rule. Modeling of the

potential BART emission controls for the non-EGU source category, including potential

emission reductions from the program, was completed using AirControlNet.
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Table 3-1. Emissions Sources and Basis for Current and Future-Year Inventoriesa,b

Sector or

Source

Emissions

Source 2001 Base Year 

Future-Year Base Case

Projections

EGU Power industry

EGUs

2001 Data from the Acid

Rain Trading Program

IPM

Non-EGU Non-utility

point,

including point

source fugitive

dust

2001 NEI Baseline including CAIR control

case: (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

BART control cases: All of the

above plus AirControlNET controls

applied to BART-eligible units

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 country distributions of

emissions as in the 2001 NEI

Average

Fire

Agricultural

burning, open

burning

2001 NEI 2001 NEI

Agriculture Livestock NH3 2002 preliminary NEIc 2015 emissions estimated with the

same approach as was used for the

2002 preliminary NEIc

Agriculture Fertilizer NH3 2001 NEI 2001 NEI

(continued)
3-3

Table 3-1. Emissions Sources and Basis for Current and Future-Year Inventoriesa,b

(continued)

Sector or

Source

Emissions

Source 2001 Base Year 

Future-Year Base Case

Projections

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 CAIR proposed and final

rule, emissions from MOBILE6.2

model

Nonroad Locomotives,

commercial

marine vessels,

and aircraft

2001 NEI; CMV adjusted

to new national totals

from Office of

Transportation Air Quality

(OTAQ)

Grown based on national totals from

OTAQ, using state/county

distribution of emissions from the

2001 NEI

Nonroad All other

nonroad

vehicles

NONROAD 2004 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 emissions inventory 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.
3-4

The emissions inventories used for the BART analysis builds on a baseline inventory

that includes CAIR promulgated March 10, 2005. The CAIR TSD for emissions inventories

discusses the development of the 2001, 2015 baseline prior to CAIR emission controls, and

2015 baseline including the 2015 CAIR emission controls (CAIR control case). The CAIR

control case provides the emissions inventory baseline for the BART analysis conducted. 

The CAIR TSD is available as follows: http://www.epa.gov/interstateairquality/

pdfs/finaltech01.pdf

Table 3-2 summarizes the 2001 baseline, 2015 CAIR control case (2015 base case for

BART), and three 2015 BART control case scenarios of NOX and SO2 emissions. Table 3-3

shows the change in NOX and SO2 emissions for each of the three BART control case

scenarios that were used in modeling air quality changes: Scenario 1, Scenario 2, and

Scenario 3. The emission reductions for these control case scenarios relate to the EGU and

non-EGU source categories that are potentially affected by the BART guidelines. For details

on EPA’s IPM results including emission reductions for the EGU sector, see Chapter 7 of

this RIA. For details on EPA’s AirControlNET modeling of potential non-EGU emission

reductions see Chapter 8 of this RIA. The BART emissions inventories TSD discusses the

development of the 2015 CAVR inventories used to develop the benefits analysis for this

final rule.

3.2 Air Quality Impacts

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

2015 base case 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, and

2. 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.

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 discuss the estimation of visibility degradation.
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Table 3-2. Summary of Modeled Baseline Emissions, CAIR Control Case, and BART

Control Strategies

Pollutant Emissions (tons)

Source NOX SO2

2001 Baselinea

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

2015 Base CAIR Control Casea

EGUs 2,172,839 5,111,436

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

Average Fire 238,931 49,108

Area 1,702,154 1,480,348

On-road 3,152,562 30,824

Nonroad 2,912,382 232,627

Total, All Sources 13,362,365 10,327,258

2015 BART, Scenario 1b

EGUs 2,057,220 5,057,652

Non-EGUs 3,011,944 3,338,983

Total, All Sources 13,075,193 10,189,543

2015 BART, Scenario 2 b

EGUs 1,963,753 4,991,850

Non-EGUs 2,807,124 3,152,277

Total, All Sources 12,776,906 9,937,034

2015 BART, Scenario 3b

EGUs 1,740,551 4,958,975

Non-EGUs 2,779,816 3,043,903

Total, All Sources 12,526,397 9,795,785

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

b With the exception of EGUs and non-EGUs, all other sectors are the same as the CAIR control case (CAVR

baseline) for 2015.
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Table 3-3. Summary of Modeled Emissions Changes for the BART Rule: 2015a

Pollutant

Item NOX SO2

2015 Emission Reductions with the BART, Scenario 1

Total Ton Reductions 287,172 137,715

Percentage of EGU Emission Reductions 5.3% 1.1%

Percentage of Non-EGU Emission Reductions 5.4% 2.5%

Percent Reduction of All Manmade Emissions 2.1% 1.3%

2015 Emission Reductions with BART, Scenario 2

Total Tons Reduction 585,459 390,224

Percentage Reduction of EGU Emissions 9.6% 2.3%

Percentage of Non-EGU Emission Reduction 11.8% 7.9%

Percentage Reduction of All Manmade Emissions 4.4% 3.8%

2015 Emission Reductions with BART, Scenario 3

Total Tons Reduction 835,968 531,473

Percentage Reduction of EGU Emissions 19.9% 3.0%

Percentage of Non-EGU Emission Reduction 12.7% 11.1%

Percentage Reduction of All Manmade Emissions 6.3% 5.1%

a Note that the emission changes and percentage changes reported are nationwide estimates.

3.2.1 PM Air Quality Estimates

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

the 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
1

Given that a large percentage of PM2.5 concentrations and visibility degradation is due to 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 that are best addressed

using an air quality model that employs a Eulerian grid model design.

3-7

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 five emissions

scenarios: a 2001 base year, a 2015 base year projection, and three 2015 projections with

varying levels of emissions controls. 

The CMAQ version 4.3 was employed for this BART 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 partitioning algorithm from both anthropogenic and

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

sulfate 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 and 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 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.
3-8

Figure 3-1. CMAQ Modeling Domain

3.2.1.1 Modeling Domain

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

extends from approximately 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

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.
3-9

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 2015 base case and control scenarios).

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 the future-year base case and control scenarios. 

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

unchanged for each future-year modeling scenario.

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

(EPA, 2005a). 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 36 km horizontal resolution used for this CMAQ

application. Further details on the CMAQ model setup can be found in the CAIR Air

Quality Modeling Technical Support Document (EPA, 2005b).

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), and 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
3-10

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

analyses can be found in “CMAQ Model Performance Evaluation for 2001: Updated March

2005” (EPA, 2005c).

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). 
3-11

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

48 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: 

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 
3-12

Table 3-4. Model Performance Statistics for BART 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

sulfate (especially in the summer) accounts for the vast majority of visibility degradation in

the East. Model performance statistics for summer average sulfate is provided in Table 3-5. 

The general range of model performance for PM2.5 species compares favorably to fractional

bias and fractional error statistics from the better performing model applications found by

others in the modeling community, as follows: 

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.
3-13

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

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. 

3.2.1.5 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, 2001). 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 BART (and for CAIR) have been revised. Full documentation of the revised SMAT

methodology is contained in “Procedures for Estimating Future PM2.5 Values for the CAIR
3-14

Final Rule by Application of the (Revised) Speciated Modeled Attainment Test (SMAT)-

Updated” (EPA, 2004). 

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 the 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 as documented for use in CAIR 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 BART PM benefits analysis with the

following exceptions:

1) The BART benefits analysis uses interpolated PM2.5 data that cover all of the

grid cells in the modeling domain (covering the entire country), whereas the

CAIR nonattainment analysis is performed at each ambient monitoring site in the

East using measured PM2.5 data (only the species data are interpolated). 
3-15

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 2015 base case and

changes associated with the rule. The table includes the annual mean concentration averaged

across all model grid cells in the nation, 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 concentration of

PM2.5 across populated eastern U.S. grid cells declines by roughly 1.28 percent (or 0.12

μg/m3) in 2015. The population-weighted average mean concentration declined by 0.94

percent (or 0.10 μg/m3) in 2015 for Scenario 2. Comparable estimates for Scenario 1 reflect

smaller PM air quality improvements than for Scenario 2, and estimates for Scenario 3 show

greater air quality improvements. This information is presented in Table 3-6.

Table 3-7 provides information on the populations in 2015 that will experience

improved PM air quality. As shown, in 2015, almost 35.4 percent of the U.S. population is

predicted to experience reductions of greater than 0.1 μg/m3. Furthermore, over 8 percent of

this population will benefit from reductions in annual mean PM2.5 concentrations of greater

than 0.2 μg/m3. 

3.2.2 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).
3-16

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

Visibility Rule in 2015

Scenario 1 Scenario 2 Scenario 3

Region PM2.5 (μg/m3

)

Base

Case Changea

Percent

Change Changea

Percent

Change Changea

Percent

Change

East

Averageb Annual

Mean

9.39 –0.04 –0.43 –0.12 –1.28 –0.16 –1.70

PopulationWeighted Average

Annual Mean c

10.64 –0.03 –0.28 –0.10 –0.94 –0.14 –1.32

West

Average Annual

Mean

6.04 –0.02 –0.33 –0.04 –0.66 –0.06 –0.99

PopulationWeighted Average

Annual Meanc

12.46 –0.01 –0.08 –0.07 –0.56 –0.10 –0.80

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 regional population.

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

Clean Air Visibility Rule in 2015

Range of Change in

Annual Mean PM2.5

Concentrations

(μg/m3

)

a

Scenario 1 Scenario 2 Scenario 3

Number

(millions)

Percent

(%)

Number

(millions)

Percent

(%)

Number

(millions)

Percent

(%)

# 0.1 301.7 95.2% 204.6 64.6% 130.8 41.3%

0.11 – 0.20 9.9 3.1% 85.6 27.0% 136.3 43.0%

0.21 – 0.30 5.2 1.6% 19.6 6.2% 35.5 11.1%

0.31 – 0.40 0.0 0.0% 5.1 1.6% 9.2 2.9%

0.41 – 0.50 0.0 0.0% 1.9 0.6% 2.7 0.8%

0.50 – 0.60 0.0 0.0% 0.0 0.0% 2.4 0.8%

a The change is defined as the control case value minus the base case value.

b Population counts and percentages are for the continental U.S. population.
2

There are 81 IMPROVE sites with complete data for 2001. Many of these sites collect data that are

“representative” of other nearby unmonitored Class I areas. A total of 116 Class I areas are represented by

the 81 sites. The matching of sites to monitors is taken from “Guidance for Tracking Progress Under the

Regional Haze Rule.”

3-17

Based on the light-extinction coefficient, we also calculated a unitless visibility

index, called a “deciview,” that 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. 

3.2.2.1 Procedures for Estimating Visibility Degradation

The impacts of the BART emissions reductions were examined in terms of the

projected improvement in annual average visibility as well as projected visibility

improvement on the 20 percent best and worst days from 2001 at Class I areas. We

quantified visibility impacts at the 116 Class I areas that have complete IMPROVE ambient

data for 2001 or are represented by IMPROVE monitors with complete data.2

 Currently 110

IMPROVE monitoring sites (representing all 156 Class I areas) are collecting ambient PM2.5

data at Class I areas, but only 81 of these sites have complete data for 2001. 

The future-year base and BART control visibility was calculated using a

methodology that applies modeling results in a relative sense similar to SMAT. The draft

PM2.5 and Regional Haze modeling guidance recommends the calculation of future-year

changes in visibility in a similar manner to the calculation of changes in PM2.5. We generally

followed the procedures in the guidance. 

In calculating visibility impairment, the extinction coefficient and deciview values

are made up of individual component species (e.g., sulfate, nitrate, organics). The predicted

change in visibility is calculated as the percentage change in the extinction coefficient for

each of the PM species (on a daily average basis). The individual daily species extinction

coefficients are summed to get a daily total extinction value. The daily extinction

coefficients are converted to deciviews and then averaged across all monitored days to get an

annual average as well as the 20 percent best and worst days (best and worst days

separately). In this way, we can calculate an average change in deciviews from the base case

to a future case at each Class I area. Additionally, subtracting the future BART control case

deciview values from the future base case deciview values gives an estimate of the visibility
3-18

benefits in Class I areas from the BART scenario. Additional details on the visibility

calculation methodology can be found in the CAIR “Better-than-BART” TSD (EPA, 2005d).

As explained above (Section 3.2.1.5), when calculating future-year PM2.5

concentrations for BART, we have updated the SMAT procedures to use PM2.5 component

species that emulate the FRM measurements. This is thought to be the most accurate

estimate of the PM2.5 species fractions at FRM sites for the purpose of calculating PM-related

health benefits. For visibility calculations, we are continuing to use the IMPROVE program

species definitions and visibility formulas that are recommended in the draft modeling

guidance. Each IMPROVE site has measurements of PM2.5 species; therefore, we do not

need to estimate the species fractions in the same way that we did for FRM sites (using

interpolation techniques and other assumptions concerning volatilization of species). 

Therefore, the methodology for calculating PM2.5 species fractions for the visibility

calculations (at IMPROVE sites) differs from the calculations that are detailed in the revised

SMAT methodology. 

Table 3-8 provides visibility improvements expected to occur in specific parks in the

nation for Scenario 2. As shown, major parks in the United States, including the Great

Smokey Mountains and Shenandoah, are expected to see 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 0.15 deciviews (0.59 percent), and Shenandoah

National Park is expected to see improvements of over 0.24 deciviews (1.02 percent). It is

important to note that the deciview changes reported are estimated with CAIR in the

baseline. This means visibility improvements for CAIR controls that meet BART

requirements are not reflected in the deciview impacts shown in this table.
3-19

Table 3-8. Summary of Deciview Visibility Impacts at Class I Areas in the Nationa,b 

Scenario 2

Federal Class I Area

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

Acadia, ME 0.09 0.41 0.05 0.39

Aqua Tibia, CA* 0.20 0.87 0.14 0.79

Alpine Lakes, WA 0.10 0.58 0.10 0.84

Anaconda- Pintler, MT 0.04 0.30 0.03 0.36

Arches, UT* 0.00 0.00 0.03 0.40

Badlands, SD 0.32 1.87 0.21 1.77

Bandelier, NM* 0.08 0.66 0.04 0.50

Big Bend, TX 0.04 0.22 0.05 0.40

Black Canyon of the Gunnison, CO* 0.06 0.61 0.06 0.80

Bob Marshall, MT 0.02 0.13 0.02 0.20

Boundary Waters Canoe Area, MN 0.20 1.04 0.09 0.77

Bridger, WY 0.06 0.56 0.04 0.62

Brigantine, NJ 0.11 0.43 0.06 0.30

Bryce Canyon, UT* 0.06 0.49 0.05 0.61

Cabinet Mountains, MT 0.03 0.23 0.03 0.35

Caney Creek, AR 0.19 0.78 0.27 1.46

Canyonlands, UT* 0.03 0.29 0.04 0.53

Cape Romain, SC 0.14 0.61 0.11 0.61

Caribou, CA* 0.06 0.46 0.04 0.55

Carlsbad Cavern, NM* 0.06 0.36 0.05 0.44

Chassahowitzka, FL* 0.10 0.44 0.07 0.37

Chiricahua, AZ* 0.08 0.59 0.04 0.47

Craters of the Moon, ID 0.07 0.51 0.04 0.43

Desolation, CA* 0.06 0.41 0.04 0.48

Dolly Sods, WV* 0.16 0.69 0.10 0.58

Dome Land, CA* 0.06 0.33 0.03 0.25

Eagle Cap, OR 0.09 0.49 0.05 0.44

Eagles Nest, CO* 0.07 0.67 0.05 0.73

Emigrant, CA* 0.05 0.30 0.02 0.21

Everglades, FL* –0.01 –0.05 0.02 0.11

Fitzpatrick, WY 0.06 0.56 0.04 0.62

(continued)
3-20

Table 3-8. Summary of Deciview Visibility Impacts at Class I Areas in the Nation a,b

Scenario 2 (continued)

Federal Class I Area

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

Flat Tops, CO* –0.03 –0.28 0.02 0.32

Galiuro, AZ* 0.09 0.67 0.04 0.47

Gates of the Mountains, MT 0.02 0.19 0.02 0.32

Gila, NM* 0.08 0.67 0.03 0.40

Glacier, MT 0.02 0.13 0.02 0.18

Glacier Peak, WA 0.05 0.38 0.04 0.55

Grand Teton, WY 0.05 0.36 0.03 0.44

Great Gulf, NH 0.04 0.20 –0.04 –0.30

Great Sand Dunes, CO* 0.07 0.62 0.07 0.80

Great Smokey Mountains, TN* 0.15 0.59 0.11 0.56

Guadalupe Mountains, TX 0.08 0.44 0.05 0.43

Hells Canyon, OR 0.06 0.31 0.03 0.30

Isle Royale, MI 0.23 1.06 0.10 0.77

James River Face, VA* 0.15 0.62 0.08 0.42

Jarbidge. MV* 0.02 0.19 0.05 0.66

Joshua Tree, CA* 0.06 0.32 0.05 0.40

Joyce Kilmer—Slickrock, NC* 0.15 0.59 0.11 0.56

Kalmiopsis, OR 0.05 0.36 0.02 0.25

Kings Canyon, CA* 0.05 0.22 0.03 0.18

La Garita, CO* 0.04 0.42 0.04 0.58

Lassen Volcanic, CA* 0.06 0.45 0.04 0.53

Lava Beds, CA* 0.05 0.34 0.03 0.33

Linville Gorge, NC* 0.25 1.01 0.15 0.84

Lostwood, ND 0.21 1.08 0.09 0.64

Lye Brook, VT 0.14 0.62 0.06 0.44

Mammoth Cave, KY* 0.13 0.49 0.11 0.52

Marble Mountain, CA* 0.04 0.26 0.02 0.18

Maroon Bells- Snowmass, CO* 0.08 0.81 0.05 0.71

Mazatzal, AZ* 0.04 0.35 0.03 0.26

Medicine Lake, MT 0.10 0.55 0.06 0.45

Mesa Verde, CO* 0.08 0.70 0.05 0.59

Mingo, MO 0.14 0.52 0.16 0.78

Mission Mountains, MT 0.01 0.08 0.01 0.17

Mokelumne, CA* 0.05 0.39 0.03 0.37

(continued)
3-21

Table 3-8. Summary of Deciview Visibility Impacts at Class I Areas in the Nationa,b 

Scenario 2 (continued)

Federal Class I Area

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

Moosehorn, ME 0.06 0.29 0.04 0.27

Mount Hood, OR 0.07 0.55 0.05 0.55

Mount Jefferson, OR 0.05 0.40 0.03 0.38

Mount Ranier, WA 0.09 0.50 0.06 0.48

Mount Washington, OR 0.05 0.35 0.03 0.33

Mount Zirkel, CO* –0.03 –0.25 0.04 0.53

Noth Cascades, WA 0.04 0.30 0.04 0.53

Okefenokee, GA* 0.17 0.69 0.14 0.73

Otter Creek, WV 0.19 0.81 0.11 0.64

Pasauten. WA 0.07 0.44 0.05 0.65

Petrified Forest, AZ* 0.08 0.70 0.06 0.65

Pine Mountain, AZ 0.04 0.28 0.04 0.38

Presidential Range—Dry, NH* 0.11 0.55 0.05 0.35

Rawah, CO* 0.09 0.82 0.08 1.05

Red Rock Lakes, WY 0.04 0.31 0.03 0.42

Redwood, CA* 0.06 0.40 0.03 0.25

Rocky Mountain, CO* 0.07 0.49 0.06 0.67

Roosevelt Campobello, ME 0.08 0.39 0.06 0.40

Salt Creek, NM* 0.09 0.52 0.09 0.73

San Gorgonio, CA* 0.08 0.38 0.06 0.43

San Jacinto, CA * 0.11 0.53 0.10 0.69

San Pedro Parks, NM* 0.05 0.46 0.05 0.62

Sawtooth, ID 0.02 0.16 0.02 0.23

Scapegoat, MT 0.01 0.08 0.02 0.17

Selway—Bitterroot, MT 0.04 0.31 0.03 0.36

Seney, MI 0.20 0.85 0.10 0.72

Sequoia, CA* 0.07 0.30 0.05 0.28

Shenandoah, VA* 0.24 1.02 0.15 0.85

Sierra Ancha, AZ* 0.04 0.33 0.03 0.26

Sipsey, AL* 0.08 0.30 0.08 0.41

South Warner, CA* 0.04 0.32 0.03 0.37

Strawberry Mountain, OR 0.08 0.45 0.03 0.31

Superstition, AZ* 0.04 0.42 0.04 0.39

Swanquarter, NC* 0.09 0.77 0.09 0.50

(continued)
3-22

Table 3-8. Summary of Deciview Visibility Impacts at Class I Areas in the Nationa,b 

Scenario 2 (continued)

Federal Class I Area

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

Sycamore Canyon, AZ* 0.02 0.15 0.02 0.17

Teton, WY 0.04 0.36 0.03 0.42

Theodore Roosevelt, ND 0.17 1.00 0.10 0.82

Thousand Lakes, CA* 0.06 0.45 0.04 0.53

Three Sisters, OR 0.05 0.35 0.03 0.33

UL Bend, MT 0.04 0.26 0.03 0.27

Upper Buffalo, AR 0.20 0.86 0.25 1.39

Voyageurs, MN 0.08 0.49 –0.11 –0.99

Weminuche, CO* 0.06 0.56 0.05 0.65

West Elk, CO* 0.03 0.25 0.03 0.53

Wind Cave, SD 0.21 1.38 0.13 1.31

Wolf Island, GA* 0.16 0.65 0.10 0.51

Yellowstone, WY 0.04 0.35 0.03 0.42

Yolla Bolly—Middle Eel, CA* 0.04 0.21 0.03 0.29

Yosemite, CA* 0.04 0.26 0.01 0.12

Zion, UT* 0.04 0.33 0.05 0.54

a The change is defined as the base case (includes CAIR controls) value minus the control case value for

Scenario 2. This means the visibility changes from CAIR controls that meet the BART requirements are not

reflected in the deciview impacts shown on this table.

b The percentage change is the “Change” divided by the “Base Case” and then multiplied by 100 to convert

the value to a percentage. 

* Visibility benefits were monetized for this park.

Negative values indicate visibility degradation.
3-23

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). 2001. 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). 2004. “Procedures for Estimating Future

PM2.5 Values for the CAIR Final Rule by Application of the (Revised) Speciated

Modeled Attainment Test (SMAT)-Updated”, Office of Air Quality Planning and

Standards, Research Triangle Park, NC.

U.S. Environmental Protection Agency (EPA). 2005a. Clean Air Interstate Rule Emission

Inventory Technical Support Document. Office of Air Quality Planning and

Standards. Research Triangle Park, NC.

U.S. Environmental Protection Agency (EPA). 2005b. 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). 2005c. CMAQ Model Performance

Evaluation for 2001: Updated March 2005. Office of Air Quality Planning and

Standards. Research Triangle Park, N.C.

U.S. Environmental Protection Agency (EPA). 2005d. Demonstration that CAIR Satisfies

the “Better-than-BART” Test As Proposed in the Guidelines for Making BART

Determinations. Office of Air Quality Planning and Standards. Research Triangle

Park, NC.
1

 Note that CAIR as promulgated does not include utilities in New Jersey, Delaware, or Arkansas. However, a

rulemaking has been proposed to include New Jersey and Delaware in the CAIR region. The emissions and

air quality modeling conducted for CAVR anticipated the inclusion of these States in the CAIR region and

thus included them in CAIR for the purpose of projecting future conditions in 2015. The inclusion of

Arkansas in the CAIR region for purposes of establishing the baseline for CAVR would tend to understate

the emission reductions and benefits estimated for CAVR. In addition, the impacts of the recently

promulgated CAMR were not considered in the baseline.

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 CAVR. 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 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 consistent with the benefits

analysis performed for the recent analysis of CAIR (EPA, 2005). The benefits analysis relies

on three major modeling components:

1) Calculation of the impact of CAVR on eligible EGU and non-EGU sources

assuming a program. This rule directs States and Tribes to establish SO2 and NOx

controls for BART-eligible sources. These controls are expected to impact

primarily EGU sources outside of the CAIR region (see Chapter 7 for more

details) and non-EGU sources nationwide. Because of the nature of the CAIR

banking and trading program for SO2 and NOx, there are also expected to be

changes in emissions at EGU sources in the Eastern U.S. CAIR region due to

changes in the relative marginal costs of electricity production.1
2

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

2) Air quality modeling for 2015 to determine changes in visibility and in ambient

concentrations of 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 visibility and ambient concentrations of PM.

A wide range of human health and welfare effects are linked to the emissions of NOx

and SO2 from BART-eligible sources and the resulting impact on visibility and 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 short-term

(acute) exposures (e.g., respiratory and cardiovascular symptoms resulting in hospital

admissions, asthma 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 multicity analysis

of 95 major U.S. urban areas (Bell et al., 2004), have linked short-term ozone exposures with

premature mortality.2

 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 CAVR for 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. 

Because of schedule and resource limitations, EPA did not conduct a quantitative

analysis of benefits from reductions (and potential disbenefits from increases) in groundlevel ozone as a result of precursor emissions reductions projected for BART. However, it is

unlikely that net benefits resulting from ozone reductions would have a significant impact on

any conclusions reached regarding the overall benefits for this rulemaking. 
3

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.

4-3

Table 4-1. Estimated Monetized Benefits of the Final CAVR

Total Benefitsa, b (billions 1999$)

Scenario 1 Scenario 2 Scenario 3 

Using a 3% discount rate $2.6 + B $10.1 + B $14.3 + B

Using a 7% discount rate $2.2 + B $8.6+ B $12.2 + 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.

Figure 4-1 illustrates the major steps in the benefits analysis. Given baseline and

postcontrol 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

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 functions3 to generate changes in the incidence of health

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, 
4-4

Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAVR

Pollutant/Effect

Quantified and Monetized in

Base Estimatesa

Quantified and/or Monetized

Effects in Sensitivity Analyses Unquantified Effects—Changes in:

Ozone/Healthb Chronic respiratory damage

Premature aging of the lungsNonasthma respiratory emergency room visitsIncreased exposure to UVb Hospital admissions: respiratory Emergency room visits for asthmaMinor restricted-activity daysSchool loss days Premature mortality: short-term exposuresc

Asthma attacksCardiovascular emergency room visitsAcute respiratory symptoms

Ozone/Welfare Decreased outdoor worker productivity 

Yields for:  – Commercial forests – Fruits and vegetables, and – Other commercial and noncommercial cropsDamage to urban ornamental plantsRecreational demand from damaged forest aestheticsEcosystem functionsIncreased exposure to UVb

(continued)
4-5

Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAVR (continued)

Pollutant/Effect

Quantified and Monetized in

Base Estimatesa

Quantified and/or Monetized

Effects in Sensitivity Analyses Unquantified Effects—Changes in

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 than chronic bronchitisNonasthma respiratory emergency room visitsUVb exposure (+/-)g

PM/Welfare Visibility in Southeastern Class I

areas Visibility in western U.S. Class Iareas

Visibility in northeastern andMidwestern Class I areasHousehold soiling

Visibility in residential and non-Class I areasUVb exposure (+/-)g

(continued)
4-6

Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAVR (continued)

Pollutant/Effect

Quantified and Monetized in

Base Estimatesa

Quantified and/or Monetized

Effects in Sensitivity Analyses Unquantified Effects—Changes in:

Nitrogen andSulfateDeposition/Welfare

Commercial forests due to acidic sulfate and nitratedepositionCommercial freshwater fishing due to acidic depositionRecreation in terrestrial ecosystems due to acidicdepositionCommercial fishing, agriculture, and forests due tonitrogen depositionRecreation in estuarine ecosystems due to nitrogendepositionEcosystem functionsPassive fertilization

SO2/Health Hospital admissions for respiratory and cardiac diseases

Respiratory symptoms in asthmatics

NOx/Health Lung irritation

Lowered resistance to respiratory infectionHospital admissions for respiratory and cardiac diseases

(continued)
4-7

Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAVR (continued)

Pollutant/Effect

Quantified and Monetized

in Base Estimatesa

Quantified and/or Monetized

Effects in Sensitivity Analyses Unquantified Effects

Mercury Healthi Incidences of neurological disorders

Incidences of learning disabilitiesIncidences in developmental delaysPotential cardiovascular effectsh, including:

 – Altered blood pressure regulationh

 – Increased heart rate variabilityh – Incidences of myocardial infarctionhPotential reproductive effectsh

Mercury DepositionWelfarei

Impact on birds and mammals (e.g., reproductive effects)Impacts to commercial, subsistence, and recreational fishing

a Primary quantified and monetized effects are those included when determining the primary estimate of total monetized benefits of CAVR. See below for

a more complete discussion of the benefit estimates.

b In addition to primary economic endpoints, a number of biological responses have been associated with ozone health, 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.

c Recent evidence suggests that short-term exposures to ozone may have a significant effect on daily mortality rates, independent of exposure to PM. d In addition to primary economic endpoints, a number of biological responses 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 quantifiedendpoints.

e Cohort estimates are designed to examine the effects of long-term exposures to ambient pollution, but relative risk estimates may also incorporate some

effects due to short-term exposures (see Kunzli et al. [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 premature mortality from

short-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 because the literature is insufficient.i Mercury emission reductions are not anticipated for BART from the EGU source category because of the cap and trade program promulgated for the

CAMR (March 2005); however, the geographic location of mercury reductions may change as a result of this rule. EPA believes any such effects forthese sources would be minimal. Mercury reductions are expected for the non-EGU source categories. The mercury reductions for BART from the nonEGU source categories is expected to be small in comparison to reductions resulting from the recently promulgated CAIR and CAMR (March 2005).
4-8

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

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 on emission

changes anticipated for the final CAVR program with one exception. The benefits estimated

in this report are slightly understated because Arkansas, New Jersey, and Delaware were

included in the CAIR region when IPM modeling was conducted for CAVR. The final

CAIR does not include these States; however, EPA has proposed a rule to include New

Jersey and Delaware in the CAIR region. Thus, the analysis presented reflects EPA’s best

estimate of the benefits of CAVR, assuming that New Jersey and Delaware become a part of
4

Expert elicitation is a formal, highly structured and well-documented process whereby expert judgments,

usually of multiple experts, are obtained (Ayyub, 2002). 

4-9

the CAIR region for PM2.5 as well as ozone, but the benefits are slightly understated because

of use of modeling that includes Arkansas in the CAIR region for SO2 and annual NOx

controls. In addition, the recently promulgated CAMR is expected to affect the emissions of

SO2 from EGUs in both the CAIR region and throughout the rest of the United States. The

base case for this analysis only included CAIR and not the expected CAMR controls. 

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-related mortality resulting from both shortterm and long-term exposure.4

 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.

The benefits estimates generated for the final CAVR 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. 
4-10

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.

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 scenario.

Because of schedule and resource limitations, EPA did not conduct a quantitative

analysis of benefits from reductions (and potential disbenefits from increases) in groundlevel ozone as a result of precursor emissions reductions projected for BART. However, it is

unlikely that net benefits resulting from ozone reductions would have a significant impact on

any conclusions reached regarding the overall benefits for this rulemaking. 

Benefits estimates for the final CAVR 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).
4-11

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 CAVR 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

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 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 alternatives, they are expected to

be small relative to the direct air pollution-related impacts.
5

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.

6

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.

4-12

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.

4.1.1 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 pay5

 (WTP) for

changes in risk prior to the regulation (Freeman, 1993).6 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
4-13

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

difficult than measuring use values. The air quality changes produced by CAVR 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. Three main

nonmarket valuation methods are 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
7

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. 

8

Income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent

change in income.

4-14

air quality regulations.7 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

value of a statistical life (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).

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 elasticity8

 of

WTP for health risk reductions is positive, although there is uncertainty about its exact value. 
4-15

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

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-SABEEAC-00-013, 2000). 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 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 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
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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 (a priori 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 2015 are presented in Table 4-3.

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. 

In addition to elasticity estimates, projections of real gross domestic product (GDP)

and populations from 1990 to 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 cohortcomponent 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
9

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.

10In 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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to 2010.9 We used projections of real GDP (in chained 1996 dollars) provided by Standard

and Poor’s (2000) for the year 2015.10

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

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). 

Table 4-4. Adjustment Factors Used to Account for Projected Real Income Growtha

Benefit Category Adjustment Factor

Minor Health Effect 1.073

Severe and Chronic Health Effects 1.254

Premature Mortality 1.222

Visibility 1.581

a Based on elasticity values reported in Table 4-3, U.S. Census population projections, and projections of real

GDP per capita.
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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;

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 
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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.

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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CAVR, 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 CAVR 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 long-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 that is used in the ancillary uncertainty analysis for

CAVR (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. 

For the final CAVR, 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 CAVR. 

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. As previously discussed, ozone benefits are
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anticipated for this rule but were not estimates for this analysis. 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. 

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. 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 believe

these benefits may be small relative to those categories we were able to quantify and

monetize.

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 and southwestern United States—are included in the monetary

benefits estimates we project for this rule. We know that additional visibility benefits will

occur in other parks in the country and in urban areas. Those benefits are described in

Chapter 3, and an analysis of the potential dollar value of the benefits is included in

Appendix F of this report. We believe the benefits associated with these nonhealth benefit

categories are likely significant. For example, we are able to quantify significant visibility

improvements in Class I areas in the Northeast, Midwest, and Northwest but are unable at

present to place a monetary value on these improvements. Similarly, we anticipate

improvement in visibility in residential areas within the CAVR region for which we are

currently unable to monetize benefits. For the Class I areas in the southeastern and

southwestern United States for the Scenario 2 alternative, we estimate annual benefits of

$242 million beginning in 2015 for visibility improvements. The value of visibility benefits

in areas where we were unable to monetize benefits could also be substantial. Annual

visibility benefits are estimated to be approximately $84 million and $416 million for

Scenarios 1 and 3, respectively. 
4-22

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 $153 million for Scenario 2. (See Appendix E 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 et al., 2004). Although the study requires

further evaluation, the RFF study does suggest that the benefits of acid deposition reductions

for CAVR could be substantial in terms of the total monetized value for ecological

endpoints.

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

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
4-23

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 are

attributable to reductions in health risks associated with air pollution. EPA’s Criteria

Document for PM lists numerous health effects known to be linked to ambient

concentrations of PM (EPA, 2004). As illustrated in Figure 4-1, quantification of health

impacts requires several inputs, including epidemiological effect estimates (concentrationresponse 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

The PM 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. We relied on the published scientific literature to ascertain the

relationship between potential PM exposure (measured by ambient concentrations) and
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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 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 induced,

effect estimates are not available for quantifying the benefits associated with reducing these

effects. 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 BenMAP User’s Manual 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 between-study variability, due, for example, to 
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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 CAVR

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. 
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. 

4-26

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, lower respiratory symptoms, and asthma

exacerbations. For more details on methods used to pool 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 
4-27

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

(annual

mean)

Abbey et al. (1995) >26 years

Nonfatal heart

attacks

PM2.5

(daily)

Peters et al. (2001) Adults

Hospital Admissions 

Respiratory PM2.5

(daily)

Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD)

Ito (2003)—ICD 490-496 (COPD)

>64 years

PM2.5

(daily)

Moolgavkar (2000)—ICD 490-496 (COPD) 20–64 years

PM2.5

(daily)

Ito (2003)—ICD 480-486 (pneumonia) >64 years

PM2.5

(daily)

Sheppard (2003)—ICD 493 (asthma) <65 years

Cardiovascular PM2.5

(daily)

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

(daily)

Moolgavkar (2000)—ICD 390-429 (all

cardiovascular)

20–64 years

Asthma-related ER visits PM2.5

(daily)

Norris et al. (1999) 0–18 years

(continued)
4-28

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

(annual

mean)

Dockery et al. (1996) 8–12 years

Upper respiratory

symptoms

PM10

(daily)

Pope et al. (1991) Asthmatics, 

9–11 years

Lower respiratory

symptoms

PM2.5

(daily)

Schwartz and Neas (2000) 7–14 years

Asthma

exacerbations

PM2.5

(daily)

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

(daily)

Ostro (1987) 18–65 years

MRADs PM2.5

(daily)

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.

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

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,
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). 

4-29

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

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
4-30

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. 

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).
4-31

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

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 CAVR 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. 
4-32

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

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 CAVR 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
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.

4-33

show a statistically significant relationship between PM10 and cardiovascular hospital

admissions. However, given that the control scenarios 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

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
14See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002. 

4-34

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. 

Acute Health Events. 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 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). 

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). 

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).
15Estimating asthma exacerbations associated with air pollution exposures is difficult, because of 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

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. 

4-35

For CAVR, 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

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
4-36

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

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 
4-37

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

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
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.

4-38

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

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. 
4-39

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

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 CAVR, 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 nonthreshold 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.
4-40

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

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 long-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), pages 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
4-41

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.

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. 
4-42

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

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
4-43

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. 

We are working to develop methods to use these data to project future incidence rates. 

However, for our primary benefits analysis of the final CAVR, 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 
4-44

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. (1993, 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)
4-45

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)

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.

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 (2015).

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. 
4-46

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

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.
4-47

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 future analysis year, 2015. Note that the unit

values for hospital admissions are the weighted averages of the ICD-9 code-specific 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

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. 
4-48

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

Health Endpoint

Central Estimate of Value Per

Statistical Incidence 

Derivation of Estimates

1990 Income

Level

2015 Income

Level

Premature Mortality (Value of aStatistical Life)

$5,500,000 $6,400,000 Point estimate is the mean of a normal distribution with a 95 percent confidence

interval between $1 and $10 million. Confidence interval is based on two metaanalyses of the wage-risk VSL literature: $1 million represents the lower end of theinterquartile range from the Mrozek and Taylor (2002) meta-analysis and $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 riskaggregated over the affected population.

Chronic Bronchitis (CB) $340,000 $400,000 Point estimate is the mean of a generated distribution of WTP to avoid a case of

pollution-related CB. WTP to avoid a case of pollution-related CB is derived byadjusting WTP (as described in Viscusi et al., [1991]) to avoid a severe case of CBfor the difference in severity and taking into account the elasticity of WTP withrespect to severity of CB. 

Nonfatal Myocardial Infarction (heartattack)

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

Age-specific cost-of-illness values reflect lost earnings and direct medical costs overa 5-year period following a nonfatal MI. Lost earnings estimates are based onCropper and Krupnick (1990). Direct medical costs are based on simple average ofestimates 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 $7,85545-54 $12,932 $11,57855-65 $74,746 $66,920Direct 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)

(continued)
4-49

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 Estimates

1990 Income

Level

2015 Income

Level

Hospital AdmissionsChronic Obstructive Pulmonary Disease(COPD)(ICD codes 490-492, 494-496)

$12,378 $12,378 The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 codelevel information (e.g., average hospital care costs, average length of hospital stay,

and weighted share of total COPD category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov). 

Pneumonia(ICD codes 480-487)

$14,693 $14,693 The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 codelevel information (e.g., average hospital care costs, average length of hospital stay,

and weighted share of total pneumonia category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov). 

Asthma Admissions $6,634 $6,634 The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 codelevel information (e.g., average hospital care costs, average length of hospital stay,

and weighted share of total asthma category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov). 

All Cardiovascular(ICD codes 390-429)

$18,387 $18,387 The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 codelevel information (e.g., average hospital care costs, average length of hospital stay,

and weighted share of total cardiovascular category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov). 

Emergency Room Visits for Asthma $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).

(continued)
4-50

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 Estimates

1990 Income

Level

2015 Income

Level

Respiratory Ailments Not Requiring HospitalizationUpper Respiratory Symptoms (URS) $25 $26 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 clusterand assuming additivity of WTPs. The dollar value for URS is the average of thedollar values for the seven different types of URS.

Lower Respiratory Symptoms (LRS) $16 $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 “symptomclusters,” each describing a “type” of LRS. A dollar value was derived for each typeof LRS, using mid-range estimates of WTP (IEc, 1994) to avoid each symptom in thecluster and assuming additivity of WTPs. The dollar value for LRS is the average ofthe dollar values for the 11 different types of LRS.

Asthma Exacerbations $42 $44 Asthma exacerbations are valued at $42 per incidence, based on the mean of average

WTP estimates for the four severity definitions of a “bad asthma day,” described inRowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP foravoidance of a “bad asthma day,” as defined by the subjects. For purposes ofvaluation, an asthma attack is assumed to be equivalent to a day in which asthma ismoderate or worse as reported in the Rowe and Chestnut (1986) study.

Acute Bronchitis $360 $380 Assumes a 6-day episode, with daily value equal to the average of low and high

values for related respiratory symptoms recommended in Neumann et al. (1994).

Restricted Activity and Work/School Loss DaysWork Loss Days (WLDs) Variable

(national median

= )

County-specific 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 byGeolytics, Inc.

Minor Restricted Activity Days (MRADs) $51 $54 Median WTP estimate to avoid one MRAD from Tolley et al. (1986).
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).

4-51

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

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 
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.

4-52

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

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 CAVR. 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

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,
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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

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. 
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C Level of risk reduction: The transferability of estimates of the VSL from the

wage-risk studies to the context of the CAVR 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

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.
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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

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.
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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.

In summary, the three different studies provided significantly different values (see

Table 4-13).

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 
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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.

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.
19A 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

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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

CAVR 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

circumstances.” Air quality models were used to predict the change in visibility, measured

in deciviews, of the areas affected by the control scenarios.19
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.

20The Clean Air Act designates 156 national parks and wilderness areas as Class I areas for visibility protection.

21For 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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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.20

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.21

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

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
22An 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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of visibility changes in recreational areas.22 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:

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.
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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

been developed. As future survey efforts are completed, EPA will incorporate values for

visibility improvements reflecting the improved survey designs. 
4-62

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 CAVR. 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 CAVR. 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 annually in Southeastern and Southwestern Class I areas brought

about by CAVR range from $84 to $420 million 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. 

We know that additional visibility benefits will occur in other parks in the country

and in urban areas. Those benefits are described in Chapter 3, and an analysis of the

potential dollar value of the benefits is included in Appendix F of this report.

The benefits resulting from visibility improvements in Southeastern and

Southwestern Class I areas included in the primary monetized benefits estimates under the

final CAVR are presented in Figure 4-2. This figure presents these benefits in terms of the

total benefits modeled for each of the Class I areas (i.e., the “Park Benefits” map) in the 81

Class I areas included in the study.

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. CAVR Final Rule Visibility Improvements in Class I Areas in the

Southeast and Southwest

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 scenarios 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
4-64

impacts. Because of resource limitations, we are unable to provide agricultural or benefits

estimates for the final CAVR 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.,

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.
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The CAVR 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 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 scenarios 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.
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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 PMrelated 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.
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4.2 Benefits Analysis—Results

Applying the impact and valuation functions described previously in this chapter to

the estimated changes in visibility and ambient PM yields estimates of the changes in

physical damages (e.g., premature mortalities, cases, admissions) and the associated

monetary values for those changes. Estimates of physical health impacts are presented in

Table 4-15. Monetized values for both health and welfare endpoints are presented in

Table 4-16, along with total aggregate monetized benefits. All of the monetary benefits are

in constant-year 1999 dollars.

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 1,600 avoided premature

deaths annually for the Scenario 2 in 2015. 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 2015 for the final rule ranges from $2.2

billion to $14.3 billion depending upon the scenario analyzed and the discount rates of 3 and

7 percent. Health benefits account for over 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 $9.2 billion in 2015 for Scenario 2, 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 
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Table 4-15. Clean Air Visibility Rule: Estimated Reduction in Incidence of Adverse

Health Effectsa

Incidence Reduction

Health Effect Scenario 1 Scenario 2 Scenario 3

Premature Mortalityb,c

 Adult, age 30 and over

 Infant, age <1 year

400

1

1,600

4

2,300

5

Chronic bronchitis (adult, age 26 and over) 230 890 1,300

Nonfatal myocardial infarction (adults, age 18 and older) 570 2,200 3,000

Hospital admissions—respiratory (all ages)d 140 510 720

Hospital admissions—cardiovascular (adults, age >18)e 120 450 640

Emergency room visits for asthma (age 18 years and younger) 370 1,300 1,800

Acute bronchitis (children, age 8–12) 550 2,100 3,000

Lower respiratory symptoms (children, age 7–14) 6,600 25,000 36,000

Upper respiratory symptoms (asthmatic children, age 9–18) 5,000 19,000 27,000

Asthma exacerbation (asthmatic children, age 6–18) 8,100 31,000 44,000

Work loss days (adults, age 18–65) 44,000 170,000 240,000

Minor restricted-activity days (adults, age 18–65) 260,000 1,000,000 1,400,000

a Incidences are rounded to two significant digits. These estimates represent benefits for CAVR Nationwide

for the final CAVR program relative to a baseline with CAIR inclusive of the proposal to include SO2 and

annual NOx controls for New Jersey and Delaware. Note these estimates may be slightly understated due to

the inclusion in CAIR of SO2 and annual NOx controls for Arkansas. The baseline used to estimates these

benefits does not consider the recently promulgated CAMR. 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.
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Table 4-16. Estimated Monetary Value of Reductions in Incidence of Health and

Welfare Effects Associated with the CAVR (millions of 1999$)a,b

Estimated Value of Reductions

Effect Scenario 1 Scenario 2 Scenario 3

Health Effects:

Premature mortalityc,d

 Adult, age 30 and over

 3% discount rate

 7% discount rate

 Infant, < 1 year

$2,330

$1,960

$6.12

$9,180

$7,730

$23.8

$13,000

$10,900

$34.2

Chronic bronchitis (adults, 26 and over) 90.5 353 498

Nonfatal acute myocardial infarctions 

 3% discount rate

 7% discount rate

$49.3

$45.8

$189

$176

$264

$245

Hospital admissions for respiratory causes 1.07 4.03 5.65

Hospital admissions for cardiovascular causes 2.6 10 14.1

Emergency room visits for asthma 0.106 0.362 0.51

Acute bronchitis (children, age 8–12) 0.207 0.79 1.12

Lower respiratory symptoms (children, 7–14) 0.109 0.415 0.587

Upper respiratory symptoms (asthma, 9–11) 0.137 0.523 0.74

Asthma exacerbations 0.367 1.4 1.98

Work loss days 5.56 22.4 31.5

Minor restricted-activity days (MRADs) 13.8 54.1 76.3

Welfare Effects:

Recreational visibility, 81 Class I areas $84 $239 $416

Monetized Total e 

 Base Estimate:

 3% discount rate

 7% discount rate 

$2,600 + B

$2,200 + B

$10,100 + B

$8,600 + B

$14,300 + B

$12,200 + 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 are associated with NOx and SO2

reductions. Visibility benefits relate to Class I areas in the southeastern and southwestern United States.

These estimates represent benefits for CAVR Nationwide for the final CAVR program relative to a baseline

with CAIR inclusive of the proposal to include SO2 and annual NOx controls for New Jersey and Delaware. 

Note these estimates may be slightly understated due to the inclusion in CAIR of SO2 and annual NOx

controls for Arkansas. Note also that the recently promulgated CAMR was not considered in the baseline

used to develop these estimates. b Monetary benefits adjusted to account for growth in real GDP per capita between 1990and 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. Column totals are rounded to the nearest 100 million dollars, and totals may

not sum due to rounding.
4-70

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 proxy measure of WTP. As such, the true

value of these effects may be higher than that reported in Table 4-16. 

4.3 Uncertainty in the Benefits Estimates

Characterization of health-related benefits associated with PM reductions is a complex

process which is subject to a variety of potential sources of uncertainty. Key assumptions

underlying the estimate of avoided premature mortality include the following: 

C 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 established, the

weight of the available epidemiological and experimental evidence supports an

assumption of causality.

C 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. 

C 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 standards and

those that do not meet the standard. 

C 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. 

Use of the Pope et al., 2002-derived mortality function to support this analysis is

associated with uncertainty resulting from: (a) potential of the study to incompletely capture
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short-term exposure-related mortality effects, (b) potential mis-match between study and

analysis populations which introduces various forms of bias into the results, and (c) failure to

identify all key confounders and effects modifiers, which could result in incorrect effects

estimates relating morality to PM2.5 exposure. EPA is researching methods to characterize all

elements of uncertainty in the dose-response function for mortality. As is discussed in detail

in the CAIR RIA (EPA, 2005), EPA has used two methods to quantify uncertainties in the

mortality function, including: the statistical uncertainty derived from the standard errors

reported in the Pope et al., 2002 study, and the use of results of a pilot expert elicitation

conducted in 2004 to investigate other uncertainties in the mortality estimate. In the CAIR

benefit analysis, the statistical uncertainty from the standard error of the Pope et al., 2002

study was twice the mean benefit estimate at the 95th percentile and one-fourth of the mean at

the 5th percentile, while the expert elicitation provided mean estimates that ranged in value

from less than one-third of the mean estimate from the Pope et al., 2002 study-based estimate

to two and one-half times the Pope et al., 2002-based estimate. The confidence intervals

from the pilot elicitation applied to the CAIR benefit analysis ranged in value from zero at

the 5th percentile to a value at the 95th percentile that is seven times higher than the Pope et

al., 2002-based estimate. These results are highly dependent on the air quality scenarios

applied to the concentration-response functions of the Pope et all, 2002 study and the pilot

expert elicitation. Thus, the characterization of uncertainty discussed in the CAIR RIA could

differ greatly from what would be observed for CAVR due to differences in populationweighted changes in concentrations of PM2.5 (i.e., the location of populations exposure

relative to the changes in air quality), and may be especially sensitive to the differences in

baseline PM2.5 air quality experienced by populations prior to implementation of the CAVR. 

Table 4-17 shows the mean estimate and estimated 5th and 95th percentiles of premature

deaths avoided for our primary estimate based on the Pope et al. (2002) study and based on

the responses for each of the 5 experts. This table shows that for each scenario, our primary

estimates are higher than four of the experts and lower than one expert and falls within the

uncertainty bounds of all but one expert. The table shows that for Scenario 2, the average

estimated annual number of premature deaths 
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Table 4-17. Results of Illustrative Application of Pilot Expert Elicitation: Annual Reductions in Premature Mortality in

2015 Associated with the Clean Air Visibility Rule

Source of

Mortality

Estimate

Scenario 1 Scenario 2 Scenario 3

5th

Percentile Mean

95th

Percentile

5th

Percentile Mean

95th

Percentile

5th

Percentile Mean

95th

Percentile

Pope et al.(2002)

170 405 640 680 1,600 2,500 960 2,300 3,500

Expert A 0 330 630 0 1,300 2,500 0 1,800 3,500Expert B 0 100 440 0 490 2,200 0 670 3,000Expert C 0 65 220 0 310 950 0 420 1,300Expert D 0 280 840 0 1,100 3,300 0 1,600 4,700Expert E 0 550 1,300 0 2,200 5,200 0 3,100 7,300
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avoided in 2015 ranges from approximately 310 (based on the judgments of Expert C) to

2,200 (based on the judgments of Expert E). The statistical uncertainty bounds (5th to 95th

percentile) 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. EPA is

continuing its research of methods to characterize uncertainty in total benefits estimates, and

is conducting a full-scale expert elicitation. The full-scale expert elicitation is scheduled to

be completed by the end of 2005. 

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 sources. The result suggests there will be significant health and welfare

benefits arising from regulating emissions from BART eligible sources in the United States. 

Our estimate that 1,600 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 these sources 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

the sources affected by this rule.

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; Ibald-
4-74

Mulli 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.

4.5 Cost Effectiveness Analysis

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. EPA included in the CAIR RIA (EPA, 2005) a preliminary and experimental

application of one type of CEA—a modified quality-adjusted life-years (QALYs) approach. 

For CAIR, we concluded that the direct usefulness of cost-effectiveness analysis is mitigated

by the lack of rule alternatives to compare relative effectiveness, but that comparisons could

still be made 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,

however, agencies are expected to provide CEAs for rules covered by Circular A-4

requirements.
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In Appendix G of the RIA for the CAIR (EPA, 2005), we conducted an extensive

cost-effectiveness analysis using morbidity inclusive life years (MILY). That analysis

concluded that the reductions in PM2.5 associated with CAIR are expected to be cost-saving

(because the value of expenditures on illnesses and non-health benefits exceed costs), and

that costs of the CAIR could have been significantly higher and still result in cost-effective

improvements in public health. Because the age distribution of the mortality and chronic

disease reductions is not expected to differ between CAVR and CAIR, one can draw

inferences by examining the relative magnitude of the costs and health impacts between them

even in the absence of a formal cost-effectiveness analysis for CAVR. While CAVR is not

expected to be cost-saving like CAIR, we expect that CAVR is likely to have a relatively low

cost per MILY. 

For Scenario 2, reductions in the incidences of mortality, chronic bronchitis, and nonfatal heart attacks are 1,600, 890, and 2,200, respectively. These are roughly 10 percent of

the corresponding incidences in the CAIR rule (16,700, 8,700, and 22,000, respectively). 

Total MILY gained in the CAIR rule in 2015 was estimated to be 250,000. Assuming the

difference in MILY between the rules would be roughly proportional to the difference in

incidence (which should be the case because both rules are analyzed for a 2015 population),

then Scenario 2 would result in roughly 25,000 MILY. The costs of Scenario 2 are estimated

at $1.4 billion (using a 3% discount rate). Costs of illness for chronic bronchitis and nonfatal heart attacks are expected to be $270 million, while the value of other health benefits

and visibility improvements is estimated to be $330 million. Subtracting these from the

CAVR costs gives a net cost of $800 million. Dividing this by the approximate estimate of

MILY yields a net cost per MILY of $32,000. This estimate is close to the median cost per

QALY for respiratory and cardiovascular interventions of $31,000 (2002$) reported in the

Harvard Cost Utility Database (http://www.hsph.harvard.edu/cearegistry/index.html). 

These results are suggestive, but should be interpreted with caution for several

reasons. First, in the analysis for CAIR the use of a 7% discount rate instead of 3 percent

significantly reduced cost-effectiveness, and CAVR estimates are likely to be similarly

affected by a 7 percent discount rate. Second, if the CAVR confidence intervals on the

number of MILY are proportional to those in CAIR, it is less clear that the net cost per

MILY will be less than the $50,000 cost-effectiveness benchmark. Finally, by construction

MILY will generally be greater than standard QALYs for a given reduction in incidence,

which will bias cost-effectiveness comparisons to common QALY benchmarks. Even with

these caveats, the results indicate that CAVR as likely to be cost-effective and to compare

favorably with other health interventions.
4-76

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EPA-SAB-COUNCIL-ADV-99-012. July 1999. “The Clean Air Act Amendments (CAAA)

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Study.” Environmental Health Perspectives 108(12):1209-1214.
5-1

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, BART 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—Impacts on Aquatic, Forest,

and Coastal Ecosystems

Reductions in atmospheric deposition of sulfur and nitrogen are anticipated to occur

across the nation as a result of this rule. 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

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,
5-2

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). BART is expected to reduce

atmospheric deposition of nitrogen and sulfur and to reduce the total nitrogen and sulfur

loads.

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.

5.2.1 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

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: 
5-3

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). 

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
5-4

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).

5.2.2 Forest Ecosystems

Reductions in sulfur and nitrogen deposition under BART 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 (U.S.

Department of the 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
5-5

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

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.
5-6

5.2.3 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.

5.2.4 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
5-7

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.

5.3 References

Adams, M.B., T.R. Angradi, and J.N. Kochenderfer. 1997. “Stream Water and Soil Solution

Responses to 5 Years of Nitrogen and Sulfur Additions at the Fernow Experimental

Forest, West Virginia.” Forest Ecology and Management 95:79-91.

Bricker, S.B., C.G. Clement, D.E. Pirhalla, S.P. Orlando, and D.R.G. Farrow. 1999. 

National Estuarine Eutrophication Assessment: Effects of Nutrient Enrichment in the

Nation’s Estuaries. National Oceanic and Atmospheric Administration, National

Ocean Service, Special Projects Office and the National Centers for Coastal Ocean

Science. Silver Spring, Maryland.

Church, M.R., P.W. Shaffer, K.W. Thornton, D.L. Cassell, C.I. Liff, M.G. Johnson, D.A.

Lammers, J.J. Lee, G.R. Holdren, J.S. Kern, L.H. Liegel, S.M. Pierson, D.L. Stevens,

B.P. Rochelle, and R.S. Turner. 1992. Direct/Delayed Response Project: Future

Effects of Long-Term Sulfur Deposition on Stream Chemistry in the Mid-Appalachian

Region of the Eastern United States. U.S. Environmental Agency, EPA/600/R92/186, Washington, DC. 384 pp.

DeHayes, D.H., P.G. Schaberg, G.J. Hawley, and G.R. Strimbeck. 1999. “Acid Rain

Impacts Calcium Nutrition and Forest Health: Alteration of Membrane-Associated

Calcium Leads to Membrane Destabilization and Foliar Injury in Red Spruce.”

Bioscience 49:789-800.

Driscoll, C.T., G. Lawrence, A. Bulger, T. Butler, C. Cronan, C. Eagar, K.F. Lambert, G.E.

Likens, J. Stoddard, and K. Weathers. 2001. “Acid Deposition in the Northeastern

U.S.: Sources and Inputs, Ecosystem Effects, and Management Strategies.” 

Bioscience 51:180-198.
5-8

Herlihy, A.T., P.R. Kaufmann, M.R. Church, P.J. Wigington, Jr., J.R. Webb, and M.J. Sale. 

1993. “The Effects of Acid Deposition on Streams in the Appalachian Mountain and

Piedmont Region of the Mid-Atlantic United States.” Water Resources Research

29:2687-2703.

Herlihy, A.T., P.R. Kaufmann, J.L. Stoddard, K.N. Eshleman, and A.J. Bulger. 1996. 

Effects of Acidic Deposition on Aquatic Resources in the Southern Appalachians with

a Special Focus on Class I Wilderness Areas. The Southern Appalachian Mountain

Initiative (SAMI). 

Horsley, S.B., R.P. Long, S.W. Bailey, R.A. Hallett, and T.J. Hall. 2000. “Factors

Associated with the Decline Disease of Sugar Maple on the Allegheny Plateau.”

Canadian Journal of Forest Research 30:1365-1378.

Howarth, Robert. 1998. “An Assessment of Human Influences on Fluxes of Nitrogen from

the Terrestrial Landscape to the Estuaries and Continental Shelves of the North

Atlantic Ocean.” Nutrient Cycling in Agroecosystems 52(2/3):213-223.

Huntington, T.G., R.P. Hooper, C.E. Johnson, B.T. Aulenbach, R. Cappellato, and A.E.

Blum. 2000. “Calcium Depletion in a Southeastern United States Forest

Ecosystem.” Soil Science Society of America Journal 64:1845-1858.

Kahl, J., S. Norton, I. Fernandez, L. Rustad, and M. Handley. 1999. “Nitrogen and Sulfur

Input-Output Budgets in the Experimental and Reference Watersheds, Bear Brook

Watershed, Maine (BBWM).” Environmental Monitoring and Assessment

55:113-131.

Kahl, J.S., S.A. Norton, I.J. Fernandez, K.J. Nadelhoffer, C.T. Driscoll, and J.D. Aber. 1993.

“Experimental Inducement of Nitrogen Saturation at the Watershed Scale.”

Environmental Science and Technology 27:565-568.

Lawrence, G.B., M.B. David, and W.C. Shortle. 1995. “A New Mechanism for Calcium

Loss in Forest-Floor Soils.” Nature 378:162-165.

Lawrence, G.W., M.B. David, S.W. Bailey, and W.C. Shortle. 1997. “Assessment of

Calcium Status in Soils of Red Spruce Forests in the Northeastern United States.”

Biogeochemistry 38:19-39.
5-9

Lawrence, G.B., M.B. David, G.M. Lovett, P.S. Murdoch, D.A. Burns, J.L. Stoddard, B.P.

Baldigo, J.H. Porter, and A.W. Thompson. 1999. “Soil Calcium Status and the

Response of Stream Chemistry to Changing Acidic Deposition Rates in the Catskill

Mountains of New York.” Ecological Applications 9:1059-1072. 

Likens, G.E., C.T. Driscoll, and D.C. Buso. 1996. “Long-Term Effects of Acid Rain: 

Responses and Recovery of a Forest Ecosystem.” Science 272:244-246.

MacDonald, N.W., A.J. Burton, H.O. Liechty, J.A. Whitter, K.S. Pregitzer, G.D. Mroz, and

D.D. Richter. 1992. “Ion Leaching in Forest Ecosystems along a Great Lakes Air

Pollution Gradient.” Journal of Environmental Quality 21:614-623.

McLaughlin S.B. and R. Wimmer. 1999. “Tansley Review No. 104, Calcium Physiology

and Terrestrial Ecosystem Processes.” New Phytologist 142:373-417.

Mitchell, M.J., M.B. David, I.J. Fernandez, R.D. Fuller, K. Nadelhoffer, L.E. Rustad, and

A.C. Stam. 1994. “Response of Buried Mineral Soil Bags to Experimental

Acidification of Forest Ecosystem.” Soil Science Society of America Journal

58:556-563.

Mitchell, M.J., C.T. Driscoll, J.S. Kahl, G.E. Likens, P.S. Murdoch, and L.H. Pardo. 1996.

“Climate Control on Nitrate Loss from Forested Watersheds in the Northeast United

States.” Environmental Science and Technology 30:2609-2612.

National Acid Precipitation Assessment Program (NAPAP). 1991. 1990 Integrated

Assessment Report. Washington, DC: National Acid Precipitation Assessment

Program Office of the Director.

Norton, S.A., J.S. Kahl, I.J. Fernandez, L.E. Rustad, J.P. Schofield, and T.A. Haines. 1994. 

“Response of the West Bear Brook Watershed, Maine, USA, to the addition of

(NH4)2SO4: 3-Year Results.” Forest and Ecology Management 68:61-73.

Norton, S.A., J.S. Kahl, I.J. Fernandez, T.A. Haines, L.E. Rustad, S. Nodvin, J.P. Scofield, T.

Strickland, H. Erickson, P. Wiggington, and J. Lee. 1999. “The Bear Brook

Watershed, Maine, (BBWP) USA.” Environmental Monitoring and Assessment

55:7-51.

Rustad, L.E., I.J. Fernandez, M.B. David, M.J. Mitchell, K.J. Nadelhoffer, and R.D. Fuller. 

1996. “Experimental Soil Acidification and Recovery at the Bear Brook Watershed

in Maine.” Soil Science of America Journal 60:1933-1943.
5-10

Schaberg, P.G., D.H. DeHayes, G.J. Hawley, G.R. Strimbeck, J.R. Cumming, P.F.

Murakami, and C.H. Borer. 2000. “Acid Mist, Soil Ca and Al Alter the Mineral

Nutrition and Physiology of Red Spruce.” Tree Physiology 20:73-85.

Smith, K.T. and W.C. Shortle. 2001. “Conservation of Element Concentration in Xylem

Sap of Red Spruce.” Trees 15:148-153.

Shortle, W.C. and K.T. Smith. 1988. “Aluminum-Induced Calcium Deficiency Syndrome in

Declining Red Spruce Trees.” Science 240:1017-1018.

Shortle, W.C., K.T. Smith, R. Minocha, G.B. Lawrence, and M.B. David. 1997. “Acidic

Deposition, Cation Mobilization, and Biochemical Indicators of Stress in Healthy

Red Spruce.” Journal of Environmental Quality 26:871-876.

U.S. Department of the Interior, National Park Service. 2003. Assessment of Air Quality

and Related Values in Shenandoah National Park. Technical Report

NPS/NERCHAL/NRTR-03/090. Philadelphia, PA: National Park Service, Northeast

Region. http://www.nps.gov/shen/air_quality.htm.

U.S. Environmental Protection Agency (EPA). 2004. Air Quality Criteria for Particulate

Matter (October 2004). <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?

deid=87903>.

U.S. Environmental Protection Agency (EPA). 2005a. Air Quality Criteria for Ozone and

Related Photochemical Oxidants (First External Review Draft). 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=114523>.

U.S. Environmental Protection Agency (EPA). 2005b. Draft Air Quality Criteria for Ozone

and Related Photochemical Oxidants E-Docket No. ORD-2004-0015 [Federal

Register: January 31, 2005 (Volume 70, Number 19)]

<http://www.epa.gov/fedrgstr/EPA-AIR/2005/January/Day-31/a1720.htm>.

Valigura, R.A., R.B. Alexander, M.S. Castro, T.P. Meyers, H.W. Paerl, P.E. Stacy, and R.E.

Turner. 2001. Nitrogen Loading in Coastal Water Bodies: An Atmospheric

Perspective. Washington, DC: American Geophysical Union. 

Van Sickle, J. and M.R. Church. 1995. Methods for Estimating the Relative Effects of Sulfur

and Nitrogen Deposition on Surface Water Chemistry. EPA/600/R-95/172. 

Washington, DC: U.S. Environmental Protection Agency. 
5-11

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.

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.
6-1

CHAPTER 6

PROFILE OF POTENTIALLY AFFECTED INDUSTRY SECTORS

Potential sources affected by the BART rulemaking are defined in Section 169A (g)

(7) of the CAA. This section defines affected sources to be “major stationary sources” with

the potential to emit 250 tons or more of any pollutant and fossil-fuel-fired steam electric

plants of more than 250 million Btus per hour heat input. Additional source categories

enumerated in the Act include coal cleaning plants; kraft pulp mills; Portland cement plants;

primary zinc smelters; iron and steel mill plants; primary aluminum ore reduction plants;

primary copper smelters; municipal incinerators; hydrofluoric, sulfuric, and nitric acid

plants; petroleum refineries; lime plants; phosphate rock processing plants; coke oven

batteries; sulfur recovery plants; carbon black plants; primary lead smelters; fuel conversion

plants; sintering plants; secondary metal production facilities; chemical process plants;

taconite ore processing facilities; glass fiber processing plants; and charcoal production

facilities (see examples in Table 6-1). States implementing the BART rule must consider

emission controls for any BART-eligible source operating in the previously enumerated list

of applicable industries. A subset of the preceding list of industries that includes those most

likely to be affected by this rulemaking are characterized in this chapter. 

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 6-2).
6-2

Table 6-1. Examples of Affected Source Categories

Source Category Name SIC NAICS

Industry Profile

Subsection

Fossil Fuel-Fired Steam Electric Plants

(>250 MMBTU heat input per hour)

4911 221111, 221112, 221113,

221119, 221121, 221122

6.1 Power Sector

Fossil Fuel-Fired Industrial Boilers (>250

MMBTU heat input per hour)

NA NA NA

Petroleum Refineries 2911 324110 6.6 Petroleum

Refining Industry

Kraft Pulp Mills 2611, 2621,

2631

322110, 322121, 322122,

322130, 322121, 322122, 322130

6.5 Paper and Allied

Products

Portland Cement Plants 3241 327310 6.2 Cement

Iron and Steel Mill Plants 3312 331111, 331221 6.7 Primary Metal

Manufacturing

Hydrofluoric, Sulfuric, and Nitric Acid

Plants

2819 211112 6.4 Crude

Petroleum and

Natural Gas

Coke Oven Batteries 3312 331111, 331221 6.7 Primary Metal

Manufacturing

Sulfur Recovery Plants 2819 325131, 325188, 325998, 331311 6.3 Industrial

Organic Chemicals

Primary Lead Smelters 3339 331419 6.7 Primary Metal

Manufacturing

Primary Copper Smelters 3331 331411 6.7 Primary Metal

Manufacturing

Primary Zinc Smelters 33xx 331419 6.7 Primary Metal

Manufacturing

Primary Aluminum Ore Reduction Plants 3334 331312 6.7 Primary Metal

Manufacturing

Municipal Incinerators (>250 tons refuse

per day)

4953 562211, 562212, 562213,

562219, 562920

NA

Lime Plants 3274 327410 NA

Phosphate Rock Processing Plants 1429 212319 NA

(continued)
6-3

Table 6-1. Examples of Affected Source Categories (continued)

Source Category Name SIC NAICS

Industry Profile

Subsection

Carbon Black Plants (furnace process) 2895 325182 6.3 Industrial

Organic Chemicals

Fuel Conversion Plants NA NA NA

Sintering Plants NA NA NA

Secondary Metal Production Facilities 3341 331314, 331423, 331492 6.7 Primary Metal

Manufacturing

Chemical Process Plants 28xx 325 6.3 Industrial

Organic Chemicals

Petroleum Storage and Transfer Facilities

(capacity > 300,000 barrels)

5171, 5172 424710, 454311, 454312,

424720, 425110, 425120

6.6 Petroleum

Refining Industry

Taconite Ore Processing Plants 3295 212324, 212325, 212393,

212399, 327992

NA

Glass Fiber Processing Plants 32xx 327212 NA

Charcoal Production Facilities 2819 211112, 325131, 325188,

325998, 331311

6.3 Industrial

Organic Chemicals

Coal Cleaning Plants (thermal dryers) 2999 324199 NA

Table 6-2. 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
6-4

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 6-3). 

Table 6-3. 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 6-4).

Table 6-4. 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.
6-5

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
6-6

entities in many State efforts. Transmissions and distribution remain price regulated

throughout the country based on the cost of service.

6.1.4 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. 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.
6-7

Figure 6-1. Status of State Electricity Industry Restructuring Activities (as of February

2003)

6.1.5 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) and to visibility impairment. 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 
6-8

Sulfur Dioxide Nitrogen Oxides

Power Sector 

(22%)

Power Sector 

(67%)

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

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

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.

6.1.6 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. 
6-9

It is generally easier to switch to a coal within the same rank (e.g., bituminous or

subbituminous) because these coals will have similar heat contents and other characteristics. 

Switching completely to subbituminous coal (which typically has a lower sulfur content)

from bituminous coal is likely to require some modifications to the unit. Limited blending of

subbituminous coal with bituminous coal can often be done with much more limited

modifications.

The two most commonly used scrubber types are 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 using 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. 

Postcombustion controls reduce NOx by removing the NOx after it has been formed. The

most common postcombustion 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.

6.1.7 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. 

Federal regulation of SO2 and NOx emissions at power plants began with the 1970

CAA. The Act required the Agency to develop performance standards for a number of
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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 controlling NOx to reduce ozone

nonattainment. This has led to the formation of several regional NOx trading programs and

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

the summer of 2004 and has resulted in the installation of significant amounts of SCR.
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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.

6.2 Cement

The product that most Americans know simply as cement is technically referred to as

Portland cement. This product received its name because it resembled the well-known

building stone quarried on the Isle of Portland in the English Channel in color and texture. 

Portland cement is used predominantly in the production of concrete. In 2002, the United

States produced 85 million metric tons of Portland cement, while U.S. producers shipped 113

million metric tons. The total value of Portland cement shipments was $8.5 billion with an

average value of $77 per metric ton shipped. 

6.2.1 The Supply Side: Production and Costs

6.2.1.1 Production Process

The Portland cement manufacturing process consists of

C quarrying and crushing the raw materials,

C grinding the carefully proportioned materials to a high degree of fineness,

C firing the raw materials mixture in a rotary kiln to produce clinker, and

C grinding the resulting clinker to a fine powder and mixing with gypsum to

produce cement.

There are basically two distinct methods of blending the raw mixture: the wet

process and the dry process. In the wet process, water is added to the materials to create a

slurry that is fed into the kiln. The water eventually is evaporated in the kiln where the raw

materials are converted into clinker. In the dry process, all grinding and blending are done

with dry materials that are fed directly into the kiln to be calcined into clinker. In 2001, wet

process kilns produced 18.5 percent of clinker produced in the United States, dry process

kilns produced 75.2 percent of clinker, and active kilns using both processes accounted for

the remaining 6.5 percent (van Oss, 2002).
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6.2.1.2 Types of Output

Although five basic types of Portland cement are produced in the United States, 

Types I and II cement are the most common type of cement shipped from U.S. plants,

comprising over 85 percent of total Portland cement production in 2001 (van Oss, 2002). A

brief description of each type is provided below.

Type I: Regular Portland cements are the usual products used in general concrete

construction, most commonly known as gray cement because of its color. Type I is provided

as a concrete without special properties. In contrast, white cement typically contains less

ferric oxide and is used for special applications. Other types of regular cements include

oil-well cement, quick-setting cement, and others for special uses.

Type II: Moderate heat-of-hardening and sulfate-resisting Portland cements are

intended for use when moderate heat of hydration is required or for general concrete

construction exposed to moderate sulfate action.

Type III: High early strength cements are made from raw materials with a lime-tosilica ratio higher than that of Type I cement and are ground finer than Type I cements. They

contain a higher proportion of tricalcium silicate than regular Portland cements.

Type IV: Low-heat Portland cements contain a lower percentage of tricalcium

silicate and tricalcium aluminate than Type I, thus lowering the heat evolution. 

Consequently, the percentage of tetracalcium aluminoferrite is increased. Type IV cements

are produced to attain a low heat of hydration.

Type V: Sulfate-resisting Portland cements are those that, by their composition or

processing, resist sulfates better than the other four types.

The use of additives, or admixtures, allows producers to alter or enhance the

attributes of the cement product and, thus, the ultimate concrete product. Admixtures affect

factors such as durability, appearance, versatility, and cost-effectiveness by altering the

hydration of Portland cement in some way, by changing the speed of reaction, or by

dispersing the cement particles more thoroughly throughout the concrete mix. 

6.2.1.3 Production Costs

There are five primary variable inputs in cement production—labor, fuel, electricity,

raw material, and maintenance. Labor is used in the quarry and for packing operations and
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accounts for approximately 20 percent of production costs (see Table 6-5). Fuel is largely

consumed by the kilns, and inputs include coal, coke, petroleum coke, and oil. Electricity is

consumed by the auxiliary equipment, and in 2001 plants used approximately 144 kilowatthours of electricity per ton of cement produced (van Oss, 2002). Raw materials serve as the

kiln feed and account for 60 percent of material costs reported by the Bureau of the Census. 

The USGS estimates approximately 1.7 metric tons of nonfuel raw materials are needed to

make 1 metric ton of Portland cement (van Oss, 2002). The cement industry’s capital

expenditures increased in 2000 and 2001 as several plants completed capacity upgrades. 

Table 6-5. Costs of Production for the Cement Industry: 1997–2001

Year

Labor Fuel, Electricity,

and Materials

($106

)

Capital

Expenditures

($106 Quantity (10 ) 3

) Payroll ($106)

1997 16.9 734.2 2,475.8 505.6

1998 17.2 756.3 2,528.9 525.4

1999 17.3 815.7 2,526.6 691.2

2000 17.2 829.5 2,589.7 1,108.0

2001 17.2 860.5 2,724.5 1,430.0

Source: U.S. Department of Commerce, Bureau of the Census. 1997. 1997 Economic Census. Manufacturing

Industry Series. Cement Manufacturing. Washington, DC: Government Printing Office. 

6.2.2 The Demand Side

Concrete and reinforced concrete are used extensively in almost all construction

applications including homes, public buildings, roads, industrial plants, dams, bridges, and

many other structures. Therefore, the demand for Portland cement is a derived demand, and

the rate of growth in demand for Portland cement largely depends on the rate of growth in

construction activities. Cement competes with other construction productions such as glass,

aluminum, steel, and asphalt. The USGS reports that ready-mixed concrete producers

consumed 75 percent of cement sales in 2002, followed by concrete product manufacturers

(13 percent), contractors (6 percent), and other (6 percent) (van Oss, 2003).

6.2.3 Industry Organization: Market Structure, Plants, and Firms

Making inferences about the behavior of producers often requires assessing barriers

to entry and developing a measure of concentration within each market, both of which should
6-14

reflect the ability of firms to raise prices above the competitive level. Markets with barriers

to entry (e.g., licenses, legal restrictions, or high fixed costs) are generally expected to be less

competitive than those without such barriers. In addition, less concentrated markets are

predicted to be more competitive and should result in a low value of the concentration

measure, while a higher value should indicate a higher price-cost margin or a higher

likelihood of noncompetitive behavior on the part of producers. Based on the evidence

presented below, the Agency used an economic model with oligopolistic market structure to

evaluate the economic impacts of recent air pollution regulations for the industry (EPA,

1999a).

Portland cement plants operate under conditions of high, location-specific fixed costs

and substantial returns to scale that act as a barrier to entry. The capital investment required

for the production of cement involves using large rotary kilns that are not readily movable or

transferable to other uses. Because the minimum efficient scale of cement operations is a

significant share of local demand, each regional Portland cement market can sustain only a

small number of firms that are able to earn positive profits without inviting entry. Entry is

expected to occur only in the event of growth in the local demand for Portland cement.

Although a substantial portion of the cement consumed domestically is imported into the

nation each year (approximately 20 percent in 2003), imports tend to fill the gap between

domestic production and fluctuating demand. However, the cost and availability of shipping

for cement imports does provide a substantial barrier to entry into this market (Portland

Cement Association, 2005).

National measures of concentration do not suggest the cement industry is particularly

concentrated. Census data (U.S. Census Bureau, 2001) show that the top four companies

account for 33 percent of cement sales, and the Herfindahl-Hirschman index (HHI) is only

466. However, closer examination of the regional nature of the cement industry suggests

these markets are concentrated and increases the likelihood that firms can raise prices above

the competitive levels. 

The Portland cement industry is characterized by regional markets because of the low

value of Portland cement and the high transportation costs. Because Portland cement is

generally regarded as a homogeneous product, buyers are prevented from distinguishing

between the product of sellers in the market so that the geographic boundaries of each market

are solely determined by the costs of transporting the Portland cement. A study of 25
6-15

regional cement markets in the United States over an 8-year period found that all of the

regional markets were in fact concentrated (Iwand and Rosenbaum, 1991). 

In 2001, approximately 40 companies and one state agency produced cement at 115

plants. Eight of the top 10 U.S. manufacturers are now owned by foreign companies. Texas,

Pennsylvania, Michigan, Missouri, and Alabama are the largest cement-producing states,

accounting for approximately 50 percent of U.S. production. 

Cement plants have operated at nearly full “practicable” capacity over the past 5

years with utilization rates near 90 percent (see Table 6-6). Although the utilization rate for

2001 fell to 80 percent, this is primarily the result of new cement capacity added late in the

year and temporary reductions in production resulting from technical problems at a cement

plant in Colorado (van Oss, 2002). 

Table 6-6. Capacity Utilization Rates: 1997–2001

Year Percentage of Clinker Capacity Used

1997 89.4

1998 90.1

1999 88.5

2000 87.5

2001 80.0

Source: Minerals Yearbook: Volume I.—Metals and Minerals: Cement http://minerals.usgs.gov/

minerals/pubs/commodity/cement/index.html#myb. Last updated October 2003.

6.2.4 Markets and Trends

U.S. production of Portland and masonry cement grew from 84 million metric tons in

1998 to 89 million metric tons in 2002 (see Table 6-7). Although consumption fell slightly

from 2001 levels, it has remained at approximately 110 million metric tons over the past 3

years. The United States has relied on foreign imports from foreign countries to meet

demand with approximately 20 percent of consumed cement being provided from outside the

United States. Canada has been a traditional source of imports, but Asian countries such as

Thailand and China have recently become important sources of cement. In contrast, little
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Table 6-7. Cement Market Statistics 1998–2002 (103 Metric Tons, Unless Otherwise

Noted)

1998 1999 2000 2001 2002

Production: 

Portland and masonry cement 83,931 85,952 87,846 88,900 89,000

Clinker 74,523 76,003 78,138 78,451 82,000

Shipments to final customers, includes exports 103,696 108,862 110,048 113,136 110,000

Imports of hydraulic cement for consumption 19,878 24,578 24,561 23,591 22,500

Imports of clinker for consumption 3,905 4,164 3,673 1,884 1,660

Exports of hydraulic cement and clinker 743 694 738 746 900

Consumption, apparent 103,457 108,862 110,470 112,710 110,000

Price, average mill value, dollars per ton 76.46 78.27 78.56 76.50 77.00

Stocks, mill, year end 5,393 6,367 7,566 6,600 7,600

Employment, mine and mill, number 17,900 18,000 18,000 18,000 18,000

Net import reliance as a percentage of

apparent consumption

19 21 20 21 19

Source: van Oss, H. 2003. Mineral Commodity Summaries: Cement. http://minerals.usgs.gov/minerals/

pubs/commodity/cement/170303.pdf. Last updated January 2003.

cement is exported (less than 1 million metric tons) because of high transportation costs. 

Prices for cement have ranged from $76 to $78 per metric ton during this period. 

The USGS has identified three key trends for the U.S. cement industry (van Oss,

2003). First, the international cement industry has experienced widespread consolidation in

the last few years. In 2001, three significant ownership changes took place in the United

States. Lafarge merged with Blue Circle cement and became the largest cement producer in

the United States. The biggest Brazilian cement producer also entered the U.S. market with

the purchase of St. Mary’s Cement Corporation, and a Mexican cement firm purchased

Decotah Cement from the state of South Dakota. Second, the industry continues to face

environmental concerns related to emissions resulting from cement production. As a result,

the industry will continue to focus on emission reduction strategies. Finally, the USGS

reports that the use of natural and synthetic substitutes for Portland cement are growing
6-17

outside of the United States. These materials have some performance advantages in some

applications and can lower energy and related environmental costs. 

6.3 Industrial Organic Chemicals

The industrial organic chemicals (not elsewhere classified) industry (NAICS 3251)

produces organic chemicals for end-use applications and for inputs into numerous other

chemical manufacturing industries. In nominal terms, it was the single largest segment of

the $419 billion chemical manufacturing industry (NAICS 325) in 1997, accounting for

approximately 27 percent of the industry’s shipments.

All organic chemicals are, by definition, carbon based and are divided into two

general categories: commodity and specialty. Commodity chemical manufacturers compete

on price and produce large volumes of staple chemicals using continuous manufacturing

processes. Specialty chemicals cater to custom markets, using batch processes to produce a

diverse range of chemicals. Specialty chemicals generally require more technical expertise

and research and development than the more standardized commodity chemicals industry

(EPA, 2002b). Consequently, specialty chemical manufacturers have a greater value added

to their products. End products for all industrial organic chemical producers are as varied as

synthetic perfumes, flavoring chemicals, glycerin, and plasticizers. 

6.3.1 The Supply Side: Production and Costs

6.3.1.1 Production Processes

Processes used to manufacture industrial organic chemicals are as varied as the end

products themselves. There are thousands of possible ingredients and hundreds of processes. 

Therefore, the discussion that follows is a general description of the ingredients and stages

involved in a typical manufacturing process.

Essentially a set of ingredients (feedstocks) is combined in a series of reactions to

produce end products and intermediates (EPA, 2002b). The typical chemical synthesis

processes incorporate multiple feedstocks in a series of chemical reactions. Commodity

chemicals are produced in a continuous reactor, and specialty chemicals are produced in

batches. Specialty chemicals may undergo a series of reaction steps, as opposed to

commodity chemicals’ one continuous reaction because a finite amount of ingredients is

prepared and used in the production process. Reactions usually take place at high

temperatures, with one or two additional components being intermittently added. As the
6-18

production advances, by-products are removed using separation, distillation, or refrigeration

techniques. The final product may undergo a drying or pelletizing stage to form a more

manageable substance. 

6.3.1.2 Types of Output

Miscellaneous industrial organic chemicals comprise nine general categories of

products:

C aliphitic and other acyclic organic chemicals (ethylene); acetic, chloroaceptic,

adipic, formic, oxalic, and tartaric acids and their metallic salts; chloral,

formaldehyde, and methylamine;

C solvents (ethyl alcohol etc.); methanol; amyl, butyl, and ethyl acetates; ethers;

acetone, carbon disulfide, and chlorinated solvents;

C polyhydric alcohols (e.g., synthetic glycerin);

C synthetic perfume and flavoring materials (e.g., citral, methyl, oinone);

C rubber processing chemicals, both accelerators and antioxidants (cyclic and

acyclic);

C cyclic and acyclic plasticizers (e.g., phosphoric acid);

C synthetic tanning agents;

C chemical warfare gases; and

C esters, amines, etc., of polyhydric alcohols and fatty and other acids. 

6.3.1.3 Major By-Products and Co-Products

By-products, co-products, and emissions vary according to the ingredients, processes,

maintenance practices, and equipment used (EPA, 2002b). Frequently, residuals from the

reaction process that are separated from the end product are resold or possibly reused in the

manufacturing process. A by-product from one process may be another’s input. The

industry is strictly regulated because it emits chemicals through many types of media,

including discharges to air, land, and water, and because of the volume and composition of

these emissions.
6-19

6.3.1.4 Production Costs

Of all the factors of production, employment in industrial organic chemicals

fluctuated the least between 1997 and 2001 (see Table 6-8). During that time, the nominal

cost of materials rose 10.7 percent to $66 billion, reaching a low of $53 billion in 1998. 

Between 1987 and 1996, employment decreased 7 percent, and this decrease continued

between 1997 and 2001. Facilities became far more computerized, incorporating advanced

technologies into the production process. Even with the drop in employment, payroll was

$300 million more in 2001 than in 1997. The cost of materials fluctuated between $29 and

$36 billion for these years, and new capital investment averaged $109 billion a year.

Table 6-8. Inputs for the Industrial Organic Chemicals Industry (NAICS 3251),

1997–2001

Year

Labor

Materials ($106)

Capital

Expenditures ($106 Quantity (10 ) 3) Payroll ($106)

1997 200.8 10,290.9 59,632.4 113,356.9

1998 202.2 10,498.4 53,294.7 106,695.1

1999 196.6 10,504.8 58,090.9 106,288.5

2000 190.8 10,530.8 69,948.1 115,707.6

2001 183.2 10,582.4 66,020.6 104,430.2

Source: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001. 

Washington, DC: Government Printing Office.

6.3.2 The Demand Side

Industrial organic chemicals are components of many chemical products. Most of

the chemical sectors (classified under NAICS 325) are downstream users of organic

chemicals. These sectors either purchase commodity chemicals or enter into contracts with

industrial organic chemical producers to obtain specialty chemicals. Consumers include

inorganic chemicals (NAICS 3251), plastics and synthetics (NAICS 3252), drugs (NAICS

3254), soaps and cleaners (NAICS 3256), paints and allied products (NAICS 3251), and

miscellaneous chemical products (NAICS 3259).
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6.3.3 Organization of the Industry: Market Concentration, Plants, and Firms

The industrial organic chemicals industry is unconcentrated. The NAICS 3251 1997

four-firm concentration ratio (CR4) was 15.6 and the eight-firm concentration ratio (CR8)

was 28.7; the industry’s HHI was 156.4. Facilities with 100 or more employees continued to

account for the majority of the industry’s shipment values. For example, in 1992, 28 percent

of all facilities had 100 or more employees (see Table 6-9), and these facilities produced

89 percent of the industry’s shipment values. The average number of facilities per firm was

1.4 in both years. Unfortunately, there is no direct correspondence between SIC 2869 and a

six-digit NAICS code industry. The 1997 Economic Census Manufacturing Series does not

present the same information as given in Table 6-9 for NAICS 3251. Table 6-10 instead

presents establishment sizes for the two largest subsectors of NAICS 3251. NAICS 325110

encompasses the petrochemical manufacturing industry, and NAICS 325199 represents

manufacturing of all other basic organic chemicals. 

Table 6-9. Historical Size of Establishments and Value of Shipments for the Industrial

Organic Chemicals Industry (SIC 2869/NAICS 3251)

1987 1992

Number of Employees in

Establishment 

Number of

Facilities

Value of Shipments

(1992 $106

)

Number of

Facilities

Value of Shipments

(1992 $106)

1 to 4 employees 97 552.8 100 102.6

5 to 9 employees 80 200.9 80 208.7

10 to 19 employees 91 484.7 97 533.9

20 to 49 employees 137 1,749.9 125 1,701.5

50 to 99 employees 99 2556.3 106 3,460.9

100 to 249 employees 110 10,361.2 111 8,855.9

250 to 499 employees 41 17,156.9 41 9,971.1

500 to 999 employees 27 9,615.5 30 13,755.0

1,000 to 2,499 employees 11 9,184.6 10 9,051.0

2,500 or more employees 6 7,156.9 5 6,613.5

Total 699 59,019.7 705 54,254.1

Sources: U.S. Department of Commerce, Bureau of the Census. 1995. 1992 Census of Manufactures, Industry

Series: Industrial Organic Chemicals. Washington, DC: Government Printing Office. 

U.S. Department of Commerce, Bureau of the Census. 1990. 1987 Census of Manufactures, Industry

Series, Industrial Organic Chemicals. Washington, DC: Government Printing Office.
6-21

Table 6-10. 1997 Size of Establishments, Value of Shipments, and Payroll for the

Industrial Organic Chemicals Industry (NAICS 3251)

NAICS 325110 NAICS 325199

Number of Employees in

Establishment

Number of

Facilities

Value of

Shipments

($106

)

Number of

Facilities

Value of

Shipments

($106

)

1 to 4 employees 4 3.3 111 178.4

5 to 9 employees 2 D 60 321.2

10 to 19 employees 5 26.0 80 844.9

20 to 49 employees 5 101.1 136 2,211.7

50 to 99 employees 10 866.9 100 4,364.9

100 to 249 employees 13 2,669.1 118 10,905.4

250 to 499 employees 9 5,211.0 33 7,592.2

500 to 999 employees 5 D 25 12,695.8

1,000 to 2,499 employees 1 D 12 D

2,500 or more employees — — 1 D

Total 54 20,534.8 676 53,542.4

D = undisclosed

Source: U.S. Department of Commerce, Bureau of the Census. 1999a. 1997 Economic Census. 

Manufacturing Industry Series. All Other Basic Organic Chemical Manufacturing. Washington, DC: 

Government Printing Office.

U.S. Department of Commerce, Bureau of the Census. 1999b. 1997 Economic Census.

Manufacturing Industry Series. Petrochemical Manufacturing. Washington, DC: Government

Printing Office.

6.3.3.1 Capacity Utilization 

The capacity utilization ratio for the industry averaged 78 over the 5-year period

presented. The varying capacity utilization ratios within this industry reflect changes in

production volumes and new production facilities and capacities going on- and off-line

between 1997 and 2001 (see Table 6-11). 
6-22

Table 6-11. Capacity Utilization Ratios for the Industrial Organic Chemicals Industry

(NAICS 3251), 1997–2001

1997 1998 1999 2000 2001

NAICS 3251 83 80 82 76 69

Note: All values are percentages. 

Source: U.S. Department of Commerce, Bureau of the Census. 2003b. Current Industrial Reports, Survey of

Plant Capacity: 2001 Washington, DC: Government Printing Office.

6.3.4 Markets and Trends

The inorganic chemicals industry’s shipments rose in 2000 to a high of $115.7

billion before declining in 2001, as seen in Table 6-12. Between 1997 and 2001, the

industry’s shipments fell 7.9 percent to $104.4 billion. This decrease largely reflects the

downturn in petrochemical production and reduced exports due to the Asian financial crisis

(Saftlas, 1999). 

Table 6-12. Value of Shipments for the Industrial Organic Chemicals, N.E.C. Industry

(SIC 2869/NAICS 3251), 1997–2001

Year Value of Shipments ($106)

1997 113,356.9

1998 106,695.1

1999 106,288.5

2000 115,707.6

2001 104,430.2

Source: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001. 

Washington, DC: Government Printing Office.

The U.S. industrial organic chemical industry is expected to expand through 2004 at

an annual rate of 3 percent (Saftlas, 1999). U.S. producers face increasing competition

domestically and abroad as chemical industries in developing nations gain market share and 

increase exports to the United States. In the coming years, the United States is expected to

remain a net exporter of industrial organic chemicals, but this surplus will decrease with

increased global competition.
6-23

6.4 Crude Petroleum and Natural Gas

The crude petroleum and natural gas industry encompasses the oil and gas extraction

process from the exploration for oil and natural gas deposits through the transportation of the

product from the production site (NAICS 211 and 213). The primary products of this

industry are natural gas, natural gas liquids (NGLs), and crude petroleum. 

6.4.1 The Supply Side: Production and Costs

6.4.1.1 Production Processes

Domestic production occurs within the contiguous 48 U.S., Alaska, and at offshore

facilities. There are four major stages in oil and gas extraction: exploration, well

development, production, and site abandonment (EPA, 2000). Exploration is the search for

rock formations associated with oil and/or natural gas deposits. Certain geological clues,

such as porous rock with an overlying layer of low-permeability rock, help guide exploration

companies to possible sources. 

After a field is located, the well development process begins. Well holes, or well

bores, are drilled to a depth of between 1,000 and 30,000 feet, with an average depth of about

5,500 feet (EPA, 2000). As the hole is drilled, casing is placed in the well to stabilize the

hole and prevent caving. Once the well has been drilled, rigging, derricks, and other

production equipment are installed. Onshore fields are equipped with a pad and roads; ships,

floating structures, or a fixed platform are procured for offshore fields. The hydrocarbons

are brought to the surface and are separated into a spectrum of products. 

6.4.1.2 Types of Output

The oil and gas industry’s principal products are crude oil, natural gas, and NGLs

Refineries process crude oil into several petroleum products. These products include motor

gasoline (40 percent of crude oil); diesel and home heating oil (20 percent); jet fuels

(10 percent); waxes, asphalts, and other nonfuel products (5 percent); feedstocks for the

petrochemical industry (3 percent); and other lesser products (EPA, 2000).

Natural gas is produced from either oil wells (known as “associated gas”) or wells

that are drilled for the primary objective of obtaining natural gas (known as “nonassociated

gas”). Methane is the predominant component of natural gas (about 85 percent), but ethane

(about 10 percent), propane, and butane are also components. Propane and butane, the

heavier components of natural gas, exist as liquids when cooled and compressed. These
6-24

latter two components are usually separated and processed as NGLs (EPA, 2000). A small

amount of the natural gas produced is consumed as fuel by the engines used in extracting and

transporting the gas, and the remainder is transported through pipelines for use by residential,

commercial, industrial, and electric utility users.

6.4.1.3 Major By-products

In addition to the various products of the oil and natural gas extraction process

described above, some additional by-products are generated during the extraction process. 

Oil and natural gas are composed of widely varying constituents and proportions depending

on the site of extraction. The removal and separation of individual hydrocarbons during

processing is possible because of the differing physical properties of the various components. 

Each component has a distinctive weight, boiling point, vapor pressure, and other

characteristics, making separation relatively simple. Most natural gas is processed to

separate hydrocarbon liquids that are more valuable as separate products, such as ethane,

propane, butane, isobutane, and natural gasoline. Finally, the engines that provide pumping

action at wells and push crude oil and natural gas through pipes to processing plants,

refineries, and storage locations produce HAPs. HAPs produced in engines include

formaldehyde, acetaldehyde, acrolein, and methanol. 

6.4.1.4 Production Costs

Automation, mergers, and corporate downsizing have made this industry less labor

intensive (Lillis, 1998). As shown in Table 6-13, the latest census data show labor costs

account for only 8 percent of production costs. Material costs (including fuel and electricity)

accounted for approximately 60 percent; the vast majority of these expenditures are for

materials and supplies. Capital expenditures accounted for the remaining 32 percent.

6.4.2 Demand-Side Characteristics

Crude oil, or unrefined petroleum, is a complex mixture of hydrocarbons that is the

most important of the primary fossil fuels. Refined petroleum products are used for

petrochemicals, lubrication, heating, and fuel. Petrochemicals derived from crude oil are the

source of chemical products such as solvents, paints, plastics, synthetic rubber and fibers,

soaps and cleansing agents, waxes, jellies, and fertilizers. Petroleum products also fuel the

engines of automobiles, airplanes, ships, tractors, trucks, and rockets. Other applications

include fuel for electric power generation, lubricants for machines, heating, and asphalt

(EPA, 1995c). 
6-25

Table 6-13. Costs of Production for the Crude Petroleum and Natural Gas Industry: 

1997

NAICS

Labor Fuel, Electricity,

and Materials

($106

)

Capital

Expenditures

($106 Quantity (10 ) 3

) Payroll ($106)

211 111.0 5,511.0 42,268.0 21,784.0

213111 53.0 1,901.0 3,796.0 2,205.0

213112 106.0 3,622.0 3,093.0 1,161.0

Natural gas is used by residential, commercial, industrial, and electric utility users. 

Total consumption of natural gas in the United States was 22,545 billion cubic feet in 2002. 

Industrial consumers accounted for the largest share of this total, consuming 7,257 billion

cubic feet, while residential, commercial, and electric utility consumption was 4,918 billion

cubic feet, 3,117 billion cubic feet, and 5,553 billion cubic feet, respectively. The remainder

of U.S. consumption was by natural gas producers in their plants and on their gas pipelines. 

The largest single application for natural gas is as a domestic or industrial fuel. Natural gas

is also becoming increasingly important for generating electricity. Although these are the

primary uses, other specialized applications have emerged over the years, such as a

nonpolluting fuel for buses and other motor vehicles (DOE, 2003c).

The primary substitutes for oil and natural gas are coal, electricity, and each other. 

Consumers of these energy products are expected to respond to changes in the relative prices

between these four energy sources by changing the proportions of these fuels they consume. 

For example, if the price of natural gas were to increase relative to other fuels, then it is

likely that consumers would substitute oil, coal, and electricity for natural gas. This effect of

changing prices is commonly referred to as fuel switching. 

The extent to which consumers change their fuel usage depends on such factors as

the availability of alternative fuels and the capital requirements involved. If they own

equipment that can run on multiple fuels, then it may be relatively easy to switch fuel usage

as prices change. However, if existing capital cannot easily be modified to run on an

alternative fuel, then it is less likely for a consumer to change fuels in the short run. 

If the relative price of the fuel currently in use remains elevated in the long run,

some additional consumers will switch fuels as they replace existing capital with new capital
6-26

capable of using relatively cheaper fuels. For example, if the price of natural gas were to

significantly increase relative to the price of electricity for residential consumers, most

consumers would be unlikely to replace their natural gas furnaces immediately. However, if

the natural gases price consistently remained higher compared to electricity prices over time,

residential consumers would be more likely to replace their natural gas furnaces with electric

heat pumps as their existing furnaces wear out.

6.4.3 Organization of the Industry: Market Concentration, Plants, and Firms

Many oil and gas firms are merging to remain competitive in both the global and

domestic marketplaces. By merging with their peers, these companies may reduce operating

expenses and reap greater economies of scale than they would otherwise. Recent mergers,

such as BP Amoco and ExxonMobil, have reduced the number of companies and facilities

operating in the United States and makes the markets more concentrated. 

Most U.S. oil and gas firms are concentrated in states with significant oil and gas

reserves, such as Texas, Louisiana, California, Oklahoma, and Alaska. In 1997, there were

over 15,000 establishments engaged in operations in NAICS 211 and 213.

The United States is home to half of the major oil and gas companies operating

around the globe. Although small firms account for approximately 47 percent of U.S. crude

oil and natural gas output, the domestic oil and gas industry is dominated by 24 integrated

petroleum and natural gas refiners and producers, such as ExxonMobil and BP Amoco

(Spancake, 1999). The latest Census data show over 7,000 companies performing oil and

gas extraction or supporting activities (see Table 6-14).

Table 6-14. Crude Petroleum and Natural Gas Establishment and Company Statistics: 

1997

NAICS Description

Number of

Companies

Number of

Establishments

211 Oil and Gas Extraction 6,859 8,312

213111 and 213112 Support Activities Mining 2,745 8,694

Source: U.S. Department of Commerce, Bureau of the Census. 1999c. 1997 Economic Census, Mining

Industry Series, Crude Petroleum and Natural Gas Extraction. Washington, DC: U.S. Department of

Commerce.
6-27

6.4.4 Markets and Trends

U.S. annual oil and gas production is a small percentage of total U.S. reserves. In

2001, oil producers extracted approximately 1.2 percent of the nation’s crude oil reserves

(see Table 6-15). A slightly larger percentage of NGLs was extracted (1.4 percent), and a

larger percentage of natural gas was extracted (3.0 percent). The United States produces

approximately 38 percent (2,118 million barrels) of its annual crude oil consumption,

importing the remainder of its crude oil from Canada, Latin America, Africa, and the Middle

East (3,405 million barrels). Approximately 17 percent (3,977 billion cubic feet) of U.S.

natural gas supply is imported. Most imported natural gas originates in Canadian fields in

the Rocky Mountains and off the Coast of Nova Scotia and New Brunswick. 

Table 6-15. Estimated U.S. Oil and Gas Reserves, Annual Production, and Imports,

2001

Category Reserves 

Annual

Production Imports

Crude Oil (106

 barrels) 174,820 2,118 3,405

Natural Gas (109

 cubic feet) 1,430,630 19,702 3,977

Natural Gas Liquids (106

 barrels) 23,570 682 1

Sources: U.S. Department of Energy, Energy Information Administration. 2002c. U.S. Crude Oil, Natural

Gas, and Natural Gas Liquids Reserves 2001 Annual Report. Washington, DC: U.S. Department of

Energy. 

U.S. Department of Energy, Energy Information Administration. 2002b. Petroleum Supply Annual

2001, Volume I. Washington, DC: U.S. Department of Energy.

U.S. Department of Energy, Energy Information Administration. 2003c. Natural Gas Annual 2001. 

Washington, DC: U.S. Department of Energy.

Between 1990 and 1998, crude oil consumption increased 1.4 percent per year, and

natural gas consumption increased 2.0 percent per year. The increase in natural gas

consumption came mostly at the expense of coal consumption (EPA, 1999b). The Energy

Information Administration (EIA) anticipates that natural gas consumption will continue to

grow, but only at an average annual growth rate of 0.3 percent (not including consumption

by electricity generators) through the year 2020. They also expect crude oil consumption to

grow at an annual rate of less than 1 percent over the same period (DOE, 2003a).
6-28

6.5 Paper and Allied Products

The paper and allied products industry (NAICS 322) is one of the largest

manufacturing industries in the United States. In 2001, the industry shipped nearly

$156 billion in paper commodities. The industry produces a wide range of wood pulp,

primary paper products, and paperboard products such as printing and writing papers,

industrial papers, tissues, container board, and boxboard. The industry also includes

manufacturers that “convert” primary paper and paperboard into finished products like

envelopes, packaging, and shipping containers (EPA, 2002c). 

6.5.1 The Supply Side: Production and Costs

6.5.1.1 Production Process

The manufacturing paper and allied products industry is capital- and resourceintensive, consuming large amounts of pulp wood and water in the manufacturing process. 

Approximately half of all paper and allied products establishments are integrated facilities,

meaning that they produce both pulp and paper on-site. The remaining half produce only

paper products; few facilities produce only pulp (EPA, 2002c). 

The paper and paperboard manufacturing process can be divided into three general

steps: pulp making, pulp processing, and paper/paperboard production. Paper and

paperboard are manufactured using what is essentially the same process. The principal

difference between the two products is that paperboard is thicker than paper’s 0.3 mm.

Producers manufacture pulp mixtures by using chemicals, machines, or both to

reduce raw material into small fibers. In the case of wood, the most common pulping

material, chemical pulping actions release cellulose fibers by selectively destroying the

chemical bonds that bind the fibers together (EPA, 2002c). Impurities are removed from the

pulp, which then may be bleached to improve brightness. Only about 20 percent of pulp and

paper mills practice bleaching (EPA, 2002c). The pulp may also be further processed to aid

in the paper-making process. 

During the paper-making stage, the pulp is strengthened and then converted into

paper. Pulp can be combined with dyes, resins, filler materials, or other additives to better

fulfill specifications for the final product. Next, the water is removed from the pulp, leaving

the pulp on a wire or wire mesh conveyor. The fibers bond together as they are carried

through heated presses and rollers. The paper is stored on large rolls before being shipped
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for conversion into another product, such as envelopes and boxes, or cut into paper sheets for

immediate consumption.

6.5.1.2 Types of Output

The paper and allied products industry’s output ranges from writing papers to

containers and packaging. Paper products include printing and writing papers; paperboard

boxes; corrugated and solid fiber boxes; fiber cans, drums, and similar products; sanitary

food containers; building paper; packaging; bags; sanitary paper napkins; envelopes;

stationary products; and other converted paper products.

6.5.1.3 Major By-Products and Co-Products

The paper and allied products industry is the largest user of industrial process water

in the United States. In 2000, a typical mill used between 4,000 and 12,000 gallons of water

per ton of pulp produced. The equivalent amount of waste water discharged per ton of pulp

ranges from 14 to 140 kg (EPA, 2002c). Most facilities operate waste water treatment

facilities on site to remove biological oxygen demand (BOD), total suspended solids (TSS),

and other pollutants before discharging the water into a nearby waterway.

6.5.1.4 Production Costs

Historical statistics for the costs of production for the paper and allied products

industry are listed in Table 6-16. From 1997 to 2001, industry payroll generally ranged from

approximately $22 to 23 billion. Employment peaked at 574,300 people in 1997 and

declined slightly to 530,200 people by 2001. Materials costs averaged $83.1 billion a year

and new capital investment averaged $7.7 billion a year.

6.5.2 The Demand Side

Paper is valued for its diversity in product types, applications, and low cost due to

ready access to raw materials. Manufacturers produce papers of varying durabilities,

textures, and colors. Consumers purchasing large quantities of papers may have papers

tailored to their specification. Papers may be simple writing papers or newsprint for personal

consumption and for the printing and publishing industry or durable for conversion into

shipping cartons, drums, or sanitary boxes. Inputs in the paper production process are

readily available in the United States because one-third of the country is forested, and

facilities generally have ready access to waterways.
6-30

Table 6-16. Inputs for the Paper and Allied Products Industry (NAICS 322), 1997–2001

Labor

Materials ($106

)

Total Capital

Expenditures

($106 Year Quantity (10 ) 3

) Payroll ($106)

1997 574.3 22,312.0 80,189.5 8,595.1

1998 572.4 22,529.5 82,419.4 8,546.7

1999 560.7 22,837.4 82,720.9 7,081.1

2000 548.3 22,680.1 87,346.6 7,383.5

2001 530.2 22,188.3 82,823.0 6,797.4

Sources: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures 2001. 

Washington, DC: Government Printing Office.

The paper and allied products industry is an integral part of the U.S. economy;

nearly every industry and service sector relies on paper products for its personal, education,

and business needs. Among a myriad of uses, papers are used for correspondence, printing

and publishing, packing and storage, and sanitary purposes. Common applications are all

manners of reading material, correspondence, sanitary containers, shipping cartons and

drums, and miscellaneous packing materials.

6.5.3 Organization of the Industry: Market Concentration, Plants, and Firms

For the paper and allied products industry, the CR4 equaled 18.5 in 1997 (see

Table 6-17). This means that the top four firms’ combined sales were 18.5 percent of the

industry’s total sales. This industry’s unconcentrated nature is also indicated by its HHI of

173.3.

Table 6-17. Measures of Market Concentration for Paper and Allied Products

Markets, 1997

NAICS Description CR4 CR8 HHI

Number of

Companies

Number of

Facilities

322 Paper Manufacturing 18.5 31.1 173.3 3,808 5,868

Source: U.S. Department of Commerce, Bureau of the Census. 2001. 1997 Economic Census, Manufacturing

Subject Series, Concentration Ratios in Manufacturing. Washington, DC: Government Printing

Office.
6-31

In 1997, 3,808 companies produced paper and allied products and operated 5,868

facilities. By way of comparison, 4,264 companies controlled 6,416 facilities in 1992. Even

though they account for only 46 percent of all facilities, those with 50 or more employees

contribute more than 92 percent of the industry’s total value of shipments (see Table 6-18). 

Table 6-18. Size of Establishments and Value of Shipments for the Paper and Allied

Products Industry (NAICS 322)

1987 1992 1997

Number of Employees in

Establishment

Number

of

Facilities

Value of

Shipments

($106

)

Number

of

Facilities

Value of

Shipments

($106

)

Number

of

Facilities

Value of

Shipments

($106

)

1 to 4 employees 729 640.6 786 216 687 D

4 to 9 employees 531 D 565 483 500 605.2

10 to 19 employees 888 1,563.4 816 1,456.5 706 1,672.7

20 to 49 employees 1,433 18,328.6 1,389 6,366.6 1,292 7,345.4

50 to 99 employees 1,018 D 1,088 12,811.5 1,033 14,686.8

100 to 249 employees 1,176 32,141.7 1,253 35,114.0 1,193 40,366.0

250 to 499 employees 308 24,221.1 298 22,281.2 265 23,940.2

500 to 999 employees 145 28,129.1 159 31,356.5 131 32,060.7

1,000 to 2,499 employees 63 24,903.1 62 23,115.4 59 26,780.6

2,500 or more employees 1 D 2 D

Total 1,732 129,927.8 6,416 133,200.7 5,868 150,295.9

D = undisclosed

Sources: U.S. Department of Commerce, Bureau of the Census. 1991. 1987 Census of Manufactures, Subject

Series, General Summary. Washington, DC: Government Printing Office.

U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of Manufactures, Subject

Series: General Summary. Washington, DC: Government Printing Office. 

U.S. Department of Commerce, Bureau of the Census. 2002a. 1997 Economic Census,

Manufacturing Subject Series, General Summary. Washington, DC: Government Printing Office. 
6-32

Capacity utilization measures are used to track a variety of economic conditions,

specific to the path of the business cycle and employment and inflationary trends. 

Table 6-19 presents the trend in capacity utilization for the paper and allied products

industry. The varying capacities reflect changes in the industry and the economy as a whole. 

The average capacity utilization ratio for the paper and allied products industry between

1997 and 2001 was approximately 81, with capacity declining in recent years. 

Table 6-19. Capacity Utilization Ratios for the Paper and Allied Products Industry,

1997–2001

1997 1998 1999 2000 2001

85 83 83 79 76

Note: All values are percentages. 

Source: U.S. Department of Commerce, Bureau of the Census. 2003b. Current Industry Reports, Survey of

Plant Capacity: 2001. Washington, DC: Government Printing Office.

6.5.4 Markets and Trends

The industry’s performance is tied to raw material prices, labor conditions, and

worldwide inventories and demand (EPA, 2002c). Industry performance was strong until

2001, when the value of shipments decreased by 6 percent (see Table 6-20). Over the entire

5-year period from 1997 to 2001, the value of shipments increased by 3.7 percent.

Table 6-20. Value of Shipments for the Paper and Allied Products Industry

(NAICS 322), 1997–2001

Year Value of Shipments ($106)

1997 150,295.9

1998 154,984.2

1999 156,914.9

2000 165,297.4

2001 155,846.0

Source: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001. 

Washington, DC: Government Printing Office.
6-33

The Department of Commerce projects that shipments of paper and allied products

will increase through 2004 by an annual average of 2.1 percent (Stanley, 1999). Because

nearly all of the industry’s products are consumer related, shipments will be most affected by

the health of the U.S. and global economy. The United States is a key competitor in the

international market for paper products and, after Canada, is the largest exporter of paper

products. U.S. exports and imports are both expected to increase 3 percent annually through

2004.

6.6 Petroleum Refining Industry

The petroleum refining industry is an important industry in the U.S. economy

accounting for approximately 4 percent of the manufacturing sector’s value of shipments. In

this section, we describe the current refinery process, raw materials used, uses and

consumers, industry organization, and markets and trends with particular emphasis on

distillate fuel uses.

6.6.1 The Supply Side: Production and Costs

6.6.1.1 Refinery Production Processes/Technology

Petroleum refining is the physical, thermal, and chemical separation of crude oil into

its major distillation fractions, followed by further processing (through a series of separation

and conversion steps) into highly valued finished petroleum products. Although refineries

are extraordinarily complex and each site has a unique configuration, we describe a generic

set of unit operations that are found in most medium and large facilities. A detailed

discussion of these processes can be found in EPA’s sector notebook of the petroleum

refining industry (EPA, 1995c); simplified descriptions are available on the Web sites of

several major petroleum producers (Flint Hills Resources, 2002; Chevron, 1998). 

After going through an initial desalting process to remove corrosive salts, crude oil

is fed to an atmospheric distillation column that separates the feed into several fractions. The

lightest of the fractions are processed through reforming and isomerization units into

gasoline or diverted to lower-value uses such as liquefied petroleum (LP) gas and

petrochemical feedstocks. The middle-boiling fractions make up the bulk of the aviation

fuel, diesel, and heating oil produced from the crude. In most refineries, the undistilled

liquid (called bottoms) is sent to a vacuum still to further fractionate this heavier material. 

Bottoms from the vacuum distillation can be further processed into low-value products such

as residual fuel oil, asphalt, and petroleum coke.
6-34

The middle fractions, however, are not suitable for sale immediately after

distillation. They are treated via one of several types of downstream processing: cracking,

which breaks large hydrocarbon molecules into smaller ones; combining (alkylation, for

example), which combines small molecules into larger, more useful entities; and reforming,

in which petroleum molecules are reshaped into higher quality molecules. It is in the

reforming operation that the octane rating of gasoline is increased to the level desired for

final sale. A downstream purification process, called hydrotreating, helps remove

chemically bound sulfur from petroleum products and is critically important for refineries to

achieve the low sulfur levels that the proposed regulations will mandate.

For each of the major products, several product streams from the refinery will be

blended into a finished mixture. For example, diesel fuel will typically contain a straight-run

fraction from crude distillation, distillate from the hydrocracker, light-cycle oil from the

catalytic cracker, and hydrotreated gas oil from the coker. Several auxiliary unit operations

are also needed in the refinery complex, including hydrogen generation, catalyst handing and

regeneration, sulfur recovery, wastewater treatment, and blending and storage tanks.

Refining, like most continuous chemical processes, has high fixed costs from the

complex and expensive capital equipment installed. In addition, shutdowns are very

expensive, because they create large amounts of off-specification product that must be

recycled and reprocessed prior to sale. As a result, refineries attempt to operate 24 hours per

day, 7 days per week, with only 2 to 3 weeks of downtime per year. Intense focus on

cost-cutting has led to large increases in capacity utilization over the past several years. A

Federal Trade Commission investigation into the gasoline price spikes in the Midwest during

the summer of 2000 disclosed an average utilization rate of 94 percent during that year, and

EIA data from 2001 show that a 92.6 percent utilization rate was maintained in 2001 (FTC,

2002; EIA, 2002b).

6.6.1.2 Potential Changes in Refining Technology Due to EPA Regulation

Over the next few years, EPA regulations will come into effect that require much

lower levels of residual sulfur for both gasoline and highway diesel fuel. To meet these

challenges, refineries are planning to add hydrotreater units to their facilities, route more

intermediate product fractions through existing hydrotreaters, and operate these units under

more severe conditions to reduce levels of chemically bound sulfur in finished products. As

has been documented in economic impact analyses for the gasoline and highway diesel rules,
6-35

these changes will require capital for equipment, new piping, and in-process storage,

increased use of catalyst and hydrogen, and different operating strategies.

The addition of lower sulfur limits for nonroad diesel fuel will result in additional

costs similar in nature to those required for the highway diesel fuel. Product streams

formerly sent directly to blending tanks will need to be routed through the hydrotreating

operation to reduce their sulfur level. In addition, because an increasing fraction of the total

volumetric output of the facility must meet ultralow sulfur requirements, flexibility will be

somewhat reduced. For example, it will become more difficult to sell off spec products if

errors or equipment failures occur during operation.

6.6.1.3 Types of Products

The major products made at petroleum refineries are unbranded commodities, which

must meet established specifications for fuel value, density, vapor pressure, sulfur content,

and several other important characteristics. These products are transported through a

distribution network to wholesalers and retailers, who may attempt to differentiate their fuel

from competitors based on the inclusion of special additives or purely through adroit

marketing. Gasoline and highway diesel are taxed prior to final sale, whereas nonroad fuel is

not. To prevent accidental or deliberate misuse, nonroad diesel fuel must be dyed prior to

final sale.

A total of $158.7 billion of petroleum products were sold in the 1997 census year,

accounting for approximately 0.4 percent of GDP. Motor gasoline is the dominant product,

both in terms of volume and value, with almost 3 billion barrels produced in 1997 (see

Table 6-21). Distillate fuels accounted for less than half as much as gasoline, with 1.3 billion

barrels produced in the United States in the same year. Data from the EIA suggest that 60

percent of that total is low-sulfur highway diesel, with the remainder split between nonroad

diesel and heating oil. Jet fuel, a fraction slightly heavier than gasoline, is the third most

important product, with a production volume of almost 600 million barrels.

6.6.1.4 Production Costs

The costs of production are divided into the main input categories of labor,

materials, and capital expenditures (see Table 6-22). Of these categories, the cost of

materials represents about 80 percent of the total value of shipments, varying from year to
6-36

Table 6-21. Types of Petroleum Products Produced by U.S. Refineries

Products

Total Produced 

(thousand barrels) Percentage of Total

Liquified refinery gases 243,322 3.9%

Finished motor gasoline 2,928,050 46.4%

Finished aviation 6,522 0.1%

Jet fuel 558,319 8.8%

Kerosene 26,679 0.4%

Distillate fuel oil 1,348,525 21.4%

Residual fuel oil 263,017 4.2%

Naphtha for feedstock 60,729 1.0%

Other oils for feedstock 61,677 1.0%

Special naphthas 18,334 0.3%

Lubricants 63,961 1.0%

Waxes 6,523 0.1%

Petroleum coke 280,077 4.4%

Asphalt and road oil 177,189 2.8%

Still gas 244,432 3.9%

Miscellaneous 21,644 0.3%

Total 6,309,000 100.0%

Source: U.S. Department of Energy, Energy Information Administration. 2002b. Petroleum Supply Annual

2001 Volume 1. Washington, DC: U.S. Department of Energy.

Table 6-22. Petroleum Refinery Costs of Production

Petroleum Refinery Costs of

Production Cost ($106

)

Cost as Percent of Product

Value (%)

Cost of materials $158,733 79.4%

Cost of labor 4,233 2.1%

Capital expenditures 6,817 3.4%

Source: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001. 

Washington, DC: Government Printing Office.
6-37

Petroleum Administration Defense Districts (PADDs)

Figure 6-3. PADD Districts of the United States

year as crude petroleum prices change. Labor and capital expenditures tend to be more

stable, accounting for about 2 to 3 percent of the value of shipments in 2001.

6.6.2 The Demand Side

This section describes the demand side of the market for refined petroleum products,

with a focus on the distillate fuel oil industry. It discusses the primary consumer markets

identified and their distribution by end use and petroleum administration defense district

(PADD) (see Figure 6-3). This section will also consider substitution possibilities available

in each of these markets and the feasibility and costs of these substitutions. 
6-38

6.6.2.1 Uses and Consumers

As Table 6-23 shows, highway diesel usage of 33.1 billion gallons represents the bulk

of distillate fuel usage (58 percent) in 2000. Residential distillate fuel usage, which in the

majority is fuel oil, accounts for 11 percent of total usage in 2000. Nonroad diesel fuel is

primarily centered on industrial, farm, and off-highway diesel (construction) usage. In 2000,

these markets consumed about 13 percent of total U.S. distillate fuels.

Table 6-23. Adjusted Sales of Distillate Fuel Oil by End Use (2000)

End Use 2000 Usage (thousand gallons) Percent Share (%)

Residential 6,204,449 10.8%

Commercial 3,372,596 5.9%

Industrial 2,149,386 3.8%

Oil company 684,620 1.2%

Farm 3,168,409 5.5%

Electric utility 793,162 1.4%

Railroad 3,070,766 5.4%

Vessel bunking 2,080,599 3.6%

On-highway diesel 33,129,664 57.9%

Military 233,210 0.4%

Off-highway diesel 2,330,370 4.1%

Total 57,217,231 100.0%

Source: U.S. Department of Energy, Energy Information Administration. 2001. Fuel Oil and Kerosene Sales

2000. Washington, DC: U.S. Department of Energy.

To determine the regional distribution of distillate fuel usage, 2000 sales of distillate

fuel are categorized by PADDs. As shown by Table 6-24, PADD I (the East Coast)

consumes the greatest amount of distillate fuel oil at 20.9 billion gallons. However,

residential, locomotive, and vessel bunking consumers account for 6.4 billion gallons of the

distillate consumed, which means that at least one-third of the distillate consumed in PADD I

is due to fuel oil and not to diesel fuel consumption.
6-39

Table 6-24. Adjusted Sales of Distillate Fuel Oil by End Use and by PADD

Enduse

PADD (Thousand Gallons)

I II III IV V

Residential 5,399,194 628,414 1,117 38,761 136,962 

Commercial 2,141,784 568,089 346,578 102,905 213,240 

Industrial 649,726 600,800 420,400 241,146 237,313 

Oil company 19,101 41,727 560,905 29,245 33,643 

Farm 432,535 1,611,956 552,104 220,437 351,377 

Electric utility 304,717 133,971 194,786 8,492 151,196 

Railroad 499,787 1,232,993 686,342 344,586 307,059 

Vessel bunking 490,150 301,356 1,033,333 173 255,586 

On-highway diesel 10,228,244 11,140,616 5,643,703 1,474,611 4,642,490 

Military 70,801 36,100 9,250 4,163 112,895 

Off-highway diesel 669,923 608,307 516,989 180,094 355,056 

Total 20,905,962 16,904,329 9,965,507 2,644,613 6,796,817 

Source: U.S. Department of Energy, Energy Information Administration. 2001. Fuel Oil and Kerosene Sales

2000. Washington, DC: U.S. Department of Energy.

6.6.2.2 Substitution Possibilities in Consumption

For engines and other combustion devices designed to operate on gasoline, there are

no practical substitutes, except among different grades of the same fuel. Because EPA

regulations apply equally to all gasoline octane grades, price increases will not lead to

substitution or misfueling. In the case of distillate fuels, it is currently possible to substitute

between low-sulfur diesel (LSD), high-sulfur diesel (HSD), and #2 fuel oil, although higher

sulfur levels are associated with increased maintenance and poorer performance.

With the consideration of more stringent nonroad fuel and emission regulations,

substitution will become less likely. Switching from nonroad ultralow-sulfur diesel (ULSD)

to highway ULSD is not financially attractive, because of the taxes levied on the highway

product. Misfueling with high-sulfur fuel oil will rapidly degrade the performance of the

exhaust system of the affected engine, with negative consequences for maintenance and

repair costs.
6-40

6.6.3 Industry Organization: Market Concentration, Plants, and Firms

To determine the ultimate effects of the EPA regulation, it is important to have a

good understanding of the overall refinery industry structure. The degree of industry

concentration, regional patterns of production and shipment, and the nature of the

corporations involved are all important aspects of this discussion. In this section, we look at

market measures for the United States as a whole and by PADD region.

6.6.3.1 Market Structure—Concentration

There is a great deal of concern among members of the public about the nature and

effectiveness of competition in the refining industry. Large price spikes following supply

disruptions and the tendency for prices to slowly fall back to more reasonable levels has

created suspicion of coordinated action or other market imperfections in certain regions. The

importance of distance in total delivered cost to various end-use markets also means that

refiners incur a wide range of costs in serving some markets; since the price is set by the

highest cost producer serving the market, profits are made by the low-cost producers in those

markets. 

There is no convincing evidence in the literature that markets should be modeled as

imperfectly competitive, however. Although the FTC study cited above concluded that the

extremely low supply and demand elasticities made large price movements likely and

inevitable given inadequate supply or unexpected increases in demand, their economic

analysis found no evidence of collusion or other anticompetitive behavior in the summer of

2000. Furthermore, the industry is not highly concentrated on a nationwide level or within

regions. The 1997 Economic Census presented the following concentration information: 

four-firm CR of 28.5 percent, eight-firm CR of 48.6 percent, and an HHI of 422 (U.S.

Department of Commerce, 2001). Merger guidelines followed by the Department of Justice

consider that there is little potential for pricing power in an industry with an HHI below

1,000 (DOJ, 1997).

6.6.3.2 Plants and Firms

As of January 2003, there were 145 operating petroleum refineries in the United

States (Table 6-25). An additional four refineries were operable but idle. Nearly 40 percent

of the nation’s refineries were contained in PADD III. This region also accounts for 45 to 50

percent of U.S. refinery net production of finished motor gasoline, distillate fuel oil, and 
6-41

Table 6-25. Number of Petroleum Refineries by PADD, 2003

PADD Number of Facilities Percent of Total

I 13 9.0

II 26 17.9

III 54 37.2

IV 16 11.0

V 36 24.8

Total 145 100.0%

Source: U.S. Department of Energy, Energy Information Administration. 2003d. Petroleum Supply Annual

2002, Volume 1. Washington, DC: U.S. Department of Energy.

residual fuel oil, which is not surprising since PADD III contains the petroleum-rich states of

Texas and Louisiana. PADD I had the fewest refineries of the five districts.

According to the EIA Petroleum Supply Annual 2001, the top three owners of crude

distillation facilities are ExxonMobil Corp (11 percent of U.S. total), Phillips Petroleum Corp

(10 percent), and BP PLC (9 percent). Information is not available on actual production of

highway diesel, nonroad diesel, and other distillate fuels for each refinery. It should be noted

that PADD III has more than 50 percent of the total crude distillation capacity and the three

largest single facilities (U.S. Department of Energy, 2002b).

6.6.3.3 Firm Characteristics

Many of the large integrated refineries are owned by major petroleum producers,

which are among the largest corporations in the United States. According to Fortune

Magazine’s Fortune 500 list, ExxonMobil is the second largest corporation in the world, as

well as in the United States. Chevron Texaco ranks as the eighth largest U.S. corporation,

placing it 14th in the world. The newly merged Phillips and Conoco entity will rank in the

top 20 in the United States, and six more U.S. petroleum firms make the top 500. BP Amoco

(fourth worldwide) and Royal Dutch Shell (eighth worldwide) are foreign owned, as is Citgo

(owned by Petroleos de Venezuela).

On the other hand, several of the smallest refineries are certified as small businesses

by EPA. A total of 21 facilities owned by 17 different parent companies qualify or have

applied for small-business status (EPA, 2002a). These small refineries are concentrated in
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the Rocky Mountain and Great Plains region of PADD IV, and their conversion to ULSD is

likely to require significant flexibility on the part of EPA. 

6.6.4 Markets and Trends

Markets for nonroad diesel and other distillate products have been growing

irregularly over the past several years. Table 6-26 shows that residential and commercial use

of fuel oil has been dropping steadily since 1984, while highway diesel use has nearly

doubled over the same period. Farm use of distillate has been flat over the 15-year period,

while off-highway use, mainly for construction, has increased by 40 percent.

6.7 Primary Metal Manufacturing

The primary metal manufacturing industries (NAICS 331) includes industries

involved in the primary and secondary smelting and refining of ferrous and nonferrous metal

from ore or scrap. Primary smelting and refining produces metals directly from ores, while

secondary processes produce metals from scrap and process waste. Nonferrous metals

produced by NAICS 331 industries include aluminum, lead, copper, and zinc. The iron and

steel sector comprises establishments involved in direct reduction of iron ore, manufacturing

pig iron and converting it into steel, and manufacturing steel and converting it to shapes or

tubes and pipes (EPA, 2003). 

6.7.1 The Supply Side: Production and Costs

6.7.1.1 Production Processes

Iron and Steel. In the United States, the highest geographic concentration of steel

mills exists in the Great Lakes region, which is home to most integrated plants. Historically,

mill sites were selected for their proximity to water sources, so very few mills operate in the

western United States. Because of the high cost of new capital and the long lead time needed

to introduce new equipment into the industry, changes in production methods and products

are generally made gradually, which makes it difficult for the industry to adjust to market

fluctuations. 

There are two types of steelmaking technology in use today. The first is the basic

oxygen furnace (BOF), which uses molten iron, scrap, and oxygen as input materials and

requires cokemaking and ironmaking as intermediate steps in steel production. The second

technique is the electric arc furnace (EAF), which uses electricity and scrap as inputs and

does not require the intermediate steps of the (BOF) method. 
6-43

Table 6-26. Sales of Distillate Fuel Oils to End Users 1984–1999 (thousands of barrels per day)

Year

Residential Com- mercial Indust- rial

Oil

Company Farm Electric Utility Rail- road Vessel Bunkering

OnHighway 

Diesel Military

OffHighway

Diesel

All

Other Total

1984 450 319 153 59 193 45 225 110 1,09345 45 109 44 2,8451985 471 294 169 57 216 34 209 124 1,12750 50 105 12 2,8681986 476 280 175 49 220 40 202 133 1,16950 50 111 9 2,9141987 484 279 190 58 211 42 205 145 1,18558 58 113 5 2,9761988 498 269 170 57 223 52 212 150 1,30464 64 119 4 3,1221989 489 252 167 55 209 70 213 154 1,37861 61 107 2 3,1571990 393 228 160 63 215 48 209 143 1,39351 51 116 (s) 3,0211991 391 226 152 59 214 39 197 141 1,33654 54 110 (s) 2,9211992 406 218 144 51 228 30 209 146 1,39142 42 113 (s) 2,9791993 429 218 128 50 211 38 190 133 1,48531 31 127 (s) 3,0411994 413 218 136 46 209 49 200 132 1,59434 34 130 (s) 3,1621995 416 216 132 36 211 39 208 129 1,66824 24 126 — 3,2071996 436 223 137 41 217 45 213 142 1,75424 24 134 — 3,3651997 423 210 141 41 216 42 200 137 1,86722 22 136 — 3,4351998 367 199 147 37 198 63 185 139 1,96718 18 142 — 3,4611999 381 196 142 38 189 60 182 135 2,09119 19 140 — 3,572

Source: U.S. Department of Energy, Energy Information Administration. 2002a. Annual Energy Review 2001. Washington, DC: U.S. Department of Energy.
6-44

For the BOF technique, the process begins with the manufacturing of coke. This

starts with bituminous pulverized coal charge, which is fed into a coke oven and heated in

the absence of oxygen. Volatile compounds are driven from the coal, and the resulting

product is coke. After heating, the coke is cooled with water and screened. This process

generates the most environmental concern, with air emissions and quench water use cited as

major problems. Industry experts predict that U.S. imports of coke will rise in the future,

because of the high cost of constructing new environmentally friendly production facilities. 

The next production stage produces molten iron from iron ore, coke, and limestone,

which are heated in a furnace. This heat burns the coke, which creates carbon monoxide to

reduce iron ore to iron. Finally, the molten iron from the furnace is combined with flux,

alloy materials, and scrap in the basic oxygen furnace, melted, and exposed to high-purity

oxygen. The end product of this process is molten steel. 

Production facilities using the EAF use scrap metal as the primary raw material. This

metal is melted and refined using electric energy. The melting process prompts the oxidation

of phosphorus, silicon, manganese, carbon and other materials, which form a slag on top of

the molten metal. 

Regardless of the production process, the output is molten steel, which is formed into

ingots or slabs that are rolled into finished products. The rolling process may consist of

reheating, rolling, cleaning, and coating of the steel. The forming process generally used in

facilities today is called the continuous casting process, in which the molten steel is cast

directly into semifinished shapes (EPA, 1995a).

Nonferrous metals. The process for manufacturing nonferrous metals involves

primary and secondary smelting and refining of ore or scrap; rolling, drawing, and alloying;

and the manufacturing and casting of basic metal products. Two recovery technologies are

used to produce refined metals. The first is pyrometallurgical technologies, which use heat

to separate desired metals from other materials. Examples of this process include drying,

calcining, roasting, sintering, retorting, and smelting. In contrast, hydrometallurgical

technologies separate desired from undesired metals by taking advantage of differences

between constituent solubilities and/or electrochemical properties while in aqueous solutions. 

During pyrometallic processing, an ore is combined by heat with materials such as

baghouse dust and flux. This combination is melted to fuse the desired materials into a

molten bullion. This bullion is again refined to increase the purity. This process varies
6-45

according to the raw ore, but the basic steps remain the same for pyrometallic processing

(EPA, 1995b).

Aluminum, copper, lead, and zinc are the more most widely used nonferrous metals

in the United States. The production of each of these metals is briefly discussed below.

Aluminum. In the early 1990s, most of the primary aluminum producers in the United

States were located in either the Northwest or the Ohio River Valley to take advantage of

hydroelectric power and coal-based energy. Most of the secondary smelters were located in

Southern California and the Great Lakes region near major industrial centers for access to

large amounts of scrap metal. 

Primary aluminum producers use a three-step process to produce aluminum alloy

ingots. In the first step alumina is extracted from bauxite oar using the Bayer process. In

this process, finely crushed bauxite is mixed with an aqueous sodium hydroxide solution to

form a slurry, which is heated and put through a series of chemical reactions to separate

aluminum hydroxide crystals from the rest of the materials. In the second step, the

dewatered aluminum oxide (alumina) is reduced to make pure molten aluminum. The

alumina is heated and electrically charged to separate the oxygen from the aluminum, which

is then siphoned off and transferred to melting and holding furnaces. The third step consists

of either adding other metals to create alloys of specific characteristics or casting aluminum

into ingots for transport to fabricating shops. 

Secondary production of aluminum involves the melting of scrap in oil- or gas-fired

reverberatory furnaces. The molten metal is then treated with chlorine or various fluxes to

remove magnesium (EPA, 1995b).

Copper. Copper ore is mined in the northern and southern hemisphere but is

primarily processed and consumed by countries in the northern hemisphere. The United

States is a major producer and consumer of copper products. Copper ore, mined from open

pits and underground mines, contains less than 1 percent copper. This ore is crushed and

concentrated by mixing it with water, chemical reagents, and air. The air attaches to the

copper minerals and rises to the top, where the new concentrate (approximately 20 to 30

percent copper) is skimmed off. 

Like steel, copper can be produced either pyrometallurgically or

hydrometallurgically. Ore concentrates, which contain high levels of copper sulfide and iron

sulfide minerals are treated by the pyrometallurgical process, while oxide ores that contain
6-46

copper oxide minerals and other oxidized waste minerals are treated by hydrometallurgical

processes (EPA, 1995b).

Lead. The United States is one of the world’s largest recycler of lead scrap and can

meet almost all domestic needs for refined lead production from scrap recycling. The

primary lead production process consists of four steps: sintering, smelting, drossing, and

pyrometallurgical refining. After a feedstock of lead concentrate completes these four steps,

the resulting refined lead will have a purity greater than 99.9 percent and can be mixed with

other minerals to form alloys. 

The secondary production process uses old scrap and new scrap from primary

production to manufacture lead. The main source of lead scrap in the United States is leadacid batteries. Once the lead battery scrap is broken and classified by lead type, it is

processed in blast furnaces or rotary reverberatory furnaces (EPA, 1995b).

Zinc. The primary production of zinc starts with zinc concentrate and reduces it to

metal either pyrometallurgically by distillation or hydrometallurgically by electrowinning. 

Nearly 80 percent of zinc refining is done hydrometallurgically. This process takes the zinc

concentrate through four steps: calcinating, leaching, purification, and electrowinning. 

Calcining involves mixing zinc-containing materials with coal then heating the mixture to

vaporize the zinc oxide, which is the desired output for this step. Next, the zinc oxide is

dissolved in a sulfuric acid solution. The third production step involves adding zinc dust,

which causes the undesirable elements to precipitate for easy filtering. The final step runs an

electric current through the aqueous zinc solution to cause the zinc to attach to aluminum

plates. These aluminum plates are then stripped and the zinc concentrate is melted and cast

into ingots (EPA, 1995b).

For secondary zinc production, zinc-containing metals are melted in a sweating

furnace and the molten zinc is recovered and refined. Recovered zinc is generally

comparable to primary zinc in quality, and secondary zinc production will most likely

increase in the future due to environmental concerns (EPA, 1995b).

6.7.1.2 Types of Output

The iron and steel industry produces iron and steel bars, strips, and sheets, as well as

finished products such as steel nails, spikes, wire, rods, pipes, and ferroalloys. By-products

derived from the coke manufacturing process such as coal tar and distillates are also

categorized as iron and steel industry products (EPA, 1995a). Both the primary and
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secondary aluminum production processes result in ingots of pure aluminum that serve as

feedstock for other materials and processes. Primary forms of aluminum include bars, foils,

pipes, plates, rods, sheets, tubes, and wire. Other nonferrous metal production results in

similar output. Facilities may also produce nonferrous nails, brads, and spikes as well as

metal power, flakes, and paste (U.S. Department of Commerce, 2002b).

6.7.1.3 Major By-Products and Co-Products

For steel production, the by-products produced depend on whether the BOF or EAF

was used. When production uses a BOF, the process of cokemaking produces coal tar, light

oil, ammonia liquor, and fuel. Coal tar is typically refined and used to produce various

commercial and industrial products. Additionally, the cokemaking process emits fine coke

particles during coke transport. Approximately 1 pound of particles per ton of coke

produced is captured by pollution control equipment. 

Ironmaking produces residual sulfur dioxide and particulates, which are most often

captured in gas during the production process. Regardless of the furnace used, the

steelmaking process emits metal dusts made of iron particulate, zinc, and other metals (EPA,

1995a).

Large amounts of sulfur are released during all of the nonferrous metal smelting

operations covered in the previous section. Primary and secondary aluminum manufacturing

release particulates, although the particulate matter generated during the calcining of

hydrated aluminum oxide is valuable enough to the production process that facilities use

extensive controls to reduce these emissions. 

Copper production creates sulfur dioxide as a by-product, and this gas is generally

collected and made into sulfuric acid, which can be sold or used in other operations. The

copper refining process removes solid matter from molten copper. This matter forms a

sludge, which is collected and scanned for precious metals, such as gold and silver. 

Particulate matter, primarily made of copper and iron oxides, is the principal air contaminant

emitted during copper smelting, conversion, and secondary copper processing. 

The particulate matter emitted from blast furnaces during lead production includes

lead oxides, quartz, limestone, iron pyrites, arsenic, and other metallic compounds. 

Additionally, about 7 percent of the total sulfur in the raw ore is emitted as sulfur dioxide. 

Fabric filters and electrostatic precipitators are most commonly used to control emissions. 
6-48

Like copper production, zinc production results in the creation of sulfur dioxide,

which is collected and made into sulfuric acid. More than 90 percent of potential sulfur

dioxide emissions are generated during the roasting process. This stage in the production

process also releases particulate matter containing zinc and lead (EPA, 1995b).

6.7.1.4 Production Costs

Similar to the trend in the value of shipments for the industry, all inputs shown in

Table 6-27 fell over the 5-year period. Low market prices and high production costs caused

many companies to close plants or cut employment in an attempt to compete with world

prices. Employment fell 12 percent and the industry payroll (in unadjusted dollars) fell 7

percent. For all inputs other than labor, there was a slight increase between 1999 and 2000,

but this upward swing was not maintained and the 2001 levels for all inputs were lower than

the 1997 levels. Total capital investment experienced the largest decrease in percentage

terms, with the 2001 level more than 20 percent less than the 1997 level. The cost of

materials fell by roughly 17 percent. 

Table 6-27. Inputs for the Primary Metal Manufacturing Industry (NAICS 331),

1997–2001

Labor

Materials ($106)

Total Capital

Investment

($106 Year Quantity (10 ) 3

) Payroll ($106)

1997 605.1 23,811.2 99,343.3 6,515.8

1998 602.3 24,146.1 96,949.7 6,451.2

1999 582.8 23,722.4 90,596.0 5,942.8

2000 577.8 24,155.1 93,736.3 6,138.3

2001 532.9 22,199.3 83,301.3 5,140.2

Sources: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures 2001. 

Washington, DC: Government Printing Office.

6.7.2 The Demand Side

The steel industry has seen significant changes over the last 30 years. Historically,

the two largest steel-consuming industries have been the automotive and construction

industry, so fluctuations in these industries affect demand for iron and steel products. 
6-49

Recently, however, foreign competition and technological advances in other materials have

reduced the market share of domestic steel producers (EPA, 1995a). For example, the recent

popularity of large sport utility vehicles pushed the auto industry to control the weight of

these large vehicles. While this trend may increase the demand for lighter-weight steel,

manufacturers are looking for substitutes of different materials (Bell, 1999).

Aluminum demand comes mainly from three industries: containers and packaging,

transportation, and building and construction. Combined, these three sectors accounted for

two-thirds of all aluminum shipments in 1999 (Cammarota, 1999). The leading domestic

consumer of refined copper in 1998 was wire rod mills, which accounted for three-quarters

of domestic consumption. The majority of the remaining quarter went to brass mills

producing copper and copper alloy semi-fabricated shapes (Shaw, 1999). The construction

and electronic products industries are the leading consumers of copper and copper alloy, and

transportation equipment such as radiators also accounts for a large share of end-use copper. 

Copper chemicals, primarily copper sulfate and the cupric and cuprous oxides, are widely

demanded for use in algaecides, fungicides, wood preservatives, copper plating, and

pigments (EPA, 1995b).

The most important end use for lead is the lead acid battery, which accounted for

approximately 72 percent of global lead consumption in the late 1990s. Although lead-acid

batteries include batteries for household use, the largest consumers are in industrial sectors. 

Lead oxides are used in television glass and computers, construction, protective coatings, and

ballasts (EPA, 1995b).

Zinc is generally consumed in metal (80 percent) or compound form. The largest

segment in the zinc market is for galvanizing steel, which is used in the automotive and

construction industries for corrosion protection (Larrabee, 1999b). Zinc compound use

varies widely but is used mainly by agricultural, chemical, paint, pharmaceutical, and rubber

sectors of the economy (EPA, 1995b). 

6.7.3 Organization of the Industry: Market Concentration, Plants, and Firms

Table 6-28 displays various measures of market concentration for the entire industry. 

A CR4 measure of 13.8 indicates that the four firms with the highest sales in the industry

account for a total of 13.8 percent of total industry sales. The HHI value of 97.4 shows that

the industry is unconcentrated. 
6-50

Table 6-28. 1997 Measures of Market Concentration for the Primary Metal

Manufacturing Industry (NAICS 331)

NAICS Industry CR4 CR8 HHI

Number of

Companies

Number of

Facilities

331 Primary Metal Manufacturing 13.8 22.3 97.4 4,076 5,059

Sources: U.S. Department of Commerce, Bureau of the Census. 2001. 1997 Economic Census, Manufacturing

Subject Series: Concentration Ratios in Manufacturing. Washington, DC: Government Printing

Office.

In the 1990s, many of the larger mills and operations closed down to save costs, and

this is reflected in the 1997 size distribution of facilities (see Table 6-29). Nearly 73 percent

of facilities in the industry employed fewer than 100 people, but these firms accounted for

only 12 percent of sales. The average company in this industry operates 1.24 facilities.

Table 6-29. Size of Establishments and Value of Shipments for the Primary Metal

Manufacturing Industry (NAICS 331)

1997

Number of Employees in Establishment Number of Facilities

Value of Shipments

($106

)

1 to 4 employees 859 272.9

4 to 9 employees 502 (D)

10 to 19 employees 621 (D)

20 to 49 employees 979 6,199.6

50 to 99 employees 720 11,084.9

100 to 249 employees 797 30,211.1

250 to 499 employees 363 35,126.8

500 to 999 employees 145 29,829.8

1,000 to 2,499 employees 55 25,247.2

2,500 or more employees 18 28,107.6

Total 5,059 168,117.7

(D) = undisclosed

Sources: U.S. Department of Commerce, Bureau of the Census. 2002a. 1997 Economic Census,

Manufacturing Subject Series, General Summary. Washington, DC: Government Printing Office. 
6-51

Over the 5-year period of 1997 through 2001, facilities produced at levels between

70 and 84 percent of their full production capacity (Table 6-30). The average level for the 5

years was 77.4, and it fell by 17 percent over the time period. 

Table 6-30. Capacity Utilization Ratios for the Primary Metal Manufacturing

Industry, 1997–2001

1997 1998 1999 2000 2001

84 78 81 74 70

Note: All values are percentages. 

Source: U.S. Department of Commerce, Bureau of the Census. 2003b. Current Industry Reports, Survey of

Plant Capacity, 2001. Washington, DC: Government Printing Office.

6.7.4 Markets and Trends

As with many other industries, the steel mill products felt the strain of the Asian

financial crisis in the late 1990s. Despite dramatic action by the U.S. Department of

Commerce in 1999, global demand for U.S. steel mill products and nonferrous metal

products decreased, reflected by the decline in the value of shipments for the industry (see

Table 6-31). Between 1997 and 2001, the industry’s value of shipments decreased

17.8 percent. 

Table 6-31. Value of Shipments for the Primary Metal Manufacturing Industry

(NAICS 331), 1997–2001

Year Value of Shipments ($106)

1997 168,118

1998 166,109

1999 156,647

2000 156,598

2001 138,245

Sources: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001. 

Washington, DC: Government Printing Office.
6-52

In 1999, industry experts predicted that domestic demand for steel would be strong

through 2004, assuming moderate growth in the U.S. economy. In terms of the global

market, analysts predict that worldwide demand will increase. The growth in worldwide

demand should help the domestic industry because foreign producers will be able to ship

their steel to places other than the United States. The steel market performance depends on

growth in the highway and private nonresidential building markets, which are both projected

to experience growth through the first part of the 21st century (Bell, 1999).

Domestic aluminum production in the near future will likely not meet domestic

demand, which will increase U.S. reliance on aluminum imports. Growth in the

transportation sector, especially light vehicles, is the driving force behind demand for

aluminum products (Cammarota, 1999). Copper production is projected to decline through

the early part of the 21st century, but consumption should follow the growth patterns of the

U.S. economy. The construction and power utilities industries represent the main domestic

consumers of copper (Shaw, 1999). The projected increase in lead-acid batteries should

boost the lead production increase. Industry analysts predict an average annual growth rate

of 11 percent in the industrial battery market, due mostly in part to strong performance in the

telecommunications and uninterruptible power supply markets. Finally, zinc production

should grow along with the economy then slow at the beginning of the 21st century. Growth

in sales in the galvanizing segment should support zinc production (Larrabee, 1999a, 1999b).

6.8 References

Bell, Jamie. 1999. “Steel Mill Products.” U.S. Industry and Trade Outlook 2000. United

States: McGraw-Hill. pp 13-1 to 13-6.

Cammarota, David. 1999. “Nonferrous Metals: Aluminum.” U.S. Industry and Trade

Outlook 2000. United States: McGraw-Hill. pp 14-1 to 14-4.

Chevron Texaco Corp. 1998. “Diesel Fuel Refining and Chemistry.” 

<http://www.chevron.com/prodserv/fuels/bulletin/diesel/L2_4_1_rf.htm>. As

accessed on October 20, 2003. 

Federal Trade Commission (FTC). 2002. Final Report of the Federal Trade Commission: 

Midwest Gasoline Price Investigation, March 29, 2001.

<http://www.ftc.gov/os/2001/03/mwgasrpt.htm>. As accessed October 20, 2003. 
6-53

Flint Hills Resources. 2002. “Refining Overview.” 

<http://www.fhr.com/Refining101/default.asp>. As accessed October 20, 2003. 

Iwand, T., and D. Rosenbaum. 1991. “Strategies in Supergames with Capacity Constraints: 

Some evidence from the U.S. Portland Cement Industry.” International Journal of

Industrial Organization 9:497-511.

Larrabee, Dave. 1999a. “Nonferrous Metals: Lead.” U.S. Industry and Trade Outlook

2000. United States: McGraw-Hill. pp 14-7 to 14-11.

Larrabee, Dave. 1999b. “Nonferrous Metals: Zinc.” U.S. Industry and Trade Outlook

2000. United States: McGraw-Hill. pp 14-14 to 14-17.

Lillis, Kevin. 1998. “Crude Petroleum and Natural Gas.” U.S. Industry and Trade Outlook

1998. United States: McGraw-Hill. 

Minerals Yearbook: Volume I.—Metals and Minerals: Cement

<http://minerals.usgs.gov/minerals/pubs/commodity/cement/index.html#myb>. Last

updated October 2003.

Portland Cement Association. March 2005. “About PCA, Tools for Concrete Thinking.” 

<www.cement.org/pca/shortageQA.asp>.

Saftlas, Herman. 1999. “Industrial Organic Chemicals.” U.S. Industry and Trade Outlook

2000. United States: McGraw-Hill. pp 11-5 to 11-7.

Shaw, Robert. 1999. “Nonferrous Metals: Copper.” U.S. Industry and Trade Outlook

2000. United States: McGraw-Hill. pp 14-4 to 14-7.

Spancake, Larry. 2000. “Crude Petroleum and Natural Gas.” U.S. Industry and Trade

Outlook 2000. United States: McGraw-Hill. 

Stanley, Gary. 1999. “Paper and Allied Products, Pulp Mills.” U.S. Industry and Trade

Outlook 2000. United States: McGraw-Hill. pp 10-1 to 10-8.

U.S. Department of Commerce, Bureau of the Census. 1990. 1987 Census of Manufactures,

Industry Series, Industrial Organic Chemicals. Washington, DC: Government

Printing Office.

U.S. Department of Commerce, Bureau of the Census. 1991. 1987 Census of Manufactures,

Subject Series, General Summary. Washington, DC: Government Printing Office.
6-54

U.S. Department of Commerce, Bureau of the Census. 1995. 1992 Census of Manufactures,

Industry Series: Industrial Organic Chemicals. Washington, DC: Government

Printing Office.

U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of Manufactures,

Subject Series: General Summary. Washington, DC: Government Printing Office.

U.S. Department of Commerce, Bureau of the Census. 1997. 1997 Economic Census. 

Manufacturing Industry Series. Cement Manufacturing. Washington, DC: 

Government Printing Office. 

U.S. Department of Commerce, Bureau of the Census. 1999a. 1997 Economic Census. 

Manufacturing Industry Series. All Other Basic Organic Chemical Manufacturing.

Washington, DC: Government Printing Office.

U.S. Department of Commerce, Bureau of the Census. 1999b. 1997 Economic Census.

Manufacturing Industry Series. Petrochemical Manufacturing. Washington, DC: 

Government Printing Office.

U.S. Department of Commerce, Bureau of the Census. 1999c. 1997 Economic Census,

Mining Industry Series, Crude Petroleum and Natural Gas Extraction. Washington,

DC: U.S. Department of Commerce. 

U.S. Department of Commerce, Bureau of the Census. 2001. 1997 Economic Census,

Manufacturing Subject Series, Concentration Ratios in Manufacturing. Washington,

DC: Government Printing Office.

U.S. Department of Commerce, Bureau of the Census. 2002a. 1997 Economic Census,

Manufacturing, Subject Series: General Summary. Washington, DC: Government

Printing Office.

U.S. Department of Commerce, Bureau of the Census. 2002b. “331 Primary Metal

Manufacturing.” 2002 NAICS Definitions. <http://www.census.gov/epcd/naics02/

def/NDEF331.HTM#N331>. As obtained October 14, 2003.

U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of

Manufactures 2001. Washington, DC: Government Printing Office.

U.S. Department of Commerce, Bureau of the Census. 2003b. Current Industrial Reports,

Survey of Plant Capacity: 2001 Washington, DC: Government Printing Office.
6-55

U.S. Department of Energy, Energy Information Administration. 2001. Fuel Oil and

Kerosene Sales 2000. Washington, DC: U.S. Department of Energy.

U.S. Department of Energy, Energy Information Administration. 2002a. Annual Energy

Review 2001. Washington, DC: U.S. Department of Energy. 

U.S. Department of Energy, Energy Information Administration. 2002b. Petroleum Supply

Annual 2001, Volume I. Washington, DC: U.S. Department of Energy. 

U.S. Department of Energy, Energy Information Administration. 2002c. U.S. Crude Oil,

Natural Gas, and Natural Gas Liquids Reserves 2001 Annual Report. Washington,

DC: U.S. Department of Energy. 

U.S. Department of Energy, Energy Information Administration. 2003a. Annual Energy

Outlook 2003. Washington, DC: U.S. Department of Energy.

U.S. Department of Energy, Energy Information Administration. 2003b. Natural Gas

Annual 2001. Washington, DC: U.S. Department of Energy. 

U.S. Department of Energy, Energy Information Administration. 2003c. Petroleum Supply

Annual 2002, Volume I. Washington, DC: U.S. Department of Energy. 

U.S. Department of Justice (DOJ), Federal Trade Commission. 1997. Horizontal Merger

Guidelines. Washington, DC: U.S. Department of Justice. 

U.S. Environmental Protection Agency (EPA), EPA Office of Compliance. 1995a. Sector

Notebook Project: Profile of the Iron and Steel Industry. Washington, DC: U.S.

Environmental Protection Agency. 

U.S. Environmental Protection Agency (EPA), EPA Office of Compliance. 1995b. Sector

Notebook Project: Profile of the Nonferrous Metals Industry. Washington, DC: 

U.S. Environmental Protection Agency.

U.S. Environmental Protection Agency (EPA), EPA Office of Compliance. 1995c. Sector

Notebook Project: Profile of the Petroleum Refining Industry. Washington, DC: 

U.S. Environmental Protection Agency.

U.S. Environmental Protection Agency (EPA). 1999a. Economic Impact Analysis of Air

Pollution Regulations: Portland Cement. <http://www.epa.gov/ttnecas1/

regdata/Portland.pdf>. Last updated October 2003. 
6-56

U.S. Environmental Protection Agency (EPA). 1999b. Economic Impact Analysis of the Oil

and Natural Gas NESHAP and the Natural Gas Transmission and Storage NESHAP. 

Research Triangle Park, NC: Office of Air Quality Planning and Standards. 

U.S. Environmental Protection Agency (EPA), EPA Office of Compliance. 2000. Sector

Notebook Project: Profile of the Oil and Gas Extraction Industry. Washington, DC: 

U.S. Environmental Protection Agency.

U.S. Environmental Protection Agency (EPA), Office of Air and Radiation. 2002a.

Highway Diesel Progress Review. Washington, DC: U.S. Environmental Protection

Agency.

U.S. Environmental Protection Agency (EPA), EPA Office of Compliance. 2002b. Sector

Notebook Project: Profile of the Organic Chemical Industry, 2nd Edition. 

Washington, DC: U.S. Environmental Protection Agency.

U.S. Environmental Protection Agency (EPA), EPA Office of Compliance. 2002c. Sector

Notebook Project: Profile of the Pulp and Paper Industry, 2nd Edition. Washington,

DC: U.S. Environmental Protection Agency.

U.S. Environmental Protection Agency (EPA), EPA Office of Compliance. 2003. “Metals.” 

<http://www.epa.gov/compliance/assistance/sectors/metals.html>. As obtained

October 14, 2003. 

van Oss, H. 2002. Minerals Yearbook: Volume I. Metals and Minerals: Cement. 

<http://minerals.usgs.gov/minerals/pubs/commodity/index.html#myb>. Last updated

October 2003. 

van Oss, H. 2003. Mineral Commodity Summaries: Cement. <http://minerals.usgs.gov/

minerals/pubs/commodity/cement/170303.pdf>. Last updated January 2003.
7-1

CHAPTER 7

COST, ECONOMIC, AND ENERGY IMPACTS

This chapter reports the cost, economic, and energy impact analysis performed for the

final BART rule. 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 fossil-fuel-fired steam electric plants (part of the

electric power sector), which are one of the 26 stationary source categories that are BARTeligible. 

The analysis presents results for three scenarios of increasing stringency for BART for

EGUs. In this rule, EPA is setting presumptive limits for a subset of the BART-eligible, coalfired EGUs (those greater than 200 MW at plants greater than 750 MW) and strong

recommendations for another subset of BART-eligible coal-fired EGUs (units greater than

200 MW at plants less than 750 MW). However, it is up to States to ultimately decide on what

BART is for their affected units. The three scenarios represent different assumptions regarding

the ultimate levels of controls imposed by States on BART-eligible EGUs, and these scenarios

are summarized in Table 7-1.

EPA assumes that the CAIR program is in the baseline for this final BART rule analysis. 

EPA further assumes that States implement the required CAIR reductions through a cap-andtrade program. For CAIR SO2 and NOx controls, EPA modeled an annual, two-phased 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 7-2 for total

annual emissions caps under CAIR. 
7-2

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

Table 7-1. BART Scenarios Modeled in IPM

Scenario 1 Scenario 2 Scenario 3

NOx Combustion controls on all

units >200 MW at plants >750

MW

Annual operation of SCR

(where existing)

No controls on O/G steam 

Combustion controls on units

>200 MW

SCR on cyclone units >200

Annual operation of SCR

(where existing)

No controls on O/G steam

SCR on units >100 MW

Combustion controls on 25

MW < units <100 MW

Annual operation of SCR

(where existing)

No controls on O/G steam

SO2 95% reduction or 0.15

lbs/MMBtu on all previously

uncontrolled units >200 MW at

plants >750 MW

No controls on units with

existing scrubbers

No controls on oil units

No additional controls (beyond

those for WRAP) on units in

five 309 States: Arizona, Utah,

Oregon, Wyoming, and New

Mexico.

95% reduction or 0.15

lbs/MMBtu on all previously

uncontrolled units >200 MW

No controls on units with

existing scrubbers

No controls on oil units

No additional controls (beyond

those for WRAP) on units in

five 309 States: Arizona, Utah,

Oregon, Wyoming, and New

Mexico.

95% reduction or 0.10

lbs/MMBtu on all previously

uncontrolled units >100 MW

No controls on units with

existing scrubbers

No controls on oil units

No additional controls (beyond

those for WRAP) on units in

five 309 States: Arizona, Utah,

Oregon, Wyoming, and New

Mexico. 

Note: For modeling, SO2 controls would require a 95 percent reduction from the 2005 base case SO2 rate.
7-3

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

2010–2014 (2009–2014 for NOx) 2015–Thereafter

SO2 3.6 2.5

NOx 1.5 1.3

BART-eligible units were defined as those that were online after August 7, 1962, and

under construction prior to August 7, 1977. EPA has developed an updated list of coal-fired

BART-eligible coal units by more accurately determining which units with on-line dates after

1978 were BART-eligible. A description of how this was done, along with a complete list of

BART-eligible coal-fired EGU sources used in the modeling, can be found in Appendix A and

Appendix B, respectively. The new list represents 491 units (ranging in size from 29 MW to

1,300 MW) totaling 218 GW of capacity, representing more than half of the total coal-fired EGU

capacity in the United States. Of these, a smaller portion (99 units totaling almost 45 GW) are in

States that are not in the final CAIR region for PM (which requires annual controls of SO2 and

NOx). Almost 17 GW of these are in the 309 WRAP States. A number of units have already

installed SO2 and NOx controls. Which of these units are required to apply controls depends on

the modeling scenario. A characterization of the complete list of BART-eligible coal units is

provided in Table 7-3.

Table 7-3. Number of Coal-Fired EGU BART-Eligible Units

Plant Size

Unit Size <750 MW >750 MW Total

CAIR Region

<200 MW 97 97

>200 MW 61 234 295

CAIR Total 158 234 392

Non-CAIR Region

<200 MW 24 24

>200 MW 22 53 75

Non-CAIR Total 46 53 99

Total 204 287 491
7-4

In the analysis, controls and reductions were assumed to be required in 2015, a date that

is generally consistent with the expected timing of the rule. States must submit SIPs relevant to

the BART requirements in January 2008. After approval of the SIP, there is a 5-year compliance

date. Thus, controls are likely to be installed by the end of 2013 or the beginning of 2014 to

comply with the rule. Additionally, facilitating the analysis, EPA had existing inventories,

modeling, and base case runs for 2015.

In our modeling, no additional necessary controls for SO2 (beyond their WRAP

obligations) were assumed for any units within the five WRAP 309 States. Also, because of

modeling limitations, no additional reductions were assumed from units with existing scrubbers,

even if they were performing at less than 95 percent removal. This assumption would tend to

understate the costs and emission reductions of the rule. 

These scenarios differ from the modeling done in the original BART proposal. In

modeling the proposed BART, SO2 affected units were given the choice of meeting a 0.10

lbs/mmBtu emission rate or achieving 90 percent reductions from base emissions. Affected

units needed to meet a 0.2 lbs/mmBtu emission rate limit for NOx. Additionally in the proposal,

EPA required controls only on BART-eligible units greater than 250 MW. 

Additionally, the final CAIR rule differs from this modeling in that it requires annual SO2

and NOx reductions in 23 States and the District of Columbia and 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 through September)

NOx requirements. 

Consequently, EPA’s modeling of CAIR in this BART analysis is similar, but not

identical to, the final CAIR requirements. EPA modeling included three additional States

(Arkansas, Delaware, and New Jersey) within the CAIR region and required these States to make

annual SO2 and NOx reductions. These three States are not required to make annual reductions

under the final CAIR. Along with finalizing CAIR, EPA has also put forth a proposal to include

Delaware and New Jersey in the CAIR region for annual SO2 and NOx reductions. Arkansas is

not included in the annual SO2 and NOx requirements either as part of Final CAIR or the

“Proposed Rule” (but is included for the ozone season CAIR requirement). The 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 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.
1

 EPA is making such a determination in this rulemaking.

2

 The interpretation of BART modeled in the CAIR TSD assumed an SO2 limit of 90 percent or 0.10 lb/mmBtu to

all coal units greater than 100 MW and a NOx limit of 0.20 lb/mmBtu on all coal units greater than 25 MW. 

7-5

EPA believes that the differences between the initial IPM run and the final IPM run have

limited impact on BART-projected control costs, emissions, and other impacts. Modeling the

CAIR region without Arkansas, Delaware, and New Jersey does not change the results presented

here in any significant way, and in any event, this generally reflects the geographic scope of the

CAIR program as EPA intends it to be ultimately. 

In assuming that CAIR is in the baseline, EPA also assumed, for the purposes of this

analysis, that the CAIR trading program is “better than BART” and will substitute for sourcespecific BART in the CAIR-affected region with annual SO2 and NOx reduction requirements.1

Source-specific BART controls were thus assumed for the ozone-season CAIR States only

(Massachusetts and Connecticut in this modeling) and non-CAIR regions of the country. 

EPA would note that its analysis of BART in this final rule differs from its analysis of

BART in the CAIR Technical Support Document (TSD) “Demonstration that CAIR Satisfies the

‘Better-than-BART’ Test as Proposed in the Guidelines for Making BART Determinations.” In

that analysis, EPA assumed a more strict interpretation of BART nationwide2 than in the final

BART and compared it to a program that included CAIR together with a more strict

interpretation of BART in the non-CAIR States. 

Consequently, this analysis does not assume BART source-specific controls for BARTeligible units in Arkansas, Delaware, and New Jersey (although they are covered for seasonal

NOx under final CAIR). As noted, sources in Delaware and New Jersey, however, would be

covered by the Delaware and New Jersey proposal, which if it were finalized, would have the

effective impact of bringing them into the CAIR trading program. 

Arkansas was included in CAIR with a 2015 SO2 budget of 34,091 tons—corresponding

to a 63,309-ton reduction in SO2 emissions relative to Title IV allocations. Under our modeling

of CAIR, however, sources in Arkansas did not put on SO2 controls and did not make SO2

reductions so that all of the required SO2 reductions from Arkansas were accomplished in other

States. Consequently, with Arkansas not included in CAIR, SO2 emissions in the other CAIR

States would increase. Arkansas has 3,100 MW of BART-eligible coal units without scrubbers

(totaling over 62,000 tons of SO2 emissions in 2001). BART would require 95 percent controls

(or a 0.15 lb/mmBtu emission rate) from these sources. Although total reductions would be

relatively similar under both CAIR and BART, all the reductions would take place in Arkansas

under BART, while all of the reductions would take place in other CAIR States under CAIR.
7-6

Applying source-by-source BART nationally—together with the CAIR program in the

CAIR region—would not be expected to achieve additional emissions reductions. Rather it

would force controls in the CAIR region at specific BART units, which do not necessarily

represent the least cost reductions in the region. Consequently, such an approach would only

serve to shift around emissions within the CAIR region and increase total program costs.

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 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

Wisconsin. IPM includes State rules that have been finalized and/or approved by a State’s

legislature or environmental agency. 

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 BART,

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.

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

The projected emissions levels under CAIR and under the three different BART

scenarios are provided in Table 7-4.
7-7

Table 7-4. Projected Emissions of SO2 and NOx with CAIR and with BART (thousand tons

from units greater than 25MW)

2015 

CAIR

CAIR + BART

(Scenario 1)

CAIR + BART

(Scenario 2)

CAIR + BART

(Scenario 3)

SO2 (annual) 4,890 4,836 4,770 4,738

NOx (annual) 2,125 2,009 1,916 1,693

Note: The emissions data presented here are EPA modeling results. “CAIR +BART” national emissions assume

BART applied only in those States not included in CAIR for PM.

7.3 Projected Costs, Control Technology, and Fuel Costs

For the modeled region, EPA projects that the annual incremental costs of Scenario 2

BART are $97 million in 2010 and $436 million in 2015 (in $1999, see Table 7-5). In 2020, the

annual costs are $439 million. Costs under Scenario 3 were at $896 million in 2015, while they

are $253 million in 2015 under Scenario 1.

Table 7-5. Annualized Cost of BART

Annualized Cost ($1999 millions) 2010 2015 2020

Scenario 1 $87 $253 $283

Scenario 2 $97 $436 $429

Scenario 3 $92 $896 $853

Source: Integrated Planning Model run by EPA.

Note: Changes occur in 2010 as people react to the policy announcement, in anticipation of future effects.

It should be kept in mind that the cost of electricity generation represents roughly onethird to one-half of total electricity costs, with transmission and distribution costs representing

the remaining portion. The impact of this rule on retail electricity prices, faced by consumers, is

shown in a later table. 

Under Scenario 2, BART is projected to result in the installation of an additional 6.2 GW

of flue gas desulfurization (scrubbers) on existing coal-fired generation capacity for SO2 control

in 2015 (relative to CAIR). For NOx control, this BART scenario is also projected to result in an

additional 24 GW of combustion control equipment and 2.4 GW of selective catalytic reduction

technology (SCR) on cyclone-boiler coal-fired generation capacity by 2015 (see Table 7-6). 
7-8

Table 7-6. National Pollution Controls by Technology under BART (GW)

Incremental Retrofits under BART

Scenario 1 Scenario 2 Scenario 3

Technology 2010 2015 2020 2010 2015 2020 2010 2015 2020

Combustion

Controls

19.0 24.0 26.0

Scrubbers 0.1 3.4 4.3 0.0 6.2 5.8 0.2 7.7 6.9

SCR 0.1 0.2 0.3 0.4 2.4 2.6 0.3 27.2 27.3

Note: Numbers may not add due to rounding. CAIR retrofits (CAIR is included in the baseline) include existing

scrubbers and SCR as well as additional retrofits for NSR settlements and various State rules.

Source: Integrated Planning Model run by EPA.

Coal-fired generation (as well as coal production) and natural gas-fired generation under

BART are projected to remain essentially unchanged in 2010 and 2015 relative to CAIR levels. 

None of the BART scenarios are expected to cause any additional capacity to be uneconomic to

maintain. It is also not expected that BART will change the composition of new generation built

to meet future growth in electric demand. BART is not expected to affect coal prices or natural

gas prices.

7.4 Projected Retail Electricity Prices

Retail electricity prices are projected to increase by small amounts in the regions of the

country that are modeled as affected by BART, such as MAPP, SPP, and RM. Electricity prices

in the CAIR region are expected to be essentially unchanged with implementation of BART

controls outside the borders of CAIR (see Table 7-7 and Figure 7-2).
7-9

Figure 7-2. NERC Power Regions

Table 7-7. Retail Electricity Prices by NERC Region with CAIR and with BART

(Mills/kWh)

Power CAIR

Scenario 2

BART

Percentage Price

Change

Region Main States Included 2000 2015 2020 2015 2020 2015 2020

ECAR OH, MI, IN, KY, WV, PA 57.4 58.5 58.0 58.4 58.0 –0.1% 0.0%

ERCOT TX 65.1 64.6 63.3 64.6 63.3 0.1% 0.0%

MAAC PA, NJ, MD, DC, DE 80.4 71.7 72.8 71.7 72.8 –0.1% 0.1%

MAIN IL, MR, WI 61.2 60.3 62.0 60.2 62.0 –0.1% 0.0%

MAPP MN, IA, SD, ND, NE 57.4 49.6 48.0 50.3 48.7 1.5% 1.5%

NY NY 104.3 88.8 88.4 88.8 88.4 0.0% 0.0%

NE VT, NH, ME, MA, CT, RI 89.9 84.7 83.0 84.8 83.0 0.1% 0.0%

FRCC FL 67.9 72.3 70.5 72.3 70.5 0.0% 0.0%

STV VA, NC, SC, GA, AL, MS, TN,

AR, LA

59.3 56.2 56.6 56.2 56.6 –0.1% –0.1%

SPP KS, OK, MR 59.3 57.5 57.0 57.9 57.4 0.7% 0.7%

PNW WA, OR, ID 45.9 47.5 46.9 47.5 46.9 0.0% 0.0%

RM MT, WY, CO, UT, NM, AZ, NV,

ID

64.1 65.6 65.4 65.9 65.7 0.5% 0.4%

CALI CA 94.7 99.1 99.5 99.1 99.5 0.0% 0.0%

NATIONAL Contiguous Lower 48 States 66.0 64.4 64.3 64.5 64.4 0.1% 0.1%

Source: EPA’s Retail Electricity Price Model. 2000 prices are from EIA’s AEO 2003.
7-10

States controlled for both SO2 and NOx

States not covered by CAIR

States controlled for Ozone Season NOx

Figure 7-3. 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.

7.5 Key Differences in EPA Model Runs for Final BART Modeling

As previously stated, the emissions, cost, air quality, and benefits analyses done for the

final BART are from a modeling scenario that assumes CAIR to require 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 what would reflect

final BART, in that Arkansas, Delaware, and New Jersey are not included in the annual SO2 and

NOx requirements; thus, they would no longer be considered part of a trading program that is

better than source-specific BART. BART-eligible EGUs in these States would consequently be

modeled to comply with source-specific BART (see Figure 7-3). Additionally, the final BART

modeling should reflect the Clean Air Mercury Rule, which imposes a 38-ton mercury cap by

2010 and a 15-ton cap by 2018.

All IPM runs done in support of BART and used as part of the final BART package are

in the final CAIR docket and can be found on EPA’s Web site: http://www.epa.gov/airmarkets/

epa-ipm/iaqr.html. A complete list of IPM runs can be found in Appendix D of this RIA.

7.6 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 generally get

particular attention because of the importance of these two key model inputs to the power sector. 
7-11

However, for this rule (which is not market based and involves source-specific installation of

controls) EPA believes that scenarios with higher gas price and demand assumptions would not

change the assessment of costs. 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 BART 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 BART 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 BART. 

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 BART (3 percent and 7 percent and a 30-year life of capital). The cost

of added insurance necessary because of BART was included in the calculations, but local taxes

were not included because they are considered to be transfer payments and not a social cost. 

Using these discount rates, the social costs of Scenario 2 BART are $248 million in 2015 (with

$119 million for Scenario 1 BART and $567 million for Scenario 3 BART) using a discount rate

of 3 percent. Using a discount rate of 7 percent, the social costs of Scenario 2 BART are $297

million in 2015 (with $141 million for Scenario 1 BART and $688 million for Scenario 3

BART).

The annualized regional cost of BART, as quantified here, is EPA’s best assessment of

the cost of implementing source-specific BART in the non-CAIR region. These costs are

generated from rigorous economic modeling of changes in the power sector due to BART. 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
3

 2010 Phase II cost estimate in $1995.

7-12

sources, and additional fuel expenditures. EPA believes that the cost assumptions used for

BART reflect, as closely as possible, the best information available to the Agency today. 

Furthermore, EPA wants to identify some unquantified costs as limits to its analysis. 

These costs include the costs of state administration of the program, which we believe are

modest. There also may be unquantified costs of transitioning to BART, such as the costs

associated with the possible retirement of smaller or less-efficient EGUs and employment shifts

as workers are retrained at the same company or reemployed elsewhere in the economy.

Cost estimates for BART are based on results from ICF’s IPM. 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 BART

requirements at least cost. 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. 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 EPA (see Carlson et al. [2000] and 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 billion3 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 the possibility of technological improvement, EPA’s mobile source program

uses adjusted engineering cost estimates of pollution control equipment and installation costs to
4

 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]).

5

 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]).

6

 The degree of substitution/curtailment depends on the price elasticity of demand for electricity.

7-13

account for this fact, which EPA has not done in this case.4 It is expected that a cap and trade

approach to BART would provide greater incentives for technology innovations than the

command and control approach analyzed here.

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.5

 In addition, EPA also conducted a survey

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). Any increased retail electricity prices

would prompt end users to curtail (to some extent) their use of electricity and encourage them to

use substitutes.6 The response would lessen the demand for electricity, resulting in electricity

price increases even 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 that we

would anticipate with BART. Although the reduction in electricity use is likely to be very small,

the cost savings from such a large industry ($250 billion in revenues in 2003) may be substantial. 

Recent research suggests that the total social costs of a new regulation may be affected

by interactions between the new regulation and preexisting distortions in the economy, such as

taxes. In particular, if cost increases due to a regulation are reflected in a general increase in the
7-14

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

analytical guidelines that discuss the tax interaction effect and its potential relevance for

estimating federal program costs and benefits. The limited empirical data available to support

quantification of any such effect lead to this qualitative identification of the costs.

On balance, after considering 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.

7.7 IPM Runs for CAIR Better-than-BART Determination

The IPM runs used in the final determination that CAIR is better than BART differed

slightly from the IPM runs used in this RIA. The IPM runs used for the final CAIR-is-betterthan-BART determination reflect the final CAIR region (New Jersey, Delaware, and Arkansas

affected only by the CAIR ozone season program). This change was not included in the IPM

runs used for this RIA because of time constraints. As discussed previously, EPA does not

believe this modification would have had a significant impact on the RIA. See the CAIR-isbetter-than-BART determination section of this rulemaking for further discussion.

7.8 References

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.

Manson, Nelson, and Neumann. 2002. “Assessing the Impact of Progress and Learning Curves

on Clean Air Act Compliance Costs.” Industrial Economics Incorporated.
8-1

SECTION 8

RESULTS OF COST, EMISSIONS REDUCTIONS, AND ECONOMIC IMPACT

ANALYSES FOR NONELECTRICITY GENERATING UNITS

This chapter presents the results of the cost analyses for the non-EGU sources in

BART source categories covered in these analyses. The cost analyses evaluate the potential

impacts associated with the final BART rule, based on assumptions about how the States

may implement controls for non-EGU sources. As mentioned in Chapter 3, there are 25 nonEGU source categories that are to be considered for controls associated with the BART

program. Section 8.1 presents a short summary of the results of the impact analyses. 

Section 8.2 provides summaries of the results of these analyses across sources categories. 

Section 8.3 presents a description of the methodological approach for these cost analyses, the

scenarios that are analyzed, and important assumptions and details that underlie the costs and

emission reductions for each scenario. Section 8.4 mentions the control technologies that are

applied to the non-EGU source categories in this analysis. Section 8.5 provides a list of the

source categories affected and summaries of analysis results by BART source category. 

Section 8.6 presents a listing of caveats and limitations associated with the cost analyses. 

More detailed results for the scenarios considered are provided in the technical support

document for these analyses (E.H. Pechan, 2005) and in Appendix B of this RIA.

8.1 Results in Brief

The results for applying the scenarios examined for controlling 2015 SO2 and NOx

emissions at non-EGU BART-eligible units range from 83,778 to 378,169 tons of SO2 and

from 165,634 to 391,101 tons of NOx nationwide for costs annualized at a 7 percent discount

rate. For costs annualized at a 3 percent discount rate, the reduction of SO2 emissions in

2015 ranges from 132,279 to 373,797 tons and the reduction of NOx emissions in 2015

ranges from 246,607 to 393,349 tons. Annualized costs associated with these reductions in

2015 range from $151.43 million to $2.24 billion (1999$) for costs estimated at a 7 percent

discount rate and from $272.23 million to $1.8 billion (1999$) for costs estimated at a 3

percent discount rate. The capital costs associated with these reductions in 2015 range from

$655.70 million to $14.75 billion for costs estimated at a 7 percent discount rate and from

$1.9 billion to $12.73 billion.
1

 Analysis of non-EGU sources considers the application of controls at the source category level up to a

specified average cost per ton cutoff. 

8-2

8.2 Summary of Results for Nonelectricity Generating Sources

In this RIA chapter, three illustrative scenarios are applied to non-EGU BARTeligible sources. Costs and emissions reductions for these sources are estimated for these

annualized costs per ton scenarios for reducing SO2 and NOx at varying levels of stringency: 

$1,000, $4,000, and $10,000.1 These scenarios, called Scenarios 1, 2, and 3 respectively, are

applied nationwide and are presented in detail in this chapter. These scenarios are meant to

be illustrative of the potential alternatives that may be available to States as they consider

what scenarios to include in their SIPs for non-EGU sources. These scenarios are also

compliant with the requirement in OMB Circular A-4 to examine alternative levels of

stringency as part of an RIA. Detailed results for two other illustrative scenarios, annualized

cost per ton of $2,000 and $3,000, are found in Appendix G. 

This section includes several summary tables in which the emission reductions and

costs for all non-EGU scenarios applied are shown by source category and also by the

discount rate for the annualized costs. Table 8-1 summarizes the SO2 emission reductions

for the analyses to the BART non-EGU source categories using a discount rate of 7 percent

and also showing results using an discount rate of 3 percent. In total, the scenarios applied in

this analysis lead to nationwide SO2 emission reductions ranging from 83,778 tons to

378,169 tons with costs at a 7 percent discount rate. These scenarios lead to SO2 emission

reductions ranging from 132,280 to 373,798 tons with costs at a 3 percent rate. These

represent a reduction of 11 to 30 percent from the 2015 baseline. For scenario 2, the SO2

emission reduction estimate is 269,992 tons or a 22 percent reduction from the 2015 baseline

with costs at a 7 percent discount rate and 290,591 tons or a 24 percent reduction from the

2015 baseline with costs at a 3 percent discount rate. 

Table 8-2 summarizes the NOx emission reductions. The nationwide NOx emission

reductions from applying these three scenarios range from 165,634 tons to 391,101 tons with

costs at a 7 percent discount rate. These represent a reduction of 24 to 57 percent from the

2015 baseline. These scenarios lead to NOx emission reductions ranging from 246,067 to

393,349 tons with costs at a 3 percent rate. These represent a reduction of 36 to 58 percent

from the 2015 baseline. For scenario 2, the NOx emission reduction estimate is 361,152 tons 
8-3

Table 8-1. SO2 Emissions and Emission Reductions for BART Source Categories in

2015

BART Source

Category

Baseline

Emissions

(tons)

Discount

Rate

Scenarios—Reductions (tons)

Scenario 1 Scenario 2 Scenario 3

Industrial boilers 420,782 7% 32,213 151,387 200,308

420,782 3% 65,783 164,438 204,217

Petroleum refineries 199,483 7% 2,097 31,319 56,462

199,483 3% 17,033 41,911 58,525

Kraft pulp mills 119,818 7% 0 10,814 28,380

119,818 3% 0 3,196 14,610

Portland cement plants 116,835 7% 0 13,383 26,716

116,835 3% 0 18,326 30,690

Hydrofluoric, sulfuric,

and nitric acid plants

96,741 7% 34,140 36,753 36,753

96,741 3% 34,140 36,753 36,753

Chemical process plants 47,700 7% 0 2,376 3,571

47,700 3% 0 2,376 3,571

Iron and steel mills 23,541 7% 0 2,914 2,914

23,541 3% 0 2,914 2,914

Coke oven batteries 9,815 7% 0 4,088 6,107

9,815 3% 0 3,724 5,564

Sulfur recovery plants 59,766 7% 13,697 13,697 13,697

59,766 3% 13,693 13,693 13,693

Primary aluminum ore

reduction plants

47,552 7% 1,630 3,260 3,260

47,552 3% 1,630 3,260 3,260

Lime kilns 9,373 7% 0 0 0

9,373 3% 0 0 0

(continued)
8-4

Table 8-1. SO2 Emissions and Emission Reductions for BART Source Categories in

2015 (continued)

BART Source

Category

Baseline

Emissions

(tons)

Discount

Rate

 Scenarios—Reductions (tons)

Scenario 1 Scenario 2 Scenario 3

Glass fiber processing

plants

2,170 7% 0 0 0

2,170 3% 0 0 0

Municipal incinerators 284 7% 0 0 0

284 3% 0 0 0

Coal cleaning plants 1,530 7% 0 0 0

1,530 3% 0 0 0

Carbon black plants 41,853 7% 0 0 0

41,853 3% 0 0 0

Phosphate rock

processing plants

21 7% 0 0 0

21 3% 0 0 0

Secondary metal

production facilities

9,988 7% 0 0 0

9,988 3% 0 0 0

Total 1,208,088 7% 83,778 236,992 378,169

1,208,088 3% 132,279 290,591 373,797

or a 53 percent reduction from the 2015 baseline with costs at a 7 percent discount rate and

390,871 tons or a 57 percent reduction from the 2015 baseline with costs at a 3 percent

discount rate. 

Table 8-3 summarizes the annualized costs associated with the three non-EGU

scenarios. In total, the three scenarios applied in this analysis have annualized costs of

$151.43 million to $2.2 billion (1999$) with costs at a 7 percent discount rate and $272.34

million to $1.8 billion (1999$) with costs at a 3 percent discount rate. The capital costs for

these three scenarios range from $655.70 million to $14.75 billion for costs estimated at a 7

percent discount rate and from $1.9 billion to $12.73 billion for costs estimated at a 3 percent

discount rate. More detailed capital cost information for these scenarios can be found in 
8-5

Table 8-2. NOx Emissions and Emission Reductions for BART Source Categories in

2015

BART Source

Category

Baseline

Emissions

(tons)

Discount

Rate

Scenarios—Reductions (tons)

Scenario 1 Scenario 2 Scenario 3

Industrial boilers 217,063 7% 67,325 130,456 130,522

217,063 3% 111,939 130,424 130,505

Petroleum refineries 86,566 7% 4,035 47,556 51,316

86,566 3% 4,579 51,259 53,005

Kraft pulp mills 103,614 7% 35,249 61,418 65,776

103,614 3% 53,729 65,792 65,797

Portland cement plants 120,567 7% 19,276 54,601 71,921

120,567 3% 30,903 71,921 71,921

Hydrofluoric, sulfuric,

and nitric acid plants

17,059 7% 11,276 11,283 11,283

17,059 3% 11,276 11,283 11,283

Chemical process plants 72,577 7% 23,970 34,636 34,801

72,577 3% 27,142 34,801 34,801

Iron and steel mills 20,963 7% 1,036 6,997 8,507

20,963 3% 1,036 8,500 8,673

Coke oven batteries 10,389 7% 0 5,768 5,768

10,389 3% 0 5,768 5,768

Sulfur recovery plants 651 7% 135 141 141

651 3% 135 141 141

Primary aluminum ore

reduction plants

1,676 7% 50 253 255

1,676 3% 50 253 255

Lime kilns 12,849 7% 2,683 4,471 7,153

12,849 3% 4,471 7,153 7,153

(continued)
8-6

Table 8-2. NOx Emissions and Emission Reductions for BART Source Categories in

2015 (continued)

BART Source

Category

Baseline

Emissions

(tons)

Discount

Rate

Scenarios—Reductions (tons)

Scenario 1 Scenario 2 Scenario 3

Glass fiber processing

plants

6,677 7% 568 2,116 2,198

6,677 3% 775 2,116 2,198

Municipal incinerators 1,656 7% 0 744 744

1,656 3% 0 744 744

Coal cleaning plants 1,110 7% 0 511 511

1,110 3% 0 511 511

Carbon black plants 4,645 7% 7 120 120

4,645 3% 7 120 120

Phosphate rock

processing plants

719 7% 0 45 48

719 3% 0 48 48

Secondary metal

production facilities

1,377 7% 25 34 35

1,377 3% 25 34 35

Total 681,765 7% 165,634 361,152 391,101

681,765 3% 246,067 390,871 393,349

Appendix B. In addition, Appendix B contains sensitivity analyses that provide capital cost

estimates based on a 10 percent rate for annualizing costs, and also other sensitivity analyses

in that appendix examine the effects of variation in labor and energy rates on the costs. 

Finally, information on the number of affected BART-eligible units by scenario, pollutant,

and source category are provided in Appendix B. 

Given the highly capital-intensive nature of the control measures included in these

analyses, it is not unreasonable that a lower discount rate would lead to more application of

these measures to reduce SO2 and NOx and vice versa. More sources would be controlled

that may not be able to control if they face relatively high interest rates for capital outlays in

pollution control equipment. At scenario 1, the annualized costs and emission reductions are

higher with a 3 percent discount rate than a 7 percent discount rate because the lower 
8-7

Table 8-3. Total Annualized Costs of Control for BART Source Categories in 2015

(million 1999$)

BART Source Category

Discount

Rate

Scenarios 

Scenario 1 Scenario 2 Scenario 3

Industrial boilers 7% 74.6 527.6 845.4

3% 135.0 401.8 643.3

Petroleum refineries 7% 4.2 180.2 428.6

3% 15.3 214.8 394.8

Kraft pulp mills 7% 20.7 141.7 313.3

3% 45.6 106.3 168.5

Portland cement plants 7% 2.9 174.8 409.5

3% 20.8 269.0 346.1

Hydrofluoric, sulfuric, and nitric acid

plants

7% 18.0 23.4 23.4

3% 16.4 21.4 21.4

Chemical process plants 7% 14.4 70.8 87.8

3% 21.1 54.4 77.1

Iron and Steel mills 7% 0.6 23.5 33.2

3% 0.4 22.7 32.4

Coke oven batteries 7% 0.0 18.7 33.8

3% 0.0 14.9 25.0

Sulfur Recovery plants 7% 11.7 12.1 12.1

3% 11.7 12.1 12.1

Primary aluminum ore reduction plants 7% 1.7 7.76 7.82

3% 1.0 5.0 5.0

Lime kilns 7% 2.0 5.0 31.8

3% 4.3 25.4 25.4

(continued)
8-8

Table 8-3. Total Annualized Costs of Control for BART Source Categories in 2015

(million 1999$) (continued)

BART Source Category

Discount

Rate

Scenarios

Scenario 1 Scenario 2 Scenario 3

Glass fiber processing plants 7% 0.5 5.3 7.8

3% 0.7 4.8 6.8

Municipal incinerators 7% 0.0 1.1 1.1

3% 0.0 0.9 0.9

Coal cleaning plants 7% 0.0 1.0 1.0

3% 0.0 0.8 0.8

Carbon black plants 7% 0.01 0.2 0.2

3% 0.005 0.1 0.1

Phosphate rock processing plants 7% 0.0 0.1 0.2

3% 0.0 0.2 0.2

Secondary metal production facilities 7% 0.02 0.04 0.1

3% 0.0 0.0 0.0

Total 7% $151.43 $1,193.24 $2,237.24

3% $272.34 $1,157.09 $1,770.21

discount rate leads to more sources having available controls under that scenario. At

scenario 2, the annualized costs and reductions are relatively close because the controls

available to sources are about the same. At scenario 3, the available controls are about

identical regardless of the discount rate, but the costs are lower for the 3 percent discount rate

because of the lower capital costs overall. 

Tables 8-4 through 8-6 summarize the results for the $2,000/ton and $3,000/ton nonEGU scenarios in the same way as for Scenarios 1 through 3. Table 8-4 summarizes the SO2

emission reductions from applying these two BART non-EGU source categories using a

discount rate of 7 percent and also showing results using an discount rate of 3 percent. In 
8-9

Table 8-4. SO2 Emissions and Emission Reductions for BART Source Categories in

2015

BART Source Category

Baseline

Emissions (tons) Discount Rate

Scenarios—Reductions (tons)

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Industrial boilers 420,782 7% 87,009 124,592

420,782 3% 120,095 148,962

Petroleum refineries 199,483 7% 23,173 23,173

199,483 3% 25,103 33,304

Kraft pulp mills 119,818 7% 0 0

119,818 3% 0 3,196

Portland cement plants 116,835 7% 2,350 2,350

116,835 3% 2,743 13,383

Hydrofluoric, sulfuric, and

nitric acid plants

96,741 7% 35,358 36,753

96,741 3% 36,753 36,753

Chemical process plants 47,700 7% 2,376 2,376

47,700 3% 2,376 2,376

Iron and steel mills 23,541 7% 2,914 2,914

23,541 3% 2,914 2,914

Coke oven batteries 9,815 7% 4,088 4,088

9,815 3% 4,088 4,088

Sulfur recovery plants 59,766 7% 13,697 13,697

59,766 3% 14,311 14,311

Primary aluminum ore

reduction plants

47,552 7% 1,630 1,630

47,552 3% 1,630 3,260

Lime kilns 9,373 7% 0 0

9,373 3% 0 0

(continued)
8-10

Table 8-4. SO2 Emissions and Emission Reductions for BART Source Categories in

2015 (continued)

BART Source Category

Baseline

Emissions (tons) Discount Rate

 Scenarios—Reductions (tons)

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Glass fiber processing plants 2,170 7% 0 0

2,170 3% 0 0

Municipal incinerators 284 7% 0 0

284 3% 0 0

Coal cleaning plants 1,530 7% 0 0

1,530 3% 0 0

Carbon black plants 41,853 7% 0 0

41,853 3% 0 0

Phosphate rock processing

plants

21 7% 0 0

21 3% 0 0

Secondary metal production

facilities

9,988 7% 0 0

9,988 3% 0 0

Total 1,208,088 7% 172,595 211,573

1,208,088 3% 210,013 262,547

total, the scenarios applied in this analysis lead to nationwide SO2 emission reductions

ranging from 172,595 tons to 211,573 tons with costs at a 7 percent discount rate. These

estimates represent a reduction of 14 to 18 percent from the 2015 baseline. These scenarios

lead to SO2 emission reductions ranging from 210,013 to 262,547 tons with costs at a 3

percent rate. These estimates represent a reduction of 17 to 22 percent from the 2015

baseline. 

Table 8-5 summarizes the NOx emission reductions for these two non-EGU scenarios. 

The nationwide NOx emission reductions from applying these three scenarios range from

242,355 tons to 291,740 tons with costs at a 7 percent discount rate. These represent a

reduction of 36 to 43 percent from the 2015 baseline. These scenarios lead to NOx emission

reductions ranging from 280,163 to 313,382 tons with costs at a 3 percent rate. These

represent a reduction of 41 to 46 percent from the 2015 baseline. 
8-11

Table 8-5. NOx Emissions and Emission Reductions for BART Source Categories in

2015

BART Source Category

Baseline

Emissions (tons) Discount Rate

Scenarios 

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Industrial boilers 217,063 7% 97,074 125,575

217,063 3% 120,151 128,640

Petroleum refineries 86,566 7% 23,173 23,173

86,566 3% 23,173 26,685

Kraft pulp mills 103,614 7% 50,221 60,985

103,614 3% 56,466 64,521

Portland cement plants 120,567 7% 26,659 26,659

120,567 3% 26,659 26,659

Hydrofluoric, sulfuric, and

nitric acid plants

17,059 7% 11,283 11,283

17,059 3% 11,283 11,283

Chemical process plants 72,577 7% 25,922 26,753

72,577 3% 27,568 31,567

Iron and steel mills 20,963 7% 2,034 3,259

20,963 3% 2,038 7,198

Coke oven batteries 10,389 7% 0 5,768

10,389 3% 5,768 5,768

Sulfur recovery plants 651 7% 0 0

651 3% 0 0

Primary aluminum ore

reduction plants

1,676 7% 70 253

1,676 3% 335 335

Lime kilns 12,849 7% 4,471 4,471

12,849 3% 4,471 7,153

(continued)
8-12

Table 8-5. NOx Emissions and Emission Reductions for BART Source Categories in

2015 (continued)

BART Source Category

Baseline

Emissions (tons) Discount Rate

Scenarios 

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Glass fiber processing plants 6,677 7% 568 2,116

6,677 3% 851 2,116

Municipal incinerators 1,656 7% 744 744

1,656 3% 744 744

Coal cleaning plants 1,110 7% 0 511

1,110 3% 511 511

Carbon black plants 4,645 7% 111 120

4,645 3% 111 120

Phosphate rock processing

plants

719 7% 0 45

719 3% 0 48

Secondary metal production

facilities

1,377 7% 25 25

1,377 3% 34 34

Total 681,765 7% 242,355 291,740

681,765 3% 280,163 313,382

Table 8-6 summarizes the annualized costs associated with these two non-EGU

scenarios. In total, the two scenarios applied in this analysis have annualized costs of

$512.36 million to $706.26 million (1999$) with costs at a 7 percent discount rate and

$507.23 million to $691.73 million (1999$) with costs at a 3 percent discount rate. 

Given the highly capital-intensive nature of the control measures included in these

analyses, it is not unreasonable that a lower discount rate would lead to more application of

these measures to reduce SO2 and NOx and vice versa. More sources would be controlled

that may not be able to control if they face relatively high interest rates for capital outlays in

pollution control equipment. At the $2,000/ton scenario, the emission reductions are higher

with a 3 percent discount rate than a 7 percent discount rate because the lower discount rate 
8-13

Table 8-6. Total Annualized Costs of Control for BART Source Categories in 2015

(million 1999$)

BART Source Category Discount Rate

Scenarios 

$2,000/ton

Scenario

$3,000/ton

Scenario 

Industrial boilers 7% 241.5 412.1

3% 255.0 337.4

Petroleum refineries 7% 71.1 71.1

3% 71.1 81.2

Kraft pulp mills 7% 75.1 75.1

3% 59.2 68.5

Portland cement plants 7% 29.6 29.6

3% 28.7 56.6

Hydrofluoric, sulfuric, and nitric acid plants 7% 20.4 21.4

3% 21.4 21.4

Chemical process plants 7% 40.5 40.5

3% 30.4 40.1

Iron and Steel mills 7% 7.9 11.0

3% 5.7 22.7

Coke oven batteries 7% 6.2 18.7

3% 14.9 14.9

Sulfur Recovery plants 7% 11.7 12.1

3% 12.1 12.1

Primary aluminum ore reduction plants 7% 1.7 2.2

3% 1.0 5.0

Lime kilns 7% 5.0 5.0

3% 4.3 25.4

(continued)
8-14

Table 8-6. Total Annualized Costs of Control for BART Source Categories in 2015

(million 1999$) (continued)

BART Source Category Discount Rate

Scenarios

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Glass fiber processing plants 7% 0.5 5.3

3% 1.7 4.7

Municipal incinerators 7% 1.1 1.1

3% 0.9 0.9

Coal cleaning plants 7% 0.0 1.0

3% 0.8 0.8

Carbon black plants 7% 0.01 0.01

3% 0.005 0.01

Phosphate rock processing plants 7% 0.01 0.01

3% 0.01 0.01

Secondary metal production facilities 7% 0.04 0.04

3% 0.01 0.01

Total 7% $512.36 $706.26

3% $507.23 $691.73

leads to more sources having available controls under that scenario and the costs are fairly

close. At $3,000/ton scenario, the annualized costs and reductions are relatively closer. More

details on these results can be found in Appendix G.

8.3 Costs and Analysis Approach

The potential costs of complying with the BART rule estimated in this chapter are

those from installation, operation, and maintenance of control devices that may be applied in

response to the provisions of SIP) that may require controls of these non-EGU sources. In

addition to these analyses, results from analyses in which important components of the costs

such as discount rates, labor rates, and energy rates are varied to determine the sensitivity of

the costs to such variation. We present such results in Appendix B. Costs from monitoring,
2

For more information on the emissions and control measures within AirControlNET, go to

www.epa.gov/ttn/ecas/AirControlNET.htm. Documentation for emissions data and control measures can be

found at this Web site.

8-15

record keeping, and reporting are not included in this cost analysis because these costs are

accounted for in the Regional Haze ICR.

Two types of costs will be incurred in association with the addition of control

technologies: a one-time capital cost for new equipment installation and increased annual

operating and maintenance costs. In general, economies of scale exist for pollution control

technologies for both capital costs and operating and maintenance costs. Thus, the size of

the unit to which controls are applied will determine, in part, the cost of implementing the

pollution control(s).

For each affected source category, EPA’s estimates of emissions reductions and costs

reflect the application of controls within AirControlNET, the Agency’s tool for estimating

impacts from control strategies applied to non-EGU criteria pollutant sources. 

AirControlNET can estimate costs and emission reductions from control strategies for

various average cost-effectiveness levels and for varying geographic scales (nationally,

regionally, and locally).2 For this analysis, we applied in AirControlNET control measures

up to the given average annualized cost per ton cutoff for SO2 and NOx to the BART-eligible

units within the non-EGU dataset described in Chapter 3. These analyses are calculated for

control measures applied to the 2015 emissions inventory, which is a product of growing the

emissions from the non-EGU dataset, a database with 2001 emissions in it. The procedure

for growing the emissions in that database to 2015 is also described in Chapter 3. 

Costs presented in this chapter are estimated at a 3 percent and 7 percent discount rate

for purposes of annualizing capital costs consistent with EPA and OMB guidelines for

preparing economic analyses (EPA, 2000; OMB, 2003). Equipment lives and control

efficiencies for each control technology are taken from the AirControlNET control measures

documentation report. Annual costs are estimated in 1999 dollars. All costs are converted

from the original source year to 1999 dollars using the gross domestic product (GDP) price

deflator. 
3

 The terms “ICI boiler” and “industrial boiler” are used interchangeably in this RIA.

8-16

8.4 Types of Emissions Control Technologies Employed in These Analyses

A number of technologies are commonly employed to reduce SO2 and NOx emissions

from the non-EGU source categories. This section of the chapter covers many of the

technologies employed in reducing these pollutants. 

8.4.1 SO2 Emissions Control Technologies

This section describes available technologies for controlling emissions of SO2 for

industrial, commercial, and institutional (ICI) boilers3 and other non-EGU source categories.

In general, FGD scrubbers are applied most commonly as the control technology for

industrial boilers and many other non-EGU sources because of their possible application to

most any industrial boiler and other combustion source application. Other issues involved in

choosing a control technology include ease of retrofit and reduction performance. While all

controls presented in this analysis are considered generally technically feasible for each class

of sources, source-specific cases may exist where a control technology is in fact not

technically feasible. In their response to the BART rule, States should consider case-specific

feasibility when establishing control requirements.

8.4.1.1 SO2 Control Technology for Non-EGU Sources

For industrial boilers, FGD scrubbers are the only technology available in our data. 

This is not to say that other technologies are not available or that a technique such as

switching from high-sulfur coal (e.g., 3 percent sulfur content by weight) to lower-sulfur coal

(e.g., 1 percent sulfur content by weight) could not be employed to achieve SO2 reductions,

but data for such technologies or technique were not available for this analysis. FGD

scrubbers are also used on units at petroleum refineries, kraft pulp mills, and Portland cement

kilns. For other BART source categories, other types of control technologies were available

that are more specific to the sources controlled. Table 8-7 lists these technologies. For more

information on these technologies, please refer to the AirControlNET 4.0 control measures

documentation report.
8-17

Table 8-7. Available SO2 Control Technologies for Industrial Boilers and Other NonEGU Sources

Source Type/Fuel Type Available Control Technology

ICI boilers—all fuel types, kraft pulp mills,

Portland cement plants (all fuel types)

FGD scrubbers

Hydrofluoric, sulfuric, and nitric acid Increase percentage sulfur conversion to meet

sulfuric acid NSPS (99.7% reduction)

Sulfur recovery plants Sulfur recovery and/or tail gas treatment 

Coke oven batteries Vacuum carbonate + sulfur recovery plant

Source: AirControlNET control measures documentation report.

8.4.2 NOx Emissions Control Technologies

This section describes available technologies for controlling emissions of NOx for ICI

boilers and other non-EGU sources. In general, low-NOx burners (LNB) are often applied as

a control technology for industrial boilers and many other non-EGU sources because of their

possible application to almost any industrial boiler and other combustion source application. 

Other issues involved in choosing a control technology include ease of retrofit and reduction

performance. While all controls presented in this analysis are considered generally

technically feasible for each class of sources, source-specific cases may exist where a control

technology is in fact not technically feasible. In their response to the BART rule, States

should consider case-specific feasibility when establishing control requirements.

8.4.2.1 NOx Control Technology for Non-EGU Sources

Several types of control technologies are considered for industrial boilers: SCR,

selective noncatalytic reduction (SNCR), natural gas reburn (NGR), coal reburn, and lowNOx burners. As stated above, the control technology chosen most often was LNB because

of its breadth of application. In some cases, LNB accompanied by flue gas reburning (FGR)

is applicable, such as when fuel-borne NOx emissions are expected to be of greater

importance than thermal NOx emissions. When circumstances suggest that combustion

controls do not make sense as a control technology (e.g., sintering processes, coke oven

batteries, sulfur recovery plants), SNCR or SCR may be an appropriate choice. 
8-18

Table 8-8 lists the control technologies available for industrial boilers and other nonEGU sources by type of fuel. For more information on these technologies, please refer to the

AirControlNET 4.0 control measures documentation report.

Table 8-8. Available NOx Control Technologies for Industrial Boilers

Source Type/Fuel Type Available Control Technology

ICI boilers—coal/wall SNCR, LNB, SCR

ICI boilers—coal/FBC (fluidized bed combustor) SNCR—urea based

ICI boilers—coal/stoker SNCR—urea based

ICI boilers—coal/cyclone SNCR, Coal Reburn, NGR, SCR

ICI boilers—residual oil LNB, SNCR, LNB + FGR, SCR

ICI boilers—distillate oil LNB, SNCR, LNB + FGR, SCR

ICI boilers—natural gas LNB, SNCR, LNB + FGR, OT + WI, SCR

ICI boilers—process gas LNB, LNB + FGR, OT + WI, SCR

ICI boilers—coke SNCR, LNB, SCR

ICI boilers—LPG (liquid petroleum gas) LNB, SNCR, LNB + FGR, SCR

Source: AirControlNET control measures documentation report.

8.4.2.2 NOx Control Technology for Other Non-EGU BART Source Categories

Other non-EGU source categories covered in the analysis include petroleum

refineries, kraft pulp mills, and cement kilns. NOx control technologies available for

petroleum refineries, particularly process heaters at these plants, include LNB, SNCR, FGR,

and SCR along with combinations of these technologies. NOx control technologies available

for kraft pulp mills include those available to industrial boilers, namely LNB, SCR, SNCR,

along with water injection (WI). NOx control technologies available for cement kilns include

those available to industrial boilers, namely LNB, SCR, and SNCR. In addition, mid-kiln

firing (MKF) and ammonia-based SNCR can be used on cement kilns where appropriate. 

Table 8-9 lists the control technologies available for these categories. For more information

on these technologies, please refer to the AirControlNET 4.0 control measures

documentation report.
8-19

Table 8-9. Available NOx Control Technologies for Other Non-EGU Source Categories

Other than Industrial Boilers

Source Type/Fuel Type Available Control Technology

Petroleum refineries (process

heaters—process gas, distillate oil)

LNB + FGR, SNCR, LNB + SNCR, SCR, LNB +

SCR

Kraft pulp mills LNB, SNCR, SCR, LNB + SNCR, SCR + WI

Cement manufacturing—dry MKF, LNB, SNCR—urea based, SNCR—ammonia

based, SCR

Cement manufacturing—wet MKF, LNB, SCR, biosolid injection

In-process; bituminous coal; cement kilns SNCR—urea based

Chemical process plants LNB, SNCR, SCR, SCR + WI

Lime kilns SCR

Iron and steel mills LNB, SNCR, LNB + SNCR, LNB + FGR, SCR

Source: AirControlNET control measures documentation report.

8.5 Listing of Affected Source Categories and Results for Each

We present below results for each BART source category affected in the analyses. 

Results presented here reflect the use of 7 percent and 3 percent discount rates as part of the

control strategy analysis for each scenario. There are no impacts for 8 of the 25 non-EGU

source categories because there are no control measures available to reduce SO2 and NOx

from these categories within AirControlNET. For seven source categories only NOx

reductions take place in these analyses because there are no control measures available

within AirControlNET or no controls available at $10,000/ton or below. Finally, there are 10

source categories for which both SO2 and NOx reductions take place in these analyses. The

first 10 source categories for which impacts are presented are those for which both SO2 and

NOx reductions take place in these analyses. These BART source categories are

C industrial boilers (250 mmBTU/hr heat input capacity and greater);

C petroleum refineries;

C kraft pulp mills;

C Portland cement plants;
8-20

C hydrofluoric, sulfuric, and nitric acid;

C chemical process plants;

C iron and steel mills;

C coke oven batteries;

C sulfur recovery plants; and

C primary aluminum ore reductions.

For seven source categories, only NOx reductions take place:

C lime kilns,

C glass fiber processing plants,

C municipal incinerators (250 tons refuse burn capacity or greater),

C coal cleaning plants (thermal dryers),

C carbon black plants (furnace process),

C phosphate rock processing plants, and

C secondary metal production facilities.

Finally, for eight source categories there are no impacts estimated because there are

no BART-eligible units in the non-EGU dataset or no controls available to reduce emissions

from BART-eligible units:

C primary lead smelters,

C primary copper smelters,

C primary zinc smelters,

C fuel conversion plants,

C sintering plants,

C charcoal production facilities,

C taconite ore processing plants, and

C petroleum storage and transfer facilities.
8-21

8.5.1 Results for Industrial Boilers

Table 8-10 shows the SO2 emissions reductions achieved in the analyses for each

illustrative scenario. Besides the standard BART eligibility criteria mentioned in Chapter 3,

boilers that are BART eligible are those with heat input design capacities of 250 mmBtu/hr

or greater and are fossil fueled. The table indicates that the scenarios achieve incremental

reductions from the 2015 baseline ranging from 8 to 48 percent given a 7 percent discount

rate for the costs and from 16 to 49 percent for costs at a 3 percent discount rate.

Table 8-10. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU Industrial Boilersa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 420,782 7% 388,569 32,213

420,782 3% 354,999 65,783

Scenario 2 420,782 7% 269,395 151,387

420,782 3% 256,344 164,438

Scenario 3 420,782 7% 220,474 200,308

420,782 3% 216,565 204,217

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-11 presents the NOx baseline emissions and reductions for each scenario. 

The table indicates that the scenarios achieve incremental reductions from the 2015 baseline,

ranging from 32 percent to 60 percent for costs at a 7 percent discount rate and from 52 to 60

percent for costs at a 3 percent discount rate. 

Table 8-12 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs range from $26.0 million to $610.3 million with costs at a 7

percent discount rate, and from $47.8 million to $504.6 million with costs at a 3 percent

discount rate. The accompanying average annualized cost-effectiveness results range from

$807 to $3,047 per ton with costs at a 7 percent rate and from $727 to $2,471 per ton with

costs at a 3 percent rate. In addition, the marginal costs range from $2,250 to $6,463 per ton

with costs at a 7 percent discount rate and from $2,202 to $6,023 per ton with costs at a 3

percent discount rate.
8-22

Table 8-11. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU Industrial Boilersa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 217,063 7% 149,738 67,325

217,063 3% 105,124 111,939

Scenario 2 217,063 7% 86,607 130,456

217,063 3% 86,639 130,424

Scenario 3 217,063 7% 86,541 130,522

217,063 3% 86,558 130,505

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-12. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Industrial Boilers

Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $26.0 $807 —

3% $47.8 $727 —

Scenario 2 7% $294.1 $1,943 $2,250

3% $265.0 $1,612 $2,202

Scenario 3 7% $610.3 $3,047 $6,463

3% $504.6 $2,471 $6,023

The costs and emission reductions reflect FGD scrubbers applied to all of these units.

The average and marginal costs rise as a result of FGD scrubbers being applied to more coalfired units with lower sulfur contents and to oil-fired units that have lower sulfur contents

than coal-fired units.
8-23

Table 8-13 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for NOx control. 

The annualized control costs range from $48.6 million to $235.1 million with costs at a 7

percent rate and from $87.2 million to $138.7 million with costs at a 3 percent rate. The

accompanying annualized average cost-effectiveness results range from $722 to $1,801 per

ton with costs at a 7 percent rate and from $779 to $1,063 per ton with costs at a 3 percent

rate. In addition, the marginal costs range from $2,929 to $24,242 per ton with costs at a 7

percent rate and from $2,684 to $22,840 per ton with costs at a 3 percent rate. 

Table 8-13. 2015 Cost and Cost-Effectiveness Results for NOx Control at BARTEligible Industrial Boilers

Scenarios

Discount

Rate

Total Annualized Costs

(million 1999$)

Annualized Average

Cost-Effectiveness ($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $48.6 $722 —

3% $87.2 $779 —

Scenario 2 7% $233.5 $1,790 $2,929

3% $136.8 $1,049 $2,684

Scenario 3 7% $235.1 $1,801 $24,242

3% $138.7 $1,063 $22,840

The average and marginal costs increase as the scenarios become more stringent as a

result of additional application of SCR. SCR is the most expensive NOx control device

available to industrial boilers in our analysis, though they also have a high control level (80

percent).

Table 8-14 shows the total annualized costs for each scenario for controlling both SO2

and NOx.

8.5.2 Results for Petroleum Refineries

Table 8-15 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 1 percent to 28 percent for costs estimated at a 7 percent

discount rate and from 9 percent to 29 percent for costs estimated at a 3 percent discount

rate.
8-24

Table 8-14. 2015 Cost Results for SO2 and NOx Control at BART-Eligible Industrial

Boilers

Scenarios Discount Rate Total Annualized Costs (million 1999$)

Scenario 1 7% $74.6

3% $135.0

Scenario 2 7% $527.6

3% $401.8

Scenario 3 7% $845.4

3% $643.3

Table 8-15. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Petroleum Refineriesa

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 199,483 7% 197,386 2,097

199,483 3% 182,450 17,033

Scenario 2 199,483 7% 168,164 31,319

199,483 3% 157,572 41,911

Scenario 3 199,483 7% 143,021 56,462

199,483 3% 140,958 58,525

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-16 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 5 to 59 percent for costs estimated at a 7 percent discount rate

and from 6 to 60 percent for costs estimated at a 3 percent discount rate.
8-25

Table 8-16. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Petroleum Refineriesa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 86,566 7% 82,531 4,035

3% 81,987 4,579

Scenario 2 86,566 7% 39,010 47,556

3% 35,307 51,259

Scenario 3 86,566 7% 35,250 51,316

3% 33,561 53,005

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-17 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs range from $1.9 million to $223.5 million with costs at a 7

percent discount rate and from $12.5 million to $167.3 million with costs at a 3 percent

discount rate. The accompanying annualized average cost-effectiveness results range from

$906 to $3,958 per ton with costs at a 7 percent discount rate and from $736 to $2,858 per

ton with costs at a 3 percent discount rate. In addition, the marginal costs range from $1,783

to $6,741 per ton with costs at a 7 percent discount rate and from $2,417 to $5,692 per ton

with costs at a 3 percent discount rate. 

The costs and emission reductions reflect FGD scrubbers applied to all of these units.

The average and marginal costs rise as a result of FGD scrubbers being applied to units such

as fluid catalytic cracking units (FCCUs) with lower sulfur contents. As sulfur content of the

fuel for a unit decreases, the cost per ton of control increases and vice versa.

Table 8-18 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for NOx control. 

The annualized control costs range from $2.3 million to $205.1 million with costs at a 7

percent discount rate and from $2.7 million to $227.6 million with costs at a 3 percent

discount rate. The accompanying annualized average cost-effectiveness results range from

$570 to $3,997 per ton with costs at a 7 percent discount rate and from $595 to $4,294 per 
8-26

Table 8-17. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Petroleum Refineries

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $1.9 $906 —

3% $12.5 $736 —

Scenario 2 7% $54.0 $1,724 $1,783

3% $72.7 $1,734 $2,417

Scenario 3 7% $223.5 $3,958 $6,741

3% $167.3 $2,858 $5,692

Table 8-18. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Petroleum Refineries

 Scenarios

Discount

Rate

Total Annualized Costs

(million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $2.3 $570 —

3% $2.7 $595 —

Scenario 2 7% $126.2 $2,654 $2,917

3% $142.1 $2,772 $2,986

Scenario 3 7% $205.1 $3,997 $20,984

3% $227.6 $4,294 $48,969

ton with costs at a 3 percent discount rate. In addition, the marginal costs range from $2,917

to $20,984 per ton with costs at a 7 percent discount rate and from $2,986 to $48,969 per ton

with costs at a 3 percent discount rate.

The average and marginal costs rise as a result of additional process heaters having to

apply LNB + SNCR. In most cases, the average cost per ton of control is between $4,000

and $5,000 per ton. 
8-27

Table 8-19 shows the total annualized costs for controlling both SO2 and NOx.

Table 8-19. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible

Units at Petroleum Refineries

Scenarios Discount Rate

Total Annualized Costs 

(million 1999$)

Scenario 1 7% $4.2

3% $15.3

Scenario 2 7% $180.2

3% $214.8

Scenario 3 7% $428.6

3% $394.8

8.5.3 Kraft Pulp Mills

Table 8-20 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 0 to 24 percent for costs at a 7 percent discount rate and from 0

to 12 percent for costs at a 3 percent discount rate.

Table 8-21 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 34 percent to 63 percent for costs at a 7 percent discount rate and

from 52 to 63 percent for costs at a 3 percent discount rate.

Table 8-22 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs range from $0 to $161.5 million with costs at a 7 percent

discount rate and from $0 to $69.0 million with costs at a 3 percent discount rate. The

accompanying annualized average cost-effectiveness results range from $0 to $5,691 per ton

with costs at a 7 percent discount rate and from $0 to $4,720 per ton with costs at a 3 percent

discount rate. In addition, the marginal costs range from $3,098 to $7,287 per ton with costs

at a 7 percent discount rate and from $2,189 to $5,432 per ton with costs at a 3 percent

discount rate.
8-28

Table 8-20. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Kraft Pulp Millsa

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 119,818 7% 119,818 0

119,818 3% 119,818 0

Scenario 2 119,818 7% 109,005 10,814

119,818 3% 116,820 3,196

Scenario 3 119,818 7% 91,488 28,330

119,818 3% 105,208 14,610

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-21. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Kraft Pulp Millsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 103,614 7% 68,365 35,249

103,614 3% 49,885 53,729

Scenario 2 103,614 7% 42,196 61,418

103,614 3% 37,822 65,792

Scenario 3 103,614 7% 37,838 65,776

103,614 3% 37,827 65,797

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.
8-29

Table 8-22. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Kraft Pulp Mills

Scenarios

Discoun

t Rate

Total Annualized Costs

(million 1999$)

Annualized

Average CostEffectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $0.0 $0 —

3% $0.0 $0 —

Scenario 2 7% $33.5 $3,098 $3,098

3% $7.0 $2,189 $2,189

Scenario 3 7% $161.5 $5,691 $7,287

3% $69.0 $4,720 $5,432

The costs and emission reductions reflect FGD scrubbers applied to all of these units.

The average and marginal costs rise as a result of FGD scrubbers being applied to units for

which the application is more expensive ($6,000 to $10,000 per ton).

Table 8-23 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for NOx control.

The annualized control costs range from $20.7 million to $151.8 million with costs at a 7

percent discount rate and from $45.6 million to $99.8 million with costs at a 3 percent rate. 

The accompanying annualized average cost-effectiveness results range from $587 to $2,308

per ton with costs at a 7 percent discount rate and from $849 to $1,512 per ton with costs at a

3 percent discount rate. In addition, the marginal costs range from $3,344 to $10,005 per ton

with costs at a 7 percent discount rate and from $4,452 to $35,800 per ton with costs at a 3

percent discount rate. 

The costs and emission reductions reflect greater applications of SCR as the cost-perton cap rises, particularly for sulfite pulping recovery furnaces.

Table 8-24 shows the total annualized costs of each scenario for controlling both SO2

and NOx.
8-30

Table 8-23. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Kraft Pulp Mills

Scenarios Discount Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Cost 

($/ton)

Scenario 1 7% $20.7 $587 —

3% $45.6 $849 —

Scenario 2 7% $108.2 $1,762 $3,344

3% $99.3 $1,510 $4,452

Scenario 3 7% $151.8 $2,308 $10,005

3% $99.5 $1,512 $35,800

Table 8-24. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible

Units at Kraft Pulp Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$)

Scenario 1 7% $20.7

3% $45.6

Scenario 2 7% $141.7

3% $106.3

Scenario 3 7% $313.3

3% $168.5

8.5.4 Results for Portland Cement Plants

Table 8-25 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 0 percent to 23 percent for costs at a 7 percent discount rate and

from 0 to 26 percent for costs at a 3 percent discount rate.
8-31

Table 8-25. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Portland Cement Plantsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 116,835 7% 116,835 0

116,835 3% 116,835 0

Scenario 2 116,835 7% 103,452 13,383

116,835 3% 98,509 18,326

Scenario 3 116,835 7% 90,119 26,716

116,835 3% 86,145 30,690

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-26 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 16 percent to 60 percent for costs at a 7 percent discount rate and

from 26 to 60 percent for costs at a 3 percent discount rate.

Table 8-26. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Portland Cement Plantsa

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 120,567 7% 101,289 19,276

120,567 3% 89,664 30,903

Scenario 2 120,567 7% 65,966 54,601

120,567 3% 48,646 71,921

Scenario 3 120,567 7% 48,646 71,921

120,567 3% 48,646 71,921

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.
8-32

Table 8-27 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs range from $0 to $134.4 million with costs at a 7 percent

discount rate and from $0 to $126.8 million with costs at a 3 percent discount rate. The

accompanying annualized average cost-effectiveness results range from $0 to $5,031 per ton

with costs at a 7 percent discount rate and from $0 to $4,131 per ton with costs at a 3 percent

discount rate. In addition, the marginal costs range from $3,250 to $6,818 per ton with costs

at a 7 percent discount rate and from $2,710 to $6,816 per ton with costs at a 3 percent

discount rate. 

Table 8-27. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Portland Cement Plants

Scenarios Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $0.0 $0 —

3% $0.0 $0 —

Scenario 2 7% $43.5 $3,250 $3,250

3% $49.7 $2,710 $2,710

Scenario 3 7% $134.4 $5,031 $6,818

3% $126.8 $4,131 $6,816

The costs and emission reductions reflect FGD scrubbers applied to all of these units.

The average and marginal costs rise as a result of FGD scrubbers being applied to more units

with lower sulfur content fuels.

Table 8-28 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for NOx control. 

The annualized control costs range from $2.9 million to $275.1 million with costs at a 7

percent discount rate and from $20.8 million to $219.3 million with costs at a 3 percent

discount rate. The accompanying annualized average cost-effectiveness results range from

$150 to $3,825 per ton with costs at a 7 percent discount rate and from $674 to $3,050 per

ton with costs at a 3 percent discount rate. In addition, the marginal costs range from $3,632

to $7,731 per ton with costs at a 7 percent discount rate and are $4,839 per ton with costs at a
8-33

Table 8-28. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Portland Cement Plants

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Cost 

($/ton)

Scenario 1 7% $2.9 $150 —

3% $20.8 $674 —

Scenario 2 7% $131.2 $2,403 $3,632

3% $219.3 $3,050 $4,839

Scenario 3 7% $275.1 $3,825 $7,731

3% $219.3 $3,050 N/A

3 percent discount rate between the $1,000/ton and the $4,000/ton scenarios. There is no

marginal costs between the $4,000/ton and $10,000/ton scenarios with costs at the 3 percent

discount rate because there is no difference in the impacts of the scenarios.

The average and marginal costs of control increase as more SCR applications take

place as the cost-per-ton cap rises. These applications take the place of less expensive but

less effective controls such as mid-kiln firing.

Table 8-29 shows the total annualized costs of each scenario for controlling both SO2

and NOx.

Table 8-29. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible

Units at Portland Cement Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

Scenario 1 7% $2.9

3% $20.8

Scenario 2 7% $174.8

3% $269.0

Scenario 3 7% $409.5

3% $346.1
8-34

8.5.5 Results for Hydrofluoric, Sulfuric, and Nitric Acid Plants

Table 8-30 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 35 to 38 percent for costs at a 7 percent discount rate and the

same for costs at a 3 percent discount rate.

Table 8-30. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plantsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 96,741 7% 62,601 34,140

96,741 3% 62,601 34,140

Scenario 2 96,741 7% 60,188 36,753

96,741 3% 60,188 36,753

Scenario 3 96,741 7% 60,188 36,753

96,741 3% 60,188 36,753

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled. 

Table 8-31 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline of about 66 percent. The degree of impact varies little between scenarios and

the discount rate of the costs.

Table 8-32 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs range from $9.8 million to $15.2 million with costs at a

7 percent discount rate and from $9.1 million to $14.1 million with costs at a 3 percent

discount rate. The accompanying annualized average cost-effectiveness results range from

$287 to $413 per ton with costs at a 7 percent discount rate and from $268 to $385 per ton

with costs at a 3 percent discount rate. In addition, the marginal costs are $2,067 per ton

with costs at a 7 percent discount rate and $1,914 per ton with costs at a 3 percent discount

rate. In this case, there are no controls between $4,000/ton and $10,000/ton; thus, there are

no marginal costs between these two scenarios.
8-35

Table 8-31. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plantsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 17,059 7% 5,783 11,276

17,059 3% 5,783 11,276

Scenario 2 17,059 7% 5,776 11,283

17,059 3% 5,776 11,283

Scenario 3 17,059 7% 5,776 11,283

17,059 3% 5,776 11,283

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-32. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants

Scenarios

Discount

Rate

Total Annualized Costs

(million 1999$)

Annualized

Average CostEffectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $9.8 $287 —

3% $9.1 $268 —

Scenario 2 7% $15.2 $413 $2,067

3% $14.1 $385 $1,914

Scenario 3 7% $15.2 $413 N/A

3% $14.1 $385 N/A
8-36

The costs and emission reductions are flat between the scenarios because there is only

one control technique available to reduce SO2 emissions from these sources—increase sulfur

conversion to meet the sulfuric acid NSPS (99.7 percent control). 

Table 8-33 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for NOx control. 

The annualized control costs range from $8.19 million to $8.21 million with costs at a 7

percent discount rate and from $7.29 million to $7.30 million with costs at a 3 percent

discount rate. The accompanying annualized average cost-effectiveness results range from

$726 to $728 per ton with costs at a 7 percent discount rate and from $646 to $647 per ton

with costs at a 3 percent discount rate. In addition, the marginal costs are $4,428 per ton

with costs at a 7 percent discount rate and $2,157 per ton with costs at a 3 percent discount

rate. In this case, there are no controls between $4,000/ton and $10,000/ton; thus, there are

no marginal costs between these two scenarios.

Table 8-33. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants

Illustrative

Regulatory

Scenarios Discount Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $8.2 $726 —

3% $7.3 $646 —

Scenario 2 7% $8.2 $728 $4,428

3% $7.3 $647 $2,157

Scenario 3 7% $8.2 $728 N/A

3% $7.3 $647 N/A

The costs and emission reductions are flat between the scenarios because there is only

one control technique available to reduce NOx emissions from these sources—SNCR applied

to nitric acid manufacturing sources. 

Table 8-34 shows the total annualized costs of each scenario for controlling both SO2

and NOx.
8-37

Table 8-34. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible

Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

Scenario 1 7% $18.0

3% $16.4

Scenario 2 7% $23.4

3% $21.4

Scenario 3 7% $23.4

3% $21.4

8.5.6 Results for Chemical Process Plants

Table 8-35 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 0 percent to 7 percent for costs at either a 7 or a 3 percent

discount rate.

Table 8-35. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Chemical Process Plantsa

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 47,700 7% 47,700 0

47,700 3% 47,700 0

Scenario 2 47,700 7% 45,324 2,376

47,700 3% 45,324 2,376

Scenario 3 47,700 7% 44,129 3,571

47,700 3% 44,129 3,571

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.
8-38

Table 8-36 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 33 percent to 48 percent for costs at a 7 percent discount rate and

from 37 to 48 percent for costs at a 3 percent discount rate.

Table 8-36. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Chemical Process Plantsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 72,577 7% 48,607 23,970

72,577 3% 45,435 27,142

Scenario 2 72,577 7% 37,941 34,636

72,577 3% 37,776 34,801

Scenario 3 72,577 7% 37,776 34,801

72,577 3% 37,385 35,192

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-37 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs range from $0 to $13.7 million with costs at a 7 percent

discount rate and from $0 to $10.2 million with costs at a 3 percent discount rate. The

accompanying annualized average cost-effectiveness results range from $0 to $3,836 per ton

with costs at a 7 percent discount rate and from $0 to $2,850 per ton with costs at a 3 percent

discount rate. The marginal costs range from $1,052 to $9,372 per ton with costs at a 7

percent discount rate and from $1,013 to $6,527 per ton with costs at a 3 percent discount

rate. 

The costs and emission reductions reflect a major difference in the impacts between

the two available control techniques: increase sulfur percentage conversion to meet the

sulfuric acid NSPS (99.7 percent control) and FGD scrubbers.
8-39

Table 8-37. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Chemical Process Plants

Scenarios Discount Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $0.0 $0 —

3% $0.0 $0 —

Scenario 2 7% $2.5 $1,052 $1,052

3% $2.4 $1,013 $1,013

Scenario 3 7% $13.7 $3,836 $9,372

3% $10.2 $2,850 $6,527

Table 8-38 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for NOx control. 

The annualized control costs range from $14.4 million to $74.1 million with costs at a 7

percent discount rate and from $21.1 million to $77.1 million with costs at a 3 percent

discount rate. The accompanying annualized average cost-effectiveness results range from

$601 to $2,129 per ton with costs at a 7 percent discount rate and from $776 to $2,191 per

ton with costs at a 3 percent discount rate. In addition, the marginal costs of the scenarios

range from $5,030 to $35,758 per ton with costs at a 7 percent discount rate and from $4,348

to $58,082 per ton with costs at a 3 percent discount rate.

The costs and emission reductions reflect the application of many different types of

NOx controls. The reason for the large increase in marginal costs associated with the

$10,000/ton scenario is the applications of LNB + SNCR or SCR that would take place at

units within these plants according to our analysis. 

Table 8-39 shows the total annualized costs of each scenario for controlling both SO2

and NOx.
8-40

Table 8-38. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Chemical Process Plants

 Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $14.4 $601 —

3% $21.1 $776 —

Scenario 2 7% $68.2 $1,969 $5,030

3% $54.4 $1,563 $4,348

Scenario 3 7% $74.1 $2,129 $35,758

3% $77.1 $2,191 $58,082

Table 8-39. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible

Units at Chemical Process Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

Scenario 1 7% $14.4

3% $21.1

Scenario 2 7% $70.8

3% $56.8

Scenario 3 7% $87.8

3% $87.3

8.5.7 Results for Iron and Steel Mills

Table 8-40 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 0 to 12 percent for costs at either a 7 or 3 percent discount rate. 
8-41

Table 8-40. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Iron and Steel Millsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 23,541 7% 23,541 0

23,541 3% 23,541 0

Scenario 2 23,541 7% 20,627 2,914

23,541 3% 20,627 2,914

Scenario 3 23,541 7% 20,627 2,914

23,541 3% 20,627 2,914

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-41 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 5 to 41 percent at costs of either a 7 or 3 percent discount rate. 

Table 8-41. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Iron and Steel Millsa

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 20,963 7% 19,927 1,036

20,963 3% 19,927 1,036

Scenario 2 20,963 7% 13,966 6,997

20,963 3% 12,463 8,500

Scenario 3 20,963 7% 12,456 8,507

20,963 3% 12,290 8,673

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.
8-42

Table 8-42 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs range from $0 to $5.3 million with costs at a 7 percent discount

rate and from $0 to $3.4 million with costs at a 3 percent discount rate. The accompanying

annualized average cost-effectiveness results range from $0 to $1,819 per ton with costs at a

7 percent discount rate and from $0 to $1,165 per ton with costs at a 3 percent discount rate. 

In addition, the marginal costs are $1,819 per ton with costs at a 7 percent discount rate and

$1,165 per ton with costs at a 3 percent discount rate. In this case, there are no controls

between $4,000/ton and $10,000/ton; thus, there are no marginal costs between these two

scenarios.

Table 8-42. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Iron and Steel Mills

Scenarios Discount Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $0.0 $0 —

3% $0.0 $0 —

Scenario 2 7% $5.3 $1,819 $1,819

3% $3.4 $1,165 $1,165

Scenario 3 7% $5.3 $1,819 N/A

3% $3.4 $1,165 N/A

The costs and emission reductions are flat between the scenarios because the two

controls available both have similar average cost-per-ton estimates below $4,000: sulfuric

acid plant and increase sulfur conversion to meet the sulfuric acid NSPS (99.7 percent

control).

Table 8-43 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for NOx control.

The annualized control costs range from $0.6 million to $27.9 million with costs at a 7

percent discount rate and from $0.4 million to $29.0 million with costs at a 3 percent

discount rate. The accompanying annualized average cost-effectiveness results range from

$579 to $3,280 per ton with costs at a 7 percent discount rate and from $431 to $3,343 per 
8-43

Table 8-43. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Iron and Steel Mills

Scenarios Discount Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $0.6 $579 —

3% $0.4 $431 —

Scenario 2 7% $18.2 $2,601 $2,953

3% $19.3 $2,271 $2,532

Scenario 3 7% $27.9 $3,280 $6,424

3% $29.0 $3,343 $56,052

ton with costs at a 3 percent discount rate. The marginal costs range from $2,953 to $6,424

per ton with costs at a 7 percent discount rate and from $2,532 to $56,052 per ton with costs

at a 3 percent discount rate.

The costs and emission reductions reflect a rise in the costs of control due to

additional applications of LNB + either SNCR or SCR. 

Table 8-44 shows the total annualized costs for controlling both SO2 and NOx.

Table 8-44. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible

Units at Iron and Steel Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$)

Scenario 1 7% $0.6

3% $0.4

Scenario 2 7% $23.5

3% $22.7

Scenario 3 7% $33.2

3% $32.4
8-44

8.5.8 Results for Coke Oven Batteries

Table 8-45 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 0 to 62 percent for costs at a 7 percent discount rate and from 0

to 57 percent for costs at a 3 percent discount rate.

Table 8-45. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Coke Oven Batteries

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 9,815 7% 9,815 0

9,815 3% 9,815 0

Scenario 2 9,815 7% 5,727 4,088

9,815 3% 6,091 3,724

Scenario 3 9,815 7% 3,708 6,107

9,815 3% 4,251 5,564

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-46 shows the emissions reductions achieved in the analyses for each scenario

for NOx control. The table indicates that the scenarios achieve incremental reductions from

the 2015 baseline ranging from 0 to 56 percent for costs at a 7 or 3 percent discount rate.

Table 8-47 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs range from $0 to $21.3 million with costs at a 7 percent

discount rate and from $0 to $14.1 million with costs at a 3 percent discount rate. The

accompanying annualized average cost-effectiveness results range from $0 to $3,488 per ton

with costs at a 7 percent discount rate and from $0 to $2,529 per ton with costs at a 3 percent

discount rate. The marginal costs range from $1,517 to $7,479 per ton with costs at a 7

percent discount rate and from $1,074 to $5,489 per ton with costs at a 3 percent discount

rate.
8-45

Table 8-46. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Coke Oven Batteriesa

Scenarios

2015 Baseline

Emissions

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 10,389 10,389 0

10,389 10,389 0

Scenario 2 10,389 4,621 5,768

10,389 4,621 5,768

Scenario 3 10,389 4,621 5,768

10,389 4,621 5,768

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-47. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Coke Oven Batteries

Scenarios Discount Rate

Total Annualized

Costs (million

1990$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $0.0 $0 —

3% $0.0 $0 —

Scenario 2 7% $6.2 $1,517 $1,517

3% $4.0 $1,074 $1,074

Scenario 3 7% $21.3 $3,488 $7,479

3% $14.1 $2,529 $5,489
8-46

The costs and emission reductions reflect application of only one control—vacuum

carbonate plus a sulfur recovery plant but also differing SO2 emissions levels at the affected

coke oven batteries.

Table 8-48 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for NOx control. 

The annualized control costs range from $0 to $12.5 million with costs at a 7 percent

discount rate and from $0 to $10.9 million with costs at a 3 percent discount rate. The

accompanying annualized average cost-effectiveness results range from $0 to $2,167 per ton

with costs at a 7 percent discount rate and from $0 to $1,898 per ton with costs at a 3 percent

discount rate. In addition, the marginal costs are $2,167 per ton with costs at a 7 percent

discount rate and $1,898 per ton at a 3 percent discount rate. In this case, there are no

controls available between $4,000/ton and $10,000/ton; thus, there are no marginal costs

between these two scenarios. 

Table 8-48. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Coke Oven Batteries

Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal

Costs ($/ton)

Scenario 1 7% $0.0 $0 —

3% $0.0 $0 —

Scenario 2 7% $12.5 $2,167 $2,167

3% $10.9 $1,898 $1,898

Scenario 3 7% $12.5 $2,167 N/A

3% $10.9 $1,898 N/A

The costs and emission reductions are flat between the scenarios because there is only

one NOx control available for these sources—SNCR.

Table 8-49 shows the total annualized costs for controlling both SO2 and NOx.
8-47

Table 8-49. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible

Units at Coke Oven Batteries

Scenarios Discount Rate Total Annualized Costs (million 1999$)

Scenario 1 7% $0.0

3% $0.0

Scenario 2 7% $18.7

3% $14.9

Scenario 3 7% $33.8

3% $25.0

8.5.9 Results for Sulfur Recovery Plants

Table 8-50 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline of 23 percent for costs at a 7 or a 3 percent discount rate. The emissions

reductions are the same for each scenario because of the few controls available between

$1,000 and $10,000 per ton average cost.

Table 8-50. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Sulfur Recovery Plantsa

Scenarios

2015

Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 59,766 7% 46,069 13,697

59,766 3% 46,073 13,693

Scenario 2 59,766 7% 46,069 13,697

59,766 3% 46,073 13,693

Scenario 3 59,766 7% 46,069 13,697

59,766 3% 46,073 13,693

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.
8-48

Table 8-51 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 21 to 22 percent for costs at a 7 or a 3 percent discount rate. For

this source category, the reductions vary little between scenarios because of the limited

number of emission controls between $1,000 and $10,000 per ton.

Table 8-51. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Sulfur Recovery Plantsa

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 651 7% 516 135

651 3% 516 135

Scenario 2 651 7% 510 141

651 3% 510 141

Scenario 3 651 7% 510 141

651 3% 510 141

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-52 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs are $11.6 million with costs at a 7 or a 3 percent discount rate. 

The accompanying annualized average cost-effectiveness results are $847 per ton with costs

at a 7 percent discount rate and $849 per ton with costs at a 3 percent discount rate, and the

marginal costs are $847 per ton with costs at a 7 percent discount rate and $849 per ton at a 3

percent discount rate. There are no available controls between $4,000/ton and $10,000/ton,

so there are no other marginal costs.

The costs and emission reductions are flat between the scenarios because there is only

one control technique available to reduce SO2 emissions from these sources—sulfur recovery

and/or tail gas treatment.
8-49

Table 8-52. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Sulfur Recovery Plants

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness ($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $11.6 $847 —

3% $11.6 $849 —

Scenario 2 7% $11.6 $847 $847

3% $11.6 $849 $849

Scenario 3 7% $11.6 $847 N/A

3% $11.6 $849 N/A

Table 8-53 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for NOx control. 

The annualized control costs range from $0.07 million to $0.52 million with costs at a 7

percent discount rate and from $0.05 million to $0.47 million with costs at a 3 percent

discount rate. The accompanying annualized average cost-effectiveness results range from

$519 to $3,652 per ton with costs at a 7 percent discount rate and from $400 to $3,315 per

ton with costs at a 3 percent discount rate. In addition, the marginal costs are $74,167 per

ton with costs at a 7 percent discount rate and $68,833 per ton with costs at a 3 percent

discount rate. There are no available controls between $4,000/ton and $10,000/ton, so there

are no other marginal costs.

The average and marginal costs rise between the scenarios because of applications of

SCR, a high cost/ton control for this type of source.

Table 8-54 shows the total annualized costs for controlling both SO2 and NOx.

8.5.10 Results for Primary Aluminum Ore Reduction Plants

Table 8-55 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 3 to 7 percent for costs at a 7 or 3 percent discount rate.
8-50

Table 8-53. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Sulfur Recovery Plants

 Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $0.1 $519 —

3% $0.1 $400 —

Scenario 2 7% $0.5 $3,652 $74,167

3% $0.5 $3,315 $68,833

Scenario 3 7% $0.5 $3,652 N/A

3% $0.5 $3,315 N/A

Table 8-54. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible

Units at Sulfur Recovery Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

Scenario 1 7% $11.7

3% $11.7

Scenario 2 7% $12.1

3% $12.1

Scenario 3 7% $12.1

3% $12.1
8-51

Table 8-55. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Primary Aluminum Ore Reduction Plantsa

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 47,552 7% 45,922 1,630

47,552 3% 45,922 1,630

Scenario 2 47,552 7% 44,292 3,260

47,552 3% 44,292 3,260

Scenario 3 47,552 7% 44,292 3,260

47,552 3% 44,292 3,260

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table 8-56 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 3 to 15 percent with costs at a 7 or 3 percent discount rate.

Table 8-56. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Primary Aluminum Ore Reduction Plantsa

Scenario

2015 Baseline

Emissions

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 1,676 1,626 50

1,676 1,626 50

Scenario 2 1,676 1,423 253

1,676 1,421 255

Scenario 3 1,676 1,421 255

1,676 1,421 255

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.
8-52

Table 8-57 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marginal costs between each scenario for SO2 control. 

The annualized control costs range from $1.6 million to $7.2 million with costs at a 7 percent

discount rate and from $1.0 million to $4.5 million with costs at a 3 percent discount rate. 

The accompanying annualized average cost-effectiveness results range from $982 to $2,209

per ton with costs at a 7 percent discount rate and from $590 to $1,383 per ton with costs at a

3 percent discount rate. The marginal costs are $3,436 per ton with costs at a 7 percent

discount rate and $1,383 per ton with costs at a 3 percent discount rate. There are no

available controls between $4,000/ton and $10,000/ton, so there are no other marginal costs.

Table 8-57. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Primary Aluminum Ore Reduction Plants

Scenarios Discount Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal

Costs ($/ton)

Scenario 1 7% $1.6 $982 —

3% $1.0 $590 —

Scenario 2 7% $7.2 $2,209 $3,436

3% $4.5 $1,383 $1,383

Scenario 3 7% $7.2 $2,209 N/A

3% $4.5 $1,383 N/A

The costs and emission reductions are flat between the scenarios because there is only

one control technique available to reduce SO2 emissions from these sources—a sulfuric acid

plant. 

Table 8-58 shows the annualized costs, resulting annualized average costeffectiveness for each scenarios, and marginal costs between each scenario for NOx control.

The annualized control costs range from $0.04 million to $0.7 million with costs at a 7

percent discount rate and from $0.04 million to $0.4 million with costs at a 3 percent

discount rate. The accompanying annualized average cost-effectiveness results range from

$740 to $2,639 per ton with costs at a 7 percent discount rate and from $509 to $1,764 per

ton with costs at a 3 percent discount rate. The marginal costs range from $2,823 to $30,500

per ton with costs at a 7 percent discount rate. With costs at a 3 percent discount rate, the 
8-53

Table 8-58. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Primary Aluminum Ore Reduction Plants

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $0.0 $740 —

3% $0.0 $509 —

Scenario 2 7% $0.6 $2,411 $2,823

3% $0.4 $1,764 $1,764

Scenario 3 7% $0.7 $2,639 $30,500

3% $0.4 $1,764 N/A

marginal costs are $1,764 per ton between the $1,000/ton and $4,000/ton scenarios, but there

is no marginal cost between the $4,000/ton and $10,000/ton scenarios because the impacts

are the same.

The costs and NOx emission reductions reflect LNB applications also with LNB +

SNCR in a very few cases. It is the application of LNB + SNCR that leads to the high

marginal cost for the $10,000/ton scenario at the 7 percent discount rate.

Table 8-59 shows the total annualized costs for controlling both SO2 and NOx.

The next seven BART source categories only have NOx controls applied to their

affected units because there are no SO2 emissions from BART-eligible units in these source

categories that can be controlled at under $10,000/ton (1999$). Hence, all the reductions and

costs for the remaining source categories are only for NOx, not SO2.

8.5.11 Results for Lime Kilns

Table 8-60 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 21 to 56 percent for costs at a 7 or 3 percent discount rate.

Table 8-61 shows the annualized costs, resulting annualized average costeffectiveness, and marginal costs for each scenario. The total annualized costs for these

scenarios range from $2 million to $31.8 million with costs at a 7 percent discount rate and

$4.3 million to $25.4 million with costs at a 3 percent discount rate. The annualized average 
8-54

Table 8-59. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible

Units at Primary Aluminum Ore Reduction Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

Scenario 1 7% $1.7

3% $1.0

Scenario 2 7% $7.8

3% $5.0

Scenario 3 7% $7.8a

3% $5.0

a The annual costs for Scenario 3 are actually $61,000 higher than for Scenario 2.

Table 8-60. 2015 NOx Emission Reductions (in tons) for BART-Eligible Lime Kilns

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 12,849 7% 10,166 2,683

12,849 3% 8,378 4,471

Scenario 2 12,849 7% 8,378 4,471

12,849 3% 5,696 7,153

Scenario 3 12,849 7% 5,696 7,153

12,849 3% 5,696 7,153

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source

category, both controlled and uncontrolled.
8-55

Table 8-61. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Lime Kilns

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $2.0 $745 —

3% $4.3 $953 —

Scenario 2 7% $5.0 $1,118 $1,678

3% $25.4 $3,552 $8,858

Scenario 3 7% $31.8 $4,446 $9,993

3% $25.4 $3,552 N/A

cost-effectiveness ranges from $745 to $4,446 per ton with costs at a 7 percent discount rate

and from $953 to $3,552 per ton with costs at a 3 percent discount rate. The marginal costs

are $1,678 per ton for reaching the $4,000/ton scenario and are $9,993 per ton between the

$4,000/ton and $10,000/ton scenarios with costs at a 7 percent discount rate. The marginal

costs are $8,858 per ton for reaching the $4,000/ton scenario, and there are no marginal costs

for reaching the $10,000/ton scenario from the $4,000/ton scenario because the impacts are

the same. These impacts reflect applications of LNB at $1,000/ton, SNCR at $4,000/ton, and

SCR at $10,000/ton. 

8.5.12 Results for Glass Fiber Processing Plants

Table 8-62 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 9 to 33 percent for costs at a 7 percent discount rate and from 12

to 33 percent for costs at a 3 percent discount rate.

Table 8-63 shows the annualized cost, resulting annualized average costeffectiveness, and marginal costs for each scenario. The total annualized costs for these

scenarios range from $0.5 million to $7.8 million with costs at a 7 percent discount rate and

from $0.7 million to $6.8 million with costs at a 3 percent discount rate. The annualized

average cost-effectiveness ranges from $937 to $3,549 per ton with costs at a 7 percent

discount rate and from $880 to $3,108 per ton with costs at a 3 percent discount rate. 

Marginal costs are $3,101 per ton for reaching the $4,000/ton scenario and $30,488 per ton

between the $4,000/ton and $10,000/ton scenario with costs at a 7 percent discount rate and 
8-56

Table 8-62. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at Glass

Fiber Processing Plants

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 6,677 7% 6,109 568

6,677 3% 5,902 775

Scenario 2 6,677 7% 4,561 2,116

6,677 3% 4,561 2,116

Scenario 3 6,677 7% 4,479 2,198

6,677 3% 4,479 2,198

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both

controlled and uncontrolled.

Table 8-63. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at Glass

Fiber Processing Plants

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal

Costs ($/ton)

Scenario 1 7% $0.5 $937 —

3% $0.7 $880 —

Scenario 2 7% $5.3 $2,505 $3,101

3% $4.8 $2,244 $3,057

Scenario 3 7% $7.8 $3,549 $30,488

3% $6.8 $3,108 $25,402
8-57

$3,057 per ton for reaching the $4,000/ton scenario and $25,402 per ton between the

$4,000/ton and $10,000/ton scenario with costs at a 3 percent discount rate. The impacts

reflect application of LNB at $1,000/ton and $4,000/ton and then oxygen-firing under the

$10,000/ton scenario.

8.5.13 Results for Municipal Incinerators

The analysis of municipal incinerators (>250 tons per day burn refuse capacity)

shows the results for each scenario. Table 8-64 shows the NOx emissions reductions

achieved in the analysis for each scenario. The table indicates that the scenarios achieve

incremental reductions from the 2015 baseline ranging from 0 to 45 percent for costs at a 7

or 3 percent discount rate.

Table 8-64. 2015 NOx Emission Reductions (in tons) for BART-Eligible Municipal

Incineratorsa

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 1,656 7% 1,656 0

1,656 3% 1,656 0

Scenario 2 1,656 7% 912 744

1,656 3% 912 744

Scenario 3 1,656 7% 912 744

1,656 3% 912 744

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both

controlled and uncontrolled.

Table 8-65 shows the annualized costs, annualized average cost-effectiveness, and

marginal costs for each scenario. The total annualized costs for these scenarios range from $0

to $1.1 million with costs at a 7 percent discount rate and from $0 to $0.9 million with costs

at a 3 percent discount rate. The annualized average cost-effectiveness is $1,478 per ton with

costs at a 7 percent discount rate and $1,207 per ton with costs at a 3 percent discount rate. 

The marginal costs are $1,478 per ton for reaching the $4,000/ton scenario with costs at the 7

percent discount rate and $1,207 per ton at the 3 percent discount rate. Because there are no

other controls between $4,000/ton and $10,000/ton, there are no additional reductions and

hence no marginal costs between these two scenarios. The only available control measure

for this source is SNCR.
8-58

Table 8-65. 2007 Cost and Cost-Effectiveness Results for BART-Eligible Municipal

Incinerators

Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

Scenario 1 7% $0.0 $0 —

3% $0.0 $0 —

Scenario 2 7% $1.1 $1,478 $1,478

3% $0.9 $1,207 $1,207

Scenario 3 7% $1.1 $1,478 N/A

3% $0.9 $1,207 N/A

8.5.14 Results for Coal Cleaning Plants

Table 8-66 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 0 to 46 percent for costs at a 7 or a 3 percent discount rate. 

Table 8-66. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at Coal

Cleaning Plants

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 1,110 7% 1,110 0

1,110 3% 1,110 0

Scenario 2 1,110 7% 599 511

1,110 3% 599 511

Scenario 3 1,110 7% 599 511

1,110 3% 599 511

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both

controlled and uncontrolled.
8-59

Table 8-67 shows the annualized costs, annualized average cost-effectiveness, and

marginal costs for each scenario. The total annualized costs for these scenarios range from

$0 to $1.0 million with costs at a 7 percent discount rate and from $0 to $0.8 million with

costs at a 3 percent discount rate. The annualized average cost-effectiveness is $1,900 per

ton with costs at a 7 percent discount rate and $1,534 per ton with costs at a 3 percent

discount rate. The marginal costs are $1,900 per ton for reaching the $4,000/ton scenario

with costs at a 7 percent discount rate and $1,534 per ton with costs at a 3 percent discount

rate. Because there are no other controls between $4,000/ton and $10,000/ton, there are no

additional reductions and hence no marginal costs between these two scenarios. Controls

available to these sources are LNB and SNCR.

Table 8-67. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at Coal

Cleaning Plants

Scenarios

Discount

Rate

Total Annualized Costs

(million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal

Costs ($/ton)

Scenario 1 7% $0.0 $0 —

3% $0.0 $0 —

Scenario 2 7% $1.0 $1,900 $1,900

3% $0.8 $1,534 $1,534

Scenario 3 7% $1.0 $1,900 N/A

3% $0.8 $1,534 N/A

8.5.15 Results for Carbon Black Plants

Table 8-68 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 0.2 to 2 percent for costs at a 7 or a 3 percent discount rate. 

Table 8-69 shows the annualized cost, resulting annualized average costeffectiveness, and marginal costs for each scenario. The total annualized costs for these

scenarios range from $0.1 million to $0.2 million with costs at a 7 percent discount rate and

from $0.006 million to $0.15 million with costs at a 3 percent discount rate. The annualized

average cost-effectiveness ranges from $957 to $1,608 per ton with costs at a 7 percent
8-60

Table 8-68. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at

Carbon Black Plants

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 4,645 7% 4,638 7

4,645 3% 4,638 7

Scenario 2 4,645 7% 4,525 120

4,645 3% 4,525 120

Scenario 3 4,645 7% 4,525 120

4,645 3% 4,525 120

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both

controlled and uncontrolled.

Table 8-69. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at

Carbon Black Plants

Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal

Costs ($/ton)

Scenario 1 7% $0.1 $957 —

3% $0.0 $830 —

Scenario 2 7% $0.2 $1,608 $1,646

3% $0.1 $1,237 $1,237

Scenario 3 7% $0.2 $1,608 N/A

3% $0.1 $1,237 N/A

discount rate and from $830 to $1,237 per ton with costs at a 3 percent discount rate. The

marginal costs between the $1,000/ton and the $4,000/ton scenarios are $1,646/ton with costs

at a 7 percent discount rate and $1,237/ton with costs at a 3 percent discount rate. Because

there are no other controls between $4,000/ton and $10,000/ton, there are no additional

reductions and hence no marginal costs between these two scenarios. NOx controls available
8-61

to these sources are SNCR and SCR, and the cost per ton for these controls is fairly similar

for these sources in this analysis. 

8.5.16 Results for Phosphate Rock Processing Plants

Table 8-70 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 0 to 7 percent for costs at either a 7 or 3 percent discount rate.

Table 8-70. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at

Phosphate Rock Processing Plants

Scenarios

2015 Baseline

Emissions

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 719 719 0

719 719 0

Scenario 2 719 674 45

719 671 48

Scenario 3 719 671 48

719 671 48

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both

controlled and uncontrolled.

Table 8-71 shows the annualized cost, resulting annualized average costeffectiveness, and marginal costs for each scenario. The total annualized costs for these

scenarios range from $0 to $0.2 million with costs at a 7 or 3 percent discount rate. The

annualized average cost-effectiveness ranges from $1,978 to $4,646 per ton with costs at a 7

percent discount rate and from $0 to $3,221 per ton with costs at a 3 percent discount rate. 

The marginal costs between the $1,000/ton and the $4,000/ton scenarios are $1,978 per ton,

while the marginal costs between the $4,000/ton and the $10,000/ton scenarios are $44,667

per ton with costs at a 7 percent discount rate. With costs at a 3 percent discount rate, the

marginal costs between the $1,000/ton and the $4,000/ton scenarios are $3,221 per ton, and

there is no marginal cost between the $4,000/ton and the $10,000/ton scenario because the

impacts are the same for each scenario. The only available NOx control for this source is

LNB + SNCR.
8-62

Table 8-71. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at

Phosphate Rock Processing Plants

Scenarios Discount Rate

Total Annualized Costs

(million 1999$)

Annualized

Average CostEffectiveness

($/ton)

Marginal

Costs ($/ton)

Scenario 1 7% $0.0 $0 —

3% $0.0 $0 —

Scenario 2 7% $0.1 $1,978 $1,978

3% $0.2 $3,221 $3,221

Scenario 3 7% $0.2 $4,646 $44,667

3% $0.2 $3,221 N/A

8.5.17 Results for Secondary Metal Production Facilities

Table 8-72 shows the NOx emissions reductions achieved in the analyses for each

scenarios. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 2 to 3 percent for costs at either a 7 or 3 percent discount rate.

Table 8-72. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at

Secondary Metal Production Facilities

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

Scenario 1 1,377 7% 1,352 25

1,377 3% 1,352 25

Scenario 2 1,377 7% 1,343 34

1,377 3% 1,342 35

Scenario 3 1,377 7% 1,342 35

1,377 3% 1,342 35

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source

category, both controlled and uncontrolled.
8-63

Table 8-73 shows the annualized cost, resulting annualized average costeffectiveness, and marginal costs for each scenario. The total annualized costs for these

scenarios range from $0.01 million to $0.12 million with costs at a 7 percent discount rate

and from $0.01 to $0.04 million with costs at a 3 percent discount rate. The annualized

average cost-effectiveness ranges from $760 to $1,800 per ton with costs at a 7 percent

discount rate and from $511 to $1,247 per ton with costs at a 3 percent discount rate. With

costs at a 7 percent discount rate, the marginal costs between the $1,000/ton and the

$4,000/ton scenarios are $2,000 per ton and the marginal costs between the $4,000/ton and

the $10,000/ton scenarios are $9,000 per ton. With costs at a 3 percent discount rate, the

marginal costs between the $1,000/ton and the $4,000/ton scenarios are $3,100/ton, and there

are no marginal costs between the $4,000/ton and the $10,000/ton scenarios because the

impacts are the same. Available NOx controls are LNB and the more expensive LNB +

SNCR.

Table 8-73. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at

Secondary Metal Processing Facilities

Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Cost

($/ton)

Scenario 1 7% $0.0 $760 —

3% $0.0 $511 —

Scenario 2 7% $0.0 $1,088 $2,000

3% $0.0 $1,247 $3,100

Scenario 3 7% $0.1 $1,800 $9,000

3% $0.0 $1,247 N/A

8.6 Caveats and Limitations of the Analyses

A number of caveats and limitations are associated with these analyses. 

C As noted above in Section 8.5, for a large number of non-EGU BART source

categories, no SO2 or NOx controls exist. There are no impacts for 8 of the 25

non-EGU source categories because no control measures are available to reduce

SO2 and NOx from these categories within AirControlNET or any other

documentation that has been found in the course of completing this analysis. For
8-64

seven source categories only NOx reductions take place in these analyses because

no control measures are available within AirControlNET or no controls are

available at $10,000/ton or below. Finally, there are a total 10 source categories

for which both SO2 and NOx reductions take place in these analyses.

C Control programs implemented as command-and-control regulation, as this

analysis models controls to affected non-EGU sources, will lead to less-induced

technological innovation when compared to a market incentives-based approach

(e.g., a cap-and-trade program).

C The technologies applied in these analyses do not reflect emerging control

devices that could be available in future years to meet any BART requirements in

SIPs or upgrades to current devices that may serve to increase control levels. For

example, there is increasing use of SCR/SNCR hybrid technologies that can serve

to lower the expected capital costs and lead to NOx control at high levels

(90 percent).

C Fuel switching is not considered as a way for BART-eligible units, especially

industrial boilers, to meet potential BART requirements in SIPs. Fuel switching

can consist of coal-fired sources switching from high- to low-sulfur coals (e.g.,

Powder River Basin coals). Many power plants have used this technique to meet

SO2 requirements imposed by the Acid Rain Program and by various regulations,

but industrial sources have used it less frequently.

C There is a considerable range of equipment lives for the control devices applied in

these analyses. For example, the equipment life for SCR can range from 10 to 40

years. We chose a middle point from this range to use in these analyses. To the

extent that we underestimated the actual equipment life from use of these devices,

we overestimate the annual costs of these controls and vice versa.

C Labor and energy rates and other parameters to the cost estimates are estimated

based on nationwide rates instead of regional and local rates. Using nationwide

parameter estimates introduces some uncertainty in these estimates at a sourcespecific level. 

C The emission reductions and controls that will be imposed on petroleum refineries

as a result of various New Source Review settlements are not included in our

regulatory baseline. Thus, this analysis is likely to overestimate costs of BART

compliance to many BART-eligible units at petroleum refineries.

C EPA wants to identify some unquantified costs as limits to its analysis. These

costs include the costs of State administration of the program, which we believe

are modest. There also may be unquantified costs of transitioning to BART, such

as the costs associated with the possible retirement of smaller or less-efficient
8-65

non-EGU units and employment shifts as workers are retrained at the same

company or reemployed elsewhere in the economy.

C Recent research suggests that the total social costs of a new regulation may be

affected by interactions between the new regulation and preexisting 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 analytical guidelines that

discuss the tax interaction effect and its potential relevance for estimating federal

program costs and benefits. The limited empirical data available to support

quantification of any such effect lead to this qualitative identification of the costs.

8.7 References

E.H. Pechan and Associates. March 2005. AirControlNET Version 4.0 Control Measure

Documentation Report. Prepared for the U.S. Environmental Protection Agency.

E.H. Pechan and Associates. June 2005. BART Non-EGU Control Strategy Analysis

Technical Support Document. Prepared for the U.S. Environmental Protection

Agency. 

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. October 29, 1992. Circular A-94. “Guidelines and

Discount Rates for Benefit-Cost Analysis of Federal Programs.” Available at

<http://www.whitehouse.gov/omb/circulars/a094/a094.html>. 

U.S. Office of Management and Budget (OMB). September 17, 2003. Circular A-4. “New

Guidelines for the Conduct of Regulatory Analysis.” Available at

<http://www.whitehouse.gov/omb/circulars/a004/a-4.html>.
9-1

SECTION 9

STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSES

Impact analysis is a general term used to describe various economic analyses that

supplement estimates of the benefits and costs of a rulemaking. These analyses are

conducted to meet the statutory and administrative requirements imposed by Congress and

the Executive Office. This chapter will address the requirements of the Regulatory

Flexibility Act (RFA), as amended by the Small Business Regulatory Enforcement Fairness

Act (SBREFA) and the Unfunded Mandates Reform Act (UMRA).

9.1 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 purposes of assessing the impacts of the this proposed rulemaking on small

entities, small entity is defined as: (1) a small business that is identified by the North

American Industry Classification System (NAICS) Code, as defined by the Small Business

Administration (SBA); (2) a small governmental jurisdiction that is a government of a city,

county, town, school district or special district with a population of less that 50,000; and (3) a

small organization that is any not-for-profit enterprise which is independently owned and

operated and is not dominant in its field. Table 9-1 lists EGU entities potentially impacted

by this proposed rule with applicable NAICS code. BART also has 25 non-EGU source

categories as defined by Clean Air Act. Table 9-2 provides a list of example non-EGU

entities with applicable NAICS codes and the Small Business Administrations Size

Standards. States implementing the rule may choose to regulate source categories in addition

to those listed in Table 9-1 and 9-2.
9-2

Table 9-1. Potentially Regulated EGU Categories and Entitiesa

Category

NAICS

Codeb Examples of Potentially Regulated Entities

Industry 221112 Fossil fuel-fired electric utility steam generating units.

Federal

government

22112c Fossil fuel-fired electric utility steam generating units owned by the

Federal government.

State/local/tribal

government

22112c 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.

Table 9-2. Examples of Potentially Regulated Non-EGU Categories and Entities

BART Source Category Name NAICS Codea NAICS Description Size Standardb

Fossil Fuel-Fired Industrial Boilers

(>250 MMBTU heat input per hour)

Various

manufacturing

industries

Varied Varied

Petroleum Refineries 324110 Petroleum Refineries 1500 Employees

Kraft Pulp Mills 322110 Pulp Mills 750 Employees

Portland Cement Plants 327310 Cement Manufacturing 750 Employees

Hydrofluoric, Sulfuric, and Nitric

Acid Plants

325 Chemical Manufacturing 1000 Employees

Primary Aluminum Ore Reduction

Plants

331312 Primary Aluminum

Production

750 Employees

Chemical Process Plants 325 Chemical Manufacturing 1000 Employees

Iron and Steel Mill Plants 3311 Iron and Steel Mill and

Ferroalloy Manufacturing

1000 Employees

a North American Industry Classification System.

b Small Business Administration Size Standards. http://www.sba.gov/size/sizetable2002.html.
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.

9-3

(9.1)

According to the SBA size standards for NAICS code 221112 Utilities-Fossil Fuel

Electric Power Generation, a firm is small if, including its affiliates, it is primarily engaged

in the generation, transmission, and or distribution of electric energy for sale and its total

electric output for the preceding fiscal year did not exceed 4 million megawatt hours

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

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 emissions

reductions, and would leave to the states the task of determining how to obtain those

reductions, including which entities to regulate. Moreover, because affected States would

have discretion to choose the sources to regulate and how much emissions reductions each

selected source would have to achieve, EPA could definitely not predict the effect of the rule

on small entities. Although not required by the RFA, a general analysis of the potential

impact on small entities of the BART Scenario 2 is conducted for informational purposes.

For the small business analysis, EPA assessed the economic and financial impacts of

the rule using the ratio of compliance costs to the value of sales (cost-to-sales ratio or CSR)

using revenues, control costs, and accounting measures of profit. The analysis assesses the

burden of the rule by assuming the affected firms absorb the control costs, rather than

passing them on to consumers in the form of higher prices. One drawback for this approach

is that it does not consider interaction between producers and consumers in a market context. 

Therefore, it likely overstates the impacts on firms affected by the rule and understates the

impacts on consumers. We used the following equation to compute the CSR:

where 

TACC = total annual compliance costs,

i = indexes the number of affected units owned by company j,
2

An ORIS code is a 4 digit number assigned by the Energy Information Administration (EIA) at the U.S.

Department of Energy to power plants owned by utilities.

3

These include common databases such as Hoover’s (2001) and ReferenceUSA (2001). We emphasize that this

is the ultimate parent company in the ownership structure. Therefore it may be different than the owner

identified in Forms EIA 860 and EIA 861.

4

This approach leads to a conservative estimate of ultimate parent company nameplate capacity because all units

in the EIA database could not be matched to an ultimate parent at this time. Therefore, we may under

estimate the true ultimate parent name plate capacity.

9-4

n = number of affected plants, and

TRj = total revenue of ultimate parent company j.

9.1.1 EGU Sector Small Business Impacts

For proposal, the engineering analysis conducted for the rulemaking identified 302

EGU units potentially affected by the rule. Using unit ORIS2

 numbers and the Energy

Information Administration’s publicly available 2002 electric generator databases (Form EIA

860 and Form EIA 861), we identified utility names and nameplate capacity for affected

units. EPA identified 66 ultimate parent companies in publically available company

databases,3 collected sales and employment information, and estimated annual electricity

output rates for these companies. After identifying these units, we excluded units that are

located in CAIR regions in order to identify those units most likely affected by the BART

regulatory program. The following sections describe the results of the data collection.

As previously discussed, the U.S. Small Business Administration established a table

of size standards, matched to North American Industry Classification System (NAICS)

industries that EPA has traditionally used to classify affected entities as small businesses. 

The size standard for firms primarily engaged in the generation, transmission, and/or

distribution of electric energy for sale is total electric less than or equal to 4 million

megawatt hours. In order to classify affected parent companies as small or large using this

standard, we collected data on nameplate capacity for each ultimate parent company that

could be consistently matched with the electric generator database.4 Next, we converted this

measure to megawatt hours using the following formula:

Capacity Factor × NamePlate Capacity (MW) × standard hours per year
9-5

and assumed an average capacity factor of 85 percent and 8,760 standard hours per year. For

Scenario 2 using this approach, we identified one affected small firm. We estimate this small

business will experience a cost-to-sales ratio of approximately 3 percent. 

9.1.2 Non-EGU Sector Small Business Impacts

The engineering analysis conducted for the rulemaking identified over 2,000 records

associated with affected non-EGU units potentially affected by the rule. Using publicly

available sales and employment databases, plant names, and locations, we identified 279

entities and potential owners.

To classify affected ultimate entities as small or large, EPA collected information on

facility names, parent company sales, and parent company employment data. Data were

compared with the appropriate size standard, and entities were classified as small or large. 

For example, ultimate parent companies of cement producers with employment exceeding

750 employees were classified as a large companies. This process identified 36 small

companies, 195 large companies. The remaining 48 entities were either government-owned

(25 entities, primarily state universities) or parent ownership could not be definitively

identified using available databases (23 entities). Those entities whose parent ownership

could not be definitively identified were not included in the analysis.

Under Scenario 2 using the CSR approach described above, EPA found that five nonEGU source category small businesses may experience a 3 percent CSR level or higher. 

Two may experience CSRs between 1 and 3 percent, and the remaining small company

CSRs are below one percent. The median CSR for non-EGU source category small

businesses is 0.3 percent and ranges from 0 to 20 percent.

9.2 Unfunded Mandates Reform Act (UMRA)

Title II of the Unfunded Mandates Reform Act 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 costbenefit 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
9-6

tribal governments,” section 421(5)(A)(i), 2 U.S.C. 658(5)(A)(i), except for, among other

things, a duty that is “a condition of federal assistance,” section 421(5)(A)(i)(I). A “federal

private sector mandate” includes a regulation that “would impose an enforceable duty upon

the private sector,” with certain exceptions, section 421(7)(A), 2 U.S.C. 658(7)(A).

Before promulgating an EPA rule for which a written statement is needed under

Section 202 of the UMRA, Section 205, 2 U.S.C. 1535, of the UMRA generally requires

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.

The EPA is not directly establishing any regulatory requirements that may

significantly or uniquely affect small governments, including tribal governments. Thus, 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. 

Notwithstanding these issues, EPA conducted an analysis that would be required by

UMRA if its statutory provisions applied, and the EPA has consulted with governmental

entities as would be required by UMRA. Consequently, it is not necessary for EPA to reach

a conclusion as to the applicability of the UMRA requirements. 

9.2.1 EGU UMRA Analysis

Using unit ORIS numbers and the Energy Information Administration’s publicly

available 2002 electric generator databases (Form EIA 860 and Form EIA 861), we identified

affected units that were owned by states or municipalities. There were seven units that met

this criteria and two state government entities owned these units. 

Under Scenario 2, the total annual compliance costs for affected governments are

estimated to be approximately $150 million (1999$). Approximately 2.7 million households

live in governmental jurisdictions that may potentially be impacted by this rulemaking. 

9.2.2 Non-EGU UMRA Analysis

This section of the analysis focuses upon the impacts for government entities owning

BART-eligible units in non-EGU source categories. Using lists of affected facility names,

EPA identified 25 affected entities that were owned by states or municipalities (primarily
9-7

universities). Under Scenario 2, the total annual compliance costs for the governments

owning the 25 affected entities are estimated to be approximately $40 million (1999$). 

9.3 Paperwork Reduction Act

The rule clarifies, but does not modify the information collection requirements for

BART. Therefore, this action does not impose any new information collection burden.

However, the OMB has previously approved the information collection requirements

contained in the existing regulations [40 CFR Part 51] under the provisions of the Paperwork

Reduction Act, 44 U.S.C. 3501 et seq. and has assigned OMB control number 2060-0421,

EPA ICR number 1813.04. 

Burden means the total time, effort, or financial resources expended by persons to

generate, maintain, retain, or disclose or provide information to or for a federal agency. This

includes the time needed to review instructions; develop, acquire, install, and use technology

and systems for the purposes of collecting, validating, and verifying information, processing

and maintaining information, and disclosing and providing information; adjust the existing

ways to comply with any previously applicable instructions and requirements; train

personnel to be able to respond to a collection of information; search data sources; complete

and review the collection of information; and transmit or otherwise disclose the information. 

9.4 Executive Order 13132: Federalism

Executive Order 13132, entitled federalism (64 FR 43255, August 10, 1999), requires

EPA to develop an accountable process to ensure “meaningful and timely input by state and

local officials in the development of regulatory policies that have federalism implications”

are defined in the Executive Order to include regulations that have “substantial direct effects

on the states, on the relationship between the national government and the states, or on the

distribution of power and responsibilities among the various levels of government.” Under

Section 6 of Executive Order 13132, EPA may not issue a regulation that has federalism

implications, that imposes substantial direct compliance costs, and that is not required by

statute, unless the federal government provides the funds necessary to pay the direct

compliance costs incurred by state and local governments, or EPA consults with State and

local officials early in the process of developing the regulation. The EPA also may not issue

a regulation that has federalism implications and that preempts state law unless EPA consults

with state and local officials early in the process of developing the regulation.

We have concluded that this rule, promulgating the BART guidelines, will not have

federalism implications, as specified in section 6 of the Executive Order 13132 (64 FR
9-8

43255, August 10, 1999), because it will not have substantial direct effects on the states, nor

substantially alter the relationship or the distribution of power and responsibilities between

the states and the federal government. Nonetheless, we consulted with a wide scope of state

and local officials, including the National Governors Association, National League of Cities,

National Conference of State Legislatures, U.S. Conference of Mayors, National Association

of Counties, Council of State Governments, International City/County Management

Association, and National Association of Towns and Townships, during the course of

developing this rule.

9.5 Executive Order 13175: Consultation and Coordination with Indian Tribal

Governments

Executive Order 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 defined by Executive Order 13175. It

does not have a substantial direct effect on one or more Indian Tribes. 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 federal government and Tribes in developing plans to attain the NAAQS, and this rule

does nothing to modify that relationship. This rule does not have tribal implications, and

Executive Order 13175 does not apply to this rulemaking. 

9.6 Executive Order 13045: Protection of Children from Environmental Health and

Safety Risks

Executive Order 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 Executive Order 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.

The BART rule and guidelines are not subject to the Executive Order, because the

rule and guidelines do not involve decisions on environmental health or safety risks that may
9-9

disproportionately affect children. The EPA believes that the emissions reductions from the

control strategies considered in this rulemaking will further improve air quality and will

further improve children’s health.

9.7 Executive Order 13211: Actions Concerning Regulations That Significantly

Affect Energy Supply, Distribution, or Use

This rule is not a “significant energy action” as defined in Executive Order 13211,

“Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or

Use” (66 Fed. Reg. 28355 [May 22, 2001]), because it is not likely to have a significant

adverse effect on the supply, distribution, or use of energy. This rule is not a “significant

energy action,” because it has less than a 1 percent impact on the cost of energy production

and does not exceed other factors described by OMB that may indicate a significant adverse

effect. (See, “Guidance for Implementing E.O. 13211,” OMB Memorandum 01-27 [July 13,

2001] www.whitehouse.gov/omb/memoranda/m01-27.html) Specifically, the presumptive

requirements for EGUs for this rule when fully implemented are expected have a 0.25

percent impact on the cost of energy production for the nation in 2015. States must use the

guidelines in making BART determinations for power plants with a generating capacity in

excess of 750 MW. Our analysis evaluates the impact of the presumptive requirements for

these sources and does not consider any possible additional controls for EGU sources or nonEGU sources that states may require. Although states may choose to use the guidelines in

establishing BART limits for non-EGUs , ultimately states will determine the sources subject

to BART and the appropriate level of control for such sources.

We are finalizing the reproposal of the rule following the D.C. Circuit’s remand of

the BART provisions in the 1999 regional haze rule. The 1999 regional haze rule provides

substantial flexibility to the states, allowing them to adopt alternative measures such as a

trading program in lieu of requiring the installation and operation of BART. This

rulemaking does not restrict the ability of the states to adopt alternative measures. The

regional haze rule accordingly already provides an alternative to BART that reduces the

overall cost of the regulation and its impact on the energy supply. The BART rule itself

offers flexibility by offering the choice of meeting SO2 requirements between an emission

rate and a removal rate. 

For a state that chooses to require case-by-case BART, this rule would establish

presumptive levels of controls for SO2 and NOx for certain EGUs that the state finds are

subject to BART. Based on its consideration of various factors set forth in the regulations;

however, a state may conclude that a different level of control is appropriate. The states will
5

 U.S. Environmental Protection Agency, 1998. Guidance for Incorporating Environmental Justice Concerns in

EPA’s NEPA Compliance Analyses. Office of Federal Activities, Washington, DC, April, 1998.

9-10

accordingly exercise substantial intervening discretion in implementing the final rule. 

Additionally, we have assessed that the compliance dates for the rule will provide adequate

time for EGUs to install the required emission controls.

9.8 National Technology Transfer and Advancement Act

Section 12(d) of the National Technology Transfer Advancement Act of 1995

(NTTAA), Public Law No. 104-113, §12(d)(15 U.S.C. 272 note) directs EPA to use

voluntary consensus standards (VCS) in its regulatory activities unless to do so would be

inconsistent with applicable law or otherwise impractical. Voluntary consensus standards

are technical standards (e.g., materials specifications, test methods, sampling procedures, and

business practices) that are developed or adopted by VCS bodies. The NTTAA directs EPA

to provide Congress, through OMB, explanations when the EPA decides not to use VCS.

This guidance does not involve technical standards; thus, EPA did not consider the

use of any VCS.

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

Minority Populations and Low-Income Populations

Executive Order 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,5

 agencies are to assess whether minority or lowincome 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. Negative

impacts to these subpopulations that appreciably exceed similar impacts to the general

population are not expected, because the Agency expects this rule to lead to reductions in air

pollution emissions and exposures generally. 
9-11

9.10 Congressional Review Act

The Congressional Review Act, 5 U.S.C. 801 et seq., as added by the Small Business

Regulatory Enforcement Fairness Act of 1996, generally provides that before a rule may take

effect, the agency promulgating the rule must submit a rule report, which includes a copy of

the rule, to each House of the Congress and to the Comptroller General of the United States. 

The EPA will submit a report containing this rule and other required information to the U.S.

Senate, the U.S. House of Representatives, and the Comptroller General of the United States

prior to publication of the rule in the Federal Register. A Major rule cannot take effect until

60 days after it is published in the Federal Register. This action is a “major rule” as defined

by 5 U.S.C. 804(2). 

9.11 References

Hoover’s. 2001. Hoover’s Online Data Service for Dun & Bradstreet. Available at

<http://www.hoovers.com/free/>.

InfoUSA. 2001. InfoUSA Sales Solutions Database of Company Sales. Available at

<http://www.infousa.com/>.

U.S. Environmental Protection Agency (EPA). 1998. Guidance for Incorporating

Environmental Justice Concerns in EPA’s NEPA Compliance Analyses. Office of

Federal Activities, Washington, DC, April.
10-1

SECTION 10

COMPARISON OF BENEFITS AND COSTS

The estimated social costs to implement BART, as described in this document, range

from approximately $0.3 to $2.9 billion annually for 2015 depending on the actions taken by

States and the discount rate (1999 dollars, 3 percent or 7 percent discount rate). Thus, the

annual net benefits (social benefits minus social costs) range from $1.9 + B billion or $12.0 +

B billion annually (1999 dollars, based on State actions taken and discount rates of 3 percent

or 7 percent) in 2015. (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 10-1 presents a

summary of the benefits, costs, and net benefits of the final rule. The benefits and costs of a

less stringent and a more stringent option than Scenario 2 are also presented in Table 10-1. 

The benefits and costs reported for BART represent estimates that assume

implementation of a complete CAIR program (includes the CAIR promulgated rule and the

proposal to include annual SO2 and NOx controls for New Jersey and Delaware) in the

baseline. Annual SO2 and NOx controls for Arkansas are included in the modeling used to

develop the CAIR baseline estimates resulting in a slight overstatement of the reported

benefits and costs for the complete CAIR program and a slight understatement of the benefits

and costs for BART. The recently promulgated CAMR has not been considered in the

baseline used to conduct the analysis of benefits and costs for BART. As with any complex

analysis of this scope, a number of uncertainties are inherent in the final estimate of benefits

and costs that are described fully in Chapters 4, 7, and 8 of the RIA. 
10-2

Table 10-1. Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air

Visibility Rule—2015a (billions of 1999 dollars)

Description Scenario 1 Scenario 2 Scenario 3

Social costsb

3 percent discount rate $0.4 $1.4 $2.3

7 percent discount rate $0.3 $1.5 $2.9

Social benefitsc,d,e

3 percent discount rate $2.6 + B $10.1 + B $14.3 + B

7 percent discount rate $2.2 + B $8.6 + B $12.2 + B

Health-related benefits:

3 percent discount rate $2.5 $9.8 $13.9

7 percent discount rate $2.1 $8.4 $11.8

Visibility benefits $0.08 $0.24 $0.42

Net benefits (benefits-costs)

e,f

3 percent discount rate $2.2 + B $8.7 + B $12.0 + B

7 percent discount rate $1.9 + B $7.1 + B $9.3 + B

a All estimates are rounded to two significant digits for ease of presentation and computation. 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 is assumed to implemented in the baseline for the BART analysis. 

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 and potentially

a minimal understatement of the benefits and costs for BART. The impact of the recently promulgated Clean

Air Mercury Rule was not been considered in the baseline for BART. 

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

non-EGU source categories nationwide in 2015. The discount rate used to conduct the analysis impacts the

control strategies chosen for the non-EGU source category resulting in greater level of controls under the 3

percent discount rate for Scenario 1. 

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 visibility) and are associated with NOx and SO2 reductions. Visibility

benefits represent benefits in Class I areas in the southeastern and southwestern United States. Ozone

benefits are likely to occur with BART, but are not estimated in this analysis. 

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.
10-3

10.1 References

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.
A-1

APPENDIX A

BART INDUSTRY-SECTOR IMPACTS 

EPA estimates the direct costs of implementing the BART guidance to range

from$0.3 to $2.9 billion in 2015 in the BART region. Given possible impacts of this

guidance on electricity generators and manufacturing industries, we believe it is important to

gauge the extent to which the guidance might affect other parts of the economy. To do so,

we conducted a limited analysis of the economy-wide effects of implementing BART. This

has been done for three alternative implementation scenarios: “Scenario 1,” “Scenario 2,”

and “Scenario 3.”

We were particularly interested in learning how possible changes in electricity prices

might affect industry sectors that are large electricity users and how changes in

manufactured-goods prices might affect other businesses and households. The models we

employed indicated those impacts would be small, even without incorporating the beneficial

economic effects of BART-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 BART 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 model are likely overstated, primarily because the

positive market impacts of the BART guidance 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 the macroeconomic model employed has yet been configured to include the

indirect economic benefits of air quality improvements, EPA employed a computable general

equilibrium (CGE) model to gauge the potential magnitude of the economy-wide effects of

BART implementation costs. The model, called EMPAX-CGE, is currently in peer review. 

As with all models, this tool has its strengths and weaknesses. The results of the CGE
1

See <http://www.epa.gov/cleanair2004/> for details.

2

See <http://www.epa.gov/airmarkt/epa-ipm/> for complete IPM documentation.

3

See <http://www.epa.gov/ttn/ecas/AirControlNET.htm> for complete AirControlNET documentation.

A-2

analysis show small impacts of the BART guidance on energy-intensive and manufacturing

industries. For example, production changes for the chemical manufacturing industry are

estimated at less than –0.1 percent in 2015. 

Furthermore, EMPAX-CGE is not configured to capture the beneficial economic

consequences of the increased labor availability and productivity expected to result from

BART-related air quality improvements. If these labor productivity improvements were

included, the small production output decreases projected by the model 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 CGE models. 

The analysis of BART by the EMPAX-CGE general equilibrium model follows.

A.1 EMPAX-CGE Regional Macroeconomic Analysis of BART

The BART guidance reduces emissions of SO2 and NOx from electricity generation

and combustion manufacturing emissions sources to improve air quality.1 To complement

the analysis of BART effects on electricity generation conducted using IPM2 and the effects

on specific manufacturing sectors conducted using AirControlNET,3 the macroeconomic

implications of this guidance have been estimated 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 the BART guidance 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. Detailed results for the BART Scenario

2 are presented in Sections A.1.4 through A.1.7. Next in Section A.1.8, comparisons of

Scenarios 1 and 3 for the BART guidance are shown for domestic industrial output and gross

domestic product (GDP). Section A.1.9 discusses alternative methods of linking EMPAXCGE to the IPM model results for electricity and consequences for the EMPAX-CGE results. 

Finally, Section A.2 provides additional information on the EMPAX-CGE model.
4

See Section A.2 for additional details on the EMPAX-CGE model.

5

See <http://www.epa.gov/cair>.

A-3

Please note that this analysis focuses on electricity-sector and relevant

manufacturing-sector impacts of the BART guidance as estimated by IPM and

AirControlNET. It does not account for other economic and noneconomic effects, especially

the substantial economic and health benefits associated with reduced emissions.

A.1.1 Background and Summary of EMPAX-CGE Model4

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 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 Clean Air

Interstate Rule, predecessor to BART.5

The dynamic version of EMPAX-CGE employed in this analysis of the BART

guidance 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. 
6

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.

7

EIA forecasts from the Annual Energy Outlook 2003 (AEO) (EIA, 2003) are used in this analysis.

A-4

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 as a result of the use of factors in fixed supply (land and inputs of

primary fuels, respectively). 

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.6 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 2025.7

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 data

sources to cover important distortions from capital and income taxes. 
8

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).

A-5

A.1.2 Modeling Approach for Electricity and Manufacturing 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,8 some other types of 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- and firm-specific

natures 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

possibly the construction of new generating units) can affect electricity prices, manufacturing

costs, and fuel use, a detailed characterization of the electricity and industrial markets is

preferable when estimating implications of policies like the BART guidance. For these

reasons, we developed an interface that allows linkages between EMPAX-CGE and the IPM

and AirControlNET models.

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.

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 regulations affecting electricity generation like the BART guidance, which require a very
A-6

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)

AirControlNET is a PC-based relational database tool for conducting control strategy

and costing analysis. It overlays a detailed control measure database on EPA emissions

inventories to compute source- and pollutant-specific emission reductions and associated

costs at various geographic levels (national, regional, local). It contains a database of control

measures and cost information for reducing the emissions of criteria pollutants (e.g., NOx,

SO2, VOC, PM10, PM2.5, NH3) as well as CO and Hg from point (utility and nonutility), area,

nonroad, and mobile sources as provided in EPA’s National Emission Inventory (NEI). For

the industries affected by the BART guidance, AirControlNET provides estimates of cost

changes by industry and region of the country to the EMPAX-CGE model.

For EMPAX-CGE to effectively incorporate these IPM and AirControlNET 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 industries in
A-7

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 EMPAX-CGE. Once these expenditures are specified, the incremental costs from

IPM and AirControlNET can be used to adjust the production technologies and input

purchases by electricity generation in the CGE model.

A.1.3 Modeling Methodology for the BART Guidance

The macroeconomic impacts of the BART guidance, as simulated by CGE models,

will be a function of the methodology used to link IPM and AirControlNET 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 and manufacturing draws

some capital, labor, and materials from other sectors of the economy. This, in turn, may

affect prices in markets supplying inputs to these industries. Similarly, changes in coal and

gas use by electric utilities 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 or fuel

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
9

See Section A.1.9 for EMPAX-CGE results using other linkages to IPM.

A-8

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.

For the electricity-generation industry, the methodology used to link IPM and

EMPAX-CGE for this BART analysis focuses on BART resource costs and implications for

coal use by utilities.9 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. Cost data from AirControlNET are used

to adjust the technologies used to manufacture goods in EMPAX-CGE to account for any

additional production costs.

A.1.4 Projected Impacts on Specific Industries

Impacts of the BART guidance on manufacturing and electricity-generation costs 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 A-1, estimates

for output changes from BART are generally less than 0.05 percent.

Some industries are affected more than others. BART has little impact on electric

utilities, with energy-intensive sectors of the economy being relatively more affected than

other firms because their costs have risen more than other segments of the economy. 

However, the largest of these declines in output (paper and allied products) is approximately

two-tenths of 1 percent.
A-9

-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 A-1. BART Impacts on U.S. Domestic Output, 2015

Source: EMPAX-CGE (BART Scenario 2)

Regional effects tend to show some variation that does not appear at the national

level. Figure A-2 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 eastern part of the United States is relatively unaffected by the policy, other than

through spillover effects reflected in both IPM and EMPAX-CGE, compared to the western

half of the country. Changes in natural gas and coal production are also distributed unevenly

across the country.
10Results for the seven components of energy-intensive sectors are aggregated in Figure A-3 for presentation

purposes, although they are all included simultaneously in the EMPAX-CGE model run.

A-10

-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 A-2. BART Impacts on Regional Energy Output, 2015

Source: EMPAX-CGE (BART Scenario 2)

Figure A-3 shows regional results for nonenergy industries in EMPAX-CGE.10

Although these sectors show more regional variation based on differences in production

methods and changes in manufacturing costs, the majority of the impacts are less than five

one-hundredths of 1 percent. Energy-intensive firms in the Northeast experience a

comparative advantage relative to other parts of the country. Impacts on other

manufacturing and 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.
A-11

-1.00%

-0.75%

-0.50%

-0.25%

0.00%

0.25%

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 A-3. BART Impacts on Regional Industrial Output, 2015

Source: EMPAX-CGE (BART Scenario 2)

Although the average effect on energy-intensive industries is negative because of

increased manufacturing costs, industries in some parts of the United States are estimated to

be raising their output. Even though costs have risen slightly, they experience an advantage

over similar firms in other regions that face proportionately larger increases. Although the

Northeast sees the greatest improvement in comparative advantage, output also rises in some

other regions (see Figure A-4). The largest decline is in paper manufacturing in the South.
A-12

-2.50%

-2.00%

-1.50%

-1.00%

-0.50%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

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 A-4. BART Impacts on Regional Energy-Intensive Output, 2015

Source: EMPAX-CGE (BART Scenario 2)

A.1.5 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 the BART guidance, consumer price levels will be affected

directly by changes in electricity and manufactured-goods prices faced by households and

indirectly by changes in goods prices that have been produced using those commodities. 

Figure A-5 shows that average consumer prices are essentially unchanged. 
A-13

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

2005 2010 2015 2020 2025

Percent Change from Reference

Figure A-5. Change in Consumer Prices Compared to Reference Case

Source: EMPAX-CGE (BART Scenario 2)

Note: Changes occur in 2005 as people react to the policy announcement, in anticipation of future effects.

A.1.6 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 is a full-employment model in which households choose between labor and

leisure time, based on both income and substitution effects.

Figure A-6 gives EMPAX-CGE’s projected impacts of BART on labor markets. The

results indicate that people are choosing to work slightly more hours to offset additional

costs of purchasing goods. These effects are extremely small, however, on the order of five

ten-thousandths of 1 percent.
A-14

-0.010%

-0.008%

-0.006%

-0.004%

-0.002%

0.000%

0.002%

0.004%

0.006%

0.008%

0.010%

2005 2010 2015 2020 2025

Percent Change from Reference

Figure A-6. Change in Labor Inputs Compared to Reference Case

Source: EMPAX-CGE (BART Scenario 2)

Note: Changes occur in 2005 as people react to the policy announcement, in anticipation of future effects.

A.1.7 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 BART 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

production in the United States, as shown in Figure A-7. Because these results are

incomplete and do not reflect potential benefits of BART, the impacts on GDP should not be

construed as the costs of the guidance. EMPAX-CGE projects decreases in GDP for BART

Scenario 2 of between 0.01 percent and 0.02 percent (two one-hundredths of 1 percent). 
A-15

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

2005 2010 2015 2020 2025

Change in GDP (Percentage Change)

Figure A-7. Change in GDP Compared to Reference Case

Source: EMPAX-CGE (BART Scenario 2)

Note: Changes occur in 2005 as people react to the policy announcement, in anticipation of future effects.

Overall, it should be noted that the estimated implications of the BART guidance for

U.S. GDP are extremely small relative to the total size of the economy. Figure A-8

illustrates GDP in the model baseline and BART 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 BART such as improvements in labor

productivity from environmental improvements.
A-16

$10,711

$12,401

$16,753

$19,272 - Reference $19,269

$16,756 - Reference

$14,435 - Reference

$12,403 - Reference

$10,712 - Reference

$14,432

$10,000

$12,500

$15,000

$17,500

$20,000

2005 2010 2015 2020 2025

Gross Domestic Product (Billion 1999$)

EMPAX Reference Case

BART Policy Case

Figure A-8. U.S. Gross Domestic Product (GDP): Reference Case vs. BART

Source: EMPAX-CGE (BART Scenario 2)

Note: Changes occur in 2005 as people react to the policy announcement, in anticipation of future effects.

National GDP effects like those in Figure A-8 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.
A-17

-0.25%

-0.20%

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

2005 2010 2015 2020 2025

Change in GDP (%)

Northeast South Midwest Plains West US

Figure A-9. Change in Regional GDP Compared to Reference Case

Source: EMPAX-CGE (BART Scenario 2)

Note: Changes occur in 2005 as people react to the policy announcement, in anticipation of future effects.

Figure A-9 presents the regional GDP changes estimated by EMPAX-CGE that

underlie the national U.S. results above. As with other types of results, northeastern States

are relatively unaffected by BART for several reasons: most of the resource costs are

experienced by electricity generators in the West, 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 Plains, have a higher proportion of energy-intensive industries such

as paper and chemicals that experience higher impacts and, as a result, have slightly larger

relative GDP declines than the U.S. average. 
A-18

Tables A-1 and A-2 show EMPAX-CGE estimates of changes in revenue and output

quantities for 2015 and 2020 for BART Scenario 2.

Table A-1. U.S. Domestic Output Changes

Variable Industry 2015 2020

Percentage Change in Revenue (%) Coal 0.07% 0.32%

Crude Oil 0.00% 0.00%

Electricity 0.06% 0.05%

Natural Gas –0.07% –0.11%

Petroleum –0.03% –0.03%

Agriculture –0.04% –0.04%

Energy-Intensive Manufacturing –0.03% –0.03%

Other Manufacturing –0.01% –0.01%

Services –0.01% –0.01%

Transportation –0.02% –0.02%

Percentage Change In Quantity (%) Coal 0.02% 0.10%

Crude Oil 0.00% 0.00%

Electricity –0.07% –0.03%

Natural Gas –0.02% –0.03%

Petroleum –0.09% –0.09%

Agriculture –0.04% –0.04%

Energy-Intensive Manufacturing –0.11% –0.11%

Other Manufacturing –0.01% –0.01%

Services –0.01% –0.01%

Transportation –0.02% –0.02%

Source: EMPAX-CGE (BART Scenario 2)
A-19

Table A-2. U.S. Domestic Energy-Intensive Sector Output Changes

Variable Industry 2015 2020

Percentage Change in Revenue (%) Food and Kindred –0.02% –0.02%

Paper and Allied 0.04% 0.03%

Chemicals –0.07% –0.08%

Glass –0.02% –0.03%

Cement –0.01% 0.00%

Iron and Steel 0.01% 0.01%

Aluminum 0.00% 0.00%

Percentage Change In Quantity (%) Food and Kindred –0.05% –0.05%

Paper and Allied –0.21% –0.22%

Chemicals –0.13% –0.14%

Glass –0.05% –0.05%

Cement –0.02% –0.02%

Iron and Steel –0.05% –0.06%

Aluminum –0.03% –0.03%

Source: EMPAX-CGE (BART Scenario 2)

A.1.8 Alternative BART Scenarios

The preceding results focus on BART Scenario 2. This section compares these

results to those for BART Scenarios 1 and 3. Figure A-10 shows how the alternative

scenarios affect industrial output. In general, the impacts follow a pattern similar to the

stringency of the individual BART scenario. However, all impacts remain uniformly small

across the three sets of results.

A comparable pattens also holds for GDP changes across the three alternatives (see

Figure A-11). Scenario 2 has the midrange GDP effects with Scenario 1 showing essentially

no GDP impacts and Scenario 3 having a decline in GDP of between two and four onehundredths of 1 percent. 
A-20

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3

Coal Crude

Oil

ElectricityNatural

Gas

PetroleumAgricultureEIS:

Food and

Kindred

EIS:

Paper

and

Allied

EIS:

Chemicals

EIS:

Glass

EIS:

Cement

EIS:

Iron and

Steel

EIS:

Aluminum

Other

Manufacturing

ServicesTransport

Percent Change from Reference

Figure A-10. Domestic Output Impacts of Alternative BART Scenarios

Source: EMPAX-CGE

A.1.9 Alternative IPM-to-EMPAX Linkages

As discussed in Section A.1.2, 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 for BART Scenario 2, as shown by changes in

GDP, using two alternative methods for linking EMPAX-CGE to the IPM results. These

alternative findings are contrasted to this “Central Case” (i.e., the results presented above for

Scenario 2) to demonstrate how the methodology used to link the two models can influence

results. One alternative linkage, 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.
A-21

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

2005 2010 2015 2020 2025

Change in GDP (

%

BART - Scenario 2 BART - Scenario 1 BART - Scenario 3

Figure A-11. GDP Impacts of Alternative BART Scenarios

Source: EMPAX-CGE

Note: Changes occur in 2005 as people react to the policy announcement, in anticipation of future effects.

Specifically, the three alternative approaches are as follows:

C “Central Case”—This BART Scenario 2 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.

C “Unconstrained Case”—This BART Scenario 2 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
A-22

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 BART Scenario 2 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. 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 A-12 illustrates the implications of these alternative linkages between IPM

and EMPAX-CGE for estimates of BART 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 between 10 and 15

percent lower than in the “Central Case.” 

The “IPM Price & Fuel Case” shows changes in GDP are generally lower than in the

“Central Case.” 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 2015 through 2025, leading to smaller

GDP effects in the “IPM Price & Fuel Case” than in the “Central Case.” 
A-23

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

2005 2010 2015 2020 2025

Change in GDP (%)

EMPAX - Central Case EMPAX - Unconstrained EMPAX - IPM Price & Fuel 

Figure A-12. GDP Impacts of Alternative Linkages (%)

Source: EMPAX-CGE

Note: Changes occur in 2005 as people react to the policy announcement, in anticipation of future effects.

A.2 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 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. 
11Although it is not illustrated in Figure A-13, 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).

A-24

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 (K LE) composite. 

Intermediate materials inputs are the 11 types of nonenergy goods, in fixed proportion for each industry. 

E n ergy V alue A d ded 

Capital Labor 

Energy 

(5 Types) 

The K LE composite is a CES function 

of energy and value-added (K L). 

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 A-13. General Production and Consumption Nesting Structure in EMPAXCGE

Nested CES functions are used to portray substitution possibilities available to

producers and consumers. Figure A-13 illustrates this general framework and gives a broad

characterization of the model.11 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.
12See Brooke et al. (1998) for a description of GAMS (http://www.gams.com/).

13Solving EMPAX-CGE as an 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.

14See Rutherford (1999) for MPSGE documentation (http://debreu.colorado.edu).

15See <http://www.implan.com/index.html> for a description of the Minnesota IMPLAN Group (2003) and its

data.

16These 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 (EIA, 2001;

undated[a and b]).

A-25

Given this basic similarity, EMPAX-CGE has adopted a comparable structure. 

EMPAX-CGE is programmed in the GAMS12 language (Generalized Algebraic Modeling

System) and solved as a mixed complementarity problem (MCP)13 using MPSGE software

(Mathematical Programming Subsystem for General Equilibrium).14 The PATH solver from

GAMS is used to solve the MCP equations generated by MPSGE.

A.2.1 Data Sources

The economic data come from state-level information provided by the Minnesota

IMPLAN Group15 and energy data come from EIA.16 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
17Energy-intensive sectors industry categories are based on EIA definitions of energy-intensive manufacturers in

the Assumptions for the Annual Energy Outlook 2003.

18Economic 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 policy

analyses, 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.

A-26

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.

Table A-3 presents the industry categories included in EMPAX-CGE for policy

analysis. Their focus is on maintaining as much detail in the energy-intensive sectors17 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 affect only

fossil-fired electricity.

Figure A-14 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).18
A-27

Table A-3. 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.

A.2.2 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 A-13 and associated substitution elasticities define current production technologies

and possible alternatives.
A-28

Figure A-14. Regions Defined in EMPAX-CGE for Policy Analysis

A.2.3 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
A-29

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

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.

A.2.4 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. 

A.2.5 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
19See 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).

A-30

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

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.

A.2.6 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.19 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
20A 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.

A-31

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.

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.20 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.

A.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. Available at <http://www.aeaweb.org/aer/>.

Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User’s Guide. 

GAMS Development Corporation. Available at http://www.gams.com.
A-32

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. Available at

<http://www.journals.uchicago.edu/JPE/home.html>

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. 

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. Undated (a). 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. Undated (b). State Energy

Price and Expenditure Report. Washington DC. Available at

http://www.eia.doe.gov/emeu/states/price_multistate.html. 
A-33

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.eia.doe.gov/oiaf/aeo/pdf/0383 (2001).
B-1

APPENDIX B

COST AND ECONOMIC IMPACT SUPPLEMENTAL INFORMATION AND

SENSITIVITY ANALYSES

This appendix presents supplemental information concerning the cost and economic

impact analyses conducted for BART. Section B.1 presents a memo containing a list of

BART EGUs potentially affected by the rule, and Section B.2 contains a list of BART EGUs

used in the modeling of control scenarios for this RIA. Section B.3 presents a number of

non-EGU cost and economic impact sensitivity analyses. 

B.1 List of EGU Units Potentially Affected by BART

Memo From Perrin Quarles Associates, Inc.

Re: Follow-Up on Units Potentially Affected by BART

July 19, 2004

Perrin Quarles Associates, Inc.

675 Peter Jefferson Parkway, Suite 200

Charlottesville, Virginia 22911

Voice: (434) 979-3700 C Fax: (434) 296-2860

Email: pqa@pqa.com

MEMORANDUM

TO: Roman Kramarchuk

FROM: Doran Stegura

RE: Follow-Up on Units Potentially Affected by BART

DATE: July 19, 2004

On March 24, 2003, PQA delivered an analysis of sources that may be subject to

controls under EPA’s Proposed Guidelines for Best Available Retrofit Technology (BART)

Determinations. This analysis provided a list of BART units, additional information on the

location and control technologies for each unit, and control cost information. The March

2003 analysis only focused on the units for which construction was started by August 7, 1977

and that were not in operation prior to August 7, 1962. Based on EPA guidance, the original
B-2

analysis also assumed that BART-eligible units are only those that are located at a plant

where the total capacity of all units within the BART timeframe exceeds 750 MW. 

This follow-up analysis provided additional information on units that are below the

750 MW threshold, but that are potentially within the specified BART timeframe. The

approach and assumptions used to identify whether units below the 750 MW threshold could

potentially be BART-eligible are consistent with the March 2003 analysis. The units that

required additional follow-up research in this regard are those with an online date on or after

1979 since the BART rule could apply if construction on these units commenced prior to

August 7, 1977. It was assumed that units with a 1977 or 1978 online date started

construction prior to the 1977 cutoff and thus, are considered to be within the BART

timeframe. Hunter, unit 1 (UT) is the only exception since it has a PSD permit with an

online date of 1978. For the units in question, PQA reviewed the RACT/BACT/LAER

Clearinghouse and other internet search information and contacted the appropriate state

environmental agencies to verify when the construction permit was issued. Note that the

date the construction permit was issued is used as an indication of when construction began

for purposes of this analysis. However, actual construction on these units may have started

well after the date the permit was issued. 

In evaluating the list of units below the 750 MW threshold and with an online date in

1979 or later, PQA assumed that: (1) units subject to Part 60, Subpart D cannot be excluded

because this NSPS subpart applies to sources that have started construction after August 17,

1971; (2) units subject to NSPS Subpart Da requirements are outside the applicable BART

time period since these requirements apply to sources that started construction after

September 18, 1978; and (3) units that received a PSD permit (with a BACT requirement)

are outside the BART time period. If a PSD permit was issued, PQA researched the issue

date in order to confirm that the unit is outside the BART time period.

Using the above assumptions, a list of 61 units was compiled that required follow-up

with the state environmental agency to confirm whether construction began prior to August

7, 1977. Of these 61 units, 43 are located in states covered under the Clean Air InterState

Rule (CAIR) and 18 are located in states not covered under CAIR. Per EPA guidance, initial

priority was given to those units not located in a state affected by CAIR. Follow-up with the

state environmental agencies revealed that of the 61 units that required follow-up, 24 units

are within the BART time period, 24 units are outside the BART time period, and 13 units

require further follow-up since the state environmental agency was not able to provide the
B-3

information needed to determine whether the unit started construction prior to August 7,

1977. 

Table B-1 summarizes the 61 units analyzed by PQA. The table provides an

indication of whether the unit is located in a state affected by CAIR, whether the unit has

been identified as within the BART timeframe, and whether additional follow-up with the

state agency for information on construction permit dates is required to determine BART

eligibility.

Table B-1. Potential BART Units Identified for Follow-Up Analysis

State Plant Name

ORIS

Code Unit ID Online NSPS CAIR

In BART

Timeframe?

Follow-Up

Needed

Nameplate

(MW)

AL Charles R Lowman 56 2 1979 D X X 233

AL Charles R Lowman 56 3 1980 D X X 233

AZ Apache Station 160 2 1979 D X 194.7

AZ Apache Station 160 3 1979 D X 194.7

AZ Springerville 8223 1 1985 D 397

AZ Springerville 8223 2 1990 D 397

CO Pawnee 6248 1 1981 D X 500

CO Ray D Nixon 8219 1 1980 D X 207

DE Indian River 594 4 1980 D X X 442.4

FL C D McIntosh 676 3 1982 D X 334

FL Deerhaven 663 B2 1981 D X 250.75

GA McIntosh (6124) 6124 1 1979 PRE X X 177.66

IA Ames 1122 8 1982 D X 65

IA George Neal South 7343 4 1979 D X X 639.9

IA Louisa 6664 101 1983 D X 738.09

IA Ottumwa 6254 1 1981 D X X 726

IN A B Brown Generating Station 6137 1 1979 D X X 265.23

KS Nearman Creek 6064 N1 1981 D X X 261

KY East Bend 6018 2 1981 D X X 669.28

KY R D Green 6639 G1 1979 D X X 263.7

KY R D Green 6639 G2 1981 D X X 263.7

KY Trimble County 6071 1 1990 D X X 566.1

LA Dolet Hills 51 1 1986 D X 720.75

LA R S Nelson 1393 6 1982 D X X 614.6

LA Rodemacher 6190 2 1982 D X X 558

MD Brandon Shores 602 1 1984 D X X 685.08

MD Brandon Shores 602 2 1991 D X X 685.08

MI Presque Isle 1769 9 1979 D X X 90

MI Wyandotte 1866 7 1982 D X 73

MN Clay Boswell 1893 4 1980 D X X 558

(continued)
B-4

Table B-1. Potential BART Units Identified for Follow-Up Analysis (continued)

State Plant Name

ORIS

Code Unit ID Online NSPS CAIR

In BART

Timeframe?

Follow-Up

Needed

Nameplate

(MW)

MO Iatan 6065 1 1980 D X X 725.85

MO Sikeston 6768 1 1981 D X X 261

NC Elizabethtown Power 10380 UNIT1 1985 D X 35

NC Elizabethtown Power 10380 UNIT2 1985 D X 35

NC Lumberton Power 10382 UNIT1 1985 D X 35

NC Lumberton Power 10382 UNIT2 1985 D X 35

NC Mayo 6250 1A 1983 D X 735.84

NC Mayo 6250 1B 1983 D X 735.84

ND Antelope Valley 6469 B1 1984 D 435

ND Antelope Valley 6469 B2 1986 D 435

ND Coyote 8222 B1 1981 D 450

NE Gerald Whelan Energy Center 60 1 1981 D 76.3

NE Nebraska City 6096 1 1979 D X 615.87

NE Platte 59 1 1982 D 109.8

NV North Valmy 8224 1 1981 D X 254.26

OH Killen Station 6031 2 1982 D X X 666.45

OK Grand River Dam Authority 165 1 1982 D 490

OK Hugo 6772 1 1982 D 400

OR Boardman 6106 1SG 1980 D X 560.5

TX Coleto Creek 6178 1 1980 D X X 600.39

TX Gibbons Creek 6136 1 1983 D X 443.97

TX Pirkey 7902 1 1985 D X 720.75

TX San Miguel 6183 SM-1 1982 D X 410

TX Sandow 6648 4 1981 D X X 590.64

UT Hunter (Emery) 6165 1 1978 D X 446.4

UT Hunter (Emery) 6165 2 1980 D X 446.4

WI Edgewater (4050) 4050 5 1985 D X X 380

WI J P Madgett 4271 B1 1979 D X X 387

WI Pleasant Prairie 6170 1 1980 D X X 616.59

WI Pleasant Prairie 6170 2 1985 D X X 616.59

WI Weston 4078 3 1981 D X X 350.46

DLS/jlj
B-5

B.2 EGU Units Presumed to be BART-Eligible for Purposes of Modeling Emissions

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

AL Barry 4 1969 404

AL Barry 5 1971 789

AL Charles R Lowman 1 1969 66

AL Charles R Lowman 2 1979 233

AL Charles R Lowman 3 1980 233

AL Colbert 5 1965 550

AL E C Gaston 5 1974 952

AL Gorgas 10 1972 789

AL Greene County 1 1965 299

AL Greene County 2 1966 269

AL James H Miller Jr 1 1978 706

AL James H Miller Jr 2 1985 706

AL Widows Creek 8 1965 550

AR Flint Creek 1 1978 558

AR Independence 1 1983 850

AR White Bluff 1 1980 850

AR White Bluff 2 1981 850

AZ Apache Station 2 1979 195

AZ Apache Station 3 1979 195

AZ Cholla 2 1978 289

AZ Cholla 3 1980 289

AZ Cholla 4 1981 414

AZ Coronado Generating Station U1B 1979 411

AZ Coronado Generating Station U2B 1980 411

AZ Irvington 4 1967 173

AZ Navajo Generating Station 1 1974 803

AZ Navajo Generating Station 2 1975 803

AZ Navajo Generating Station 3 1976 803

CO Cherokee 3 1962 150

CO Cherokee 4 1968 350

CO Comanche (470) 1 1973 350

(continued)
B-6

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

CO Comanche (470) 2 1975 350

CO Craig C1 1980 446

CO Craig C2 1979 446

CO Hayden H1 1965 190

CO Hayden H2 1976 275

CO Martin Drake 5 1962 50

CO Martin Drake 6 1968 75

CO Martin Drake 7 1974 132

CO Pawnee 1 1981 500

CO Ray D Nixon 1 1980 207

CO Valmont 5 1964 166

CT Bridgeport Harbor Station BHB3 1968 400

DE Edge Moor 4 1966 177

DE Indian River 3 1970 177

DE Indian River 4 1980 442

FL Big Bend BB01 1970 446

FL Big Bend BB02 1973 446

FL Big Bend BB03 1976 446

FL Crist Electric Generating Plant 6 1970 370

FL Crist Electric Generating Plant 7 1973 578

FL Crystal River 1 1966 441

FL Crystal River 2 1969 524

FL Crystal River 4 1982 739

FL Crystal River 5 1984 739

FL F J Gannon GB04 1963 187

FL F J Gannon GB05 1965 239

FL F J Gannon GB06 1967 414

FL Lansing Smith 1 1965 150

FL Lansing Smith 2 1967 190

GA Bowen 1BLR 1971 700

GA Bowen 2BLR 1972 700

GA Bowen 3BLR 1974 880

(continued)
B-7

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

GA Bowen 4BLR 1975 880

GA Hammond 4 1970 500

GA Harllee Branch 1 1965 250

GA Harllee Branch 2 1967 319

GA Harllee Branch 3 1968 481

GA Harllee Branch 4 1969 490

GA Jack McDonough MB1 1963 245

GA Jack McDonough MB2 1964 245

GA Kraft 3 1965 104

GA McIntosh (6124) 1 1979 178

GA Mitchell 3 1964 125

GA Scherer 1 1982 818

GA Scherer 2 1984 818

GA Wansley (6052) 1 1976 865

GA Wansley (6052) 2 1978 865

GA Yates Y6BR 1974 350

GA Yates Y7BR 1974 350

IA Ames 7 1968 33

IA Burlington (IA) 1 1968 212

IA Council Bluffs 3 1978 726

IA Fair Station 2 1967 38

IA George Neal North 1 1964 147

IA George Neal North 2 1972 349

IA George Neal North 3 1975 550

IA George Neal South 4 1979 640

IA Lansing 4 1977 275

IA Milton L Kapp 2 1967 218

IA Muscatine 8 1969 75

IA Ottumwa 1 1981 726

IA Pella 6 1963 38

IA Pella 7 1973 38

IA Prairie Creek 4 1967 149

(continued)
B-8

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

IA Sixth Street 2 1970 85

IA Sixth Street 4 1970 85

IA Streeter Station 7 1973 35

IL Baldwin 1 1970 623

IL Baldwin 2 1973 635

IL Baldwin 3 1975 635

IL Coffeen 01 1965 389

IL Coffeen 02 1972 617

IL Dallman 31 1968 90

IL Dallman 32 1972 90

IL Dallman 33 1978 207

IL Duck Creek 1 1976 441

IL E D Edwards 2 1968 281

IL E D Edwards 3 1972 364

IL Havana 9 1978 488

IL Joliet 29 71 1965 660

IL Joliet 29 72 1965 660

IL Joliet 29 81 1965 660

IL Joliet 29 82 1965 660

IL Kincaid 1 1967 660

IL Kincaid 2 1968 660

IL Lakeside 7 1965 38

IL Lakeside 8 1965 38

IL Marion 1 1963 33

IL Marion 2 1963 33

IL Marion 3 1963 33

IL Marion 4 1978 173

IL Newton 1 1977 617

IL Newton 2 1982 617

IL Powerton 51 1972 893

IL Powerton 52 1972 893

IL Powerton 61 1975 893

(continued)
B-9

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

IL Powerton 62 1975 893

IL Waukegan 8 1962 355

IL Will County 4 1963 598

IL Wood River 5 1964 388

IN A B Brown Generating Station 1 1979 265

IN Bailly 7 1962 194

IN Bailly 8 1968 422

IN Cayuga 1 1970 531

IN Cayuga 2 1972 531

IN Dean H Mitchell 11 1970 115

IN F B Culley Generating Station 2 1966 104

IN F B Culley Generating Station 3 1973 265

IN Frank E Ratts 1SG1 1970 117

IN Frank E Ratts 2SG1 1970 117

IN Gibson 1 1976 668

IN Gibson 2 1975 668

IN Gibson 3 1978 668

IN Gibson 4 1979 668

IN Harding Street Station (EW Stout) 70 1973 471

IN Merom 1SG1 1983 540

IN Merom 2SG1 1982 540

IN Michigan City 12 1974 540

IN Petersburg 1 1967 253

IN Petersburg 2 1969 471

IN Petersburg 3 1977 574

IN R M Schahfer 14 1976 540

IN R M Schahfer 15 1979 556

IN State Line Generating Station (IN) 4 1962 389

IN Tanners Creek U4 1964 580

IN Wabash River 6 1968 387

IN Warrick 2 1964 144

IN Warrick 3 1965 144

(continued)
B-10

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

IN Warrick 4 1970 323

IN Whitewater Valley 2 1973 60

KS Jeffrey Energy Center 1 1978 720

KS Jeffrey Energy Center 2 1980 720

KS La Cygne 1 1973 893

KS La Cygne 2 1977 685

KS Lawrence Energy Center 5 1971 458

KS Nearman Creek N1 1981 261

KS Quindaro 1 1965 82

KS Quindaro 2 1971 158

KS Tecumseh Energy Center 10 1962 176

KY Big Sandy BSU1 1963 281

KY Big Sandy BSU2 1969 816

KY Cane Run 4 1962 163

KY Cane Run 5 1966 209

KY Cane Run 6 1969 272

KY Coleman C1 1969 174

KY Coleman C2 1970 174

KY Coleman C3 1971 173

KY Cooper 1 1965 100

KY Cooper 2 1969 221

KY E W Brown 2 1963 180

KY E W Brown 3 1971 446

KY East Bend 2 1981 669

KY Elmer Smith 1 1964 151

KY Elmer Smith 2 1974 265

KY Ghent 1 1974 557

KY Ghent 2 1977 556

KY H L Spurlock 1 1977 305

KY H L Spurlock 2 1981 508

KY Henderson I 6 1968 32

KY HMP&L Station 2 H1 1973 180

(continued)
B-11

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

KY HMP&L Station 2 H2 1974 185

KY Mill Creek 1 1972 356

KY Mill Creek 2 1974 356

KY Mill Creek 3 1978 463

KY Mill Creek 4 1982 544

KY Paradise 1 1963 704

KY Paradise 2 1963 704

KY Paradise 3 1970 1150

KY R D Green G1 1979 264

KY R D Green G2 1981 264

KY Robert Reid R1 1965 82

KY Trimble County 1 1990 566

LA Big Cajun 2 2B1 1980 559

LA Big Cajun 2 2B2 1981 559

LA R S Nelson 6 1982 615

LA Rodemacher 2 1982 558

MA Brayton Point 1 1963 241

MA Brayton Point 2 1964 241

MA Brayton Point 3 1969 643

MD Brandon Shores 1 1984 685

MD Brandon Shores 2 1991 685

MD C P Crane 2 1963 209

MD Chalk Point 1 1964 364

MD Chalk Point 2 1965 364

MD Dickerson 3 1962 196

MD Herbert a Wagner 3 1966 359

MD Morgantown 1 1970 626

MD Morgantown 2 1971 626

MI Belle River 1 1984 698

MI Belle River 2 1985 698

MI Eckert Station 4 1964 80

MI Eckert Station 5 1968 80

(continued)
B-12

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

MI Eckert Station 6 1970 80

MI Erickson 1 1973 155

MI Harbor Beach 1 1968 121

MI J H Campbell 1 1962 265

MI J H Campbell 2 1967 385

MI J H Campbell 3 1980 871

MI James De Young 5 1969 29

MI Monroe 1 1971 817

MI Monroe 2 1973 823

MI Monroe 3 1973 823

MI Monroe 4 1974 817

MI Presque Isle 2 1962 38

MI Presque Isle 3 1964 54

MI Presque Isle 4 1966 58

MI Presque Isle 5 1974 90

MI Presque Isle 6 1975 90

MI Presque Isle 7 1978 90

MI Presque Isle 8 1978 90

MI Presque Isle 9 1979 90

MI St. Clair 7 1969 545

MI Trenton Channel 9A 1968 536

MN Allen S King 1 1968 598

MN Clay Boswell 3 1973 365

MN Clay Boswell 4 1980 558

MN Hoot Lake 3 1964 75

MN Northeast Station NEPP 1971 32

MN Riverside (1927) 8 1964 239

MN Sherburne County 1 1976 660

MN Sherburne County 2 1977 660

MN Silver Lake 4 1969 54

MO Asbury 1 1970 232

MO Blue Valley 3 1965 58

(continued)
B-13

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

MO Columbia 7 1965 74

MO Iatan 1 1980 726

MO James River 4 1964 60

MO James River 5 1970 105

MO Labadie 1 1970 574

MO Labadie 2 1971 574

MO Labadie 3 1972 621

MO Labadie 4 1973 621

MO Lake Road 6 1970 90

MO Montrose 3 1964 188

MO New Madrid 1 1972 600

MO New Madrid 2 1977 600

MO Rush Island 1 1976 621

MO Rush Island 2 1977 621

MO Sibley 2 1962 50

MO Sibley 3 1969 419

MO Sikeston 1 1981 261

MO Sioux 1 1967 550

MO Sioux 2 1968 550

MO Southwest 1 1976 194

MO Thomas Hill MB1 1966 180

MO Thomas Hill MB2 1969 285

MS Daniel Electric Generating Plant 1 1977 500

MS Daniel Electric Generating Plant 2 1981 500

MS R D Morrow 1 1978 200

MS R D Morrow 2 1978 200

MS Watson Electric Generating Plant 4 1968 250

MS Watson Electric Generating Plant 5 1973 500

MT Colstrip 1 1975 358

MT Colstrip 2 1976 358

MT J E Corette 2 1968 191

NC Asheville 1 1964 207

(continued)
B-14

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

NC Asheville 2 1971 207

NC Belews Creek 1 1974 1080

NC Belews Creek 2 1975 1080

NC Cliffside 5 1972 571

NC L V Sutton 3 1972 447

NC Lee 3 1962 252

NC Marshall 1 1965 350

NC Marshall 2 1966 350

NC Marshall 3 1969 648

NC Marshall 4 1970 648

NC Roxboro 1 1966 411

NC Roxboro 2 1968 657

NC Roxboro 3A 1973 745

NC Roxboro 3B 1973 745

NC Roxboro 4A 1980 745

NC Roxboro 4B 1980 745

ND Coal Creek 1 1979 506

ND Coal Creek 2 1981 506

ND Leland Olds 1 1966 216

ND Leland Olds 2 1975 440

ND Milton R Young B1 1970 257

ND Milton R Young B2 1977 477

ND R M Heskett B2 1963 75

ND Stanton 1 1967 172

NE Gerald Gentleman Station 1 1979 681

NE Gerald Gentleman Station 2 1982 681

NE Lon D Wright Power Plant 8 1976 92

NE Nebraska City 1 1979 616

NE North Omaha 4 1963 136

NE North Omaha 5 1968 218

NE Sheldon 1 1968 109

NH Merrimack 2 1968 346

(continued)
B-15

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

NJ B L England 1 1962 136

NJ B L England 2 1964 163

NJ Hudson 2 1968 660

NM Four Corners 1 1963 190

NM Four Corners 2 1963 190

NM Four Corners 3 1964 253

NM Four Corners 4 1969 818

NM Four Corners 5 1970 818

NM San Juan 1 1976 361

NM San Juan 2 1973 350

NM San Juan 3 1979 534

NM San Juan 4 1982 534

NV Mohave 1 1971 818

NV Mohave 2 1971 818

NV North Valmy 1 1981 254

NV Reid Gardner 1 1965 114

NV Reid Gardner 2 1968 114

NV Reid Gardner 3 1976 114

NY Dynegy Danskammer 4 1967 239

NY Lovett 4 1966 180

NY Lovett 5 1969 201

NY S A Carlson 12 1963 58

OH Avon Lake Power Plant 12 1970 680

OH Bay Shore 3 1963 141

OH Bay Shore 4 1968 218

OH Cardinal 1 1967 615

OH Cardinal 2 1967 615

OH Cardinal 3 1977 650

OH Conesville 3 1962 162

OH Conesville 4 1973 842

OH Conesville 5 1976 444

OH Conesville 6 1978 444

(continued)
B-16

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

OH Eastlake 5 1972 680

OH Gen J M Gavin 1 1974 1300

OH Gen J M Gavin 2 1975 1300

OH Hamilton 9 1974 51

OH J M Stuart 1 1971 610

OH J M Stuart 2 1970 610

OH J M Stuart 3 1972 610

OH J M Stuart 4 1974 610

OH Killen Station 2 1982 666

OH Lake Shore 18 1962 256

OH Miami Fort 7 1975 557

OH Miami Fort 8 1978 558

OH Muskingum River 5 1968 615

OH W H Sammis 4 1962 185

OH W H Sammis 5 1967 318

OH W H Sammis 6 1969 623

OH W H Sammis 7 1971 623

OH Walter C Beckjord 5 1962 245

OH Walter C Beckjord 6 1969 461

OK Muskogee 4 1977 572

OK Muskogee 5 1978 572

OK Northeastern 3313 1979 473

OK Northeastern 3314 1980 473

OK Sooner 1 1979 569

OK Sooner 2 1980 569

OR Boardman 1SG 1980 561

PA Bruce Mansfield 1 1976 914

PA Bruce Mansfield 2 1977 914

PA Bruce Mansfield 3 1980 914

PA Brunner Island 2 1965 405

PA Brunner Island 3 1969 790

PA Cheswick 1 1970 565

(continued)
B-17

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

PA Conemaugh 1 1970 936

PA Conemaugh 2 1971 936

PA Hatfields Ferry 1 1969 576

PA Hatfields Ferry 2 1970 576

PA Hatfields Ferry 3 1971 576

PA Homer City 1 1969 660

PA Homer City 2 1969 660

PA Homer City 3 1977 692

PA Keystone 1 1967 936

PA Keystone 2 1968 936

PA Mitchell 33 1963 299

PA Montour 1 1972 823

PA Montour 2 1973 819

PA New Castle 5 1964 136

PA Portland 2 1962 255

SC Canadys Steam CAN1 1962 136

SC Canadys Steam CAN2 1964 136

SC Canadys Steam CAN3 1967 218

SC Dolphus M Grainger 1 1966 82

SC Dolphus M Grainger 2 1966 82

SC Jefferies 3 1970 173

SC Jefferies 4 1970 173

SC Wateree WAT1 1970 386

SC Wateree WAT2 1971 386

SC Williams WIL1 1973 633

SC Winyah 1 1975 315

SC Winyah 2 1977 315

SD Big Stone 1 1975 456

TN Bull Run 1 1967 950

TN Cumberland 1 1973 1300

TN Cumberland 2 1973 1300

TX Big Brown 1 1971 593

(continued)
B-18

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

TX Big Brown 2 1972 593

TX Coleto Creek 1 1980 600

TX Harrington Station 061B 1976 360

TX Harrington Station 062B 1978 360

TX J T Deely 1 1977 446

TX J T Deely 2 1978 446

TX Martin Lake 1 1977 793

TX Martin Lake 2 1978 793

TX Monticello 1 1974 593

TX Monticello 2 1975 593

TX Monticello 3 1978 793

TX Sam Seymour 1 1979 615

TX Sam Seymour 2 1980 615

TX Sandow 4 1981 591

TX W A Parish WAP5 1977 734

TX W A Parish WAP6 1978 734

TX Welsh 1 1977 558

TX Welsh 2 1980 558

TX Welsh 3 1982 558

UT Hunter (Emery) 1 1978 446

UT Hunter (Emery) 2 1980 446

UT Huntington 1 1977 446

UT Huntington 2 1974 446

VA Chesapeake 4 1962 239

VA Chesterfield 5 1964 359

VA Chesterfield 6 1969 694

VA Possum Point Power Station 4 1962 239

WA Centralia BW21 1972 730

WA Centralia BW22 1973 730

WI Columbia 1 1975 512

WI Columbia 2 1978 512

WI Edgewater (4050) 4 1969 351

(continued)
B-19

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

WI Edgewater (4050) 5 1985 380

WI Genoa 1 1969 346

WI J P Madgett B1 1979 387

WI Manitowoc 7 1962 79

WI Nelson Dewey 2 1962 114

WI Pleasant Prairie 1 1980 617

WI Pleasant Prairie 2 1985 617

WI Pulliam 8 1964 136

WI South Oak Creek 7 1965 318

WI South Oak Creek 8 1967 324

WI Valley (Wepco) 1 1968 136

WI Valley (Wepco) 2 1968 136

WI Valley (Wepco) 3 1969 136

WI Valley (Wepco) 4 1969 136

WI Weston 3 1981 350

WV Fort Martin 1 1967 576

WV Fort Martin 2 1968 576

WV Harrison 1 1972 684

WV Harrison 2 1973 684

WV Harrison 3 1974 684

WV John E Amos 1 1971 816

WV John E Amos 2 1972 816

WV John E Amos 3 1973 1300

WV Mitchell 1 1971 816

WV Mitchell 2 1971 816

WV Mount Storm Power Station 1 1965 570

WV Mount Storm Power Station 2 1966 570

WV Mount Storm Power Station 3 1973 522

WV Mountaineer (1301) 1 1980 1300

WV Pleasants 1 1979 684

WV Pleasants 2 1980 684

WY Dave Johnston BW43 1964 230

(continued)
B-20

Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling

Emissions (continued)

State FACILITY_NAME UNITID Online Year Nameplate Capacitya

WY Dave Johnston BW44 1972 360

WY Jim Bridger BW71 1974 561

WY Jim Bridger BW72 1975 561

WY Jim Bridger BW73 1976 561

WY Jim Bridger BW74 1979 561

WY Laramie River 1 1980 570

WY Laramie River 2 1981 570

WY Laramie River 3 1982 570

WY Naughton 1 1963 163

WY Naughton 2 1968 218

WY Naughton 3 1971 326

WY Wyodak BW91 1978 362

a Nameplate capacity of generator connected to boiler.
B-21

B.3 Non-EGU Cost and Economic Impact Sensitivity Analyses

This appendix contains a number of sensitivity analyses for Scenarios 1 through 3

($1,000/ton, $4,000/ton, and $10,000/ton) applied to the non-EGU source categories. These

sensitivity analyses are the following:

C total capital costs of controlling both SO2 and NOx in 2015 for each illustrative

scenario—calculated at a 7 percent discount rate (see Table B-3)

C total capital costs of controlling both SO2 and NOx in 2015—calculated at a 3

percent discount rate (see Table B-4)

C total capital costs of controlling both SO2 and NOx in 2015 for Scenario

2—calculated at a 10 percent discount rate (see Table B-5)

C total annualized costs of controlling both SO2 and NOx in 2015 for Scenario

2—calculated at a 10 percent discount rate (see Table B-6)

C total annualized and capital costs of controlling both SO2 and NOx for Scenario 2

in 2015 for a 25 percent increase in labor rates—calculated at a 7 percent discount

rate (see Table B-7)

C total annualized and capital costs of controlling both SO2 and NOx for Scenario 2

in 2015 for a 25 percent decrease in labor rates—calculated at a 7 percent

discount rate (see Table B-8)

C total annualized and capital costs of controlling both SO2 and NOx for Scenario 2

in 2015 for a 25 percent increase in energy prices—calculated at a 7 percent

discount rate (see Table B-9)

C total annualized and capital costs of controlling both SO2 and NOx for Scenario 2

in 2015 for a 25 percent decrease in energy prices—calculated at a 7 percent

discount rate (see Table B-10)
B-22

Table B-3. Total Capital Costs of Controlling Both SO2 and NOx for the Non-EGU

BART Source Categories in 2015—7 Percent Discount Rate (million 1999$)

BART Source Category

Scenarios

Scenario 1

$1,000/ton

Scenario 2

$4,000/ton

Scenario 3

$10,000/ton

Industrial boilers $422.3 $4,132.2 $6,324.9

Petroleum refineries 22.6 1,220.9 2,968.6

Kraft pulp mills 70.7 1,131.6 2,168.2

Portland cement plants 18.4 817.6 2,092.7

Hydrofluoric, sulfuric, and nitric

acid plants

34.6 42.1 42.1

Chemical process plants 58.4 392.4 503.8

Iron and steel mills 2.6 162.9 227.6

Coke oven batteries 0.0 81.0 191.3

Sulfur recovery plants 0.3 1.0 1.0

Primary aluminum ore reduction

plants

14.6 62.0 62.4

Lime kilns 9.9 16.6 140.1

Glass fiber processing plants 1.2 11.8 20.1

Municipal incinerators 0.0 4.6 4.6

Coal cleaning plants 0.0 4.0 4.0

Carbon black plants 0.02 0.9 0.9

Phosphate rock processing plants 0.0 0.7 1.5

Secondary metal production

facilities

0.1 0.3 0.4

Total $655.7 $8,082.7 $14,754.1
B-23

Table B-4. Total Capital Costs of Controlling Both SO2 and NOx for the Non-EGU

BART Source Categories in 2015—3 Percent Discount Rate (million 1999$)

BART Source Category

Scenarios

Scenario 1

$1,000/ton

Scenario 2

$4,000/ton

Scenario 3

$10,000/ton

Industrial boilers $1,101.3 $3,547.2 $4,549.1

Petroleum refineries 164.6 1,632.2 2,999.0

Kraft pulp mills 378.4 880.9 1,299.9

Portland cement plants 69.5 1,268.6 1,844.5

Hydrofluoric, sulfuric, and nitric

acid plants

27.5 33.3 33.3

Chemical process plants 158.9 317.0 530.8

Iron and steel mills 2.2 173.7 240.7

Coke oven batteries 0.0 63.9 148.0

Sulfur recovery plants 0.2 0.8 0.8

Primary aluminum ore reduction

plants

47.6 47.6 47.6

Lime kilns 12.6 106.9 106.9

Glass fiber processing plants 1.9 10.1 17.0

Municipal incinerators 0.0 3.2 3.2

Coal cleaning plants 0.0 3.3 3.3

Carbon black plants 0.02 0.7 0.7

Phosphate rock processing plants 0.0 1.1 1.1

Secondary metal production

facilities

0.1 0.3 0.3

Total $1,881.70 $8,090.92 $12,726.09
B-24

Table B-5. Total Capital Costs of Controlling Both SO2 and NOx for Scenario 2 ($4,000

per ton) Applied to the Non-EGU BART Source Categories in 2015 (million 1999$)—10

Percent Discount Rate

BART Source Category

Scenario 2

($4,000 per ton)

Industrial boilers $3,054.1

Petroleum refineries 862.6

Kraft pulp mills 788.4

Portland cement plants 154.1

Hydrofluoric, sulfuric, and nitric acid plants 49.3

Chemical process plants 398.7

Iron and steel mills 117.0

Coke oven batteries 95.2

Sulfur recovery plants 1.5

Primary aluminum ore reduction plants 22.4

Lime kilns 19.8

Glass fiber processing plants 13.1

Municipal incinerators 5.7

Coal cleaning plants 4.6

Carbon black plants 0.9

Phosphate rock processing plants 0.8

Secondary metal production facilities 0.3

Total $5,588.6
B-25

Table B-6. Total Annualized Costs of Controlling Both SO2 and NOx for the Scenario 2

Applied to the Non-EGU BART Source Categories in 2015—10 Percent Discount Rate

(million 1999$)

BART Source Category

Scenario 2

($4,000 per ton)

Industrial boilers $460.6

Petroleum refineries 137.0

Kraft pulp mills 116.7

Portland cement plants 36.0

Hydrofluoric, sulfuric, and nitric acid plants 25.3

Chemical process plants 72.7

Iron and steel mills 18.1

Coke oven batteries 22.4

Sulfur recovery plants 11.9

Primary aluminum ore reduction plants 3.1

Lime kilns 5.8

Glass fiber processing plants 5.8

Municipal incinerators 1.3

Coal cleaning plants 1.1

Carbon black plants 0.2

Phosphate rock processing plants 0.1

Secondary metal production facilities 0.05

Total $928.29
B-26

Table B-7. Total Annualized and Capital Costs of Controlling Both SO2 and NOx for

Scenario 2— Applied to the BART Non-EGU Source Categories in 2015—7 Percent

Discount Rate—25 Percent Labor Rate Increase (million 1999$)

BART Source Category Annualized Costs Capital Costs

Industrial boilers $521.7 $4,074.1

Petroleum refineries 180.8 1,221.0

Kraft pulp mills 119.1 1,012.2

Portland cement plants 175.6 817.6

Hydrofluoric, sulfuric, and nitric acid plants 23.4 42.1

Chemical process plants 71.5 392.4

Iron and steel mills 23.5 162.9

Coke oven batteries 18.6 80.0

Sulfur recovery plants 12.2 1.0

Primary aluminum ore reduction plants 7.8 62.0

Lime kilns 5.1 16.6

Glass fiber processing plants 5.3 11.8

Municipal incinerators 1.1 4.6

Coal cleaning plants 1.0 4.0

Carbon black plants 0.2 0.9

Phosphate rock processing plants 0.1 0.7

Secondary metal production facilities 0.04 0.3

Total $1,167.13 $7,906.20
B-27

Table B-8. Total Annualized and Capital Costs of Controlling Both SO2 and NOx for

Scenario 2 Applied to the Non-EGU BART Source Categories in 2015—7 Percent

Discount Rate—25 Percent Labor Rate Decrease (million 1999$)

BART Source Category Annualized Costs Capital Costs

Industrial boilers $525.5 $4,128.5

Petroleum refineries 179.5 1,221.0

Kraft pulp mills 118.9 1,012.2

Portland cement plants 173.9 817.6

Hydrofluoric, sulfuric, and nitric acid plants 23.3 42.1

Chemical process plants 70.1 392.4

Iron and steel mills 23.5 162.9

Coke oven batteries 18.7 81.0

Sulfur recovery plants 12.1 1.0

Primary aluminum ore reduction plants 7.8 62.0

Lime kilns 5.0 16.6

Glass fiber processing plants 5.3 11.8

Municipal incinerators 1.1 4.6

Coal cleaning plants 1.0 4.0

Carbon black plants 0.2 0.9

Phosphate rock processing plants 0.1 0.7

Secondary metal production facilities 0.04 0.3

Total $1,165.83 $7,960.59
B-28

Table B-9. Total Annualized and Capital Costs of Controlling Both SO2 and NOx for

Scenario 2— Applied to the BART Non-EGU Source Categories in 2015—7 Percent

Discount Rate—25 Percent Energy Price Increase (million 1999$)

BART Source Category Annualized Costs Capital Costs

Industrial boilers $522.2 $4,074.2

Petroleum refineries 180.7 1,220.0

Kraft pulp mills 118.2 1,012.2

Portland cement plants 179.3 817.6

Hydrofluoric, sulfuric, and nitric acid plants 24.2 42.1

Chemical process plants 70.8 392.4

Iron and steel mills 23.5 162.9

Coke oven batteries 18.7 81.1

Sulfur recovery plants 12.1 1.0

Primary aluminum ore reduction plants 7.8 62.0

Lime kilns 5.0 16.6

Glass fiber processing plants 5.3 11.8

Municipal incinerators 1.1 4.6

Coal cleaning plants 1.0 4.0

Carbon black plants 0.2 0.9

Phosphate rock processing plants 0.1 0.7

Secondary metal production facilities 0.04 0.3

Total $1,171.27 $7,906.20
B-29

Table B-10. Total Annualized and Capital Costs of Controlling Both SO2 and NOx for

Scenario 2 Applied to the BART Non-EGU Source Categories in 2015—7 Percent

Discount Rate—25 Percent Energy Price Increase (million 1999$)

BART Source Category Annualized Costs Capital Costs

Industrial boilers $25.0 $4,128.5

Petroleum refineries 179.7 1,221.0

Kraft pulp mills 118.8 1,012.2

Portland cement plants 170.2 817.6

Hydrofluoric, sulfuric, and nitric acid plants 22.6 42.1

Chemical process plants 70.7 392.4

Iron and steel mills 23.4 162.9

Coke oven batteries 18.6 81.1

Sulfur recovery plants 12.1 1.0

Primary aluminum ore reduction plants 7.8 62.0

Lime kilns 5.0 16.6

Glass fiber processing plants 5.3 11.8

Municipal incinerators 1.1 4.6

Coal cleaning plants 1.0 4.0

Carbon black plants 0.2 0.9

Phosphate rock processing plants 0.1 0.7

Secondary metal production facilities 0.04 0.3

Total $1,161.68 $7,960.59
C-1

*

*

*

#

# = Center Grid-Cell “E”

* = Air Pollution Monitor

*

*

*

*

APPENDIX C

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. 

C.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.
C-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:
C-3

The weights for the other monitors would be calculated in a similar fashion. 

C.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,
C-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.

C.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
C-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.

C.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.
C-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 C-1 shows the relevant calculations for this pooling for three sample studies.

Table C-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).
C-7

C.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 $i

s

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
C-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, 
C-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.

C.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. 

C-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.

C.6 Reference

DerSimonian, R. and N. Laird 1986. “Meta-analysis in Clinical Trials.” Controlled Clinical

Trials 7(3):177-188.
1

 Hereafter referred to as Class I areas, which are defined as areas of the country such as national parks, national

wilderness areas, and national monuments that have been set aside under Section 162(a) of the Clean Air

Act to receive the most stringent degree of air quality protection. Class I federal lands fall under the

jurisdiction of three federal agencies, the National Park Service, the Fish and Wildlife Service, and the

Forest Service.

D-1

APPENDIX D

VISIBILITY BENEFITS METHODOLOGY

Visibility degradation estimates used in this analysis are generated by the CMAQ

model. To conduct the visibility benefits analysis, however, we need visibility data at the

county level. To convert CMAQ visibility data from the square grid to the county level, we

use the following rule: if a county center falls within a given CMAQ grid cell, we assign that

CMAQ grid cell’s visibility values to that county. Because the modeled air quality-related

changes in visibility are directly used in the benefits analysis, the methodology for predicting

visibility changes is not discussed here. The visibility estimation procedure is described in

detail in EPA (2000), and is based on the methods in Sisler (1996).

Economic benefits may result from two broad categories of visibility changes: 

(1) changes in “residential” visibility—i.e., the visibility in and around the locations where

people live; and (2) changes in “recreational” visibility at Class I areas—i.e., visibility at

Class I national parks and wilderness areas.1 In this analysis, only those recreational benefits

in Class I areas that have been directly studied (in California, the Southeast, and the

Southwest) are included in the primary presentation of benefits; residential benefits and

recreational benefits in all U.S. Class I areas are presented as alternative calculations of

visibility benefits.

Within the category of recreational visibility, further distinctions have been made. 

There is evidence (Chestnut and Rowe, 1990) that an individual’s WTP for improvements in

visibility at a Class I area is influenced by whether it is in the region in which the individual

lives, or whether it is somewhere else. In general people appear to be willing to pay more for

visibility improvements at parks and wilderness areas that are “in-region” than at those that
2

 The constant elasticity of substitution utility function has been chosen for use in this analysis because of its

flexibility when illustrating the degree of substitutability present in various economic relationships (in this

case, the trade-off between income and improvements in visibility).

3

We remind the reader that, although residential and recreational visibility benefits estimation is discussed

simultaneously in this section, benefits are calculated and presented separately for each visibility category.

D-2

are “out-of-region.” This is plausible, because people are more likely to visit, be familiar

with, and care about parks and wilderness areas in their own part of the country.

To value estimated visibility changes, we are using an approach consistent with

economic theory. Below we discuss an application of the Constant Elasticity of Substitution

(CES) utility function approach2 to value both residential visibility improvements and

visibility improvements at Class I areas in the United States. This approach is based on the

preference calibration method developed by Smith, Van Houtven, and Pattanayak (1999). 

The presentation of this methodology is organized as follows. The basic utility model is

presented in Section D.1. In Section D.2 we discuss the measurement of visibility, and the

mapping from environmental “bads” to environmental “goods.” In Sections D.3 and D.4 we

summarize the information that is available to estimate the parameters of the model

corresponding to visibility at in-region and out-of-region Class I areas, and visibility in

residential areas, respectively, and we describe the methods used to estimate these

parameters. Section D.5 synthesizes the results.

D.1 Basic Utility Model

We begin with a CES utility function in which a household derives utility from 

(1) “all consumption goods,” X, 

(2) visibility in the residential area in which the household is located (“residential

visibility”),3

(3) visibility at Class I areas in the same region as the household (“in-region

recreational visibility”), and 

(4) visibility at Class I areas outside the household’s region (“out-of-region

recreational visibility”). 
D-3

UXZ Q Q

ik jk

ni n ik ik

k

N

jk jk

k

N

j i

ik jk

i j

= ++ +

> >∀ >∀ ≤

= = ≠

( ), ∑ ∑ ∑

, , ,, , ,, .

ρ ρ ρ ρρ/ θγ δ

θγ δ ρ

1 1

1

00 0 1

m pX −⋅ ≤ 0 ,

There are a total of six regions being considered, so there are five regions for which

any household is out of region. The utility function of a household in the nth residential area

and the ith region of the country is:

where

Zn = the level of visibility in the nth residential area;

Qik = the level of visibility at the kth in-region park (i.e., the kth park in the ith

region);

Qjk = the level of visibility at the kth park in the jth region ( for which the household

is out of region), j...i;

Ni

 = the number of Class I areas in the ith region;

Nj = the number of Class I areas in the jth region (for which the household is out of

region), j...i; and 

2, the (’s and *’s are parameters of the utility function corresponding to the visibility levels

at residential areas and at in-region and out-of-region Class I areas, respectively. In

particular, the (ik’s are the parameters corresponding to visibility at in-region Class I areas;

the *1’s are the parameters corresponding to visibility at Class I areas in region 1

(California), if i...1; the *2’s are the parameters corresponding to visibility at Class I areas in

region 2 (Colorado Plateau), if i...2, and so forth. Because the model assumes that the

relationship between residential visibility and utility is the same everywhere, there is only

one 2. The parameter D in this CES utility function is an important determinant of the slope

of the marginal WTP curve associated with any of the environmental quality variables. 

When D=1, the marginal WTP curve is horizontal. When D<1, it is downward sloping. 

The household’s budget constraint is:
D-4

V mZ Q m Z Q Q ni n n ik ik

k

N

jk jk

k

N

j i

i j

(, ,;,,,) ( ) , / θγδρ θ γ δ ρ ρ ρ ρρ =++ + = = ≠

∑ ∑ ∑ 1 1

1

WTP Z Q m m Z Z Q Q Q Q ni n n ik ik ik

k

N

jk jk jk

k

N

j i

i j

( , ) [ ( ) ( ) ( )] . / ∆∆ = − + − + − + − = = ≠

∑ ∑ ∑ ρ ρρ ρ ρ ρ ρ ρ θγ δ 01 0 1

1

0 1

1

1

WTP Q m m Q Q i ii i ( ) [ ( )] , / ∆ 1 1 01 11

1 =− + − ρ ρ ρρ γ

where m is income, and p is the price of X. Without loss of generality, set p = 1. The only

choice variable is X. The household maximizes its utility by choosing X=m. The indirect

utility function for a household in the nth residential area and the ith region is therefore

where Q denotes the vector of vectors, Q1, Q2, Q3, Q4, Q5, and Q6, and the unsubscripted (

and * denote vectors as well.

Given estimates of D, 2, the (’s and the *’s, the household’s utility function and the

corresponding WTP functions are fully specified. The household’s WTP for any set of

changes in the levels of visibility at in-region Class I areas, out-of-region Class I areas, and

the household’s residential area can be shown to be:

The household’s WTP for a single visibility improvement will depend on its order in

the series of visibility improvements the household is valuing. If it is the first visibility

improvement to be valued, the household’s WTP for it follows directly from the previous

equation. For example, the household’s WTP for an improvement in visibility at the first inregion park, from Qi1 = Q0i1 to Qi1 = Q1i1, is

if this is the first (or only) visibility change the household values.

D.2 Measure of Visibility: Environmental “Goods” Versus “Bads”

In the above model, Q and Z are environmental “goods.” As the level of visibility

increases, utility increases. The utility function and the corresponding WTP function both

have reasonable properties. The first derivative of the indirect utility function with respect to

Q (or Z) is positive; the second derivative is negative. WTP for a change from Q0 to a higher
4

 There may be more than one “good” related to a given environmental “bad.” To simplify the discussion,

however, we assume only a single “good.”

D-5

∂

∂

V

B

< 0 ,

Q FB = ( ),

V V mQ V mF B = = ( , ) ( , ( )) .

(improved) level of visibility, Q1, is therefore a concave function of Q1, with decreasing

marginal WTP.

The measure of visibility that is currently preferred by air quality scientists is the

deciview, which increases as visibility decreases. Deciview, in effect, is a measure of the

lack of visibility. As deciviews increase, visibility, and therefore utility, decreases. The

deciview, then, is a measure of an environmental “bad.” There are many examples of

environmental “bads”—all types of pollution are environmental “bads.” Utility decreases,

for example, as the concentration of particulate matter in the atmosphere increases. 

One way to value decreases in environmental bads is to consider the “goods” with

which they are associated, and to incorporate those goods into the utility function. In

particular, if B denotes an environmental “bad,” such that:

and the environmental “good,” Q, is a function of B,

then the environmental “bad” can be related to utility via the corresponding environmental

“good”:4

The relationship between Q and B, F(B), is an empirical relationship that must be estimated.

There is a potential problem with this approach, however. If the function relating B

and Q is not the same everywhere (i.e., if for a given value of B, the value of Q depends on

other factors as well), then there can be more than one value of the environmental good

corresponding to any given value of the environmental bad, and it is not clear which value to

use. This has been identified as a problem with translating deciviews (an environmental

“bad”) into visual range (an environmental “good”). It has been noted that, for a given

deciview value, there can be many different visual ranges, depending on the other factors

that affect visual range—such as light angle and altitude. We note here, however, that this
Q e PM = − 1 α β .

5

 Another example of an environmental “bad” is particulate matter air pollution (PM). The relationship between

survival probability (Q) and the ambient PM level is generally taken to be of the form

where " denotes the mortality rate (or level) when there is no ambient PM (i.e., when PM=0). However, " is

implicitly a function of all the factors other than PM that affect mortality. As these factors change (e.g.,

from one location to another), " will change (just as visual range changes as light angle changes). It is

therefore possible to have many values of Q corresponding to a given value of PM, as the values of " vary.

6

 Ideally, we would want the location-, time-, and meteorological condition-specific relationships between

deciviews and visual range, which could be applied as appropriate. This is probably not feasible, however.

D-6

DV 10* ln(

391

VR = ) ,

VR 391* e . 0.1DV = −

problem is not unique to visibility, but is a general problem when trying to translate

environmental “bads” into “goods.”5

In order to translate deciviews (a “bad”) into visual range (a “good”), we use a

relationship derived by Pitchford and Malm (1994) in which

where DV denotes deciview and VR denotes visual range (in kilometers). Solving for VR as

a function of DV yields

This conversion is based on specific assumptions characterizing the “average” conditions of

those factors, such as light angle, that affect visual range. To the extent that specific

locations depart from the average conditions, the relationship will be an imperfect

approximation.6

D.3 Estimating the Parameters for Visibility at Class I Areas: the (’s and *’s

As noted in Section 2, if we consider a particular visibility change as the first or the

only visibility change valued by the household, the household’s WTP for that change in

visibility can be calculated, given income (m), the “shape” parameter, D, and the

corresponding recreational visibility parameter. For example, a Southeast household’s WTP

for a change in visibility at in-region parks (collectively) from Q1 = Q01 to Q1 = Q11 is:
D-7

γ

ρ ρ

j ρ ρ

j

in

j j

m WTP m

Q Q = − −

−

( )

( ) 0 1

WTP(DQ ) m [m g (Q Q )] 1

r

1 01

r

11

r 1/r =− + −

if this is the first (or only) visibility change the household values.

Alternatively, if we have estimates of m as well as WTP1

in and WTP1out of in-region

and out-of-region households, respectively, for a given change in visibility from Q01 to Q11 in

Southeast parks, we can solve for (1 and *1 as a function of our estimates of m, WTP1

in and

WTP1

out, for any given value of D. Generalizing, we can derive the values of ( and * for the

j

th region as follows:

and

δ

ρ ρ

j ρ ρ

j

out

j j

m WTP m

Q Q = − −

−

( )

( ) . 0 1

Chestnut and Rowe (1990) and Chestnut (1997) estimated WTP (per household) for

specific visibility changes at national parks in three regions of the United States—both for

households that are in-region (in the same region as the park) and for households that are

out-of-region. The Chestnut and Rowe study asked study subjects what they would be

willing to pay for each of three visibility improvements in the national parks in a given

region. Study subjects were shown a map of the region, with dots indicating the locations of

the parks in question. The WTP questions referred to the three visibility improvements in all

the parks collectively; the survey did not ask subjects’ WTP for these improvements in

specific parks individually. Responses were categorized according to whether the

respondents lived in the same region as the parks in question (“in-region” respondents) or in

a different region (“out-of-region” respondents). The areas for which in-region and out-ofregion WTP estimates are available from Chestnut and Rowe (1990), and the sources of

benefits transfer-based estimates that we employ in the absence of estimates, are summarized

in Table D-1. In all cases, WTP refers to WTP per household.
D-8

Table D-1. Available Information on WTP for Visibility Improvements in National

Parks

Region of Park

Region of Household

In Regiona Out of Regionb

1. California WTP estimate from study WTP estimate from study

2. Colorado Plateau WTP estimate from study WTP estimate from study

3. Southeast United States WTP estimate from study WTP estimate from study

4. Northwest United States (based on benefits transfer from California)

5. Northern Rockies (based on benefits transfer from Colorado Plateau)

6. Rest of United States (based on benefits transfer from Southeast U.S.)

a In-region” WTP is WTP for a visibility improvement in a park in the same region as that in which the

household is located. For example, in-region WTP in the “Southeast” row is the estimate of the average

Southeast household’s WTP for a visibility improvement in a Southeast park. 

b Out-of-region” WTP is WTP for a visibility improvement in a park that is not in the same region in which the

household is located. For example, out-of-region WTP in the “Southeast” row is the estimate of WTP for a

visibility improvement in a park in the Southeast by a household outside of the Southeast.

In the primary calculation of visibility benefits for this analysis, only visibility

changes at parks within visibility regions for which a WTP estimate was available from

Chestnut and Rowe (1990) are considered (for both in- and out-of-region benefits). Primary

estimates will not include visibility benefits calculated by transferring WTP values to

visibility changes at parks not included in the Chestnut and Rowe study. Transferred

benefits at parks located outside of the Chestnut and Rowe visibility regions will, however,

be included as an alternative calculation.

The values of the parameters in a household’s utility function will depend on where

the household is located. The region-specific parameters associated with visibility at Class I

areas (that is, all parameters except the residential visibility parameter) are arrayed in

Table D-2. The parameters in columns 1 through 3 can be directly estimated using WTP

estimates from Chestnut and Rowe (1990) (the columns labeled “Region 1,” “Region 2,” and

“Region 3”).

Table D-2. Summary of Region-Specific Recreational Visibility Parameters to be

Estimated in Household Utility Functions
D-9

Region of

Household

Region of Park

Region 1 Region 2 Region 3 Region 4 Region 5 Region 6

Region 1 (1

a *2 *3 *4 *5 *6

Region 2 *1 (2 *3 *4 *5 *6

Region 3 *1 *2 (3 *4 *5 *6

Region 4 *1 *2 *3 (4 *5 *6

Region 5 *1 *2 *3 *4 (5 *6

Region 6 *1 *2 *3 *4 *5 (6

a The parameters arrayed in this table are region specific rather than park specific or wilderness area specific. 

For example, *1 is the parameter associated with visibility at “ Class I areas in region 1” for a household in

any region other than region 1. The benefits analysis must derive Class I area-specific parameters (e.g., *1k,

for the kth Class I area in the first region).

For the three regions covered in Chestnut and Rowe (1990) (California, the Colorado

Plateau, and the Southeast United States), we can directly use the in-region WTP estimates

from the study to estimate the parameters in the utility functions corresponding to visibility

at in-region parks ((1); similarly, we can directly use the out-of-region WTP estimates from

the study to estimate the parameters for out-of-region parks (*1). For the other three regions

not covered in the study, however, we must rely on benefits transfer to estimate the necessary

parameters.

While Chestnut and Rowe (1990) provide useful information on households’ WTP

for visibility improvements in national parks, there are several significant gaps remaining

between the information provided in that study and the information necessary for the benefits

analysis. First, as noted above, the WTP responses were not park specific, but only region

specific. Because visibility improvements vary from one park in a region to another, the

benefits analysis must value park-specific visibility changes. Second, not all Class I areas in

each of the three regions considered in the study were included on the maps shown to study

subjects. Because the focus of the study was primarily national parks, most Class I

wilderness areas were not included. Third, only three regions of the United States were

included, leaving the three remaining regions without direct WTP estimates.

In addition, Chestnut and Rowe (1990) elicited WTP responses for three different

visibility changes, rather than a single change. In theory, if the CES utility function
D-10

accurately describes household preferences, and if all households in a region have the same

preference structure, then households’ three WTP responses corresponding to the three

different visibility changes should all produce the same value of the associated recreational

visibility parameter, given a value of D and an income, m. In practice, of course, this is not

the case.

In addressing these issues, we take a three-phase approach:

(1) We estimate region-specific parameters for the region in the modeled domain

covered by Chestnut and Rowe (1990) (California, the Colorado Plateau, and

the Southeast)—(1, (2, and (3 and *1, *2, and *3. 

(2) We infer region-specific parameters for those regions not covered by the

Chestnut and Rowe study (the Northwest United States, the Northern Rockies,

and the rest of the United States)— (4, (5, and (6 and *4, *5, and *6. 

(3) We derive park- and wilderness area-specific parameters within each region ((1k

and *1k, for k=1, ..., N1; (2k and *2k, for k=1, ..., N2; and so forth).

The question that must be addressed in the first phase is how to estimate a single

region-specific in-region parameter and a single region-specific out-of-region parameter for

each of the three regions covered in Chestnut and Rowe (1990) from study respondents’

WTPs for three different visibility changes in each region. All parks in a region are treated

collectively as if they were a single “regional park” in this first phase. In the second phase,

we infer region-specific recreational visibility parameters for regions not covered in the

Chestnut and Rowe study (the Northwest United States, the Northern Rockies, and the rest of

the United States). As in the first phase, we ignore the necessity to derive park-specific

parameters at this phase. Finally, in the third phase, we derive park- and wilderness areaspecific parameters for each region.

D.3.1 Estimating Region-Specific Recreational Visibility Parameters for the Region

Covered in the Chestnut and Rowe Study (Regions 1, 2, and 3) 

Given a value of D and estimates of m and in-region and out-of-region WTPs for a

change from Q0 to Q1 in a given region, the in-region parameter, (, and the out-of-region

parameter, *, for that region can be solved for. Chestnut and Rowe (1990), however,

considered not just one, but three visibility changes in each region, each of which results in a

different calibrated ( and a different calibrated *, even though in theory all the (’s should be
D-11

( ( WTP WTP $ ) ) ij j ij

i

γ − =

∑ 2

1

3

the same and similarly, all the *’s should be the same. For each region, however, we must

have only a single ( and a single *.

Denoting as our estimate of ( for the jth γ$ j region, based on all three visibility

changes, we chose to best predict the thr γ$ j ee WTPs observed in the study for the three

visibility improvements in the jth region. First, we calculated , i=1, 2, 3, corresponding to γ$ ji

each of the three visibility improvements considered in the study. Then, using a grid search

method beginning at the average of the three ’s, we chose to minimize the sum of the γ$ ji γ$ j

squared differences between the WTPs we predict using and the three region-specific γ$ j

WTPs observed in the study. That is, we selected to minimize: γ$ j

where WTPij and WTPij( ) are the observed and the predicted WTPs for a change in γ$ j

visibility in the jth region from Q0 = Q0i to Q1= Q1i, i=1, ..., 3. An analogous procedure was

used to select an optimal *, for each of the three regions in the Chestnut and Rowe study.

D.3.2 Inferring Region-Specific Recreational Visibility Parameters for Regions Not

Covered in the Chestnut and Rowe Study (Regions 4, 5, and 6) 

One possible approach to estimating region-specific parameters for regions not

covered by Chestnut and Rowe (1990) ((4, (5, and (6 and *4, *5, and *6) is to simply assume

that households’ utility functions are the same everywhere, and that the environmental goods

being valued are the same—e.g., that a change in visibility at national parks in California is

the same environmental good to a Californian as a change in visibility at national parks in

Minnesota is to a Minnesotan.

For example, to estimate *4 in the utility function of a California household,

corresponding to visibility at national parks in the Northwest United States, we might assume

that out-of-region WTP for a given visibility change at national parks in the Northwest

United States is the same as out-of-region WTP for the same visibility change at national

parks in California (income held constant). Suppose, for example, that we have an estimated

mean WTP of out-of-region households for a visibility change from Q01 to Q11 at national
7

 We acknowledge that reliance on visitation rates does not get at nonuse value.

D-12

δ

ρ ρ

4 ρ ρ

4

04 14

= − −

−

( ) m WTP m

Q Q

out

parks in California (region 1), denoted WTP1

out. Suppose the mean income of the out-ofregion subjects in the study was m. We might assume that, for the same change in visibility

at national parks in the Northwest United States, WTP4

out = WTP1out among out-of-region

individuals with income m.

We could then derive the value of *4, given a value of D as follows:

where Q04 = Q01 and Q14 = Q11, (i.e., where it is the same visibility change in parks in region

4 that was valued at parks in the region 1). 

This benefits transfer method assumes that (1) all households have the same

preference structures and (2) what is being valued in the Northwest United States (by a

California household) is the same as what is being valued in the California (by all out-ofregion households). While we cannot know the extent to which the first assumption

approximates reality, the second assumption is clearly problematic. National parks in one

region are likely to differ from national parks in another region in both quality and quantity

(i.e., number of parks).

One statistic that is likely to reflect both the quality and quantity of national parks in

a region is the average annual visitation rate to the parks in that region. A reasonable way to

gauge the extent to which out-of-region people would be willing to pay for visibility changes

in parks in the Northwest United States versus in California might be to compare visitation

rates in the two regions.7 Suppose, for example, that twice as many visitor-days are spent in

California parks per year as in parks in the Northwest United States per year. This could be

an indication that the parks in California are in some way more desirable than those in the

Northwest United States and/or that there are more of them—i.e., that the environmental

goods being valued in the two regions (“visibility at national parks”) are not the same.

A preferable way to estimate *4, then, might be to assume the following relationship:
D-13

WTP

WTP

n

n

out

out

4

1

4

1

=

WTP

n

n

WTP out out

4

4

1

= 1*

WTP

n

n

WTP in in

4

4

1

1 = * .

(income held constant), where n1 = the average annual number of visitor-days to California

parks and n4 = the average annual number of visitor-days to parks in the Northwest United

States. This implies that 

for the same change in visibility in region 4 parks among out-of-region individuals with

income m. If, for example, n1 = 2n4, WTP4

out would be half of WTP1out. The interpretation

would be the following: California national parks have twice as many visitor-days per year

as national parks in the Northwest United States; therefore they must be twice as

desirable/plentiful; therefore, out-of-region people would be willing to pay twice as much for

visibility changes in California parks as in parks in the Northwest United States; therefore a

Californian would be willing to pay only half as much for a visibility change in national

parks in the Northwest United States as an out-of-region individual would be willing to pay

for the same visibility change in national parks in California. This adjustment, then, is based

on the premise that the environmental goods being valued (by people out of region) are not

the same in all regions.

The parameter *4 is estimated as shown above, using this adjusted WTP4

out. The same

procedure is used to estimate *5 and *6. We estimate (4, (5, and (6 in an analogous way, using

the in-region WTP estimates from the transfer regions, e.g.,

D.3.3 Estimating Park- and Wilderness Area-Specific Parameters

As noted above, Chestnut and Rowe (1990) estimated WTP for a region’s national

parks collectively, rather than providing park-specific WTP estimates. The (’s and *’s are

therefore the parameters that would be in household utility functions if there were only a
D-14

single park in each region, or if the many parks in a region were effectively indistinguishable

from one another. Also noted above is the fact that the Chestnut and Rowe study did not

include all Class I areas in the regions it covered, focusing primarily on national parks rather

than wilderness areas. Most Class I wilderness areas were not represented on the maps

shown to study subjects. In California, for example, there are 31 Class I areas, including 6

national parks and 25 wilderness areas. The Chestnut and Rowe study map of California

included only 10 of these Class I areas, including all 6 of the national parks. It is unclear

whether subjects had in mind “all parks and wilderness areas” when they offered their WTPs

for visibility improvements, or whether they had in mind the specific number of (mostly)

parks that were shown on the maps. The derivation of park- and wilderness area-specific

parameters depends on this.

D.3.4 Derivation of Region-Specific WTP for National Parks and Wilderness Areas

If study subjects were lumping all Class I areas together in their minds when giving

their WTP responses, then it would be reasonable to allocate that WTP among the specific

parks and wilderness areas in the region to derive park- and wilderness area-specific (’s and

*’s for the region. If, on the other hand, study subjects were thinking only of the (mostly)

parks shown on the map when they gave their WTP response, then there are two possible

approaches that could be taken. One approach assumes that households would be willing to

pay some additional amount for the same visibility improvement in additional Class I areas

that were not shown, and that this additional amount can be estimated using the same

benefits transfer approach used to estimate region-specific WTPs in regions not covered by

Chestnut and Rowe (1990).

However, even if we believe that households would be willing to pay some additional

amount for the same visibility improvement in additional Class I areas that were not shown,

it is open to question whether this additional amount can be estimated using benefits transfer

methods. A third possibility, then, is to simply omit wilderness areas from the benefits

analysis. For this analysis we calculate visibility benefits assuming that study subjects

lumped all Class I areas together when stating their WTP, even if these Class I areas were

not present on the map.
8

 This might be thought of as two assumptions: (1) that the relative frequencies of visits to the parks in a region 

from everyone in the world is a reasonable representation of the relative frequency of visits from people in

the United States—i.e., that the parks that are most popular (receive the most visitors per year) in general are

also the most popular among Americans; and (2) that the relative frequency with which Americans visit each

D-15

WTP

n

n k WTP in k in

1

1

1

= * , 1

WTP 

n

n

 *WTP . 1k

out 1k

1

1

out =

D.3.5 Derivation of Park- and Wilderness Area-Specific WTPs, Given Region-Specific

WTPs for National Parks and Wilderness Areas

The first step in deriving park- and wilderness area-specific parameters is the

estimation of park- and wilderness area-specific WTPs. To derive park and wilderness areaspecific WTPs, we apportion the region-specific WTP to the specific Class I areas in the

region according to each area’s share of the region’s visitor-days. For example, if WTP1

in

and WTP1

out denote the mean household WTPs in the Chestnut and Rowe (1990) study

among respondents who were in-region-1 and out-of-region-1, respectively, n1k denotes the

annual average number of visitor-days to the kth Class I area in California, and n1 denotes the

annual average number of visitor-days to all Class I areas in California (that are included in

the benefits analysis), then we assume that

and

Using WTPj

in and WTPjout, either from the Chestnut and Rowe study (for j = 1, 2, and 3) or

derived by the benefits transfer method (for j = 4, 5, and 6), the same method is used to

derive Class I area-specific WTPs in each of the six regions. 

While this is not a perfect allocation scheme, it is a reasonable scheme, given the

limitations of data. Visitors to national parks in the United States are not all from the United

States, and certainly not all from the region in which the park is located. A very large

proportion of the visitors to Yosemite National Park in California, for example, may come

from outside the United States. The above allocation scheme implicitly assumes that the

relative frequencies of visits to the parks in a region from everyone in the world is a

reasonable index of the relative WTP of an average household in that region (WTPj

in) or out

of that region (but in the United States) (WTPj

out) for visibility improvements at these parks.8
of their parks is a good index of their relative WTPs for visibility improvements at these parks.

D-16

A possible problem with this allocation scheme is that the relative frequency of visits

is an indicator of use value but not necessarily of nonuse value, which may be a substantial

component of the household’s total WTP for a visibility improvement at Class I areas. If

park A is twice as popular (i.e., has twice as many visitors per year) as park B, this does not

necessarily imply that a household’s WTP for an improvement in visibility at park A is twice

its WTP for the same improvement at park B. Although an allocation scheme based on

relative visitation frequencies has some obvious problems, however, it is still probably the

best way to allocate a collective WTP.

D.3.6 Derivation of Park- and Wilderness Area-Specific Parameters, Given Park- and

Wilderness Area-Specific WTPs

Once the Class I area-specific WTPs have been estimated, we could derive the parkand wilderness area-specific (’s and *’s using the method used to derive region-specific (’s

and *’s. Recall that method involved (1) calibrating ( and * to each of the three visibility

improvements in the Chestnut and Rowe study (producing three (’s and three *’s),

(2) averaging the three (’s and averaging the three *’s, and finally, (3) using these average (

and * as starting points for a grid search to find the optimal ( and the optimal *—i.e., the (

and * that would allow us to reproduce, as closely as possible, the three in-region and three

out-of-region WTPs in the study for the three visibility changes being valued.

Going through this procedure for each national park and each wilderness area

separately would be very time consuming, however. We therefore used a simpler approach,

which produces very close approximations to the (’s and *’s produced using the above

approach. If:

WTPj

in = the in-region WTP for the change in visibility from Q0 to Q1 in the jth

region;

WTPjk

in= the in-region WTP for the same visibility change (from Q0 to Q1) in the

kth Class I area in the jth region (= sjk*WTPj

in, where sjk is the kth area’s

share of visitor-days in the jth region);

m = income;

(j* = the optimal value of ( for the jth region; and
9 (j

* is only approximately equal to the right-hand side because, although it is the optimal value designed to

reproduce as closely as possible all three of the WTPs corresponding to the three visibility changes in the

Chestnut and Rowe study, (j

* will not exactly reproduce any of these WTPs. 

10 This method uses a single in-region WTP and a single out-of-region WTP per region. Although the choice of

WTP will affect the resulting adjustment factors (the ajk’s) and therefore the resulting (jk’s and *jk’s, the

effect is negligible. We confirmed this by using each of the three in-region WTPs in California and

comparing the resulting three sets of (jk’s and *jk’s, which were different from each other by about one onehundredth of a percent.

D-17

γ

ρ ρ

j ρ ρ

j

in m WTP m

Q Q

* ( )

( ) ≈ − −

0 1 −

γ

ρ ρ

jk ρ ρ

jk

in m WTP m

Q Q = − −

−

( )

( ) 0 1

γ γ jk jk j ≈ a * , *

a m WTP m

m WTP m jk

jk

in

j

= in

− −

− −

( )

( ) .

ρ ρ

ρ ρ

(jk = the value of (jk calibrated to WTPjk

in and the change from Q0 to Q1;

then9

:

and

which implies that:

where:

We use the adjustment factor, ajk, to derive (jk from (j

*, for the kth Class I area in the jth

region. We use an analogous procedure to derive *jk from *j* for the kth Class I area in the jth

region (where, in this case, we use WTPj

out and WTPjkout instead of WTPjin and WTPjkin).10
D-18

θ

ρ ρ

ρ ρ = − −

−

( )

( ) . m WTP mZ Z 0 1

WTP Z Q m m Z Z Q Q Q Q ni n n ik ik ik

k

N

jk jk jk

k

N

j i

i j

( , ) [ ( ) ( ) ( )] . / ∆∆ = − + − + − + − = = ≠

∑ ∑ ∑ ρ ρρ ρ ρ ρ ρ ρ θγ δ 01 0 1

1

0 1

1

1

D.4 Estimating the Parameter for Visibility in Residential Areas: 2

The estimate of 2 is based on McClelland et al. (1991), in which household WTP for

improvements in residential visibility was elicited from respondents in Chicago and Atlanta. 

A notable difference between the Chestnut and Rowe study and the McClelland study is that,

while the former elicited WTP responses for three different visibility changes, the latter

considered only one visibility change. The estimation of 2 was therefore a much simpler

procedure, involving a straightforward calibration to the single income and WTP in the

study:

D.5 Putting it All Together: The Household Utility and WTP Functions

Given an estimate of 2, derived as shown in Section D.4, and estimates of the (’s and

*’s, derived as shown in Section D.3, based on an assumed or estimated value of D, the utility

and WTP functions for a household in any region are fully specified. We can therefore

estimate the value to that household of visibility changes from any baseline level to any

alternative level in the household’s residential area and/or at any or all of the Class I areas in

the United States, in a way that is consistent with economic theory. In particular, the WTP

of a household in the ith region and the nth residential area for any set of changes in the

levels of visibility at in-region Class I areas, out-of-region Class I areas, and the household’s

residential area (given by equation (24)) is:

The national benefits associated with any suite of visibility changes is properly

calculated as the sum of these household WTPs for those changes. The benefit of any subset

of visibility changes (e.g., changes in visibility only at Class I areas in California) can be

calculated by setting all the other components of the WTP function to zero (that is, by

assuming that all other visibility changes that are not of interest are zero). This is effectively

the same as assuming that the subset of visibility changes of interest is the first or the only

set of changes being valued by households. Estimating benefit components in this way will

yield slightly upward biased estimates of benefits, because disposable income, m, is not

being reduced by the WTPs for any prior visibility improvements. That is, each visibility
D-19

improvement (e.g., visibility at Class I areas in the California) is assumed to be the first, and

they cannot all be the first. The upward bias should be extremely small, however, because

all of the WTPs for visibility changes are likely to be very small relative to income.

D.6 References

Chestnut, L.G. April 15, 1997. Draft Memorandum: Methodology for Estimating Values

for Changes in Visibility at National Parks.

Chestnut, L.G., and R.D. Rowe. 1990. “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.

McClelland, G., W. Schulze, D. Waldman, J. Irwin, D. Schenk, T. Stewart, L. Deck, and M.

Thayer. 1991. Valuing Eastern Visibility: A Field Test of the Contingent Valuation

Method. Prepared for U.S. Environmental Protection Agency, Office of Policy,

Planning and Evaluation. June.

Pitchford, M.L., and W.C. Malm. 1994. “Development and Applications of a Standard

Visual Index.” Atmospheric Environment 28(5):1049-1054.

Sisler, J.F. 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. Colorado State University, Cooperative Institute for Research

in the Atmosphere. Fort Collins, CO. July. See EPA Air Docket A-96-56,

Document No. VI-B-09-(ee).

Smith, V.K., G. Van Houtven, and S. Pattanayak. 1999. Benefits Transfer as Preference

Calibration. Resources for the Future Working Paper (Unnumbered).

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

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

E-1

APPENDIX E

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 A comparison of

the BART nationwide scenario with the CAIR plus BART in the Non-CAIR Region scenario

provides some information on the possible benefits and costs for BART for EGU sources in

the Non-CAIR Region.

The control strategy assumptions for the BART nationwide and BART portion of the

CAIR Plus BART in the Non-CAIR Region scenarios differ from the BART scenarios

(Scenario 1, Scenario 2, and Scenario 3) analyzed in this RIA. Because of the differing

control strategy modeling for the EGU source category, the benefits and costs reported in this

appendix for EGU sources will differ from these scenarios. The analysis conducted for this

appendix assumes that all units greater than 100 MW that do not currently have scrubbers are

required to reduce emissions from uncontrolled levels by 90 percent or meet a 0.1 lb/mmbtu

SO2 emission rate limit. It also assumes that all BART units greater than 25 MW are
E-2

required to meet an emission rate limit of 0.2 lbs/mmbtu. This analysis was conducted to

support the “Better than BART” analysis conducted for the CAIR final rule and was a

conservative (i.e., control on most units) look at controls that states might choose to require

on sources not subject to presumptive BART.

As Table E-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 E-2 lists the

reduction in health incidence resulting from the CAIR program, CAIR Plus BART in the

Non-CAIR Region, and BART nationwide. Table E-3 depicts the monetary value of the

benefit categories listed on Table E-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.
E-3

Table E-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 and a conservative (i.e.,

controls on most units) look at controls that states might choose to require on sources not subject to

presumptive BART. For these reasons, the benefits and costs of BART in this appendix differ from Scenarios

1, 2, and 3 in this RIA. 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 scenarios. 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.
E-4

Table E-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 and a conservative (i.e, controls on most units) look at controls that states might choose

to require on sources not subject to presumptive BART. For these reasons, the benefits and costs of BART in

this appendix differ from Scenarios 1, 2, and 3 in this RIA. b Premature mortality benefits associated with ozone are not quantified in the primary analysis. Adult

premature mortality estimates are based on studies by Pope et al. (2002). Infant premature mortality

estimates are based on 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
E-5

Table E-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 and a conservative (i.e, controls on most units) look at

controls that states might choose to require on sources not subject to presumptive BART. For these reasons, the benefits

and costs of BART in this appendix differ from Scenarios 1, 2, and 3 in this RIA. 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 NonCAIR 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 on 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
E-6

E.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.
F-1

APPENDIX F

SENSITIVITY ANALYSES OF SOME KEY PARAMETERS IN THE BENEFITS

ANALYSIS

The primary analysis of benefits of the Clean Air Visibility Rule (CAVR) 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. 

F.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 F-1.
F-2

Table F-1. Sensitivity of Benefits of Premature Mortality Reductions to Alternative Assumptions (RelaEstimate Benefits of the Final CAVR)

Description of Sensitivity Analysis

Avoided Incidences in 2015a Value 

Scenario 1 Scenario 2 Scenario 3 Scenario 1 S

Alternative Concentration-Response Functions for PM-Related Premature MortalityPope/ACS Study (2002)c

Lung Cancer 310 1,200 1,700 $1,800 Cardiopulmonary 65 250 360 $370 

Krewski/Harvard Six-Cities Study 930 3,700 5,200 $5,300 Alternative Lag Structures for PM-Related Premature Mortality None Incidences all occur in the first year 400 1,600 2,300 $2,500 8-year Incidences all occur in the 8th year

3% discount rate 400 1,600 2,300 $2,100 7% discount rate 400 1,600 2,300 $1,600 

15-year Incidences all occur in the 15th year

3% discount rate 400 1,600 2,300 $1,700 7% discount rate 400 1,600 2,300 $1,000 

AlternativeSegmented

20 percent of incidences occur in 1st year, 50

percent in years 2 to 5, and 30 percent in years 6 to20

3% discount rate 400 1,600 2,300 $2,200 7% discount rate 400 1,600 2,300 $1,800 
F-3

Table F-1. Sensitivity of Benefits of Premature Mortality Reductions to Alternative Assumptions (RelaEstimate Benefits of the Final CAVR) (continued)

Description of Sensitivity Analysis

Avoided Incidences in 2015a Value 

Scenario 1 Scenario 2 Scenario 3 Scenario 1 S

5-YearDistributed

50 percent of incidences occur in years 1 and 2and 50 percent in years 2 to 5

3% discount rate 400 1,600 2,300 $2,400 7% discount rate 400 1,600 2,300 $2,300 

Exponential Incidences occur at an exponentially declining rate

following year of change in exposure

3% discount rate 400 1,600 2,300 $2,400 7% discount rate 400 1,600 2,300 $2,200 

Alternative Thresholds

No Threshold (base estimate) 400 1,600 2,300 $2,300 

5 :g/m3 400 1,600 2,200 $2,300 

10 :g/m3 230 1,100 1,500 $1,300 15 :g/m3 0 130 170 $0 

20 :g/m3 0 54 70 $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 residu

with long-term exposures to PM2.5 that is not captured by the cardiopulmonary and lung cancer categories.
F-4

F.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 F-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 CR 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 CAVR. Thus it is used for our primary

estimate of this important health effect. 
F-5

F.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.

F-6

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-COUNCILLTR-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).
F-7

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

. (F.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,

Schwartz, and Ranson [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 F-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 mortality results based on the pilot expert elicitation, described in Chapter 4 and more

completely in Appendix B of the CAIR RIA presents the potential implications of assuming some

probability of a threshold on the benefits estimate.

F-8

F.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” (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 of the CAIR RIA provides some information on the impact

of applying nonlinear and threshold-based C-R functions. 

F-9

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. 

F.1.4 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 to 50 percent (for 3 and 7 percent discount rates,

respectively), 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 a 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 for Scenario 2, over 68 percent

of the premature mortality-related benefits are due to changes in PM2.5 concentrations
F-10

occurring above 10 :g/m3, and around 8 percent are due to changes above 15 :g/m3, the

current PM2.5 standard.

F.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 F-2, we show the impact of alternative assumptions about

other parameters, including treatment of reversals in CB, 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 F-2. Additional Parameter Sensitivity Analyses

Alternative

Calculation Description of Estimate

Impact on Primary Benefit

Estimate (million 2000$)

Least

Stringent Expected

Most

Stringent

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 for the least

stringent Scenario 1 increases from 230 to 430

(87%). The increase for Scenario 2 is from

890 to 1,670 (87%). The increase for Scenario

3 is from 1,300 to 2,400 (85%).

+$34 +$130 +$190

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.

+$54 +$120 +$200

3 Household

soiling

damage

Value of decreases in expenditures on cleaning

are estimated using values derived from

Manuel et al. (1983).

+$8 +$33 +$47

An important assumption related to chronic conditions is the possible reversal in CB

incidences (row 1 of Table F-2). 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
F-11

a range of severities, one way to address this is to treat reversals as cases of CB that are at the

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 F-2) 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 3 of Table F-2) 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, Johnson, and

Banzhaf, 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.

F.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 F-3 lists

the ranges of elasticity values used to calculate the income adjustment factors, while 
F-12

Table F-3. 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 F-4 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 F-5.

Table F-4. Ranges of Adjustment Factors Used to Account for Projected Real Income

Growtha

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effect 1.015 1.114

Severe and Chronic Health Effects 1.094 1.241

Premature Mortality 1.029 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.
F-13

Table F-5. Sensitivity Analysis of Alternative Income Elasticitiesa

Benefit Category

Benefits in Millions of 1999$

Lower Sensitivity Bound Upper Sensitivity Bound

Least

Stringent Expected

Most

Stringent

Least

Stringent Expected

Most

Stringent

Minor Health

Effect

$20 $78 $110 $21 $83 $120

Severe and

Chronic Health

Effects

$140 $530 $750 $150 $580 $810

Premature

Mortalityc

$2,100 $8,200 $12,000 $2,900 $11,000 $16,000

Visibilityb $84 $240 $420 $84 $240 $420

Total Benefitsc $2,300 $9,100 $13,000 $3,200 $12,000 $18,000

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.

c Assuming a 3 percent discount rate for mortality benefits.

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.

F.4 References

Chestnut, L.G. 1997. “Draft Memorandum: Methodology for Estimating Values for

Changes in Visibility at National Parks.” April 15.

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.
F-14

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-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. 

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.

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.
F-15

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. 

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.

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.
G-1

APPENDIX G

RESULTS FOR TWO ADDITIONAL SCENARIOS APPLIED TO BART NON-EGU

SOURCE CATEGORIES

We present below results for each BART source category affected by these two nonEGU scenarios ($2,000/ton and $3,000/ton, respectively). Cost and emission reductions are

available for these scenarios; no benefits analyses were conducted for these scenarios. Results

presented here reflect the use of 7 percent and 3 percent discount rates as part of the control

strategy analysis for each scenario. There are no impacts for 8 of the 25 non-EGU source

categories because there are no control measures available to reduce SO2 and NOx from these

categories within AirControlNET. For seven source categories only, NOx reductions take place

in these analyses because there are no control measures available within AirControlNET at or

below these cost/ton levels.

G.1 Summary of Results for Two Additional Non-EGU Scenarios

The two scenarios presented in this appendix are applied nationwide and are presented in

detail in this appendix. These scenarios are meant to be illustrative of the potential alternatives

that may be available to states as they consider what scenarios to include in their SIPs for nonEGU sources. These scenarios are also compliant with the requirement in OMB Circular A-4 to

examine alternative levels of stringency as part of an RIA. 

This section includes several summary tables in which the emission reductions and costs

for these two non-EGU scenarios applied are shown by source category and also by the discount

rate for the annualized costs. Table G-1 summarizes the SO2 emission reductions for these

BART non-EGU source categories using a discount rate of 7 percent and also showing results

using a discount rate of 3 percent. In total, the scenarios applied in this analysis lead to

nationwide SO2 emission reductions ranging from 172,595 tons to 211,573 tons with costs at a 7

percent discount rate. These estimates represent a reduction of 14 to 18 percent from the 2015

baseline. These scenarios lead to SO2 emission reductions ranging from 210,013 to 262,547 tons

with costs at a 3 percent rate. These estimates represent a reduction of 17 to 22 percent from the

2015 baseline.
G-2

Table G-1. SO2 Emissions and Emission Reductions for BART Source Categories in 2015

BART Source Category

Baseline

Emissions (tons) Discount Rate

Scenarios—Reductions (tons)

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Industrial boilers 420,782 7% 87,009 124,592

420,782 3% 120,095 148,962

Petroleum refineries 199,483 7% 23,173 23,173

199,483 3% 25,103 33,304

Kraft pulp mills 119,818 7% 0 0

119,818 3% 0 3,196

Portland cement plants 116,835 7% 2,350 2,350

116,835 3% 2,743 13,383

Hydrofluoric, sulfuric, and

nitric acid plants

96,741 7% 35,358 36,753

96,741 3% 36,753 36,753

Chemical process plants 47,700 7% 2,376 2,376

47,700 3% 2,376 2,376

Iron and steel mills 23,541 7% 2,914 2,914

23,541 3% 2,914 2,914

Coke oven batteries 9,815 7% 4,088 4,088

9,815 3% 4,088 4,088

Sulfur recovery plants 59,766 7% 13,697 13,697

59,766 3% 14,311 14,311

Primary aluminum ore

reduction plants

47,552 7% 1,630 1,630

47,552 3% 1,630 3,260

Lime kilns 9,373 7% 0 0

9,373 3% 0 0

(continued)
G-3

Table G-1. SO2 Emissions and Emission Reductions for BART Source Categories in 2015

(continued)

BART Source Category

Baseline

Emissions (tons) Discount Rate

 Scenarios—Reductions (tons)

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Glass fiber processing plants 2,170 7% 0 0

2,170 3% 0 0

Municipal incinerators 284 7% 0 0

284 3% 0 0

Coal cleaning plants 1,530 7% 0 0

1,530 3% 0 0

Carbon black plants 41,853 7% 0 0

41,853 3% 0 0

Phosphate rock processing

plants

21 7% 0 0

21 3% 0 0

Secondary metal production

facilities

9,988 7% 0 0

9,988 3% 0 0

Total 1,208,088 7% 172,595 211,573

1,208,088 3% 210,013 262,547

Table G-2 summarizes the NOx emission reductions. The nationwide NOx emission

reductions from applying these two scenarios range from 242,355 tons to 291,740 tons with costs

at a 7 percent discount rate. These represent a reduction of 36 to 43 percent from the 2015

baseline. These scenarios lead to NOx emission reductions ranging from 280,163 to 313,382

tons with costs at a 3 percent rate. These represent a reduction of 41 to 46 percent from the 2015

baseline. 
G-4

Table G-2. NOx Emissions and Emission Reductions for BART Source Categories in 2015

BART Source Category

Baseline

Emissions (tons) Discount Rate

Scenarios 

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Industrial boilers 217,063 7% 97,074 125,575

217,063 3% 120,151 128,640

Petroleum refineries 86,566 7% 23,173 23,173

86,566 3% 23,173 26,685

Kraft pulp mills 103,614 7% 50,221 60,985

103,614 3% 56,466 64,521

Portland cement plants 120,567 7% 26,659 26,659

120,567 3% 26,659 26,659

Hydrofluoric, sulfuric, and nitric

acid plants

17,059 7% 11,283 11,283

17,059 3% 11,283 11,283

Chemical process plants 72,577 7% 25,922 26,753

72,577 3% 27,568 31,567

Iron and steel mills 20,963 7% 2,034 3,259

20,963 3% 2,038 7,198

Coke oven batteries 10,389 7% 0 5,768

10,389 3% 5,768 5,768

Sulfur recovery plants 651 7% 0 0

651 3% 0 0

Primary aluminum ore reduction

plants

1,676 7% 70 253

1,676 3% 335 335

Lime kilns 12,849 7% 4,471 4,471

12,849 3% 4,471 7,153

(continued)
G-5

Table G-2. NOx Emissions and Emission Reductions for BART Source Categories in 2015

(continued)

BART Source Category

Baseline

Emissions (tons) Discount Rate

Scenarios 

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Glass fiber processing plants 6,677 7% 568 2,116

6,677 3% 851 2,116

Municipal incinerators 1,656 7% 744 744

1,656 3% 744 744

Coal cleaning plants 1,110 7% 0 511

1,110 3% 511 511

Carbon black plants 4,645 7% 111 120

4,645 3% 111 120

Phosphate rock processing plants 719 7% 0 45

719 3% 0 48

Secondary metal production

facilities

1,377 7% 25 25

1,377 3% 34 34

Total 681,765 7% 242,355 291,740

681,765 3% 280,163 313,382

Table G-3 summarizes the annualized costs associated with the two non-EGU scenarios. 

In total, the two scenarios applied in this analysis have annualized costs of $512.36 million to

$706.26 million (1999$) with costs at a 7 percent discount rate and $507.23 million to $691.73

million (1999$) with costs at a 3 percent discount rate. 
G-6

Table G-3. Total Annualized Costs of Control for BART Source Categories in 2015

(million 1999$)

BART Source Category Discount Rate

Scenarios 

$2,000/ton

Scenario

$3,000/ton

Scenario 

Industrial boilers 7% 241.5 412.1

3% 255.0 337.4

Petroleum refineries 7% 71.1 71.1

3% 71.1 81.2

Kraft pulp mills 7% 75.1 75.1

3% 59.2 68.5

Portland cement plants 7% 29.6 29.6

3% 28.7 56.6

Hydrofluoric, sulfuric, and nitric acid plants 7% 20.4 21.4

3% 21.4 21.4

Chemical process plants 7% 40.5 40.5

3% 30.4 40.1

Iron and Steel mills 7% 7.9 11.0

3% 5.7 22.7

Coke oven batteries 7% 6.2 18.7

3% 14.9 14.9

Sulfur recovery plants 7% 11.7 12.1

3% 12.1 12.1

Primary aluminum ore reduction plants 7% 1.7 2.2

3% 1.0 5.0

Lime kilns 7% 5.0 5.0

3% 4.3 25.4

(continued)
G-7

Table G-3. Total Annualized Costs of Control for BART Source Categories in 2015

(million 1999$) (continued)

BART Source Category Discount Rate

Scenarios

$2,000/ton

Scenario 

$3,000/ton

Scenario 

Glass fiber processing plants 7% 0.5 5.3

3% 1.7 4.7

Municipal incinerators 7% 1.1 1.1

3% 0.9 0.9

Coal cleaning plants 7% 0.0 1.0

3% 0.8 0.8

Carbon black plants 7% 0.01 0.01

3% 0.005 0.01

Phosphate rock processing plants 7% 0.01 0.01

3% 0.01 0.01

Secondary metal production facilities 7% 0.04 0.04

3% 0.01 0.01

Total 7% $512.36 $706.26

3% $507.23 $691.73

Given the highly capital-intensive nature of the control measures included in these

analyses, it is not unreasonable that a lower discount rate would lead to more application of these

measures to reduce SO2 and NOx and vice versa. More sources would be controlled that may not

be able to control if they face relatively high interest rates for capital outlays in pollution control

equipment. At the $2,000/ton scenario, the emission reductions are higher with a 3 percent

discount rate than a 7 percent discount rate because the lower discount rate leads to more sources

having available controls under that scenario and the costs are fairly close. At $3,000/ton

scenario, the annualized costs and reductions are relatively closer.
G-8

G.2 Results for Industrial Boilers

Table G-4 shows the SO2 emissions reductions achieved in the analyses for each of these

two scenarios. The table indicates that these two scenarios achieve incremental reductions from

the 2015 baseline ranging from 21 to 30 percent given a 7 percent discount rate for the costs and

from 29 to 35 percent for costs at a 3 percent discount rate.

Table G-4. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

Industrial Boilersa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 420,782 7% 333,773 87,009

420,782 3% 300,687 120,095

$3,000/ton Scenario 420,782 7% 296,190 124,592

420,782 3% 271,820 148,962

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table G-5 presents the NOx baseline emissions and reductions for each scenario. The

table indicates that the scenarios achieve incremental reductions from the 2015 baseline, ranging

from 45 percent to 58 percent for costs at a 7 percent discount rate and from 55 to 59 percent for

costs at a 3 percent discount rate. 

Table G-5. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for Non-EGU

Industrial Boilers

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 217,063 7% 119,989 97,074

217,063 3% 96,912 120,151

$3,000/ton Scenario 217,063 7% 91,488 125,575

217,063 3% 88,423 128,640

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.
G-9

Table G-6 shows the annualized costs, resulting annualized average cost-effectiveness for

each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs range from $109 million to $197 million with costs at a 7 percent discount rate, and

from $133 million to $208 million with costs at a 3 percent discount rate. The accompanying

average annualized cost-effectiveness results range from $1,256 to $1,580 per ton with costs at a

7 percent rate and from $1,111 to $1,398 per ton with costs at a 3 percent rate. In addition, the

marginal costs between these scenarios are $2,328 per ton with costs at a 7 percent discount rate

and $2,595 per ton with costs at a 3 percent discount rate.

Table G-6. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU BARTEligible Industrial Boilers

 Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $109.3 $1,256 —

3% $133.4 $1,111 —

$3,000/ton Scenario 7% $196.8 $1,580 $2,328

3% $208.3 $1,398 $2,595

The costs and emission reductions reflect FGD scrubbers applied to all of these units.

The average and marginal costs rise as a result of FGD scrubbers being applied to more coalfired units with lower sulfur contents and to oil-fired units that have lower sulfur contents than

coal-fired units.

Table G-7 shows the annualized costs, resulting annualized average cost-effectiveness for

each scenario, and marginal costs between each scenario for NOx control. The annualized

control costs range from $132.2 million to $215.3 million with costs at a 7 percent rate and from

$121.6 million to $129.1 million with costs at a 3 percent rate. The accompanying annualized

average cost-effectiveness results range from $1,360 to $1,715 per ton with costs at a 7 percent

rate and from $1,012 to $1,044 per ton with costs at a 3 percent rate. In addition, the marginal

costs are $2,929 per ton with costs at a 7 percent rate and $2,684 per ton with costs at a 3 percent

rate. 

The average and marginal costs increase as the scenarios become more stringent as a

result of additional application of SCR. SCR is the most expensive NOx control device available

to industrial boilers in our analysis, though they also have a high control level (80 percent).
G-10

Table G-7. 2015 Cost and Cost-Effectiveness Results for NOx Control at BART-Eligible

Industrial Boilers

 Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness ($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $132.2 $1,360 —

3% $121.6 $1,012 —

$3,000/ton Scenario 7% $215.3 $1,715 $2,929

3% $129.1 $1,044 $2,684

Table G-8 shows the total annualized costs for each scenario for controlling both SO2 and

NOx.

Table G-8. 2015 Cost Results for SO2 and NOx Control at BART-Eligible Industrial

Boilers

Scenarios Discount Rate Total Annualized Costs (million 1999$)

$2,000/ton Scenario 7% $241.5

3% $255.0

$3,000/ton Scenario 7% $412.1

3% $337.4

G.3 Results for Petroleum Refineries

Table G-9 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline of 12 percent with costs estimated at a 7 percent discount rate and from 13 percent to 17

percent for costs estimated at a 3 percent discount rate.
G-11

Table G-9. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Petroleum Refineriesa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 199,483 7% 176,310 23,173

199,483 3% 174,380 25,103

$3,000/ton Scenario 199,483 7% 176,310 23,173

199,483 3% 166,179 33,304

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled. 

Table G-10 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline of 27 percent for costs estimated at a 7 percent discount rate and from 27 to 34 percent

for costs estimated at a 3 percent discount rate.

Table G-10. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Petroleum Refineriesa

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 86,566 7% 64,512 23,173

3% 64,512 23,173

$3,000/ton Scenario 86,566 7% 64,512 23,173

3% 59,881 26,685

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table G-11 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs are $28.5 million with costs at a 7 percent discount rate and range from $28.5

million to $43.9 million with costs at a 3 percent discount rate. The accompanying annualized

average cost-effectiveness results are $1,231 per ton with costs at a 7 percent discount rate and

range from $1,045 to $1,479 per ton with costs at a 3 percent discount rate. In addition, the

marginal costs are zero per ton with costs at a 7 percent discount rate and $3,025 per ton with

costs at a 3 percent discount rate. 
G-12

Table G-11. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Petroleum Refineries

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $28.5 $1,231 —

3% $28.5 $1,045 —

$3,000/ton Scenario 7% $28.5 $1,231 $0

3% $43.9 $1,479 $3,025

The costs and emission reductions reflect FGD scrubbers applied to all of these units.

The average and marginal costs rise as a result of FGD scrubbers being applied to units such as

fluid catalytic cracking units (FCCUs) with lower sulfur contents. As sulfur content of the fuel

for a unit decreases, the cost per ton of control increases and vice versa.

Table G-12 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for NOx control. The annualized

control cost is $42.6 million with costs at a 7 percent discount rate and ranges from $42.6 million

to $52.7 million with costs at a 3 percent discount rate. The accompanying annualized average

cost-effectiveness results are $1,930 per ton with costs at a 7 percent discount rate and range

from $1,930 to $1,975 per ton with costs at a 3 percent discount rate. In addition, the marginal

costs are zero with costs at a 7 percent discount rate and $2,876 per ton with costs at a 3 percent

discount rate. 

The average and marginal costs rise as a result of additional process heaters having to

apply LNB + SNCR. 
G-13

Table G-12. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Petroleum Refineries

 Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness ($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $42.6 $1,930 —

3% $42.6 $1,930 —

$3,000/ton Scenario 7% $42.6 $1,930 $0

3% $52.7 $1,975 $2,876

Table G-13 shows the total annualized costs for controlling both SO2 and NOx. 

Table G-13. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible Units

at Petroleum Refineries

Scenarios Discount Rate

Total Annualized Costs 

(million 1999$)

$2,000/ton Scenario 7% $71.1

3% $71.1

$3,000/ton Scenario 7% $71.1

3% $81.2

G.4 Kraft Pulp Mills

Table G-14 shows the SO2 emissions reductions achieved in the analyses for each of

these scenarios. The table indicates that the scenarios achieve no incremental reductions from

the 2015 baseline percent for costs at a 7 percent discount rate and from 0 to 3 percent for costs

at a 3 percent discount rate.

Table G-15 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 48 percent to 59 percent for costs at a 7 percent discount rate and from 54

to 65 percent for costs at a 3 percent discount rate.
G-14

Table G-14. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Kraft Pulp Millsa

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 119,818 7% 119,818 0

119,818 3% 119,818 0

$3,000/ton Scenario 119,818 7% 119,818 0

119,818 3% 116,820 3,196

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table G-15. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Kraft Pulp Millsa

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 103,614 7% 53,393 50,221

103,614 3% 47,148 56,466

$3,000/ton Scenario 103,614 7% 42,629 60,985

103,614 3% 36,093 67,521

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled. 

Table G-16 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs are $0 with costs at a 7 percent discount rate and range from $0 to $7.0 million with

costs at a 3 percent discount rate. The accompanying annualized average cost-effectiveness

results range from $0 per ton with costs at a 7 percent discount rate (since there are no

reductions) and from $0 to $2,189 per ton with costs at a 3 percent discount rate. In addition, the

marginal costs are zero with costs at a 7 percent discount rate and $2,189 per ton with costs at a

3 percent discount rate.
G-15

Table G-16. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Kraft Pulp Mills

 Scenarios

Discount

Rate

Total Annualized Costs

(million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $0.0 $0 —

3% $0.0 $0 —

$3,000/ton Scenario 7% $0.0 $0 —

3% $7.0 $2,189 $2,189

The costs and emission reductions reflect FGD scrubbers applied to all of these units.

The average and marginal costs rise as a result of FGD scrubbers being applied to units for

which the application is more expensive. 

Table G-17 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for NOx control. The annualized

control costs range from $75.1 million to $106.7 million with costs at a 7 percent discount rate

and range from $59.2 million to $61.5 million with costs at a 3 percent rate. The accompanying

annualized average cost-effectiveness results range from $1,496 to $1,749 per ton with costs at a

7 percent discount rate and from $1,048 to $1,069 per ton with costs at a 3 percent discount rate.

In addition, the marginal costs are $3,344 per ton with costs at a 7 percent discount rate and 

$4,452 per ton with costs at a 3 percent discount rate. 

The costs and emission reductions reflect greater applications of SCR as the cost-per-ton

cap rises, particularly for sulfite pulping recovery furnaces.

Table G-18 shows the total annualized costs of each scenario for controlling both SO2

and NOx.
G-16

Table G-17. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Kraft Pulp Mills

 Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Cost

($/ton)

$2,000/ton Scenario 7% $75.1 $1,496 —

3% $59.2 $1,048 —

$3,000/ton Scenario 7% $106.7 $1,749 $3,344

3% $61.5 $1,069 $4,452

Table G-18. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible Units

at Kraft Pulp Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$)

$2,000/ton Scenario 7% $75.1

3% $59.2

$3,000/ton Scenario 7% $75.1

3% $68.5

G.5 Results for Portland Cement Plants

Table G-19 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 2 percent for costs at a 7 percent discount rate and from 2 to 11 percent for

costs at a 3 percent discount rate.

Table G-20 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline of 28 percent for costs at a 7 or a 3 percent discount rate.
G-17

Table G-19. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Portland Cement Plantsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 116,835 7% 114,485 2,350

116,835 3% 114,092 2,743

$3,000/ton Scenario 116,835 7% 114,485 2,350

116,835 3% 103,452 13,383

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table G-20. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Portland Cement Plantsa

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 120,567 7% 93,908 26,659

120,567 3% 93,908 26,659

$3,000/ton Scenario 120,567 7% 93,908 26,659

120,567 3% 93,908 26,659

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled. 

Table G-21 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs are $4.6 million with costs at a 7 percent discount rate and range from $3.7 to $31.6

million with costs at a 3 percent discount rate. The accompanying annualized average costeffectiveness results are $1,973 per ton with costs at a 7 percent discount rate and from $1,344 to

$2,362 per ton with costs at a 3 percent discount rate. In addition, the marginal costs are zero

with costs at a 7 percent discount rate and $2,622 per ton with costs at a 3 percent discount rate. 
G-18

Table G-21. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Portland Cement Plants

 Scenarios Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $4.6 $1,973 —

3% $3.7 $1,344 —

$3,000/ton Scenario 7% $4.6 $1,973 __

3% $31.6 $2,362 $2,622

The costs and emission reductions reflect FGD scrubbers applied to all of these units.

The average and marginal costs rise as a result of FGD scrubbers being applied to more units

with lower sulfur content fuels.

Table G-22 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for NOx control. The annualized

control costs are $25.0 million at either a 7 or a 3 percent discount rate for each scenario. The

annualized average cost-effectiveness is $937 per ton. Since there is no difference in costs

between these scenarios for NOx control, the marginal costs are zero.

Table G-22. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Portland Cement Plants

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average CostEffectiveness ($/ton)Marginal Cost 

($/ton)

$2,000/ton

Scenario 

7% $25.0 $937 —

3% $25.0 $937 —

$3,000/ton

Scenario 

7% $25.0 $937 __

3% $25.0 $937 —
G-19

The average and marginal costs of control increase as more SCR applications take place

as the cost-per-ton cap rises. These applications take the place of less expensive but less

effective controls such as mid-kiln firing. 

Table G-23 shows the total annualized costs of each scenario for controlling both SO2

and NOx.

Table G-23. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible Units

at Portland Cement Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

$2,000/ton Scenario 7% $29.6

3% $28.7

$3,000/ton Scenario 7% $29.6

3% $56.6

G.6 Results for Hydrofluoric, Sulfuric, and Nitric Acid Plants

Table G-24 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 35 to 38 percent for costs at a 7 percent discount rate and the same for

costs at a 3 percent discount rate.

Table G-25 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline of about 66 percent. The degree of impact varies little between scenarios and the

discount rate of the costs.

Table G-26 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs range from $12.2 million to $14.1 million with costs at a 7 percent discount rate

and are $14.1 million with costs at a 3 percent discount rate. The accompanying annualized

average cost-effectiveness results range from $345 to $385 per ton with costs at a 7 percent

discount rate and are $385 per ton with costs at a 3 percent discount rate. In addition, the

marginal costs are $1,362 per ton with costs at a 7 percent discount rate and zero (no reductions)

with costs at a 3 percent discount rate.
G-20

Table G-24. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plantsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 96,741 7% 61,383 35,358

96,741 3% 60,188 36,753

$3,000/ton Scenario 96,741 7% 60,188 36,753

96,741 3% 60,188 36,753

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled. 

Table G-25. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plantsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 17,059 7% 5,776 11,283

17,059 3% 5,776 11,283

$3,000/ton Scenario 17,059 7% 5,776 11,283

17,059 3% 5,776 11,283

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled. 

Table G-26. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants

 Scenarios

Discount

Rate

Total Annualized Costs

(million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $12.2 $345 —

3% $14.1 $385 —

$3,000/ton Scenario 7% $14.1 $385 $1,362

3% $14.1 $385 —
G-21

The costs and emission reductions are flat between the scenarios because there is only

one control technique available to reduce SO2 emissions from these sources—increase sulfur

conversion to meet the sulfuric acid NSPS (99.7 percent control). 

Table G-27 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for NOx control. The annualized

control costs are $8.2 million with costs at a 7 percent discount rate and $7.3 million with costs

at a 3 percent discount rate. The accompanying annualized average cost-effectiveness results are

$728 per ton with costs at a 7 percent discount rate and $647 per ton with costs at a 3 percent

discount rate. The marginal costs are zero between these scenarios because no additional

emission reductions occur.

Table G-27. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants

 Scenarios Discount Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $8.2 $728 —

3% $7.3 $647 —

$3,000/ton Scenario 7% $8.2 $728 —

3% $7.3 $647 —

The costs and emission reductions are flat between the scenarios because there is only

one control technique available to reduce NOx emissions from these sources—SNCR applied to

nitric acid manufacturing sources.

Table G-28 shows the total annualized costs of each scenario for controlling both SO2

and NOx.

G.7 Results for Chemical Process Plants

Table G-29 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline of 7 percent for costs at either a 7 or a 3 percent discount rate.
G-22

Table G-28. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible Units

at Hydrofluoric, Sulfuric, and Nitric Acid Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

$2,000/ton Scenario 7% $20.4

3% $21.4

$3,000/ton Scenario 7% $21.4

3% $21.4

Table G-29. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Chemical Process Plantsa

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 47,700 7% 45,324 2,376

47,700 3% 45,324 2,376

$3,000/ton Scenario 47,700 7% 45,324 2,376

47,700 3% 45,324 2,376

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table G-30 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 36 percent to 37 percent for costs at a 7 percent discount rate and from 38

to 43 percent for costs at a 3 percent discount rate.

Table G-31 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs are $2.5 million with costs at a 7 percent discount rate and are $2.4 million with

costs at a 3 percent discount rate. The accompanying annualized average cost-effectiveness

results are $1,052 per ton with costs at a 7 percent discount rate and $1,013 per ton with costs at

a 3 percent discount rate. 
G-23

Table G-30. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Chemical Process Plantsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 72,577 7% 46,655 25,922

72,577 3% 45,009 27,568

$3,000/ton Scenario 72,577 7% 45,824 26,753

72,577 3% 41,010 31,567

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled. 

Table G-31. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Chemical Process Plants

 Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $2.5 $1,052 —

3% $2.4 $1,013 —

$3,000/ton Scenario 7% $2.5 $1,052 $1,052

3% $2.4 $1,013 $1,013

The costs and emission reductions reflect a major difference in the impacts between the

two available control techniques: increase sulfur percentage conversion to meet the sulfuric acid

NSPS (99.7 percent control) and FGD scrubbers.

Table G-32 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for NOx control. The annualized

control costs are $38 million with costs at a 7 percent discount rate and from $28.0 million to

$37.7 million with costs at a 3 percent discount rate. The accompanying annualized average

cost-effectiveness results are $1,466 per ton with costs at a 7 percent discount rate and range

from $1,015 per ton to $1,192 per ton with costs at a 3 percent discount rate. In addition, the

marginal costs of the scenarios are zero (no additional reductions) with costs at a 7 percent

discount rate and $4,348 per ton with costs at a 3 percent discount rate.
G-24

Table G-32. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Chemical Process Plants

 Scenarios Discount Rate

Total Annualized Costs

(million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $38.0 $1,466 —

3% $28.0 $1,015 —

$3,000/ton Scenario 7% $38.0 $1,466 —

3% $37.7 $1,192 $4,348

Table G-33 shows the total annualized costs of each scenario for controlling both SO2

and NOx.

Table G-33. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible Units

at Chemical Process Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

$2,000/ton Scenario 7% $40.5

3% $30.4

$3,000/ton Scenario 7% $40.5

3% $40.1

G.8 Results for Iron and Steel Mills

Table G-34 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline of 12 percent for costs at either a 7 or 3 percent discount rate.

Table G-35 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 5 to 41 percent for costs of either a 7 or 3 percent discount rate. 
G-25

Table G-34. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Iron and Steel Millsa

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 23,541 7% 20,627 2,914

23,541 3% 20,627 2,914

$3,000/ton Scenario 23,541 7% 20,627 2,914

23,541 3% 20,627 2,914

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled. 

Table G-35. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Iron and Steel Millsa

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 20,963 7% 18,929 2,034

20,963 3% 18,925 2,038

$3,000/ton Scenario 20,963 7% 17,704 3,259

20,963 3% 13,765 7,198

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table G-36 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs are $5.3 million with costs at a 7 percent discount rate and $3.4 million with costs

at a 3 percent discount rate. The accompanying annualized average cost-effectiveness results are

$1,819 per ton with costs at a 7 percent discount rate and $1,165 per ton with costs at a 3 percent

discount rate. Marginal costs are zero between the scenarios because there are no additional

reductions from going to the $3,000 per ton scenario from the $2,000 per ton scenario.
G-26

Table G-36. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Iron and Steel Mills

 Scenarios Discount Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $5.3 $1,819 —

3% $3.4 $1,165 —

$3,000/ton Scenario 7% $5.3 $1,819 —

3% $3.4 $1,165 —

Table G-37 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for NOx control. The annualized

control costs range from $2.6 million to $5.8 million with costs at a 7 percent discount rate and

from $2.3 million to $15.0 million with costs at a 3 percent discount rate. The accompanying

annualized average cost-effectiveness results range from $1,302 to $1,770 per ton with costs at a

7 percent discount rate and from $1,109 to $2,271 per ton with costs at a 3 percent discount rate. 

The marginal costs are $2,953 per ton with costs at a 7 percent discount rate and $2,532 per ton

with costs at a 3 percent discount rate.

The costs and emission reductions reflect a rise in the costs of control due to additional

applications of LNB + either SNCR or SCR. 

Table G-38 shows the total annualized costs for controlling both SO2 and NOx.

G.9 Results for Coke Oven Batteries

Table G-39 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 0 to 62 percent for costs at a 7 percent discount rate and from 0 to 57

percent for costs at a 3 percent discount rate.
G-27

Table G-37. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Iron and Steel Mills

 Scenarios Discount Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $2.6 $1,302 —

3% $2.3 $1,109 —

$3,000/ton Scenario 7% $5.8 $1,770 $2,953

3% $15.0 $2,271 $2,532

Table G-38. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible Units

at Iron and Steel Mills

 Scenarios Discount Rate Total Annualized Costs (million 1999$)

$2,000/ton Scenario 7% $7.9

3% $5.7

$3,000/ton Scenario 7% $11.0

3% $22.7

Table G-39. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Coke Oven Batteries

Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 9,815 7% 5,727 4,088

9,815 3% 5,727 4,088

$3,000/ton Scenario 9,815 7% 5,727 4,088

9,815 3% 5,727 4,088

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.
G-28

Table G-40 shows the emissions reductions achieved in the analyses for each scenario for

NOx control. The table indicates that the scenarios achieve incremental reductions from the

2015 baseline ranging from 0 to 56 percent for costs at a 7 or 3 percent discount rate.

Table G-40. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Coke Oven Batteriesa

 Scenarios 2015 Baseline Emissions

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 10,389 10,389 0

10,389 4,621 5,768

$3,000/ton Scenario 10,389 4,621 5,768

10,389 4,621 5,768

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled. 

Table G-41 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs rare $6.2 million with costs at a 7 percent discount rate and $4.0 million with costs

at a 3 percent discount rate. The accompanying annualized average cost-effectiveness results are

$1,517 per ton with costs at a 7 percent discount rate and from $1,074 per ton with costs at a 3

percent discount rate. The marginal costs are $1,517 per ton with costs at a 7 percent discount

rate and $1,074 per ton with costs at a 3 percent discount rate. 

Table G-41. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Coke Oven Batteries

 Scenarios Discount Rate

Total Annualized

Costs (million 1990$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $6.2 $1,517 —

3% $4.0 $1,074 —

$3,000/ton Scenario 7% $6.2 $1,517 $1,517

3% $4.0 $1,074 $1,074
G-29

The costs and emission reductions reflect application of only one control—vacuum

carbonate plus a sulfur recovery plant but also differing SO2 emissions levels at the affected coke

oven batteries.

Table G-42 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for NOx control. The annualized

control costs range from $0 million to $12.5 million with costs at a 7 percent discount rate and

are $10.9 million with costs at a 3 percent discount rate. The accompanying annualized average

cost-effectiveness results range from $0 to $2,167 per ton with costs at a 7 percent discount rate

and are $1,898 per ton with costs at a 3 percent discount rate. In addition, the marginal costs are

$2,167 per ton with costs at a 7 percent discount rate and zero with costs at a 3 percent discount

rate.

Table G-42. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Coke Oven Batteries

 Scenarios Discount Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal

Costs ($/ton)

$2,000/ton Scenario 7% $0.0 $0 —

3% $10.9 $1,898 —

$3,000/ton Scenario 7% $12.5 $2,167 $2,167

3% $10.9 $1,898 —

Table G-43 shows the total annualized costs for controlling both SO2 and NOx.

Table G-43. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible Units

at Coke Oven Batteries

Scenarios Discount Rate Total Annualized Costs (million 1999$)

$2,000/ton Scenario 7% $6.2

3% $14.9

$3,000/ton Scenario 7% $18.7

3% $14.9
G-30

G.10 Results for Sulfur Recovery Plants

Table G-44 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline of 23 percent for costs at a 7 percent discount rate and 24 percent for costs at a 3 percent

discount rate. The emission reductions are the same for each scenario because of the few

controls available under these scenarios. 

Table G-44. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Sulfur Recovery Plantsa

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 59,766 7% 46,069 13,697

59,766 3% 45,455 14,311

$3,000/ton Scenario 59,766 7% 46,069 13,697

59,766 3% 45,455 14,311

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

There are no reductions of NOx from sulfur recovery units under either of these scenarios.

Table G-45 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs are $11.6 million with costs at a 7 or a 3 percent discount rate. The accompanying

annualized average cost-effectiveness results are $847 per ton with costs at a 7 percent discount

rate and $849 per ton with costs at a 3 percent discount rate.

The costs and emission reductions are flat between the scenarios because there is only

one control technique available to reduce SO2 emissions from these sources—sulfur recovery

and/or tail gas treatment.

Table G-46 shows the total annualized costs for controlling SO2 from these sources.
G-31

Table G-45. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Sulfur Recovery Plants

 Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $11.6 $847 —

3% $12.2 $849 —

$3,000/ton Scenario 7% $11.6 $847 $847

3% $12.2 $849 $849

Table G-46. 2015 Cost Results for SO2 Control at Non-EGU BART-Eligible Units at Sulfur

Recovery Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

$2,000/ton Scenario 7% $11.7

3% $12.1

$3,000/ton Scenario 7% $12.1

3% $12.1

G.11 Results for Primary Aluminum Ore Reduction Plants

Table G-47 shows the SO2 emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 3 to 7 percent for costs at a 7 or 3 percent discount rate.

Table G-48 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 4 to 15 percent with costs at a 7 percent discount rate and are 20 percent

with costs at a 3 percent discount rate.
G-32

Table G-47. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Primary Aluminum Ore Reduction Plantsa

 Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 47,552 7% 45,922 1,630

47,552 3% 45,922 1,630

$3,000/ton Scenario 47,552 7% 45,922 1,630

47,552 3% 44,292 3,260

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table G-48. 2015 NOx Baseline Emissions and Emission Reductions (in tons) for Non-EGU

BART-Eligible Units at Primary Aluminum Ore Reduction Plantsa

 Scenario

2015 Baseline

Emissions

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 1,676 1,606 70

1,676 1,341 335

$3,000/ton Scenario 1,676 1,423 253

1,676 1,341 335

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source

categories, both controlled and uncontrolled.

Table G-49 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for SO2 control. The annualized

control costs are $1.6 million with costs at a 7 percent discount rate and range from $1.0 million

to $4.5 million with costs at a 3 percent discount rate. The accompanying annualized average

cost-effectiveness results range from $982 per ton with costs at a 7 percent discount rate and

from $590 to $1,381 per ton with costs at a 3 percent discount rate. The marginal costs are zero

with costs at a 7 percent discount rate and $2,147 per ton with costs at a 3 percent discount rate. 
G-33

Table G-49. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU

BART-Eligible Units at Primary Aluminum Ore Reduction Plants

 Scenarios

Discount

Rate

Total Annualized Costs

(million 1999$)

Annualized Average

Cost-Effectiveness ($/ton)

Marginal

Costs ($/ton)

$2,000/ton Scenario 7% $1.6 $982 —

3% $1.0 $590 —

$3,000/ton Scenario 7% $1.6 $982 __

3% $4.5 $1,381 $2,147

Table G-50 shows the annualized costs, resulting annualized average cost-effectiveness

for each scenario, and marginal costs between each scenario for NOx control. The annualized

control costs range from $0.04 million to $0.6 million with costs at a 7 percent discount rate and

from $0.04 to $0.5 million with costs at a 3 percent discount rate. The accompanying annualized

average cost-effectiveness results range from $1,114 to $2,411 per ton with costs at a 7 percent

discount rate and from $509 to $1,614 per ton with costs at a 3 percent discount rate. The

marginal costs are $2,823 per ton with costs at a 7 percent discount rate and $1,764 per ton with

costs at a 3 percent discount rate. The costs and NOx emission reductions reflect LNB

applications. 

Table G-50. 2015 Cost and Cost-Effectiveness Results for NOx Control at Non-EGU

BART-Eligible Units at Primary Aluminum Ore Reduction Plants

 Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness ($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $0.1 $1,114 —

3% $0.0 $509 —

$3,000/ton Scenario 7% $0.6 $2,411 $2,823

3% $0.5 $1,614 $1,764

Table G-51 shows the total annualized costs for controlling both SO2 and NOx.
G-34

Table G-51. 2015 Cost Results for SO2 and NOx Control at Non-EGU BART-Eligible Units

at Primary Aluminum Ore Reduction Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$)

$2,000/ton Scenario 7% $1.7

3% $1.0

$3,000/ton Scenario 7% $2.2

3% $5.0

The next seven BART source categories only have NOx controls applied to their affected

units because there are no SO2 emissions from BART-eligible units in these source categories

that can be controlled at under $3,000 per ton. Hence, all the reductions and costs for the

remaining source categories are only for NOx, not SO2.

G.12 Results for Lime Kilns

Table G-52 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the options achieve incremental reductions from the 2015

baseline ranging from 21 to 56 percent for costs at a 7 or 3 percent discount rate.

Table G-52. 2015 NOx Emission Reductions (in tons) for BART-Eligible Lime Kilns

 Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 12,849 7% 8,378 4,471

12,849 3% 8,378 4,471

$3,000/ton Scenario 12,849 7% 8,378 4,471

12,849 3% 5,696 7,153

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source category,

both controlled and uncontrolled.
G-35

Table G-53 shows the annualized costs, resulting annualized average cost-effectiveness,

and marginal costs for each scenario. The total annualized costs for these scenarios are $5

million with costs at a 7 percent discount rate and range from $4.3 million to $25.4 million with

costs at a 3 percent discount rate. The annualized average cost-effectiveness is $1,118 per ton

with costs at a 7 percent discount rate and ranges from $953 to $3,552 per ton with costs at a 3

percent discount rate. The marginal costs are zero (no additional reductions) with costs at a 7

percent discount rate and $7,867 per ton with costs at a 3 percent discount rate. These impacts

reflect applications of SNCR.

Table G-53. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Lime Kilns

 Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal

Costs ($/ton)

$2,000/ton Scenario 7% $5.0 $1,118 —

3% $4.3 $953 —

$3,000/ton Scenario 7% $5.0 $1,118 __

3% $25.4 $3,552 $7,867

G.13 Results for Glass Fiber Processing Plants

Table G-54 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the options achieve incremental reductions from the 2015

baseline ranging from 9 to 32 percent for costs at a 7 or 3 percent discount rate.

Table G-54. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at Glass

Fiber Processing Plants

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 6,677 7% 6,109 568

6,677 3% 5,826 851

$3,000/ton Scenario 6,677 7% 4,561 2,116

6,677 3% 4,561 2,116

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled

and uncontrolled.
G-36

Table G-55 shows the annualized cost, resulting annualized average cost-effectiveness,

and marginal costs for each scenario. The total annualized costs for these scenarios range from

$0.5 million to $5.3 million with costs at a 7 percent discount rate and from $1.7 million to $4.7

million with costs at a 3 percent discount rate. The annualized average cost-effectiveness ranges

from $937 to $2,505 per ton with costs at a 7 percent discount rate and from $1,972 to $2,244

per ton with costs at a 3 percent discount rate. 

Table G-55. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at Glass

Fiber Processing Plants

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average CostEffectiveness ($/ton)Marginal

Costs ($/ton)

$2,000/ton Scenario 7% $0.5 $937 —

3% $1.7 $1,972 —

$3,000/ton Scenario 7% $5.3 $2,505 $3,101

3% $4.7 $2,244 $3,057

G.14 Results for Municipal Incinerators

The analysis of municipal incinerators (>250 tons per day burn refuse capacity) shows

the results for each scenario. Table G-56 shows the NOx emissions reductions achieved in the

analysis for each scenario. The table indicates that the scenarios achieve incremental reductions

from the 2015 baseline of 45 percent for costs at a 7 or 3 percent discount rate.

Table G-56. 2015 NOx Emission Reductions (in tons) for BART-Eligible Municipal

Incineratorsa

 Scenarios

2015 Baseline

Emissions

Discount

Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 1,656 7% 912 744

1,656 3% 912 744

$3,000/ton Scenario 1,656 7% 912 744

1,656 3% 912 744

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled

and uncontrolled.
G-37

Table G-57 shows the annualized costs, annualized average cost-effectiveness, and

marginal costs for each scenario. The total annualized costs for these scenarios range is $1.1

million with costs at a 7 percent discount rate and $0.9 million with costs at a 3 percent discount

rate. The annualized average cost-effectiveness is $1,478 per ton with costs at a 7 percent

discount rate and $1,207 per ton with costs at a 3 percent discount rate. The marginal costs are

$1,478 per ton for reaching the $3,000 per ton scenario with costs at the 7 percent discount rate

and $1,207 per ton at the 3 percent discount rate. The only available control measure for this

source is SNCR.

Table G-57. 2007 Cost and Cost-Effectiveness Results for BART-Eligible Municipal

Incinerators

Scenarios

Discount

Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $1.1 $1,478 —

3% $0.9 $1,207

$3,000/ton Scenario 7% $1.1 $1,478 $1,478

3% $0.9 $1,207 $1,207

G.15 Results for Coal Cleaning Plants

Table G-58 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 0 to 46 percent for costs at a 7 or a 3 percent discount rate. 

Table G-59 shows the annualized costs, annualized average cost-effectiveness, and

marginal costs for each scenario. The total annualized costs for these scenarios range from $0 to

$1 million with costs at a 7 percent discount rate and $0.8 million with costs at a 3 percent

discount rate. The annualized average cost-effectiveness is $0 to $1,900 per ton with costs at a 7

percent discount rate and $1,534 per ton with costs at a 3 percent discount rate. Marginal costs

are $1,900 per ton between the scenarios with costs at a 7 percent discount rate and are zero (no

additional reductions) with costs at a 3 percent discount rate. Controls available to these sources

are LNB and SNCR.
G-38

Table G-58. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at Coal

Cleaning Plants

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 1,110 7% 1,110 0

1,110 3% 599 511

$3,000/ton Scenario 1,110 7% 599 511

1,110 3% 599 511

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled

and uncontrolled.

Table G-59. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at Coal

Cleaning Plants

 Scenarios Discount Rate

Total Annualized

Costs (million 1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $0.0 $0 —

3% $0.8 $1,534 —

$3,000/ton Scenario 7% $1.0 $1,900 $1,900

3% $0.8 $1,534 —

G.16 Results for Carbon Black Plants

Table G-60 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline of about 2 percent for costs at a 7 or a 3 percent discount rate. 
G-39

Table G-60. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at Carbon

Black Plants

 Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 4,645 7% 4,534 111

4,645 3% 4,534 111

$3,000/ton Scenario 4,645 7% 4,525 120

4,645 3% 4,525 120

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled

and uncontrolled.

Table G-61 shows the annualized cost, resulting annualized average cost-effectiveness,

and marginal costs for each scenario. The total annualized costs for these scenarios are about

$0.01 million with costs at a 7 percent discount rate and about $0.006 million with costs at a 3

percent discount rate. The annualized average cost-effectiveness is $1,608 per ton with costs at a

7 percent discount rate and $1,495 per ton with costs at a 3 percent discount rate. Marginal costs

are zero since there are no reductions between the scenarios. NOx controls available to these

sources are SNCR and SCR, and the cost per ton for these controls is fairly similar for these

sources in this analysis. 

Table G-61. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at Carbon

Black Plants

 Scenarios Discount Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Costs

($/ton)

$2,000/ton Scenario 7% $0.0 $1,608 —

3% $0.0 $1,495 —

$3,000/ton Scenario 7% $0.0 $1,608 —

3% $0.0 $1,495 —
G-40

G.17 Results for Secondary Metal Production Facilities

Table G-62 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 2 to 3 percent for costs at either a 7 or 3 percent discount rate.

Table G-62. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at

Secondary Metal Production Facilities

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 1,377 7% 1,352 25

1,377 3% 1,343 34

$3,000/ton Scenario 1,377 7% 1,352 25

1,377 3% 1,343 34

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source category,

both controlled and uncontrolled.

Table G-63 shows the annualized cost, resulting annualized average cost-effectiveness,

and marginal costs for each scenario. The total annualized costs for these scenarios range from

$0.01 million to $0.04 million with costs at a 7 percent discount rate and the same with costs at a

3 percent discount rate. The annualized average cost-effectiveness ranges from $511 to $760 per

ton with costs at a either a 3 or 7 percent discount rate. The marginal costs between the

scenarios are zero since there are no additional reductions with increasing stringency.

Available NOx controls are LNB and the more expensive LNB + SNCR.

G.18 Results for Phosphate Rock Ore Processing Facilities

Table G-64 shows the NOx emissions reductions achieved in the analyses for each

scenario. The table indicates that the scenarios achieve incremental reductions from the 2015

baseline ranging from 2 percent for costs at a 7 percent discount rate and from 2 to 47 percent for

costs at a 3 percent discount rate.
G-41

Table G-63. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at

Secondary Metal Processing Facilities

 Scenarios

Discount

Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Cost

($/ton)

$2,000/ton Scenario 7% $0.0 $760 —

3% $0.0 $511 —

$3,000/ton Scenario 7% $0.0 $760 —

3% $0.0 $511 —

Table G-64. 2015 NOx Emission Reductions (in tons) for BART-Eligible Units at

Phosphate Rock Ore Processing Facilities

Scenarios

2015 Baseline

Emissions Discount Rate

2015 Postcontrol

Emissions

2015 Emission

Reductions

$2,000/ton Scenario 719 7% 689 30

719 3% 689 30

$3,000/ton Scenario 719 7% 689 30

719 3% 689 30

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source category,

both controlled and uncontrolled.

Table G-65 shows the annualized cost, resulting annualized average cost-effectiveness,

and marginal costs for each scenario. The total annualized costs for these scenarios range from

$0.01 million with costs at either a 7 or 3 percent discount rate. The annualized average costeffectiveness is $760 per ton with costs at a either a 3 or 7 percent discount rate. The marginal

costs between the scenarios are zero since there are no additional reductions with increasing

stringency.
G-42

Table G-65. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at

Phosphate Rock Ore Processing Facilities

 Scenarios Discount Rate

Total Annualized

Costs (million

1999$)

Annualized Average

Cost-Effectiveness

($/ton)

Marginal Cost

($/ton)

$2,000/ton Scenario 7% $0.0 $760 —

3% $0.0 $760 —

$3,000/ton Scenario 7% 0.0 760 —

3% 0.0 760 —
United States Office of Air Quality Planning and Standards Publication No

Environmental Protection Air Quality Strategies and Standards Division June 2005Agency Research Triangle Park, NC