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

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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
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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

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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 Vegetation-
Related 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
IV
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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
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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
VI
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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 Analyse8-16
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

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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

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10. COMPARISON OF BENEFITS AND COSTS 10-1

10.1 References 10-3

Appendix A: BART Industry-Sector Impacts A-l

Appendix B: Cost and Economic Impact Supplemental Information and Sensitivity
Analyses B-l

Appendix C: Additional Technical Information Supporting the Benefits Analysis C-l

Appendix D: Visibility Benefits Methodology D-l

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-l

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

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

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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
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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$) 8-7
8-4 SO2 Emissions and Emission Reductions for BART Source Categories
in 2015 8-9
8-5 NOX Emissions and Emission Reductions for BART Source Categories
in 2015 8-11
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8-6 Total Annualized Costs of Control for BART Source Categories in 2015
(million 1999$) 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

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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
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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
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8-72 2015 NOX Emission Reductions (in tons) for BART-Eligible Units at Secondary
Metal Production Facilities 8-62

9-1 Potentially Regulated ECU 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
XVII
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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.
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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 electric-
generating sector (ECU) 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 ECU 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.
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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 costs'"

3 percent discount rate

7 percent discount rate
Social benefits0 de
$0.4

$0.3
$1.4

$1.5
$2.3

$2.9
3 percent discount rate
7 percent discount rate
Health-related benefits:
3 percent discount rate
7 percent discount rate
Visibility benefits
Net benefits (benefits-costs)0''
3 percent discount rate
7 percent discount rate
$2.6 + B
$2.2 + B

$2.5
$2.1
$0.08

$2.2 + B
$1.9 + B
$10.1 +B
$8.6 + B

$9.8
$8.4
$0.24

$8.7 + B
$7.1 +B
$14.3 + B
$12.2 + B

$13.9
$11.8
$0.42

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.
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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 (PM25), and NOX contributes to the formation of both PM25 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

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 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).

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Table 1-2. Clean Air Visibility Rule: Estimated Reduction in Incidence of Adverse
Health Effects in 2015ab
Health Effect

Scenario 1
Incidence Reduction
Scenario 2

Scenario 3
PM-Related Endpoints:
Premature mortality0
Adult, age 30 and over
Infant, age18)e
Emergency room visits for asthma (age
18 years and younger)
Acute bronchitis (children, age 8-12)
Lower respiratory symptoms (children,
400
1
230
570
140
120
370
550
6,600
1,600
4
890
2,200
510
450
1,300
2,100
25,000
2,300
5
1,300
3,000
720
640
1,800
3,000
36,000
age 7-14)
Upper respiratory symptoms (asthmatic
children, age 9-18)
Asthma exacerbation (asthmatic
children, age 6-18)
Work loss days (adults, age 18-65)
Minor restricted-activity days (MRADs)
(adults, age 18-65)
44,000
260,000
19,000

31,000

170,000
1,000,000
27,000

44,000

240,000
1,400,000
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.
Ozone benefits are expected for CAVR but are not estimated for this analysis.
Adult premature mortality based on studies by Pope et al. (2002). Infant premature mortality is based on studies by
Woodruff, Grille, and Schoendorf (1997).
Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD),
pneumonia, and asthma.
Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart disease,
dysrhythmias, and heart failure.
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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$)ab
Effect
Scenario 1
Scenario 2
Scenario 3
Health Effects:
Premature mortality04
Adult >30 years
3% discount rate
7% discount rate
Child <1 year
Chronic bronchitis (adults, 26 and over)
Nonfatal acute myocardial infarctions
3% discount rate
7% discount rate
Hospital admissions for respiratory causes
Hospital admissions for cardiovascular causes
Emergency room visits for asthma
Acute bronchitis (children, age 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthma, 9-11)
Asthma exacerbations
Work loss days
Minor restricted-activity days (MRADs)
Welfare Effects:
Recreational visibility, southeastern and
southwestern Class I areas
$2,330
$1,960
$6.12
$90.5
$49.3
$45.8
$1.07
$2.6
$0.106
$0.207
$0.109
$0.137
$0.367
$5.56
$13.8

$84
$9,180
$7,730
$23.8
$353
$189
$176
$4.03
$10.0
$0.362
$0.79
$0.415
$0.523
$1.4
$22.4
$54.1

$239
$13,000
$10,900
$34.2
$498
$264
$245
$5.65
$14.1
$0.51
$1.12
$0.587
$0.74
$1.98
$31.5
$76.3

$416
Monetized TotaF
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
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.
Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis year of 2015.
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).
Adult premature mortality based on studies by Pope et al. (2002). Infant premature mortality based on studies by
Woodruff, Grille, and Schoendorf (1997).
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.
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7.2.2 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.

7.2.3 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:

• 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.

• 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.

• 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.

• 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 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 PM25 (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.
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Table 1-4. Unquantified and Nonmonetized Effects of the Clean Air Visibility Rule
Pollutant/Effect
Effects Not Included in Primary Estimates—Changes in:
Ozone—Health3
Ozone—Welfare
PM—Health'
PM—Welfare
Nitrogen and Sulfate
Deposition—Welfare
Premature mortality13
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
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
Premature mortality: short-term exposures'1
Low birth weight
Pulmonary function
Chronic respiratory diseases other than chronic bronchitis
Nonasthma respiratory emergency room visits
Exposure to UVb (+/-)e
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
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)
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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 effects'
• Potential cardiovascular effects', including:
- Altered blood pressure regulation'
- Increased heart rate variability'
- Incidence of myocardial infarction'

Mercury Deposition85 • Impacts on birds and mammals (e.g., reproductive effects)
Welfare • 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.
° 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 cost-
effective 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
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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 ECU 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
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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., HI, R.T. Burnett, MJ. 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.
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Woodruff, T.J., J. Grille, and K.C. Schoendorf. 1997. "The Relationship Between Selected
Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and
Particulate Air Pollution in the United States." Environmental Health Perspectives
105(6):608-612.
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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 (CAYR) 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 Federally-
protected 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

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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)(l)(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)(l) 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 source-
specific 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
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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,
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(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

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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 Ibs/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 cost-
effective. 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 coal-
fired units, our analysis assumed these units will install current combustion control
1 These levels are commonly achievable by flue gas desulfurization controls ("scrubbers").

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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 Ibs 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

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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 non-
EGU 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 B ART-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.

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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:

• Chapter 3, Emissions and Air Quality Impacts, describes emission inventories and
air quality modeling that are essential inputs into the benefits assessment.

• Chapter 4, Benefits Analysis and Results, describes the methodology and results
of the benefits analysis.

• Chapter 5, Qualitative Assessment of Nonmonetized Benefits, describes benefits
that are not monetized for this rulemaking.

• Chapter 6, Profile of Potentially Affected Industries, describes the major
industries that may be affected by this rule.

• Chapter 7, Cost and Economic Impacts for the ECU Sector, describes the costs of
the rule to the power section and related economic impacts.

• 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.

• Chapter 9, Executive Order and Statutory Requirements, describes impact
analyses conducted to meet executive order and statutory requirements.

• Chapter 10, Comparison of Benefits and Costs, provides a comparison of the
monetized benefits and estimated annual costs of the proposed rule.

• Appendix A, BART Industry-Sector Impacts

• Appendix B, Cost and Economic Impact Supplemental Information and
Sensitivity Analyses

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Appendix C, Additional Technical Information Supporting
the Benefits Analysis

Appendix D, Visibility Benefits Methodology

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

Appendix F, Sensitivity Analyses of Some Key Parameters in the Benefits
Analysis

Appendix G, Additional Control Scenarios for Non-EGU Source Categories
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SECTION 3

EMISSIONS AND AIR QUALITY IMPACTS
This chapter summarizes the emissions inventories and air quality modeling that
serve as the inputs to the benefits analysis of this rule as detailed in Chapter 4. EPA uses
sophisticated photochemical air quality models to estimate baseline and post-control ambient
concentrations of 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 Inventories
,a,b
Sector or
Source
Emissions
Source
2001 Base Year
Future-Year Base Case
Projections
EGU
Non-EGU
Power industry 2001 Data from the Acid
EGUs Rain Trading Program
Non-utility
point,
including point
source fugitive
dust
2001NEI
Average Wildfire, Same as future year
Fire prescribed
burning
Average Agricultural 2001NEI
Fire burning, open
burning

Agriculture Livestock NH3 2002 preliminary NEP
Agriculture Fertilizer NH3 2001 NEI
IPM
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 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

2001 NEI
2015 emissions estimated with the
same approach as was used for the
2002 preliminary NEF
2001 NEI
(continued)
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Table 3-1. Emissions Sources and Basis for Current and Future-Year Inventories"'15
(continued)
Sector or
Source
Area

On-road

Emissions
Source 2001 Base Year
All other 1999 NEI, version 3
stationary area grown to 200 1
sources,
including area-
source fugitive
dust
Highway Mobile6.2 model
vehicles
Future- Year Base Case
Projections
(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
Projected vehicle miles traveled
same as CAIR proposed and final
Nonroad
Nonroad
Locomotives,
commercial
marine vessels,
and aircraft
All other
nonroad
vehicles
2001 NEI; CMV adjusted
to new national totals
from Office of
Transportation Air Quality
(OTAQ)
rule, emissions from MOBILE6.2
model

Grown based on national totals from
OTAQ, using state/county
distribution of emissions from the
2001 NEI
NONROAD 2004 model NONROAD 2004 model
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.
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.
ftp://ftp.epa.gov/EmisInventory/prelim2002nei/nonpoint/documentation/nh3inventorydraftJan2004.pdf.
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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/finaltechO 1 .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 parti culate 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
2001 Baseline"
EGUs
Non-EGUs
Average Fire
Area
On-road
Nonroad
Total, All Sources
2015 Base CAIR Control Case3
EGUs
Non-EGUs
Average Fire
Area
On-road
Nonroad
Total, All Sources
2015 BART, Scenario 1"
EGUs
Non-EGUs
Total, All Sources
2015 BART, Scenario 2"
EGUs
Non-EGUs
Total, All Sources
2015 BART, Scenario 3"
EGUs
Non-EGUs
Total, All Sources
NOX

4,937,398
2,942,618
238,931
1,462,276
8,064,067
4,050,655
21,695,945

2,172,839
3,183,499
238,931
1,702,154
3,152,562
2,912,382
13,362,365

2,057,220
3,011,944
13,075,193

1,963,753
2,807,124
12,776,906

1,740,551
2,779,816
12,526,397
SO2

10,901,127
2,958,692
49,108
1,295,146
271,026
433,250
15,908,349

5,111,436
3,422,915
49,108
1,480,348
30,824
232,627
10,327,258

5,057,652
3,338,983
10,189,543

4,991,850
3,152,277
9,937,034

4,958,975
3,043,903
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
Item
2015 Emission Reductions with the BART, Scenario 1
Total Ton Reductions
Percentage of EGU Emission Reductions
Percentage of Non-EGU Emission Reductions
Percent Reduction of All Manmade Emissions
2015 Emission Reductions with BART, Scenario 2
Total Tons Reduction
Percentage Reduction of EGU Emissions
Percentage of Non-EGU Emission Reduction
Percentage Reduction of All Manmade Emissions
2015 Emission Reductions with BART, Scenario 3
Total Tons Reduction
Percentage Reduction of EGU Emissions
Percentage of Non-EGU Emission Reduction
Percentage Reduction of All Manmade Emissions
Pollutant
NOX

287,172
5.3%
5.4%
2.1%

585,459
9.6%
11.8%
4.4%

835,968
19.9%
12.7%
6.3%

SO2

137,715
1.1%
2.5%
1.3%

390,224
2.3%
7.9%
3.8%

531,473
3.0%
11.1%
5.1%
Note that the emission changes and percentage changes reported are nationwide estimates.
3.2.1 PMAir 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

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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.
'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.

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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.
Figure 3-1. CMAQ Modeling Domain
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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 three-
dimensional 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
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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 PM25 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 PM25 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).
3-10
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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 PM25 mass and/or PM25 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:
FERROR = —
N
Pred' - Obs'
*x,t
Predit + Obs*.
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 PM25 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 PM25 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
3-11
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Table 3-4. Model Performance Statistics for BART CMAQ 2001
CAIR CMAQ 2001 Annual
STN
PM25
Total Mass IMPROVE

STN

IMPROVE
Sulfate
CASTNet

STN
Nltate IMPROVE

Total Nitrate CASTNet
(NO3 + HNO3)
STN
Elemental Carbon

STN
Organic Carbon

East
West
East
West
East
West
East
West
East
West
East
West
East
West
East
West
East
West
East
West
East
West
East
West
Fractional Bias
-12
-51
8
14
8
-32
7
-2
-2
-35
-12
-92
-32
-44
16
-60
32
-20
-23
-13
-3
-31
-10
51
Fractional Error
44
64
43
57
45
52
40
49
24
50
86
115
107
115
81
105
63
67
51
66
75
66
52
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 PM25 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:

• 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
• winter nitrate is in the range of+50 percent to +70 percent for fractional bias and
85 percent to 105 percent for fractional error.
3-12
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Table 3-5. Selected Performance Evaluation Statistics from the CMAQ 2001
Simulation

CMAQ 2001
Eastern United States Fractional Bias (%) Fractional Error (%)
STN 14 44
Sulfate IMPROVE 10 42
(Summer)
CASTNet 3 22
Thus, CMAQ is considered appropriate for use in projecting changes in future year PM25
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). PM25 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 PM25 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 PM25 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
3-13
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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
PM25 mass.

For the purposes of projecting future PM25 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 PM25 monitor data from 2002 as the set of base-year measured
concentrations. Temporal scaling is based on ratios of model-predicted future case PM25
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 PM25 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).
3-14
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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 PM25 design values at each monitoring site.

3. 2. 1. 6 PMAir Quality Results

Table 3-6 summarizes the projected PM25 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
PM25 across populated eastern U.S. grid cells declines by roughly 1.28 percent (or 0.12
[J,g/m3) in 2015. The population-weighted average mean concentration declined by 0.94
percent (or 0.10 ^ig/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 ug/m3. Furthermore, over 8 percent of
this population will benefit from reductions in annual mean PM25 concentrations of greater
than 0.2
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).
3-15
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Table 3-6. Summary of Base Case PM Air Quality and Changes Due to Clean Air
Visibility Rule in 2015

Region

-^
1

PM2 5 ((ig/m3)
Average15 Annual
Mean
Population-
Weighted Average
Annual Mean °
Average Annual
Mean
Population-
Weighted Average
Annual Mean0

Base
Case
9.39

10.64

6.04

12.46

Scenario 1

Change"
-0.04

-0.03

-0.02

-0.01

Percent
Change
-0.43

-0.28

-0.33

-0.08

Scenario 2

Change"
-0.12

-0.10

-0.04

-0.07

Percent
Change
-1.28

-0.94

-0.66

-0.56

Scenario 3

Change"
-0.16

-0.14

-0.06

-0.10

Percent
Change
-1.70

-1.32

-0.99

-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.
0 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 s
Concentrations
(jig/m3)a
<0.1
0.11-0.20
0.21-0.30
0.31-0.40
0.41-0.50
0.50-0.60
Scenario 1
Number
(millions)
301.7
9.9
5.2
0.0
0.0
0.0
Percent
(%)
95.2%
3.1%
1.6%
0.0%
0.0%
0.0%
Scenario 2
Number
(millions)
204.6
85.6
19.6
5.1
1.9
0.0
Percent
(%)
64.6%
27.0%
6.2%
1.6%
0.6%
0.0%
Scenario 3
Number
(millions)
130.8
136.3
35.5
9.2
2.7
2.4
Percent
(%)
41.3%
43.0%
11.1%
2.9%
0.8%
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.
3-16
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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 PM25
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
PM25 and Regional Haze modeling guidance recommends the calculation of future-year
changes in visibility in a similar manner to the calculation of changes in PM25. 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
2There 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
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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 PM25
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 PM25 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 PM25 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.
3-18
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Table 3-8. Summary of Deciview Visibility Impacts at Class I Areas in the Nationa'b
Scenario 2
2015
Change in Average Percent Change in Average
Federal Class I Area of 20% Worst Days of 20% Worst Days
Acadia, ME
Aqua Tibia, CA*
Alpine Lakes, WA
Anaconda- Pintler, MT
Arches, UT*
Badlands, SD
Bandelier, NM*
Big Bend, TX
Black Canyon of the Gunnison, CO*
Bob Marshall, MT
Boundary Waters Canoe Area, MN
Bridger, WY
Brigantine, NJ
Bryce Canyon, UT*
Cabinet Mountains, MT
Caney Creek, AR
Canyonlands, UT*
Cape Remain, SC
Caribou, CA*
Carlsbad Cavern, NM*
Chassahowitzka, FL*
Chiricahua, AZ*
Craters of the Moon, ID
Desolation, CA*
Dolly Sods, WV*
Dome Land, CA*
Eagle Cap, OR
Eagles Nest, CO*
Emigrant, CA*
Everglades, FL*
Fitzpatrick, WY
0.09
0.20
0.10
0.04
0.00
0.32
0.08
0.04
0.06
0.02
0.20
0.06
0.11
0.06
0.03
0.19
0.03
0.14
0.06
0.06
0.10
0.08
0.07
0.06
0.16
0.06
0.09
0.07
0.05
-0.01
0.06
0.41
0.87
0.58
0.30
0.00
1.87
0.66
0.22
0.61
0.13
1.04
0.56
0.43
0.49
0.23
0.78
0.29
0.61
0.46
0.36
0.44
0.59
0.51
0.41
0.69
0.33
0.49
0.67
0.30
-0.05
0.56
Change in
Annual Average
0.05
0.14
0.10
0.03
0.03
0.21
0.04
0.05
0.06
0.02
0.09
0.04
0.06
0.05
0.03
0.27
0.04
0.11
0.04
0.05
0.07
0.04
0.04
0.04
0.10
0.03
0.05
0.05
0.02
0.02
0.04
Percent Change
in Annual Average
0.39
0.79
0.84
0.36
0.40
1.77
0.50
0.40
0.80
0.20
0.77
0.62
0.30
0.61
0.35
1.46
0.53
0.61
0.55
0.44
0.37
0.47
0.43
0.48
0.58
0.25
0.44
0.73
0.21
0.11
0.62
(continued)
3-19
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Table 3-8. Summary of Deciview Visibility Impacts at Class I Areas in the Nation a'b
Scenario 2 (continued)
Federal Class I Area
Flat Tops, CO*
Galiuro, AZ*
Gates of the Mountains, MT
Gila, NM*
Glacier, MT
Glacier Peak, WA
Grand Teton, WY
Great Gulf, NH
Great Sand Dunes, CO*
Great Smokey Mountains, TN*
Guadalupe Mountains, TX
Hells Canyon, OR
Isle Royale, MI
James River Face, VA*
Jarbidge. MV*
Joshua Tree, CA*
Joyce Kilmer — Slickrock, NC*
Kalmiopsis, OR
Kings Canyon, CA*
La Garita, CO*
Lassen Volcanic, CA*
Lava Beds, CA*
Linville Gorge, NC*
Lostwood, ND
Lye Brook, VT
Mammoth Cave, KY*
Marble Mountain, CA*
Maroon Bells- Snowmass, CO*
Mazatzal, AZ*
Medicine Lake, MT
Mesa Verde, CO*
Mingo, MO
Mission Mountains, MT
Mokelumne, CA*

2015

Change in Average Percent Change in Average Change in
of 20% Worst Days of 20% Worst Days Annual Average
-0.03
0.09
0.02
0.08
0.02
0.05
0.05
0.04
0.07
0.15
0.08
0.06
0.23
0.15
0.02
0.06
0.15
0.05
0.05
0.04
0.06
0.05
0.25
0.21
0.14
0.13
0.04
0.08
0.04
0.10
0.08
0.14
0.01
0.05
-0.28
0.67
0.19
0.67
0.13
0.38
0.36
0.20
0.62
0.59
0.44
0.31
1.06
0.62
0.19
0.32
0.59
0.36
0.22
0.42
0.45
0.34
1.01
1.08
0.62
0.49
0.26
0.81
0.35
0.55
0.70
0.52
0.08
0.39
0.02
0.04
0.02
0.03
0.02
0.04
0.03
-0.04
0.07
0.11
0.05
0.03
0.10
0.08
0.05
0.05
0.11
0.02
0.03
0.04
0.04
0.03
0.15
0.09
0.06
0.11
0.02
0.05
0.03
0.06
0.05
0.16
0.01
0.03

Percent Change
in Annual Average
0.32
0.47
0.32
0.40
0.18
0.55
0.44
-0.30
0.80
0.56
0.43
0.30
0.77
0.42
0.66
0.40
0.56
0.25
0.18
0.58
0.53
0.33
0.84
0.64
0.44
0.52
0.18
0.71
0.26
0.45
0.59
0.78
0.17
0.37
(continued)
3-20
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Table 3-8. Summary of Deciview Visibility Impacts at Class I Areas in the Nationa'b
Scenario 2 (continued)
Federal Class I Area
Moosehorn, ME
Mount Hood, OR
Mount Jefferson, OR
Mount Ranier, WA
Mount Washington, OR
Mount Zirkel, CO*
Noth Cascades, WA
Okefenokee, GA*
Otter Creek, WV
Pasauten. WA
Petrified Forest, AZ*
Pine Mountain, AZ
Presidential Range — Dry, NH*
Rawah, CO*
Red Rock Lakes, WY
Redwood, CA*
Rocky Mountain, CO*
Roosevelt Campobello, ME
Salt Creek, NM*
San Gorgonio, CA*
San Jacinto, CA *
San Pedro Parks, NM*
Sawtooth, ID
Scapegoat, MT
Selway— Bitterroot, MT
Seney, MI
Sequoia, CA*
Shenandoah, VA*
Sierra Ancha, AZ*
Sipsey, AL*
South Warner, CA*
Strawberry Mountain, OR
Superstition, AZ*
Swanquarter, NC*

2015
Change in Average Percent Change in Average
of 20% Worst Days of 20% Worst Days
0.06
0.07
0.05
0.09
0.05
-0.03
0.04
0.17
0.19
0.07
0.08
0.04
0.11
0.09
0.04
0.06
0.07
0.08
0.09
0.08
0.11
0.05
0.02
0.01
0.04
0.20
0.07
0.24
0.04
0.08
0.04
0.08
0.04
0.09
0.29
0.55
0.40
0.50
0.35
-0.25
0.30
0.69
0.81
0.44
0.70
0.28
0.55
0.82
0.31
0.40
0.49
0.39
0.52
0.38
0.53
0.46
0.16
0.08
0.31
0.85
0.30
1.02
0.33
0.30
0.32
0.45
0.42
0.77

Change in
Annual Average
0.04
0.05
0.03
0.06
0.03
0.04
0.04
0.14
0.11
0.05
0.06
0.04
0.05
0.08
0.03
0.03
0.06
0.06
0.09
0.06
0.10
0.05
0.02
0.02
0.03
0.10
0.05
0.15
0.03
0.08
0.03
0.03
0.04
0.09

Percent Change
in Annual Average
0.27
0.55
0.38
0.48
0.33
0.53
0.53
0.73
0.64
0.65
0.65
0.38
0.35
1.05
0.42
0.25
0.67
0.40
0.73
0.43
0.69
0.62
0.23
0.17
0.36
0.72
0.28
0.85
0.26
0.41
0.37
0.31
0.39
0.50
(continued)
3-21
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Table 3-8. Summary of Deciview Visibility Impacts at Class I Areas in the Nationa'b
Scenario 2 (continued)
Federal Class I Area
Sycamore Canyon, AZ*
Teton, WY
Theodore Roosevelt, ND
Thousand Lakes, CA*
Three Sisters, OR
UL Bend, MT
Upper Buffalo, AR
Voyageurs, MN
Weminuche, CO*
West Elk, CO*
Wind Cave, SD
Wolf Island, GA*
Yellowstone, WY
Yolla Bolly— Middle Eel, CA*
Yosemite, CA*
Zion. UT*

2015

Change in Average Percent Change in Average Change in
of 20% Worst Days of 20% Worst Days Annual Average
0.02
0.04
0.17
0.06
0.05
0.04
0.20
0.08
0.06
0.03
0.21
0.16
0.04
0.04
0.04
0.04
0.15
0.36
1.00
0.45
0.35
0.26
0.86
0.49
0.56
0.25
1.38
0.65
0.35
0.21
0.26
0.33
0.02
0.03
0.10
0.04
0.03
0.03
0.25
-0.11
0.05
0.03
0.13
0.10
0.03
0.03
0.01
0.05

Percent Change
in Annual Average
0.17
0.42
0.82
0.53
0.33
0.27
1.39
-0.99
0.65
0.53
1.31
0.51
0.42
0.29
0.12
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.
3-22
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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
IMP ROVE 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 PM25 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
PM25 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.
3-23
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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
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
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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 ground-
level 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.
2Short-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
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Table 4-1. Estimated Monetized Benefits of the Final CAVR

Scenario 1
Using
Using
a 3% discount rate
a 7% discount rate
$2
$2
,6H
,2H
hB
hB
Total Benefits3'
b (billions 1999$)
Scenario 2
$10.1+B
$8.6+ B

Scenario
$14.3 +
$12.2 +

3
B
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,
3The 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
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-------
INPUTS
PROCESSES
INPUTS

Emissions inventories
(2001 CEM, 1996 NEI,
MOBILE 5b and 6 PARTS
model, NONROAD2002)

Air quality monitoring data
AIRS (ozone), FRM (total
PM), STN (speciated PM)

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functions

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health endpoints

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demographic data (with
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ncentration surfaces (base and control)
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Sum health and welfare monetary
values to obtain total monetary benefits
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
4-8
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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 short-
term 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.
"Expert elicitation is a formal, highly structured and well-documented process whereby expert judgments,
usually of multiple experts, are obtained (Ayyub, 2002).

4-9
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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.

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).
4-10
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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.
4-11
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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
5For 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.

6In 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
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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;
Bergeretal., 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. Well-
designed and well-executed stated preference studies are valid for estimating the benefits of
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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.
7Concerns 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.

8Income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent
change in income.

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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-SAB-
EEAC-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 cohort-
component model applied to 1990 U.S. Census data projections (U.S. Bureau of Census,
2000). For the years between 2000 and 2015, we applied growth rates based on the U.S.
Census Bureau projections to the U.S. Census estimate of national population in 2000. We
used projections of real GDP provided in Kleckner and Neumann (1999) for the years 1990
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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 Growth"

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.
9U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at http://www.bea.doc.gov^ea/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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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:

• gaps in scientific data and inquiry;
• variability in estimated relationships, such as epidemiological effect estimates,
introduced through differences in study design and statistical modeling;
• errors in measurement and projection for variables such as population growth
rates;
• 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
• 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
— The value of the ozone or PM effect estimate in each impact function.
- Application of a single impact function to pollutant changes and populations in all locations.
- Similarity of future-year impact functions to current impact functions.
- Correct functional form of each impact function.
- Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source
epidemiological study.
- Application of impact functions only to those subpopulations matching the original study population.
2. Uncertainties Associated with Ozone and PM Concentrations
— Responsiveness of the models to changes in precursor emissions resulting from the control policy.
- Projections of future levels of precursor emissions, especially ammonia and crustal materials.
- Model chemistry for the formation of ambient nitrate concentrations.
- Lack of ozone monitors in rural areas requires extrapolation of observed ozone data from urban to rural areas.
- Use of separate air quality models for ozone and PM does not allow for a fully integrated analysis of pollutants and
their interactions.
- Full ozone season air quality distributions are extrapolated from a limited number of simulation days.
3. Uncertainties Associated -with PM Mortality Risk
- Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.
- Direct causal agents within the complex mixture of PM have not been identified.
- The extent to which adverse health effects are associated with low-level exposures that occur many times in the year
versus peak exposures.
- 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.
- Reliability of the limited ambient PM25 monitoring data in reflecting actual PM25 exposures.
4. Uncertainties Associated with Possible Lagged Effects
- 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
— 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.
- Current baseline incidence rates may not approximate well baseline incidence rates in 2015.
- Projected population and demographics may not represent well future-year population and demographics.
6. Uncertainties Associated with Economic Valuation
— Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and therefore have
uncertainty surrounding them.
- 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
- Health and welfare benefits estimates are limited to the available impact functions. Thus, unqualified 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
uncertainly 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 long-
term 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.
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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 industrial-
sector 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 cohort-
component 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
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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 (concentration-
response functions), baseline incidence and prevalence rates, potentially affected
populations, and estimates of changes in ambient concentrations of air pollution. Previous
sections have described the population and air quality inputs. This section describes the
effect estimates and baseline incidence and prevalence inputs and the methods used to
quantify and monetize changes in the expected number of incidences of various health
effects.
4.1.5.1 Selecting Health Endpoints and Epidemiological Effect Estimates

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

Study Type
Study Period
Population
Attributes
Study Size

Study Location

Pollutants
Included in
Model

Measure of PM
Economically
Valuable Health
Effects

Nonoverlapping
Endpoints
Peer-reviewed research is preferred to research that has not undergone the peer-review
process.

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.

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.

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.

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).

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.

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.

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.

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.

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.
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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
"In 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.

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Table 4-7. Endpoints and Studies Used to Calculate Total Monetized Health Benefits
Endpoint
Premature Mortality
Premature mortality
— cohort study, all-
cause
Premature mortality
— all-cause
Chronic Illness
Chronic bronchitis
Nonfatal heart
attacks
Hospital Admissions
Respiratory

Cardiovascular

Asthma-related ER visits
Pollutant

PM25
(annual
mean)
PM25
(annual
mean)

PM25
(annual
mean)
PM25
(daily)

PM25
(daily)
PM25
(daily)
PM25
(daily)
PM25
(daily)
PM25
(daily)
PM25
(daily)
PM25
(daily)
Study

Pope et al. (2002)
Woodruff etal. (1997)

Abbey etal. (1995)
Peters etal. (2001)

Pooled estimate:
Moolgavkar (2003)— ICD 490-496 (COPD)
Ito (2003)— ICD 490-496 (COPD)
Moolgavkar (2000)— ICD 490-496 (COPD)
Ito (2003)— ICD 480-486 (pneumonia)
Sheppard (2003)— ICD 493 (asthma)
Pooled estimate:
Moolgavkar (2003)— ICD 390-429 (all
cardiovascular)
Ito (2003)— ICD 410-414, 427-428 (ischemic heart
disease, dysrhythmia, heart failure)
Moolgavkar (2000)— ICD 390-429 (all
cardiovascular)
Norris etal. (1999)
Study
Population

>29 years
Infant (<1 year)

>26 years
Adults

>64 years
20-64 years
>64 years
<65 years
>64 years
20-64 years
0-18 years
(continued)
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Table 4-7. Endpoints and Studies Used to Calculate Total Monetized Health Benefits
(continued)
Endpoint
Pollutant
Study
Study
Population
Other Health Endpoints
Acute bronchitis
Upper respiratory
symptoms
PM2 5 Dockery et al. (1996)
(annual
mean)
PM10
(daily)
Lower respiratory PM2 <
Pope etal. (1991)
Schwartz and Neas (2000)
symptoms
Asthma
exacerbations
Work loss days

MRADs
(daily)
PM25 Pooled estimate:
(daily) Ostro et al. (2001) (cough, wheeze and shortness of
breath)
Vedal et al. (1998) (cough)
PM25
(daily)
PM25
(daily)
Ostro (1987)
Ostro and Rothschild (1989)
8-12 years

Asthmatics,
9-11 years
7-14 years

6-18 years3

18-65 years

18-65 years
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,
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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
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).

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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).
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The SAB-HES also recommended using the estimated relative risks from the Pope et
al. (2002) study based on the average exposure to PM25, measured by the average of two
PM25 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 PM25.

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
PM25, this analysis uses only the Abbey et al. (1995) study, because it is the only study
focusing on the relationship between PM25 and new incidences of CB.
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NonfatalMyocardialInfarctions (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
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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 PM25, we focus on the two studies that examine PM25. 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
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.

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likely capture the majority of the impact of PM25 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
PM25 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 PM25 and ozone on MRAD was estimated using an effect estimate
derived from Ostro and Rothschild (1989).
14See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002.

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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 PM25, 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
''Estimating 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.

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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 asthma-
related 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
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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
Cough
Wheeze
Asthma
exacerbation
Prevalence of shortness of PM2 5
breath; incidence of
shortness of breath
Prevalence of cough; PM2 5
incidence of cough
Prevalence of wheeze; PM25
incidence of wheeze
> 1 mild asthma symptom: PM10,
wheeze, cough, chest PM{ „
tightness, shortness of breath
Ostroetal. (2001)
Ostroetal. (2001)
Ostroetal. (2001)
Yu et al. (2000)
African-American
asthmatics, 8-13
African-American
asthmatics, 8-13
African-American
asthmatics, 8-13
Asthmatics, 5-13
Cough
Prevalence of cough
Other Symptoms/Illness Endpoints
Upper
respiratory
symptoms
Moderate or
worse asthma
> 1 of the following: runny
or stuffy nose; wet cough;
burning, aching, or red eyes
Probability of moderate (or
worse) rating of overall
asthma status
PM1
PM
PM25
Vedal et al. (1998) Asthmatics, 6-13
Pope etal. (1991)

Ostroetal. (1991)
Asthmatics, 9-11
Asthmatics, all ages
Acute bronchitis
Phlegm
Asthma attacks
> 1 episodes of bronchitis in
the past 12 months
"Other than with colds, does
this child usually seem
congested in the chest or
bring up phlegm?"
Respondent-defined asthma
attack
PM25
PM25
PM25,
ozone
McConnell et al.
(1999)
McConnell et al.
(1999)
Whittemore and
Korn(1980)
Asthmatics, 9-15
Asthmatics, 9-15
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
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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.
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.

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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 PMMortality 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.
4-39
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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 |ig/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 |ig/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
4-40
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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.
4-41
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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 |ig/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

4-42
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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
4-43
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Table 4-9. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact
Functions, General Population
Rates
Endpoint
Parameter
Value
Source3
Mortality
Daily or annual mortality
rate
Hospitalizations Daily hospitalization rate
Age-, cause-, and
county-specific
rate

Age-, region-, and
cause-specific rate
CDC Wonder (1996-1998)
1999 NHDS public use data filesb
Asthma ER
Visits
Chronic
Bronchitis

Nonfatal
Myocardial
Infarction (heart
attacks)

Asthma
Exacerbations

Acute Bronchitis

Daily asthma ER visit
rate
Annual prevalence rate
per person
• Aged 18^4
• Aged 45-64
• Aged 65 and older
Annual incidence rate per
person
Daily nonfatal myocardial
infarction incidence rate
per person, 18+
• Northeast
• Midwest
• South
• West
Incidence (and
prevalence) among
asthmatic African-
American children
• daily wheeze
• daily cough
• daily dyspnea
Prevalence among
asthmatic children
• daily wheeze
• daily cough
• daily dyspnea
Annual bronchitis
incidence rate, children
Age- and region-
specific visit rate

0.0367
0.0505
0.0587
0.00378

0.0000159
0.0000135
0.0000111
0.0000100

0.076(0.173)
0.067(0.145)
0.037 (0.074)

0.038
0.086
0.045
0.043

2000 NHAMCS public use data
files0; 1999 NHDS public use data
filesb
1999 NHIS (American Lung
Association, 2002b, Table 4)

Abbey etal. (1993, Table 3)

1999 NHDS public use data filesb;
adjusted by 0.93 for probability of
surviving after 28 days (Rosamond et
al., 1999)

Ostro etal. (2001)

Vedal etal. (1998)

American Lung Association (2002c,
Table 11)
(continued)
4-44
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Table 4-9. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact
Functions, General Population (continued)
Endpoint
Lower
Respiratory
Symptoms
Upper
Respiratory
Symptoms
Work Loss Days
Minor
Restricted-
Activity Days

Parameter
Daily lower respiratory
symptom incidence
among childrend
Daily upper respiratory
symptom incidence
among asthmatic children
Daily WLD incidence
rate per person (18-65)
• Aged 18-24
• Aged 25^4
• Aged 45-64
Daily MRAD incidence
rate per person

Value
0.0012
0.3419
0.00540
0.00678
0.00492
0.02137
Rates
Source3
Schwartz et al. (1994, Table
2)
Pope etal. ( 1991, Table 2)
1996 HIS (Adams et al., 1999,
Table 41); U.S. Bureau of the
Census (2000)
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/.
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.
4-45
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Table 4-10. Asthma Prevalence Rates Used to Estimate Asthmatic Populations in
Impact Functions
Asthma Prevalence Rates
Population Group
All Ages
<18
5-17
18-44
45-64
65+
Male, 27+
African American, 5 to 17
African American, <18
Value
0
0
0
0
0
0
0
0
0
.0386
.0527
.0567
.0371
.0333
.0221
.021
.0726
.0735
American Lung
American Lung
American Lung
American Lung
American Lung
American Lung
Association
Association
Association
Association
Association
Association
Source
(2002a,
(2002a,
(2002a,
(2002a,
(2002a,
(2002a,
Table
Table
Table
Table
Table
Table
7) — based
7) — based
7) — based
7) — based
7) — based
7) — based
on
on
on
on
on
on
1999
1999
1999
1999
1999
1999
HIS
HIS
HIS
HIS
HIS
HIS
2000 HIS public use data files3
American Lung
American Lung
Association
Association
(2002a,
(2002a,
Table
Table
9)— based
9)— based
on
on
1999
1999
HIS
HIS
a Seeftp://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.
4-46
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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 meta-
analyses 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.
4-47
-------

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4-49
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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 clusti
each describing a "type" of URS. A dollar value was derived for each type of U]
using mid-range estimates of WTP (lEc, 1994) to avoid each symptom in the clu
and assuming additivity of WTPs. The dollar value for URS is the average of th
dollar values for the seven different types of URS.
Combinations of the four symptoms for which WTP estimates are available that
closely match those listed by Schwartz et al. result in 1 1 different "symptom
clusters," each describing a "type" of LRS. A dollar value was derived for each
of LRS, using mid-range estimates of WTP (lEc, 1994) to avoid each symptom i
cluster and assuming additivity of WTPs. The dollar value for LRS is the averag
the dollar values for the 1 1 different types of LRS.
Asthma exacerbations are valued at $42 per incidence, based on the mean of ave:
WTP estimates for the four severity definitions of a "bad asthma day," described
Rowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP foi
avoidance of a "bad asthma day," as defined by the subjects. For purposes of
valuation, an asthma attack is assumed to be equivalent to a day in which asthma
moderate or worse as reported in the Rowe and Chestnut (1986) study.
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).

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4-50
-------
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
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
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Table 4-12. Expected Impact on Estimated Benefits of Premature Mortality Reductions
of Differences Between Factors Used in Developing Applied VSL and Theoretically
Appropriate VSL
Attribute
Expected Direction of Bias
Age
Life Expectancy/Health Status
Attitudes Toward Risk
Income
Voluntary vs. Involuntary
Catastrophic vs. Protracted Death
Uncertain, perhaps overestimate
Uncertain, perhaps overestimate
Underestimate
Uncertain
Uncertain, perhaps underestimate
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,
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
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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:

• 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.
4-53
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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).

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.

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.

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.
4-54
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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 pollution-
related 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 A ttacks).
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.

4-55
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We found three possible sources in the literature of estimates of the direct medical
costs of myocardial infarction:

• 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.

• 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.

• 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
4-56
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Table 4-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart
Attacks
Study
Wittelsetal. (1990)
Russell etal. (1998)
Eisenstein et al. (2001)
Russell etal. (1998)
Direct Medical Costs (2000$)
$109,474a
$22,33 lb
$49,65 lb
$27,242b
Over an x-Year Period, for x =
5
5
10
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
0-24
25-44
45-54
55-65
>65
Opportunity Cost
$0
$8,774b
$12,253b
$70,619b
$0
Medical Cost3
$65,902
$65,902
$65,902
$65,902
$65,902
Total Cost
$65,902
$74,676
$78,834
$140,649
$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.
4-57
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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
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

4-58
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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
EP A'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
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).

4-59
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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-COUNCIL-
ADV-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 (NFS) 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:

• 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.
22 An 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).

4-60
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• 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.

• 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.

• 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.

• 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.

• 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.

• 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.
4-61
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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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Benefits (1999$) * s /
» 1-2,000,000$
» 2,000,000-7,000,000$
• 7,000,000 -15,000,000 $
- 98,000,000 $
* Map shows monetised primary visitility benefits in the Southeast and Southwest
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

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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 PM-
related damage to historic buildings and outdoor works of art. Existing studies of damage to
this latter category in Sweden (Grosclaude and Soguel, 1994) indicate that these benefits
could be an order of magnitude larger than household soiling benefits.
4.1.6.4 Benefits from Reduced Ecosystem Damage

The effects of air pollution on the health and stability of ecosystems are potentially
very important but are at present poorly understood and difficult to measure. The reductions
in NOX caused by the final rule could produce significant benefits. Excess nutrient loads,
especially of nitrogen, cause a variety of adverse consequences to the health of estuarine and
coastal waters. These effects include toxic and/or noxious algal blooms such as brown and
red tides, low (hypoxic) or zero (anoxic) concentrations of dissolved oxygen in bottom
waters, the loss of submerged aquatic vegetation due to the light-filtering effect of thick algal
mats, and fundamental shifts in phytoplankton community structure (Bricker et al., 1999).

Direct functions relating changes in nitrogen loadings to changes in estuarine benefits
are not available. The preferred WTP-based measure of benefits depends on the availability
of these functions and on estimates of the value of environmental responses. Because neither
appropriate functions nor sufficient information to estimate the marginal value of changes in
water quality exist at present, calculation of a WTP measure is not possible.

If better models of ecological effects can be defined, EPA believes that progress can
be made in estimating WTP measures for ecosystem functions. These estimates would be
superior to avoided cost estimates in placing economic values on the welfare changes
associated with air pollution damage to ecosystem health. For example, if nitrogen or sulfate
loadings can be linked to measurable and definable changes in fish populations or definable
indexes of biodiversity, then CV studies can be designed to elicit individuals' WTP for
changes in these effects. This is an important area for further research and analysis and will
require close collaboration among air quality modelers, natural scientists, and economists.
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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 Effects3
Incidence Reduction
Health Effect
Premature Mortality15'0
Adult, age 30 and over
Infant, age <1 year
Chronic bronchitis (adult, age 26 and over)
Nonfatal myocardial infarction (adults, age 18 and older)
Hospital admissions — respiratory (all ages)d
Hospital admissions — cardiovascular (adults, age >18)e
Emergency room visits for asthma (age 18 years and younger)
Acute bronchitis (children, age 8-12)
Lower respiratory symptoms (children, age 7-14)
Upper respiratory symptoms (asthmatic children, age 9-18)
Asthma exacerbation (asthmatic children, age 6-18)
Work loss days (adults, age 18-65)
Minor restricted-activity days (adults, age 18-65)
Scenario 1
400
1
230
570
140
120
370
550
6,600
5,000
8,100
44,000
260,000
Scenario 2 Scenario 3
1,600
4
890
2,200
510
450
1,300
2,100
25,000
19,000
31,000
170,000
1,000,000
2,300
5
1,300
3,000
720
640
1,800
3,000
36,000
27,000
44,000
240,000
1,400,000
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.
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.
Respiratory hospital admissions for PM include admissions for COPD, pneumonia, and asthma.
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 mortality°'d
Adult, age 30 and over
3% discount rate
7% discount rate
Infant, < 1 year
Chronic bronchitis (adults, 26 and over)
Nonfatal acute myocardial infarctions
3% discount rate
7% discount rate
Hospital admissions for respiratory causes
Hospital admissions for cardiovascular causes
Emergency room visits for asthma
Acute bronchitis (children, age 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthma, 9-11)
Asthma exacerbations
Work loss days
Minor restricted-activity days (MRADs)
Welfare Effects:
Recreational visibility, 81 Class I areas
$2,330
$1,960
$6.12
90.5

$49.3
$45.8
1.07
2.6
0.106
0.207
0.109
0.137
0.367
5.56
13.8

$84
$9,180
$7,730
$23.8
353

$189
$176
4.03
10
0.362
0.79
0.415
0.523
1.4
22.4
54.1

$239
$13,000
$10,900
$34.2
498

$264
$245
5.65
14.1
0.51
1.12
0.587
0.74
1.98
31.5
76.3

$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
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.
Monetary benefits adjusted to account for growth in real GDP per capita between 1990and 2015.
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).
Adult premature mortality estimates based upon studies by Pope et al., 2002. Infant premature mortality
based upon Woodruff etal., 1997.
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.
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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:

• 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.

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 population-
weighted changes in concentrations of PM25 (i.e., the location of populations exposure
relative to the changes in air quality), and may be especially sensitive to the differences in
baseline PM25 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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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 source-
level 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-

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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: non-
health 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 non-
fatal 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 non-
fatal 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.

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4.6 References

Abbey, D.E., S.D. Colome, P.K. Mills, R. Burchette, W.L. Beeson, and Y. Tian. 1993.
"Chronic Disease Associated With Long-Term Concentrations of Nitrogen Dioxide."
Journal of Exposure Analysis and Environmental Epidemiology 3(2): 181-202.

Abbey, D.E., B.L. Hwang, RJ. Burchette, T. Vancuren, and P.K. Mills. 1995. "Estimated
Long-Term Ambient Concentrations of PM(10) and Development of Respiratory
Symptoms in a Nonsmoking Population." Archives of Environmental Health 50(2):
139-152.

Abbey, D.E., N. Nishino, W.F. McDonnell, RJ. Burchette, S.F. Knutsen, W. Lawrence
Beeson, and J.X. Yang. 1999. "Long-term inhalable particles and other air pollutants
related to mortality in nonsmokers [see comments]." American Journal of
Respiratory and Critical Care Medicine 159(2):373-382.

Abt Associates, Inc. 1995. Urban Ornamental Plants: Sensitivity to Ozone and Potential
Economic Losses. Prepared for the U.S. Environmental Protection Agency, Office of
Air Quality Planning and Standards; Research Triangle Park, NC.
Abt Associates, Inc. April 2003. Proposed Nonroad Land-based Diesel Engine Rule: Air
Quality Estimation, Selected Health and Welfare Benefits Methods, and Benefit
Analysis Results. Prepared for Office of Air Quality Planning and Standards, U.S.
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CHAPTER 5

QUALITATIVE ASSESSMENT OF NONMONETIZED BENEFITS
5.1 Introduction

In addition to the enumerated human health and welfare benefits resulting from
reductions in ambient levels of PM and ozone, 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

• acidify lakes and streams,
• harm sensitive forests, and
• 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,

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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 high-
elevation 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:

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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

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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
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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.
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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
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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
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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.
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Church, M.R., P.W. Shaffer, K.W. Thornton, D.L. Cassell, C.I. Liff, M.G. Johnson, D.A.
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DeHayes, D.H., P.O. Schaberg, GJ. 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."
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Schaberg, P.O., D.H. DeHayes, GJ. Hawley, G.R. Strimbeck, J.R. Gumming, P.P.
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). .

U.S. Environmental Protection Agency (EPA). 2005a. Air Quality Criteria for Ozone and
Related Photochemical Oxidants (First External Review Draft).
.

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)]
.

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.
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Webb, J.R., F.A. Deviney, Jr., BJ. Cosby, AJ. 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.
5-11
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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).
6-1
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Table 6-1. Examples of Affected Source Categories
Source Category Name
SIC
NAICS
Industry Profile
Subsection
Fossil Fuel-Fired Steam Electric Plants 4911
(>250 MMBTU heat input per hour)
Fossil Fuel-Fired Industrial Boilers (>250 NA
MMBTU heat input per hour)
221111,221112,221113,
221119,221121,221122
NA
6.1 Power Sector
NA
Petroleum Refineries

Kraft Pulp Mills

Portland Cement Plants
Iron and Steel Mill Plants

Hydrofluoric, Sulfuric, and Nitric Acid
Plants

Coke Oven Batteries

Sulfur Recovery Plants

Primary Lead Smelters

Primary Copper Smelters

Primary Zinc Smelters

Primary Aluminum Ore Reduction Plants

Municipal Incinerators (>250 tons refuse
per day)
Lime Plants
Phosphate Rock Processing Plants
2911

2611,2621,
2631
3241
3312

2819

3312

2819

3339

3331

33xx

3334

4953

3274
1429
324110

322110,322121,322122,
322130, 322121, 322122, 322130
327310
331111,331221

211112

331111,331221

325131, 325188, 325998, 331311

331419

331411

331419

331312

562211,562212,562213,
562219, 562920
327410
212319
6.6 Petroleum
Refining Industry
6.5 Paper and Allied
Products
6.2 Cement
6.7 Primary Metal
Manufacturing
6.4 Crude
Petroleum and
Natural Gas
6.7 Primary Metal
Manufacturing
6.3 Industrial
Organic Chemicals
6.7 Primary Metal
Manufacturing
6.7 Primary Metal
Manufacturing
6.7 Primary Metal
Manufacturing
6.7 Primary Metal
Manufacturing
NA

NA
NA
(continued)
6-2
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Table 6-1. Examples of Affected Source Categories (continued)
Source Category Name
Carbon Black Plants (furnace process)

Fuel Conversion Plants
Sintering Plants
Secondary Metal Production Facilities
SIC
2895

NA
NA
3341
NAICS
325182

NA
NA
331314,331423,331492
Industry Profile
Subsection
6.3 Industrial
Organic Chemicals
NA
NA
6.7 Primary Metal
Chemical Process Plants
28xx
Petroleum Storage and Transfer Facilities 5171, 5172
(capacity > 300,000 barrels)
Taconite Ore Processing Plants 3295

Glass Fiber Processing Plants 32xx
Charcoal Production Facilities 2819

Coal Cleaning Plants (thermal dryers) 2999
325

424710,454311,454312,
424720,425110,425120
212324,212325,212393,
212399, 327992
327212
211112,325131,325188,
325998,331311
324199
Manufacturing
6.3 Industrial
Organic Chemicals
6.6 Petroleum
Refining Industry
NA

NA
6.3 Industrial
Organic Chemicals
NA
Table 6-2. Existing Electricity Generating Capacity by Energy Source, 2002
Energy Source
Coal
Petroleum
Natural Gas
Dual Fired
Other Gases
Nuclear
Hydroelectric
Other Renewables
Other
Total
Number of Generators
1,566
3,076
2,890
2,974
104
104
4,157
1,501
41
16,413
Generator Nameplate Capacity (MW)
338,199
43,206
194,968
180,174
2,210
104,933
96,343
18,797
756
979,585
Source: El A
6-3
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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
Commercial
Industrial
Other
All Sectors
1,280
1,119
991
109
3,500
37%
32%
28%
3%
100%
Source: El A
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
Petroleum
Natural Gas
Other Gases
Nuclear
Hydroelectric
Other
Total
1,970
118
629
11
764
275
81
3,848
51%
3%
16%
0.3%
20%
7%
2%
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.
6-4
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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
6-5
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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.
6-6
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t>
RestructutingActwe
Restructuring Delayed
Restructuring Suspended
Restructuring NotActive
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 Ibs/mmBtu (for a unit with selective catalytic reduction for NOX
removal) to over 1 Ib/mmBtu for an uncontrolled cyclone boiler. NOX emissions from
6-7
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Sulfur Dioxide
Nitrogen Oxides
;or
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 Ibs/mmBtu (for
some units with flue gas desulfurization for SO2 removal) to over 5 Ibs/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 Ibs/mmBtu, while completely uncontrolled units can have
emission rates in excess of 0.5 Ibs/mmBtu. Gas-fired units have very little SO2 emissions.
NOX emission rates on oil-fired units can range from under 0.1 Ibs/mmBtu (for units with
new combustion controls) to over 0.6 Ibs/mmBtu for units without combustion controls. SO2
emissions for oil-fired units can range from under 0.1 Ibs/mmBtu for units burning low sulfur
distillate oil to over 2 Ibs/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 Ibs/mmBtu to over 5 Ibs/mmBtu depending on the type
of coal combusted.
6-8
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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 Ibs/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

6-9
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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.
6-10
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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

• quarrying and crushing the raw materials,
• grinding the carefully proportioned materials to a high degree of fineness,
• firing the raw materials mixture in a rotary kiln to produce clinker, and
• 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 HI: High early strength cements are made from raw materials with a lime-to-
silica 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-resi sting 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
6-12
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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 kilowatt-
hours 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
Labor
Year
1997
1998
1999
2000
2001
Quantity (103)
16.9
17.2
17.3
17.2
17.2
Payroll ($106)
734.2
756.3
815.7
829.5
860.5
Fuel, Electricity,
and Materials
($106)
2,475.8
2,528.9
2,526.6
2,589.7
2,724.5
Capital
Expenditures
($106)
505.6
525.4
691.2
1,108.0
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
6-13
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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
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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
Clinker
Shipments to final customers, includes exports
Imports of hydraulic cement for consumption
Imports of clinker for consumption
Exports of hydraulic cement and clinker
Consumption, apparent
Price, average mill value, dollars per ton
Stocks, mill, year end
Employment, mine and mill, number
Net import reliance as a percentage of
apparent consumption
83,931 85,952 87,846 88,900 89,000
74,523 76,003 78,138 78,451 82,000
103,696 108,862 110,048 113,136 110,000
19,878 24,578 24,561 23,591 22,500
3,905 4,164 3,673 1,884 1,660
743 694 738 746 900
103,457 108,862 110,470 112,710 110,000
76.46 78.27 78.56 76.50 77.00
5,393 6,367 7,566 6,600 7,600
18,000 18,000 18,000
20
17,900
19
21
21
18,000
19
Source: vanOss, 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
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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
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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:

• aliphitic and other acyclic organic chemicals (ethylene); acetic, chloroaceptic,
adipic, formic, oxalic, and tartaric acids and their metallic salts; chloral,
formaldehyde, and methylamine;
• solvents (ethyl alcohol etc.); methanol; amyl, butyl, and ethyl acetates; ethers;
acetone, carbon disulfide, and chlorinated solvents;
• polyhydric alcohols (e.g., synthetic glycerin);
• synthetic perfume and flavoring materials (e.g., citral, methyl, oinone);
• rubber processing chemicals, both accelerators and antioxidants (cyclic and
acyclic);
• cyclic and acyclic plasticizers (e.g., phosphoric acid);
• synthetic tanning agents;
• chemical warfare gases; and
• 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.
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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
Labor
Year
1997
1998
1999
2000
2001
Quantity (103)
200.8
202.2
196.6
190.8
183.2
Payroll ($106)
10,290.9
10,498.4
10,504.8
10,530.8
10,582.4
Materials ($106)
59,632.4
53,294.7
58,090.9
69,948.1
66,020.6
Capital
Expenditures ($106)
113,356.9
106,695.1
106,288.5
115,707.6
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
Number of Employees in
Establishment
1 to 4 employees
5 to 9 employees
10 to 19 employees
20 to 49 employees
50 to 99 employees
100 to 249 employees
250 to 499 employees
500 to 999 employees
1,000 to 2,499 employees
2,500 or more employees
Total
Number of Value of Shipments
Facilities (1992 $106)
97
80
91
137
99
110
41
27
11
6
699
552.8
200.9
484.7
1,749.9
2556.3
10,361.2
17,156.9
9,615.5
9,184.6
7,156.9
59,019.7
1992
Number of Value of Shipments
Facilities (1992 $106)
100
80
97
125
106
111
41
30
10
5
705
102.6
208.7
533.9
1,701.5
3,460.9
8,855.9
9,971.1
13,755.0
9,051.0
6,613.5
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.

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Table 6-10. 1997 Size of Establishments, Value of Shipments, and Payroll for the
Industrial Organic Chemicals Industry (NAICS 3251)

Number of Employees in
Establishment
1 to 4 employees
5 to 9 employees
10 to 19 employees
20 to 49 employees
50 to 99 employees
100 to 249 employees
250 to 499 employees
500 to 999 employees
1,000 to 2,499 employees
2,500 or more employees
Total
NAICS

Number of
Facilities
4
2
5
5
10
13
9
5
1
—
54
325110
Value of
Shipments
($106)
3.3
D
26.0
101.1
866.9
2,669.1
5,211.0
D
D
—
20,534.8
NAICS

Number of
Facilities
111
60
80
136
100
118
33
25
12
1
676
325199
Value of
Shipments
($106)
178.4
321.2
844.9
2,211.7
4,364.9
10,905.4
7,592.2
12,695.8
D
D
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).
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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.

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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

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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).

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Table 6-13. Costs of Production for the Crude Petroleum and Natural Gas Industry:
1997
Labor
NAICS
211
213111
213112
Quantity (103)
111.0
53.0
106.0
Payroll ($106)
5,511.0
1,901.0
3,622.0
Fuel, Electricity,
and Materials
($106)
42,268.0
3,796.0
3,093.0
Capital
Expenditures
($106)
21,784.0
2,205.0
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

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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
211
213111 and213112
Description
Oil and Gas Extraction
Support Activities Mining
Number of
Companies
6,859
2,745
Number of
Establishments
8,312
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.
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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
Crude Oil (106 barrels)
Natural Gas (109 cubic feet)
Natural Gas Liquids (106 barrels)

Reserves
174,820
1,430,630
23,570
Annual
Production
2,118
19,702
682

Imports
3,405
3,977
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).
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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 resource-
intensive, 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.
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Table 6-16. Inputs for the Paper and Allied Products Industry (NAICS 322), 1997-2001
Labor
Year
1997
1998
1999
2000
2001
Quantity (103)
574.3
572.4
560.7
548.3
530.2
Payroll ($106)
22,312.0
22,529.5
22,837.4
22,680.1
22,188.3
Materials ($106)
80,189.5
82,419.4
82,720.9
87,346.6
82,823.0
Total Capital
Expenditures
($106)
8,595.1
8,546.7
7,081.1
7,383.5
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

Number of Number of
NAICS Description CR4 CR8 HHI Companies 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.
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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
Number of Employees in
Establishment
1 to 4 employees
4 to 9 employees
10 to 19 employees
20 to 49 employees
50 to 99 employees
100 to 249 employees
250 to 499 employees
500 to 999 employees
1,000 to 2,499 employees
2,500 or more employees
Total
Number
of
Facilities
729
531
888
1,433
1,018
1,176
308
145
63
1
1,732
Value of
Shipments
(S106)
640.6
D
1,563.4
18,328.6
D
32,141.7
24,221.1
28,129.1
24,903.1
D
129,927.8
1992
Number
of
Facilities
786
565
816
1,389
1,088
1,253
298
159
62

6,416
Value of
Shipments
(S106)
216
483
1,456.5
6,366.6
12,811.5
35,114.0
22,281.2
31,356.5
23,115.4

133,200.7
1997
Number
of
Facilities
687
500
706
1,292
1,033
1,193
265
131
59
2
5,868
Value of
Shipments
(S106)
D
605.2
1,672.7
7,345.4
14,686.8
40,366.0
23,940.2
32,060.7
26,780.6
D
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.
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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.
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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.

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

Products
Liquified refinery gases
Finished motor gasoline
Finished aviation
Jet fuel
Kerosene
Distillate fuel oil
Residual fuel oil
Naphtha for feedstock
Other oils for feedstock
Special naphthas
Lubricants
Waxes
Petroleum coke
Asphalt and road oil
Still gas
Miscellaneous
Total
Total Produced
(thousand barrels)
243,322
2,928,050
6,522
558,319
26,679
1,348,525
263,017
60,729
61,677
18,334
63,961
6,523
280,077
177,189
244,432
21,644
6,309,000

Percentage of Total
3.9%
46.4%
0.1%
8.8%
0.4%
21.4%
4.2%
1.0%
1.0%
0.3%
1.0%
0.1%
4.4%
2.8%
3.9%
0.3%
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 of materials
Cost of labor
Capital expenditures
Cost ($106)
$158,733
4,233
6,817
Cost as Percent of Product
Value (%)
79.4%
2.1%
3.4%
Source: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001.
Washington, DC: Government Printing Office.
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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.
Petroleum Administration Defense Districts (PADDs)
HAWAII
Figure 6-3. PADD Districts of the United States
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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
Residential
Commercial
Industrial
Oil company
Farm
Electric utility
Railroad
Vessel bunking
On-highway diesel
Military
Off-highwav diesel
Total
2000 Usage (thousand gallons)
6,204,449
3,372,596
2,149,386
684,620
3,168,409
793,162
3,070,766
2,080,599
33,129,664
233,210
2.330.370
57,217,231
Percent Share (%)
10.8%
5.9%
3.8%
1.2%
5.5%
1.4%
5.4%
3.6%
57.9%
0.4%
4.1%
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.
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Table 6-24. Adjusted Sales of Distillate Fuel Oil by End Use and by PADD
PADD (Thousand Gallons)
Enduse
Residential
Commercial
Industrial
Oil company
Farm
Electric utility
Railroad
Vessel bunking
On-highway diesel
Military
Off-highway diesel
Total
I
5,399,194
2,141,784
649,726
19,101
432,535
304,717
499,787
490,150
10,228,244
70,801
669,923
20,905,962
II
628,414
568,089
600,800
41,727
1,611,956
133,971
1,232,993
301,356
11,140,616
36,100
608,307
16,904,329
III
1,117
346,578
420,400
560,905
552,104
194,786
686,342
1,033,333
5,643,703
9,250
516,989
9,965,507
IV
38,761
102,905
241,146
29,245
220,437
8,492
344,586
173
1,474,611
4,163
180,094
2,644,613
V
136,962
213,240
237,313
33,643
351,377
151,196
307,059
255,586
4,642,490
112,895
355,056
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.
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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 FtHI 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 IJJ. This region also accounts for 45 to 50
percent of U.S. refinery net production of finished motor gasoline, distillate fuel oil, and
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Table 6-25. Number of Petroleum Refineries by PADD, 2003
PADD
I
II
III
IV
V
Total
Number of Facilities
13
26
54
16
36
145
Percent of Total
9.0
17.9
37.2
11.0
24.8
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 in 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 in 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.

6-42
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For the EOF 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
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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

6-45
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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, dressing, 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 lead-
acid 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.
6-47
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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
Year
1997
1998
1999
2000
2001
Quantity (103)
605.1
602.3
582.8
577.8
532.9
Payroll ($106)
23,811.2
24,146.1
23,722.4
24,155.1
22,199.3
Materials ($106)
99,343.3
96,949.7
90,596.0
93,736.3
83,301.3
Total Capital
Investment
($106)
6,515.8
6,451.2
5,942.8
6,138.3
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.
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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.
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Table 6-28. 1997 Measures of Market Concentration for the Primary Metal
Manufacturing Industry (NAICS 331)
NAICS
331
Industry
Primary Metal Manufacturing
CR4
13.8
CR8
22.3
HHI
97.4
Number of
Companies
4,076
Number of
Facilities
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)

Number of Employees in Establishment
1 to 4 employees
4 to 9 employees
10 to 19 employees
20 to 49 employees
50 to 99 employees
100 to 249 employees
250 to 499 employees
500 to 999 employees
1,000 to 2,499 employees
2,500 or more employees
Total

Number of
859
502
621
979
720
797
363
145
55
18
5,059
1997
Value of Shipments
Facilities ($106)
272.9
(D)
(D)
6,199.6
11,084.9
30,211.1
35,126.8
29,829.8
25,247.2
28,107.6
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.
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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.
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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."
. 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.
. As accessed October 20, 2003.
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Flint Hills Resources. 2002. "Refining Overview."
. 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." InternationalJournal 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
. Last
updated October 2003.

Portland Cement Association. March 2005. "About PCA, Tools for Concrete Thinking."
.

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. 7957 Census of Manufactures,
Industry Series, Industrial Organic Chemicals. Washington, DC: Government
Printing Office.

U.S. Department of Commerce, Bureau of the Census. 1991. 7957 Census of Manufactures,
Subject Series, General Summary. Washington, DC: Government Printing Office.

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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 NAICSDefinitions. . 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.

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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,
-------
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."
. As obtained
October 14, 2003.

van Oss, H. 2002. Minerals Yearbook: Volume I. Metals and Minerals: Cement.
. Last updated
October 2003.

van Oss, H. 2003. Mineral Commodity Summaries: Cement, . Last updated January 2003.
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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 BART-
eligible.

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, coal-
fired 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-and-
trade 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.
7-1
-------
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

SO2 95% reduction or 0.15
Ibs/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.
Combustion controls on units
>200 MW

SCR on cyclone units >200

Annual operation of SCR
(where existing)

No controls on O/G steam
95% reduction or 0.15
Ibs/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.
SCR on units >100 MW

Combustion controls on 25
MW< units <100MW

Annual operation of SCR
(where existing)

No controls on O/G steam
95% reduction or 0.10
Ibs/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^/ro/w the 2005 base case SO2 rate.
States controlled for both SO2 and NOx
States controlled for ozone season NOx only
D States not covered by CAIR
Figure 7-1. CAIR Modeled Region
7-2
-------
Table 7-2. CAIR Annual Emissions Caps (Million Tons)

S02
NOX
2010-2014 (2009-2014 for NOX)
3.6
1.5
2015-Thereafter
2.5
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
Unit Size
CAIR Region
<200MW
>200MW
CAIR Total
Plant Size
<750 MW

97
61
158

>750 MW

234
234
Total

97
295
392
Non-CAIR Region
<200 MW 24 24
>200MW 22 53 75
Non-CAIR Total 46 53 99
Total 204 287 491
7-3
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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
Ibs/mmBtu emission rate or achieving 90 percent reductions from base emissions. Affected
units needed to meet a 0.2 Ibs/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 SO, and MX.
7-4
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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 source-
specific 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 BART-
eligible 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 Ib/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.
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 Ib/mmBtu to
all coal units greater than 100 MW and a NOX limit of 0.20 Ib/mmBtu on all coal units greater than 25 MW.

7-5
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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.
7-6
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Table 7-4. Projected Emissions of SO2 and NOX with CAIR and with BART (thousand tons
from units greater than 25MW)
2015

SO2 (annual)
NOX (annual)
CAIR
4,890
2,125
CAIR + BART
(Scenario 1)
4,836
2,009
CAIR + BART
(Scenario 2)
4,770
1,916
CAIR + BART
(Scenario 3)
4,738
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)
Scenario 1
Scenario 2
Scenario 3
2010
$87
$97
$92
2015
$253
$436
$896
2020
$283
$429
$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 one-
third 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).
7-7
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Table 7-6. National Pollution Controls by Technology under BART (GW)

Technology
Combustion
Controls
Scrubbers
SCR
Incremental Retrofits
Scenario 1
2010 2015 2020
19.0

0.1 3.4 4.3
0.1 0.2 0.3
Scenario
2010 2015
24.0

0.0 6.2
0.4 2.4
under BART
2
2020

5.8
2.6

Scenario 3
2010 2015
26.0

0.2 7.7
0.3 27.2
2020

6.9
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).
-------
Table 7-7. Retail Electricity Prices by NERC Region with CAIR and with BART
(Mills/kWh)

Power
Region
ECAR
ERCOT
MAAC
MAIN
MAPP
NY
NE
FRCC
STV

SPP
PNW
RM

CALI
NATIONAL

Main States Included
OH, MI, IN, KY, WV, PA
TX
PA, NJ, MD, DC, DE
IL, MR, WI
MN, IA, SD, ND, NE
NY
VT, NH, ME, MA, CT, RI
FL
VA, NC, SC, GA, AL, MS, TN,
AR,LA
KS, OK, MR
WA, OR, ID
MT, WY, CO, UT, MM, AZ, NV,
ID
CA
Contiguous Lower 48 States

2000
57.4
65.1
80.4
61.2
57.4
104.3
89.9
67.9
59.3

59.3
45.9
64.1

94.7
66.0

CAIR
2015 2(
58.5
64.6
71.7
60.3
49.6
88.8
84.7
72.3
56.2

57.5
47.5
65.6

99.1
64.4

120
58.0
63.3
72.8
62.0
48.0
88.4
83.0
70.5
56.6

57.0
46.9
65.4

99.5
64.3
Scenario 2
BART
2015 2020
58.4 58.0
64.6 63.3
71.7 72.8
60.2 62.0
50.3 48.7
88.8 88.4
84.8 83.0
72.3 70.5
56.2 56.6

57.9 57.4
47.5 46.9
65.9 65.7

99.1 99.5
64.5 64.4
Percentage Price
Change
2015 2020
-0.1% 0.0%
0.1% 0.0%
-0.1% 0.1%
-0.1% 0.0%
1.5% 1.5%
0.0% 0.0%
0.1% 0.0%
0.0% 0.0%
-0.1% -0.1%

0.7% 0.7%
0.0% 0.0%
0.5% 0.4%

0.0% 0.0%
0.1% 0.1%
Source: EPA's Retail Electricity Price Model. 2000 prices are from EIA's AEO 2003.
Figure 7-2. NERC Power Regions
7-9
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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.
States controlled for both SO2 and NOx
States controlled for Ozone Season NOx
States not covered by CAIR
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.
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.
7-10
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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

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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 transit!oning 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
' 2010 Phase II cost estimate in $1995.

7-12
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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
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.

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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-better-
than-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-is-
better-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.
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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 non-
EGU 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.
8-1
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8.2 Summary of Results for Nonelectricity Generating Sources

In this RIA chapter, three illustrative scenarios are applied to non-EGU BART-
eligible 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.l 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
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.

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Table 8-1. SO2 Emissions and Emission Reductions for BART Source Categories in
2015
BART Source
Category
Industrial boilers

Petroleum refineries

Kraft pulp mills

Portland cement plants

Hydrofluoric, sulfuric,
and nitric acid plants
Chemical process plants

Iron and steel mills

Coke oven batteries

Sulfur recovery plants

Primary aluminum ore
reduction plants
Lime kilns

Baseline
Emissions
(tons)
420,782
420,782
199,483
199,483
119,818
119,818
116,835
116,835
96,741
96,741
47,700
47,700
23,541
23,541
9,815
9,815
59,766
59,766
47,552
47,552
9,373
9,373
Discount
Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
Scenario
Scenario 1
32,213
65,783
2,097
17,033
0
0
0
0
34,140
34,140
0
0
0
0
0
0
13,697
13,693
1,630
1,630
0
0
s — Reductions
Scenario 2
151,387
164,438
31,319
41,911
10,814
3,196
13,383
18,326
36,753
36,753
2,376
2,376
2,914
2,914
4,088
3,724
13,697
13,693
3,260
3,260
0
0
(tons)
Scenario 3
200,308
204,217
56,462
58,525
28,380
14,610
26,716
30,690
36,753
36,753
3,571
3,571
2,914
2,914
6,107
5,564
13,697
13,693
3,260
3,260
0
0
(continued)
8-3
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Table 8-1. SO2 Emissions and Emission Reductions for BART Source Categories in
2015 (continued)
BART Source
Category
Glass fiber processing
plants
Municipal incinerators

Coal cleaning plants

Carbon black plants

Phosphate rock
processing plants
Secondary metal
production facilities
Total

Baseline
Emissions
(tons)
2,170
2,170
284
284
1,530
1,530
41,853
41,853
21
21
9,988
9,988
1,208,088
1,208,088
Discount
Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
Scenarios — Reductions (tons)
Scenario 1
0
0
0
0
0
0
0
0
0
0
0
0
83,778
132,279
Scenario 2
0
0
0
0
0
0
0
0
0
0
0
0
236,992
290,591
Scenario 3
0
0
0
0
0
0
0
0
0
0
0
0
378,169
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
8-4
-------
Table 8-2. NOX Emissions and Emission Reductions for BART Source Categories in
2015
BART Source
Category
Industrial boilers

Petroleum refineries

Kraft pulp mills

Portland cement plants

Hydrofluoric, sulfuric,
and nitric acid plants
Chemical process plants

Iron and steel mills

Coke oven batteries

Sulfur recovery plants

Primary aluminum ore
reduction plants
Lime kilns

Baseline
Emissions
(tons)
217,063
217,063
86,566
86,566
103,614
103,614
120,567
120,567
17,059
17,059
72,577
72,577
20,963
20,963
10,389
10,389
651
651
1,676
1,676
12,849
12,849
Discount
Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
Scenario
Scenario 1
67,325
111,939
4,035
4,579
35,249
53,729
19,276
30,903
11,276
11,276
23,970
27,142
1,036
1,036
0
0
135
135
50
50
2,683
4,471
s — Reductions
Scenario 2
130,456
130,424
47,556
51,259
61,418
65,792
54,601
71,921
11,283
11,283
34,636
34,801
6,997
8,500
5,768
5,768
141
141
253
253
4,471
7,153
(tons)
Scenario 3
130,522
130,505
51,316
53,005
65,776
65,797
71,921
71,921
11,283
11,283
34,801
34,801
8,507
8,673
5,768
5,768
141
141
255
255
7,153
7,153
(continued)
8-5
-------
Table 8-2. NOX Emissions and Emission Reductions for BART Source Categories in
2015 (continued)
BART Source
Category
Glass fiber processing
plants
Municipal incinerators

Coal cleaning plants

Carbon black plants

Phosphate rock
processing plants
Secondary metal
production facilities
Total

Baseline
Emissions
(tons)
6,677
6,677
1,656
1,656
1,110
1,110
4,645
4,645
719
719
1,377
1,377
681,765
681,765
Discount
Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
Scenarios — Reductions (tons)
Scenario 1
568
775
0
0
0
0
7
7
0
0
25
25
165,634
246,067
Scenario 2
2,116
2,116
744
744
511
511
120
120
45
48
34
34
361,152
390,871
Scenario 3
2,198
2,198
744
744
511
511
120
120
48
48
35
35
391,101
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
8-6
-------
Table 8-3. Total Annualized Costs of Control for BART Source Categories in 2015
(million 1999$)
BART Source Category
Industrial boilers

Petroleum refineries

Kraft pulp mills

Portland cement plants

Hydrofluoric, sulfuric, and nitric acid
plants
Chemical process plants

Iron and Steel mills

Coke oven batteries

Sulfur Recovery plants

Primary aluminum ore reduction plants

Lime kilns

Discount
Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%

Scenario 1
74.6
135.0
4.2
15.3
20.7
45.6
2.9
20.8
18.0
16.4
14.4
21.1
0.6
0.4
0.0
0.0
11.7
11.7
1.7
1.0
2.0
4.3
Scenarios
Scenario 2
527.6
401.8
180.2
214.8
141.7
106.3
174.8
269.0
23.4
21.4
70.8
54.4
23.5
22.7
18.7
14.9
12.1
12.1
7.76
5.0
5.0
25.4

Scenario 3
845.4
643.3
428.6
394.8
313.3
168.5
409.5
346.1
23.4
21.4
87.8
77.1
33.2
32.4
33.8
25.0
12.1
12.1
7.82
5.0
31.8
25.4
(continued)
8-7
-------
Table 8-3. Total Annualized Costs of Control for BART Source Categories in 2015
(million 1999$) (continued)
BART Source Category
Glass fiber processing plants

Municipal incinerators

Coal cleaning plants

Carbon black plants

Phosphate rock processing plants

Secondary metal production facilities

Total

Discount
Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%

Scenario 1
0.5
0.7
0.0
0.0
0.0
0.0
0.01
0.005
0.0
0.0
0.02
0.0
$151.43
$272.34
Scenarios
Scenario 2
5.3
4.8
1.1
0.9
1.0
0.8
0.2
0.1
0.1
0.2
0.04
0.0
$1,193.24
$1,157.09

Scenario 3
7.8
6.8
1.1
0.9
1.0
0.8
0.2
0.1
0.2
0.2
0.1
0.0
$2,237.24
$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 non-
EGU 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
8-8
-------
Table 8-4. SO2 Emissions and Emission Reductions for BART Source Categories in
2015
Scenarios — Reductions (tons)
BART Source Category
Industrial boilers

Petroleum refineries

Kraft pulp mills

Portland cement plants

Hydrofluoric, sulfuric, and
nitric acid plants
Chemical process plants

Iron and steel mills

Coke oven batteries

Sulfur recovery plants

Primary aluminum ore
reduction plants
Lime kilns

Baseline
Emissions (tons)
420,782
420,782
199,483
199,483
119,818
119,818
116,835
116,835
96,741
96,741
47,700
47,700
23,541
23,541
9,815
9,815
59,766
59,766
47,552
47,552
9,373
9,373
Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
87,009
120,095
23,173
25,103
0
0
2,350
2,743
35,358
36,753
2,376
2,376
2,914
2,914
4,088
4,088
13,697
14,311
1,630
1,630
0
0
$3,000/ton
Scenario
124,592
148,962
23,173
33,304
0
3,196
2,350
13,383
36,753
36,753
2,376
2,376
2,914
2,914
4,088
4,088
13,697
14,311
1,630
3,260
0
0
(continued)
8-9
-------
Table 8-4. SO2 Emissions and Emission Reductions for BART Source Categories in
2015 (continued)
Scenarios — Reductions (tons)

BART Source Category
Glass fiber processing plants

Municipal incinerators

Coal cleaning plants

Carbon black plants

Phosphate rock processing
plants
Secondary metal production
facilities
Total

Baseline
Emissions (tons)
2,170
2,170
284
284
1,530
1,530
41,853
41,853
21
21
9,988
9,988
1,208,088
1,208,088

Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
0
0
0
0
0
0
0
0
0
0
0
0
172,595
210,013
$3,000/ton
Scenario
0
0
0
0
0
0
0
0
0
0
0
0
211,573
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.
8-10
-------
Table 8-5. NOX Emissions and Emission Reductions for BART Source Categories in
2015
Scenarios
BART Source Category
Industrial boilers

Petroleum refineries

Kraft pulp mills

Portland cement plants

Hydrofluoric, sulfuric, and
nitric acid plants
Chemical process plants

Iron and steel mills

Coke oven batteries

Sulfur recovery plants

Primary aluminum ore
reduction plants
Lime kilns

Baseline
Emissions (tons)
217,063
217,063
86,566
86,566
103,614
103,614
120,567
120,567
17,059
17,059
72,577
72,577
20,963
20,963
10,389
10,389
651
651
1,676
1,676
12,849
12,849
Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
97,074
120,151
23,173
23,173
50,221
56,466
26,659
26,659
11,283
11,283
25,922
27,568
2,034
2,038
0
5,768
0
0
70
335
4,471
4,471
$3,000/ton
Scenario
125,575
128,640
23,173
26,685
60,985
64,521
26,659
26,659
11,283
11,283
26,753
31,567
3,259
7,198
5,768
5,768
0
0
253
335
4,471
7,153
(continued)
8-11
-------
Table 8-5. NOX Emissions and Emission Reductions for BART Source Categories in
2015 (continued)
Scenarios
Baseline
BART Source Category Emissions (tons)
Glass fiber processing plants

Municipal incinerators

Coal cleaning plants

Carbon black plants

Phosphate rock processing
plants
Secondary metal production
facilities

Total

6,677
6,677
1,656
1,656
1,110
1,110
4,645
4,645
719
719
1,377
1,377
681,765
681,765
Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
568
851
744
744
0
511
111
111
0
0
25
34
242,355
280,163
$3,000/ton
Scenario
2,116
2,116
744
744
511
511
120
120
45
48
25
34
291,740
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

8-12
-------
Table 8-6. Total Annualized Costs of Control for BART Source Categories in 2015
(million 1999$)
Scenarios
BART Source Category
Industrial boilers

Petroleum refineries

Kraft pulp mills

Portland cement plants

Hydrofluoric, sulfuric, and nitric acid plants

Chemical process plants

Iron and Steel mills

Coke oven batteries

Sulfur Recovery plants

Primary aluminum ore reduction plants

Lime kilns

Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
241.5
255.0
71.1
71.1
75.1
59.2
29.6
28.7
20.4
21.4
40.5
30.4
7.9
5.7
6.2
14.9
11.7
12.1
1.7
1.0
5.0
4.3
$3,000/ton
Scenario
412.1
337.4
71.1
81.2
75.1
68.5
29.6
56.6
21.4
21.4
40.5
40.1
11.0
22.7
18.7
14.9
12.1
12.1
2.2
5.0
5.0
25.4
(continued)
8-13
-------
Table 8-6. Total Annualized Costs of Control for BART Source Categories in 2015
(million 1999$) (continued)
Scenarios
BART Source Category
Glass fiber processing plants

Municipal incinerators

Coal cleaning plants

Carbon black plants

Phosphate rock processing plants

Secondary metal production facilities

Total

Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
0.5
1.7
1.1
0.9
0.0
0.8
0.01
0.005
0.01
0.01
0.04
0.01
$512.36
$507.23
$3,000/ton
Scenario
5.3
4.7
1.1
0.9
1.0
0.8
0.01
0.01
0.01
0.01
0.04
0.01
$706.26
$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,

8-14
-------
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.
2For 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
-------
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.
3 The terms "ICI boiler" and "industrial boiler" are used interchangeably in this RIA.

8-16
-------
Table 8-7. Available SO2 Control Technologies for Industrial Boilers and Other Non-
EGU Sources

Source Type/Fuel Type Available Control Technology
ICI boilers—all fuel types, kraft pulp mills, FGD scrubbers
Portland cement plants (all fuel types)
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 (NCR), coal reburn, and low-
NOX 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.
8-17
-------
Table 8-8 lists the control technologies available for industrial boilers and other non-
EGU 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
ICI boilers—coal/FBC (fluidized bed combustor)
ICI boilers—coal/stoker
ICI boilers—coal/cyclone
ICI boilers—residual oil
ICI boilers—distillate oil
ICI boilers—natural gas
ICI boilers—process gas
ICI boilers—coke
ICI boilers—LPG (liquid petroleum gas)
SNCR, LNB, SCR
SNCR—urea based
SNCR—urea based
SNCR, Coal Reburn, NCR, SCR
LNB, SNCR, LNB + FOR, SCR
LNB, SNCR, LNB + FOR, SCR
LNB, SNCR, LNB + FOR, OT + WI, SCR
LNB, LNB + FOR, OT + WI, SCR
SNCR, LNB, SCR
LNB, SNCR, LNB + FOR, 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.
8-18
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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 LNB + FOR, SNCR, LNB + SNCR, SCR, LNB +
heaters—process gas, distillate oil) 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 + FOR, 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

• industrial boilers (250 mmBTU/hr heat input capacity and greater);
• petroleum refineries;
• kraft pulp mills;
• Portland cement plants;
8-19
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• hydrofluoric, sulfuric, and nitric acid;
• chemical process plants;
• iron and steel mills;
• coke oven batteries;
• sulfur recovery plants; and

• primary aluminum ore reductions.
For seven source categories, only NOX reductions take place:

• lime kilns,
• glass fiber processing plants,
• municipal incinerators (250 tons refuse burn capacity or greater),
• coal cleaning plants (thermal dryers),
• carbon black plants (furnace process),
• phosphate rock processing plants, and
• 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:

• primary lead smelters,
• primary copper smelters,
• primary zinc smelters,
• fuel conversion plants,
• sintering plants,

• charcoal production facilities,
• taconite ore processing plants, and
• petroleum storage and transfer facilities.
8-20
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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 Non-
EGU Industrial Boilers3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
420,782
420,782
420,782
420,782
420,782
420,782
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
388,569
354,999
269,395
256,344
220,474
216,565
2015 Emission
Reductions
32,213
65,783
151,387
164,438
200,308
204,217
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 cost-
effectiveness 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.
8-21
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Table 8-11. 2015 NOX Baseline Emissions and Emission Reductions (in tons) for Non-
EGU Industrial Boilers3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
217,063
217,063
217,063
217,063
217,063
217,063
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
149,738
105,124
86,607
86,639
86,541
86,558
2015 Emission
Reductions
67,325
111,939
130,456
130,424
130,522
130,505
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
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$26.0
$47.8
$294.1
$265.0
$610.3
$504.6
Annualized Average
Cost-Effectiveness
($/ton)
$807
$727
$1,943
$1,612
$3,047
$2,471
Marginal Costs
($/ton)
—
—
$2,250
$2,202
$6,463
$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 coal-
fired units with lower sulfur contents and to oil-fired units that have lower sulfur contents
than coal-fired units.
8-22
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Table 8-13 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 $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 BART-
Eligible Industrial Boilers
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs
(million 1999$)
$48.6
$87.2
$233.5
$136.8
$235.1
$138.7
Annualized Average
Cost-Effectiveness ($/ton)
$722
$779
$1,790
$1,049
$1,801
$1,063
Marginal Costs
($/ton)
—
—
$2,929
$2,684
$24,242
$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.
8-23
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Table 8-14. 2015 Cost Results for SO2 and NOX Control at BART-Eligible Industrial
Boilers
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs (million
$74.6
$135.0
$527.6
$401.8
$845.4
$643.3
1999$)

Table 8-15. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-
EGU BART-Eligible Units at Petroleum Refineries3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
199,483
199,483
199,483
199,483
199,483
199,483
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
197,386
182,450
168,164
157,572
143,021
140,958
2015 Emission
Reductions
2,097
17,033
31,319
41,911
56,462
58,525
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.
8-24
-------
Table 8-16. 2015 NOX Baseline Emissions and Emission Reductions (in tons) for Non-
EGU BART-Eligible Units at Petroleum Refineries3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
86,566

86,566

Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
82,531
81,987
39,010
35,307
35,250
33,561
2015 Emission
Reductions
4,035
4,579
47,556
51,259
51,316
53,005
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 cost-
effectiveness 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 cost-
effectiveness 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
8-25
-------
Table 8-17. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU
BART-Eligible Units at Petroleum Refineries
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$1.9
$12.5
$54.0
$72.7
$223.5
$167.3
Annualized Average
Cost-Effectiveness
($/ton)
$906
$736
$1,724
$1,734
$3,958
$2,858
Marginal Costs
($/ton)
—
—
$1,783
$2,417
$6,741
$5,692
Table 8-18. 2015 Cost and Cost-Effectiveness Results for NOX Control at Non-EGU
BART-Eligible Units at Petroleum Refineries
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs
(million 1999$)
$2.3
$2.7
$126.2
$142.1
$205.1
$227.6
Annualized Average
Cost-Effectiveness
($/ton)
$570
$595
$2,654
$2,772
$3,997
$4,294
Marginal Costs
($/ton)
—
—
$2,917
$2,986
$20,984
$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.
8-26
-------
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
Scenario 1 7%
3%
Scenario 2 7%
3%
Scenario 3 7%
3%
Total Annualized Costs
(million 1999$)
$4.2
$15.3
$180.2
$214.8
$428.6
$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 cost-
effectiveness 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.
8-27
-------
Table 8-20. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-
EGU BART-Eligible Units at Kraft Pulp Mills3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
119,818
119,818
119,818
119,818
119,818
119,818
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
119,818
119,818
109,005
116,820
91,488
105,208
2015 Emission
Reductions
0
0
10,814
3,196
28,330
14,610
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 Non-
EGU BART-Eligible Units at Kraft Pulp Mills3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
103,614
103,614
103,614
103,614
103,614
103,614
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
68,365
49,885
42,196
37,822
37,838
37,827
2015 Emission
Reductions
35,249
53,729
61,418
65,792
65,776
65,797
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source
categories, both controlled and uncontrolled.
8-28
-------
Table 8-22. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU
BART-Eligible Units at Kraft Pulp Mills

Scenarios
Scenario 1

Scenario 2

Scenario 3

Discoun
tRate
7%
3%
7%
3%
7%
3%

Total Annualized Costs
(million 1999$)
$0.0
$0.0
$33.5
$7.0
$161.5
$69.0
Annualized
Average Cost-
Effectiveness
($/ton)
$0
$0
$3,098
$2,189
$5,691
$4,720

Marginal Costs
($/ton)
—
—
$3,098
$2,189
$7,287
$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 cost-
effectiveness 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-per-
ton cap rises, particularly for sulfite pulping recovery furnaces.

Table 8-24 shows the total annualized costs of each scenario for controlling both SO2
and NCL.
8-29
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Table 8-23. 2015 Cost and Cost-Effectiveness Results for NOX Control at Non-EGU
BART-Eligible Units at Kraft Pulp Mills
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$20.7
$45.6
$108.2
$99.3
$151.8
$99.5
Annualized Average
Cost-Effectiveness
($/ton)
$587
$849
$1,762
$1,510
$2,308
$1,512
Marginal Cost
($/ton)
—
—
$3,344
$4,452
$10,005
$35,800
Table 8-24. 2015 Cost Results for SO2 and NOX Control at Non-EGU BART-Eligible
Units at Kraft Pulp Mills
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs (million 1999$)
$20.7
$45.6
$141.7
$106.3
$313.3
$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.
8-30
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Table 8-25. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-
EGU BART-Eligible Units at Portland Cement Plants3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
116,835
116,835
116,835
116,835
116,835
116,835
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
116,835
116,835
103,452
98,509
90,119
86,145
2015 Emission
Reductions
0
0
13,383
18,326
26,716
30,690
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 Non-
EGU BART-Eligible Units at Portland Cement Plants3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
120,567
120,567
120,567
120,567
120,567
120,567
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
101,289
89,664
65,966
48,646
48,646
48,646
2015 Emission
Reductions
19,276
30,903
54,601
71,921
71,921
71,921
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source
categories, both controlled and uncontrolled.
8-31
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Table 8-27 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 $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
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$0.0
$0.0
$43.5
$49.7
$134.4
$126.8
Annualized Average
Cost-Effectiveness
($/ton)
$0
$0
$3,250
$2,710
$5,031
$4,131
Marginal Costs
($/ton)
—
—
$3,250
$2,710
$6,818
$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 cost-
effectiveness 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

8-32
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Table 8-28. 2015 Cost and Cost-Effectiveness Results for NOX Control at Non-EGU
BART-Eligible Units at Portland Cement Plants
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$2.9
$20.8
$131.2
$219.3
$275.1
$219.3
Annualized Average
Cost-Effectiveness
($/ton)
$150
$674
$2,403
$3,050
$3,825
$3,050
Marginal Cost
($/ton)
—
—
$3,632
$4,839
$7,731
N/A
3 percent discount rate between the $l,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
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs (million
$2.9
$20.8
$174.8
$269.0
$409.5
$346.1
1999$)

8-33
-------
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 Non-
EGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
96,741
96,741
96,741
96,741
96,741
96,741
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
62,601
62,601
60,188
60,188
60,188
60,188
2015 Emission
Reductions
34,140
34,140
36,753
36,753
36,753
36,753
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 cost-
effectiveness 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.

8-34
-------
Table 8-31. 2015 NOX Baseline Emissions and Emission Reductions (in tons) for Non-
EGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
17,059
17,059
17,059
17,059
17,059
17,059
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
5,783
5,783
5,776
5,776
5,776
5,776
2015 Emission
Reductions
11,276
11,276
11,283
11,283
11,283
11,283
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
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%

Total Annualized Costs
(million 1999$)
$9.8
$9.1
$15.2
$14.1
$15.2
$14.1
Annualized
Average Cost-
Effectiveness
($/ton)
$287
$268
$413
$385
$413
$385

Marginal Costs
($/ton)
—
—
$2,067
$1,914
N/A
N/A
8-35
-------
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 cost-
effectiveness 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
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$8.2
$7.3
$8.2
$7.3
$8.2
$7.3
Annualized Average
Cost-Effectiveness
($/ton)
$726
$646
$728
$647
$728
$647
Marginal Costs
($/ton)
—
—
$4,428
$2,157
N/A
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.
8-36
-------
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
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs (million
$18.0
$16.4
$23.4
$21.4
$23.4
$21.4
1999$)

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 Non-
EGU BART-Eligible Units at Chemical Process Plants3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
47,700
47,700
47,700
47,700
47,700
47,700
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
47,700
47,700
45,324
45,324
44,129
44,129
2015 Emission
Reductions
0
0
2,376
2,376
3,571
3,571
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source
categories, both controlled and uncontrolled.
8-37
-------
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 Non-
EGU BART-Eligible Units at Chemical Process Plants3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
72,577
72,577
72,577
72,577
72,577
72,577
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
48,607
45,435
37,941
37,776
37,776
37,385
2015 Emission
Reductions
23,970
27,142
34,636
34,801
34,801
35,192
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 cost-
effectiveness 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.
8-38
-------
Table 8-37. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU
BART-Eligible Units at Chemical Process Plants
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$0.0
$0.0
$2.5
$2.4
$13.7
$10.2
Annualized Average
Cost-Effectiveness
($/ton)
$0
$0
$1,052
$1,013
$3,836
$2,850
Marginal Costs
($/ton)
—
—
$1,052
$1,013
$9,372
$6,527
Table 8-38 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 $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 NCL.
8-39
-------
Table 8-38. 2015 Cost and Cost-Effectiveness Results for NOX Control at Non-EGU
BART-Eligible Units at Chemical Process Plants
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$14.4
$21.1
$68.2
$54.4
$74.1
$77.1
Annualized Average
Cost-Effectiveness
($/ton)
$601
$776
$1,969
$1,563
$2,129
$2,191
Marginal Costs
($/ton)
—
—
$5,030
$4,348
$35,758
$58,082
Table 8-39. 2015 Cost Results for SO2 and NOX Control at Non-EGU BART-Eligible
Units at Chemical Process Plants
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs (million
$14.4
$21.1
$70.8
$56.8
$87.8
$87.3
1999$)

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.
8-40
-------
Table 8-40. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-
EGU BART-Eligible Units at Iron and Steel Mills3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
23,541
23,541
23,541
23,541
23,541
23,541
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
23,541
23,541
20,627
20,627
20,627
20,627
2015 Emission
Reductions
0
0
2,914
2,914
2,914
2,914
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 Non-
EGU BART-Eligible Units at Iron and Steel Mills3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
20,963
20,963
20,963
20,963
20,963
20,963
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
19,927
19,927
13,966
12,463
12,456
12,290
2015 Emission
Reductions
1,036
1,036
6,997
8,500
8,507
8,673
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source
categories, both controlled and uncontrolled.
8-41
-------
Table 8-42 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 $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
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$0.0
$0.0
$5.3
$3.4
$5.3
$3.4
Annualized Average
Cost-Effectiveness
($/ton)
$0
$0
$1,819
$1,165
$1,819
$1,165
Marginal Costs
($/ton)
—
—
$1,819
$1,165
N/A
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 cost-
effectiveness 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
8-42
-------
Table 8-43. 2015 Cost and Cost-Effectiveness Results for NOX Control at Non-EGU
BART-Eligible Units at Iron and Steel Mills
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$0.6
$0.4
$18.2
$19.3
$27.9
$29.0
Annualized Average
Cost-Effectiveness
($/ton)
$579
$431
$2,601
$2,271
$3,280
$3,343
Marginal Costs
($/ton)
—
—
$2,953
$2,532
$6,424
$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
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs (million
$0.6
$0.4
$23.5
$22.7
$33.2
$32.4
1999$)

8-43
-------
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 Non-
EGU BART-Eligible Units at Coke Oven Batteries
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
9,815
9,815
9,815
9,815
9,815
9,815
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
9,815
9,815
5,727
6,091
3,708
4,251
2015 Emission
Reductions
0
0
4,088
3,724
6,107
5,564
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 cost-
effectiveness 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.
8-44
-------
Table 8-46. 2015 NOX Baseline Emissions and Emission Reductions (in tons) for Non-
EGU BART-Eligible Units at Coke Oven Batteries3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
10,389
10,389
10,389
10,389
10,389
10,389
2015 Postcontrol
Emissions
10,389
10,389
4,621
4,621
4,621
4,621
2015 Emission
Reductions
0
0
5,768
5,768
5,768
5,768
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
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1990$)
$0.0
$0.0
$6.2
$4.0
$21.3
$14.1
Annualized Average
Cost-Effectiveness
($/ton)
$0
$0
$1,517
$1,074
$3,488
$2,529
Marginal Costs
($/ton)
—
—
$1,517
$1,074
$7,479
$5,489
8-45
-------
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 cost-
effectiveness 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
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$0.0
$0.0
$12.5
$10.9
$12.5
$10.9
Annualized Average
Cost-Effectiveness
($/ton)
$0
$0
$2,167
$1,898
$2,167
$1,898
Marginal
Costs ($/ton)
—
—
$2,167
$1,898
N/A
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.
8-46
-------
Table 8-49. 2015 Cost Results for SO2 and NOX Control at Non-EGU BART-Eligible
Units at Coke Oven Batteries
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs (million 1999$)
$0.0
$0.0
$18.7
$14.9
$33.8
$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 Non-
EGU BART-Eligible Units at Sulfur Recovery Plants3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015
Baseline
Emissions
59,766
59,766
59,766
59,766
59,766
59,766
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
46,069
46,073
46,069
46,073
46,069
46,073
2015 Emission
Reductions
13,697
13,693
13,697
13,693
13,697
13,693
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source
categories, both controlled and uncontrolled.
8-47
-------
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 Non-
EGU BART-Eligible Units at Sulfur Recovery Plants3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
651
651
651
651
651
651
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
516
516
510
510
510
510
2015 Emission
Reductions
135
135
141
141
141
141
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 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, 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.
8-48
-------
Table 8-52. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU
BART-Eligible Units at Sulfur Recovery Plants
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$11.6
$11.6
$11.6
$11.6
$11.6
$11.6
Annualized Average
Cost-Effectiveness ($/ton)
$847
$849
$847
$849
$847
$849
Marginal Costs
($/ton)
—
—
$847
$849
N/A
N/A
Table 8-53 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 $0.07 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.
8-49
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Table 8-53. 2015 Cost and Cost-Effectiveness Results for NOX Control at Non-EGU
BART-Eligible Units at Sulfur Recovery Plants
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Total Annualized
Rate Costs (million 1999$)
7%
3%
7%
3%
7%
3%
$0.1
$0.1
$0.5
$0.5
$0.5
$0.5
Annualized Average
Cost-Effectiveness
($/ton)
$519
$400
$3,652
$3,315
$3,652
$3,315
Marginal Costs
($/ton)
—
—
$74,167
$68,833
N/A
N/A
Table 8-54. 2015 Cost Results for SO2 and NOX Control at Non-EGU BART-Eligible
Units at Sulfur Recovery Plants
Scenarios
Discount Rate
Total Annualized Costs (million 1999$)
Scenario 1
Scenario 2
Scenario 3
7%

3%
$11.7

$11.7

$12.1

$12.1
8-50
-------
Table 8-55. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-
EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
47,552
47,552
47,552
47,552
47,552
47,552
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
45,922
45,922
44,292
44,292
44,292
44,292
2015 Emission
Reductions
1,630
1,630
3,260
3,260
3,260
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 Non-
EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants3
Scenario
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
1,676
1,676
1,676
1,676
1,676
1,676
2015 Postcontrol
Emissions
1,626
1,626
1,423
1,421
1,421
1,421
2015 Emission
Reductions
50
50
253
255
255
255
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source
categories, both controlled and uncontrolled.
8-51
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Table 8-57 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 $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
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$1.6
$1.0
$7.2
$4.5
$7.2
$4.5
Annualized Average
Cost-Effectiveness
($/ton)
$982
$590
$2,209
$1,383
$2,209
$1,383
Marginal
Costs ($/ton)
—
—
$3,436
$1,383
N/A
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 cost-
effectiveness 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
8-52
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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
Scenario 1

Scenario 2

Scenario 3

Discount Total Annualized
Rate Costs (million 1999$)
7%
3%
7%
3%
7%
3%
$0.0
$0.0
$0.6
$0.4
$0.7
$0.4
Annualized Average
Cost-Effectiveness
($/ton)
$740
$509
$2,411
$1,764
$2,639
$1,764
Marginal Costs
($/ton)
—
—
$2,823
$1,764
$30,500
N/A
marginal costs are $1,764 per ton between the $l,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 cost-
effectiveness, 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

8-53
-------
Table 8-59. 2015 Cost Results for SO2 and NOX Control at Non-EGU BART-Eligible
Units at Primary Aluminum Ore Reduction Plants
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs (million 1999$)
$1.7
$1.0
$7.8
$5.0
$7.8a
$5.0
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
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
12,849
12,849
12,849
12,849
12,849
12,849
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
10,166
8,378
8,378
5,696
5,696
5,696
2015 Emission
Reductions
2,683
4,471
4,471
7,153
7,153
7,153
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source
category, both controlled and uncontrolled.
8-54
-------
Table 8-61. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Lime Kilns
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Total Annualized
Rate Costs (million 1999$)
7%
3%
7%
3%
7%
3%
$2.0
$4.3
$5.0
$25.4
$31.8
$25.4
Annualized Average
Cost-Effectiveness
($/ton)
$745
$953
$1,118
$3,552
$4,446
$3,552
Marginal Costs
($/ton)
—
—
$1,678
$8,858
$9,993
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 $l,000/ton, SNCR at $4,000/ton, and
SCRat$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 cost-
effectiveness, 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
8-55
-------
Table 8-62. 2015 NOX Emission Reductions (in tons) for BART-Eligible Units at Glass
Fiber Processing Plants
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
6,677
6,677
6,677
6,677
6,677
6,677
a The 20 1 5 baseline emissions estimate reflects
controlled and uncontrolled.
Table 8-63. 2015
Fiber Processing
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
emissions from all
Cost and Cost-Effectiveness Results
Plants
Discount Total Annualized
Rate Costs (million 1999$)
7%
3%
7%
3%
7%
3%
$0.5
$0.7
$5.3
$4.8
$7.8
$6.8
2015 Postcontrol
Emissions
6,109
5,902
4,561
4,561
4,479
4,479
2015 Emission
Reductions
568
775
2,116
2,116
2,198
2,198
sources in this source category, both
for BART-Eligible
Annualized Average
Cost-Effectiveness
($/ton)
$937
$880
$2,505
$2,244
$3,549
$3,108
Units at Glass
Marginal
Costs ($/ton)
—
—
$3,101
$3,057
$30,488
$25,402
8-56
-------
$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 $l,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
Incinerators3
Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
1,656
1,656
1,656
1,656
1,656
1,656
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
1,656
1,656
912
912
912
912
2015 Emission
Reductions
0
0
744
744
744
744
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.

8-57
-------
Table 8-65. 2007 Cost and Cost-Effectiveness Results for BART-Eligible Municipal
Incinerators
Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$0.0
$0.0
$1.1
$0.9
$1.1
$0.9
Annualized Average
Cost-Effectiveness
($/ton)
$0
$0
$1,478
$1,207
$1,478
$1,207
Marginal Costs
($/ton)
—
—
$1,478
$1,207
N/A
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
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
1,110
1,110
1,110
1,110
1,110
1,110
Discount Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
1,110
1,110
599
599
599
599
2015 Emission
Reductions
0
0
511
511
511
511
The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both
controlled and uncontrolled.
8-58
-------
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
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized Costs
(million 1999$)
$0.0
$0.0
$1.0
$0.8
$1.0
$0.8
Annualized Average
Cost-Effectiveness
($/ton)
$0
$0
$1,900
$1,534
$1,900
$1,534
Marginal
Costs ($/ton)
—
—
$1,900
$1,534
N/A
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 cost-
effectiveness, 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
8-59
-------
Table 8-68. 2015 NOX Emission Reductions (in tons) for BART-Eligible Units at
Carbon Black Plants

Scenarios
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
4,645
4,645
4,645
4,645
4,645
4,645

Discount Rate
7%
3%
7%
3%
7%
3%
a The 2015 baseline emissions estimate reflects emissions from all
controlled and uncontrolled.
Table 8-69. 2015 Cost
Carbon Black Plants
Scenarios
Scenario 1

Scenario 2

Scenario 3

and Cost-Effectiveness Results
Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$0.1
$0.0
$0.2
$0.1
$0.2
$0.1
2015 Postcontrol
Emissions
4,638
4,638
4,525
4,525
4,525
4,525
2015 Emission
Reductions
7
7
120
120
120
120
sources in this source category, both
for BART-Eligible
Annualized Average
Cost-Effectiveness
($/ton)
$957
$830
$1,608
$1,237
$1,608
$1,237
Units at
Marginal
Costs ($/ton)
—
—
$1,646
$1,237
N/A
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 $l,000/ton and the $4,000/ton scenarios are $l,646/ton with costs
at a 7 percent discount rate and $l,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
8-60
-------
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
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
719
719
719
719
719
719
2015 Postcontrol
Emissions
719
719
674
671
671
671
2015 Emission
Reductions
0
0
45
48
48
48
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 cost-
effectiveness, 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 $l,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 $l,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.
8-61
-------
Table 8-71. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at
Phosphate Rock Processing Plants

Scenarios
Scenario 1

Scenario 2

Scenario 3

Discount Rate
7%
3%
7%
3%
7%
3%

Total Annualized Costs
(million 1999$)
$0.0
$0.0
$0.1
$0.2
$0.2
$0.2
Annualized
Average Cost-
Effectiveness
($/ton)
$0
$0
$1,978
$3,221
$4,646
$3,221

Marginal
Costs ($/ton)
—
—
$1,978
$3,221
$44,667
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
Scenario 1

Scenario 2

Scenario 3

2015 Baseline
Emissions
1,377
1,377
1,377
1,377
1,377
1,377
Discount
Rate
7%
3%
7%
3%
7%
3%
2015 Postcontrol
Emissions
1,352
1,352
1,343
1,342
1,342
1,342
2015 Emission
Reductions
25
25
34
35
35
35
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source
category, both controlled and uncontrolled.
8-62
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Table 8-73 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 $0.12 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 $l,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 $l,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
Scenario 1

Scenario 2

Scenario 3

Discount
Rate
7%
3%
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$0.0
$0.0
$0.0
$0.0
$0.1
$0.0
Annualized Average
Cost-Effectiveness
($/ton)
$760
$511
$1,088
$1,247
$1,800
$1,247
Marginal Cost
($/ton)
—
—
$2,000
$3,100
$9,000
N/A
8.6 Caveats and Limitations of the Analyses

A number of caveats and limitations are associated with these analyses.

• 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
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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.

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).

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).

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.

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.

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 source-
specific level.

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.

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
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non-EGU units and employment shifts as workers are retrained at the same
company or reemployed elsewhere in the economy.

• 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
.

U.S. Office of Management and Budget (OMB). September 17, 2003. Circular A-4. "New
Guidelines for the Conduct of Regulatory Analysis." Available at
.
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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 ECU 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.
9-1
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Table 9-1. Potentially Regulated EGU Categories and Entities3
Category
Industry
Federal
government
State/local/tribal
government

NAICS
Codeb
221112
221 12C
221 12C
921150
Examples of Potentially Regulated Entities
Fossil fuel-fired electric utility steam generating units
Fossil fuel-fired electric utility steam generating units
Federal government.
Fossil fuel-fired electric utility steam generating units
municipalities.
Fossil fuel-fired electric utility steam generating units
Country.

owned by the
owned by
in Indian
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
Fossil Fuel-Fired Industrial Boilers
(>250 MMBTU heat input per hour)
Petroleum Refineries
Kraft Pulp Mills
Portland Cement Plants
Hydrofluoric, Sulfuric, and Nitric
Acid Plants
Primary Aluminum Ore Reduction
Plants
Chemical Process Plants
Iron and Steel Mill Plants
NAICS Codea
Various
manufacturing
industries
324110
322110
327310
325
331312
325
3311
NAICS Description
Varied
Petroleum Refineries
Pulp Mills
Cement Manufacturing
Chemical Manufacturing
Primary Aluminum
Production
Chemical Manufacturing
Iron and Steel Mill and
Ferroalloy Manufacturing
Size Standard15
Varied
1500 Employees
750 Employees
750 Employees
1000 Employees
750 Employees
1000 Employees
1000 Employees
a North American Industry Classification System.
b Small Business Administration Size Standards, http://www.sba.gov/size/sizetable2002.html.
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According to the SB A 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:

ETACC
CSR
where

TACC = total annual compliance costs,

i = indexes the number of affected units owned by company],
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.

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n = number of affected plants, and

TRj = total revenue of ultimate parent company j.

9.1.1 ECU Sector Small Business Impacts

For proposal, the engineering analysis conducted for the rulemaking identified 302
ECU 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 x NamePlate Capacity (MW) x standard hours per year
2An 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.

3These 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.

4This 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.

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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 non-
EGU 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 H 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 cost-
benefit analysis, for any proposed or final rule that "includes any federal mandate that may
result in the expenditure by state, local, and tribal governments, in the aggregate, or by the
private sector, of $100,000,000 or more ... in any one year." A "federal mandate" is defined
under section 421(6), 2 U.S.C. 658(6), to include a "federal intergovernmental mandate" and
a "federal private sector mandate." A "federal intergovernmental mandate," in turn, is
defined to include a regulation that "would impose an enforceable duty upon state, local, or

9-5
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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 ECU 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
9-6
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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

9-7
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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

9-8
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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 ECU sources or non-
EGU 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
-------
accordingly exercise substantial intervening discretion in implementing the final rale.
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 low-
income populations face risks or a rate of exposure to hazards that are significant and that
"appreciably exceed or is likely to appreciably exceed the risk or rate to the general
population or to the appropriate comparison group" (EPA, 1998).

In accordance with E.O. 12898, the Agency has considered whether this rule may
have disproportionate negative impacts on minority or low income populations. Negative
impacts to these subpopulations that appreciably exceed similar impacts to the general
population are not expected, because the Agency expects this rale to lead to reductions in air
pollution emissions and exposures generally.
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.

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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
.

InfoUSA. 2001. InfoUSA Sales Solutions Database of Company Sales. Available at
.

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.
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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 eutrophication-
related 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.
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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 costs'5
3 percent discount rate
7 percent discount rate
Social benefits'de
3 percent discount rate
7 percent discount rate
Health-related benefits:
3 percent discount rate
7 percent discount rate
Visibility benefits
Net benefits (benefits-costs)'1'
3 percent discount rate
7 percent discount rate
$0.4
$0.3

$2.6 + B
$2.2 + B

$2.5
$2.1
$0.08

$2.2 + B
$1.9 + B
$1.4
$1.5

$10.1 +B
$8.6+ B

$9.8
$8.4
$0.24

$8.7 + B
$7.1 +B
$2.3
$2.9

$14.3+ B
$12.2+ B

$13.9
$11.8
$0.42

$12.0+ 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 SO2for 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.
c 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.
10-2
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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.
10-3
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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

A-l
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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 EMPAX-
CGE 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.
'See for details.

2See for complete IPM documentation.

3See for complete AirControlNET documentation.

A-2
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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 ofEMPAX-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.
4See Section A.2 for additional details on the EMPAX-CGE model.

5See .

A-3
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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 EVIPLAN 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 EVIPLAN 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.
6Static 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.

7EIA forecasts from the Annual Energy Outlook 2003 (AEO) (EIA, 2003) are used in this analysis.

A-4
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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
8See, 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
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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:

• electricity prices (percentage change in retail prices)
• coal and gas consumption for electricity (percentage changes in Btus)
• coal and gas prices (percentage changes in prices)
• coal and gas expenditures ($ changes—Btus of energy input times $/MMBtu)
• capital costs ($ changes)
• fixed operating costs ($ changes)
• 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-6
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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
A-7
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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-l, 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.
9See Section A. 1.9 for EMPAX-CGE results using other linkages to IPM.

A-8
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Coal
Crude Oil
Electricity
Natural Gas
Petroleum
Agriculture
EIS: Food and Kindred
EIS: Paper and Allied
EIS: Chemicals
EIS: Glass
EIS: Cement
EIS: Iron and Steel
EIS: Aluminum
Other Manufacturing
Services
Transportation
-5.0% -4.0% -3.0% -2.0% -1.0%
Percent Change from Reference
0.0%
1.0%
Figure A-l. 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.
A-9
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1.0%
0.0%
u
u
e

-------
U.i3 /O
g 0.00% -
1
&
«
g -0.25% -
1
o
J3 -0.50%
U
& -0.75% -
-1.00% -

1 _
"•'•"• I'l M • !

!
I

C§ "g « .3 «P C§ "
o S o
z z

iiii^iliii^iilii^iiin^
§ 0 § 0 § 0 §
Agriculture Energy-Intensive Other Services Transportation
Manufacturing Manufacturing
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-ll
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2.50%

2.00%

1.50%

1.00%

0.50%

0.00%

-0.50%
u -1
g
& ,
-2.50%
n i
Food and
Kindred
Paper and
Allied
Chemicals
Glass
Cement
Iron and Steel
Aluminum
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-12
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0.10%
0.08%
0.06%
E
o
* 0.04%
M
e
8
I 0.02%
0.00%
-0.02%
2005
2010
2015
2020
2025
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-13
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Percent Change from Reference
U.U1U /O
ft ftftQO/
A ftft/;o/
A ftft/IO/
A ftft7 O/

A ftftftO/ _,
A ftft'JO/
A AA/IO/
A ftft/;o/
A ft AGO/
ft ftlfto/.

^ — -*• — ^

2005 2010 2015 2020 2025

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-14
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Change in GDP (Percentage Change)
u.iu /o -
n ns«/»
n ft^o/.
n ftzio/.

A AAO/
4
-0.02%
n n4»x.
n n(*«/»
n (is0/.
n mo/, _

2005 2010 2015 2020 2025

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-15
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$20,000
O\ '
o\
0\

a
o
$15,000
I
as
VI
O
rS $12,500
$10,712 -
$10,000
—»— EMPAX Reference Case

—O—BART Policy Case
$19,272 - Reference • $19,269
$16,756 - Reference^ $16,753
$14,435 - Reference* $14,432
$12,40.5 - Reference^ 572,407
X
X

Reference wf$10,711
2005 2010 2015 2020 2025

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:

• 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;

• differences in regional production and consumption patterns for electricity and

nonelectricity energy goods;

• differences in industrial composition of regional economies;

• differences in household consumption patterns; and

• differences in regional growth forecasts.
A-16
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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.
0.10%
0.05%
0.00%
-0.05%
o
.£
-0.10%
eS
u
-0.15%
-0.20%
-0.25%
2005
2010
2015
2020
2025
•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.
A-17
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Tables A-l and A-2 show EMPAX-CGE estimates of changes in revenue and output
quantities for 2015 and 2020 for BART Scenario 2.

Table A-l. U.S. Domestic Output Changes
Variable Industry
Percentage Change in Revenue (%) Coal
Crude Oil
Electricity
Natural Gas
Petroleum
Agriculture
Energy-Intensive Manufacturing
Other Manufacturing
Services
Transportation
Percentage Change In Quantity (%) Coal
Crude Oil
Electricity
Natural Gas
Petroleum
Agriculture
Energy-Intensive Manufacturing
Other Manufacturing
Services
Transportation
2015
0.07%
0.00%
0.06%
-0.07%
-0.03%
-0.04%
-0.03%
-0.01%
-0.01%
-0.02%
0.02%
0.00%
-0.07%
-0.02%
-0.09%
-0.04%
-0.11%
-0.01%
-0.01%
-0.02%
2020
0.32%
0.00%
0.05%
-0.11%
-0.03%
-0.04%
-0.03%
-0.01%
-0.01%
-0.02%
0.10%
0.00%
-0.03%
-0.03%
-0.09%
-0.04%
-0.11%
-0.01%
-0.01%
-0.02%
Source: EMPAX-CGE (BART Scenario 2)
A-18
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Table A-2. U.S. Domestic Energy-Intensive Sector Output Changes
Variable Industry
Percentage Change in Revenue (%) Food and Kindred
Paper and Allied
Chemicals
Glass
Cement
Iron and Steel
Aluminum
Percentage Change In Quantity (%) Food and Kindred
Paper and Allied
Chemicals
Glass
Cement
Iron and Steel
Aluminum
2015
-0.02%
0.04%
-0.07%
-0.02%
-0.01%
0.01%
0.00%
-0.05%
-0.21%
-0.13%
-0.05%
-0.02%
-0.05%
-0.03%
2020
-0.02%
0.03%
-0.08%
-0.03%
0.00%
0.01%
0.00%
-0.05%
-0.22%
-0.14%
-0.05%
-0.02%
-0.06%
-0.03%
Source: EMPAX-CGE (BART Scenario 2)

A. 1.8 A Iternative BAR T 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-l 1). 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 one-
hundredths of 1 percent.
A-19
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1.0%
0.0%
-1.0%
-2.0%
-3.0%
-4.0%
-5.0%
-rig-
oooooooooooooooooooooooooooooooooooooooooooooooo
ill
« « «
ill
« « «
ill
»5 »5 »5
Elec-
tricity
ill
« « «
ill
« « «
ill
»5 »5 »5
Agri-
1 1 1
« « «
Foodand
Kindred
ill
« « «
Paper
and
ill
»5 »5 »5
Chem-
icals
ill
»5 »5 »5
Glass
ill
« « «
ill
« « «
ill
« « «
ill
« « «
ill
« « «
ill
»5 »5 »5
Trans por1
Manu-
facturing
Allied
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-20
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0.00%
-0.10%
2005
2010
2015
2020
2025
BART - Scenario 2 -•- BART - Scenario 1 -•- BART - Scenario 3
Figure A-ll. 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:

• "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.

• "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-21
-------
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.

• "IPMPrice & 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-22
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0.00%
-0.02%
-0.04%
U
-0.06%
-0.08%
-0.10%
2005
2010
2015
2020
2025
-Q-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.
A-23
-------
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.
Utility
Consumption Leisure
Foreign
Local
Output
Regional
Output
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 goods are a CES composite
of locally produced goods and goods
from other regions.

Most producer goods use fixed pro-
portions of intermediate inputs and a
capital-labor-energy (KLE) composite.
KLE
Intermediates
Energy
Value Added
Energy
(5 Types)
Capital
Labor
Intermediate materials inputs are the 11 types ofnon-
energy goods, in fixed proportion for each industry.

The KLE composite is a CES function
of energy and value-added (KL).

Energy is a CES composite of 5 types of fuel. The
structure of this function varies across industries.
Value added is a Cobb-Douglas
composite of capital and labor.
Figure A-13. General Production and Consumption Nesting Structure in EMPAX-
CGE
"Although 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
-------
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
EVIPLAN Group15 and energy data come from EIA.16 Although EVIPLAN 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 EVIPLAN 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 EVIPLAN does not. Finally, although the EVIPLAN 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 EVIPLAN 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
12See Brooke et al. (1998) for a description of GAMS (http://www.gams.com/).

"Solving 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 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[aandb]).

A-25
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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
17Energy-intensive sectors industry categories are based on El A 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 for
further discussion of these regions.

A-26
-------
Table A-3. EMPAX-CGE Industries
EMPAX Industry NAICS Classifications
Coal 2121
Crude Oil" 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-27
-------
""West" also includes
Alaska and Hawaii.
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
• $0 to $14,999,
• $15,000 to $29,999,
• $30,000 to $49,999, and
• $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-28
-------
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 EVIPLAN 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

A-29
-------
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
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
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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, IS. 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 .

Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User's Guide.
GAMS Development Corporation. Available at http://www.gams.com.
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.

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Feenberg, D., and E. Courts. 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

Minnesota EVIPLAN 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/llf680ff78df42f585256b45007e6235/41
b8b642ab9371df852564500004b543/$FILE/EE-0217A-l.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(l):l-46. Also available at
http: //www. gam s. com/sol vers/mp sge/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_multi state, 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/state s/pri ce_multi state. html.
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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/03 83(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).
A-33
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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.I 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 • 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-l
-------
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-2
-------
information needed to determine whether the unit started construction prior to August 7,
1977.

Table B-l 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-l. Potential BART Units Identified for Follow-Up Analysis
State Plant Name
AL
AL
AZ
AZ
AZ
AZ
CO
CO
DE
FL
FL
GA
IA
IA
IA
IA
IN
KS
KY
KY
KY
KY
LA
LA
LA
MD
MD
MI
MI
MN
Charles R Lowman
Charles R Lowman
Apache Station
Apache Station
Springerville
Springerville
Pawnee
Ray D Nixon
Indian River
C D Mclntosh
Deerhaven
Mclntosh (6124)
Ames
George Neal South
Louisa
Ottumwa
A B Brown Generating Station
Nearman Creek
East Bend
R D Green
R D Green
Trimble County
Dolet Hills
R S Nelson
Rodemacher
Brandon Shores
Brandon Shores
Presque Isle
Wyandotte
Clay Boswell
ORIS
Code
56
56
160
160
8223
8223
6248
8219
594
676
663
6124
1122
7343
6664
6254
6137
6064
6018
6639
6639
6071
51
1393
6190
602
602
1769
1866
1893
Unit ID
2
3
2
3
1
2
1
1
4
3
B2
1
8
4
101
1
1
Nl
2
Gl
G2
1
1
6
2
1
2
9
7
4
Online
1979
1980
1979
1979
1985
1990
1981
1980
1980
1982
1981
1979
1982
1979
1983
1981
1979
1981
1981
1979
1981
1990
1986
1982
1982
1984
1991
1979
1982
1980
NSPS
D
D
D
D
D
D
D
D
D
D
D
PRE
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
In BART
CAIR Timeframe?
X
X

X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X

X
X

X
X
X

X
X
X
X
X

X
Follow-Up Nameplate
Needed (MW)
233
233
194.7
194.7
397
397
X 500
207
442.4
334
250.75
177.66
65
639.9
738.09
726
265.23
261
X 669.28
X 263.7
X 263.7
X 566.1
720.75
614.6
558
685.08
685.08
90
73
558
(continued)
B-3
-------
Table B-l. Potential BART Units Identified for Follow-Up Analysis (continued)
State
MO
MO
NC
NC
NC
NC
NC
NC
ND
ND
ND
NE
NE
NE
NV
OH
OK
OK
OR
TX
TX
TX
TX
TX
UT
UT
WI
WI
WI
WI
WT
Plant Name
latan
Sikeston
Elizabethtown Power
Elizabethtown Power
Lumberton Power
Lumberton Power
Mayo
Mayo
Antelope Valley
Antelope Valley
Coyote
Gerald Whelan Energy Center
Nebraska City
Platte
North Valmy
Killen Station
Grand River Dam Authority
Hugo
Boardman
Coleto Creek
Gibbons Creek
Pirkey
San Miguel
Sandow
Hunter (Emery)
Hunter (Emery)
Edgewater (4050)
J P Madgett
Pleasant Prairie
Pleasant Prairie
Weston
ORIS
Code
6065
6768
10380
10380
10382
10382
6250
6250
6469
6469
8222
60
6096
59
8224
6031
165
6772
6106
6178
6136
7902
6183
6648
6165
6165
4050
4271
6170
6170
4078
Unit ID
1
1
UNIT1
UNIT2
UNIT1
UNIT2
1A
IB
Bl
B2
Bl
1
1
1
1
2
1
1
1SG
1
1
1
SM-1
4
1
2
5
Bl
1
2
3
Online
1980
1981
1985
1985
1985
1985
1983
1983
1984
1986
1981
1981
1979
1982
1981
1982
1982
1982
1980
1980
1983
1985
1982
1981
1978
1980
1985
1979
1980
1985
1981
NSPS
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
CAIR
X
X
X
X
X
X
X
X

X
X
X
X
X

X
X
X
X
X
In BART Follow-Up Nameplate
Timeframe? Needed (MW)
X 725.85
X 261
35
35
35
35
735.84
735.84
435
435
450
76.3
X 615.87
109.8
X 254.26
X 666.45
490
400
X 560.5
X 600.39
443.97
720.75
410
X 590.64
X 446.4
X 446.4
X 380
X 387
X 616.59
X 616.59
X 350.46
DLS/jlj
B-4
-------
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
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AR
AR
AR
AR
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
CO
CO
CO
Barry
Barry
Charles R Lowman
Charles R Lowman
Charles R Lowman
Colbert
E C Gaston
Gorgas
Greene County
Greene County
James H Miller Jr
James H Miller Jr
Widows Creek
Flint Creek
Independence
White Bluff
White Bluff
Apache Station
Apache Station
Cholla
Cholla
Cholla
Coronado Generating Station
Coronado Generating Station
Irvington
Navajo Generating Station
Navajo Generating Station
Navajo Generating Station
Cherokee
Cherokee
Comanche (470)
UNITID
4
5
1
2
3
5
5
10
1
2
1
2
8
1
1
1
2
2
3
2
3
4
U1B
U2B
4
1
2
3
3
4
1
Online Year Nameplate Capacity3
1969
1971
1969
1979
1980
1965
1974
1972
1965
1966
1978
1985
1965
1978
1983
1980
1981
1979
1979
1978
1980
1981
1979
1980
1967
1974
1975
1976
1962
1968
1973
404
789
66
233
233
550
952
789
299
269
706
706
550
558
850
850
850
195
195
289
289
414
411
411
173
803
803
803
150
350
350
(continued)
B-5
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CT
DE
DE
DE
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
GA
GA
GA
Comanche (470)
Craig
Craig
Hayden
Hayden
Martin Drake
Martin Drake
Martin Drake
Pawnee
Ray D Nixon
Valmont
Bridgeport Harbor Station
Edge Moor
Indian River
Indian River
Big Bend
Big Bend
Big Bend
Crist Electric Generating Plant
Crist Electric Generating Plant
Crystal River
Crystal River
Crystal River
Crystal River
F J Gannon
F J Gannon
F J Gannon
Lansing Smith
Lansing Smith
Bowen
Bowen
Bowen
UNITID
2
Cl
C2
HI
H2
5
6
7
1
1
5
BHB3
4
3
4
BB01
BB02
BB03
6
7
1
2
4
5
GB04
GB05
GB06
1
2
1BLR
2BLR
3BLR
Online Year Nameplate Capacity3
1975
1980
1979
1965
1976
1962
1968
1974
1981
1980
1964
1968
1966
1970
1980
1970
1973
1976
1970
1973
1966
1969
1982
1984
1963
1965
1967
1965
1967
1971
1972
1974
350
446
446
190
275
50
75
132
500
207
166
400
177
177
442
446
446
446
370
578
441
524
739
739
187
239
414
150
190
700
700
880
(continued)
B-6
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
Bowen
Hammond
Harllee Branch
Harllee Branch
Harllee Branch
Harllee Branch
Jack McDonough
Jack McDonough
Kraft
Mclntosh (6124)
Mitchell
Scherer
Scherer
Wansley (6052)
Wansley (6052)
Yates
Yates
Ames
Burlington (IA)
Council Bluffs
Fair Station
George Neal North
George Neal North
George Neal North
George Neal South
Lansing
Milton L Kapp
Muscatine
Ottumwa
Pella
Pella
Prairie Creek
UNITID
4BLR
4
1
2
3
4
MB1
MB2
3
1
3
1
2
1
2
Y6BR
Y7BR
7
1
3
2
1
2
3
4
4
2
8
1
6
7
4
Online Year Nameplate Capacity3
1975
1970
1965
1967
1968
1969
1963
1964
1965
1979
1964
1982
1984
1976
1978
1974
1974
1968
1968
1978
1967
1964
1972
1975
1979
1977
1967
1969
1981
1963
1973
1967
880
500
250
319
481
490
245
245
104
178
125
818
818
865
865
350
350
33
212
726
38
147
349
550
640
275
218
75
726
38
38
149
(continued)
B-7
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
IA
IA
IA
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
Sixth Street
Sixth Street
Streeter Station
Baldwin
Baldwin
Baldwin
Coffeen
Coffeen
Dallman
Dallman
Dallman
Duck Creek
E D Edwards
E D Edwards
Havana
Joliet 29
Joliet 29
Joliet 29
Joliet 29
Kincaid
Kincaid
Lakeside
Lakeside
Marion
Marion
Marion
Marion
Newton
Newton
Powerton
Powerton
Powerton
UNITID
2
4
7
1
2
3
01
02
31
32
33
1
2
3
9
71
72
81
82
1
2
7
8
1
2
3
4
1
2
51
52
61
Online Year Nameplate Capacity3
1970
1970
1973
1970
1973
1975
1965
1972
1968
1972
1978
1976
1968
1972
1978
1965
1965
1965
1965
1967
1968
1965
1965
1963
1963
1963
1978
1977
1982
1972
1972
1975
85
85
35
623
635
635
389
617
90
90
207
441
281
364
488
660
660
660
660
660
660
38
38
33
33
33
173
617
617
893
893
893
(continued)
B-8
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
IL
IL
IL
IL
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
Powerton
Waukegan
Will County
Wood River
A B Brown Generating Station
Bailly
Bailly
Cayuga
Cayuga
Dean H Mitchell
F B Culley Generating Station
F B Culley Generating Station
Frank E Ratts
Frank E Ratts
Gibson
Gibson
Gibson
Gibson
Harding Street Station (EW Stout)
Merom
Merom
Michigan City
Petersburg
Petersburg
Petersburg
R M Schahfer
R M Schahfer
State Line Generating Station (IN)
Tanners Creek
Wabash River
Warrick
Warrick
UNITID
62
8
4
5
1
7
8
1
2
11
2
3
1SG1
2SG1
1
2
3
4
70
1SG1
2SG1
12
1
2
3
14
15
4
U4
6
2
3
Online Year Nameplate Capacity3
1975
1962
1963
1964
1979
1962
1968
1970
1972
1970
1966
1973
1970
1970
1976
1975
1978
1979
1973
1983
1982
1974
1967
1969
1977
1976
1979
1962
1964
1968
1964
1965
893
355
598
388
265
194
422
531
531
115
104
265
117
117
668
668
668
668
471
540
540
540
253
471
574
540
556
389
580
387
144
144
(continued)
B-9
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
IN
IN
KS
KS
KS
KS
KS
KS
KS
KS
KS
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
Warrick
Whitewater Valley
Jeffrey Energy Center
Jeffrey Energy Center
La Cygne
La Cygne
Lawrence Energy Center
Nearman Creek
Quindaro
Quindaro
Tecumseh Energy Center
Big Sandy
Big Sandy
Cane Run
Cane Run
Cane Run
Coleman
Coleman
Coleman
Cooper
Cooper
E W Brown
E W Brown
East Bend
Elmer Smith
Elmer Smith
Ghent
Ghent
H L Spurlock
H L Spurlock
Henderson I
HMP&L Station 2
UNITID
4
2
1
2
1
2
5
Nl
1
2
10
BSU1
BSU2
4
5
6
Cl
C2
C3
1
2
2
3
2
1
2
1
2
1
2
6
HI
Online Year Nameplate Capacity3
1970
1973
1978
1980
1973
1977
1971
1981
1965
1971
1962
1963
1969
1962
1966
1969
1969
1970
1971
1965
1969
1963
1971
1981
1964
1974
1974
1977
1977
1981
1968
1973
323
60
720
720
893
685
458
261
82
158
176
281
816
163
209
272
174
174
173
100
221
180
446
669
151
265
557
556
305
508
32
180
(continued)
B-10
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
LA
LA
LA
LA
MA
MA
MA
MD
MD
MD
MD
MD
MD
MD
MD
MD
MI
MI
MI
MI
FACILITY_NAME
HMP&L Station 2
Mill Creek
Mill Creek
Mill Creek
Mill Creek
Paradise
Paradise
Paradise
R D Green
R D Green
Robert Reid
Trimble County
Big Cajun 2
Big Cajun 2
R S Nelson
Rodemacher
Brayton Point
Brayton Point
Brayton Point
Brandon Shores
Brandon Shores
C P Crane
Chalk Point
Chalk Point
Dickerson
Herbert a Wagner
Morgantown
Morgantown
Belle River
Belle River
Eckert Station
Eckert Station
UNITID
H2
1
2
3
4
1
2
3
Gl
G2
Rl
1
2B1
2B2
6
2
1
2
3
1
2
2
1
2
3
3
1
2
1
2
4
5
Online Year
1974
1972
1974
1978
1982
1963
1963
1970
1979
1981
1965
1990
1980
1981
1982
1982
1963
1964
1969
1984
1991
1963
1964
1965
1962
1966
1970
1971
1984
1985
1964
1968
Nameplate Capacity3
185
356
356
463
544
704
704
1150
264
264
82
566
559
559
615
558
241
241
643
685
685
209
364
364
196
359
626
626
698
698
80
80
(continued)
B-ll
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MI
MN
MN
MN
MN
MN
MN
MN
MN
MN
MO
MO
Eckert Station
Erickson
Harbor Beach
J H Campbell
J H Campbell
J H Campbell
James De Young
Monroe
Monroe
Monroe
Monroe
Presque Isle
Presque Isle
Presque Isle
Presque Isle
Presque Isle
Presque Isle
Presque Isle
Presque Isle
St. Clair
Trenton Channel
Allen S King
Clay Boswell
Clay Boswell
Hoot Lake
Northeast Station
Riverside (1927)
Sherburne County
Sherburne County
Silver Lake
Asbury
Blue Valley
UNITID
6
1
1
1
2
3
5
1
2
3
4
2
3
4
5
6
7
8
9
7
9A
1
3
4
3
NEPP
8
1
2
4
1
3
Online Year Nameplate Capacity3
1970
1973
1968
1962
1967
1980
1969
1971
1973
1973
1974
1962
1964
1966
1974
1975
1978
1978
1979
1969
1968
1968
1973
1980
1964
1971
1964
1976
1977
1969
1970
1965
80
155
121
265
385
871
29
817
823
823
817
38
54
58
90
90
90
90
90
545
536
598
365
558
75
32
239
660
660
54
232
58
(continued)
B-12
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MS
MS
MS
MS
MS
MS
MT
MT
MT
NC
Columbia
latan
James River
James River
Labadie
Labadie
Labadie
Labadie
Lake Road
Montrose
New Madrid
New Madrid
Rush Island
Rush Island
Sibley
Sibley
Sikeston
Sioux
Sioux
Southwest
Thomas Hill
Thomas Hill
Daniel Electric Generating Plant
Daniel Electric Generating Plant
R D Morrow
R D Morrow
Watson Electric Generating Plant
Watson Electric Generating Plant
Colstrip
Colstrip
J E Corette
Asheville
UNITID
7
1
4
5
1
2
3
4
6
3
1
2
1
2
2
3
1
1
2
1
MB1
MB2
1
2
1
2
4
5
1
2
2
1
Online Year Nameplate Capacity3
1965
1980
1964
1970
1970
1971
1972
1973
1970
1964
1972
1977
1976
1977
1962
1969
1981
1967
1968
1976
1966
1969
1977
1981
1978
1978
1968
1973
1975
1976
1968
1964
74
726
60
105
574
574
621
621
90
188
600
600
621
621
50
419
261
550
550
194
180
285
500
500
200
200
250
500
358
358
191
207
(continued)
B-13
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
ND
ND
ND
ND
ND
ND
ND
ND
NE
NE
NE
NE
NE
NE
NE
NH
Asheville
Belews Creek
Belews Creek
Cliffside
L V Sutton
Lee
Marshall
Marshall
Marshall
Marshall
Roxboro
Roxboro
Roxboro
Roxboro
Roxboro
Roxboro
Coal Creek
Coal Creek
Leland Olds
Leland Olds
Milton R Young
Milton R Young
R M Heskett
Stanton
Gerald Gentleman Station
Gerald Gentleman Station
Lon D Wright Power Plant
Nebraska City
North Omaha
North Omaha
Sheldon
Merrimack
UNITID
2
1
2
5
3
3
1
2
3
4
1
2
3A
3B
4A
4B
1
2
1
2
Bl
B2
B2
1
1
2
8
1
4
5
1
2
Online Year
1971
1974
1975
1972
1972
1962
1965
1966
1969
1970
1966
1968
1973
1973
1980
1980
1979
1981
1966
1975
1970
1977
1963
1967
1979
1982
1976
1979
1963
1968
1968
1968
Nameplate Capacity3
207
1080
1080
571
447
252
350
350
648
648
411
657
745
745
745
745
506
506
216
440
257
477
75
172
681
681
92
616
136
218
109
346
(continued)
B-14
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
NJ
NJ
NJ
NM
NM
NM
NM
NM
NM
NM
NM
NM
NV
NV
NV
NV
NV
NV
NY
NY
NY
NY
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
B L England
B L England
Hudson
Four Corners
Four Corners
Four Corners
Four Corners
Four Corners
San Juan
San Juan
San Juan
San Juan
Mohave
Mohave
North Valmy
Reid Gardner
Reid Gardner
Reid Gardner
Dynegy Danskammer
Lovett
Lovett
S A Carlson
Avon Lake Power Plant
Bay Shore
Bay Shore
Cardinal
Cardinal
Cardinal
Conesville
Conesville
Conesville
Conesville
UNITID
1
2
2
1
2
3
4
5
1
2
3
4
1
2
1
1
2
3
4
4
5
12
12
3
4
1
2
3
3
4
5
6
Online Year Nameplate Capacity3
1962
1964
1968
1963
1963
1964
1969
1970
1976
1973
1979
1982
1971
1971
1981
1965
1968
1976
1967
1966
1969
1963
1970
1963
1968
1967
1967
1977
1962
1973
1976
1978
136
163
660
190
190
253
818
818
361
350
534
534
818
818
254
114
114
114
239
180
201
58
680
141
218
615
615
650
162
842
444
444
(continued)
B-15
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OK
OK
OK
OK
OK
OK
OR
PA
PA
PA
PA
PA
PA
Eastlake
Gen J M Gavin
Gen J M Gavin
Hamilton
J M Stuart
J M Stuart
J M Stuart
J M Stuart
Killen Station
Lake Shore
Miami Fort
Miami Fort
Muskingum River
W H Sammis
W H Sammis
W H Sammis
W H Sammis
Walter C Beckjord
Walter C Beckjord
Muskogee
Muskogee
Northeastern
Northeastern
Sooner
Sooner
Boardman
Bruce Mansfield
Bruce Mansfield
Bruce Mansfield
Brunner Island
Brunner Island
Cheswick
UNITID
5
1
2
9
1
2
3
4
2
18
7
8
5
4
5
6
7
5
6
4
5
3313
3314
1
2
ISO
1
2
3
2
3
1
Online Year
1972
1974
1975
1974
1971
1970
1972
1974
1982
1962
1975
1978
1968
1962
1967
1969
1971
1962
1969
1977
1978
1979
1980
1979
1980
1980
1976
1977
1980
1965
1969
1970
Nameplate Capacity3
680
1300
1300
51
610
610
610
610
666
256
557
558
615
185
318
623
623
245
461
572
572
473
473
569
569
561
914
914
914
405
790
565
(continued)
B-16
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SD
TN
TN
TN
TX
FACILITY_NAME
Conemaugh
Conemaugh
Hatfields Ferry
Hatfields Ferry
Hatfields Ferry
Homer City
Homer City
Homer City
Keystone
Keystone
Mitchell
Montour
Montour
New Castle
Portland
Canadys Steam
Canadys Steam
Canadys Steam
Dolphus M Grainger
Dolphus M Grainger
Jefferies
Jefferies
Wateree
Wateree
Williams
Winyah
Winyah
Big Stone
Bull Run
Cumberland
Cumberland
Big Brown
UNITID
1
2
1
2
3
1
2
3
1
2
33
1
2
5
2
CAN1
CAN2
CANS
1
2
3
4
WAT1
WAT2
WIL1
1
2
1
1
1
2
1
Online Year
1970
1971
1969
1970
1971
1969
1969
1977
1967
1968
1963
1972
1973
1964
1962
1962
1964
1967
1966
1966
1970
1970
1970
1971
1973
1975
1977
1975
1967
1973
1973
1971
Nameplate Capacity3
936
936
576
576
576
660
660
692
936
936
299
823
819
136
255
136
136
218
82
82
173
173
386
386
633
315
315
456
950
1300
1300
593
(continued)
B-17
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
UT
UT
UT
UT
VA
VA
VA
VA
WA
WA
WI
WI
WI
Big Brown
Coleto Creek
Harrington Station
Harrington Station
J T Deely
J T Deely
Martin Lake
Martin Lake
Monticello
Monticello
Monticello
Sam Seymour
Sam Seymour
Sandow
W A Parish
W A Parish
Welsh
Welsh
Welsh
Hunter (Emery)
Hunter (Emery)
Huntington
Huntington
Chesapeake
Chesterfield
Chesterfield
Possum Point Power Station
Centralia
Centralia
Columbia
Columbia
Edgewater (4050)
UNITID
2
1
061B
062B
1
2
1
2
1
2
3
1
2
4
WAP5
WAP6
1
2
3
1
2
1
2
4
5
6
4
BW21
BW22
1
2
4
Online Year Nameplate Capacity3
1972
1980
1976
1978
1977
1978
1977
1978
1974
1975
1978
1979
1980
1981
1977
1978
1977
1980
1982
1978
1980
1977
1974
1962
1964
1969
1962
1972
1973
1975
1978
1969
593
600
360
360
446
446
793
793
593
593
793
615
615
591
734
734
558
558
558
446
446
446
446
239
359
694
239
730
730
512
512
351
(continued)
B-18
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
WY
FACILITY_NAME
Edgewater (4050)
Genoa
J P Madgett
Manitowoc
Nelson Dewey
Pleasant Prairie
Pleasant Prairie
Pulliam
South Oak Creek
South Oak Creek
Valley (Wepco)
Valley (Wepco)
Valley (Wepco)
Valley (Wepco)
Weston
Fort Martin
Fort Martin
Harrison
Harrison
Harrison
John E Amos
John E Amos
John E Amos
Mitchell
Mitchell
Mount Storm Power Station
Mount Storm Power Station
Mount Storm Power Station
Mountaineer (1301)
Pleasants
Pleasants
Dave Johnston
UNITID
5
1
Bl
7
2
1
2
8
7
8
1
2
3
4
3
1
2
1
2
3
1
2
3
1
2
1
2
3
1
1
2
BW43
Online Year
1985
1969
1979
1962
1962
1980
1985
1964
1965
1967
1968
1968
1969
1969
1981
1967
1968
1972
1973
1974
1971
1972
1973
1971
1971
1965
1966
1973
1980
1979
1980
1964
Nameplate Capacity3
380
346
387
79
114
617
617
136
318
324
136
136
136
136
350
576
576
684
684
684
816
816
1300
816
816
570
570
522
1300
684
684
230
(continued)
B-19
-------
Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling
Emissions (continued)
State FACILITY_NAME UNITID
WY
WY
WY
WY
WY
WY
WY
WY
WY
WY
WY
WY
Dave Johnston
Jim Bridger
Jim Bridger
Jim Bridger
Jim Bridger
Laramie River
Laramie River
Laramie River
Naughton
Naughton
Naughton
Wyodak
BW44
BW71
BW72
BW73
BW74
1
2
3
1
2
3
BW91
Online Year Nameplate Capacity3
1972
1974
1975
1976
1979
1980
1981
1982
1963
1968
1971
1978
360
561
561
561
561
570
570
570
163
218
326
362
Nameplate capacity of generator connected to boiler.
B-20
-------
B.3 Non-EGU Cost and Economic Impact Sensitivity Analyses

This appendix contains a number of sensitivity analyses for Scenarios 1 through 3
($l,000/ton, $4,000/ton, and $10,000/ton) applied to the non-EGU source categories. These
sensitivity analyses are the following:

• 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)

• total capital costs of controlling both SO2 and NOX in 2015—calculated at a 3
percent discount rate (see Table B-4)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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-21
-------
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
Industrial boilers
Petroleum refineries
Kraft pulp mills
Portland cement plants
Hydrofluoric, sulfuric, and nitric
acid plants
Chemical process plants
Iron and steel mills
Coke oven batteries
Sulfur recovery plants
Primary aluminum ore reduction
plants
Lime kilns
Glass fiber processing plants
Municipal incinerators
Coal cleaning plants
Carbon black plants
Phosphate rock processing plants
Secondary metal production
facilities
Total

Scenario 1
$l,000/ton
$422.3
22.6
70.7
18.4
34.6
58.4
2.6
0.0
0.3
14.6
9.9
1.2
0.0
0.0
0.02
0.0
0.1
$655.7
Scenarios
Scenario 2
$4,000/ton
$4,132.2
1,220.9
1,131.6
817.6
42.1
392.4
162.9
81.0
1.0
62.0
16.6
11.8
4.6
4.0
0.9
0.7
0.3
$8,082.7

Scenario 3
$10,000/ton
$6,324.9
2,968.6
2,168.2
2,092.7
42.1
503.8
227.6
191.3
1.0
62.4
140.1
20.1
4.6
4.0
0.9
1.5
0.4
$14,754.1
B-22
-------
Table B-4. Total Capital Costs of Controlling Both SO2 and NOX for the Non-EGU
BART Source Categories in 2015—3 Percent Discount Rate (million 1999$)
BART Source Category
Industrial boilers
Petroleum refineries
Kraft pulp mills
Portland cement plants
Hydrofluoric, sulfuric, and nitric
acid plants
Chemical process plants
Iron and steel mills
Coke oven batteries
Sulfur recovery plants
Primary aluminum ore reduction
plants
Lime kilns
Glass fiber processing plants
Municipal incinerators
Coal cleaning plants
Carbon black plants
Phosphate rock processing plants
Secondary metal production
facilities
Total

Scenario 1
$l,000/ton
$1,101.3
164.6
378.4
69.5
27.5
158.9
2.2
0.0
0.2
47.6
12.6
1.9
0.0
0.0
0.02
0.0
0.1
$1,881.70
Scenarios
Scenario 2
$4,000/ton
$3,547.2
1,632.2
880.9
1,268.6
33.3
317.0
173.7
63.9
0.8
47.6
106.9
10.1
3.2
3.3
0.7
1.1
0.3
$8,090.92

Scenario 3
$10,000/ton
$4,549.1
2,999.0
1,299.9
1,844.5
33.3
530.8
240.7
148.0
0.8
47.6
106.9
17.0
3.2
3.3
0.7
1.1
0.3
$12,726.09
B-23
-------
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

Scenario 2
BART Source Category ($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
Limekilns 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 $5,588.6
B-24
-------
Table B-6. Total Annualized Costs of Controlling Both SO2 and NOX for the Scenario 2
Applied to the Non-EGU BART Source Categories in 2015—10 Percent Discount Rate
(million 1999$)

Scenario 2
BART Source Category ($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 $928.29
B-25
-------
Table B-7. Total Annualized and Capital Costs of Controlling Both SO2 and NOX for
Scenario 2— Applied to the BART Non-EGU Source Categories in 2015—7 Percent
Discount Rate—25 Percent Labor Rate Increase (million 1999$)
BART Source Category
Industrial boilers
Petroleum refineries
Kraft pulp mills
Portland cement plants
Hydrofluoric, sulfuric, and nitric acid plants
Chemical process plants
Iron and steel mills
Coke oven batteries
Sulfur recovery plants
Primary aluminum ore reduction plants
Lime kilns
Glass fiber processing plants
Municipal incinerators
Coal cleaning plants
Carbon black plants
Phosphate rock processing plants
Secondary metal production facilities
Total
Annualized Costs
$521.7
180.8
119.1
175.6
23.4
71.5
23.5
18.6
12.2
7.8
5.1
5.3
1.1
1.0
0.2
0.1
0.04
$1,167.13
Capital Costs
$4,074.1
1,221.0
1,012.2
817.6
42.1
392.4
162.9
80.0
1.0
62.0
16.6
11.8
4.6
4.0
0.9
0.7
0.3
$7,906.20
B-26
-------
Table B-8. Total Annualized and Capital Costs of Controlling Both SO2 and NOX for
Scenario 2 Applied to the Non-EGU BART Source Categories in 2015—7 Percent
Discount Rate—25 Percent Labor Rate Decrease (million 1999$)
BART Source Category
Industrial boilers
Petroleum refineries
Kraft pulp mills
Portland cement plants
Hydrofluoric, sulfuric, and nitric acid plants
Chemical process plants
Iron and steel mills
Coke oven batteries
Sulfur recovery plants
Primary aluminum ore reduction plants
Lime kilns
Glass fiber processing plants
Municipal incinerators
Coal cleaning plants
Carbon black plants
Phosphate rock processing plants
Secondary metal production facilities
Total
Annualized Costs
$525.5
179.5
118.9
173.9
23.3
70.1
23.5
18.7
12.1
7.8
5.0
5.3
1.1
1.0
0.2
0.1
0.04
$1,165.83
Capital Costs
$4,128.5
1,221.0
1,012.2
817.6
42.1
392.4
162.9
81.0
1.0
62.0
16.6
11.8
4.6
4.0
0.9
0.7
0.3
$7,960.59
B-27
-------
Table B-9. Total Annualized and Capital Costs of Controlling Both SO2 and NOX for
Scenario 2— Applied to the BART Non-EGU Source Categories in 2015—7 Percent
Discount Rate—25 Percent Energy Price Increase (million 1999$)
BART Source Category
Industrial boilers
Petroleum refineries
Kraft pulp mills
Portland cement plants
Hydrofluoric, sulfuric, and nitric acid plants
Chemical process plants
Iron and steel mills
Coke oven batteries
Sulfur recovery plants
Primary aluminum ore reduction plants
Lime kilns
Glass fiber processing plants
Municipal incinerators
Coal cleaning plants
Carbon black plants
Phosphate rock processing plants
Secondary metal production facilities
Total
Annualized Costs
$522.2
180.7
118.2
179.3
24.2
70.8
23.5
18.7
12.1
7.8
5.0
5.3
1.1
1.0
0.2
0.1
0.04
$1,171.27
Capital Costs
$4,074.2
1,220.0
1,012.2
817.6
42.1
392.4
162.9
81.1
1.0
62.0
16.6
11.8
4.6
4.0
0.9
0.7
0.3
$7,906.20
B-28
-------
Table B-10. Total Annualized and Capital Costs of Controlling Both SO2 and NOX for
Scenario 2 Applied to the BART Non-EGU Source Categories in 2015—7 Percent
Discount Rate—25 Percent Energy Price Increase (million 1999$)
BART Source Category
Industrial boilers
Petroleum refineries
Kraft pulp mills
Portland cement plants
Hydrofluoric, sulfuric, and nitric acid plants
Chemical process plants
Iron and steel mills
Coke oven batteries
Sulfur recovery plants
Primary aluminum ore reduction plants
Lime kilns
Glass fiber processing plants
Municipal incinerators
Coal cleaning plants
Carbon black plants
Phosphate rock processing plants
Secondary metal production facilities
Total
Annualized Costs
$25.0
179.7
118.8
170.2
22.6
70.7
23.4
18.6
12.1
7.8
5.0
5.3
1.1
1.0
0.2
0.1
0.04
$1,161.68
Capital Costs
$4,128.5
1,221.0
1,012.2
817.6
42.1
392.4
162.9
81.1
1.0
62.0
16.6
11.8
4.6
4.0
0.9
0.7
0.3
$7,960.59
B-29
-------
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.I 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.
# = Center Grid-Cell "E"
= Air Pollution Monitor
C-l
-------
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.
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
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:
J_
—
14
0 27
~ u-z/ •
C-2
-------
10 miles * " # •• * 15 miles
= Air Pollution Monitor
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, p, 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 p and therefore more reliable estimates of the incidence change predicted using
P. 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 p provides a second-best but still valuable way to synthesize information
(DerSimonian and Laird, 1986). This is referred to as pooling. Pooling PS requires that all
of the studies contributing estimates of p use the same functional form for the C-R function.
That is, the PS 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 PS themselves. For a variety of
reasons, this is often possible when it is not feasible to pool the underlying PS. For example,

C-3
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if one study is log-linear and another is linear, we could not pool the PS 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-4
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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 PS), 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 PS.

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 p that applies everywhere. Differences among
PS reported by different studies are therefore simply the result of sampling error. That is,
each reported p 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 P4 with variance
Vi(I=l,...,n). Let
denote the sum of the inverse variances. Then the weight, wi? given to the ith estimate, P4, 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-5
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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,

P/e = E "i * P, •

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-l. Example of Fixed Effects Model Calculations
Study
1
2
3
Sum
Pi
0.75
1.25
1.00

Vi
0.1225
0.0025
0.0100

1/v,
8.16
400
100
£ = 508.16
Wj
0.016
0.787
0.197
£ = 1.000
w,*P,
0.012
0.984
0.197
£=1.193
The sum of weighted contributions in the last column is the pooled estimate of p
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 pt and v; are not shown, because they are of no importance. The
sum of the 1/Vj is S, used to calculate the weights. The sum of the weights, w4, i=l,..., n, is
1.0, as expected).
C-6
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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 P4 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
P in Philadelphia to be greater than the true value of p 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
vl
Under the null hypothesis that there is a single underlying parameter, p, of which all the PJS
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 p. (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, r|2, is
C-7
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•S^W V

Xl/vt ~ ^
It can be shown that the denominator is always positive. Therefore, if the numerator
is negative (i.e., if Qw < n-1), then r\2 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 r\2 is
positive.

Given a value for r\2, the random effects estimate is calculated in almost the same
way as the fixed effects estimate. However, the weights now incorporate both the within-
study variance (Vj) and the between-study variance (r|2). Whereas the weights implied by the
fixed effects model used only v;, the within-study variance, the weights implied by the
random effects model use v; +r|2.
Let v4* = Vi +Ti2. Then
o* _ 1
S - E — »
and
5*
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-8
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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:
rand
1
S 1/v/
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 p. 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 PS 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
PS 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 PS. 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
C-9
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change for the ith C-R function for P4 and the variance of incidence change for the ith C-R
function for Vj.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 PS 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 PS. 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 P4 and the variance of dollar benefits
for the ith valuation for v^

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
7(3): 177-188.
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
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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
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-l
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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.I. 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.I 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").
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).

3We 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
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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:
v—\ v—\ v—\
11 — ( ~Y P 4- fff' P 4- > V f)P 4- 7 7 f\
U ni ~ \A T U^n T Z_i iik^-ik T Z_( Z_( ^j*

6>> 0, r.t > 0, Vi,k, Slk > 0, Vj,k, p
-------
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 n111 residential area and the ith region is therefore
Vm(m,Zn,Q-0,y,S,p)=(mf
.11 p
where Q denotes the vector of vectors, Q1? Q2, Q3, Q4, Q5, and Q6, and the unsubscripted y
and d denote vectors as well.

Given estimates of p, 6, the y's and the 6'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:
WTPni(AZ,AQ)=m- [nf + 9(Zp0n - ZfJ + ^MQS* ~ QS*)+ 1 1
k=l ;'*]' k=l
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 in-
region park, from Qa = Q0il to Qa = Qm, 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

D-4
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(improved) level of visibility, Q1? is therefore a concave function of Qx, 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:

dV
and the environmental "good," Q, is a function of B,

Q=F(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
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
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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

DV= 10*ln(-),
where DV denotes deciview and VR denotes visual range (in kilometers). Solving for VR as
a function of DV yields
-0.1DV
VR=391*e
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 y's and 8'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, p, and the
corresponding recreational visibility parameter. For example, a Southeast household's WTP
for a change in visibility at in-region parks (collectively) from Qx = Q01 to Qx = Qn is:
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
e=i- ^pu.
where a denotes the mortality rate (or level) when there is no ambient PM (i.e., when PM=0). However, a 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), a 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 a 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
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WTP(DQ1 )= m- [mr + gl(Qr01 - Qr
-,1/r
n
if this is the first (or only) visibility change the household values.

Alternatively, if we have estimates of m as well as WTPj™ and WTPj0"1 of in-region
and out-of-region households, respectively, for a given change in visibility from Q01 to Qn in
Southeast parks, we can solve for YI and 6X as a function of our estimates of m, WTP/" and
WTPj0"1, for any given value of p. Generalizing, we can derive the values of y and 6 for the
jth region as follows:
Jl=
- nf
and
(m-WTPj01" )p -mp
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-of-
region 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-l. In all cases, WTP refers to WTP per household.

D-7
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Table D-l. Available Information on WTP for Visibility Improvements in National
Parks

Region of Household
Region of Park In Region" 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-8
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Region of
Household
Region 1
Region 2
Region 3
Region 4
Region 5
Region 6
Region of Park
Region 1
Yia
81
81
81
81
6,
Region 2 Region 3
62 63
Y2 83
82 Ys
82 83
82 83
62 6,
Region 4 Region 5 Region 6
64 65 66
64 6= 6f:
64 65 66
Y4 ^5 ^6
S4 Ys S6
S4 S-; Y6
The parameters arrayed in this table are region specific rather than park specific or wilderness area specific.
For example, 6j 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., 6lk,
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 (yx); similarly, we can directly use the out-of-region WTP estimates from
the study to estimate the parameters for out-of-region parks (6J. 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-9
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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 p 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)—YI> Y2> and Y3 and 81? 82 and 83.
(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)— y4, ys, and y6 and 84, 85, and 86

(3) We derive park- and wilderness area-specific parameters within each region (ylk
and 8lk, for k=l,..., Nx; y2k and 82k, f°r k=l,..., 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 area-
specific 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 p and estimates of m and in-region and out-of-region WTPs for a
change from Q0 to Qx in a given region, the in-region parameter, y, and the out-of-region
parameter, 8, 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 y and a different calibrated 8, even though in theory all the y's should be
D-10
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the same and similarly, all the 6's should be the same. For each region, however, we must
have only a single y and a single 6.

Denoting f j as our estimate of y for the jth region, based on all three visibility
changes, we chose fj to best predict the three WTPs observed in the study for the three
visibility improvements in the j111 region. First, we calculated f Jt, i=l, 2, 3, corresponding to
each of the three visibility improvements considered in the study. Then, using a grid search
method beginning at the average of the three y Jt 's, we chose fj to minimize the sum of the
squared differences between the WTPs we predict using fj and the three region-specific
WTPs observed in the study. That is, we selected fj to minimize:
where WTP^ and WTP^ fj) are the observed and the predicted WTPs for a change in
visibility in the f1 region from Q0 = Q0i to Qx= QH, i=l,..., 3. An analogous procedure was
used to select an optimal 6, 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) (y4, Ys> and Ye and S4, $5, and 66) 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 64 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 Qn at national

D-ll
-------
parks in California (region 1), denoted WTPj0"1. Suppose the mean income of the out-of-
region subjects in the study was m. We might assume that, for the same change in visibility
at national parks in the Northwest United States, WTP4out = WTPj0"1 among out-of-region
individuals with income m.

We could then derive the value of 64, given a value of p as follows:

(m- WTP°utY - nf
°*~ np - p
where Q04 = Q01 and Q14 = Qn, (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-of-
region 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 64, then, might be to assume the following relationship:
7 We acknowledge that reliance on visitation rates does not get at nonuse value.

D-12
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WTP™
WTP°
(income held constant), where nx = 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

H4
°ut = * WTPout
for the same change in visibility in region 4 parks among out-of-region individuals with
income m. If, for example, nt = 2n4, WTP4out would be half of WTPj0"1. 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 64 is estimated as shown above, using this adjusted WTP4out. The same
procedure is used to estimate 65 and 66 We estimate y4 YS> and Yem 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 Y'S and 8's are
therefore the parameters that would be in household utility functions if there were only a
D-13
-------
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 WTPfor 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 y's and
6'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.
D-14
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D.3.5 Derivation of Park- and Wilderness Area-Specific WTPs, Given Region-Specific
WTPsfor 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 area-
specific 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 WTPj"1
and WTPj0"1 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, nlk denotes the
annual average number of visitor-days to the kth Class I area in California, and nx 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

; = — * WTP™ ,
and
n
'Ik
WTP™1 = — *WTP;
Using WTP/n and WTPj°ut, 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 (WTPjin) or out
of that region (but in the United States) (WTPj°ut) for visibility improvements at these parks.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
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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 park-
and wilderness area-specific y's and 6's using the method used to derive region-specific y's
and 6's. Recall that method involved (1) calibrating y and 6 to each of the three visibility
improvements in the Chestnut and Rowe study (producing three y's and three 6's),
(2) averaging the three y's and averaging the three 6's, and finally, (3) using these average y
and 6 as starting points for a grid search to find the optimal y and the optimal 6—i.e., the y
and 6 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 y's and 6's produced using the above
approach. If:

WTPjm = the in-region WTP for the change in visibility from Q0 to Qx in the j"1
region;

WTPjkm= the in-region WTP for the same visibility change (from Q0 to Qx) in the
k"1 Class I area in the jth region (= sjk*WTPjin, where sjk is the k"1 area's
share of visitor-days in the jth region);

m = income;

Yj* = the optimal value of y for the jth region; and
of their parks is a good index of their relative WTPs for visibility improvements at these parks.

D-16
-------
Yjk = the value of yjk calibrated to WTPjkm and the change from Q0 to

then9:

, (m- WTP/" f - nf
(OS -OS)
and
_ (m- WTP™f-m»
7ik = (QS-Qf)
which implies that:
where:

ajt = Tm- WTP/" f - mp '

We use the adjustment factor, ajk, to derive yjk from YJ*, for the k"1 Class I area in the f1
region. We use an analogous procedure to derive 6jk from 6j* for the kth Class I area in the jth
region (where, in this case, we use WTP°ut and WTPjkout instead of WTP/n and WTPjkin).10
9 YJ* 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, YJ* 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 a]k's) and therefore the resulting Y]k's and 8jk'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 Y]IC'S and 8jk's, which were different from each other by about one one-
hundredth of a percent.

D-17
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D.4 Estimating the Parameter for Visibility in Residential Areas: 6
The estimate of 6 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 6 was therefore a much simpler
procedure, involving a straightforward calibration to the single income and WTP in the
study:

_ (m- WTPY - mp
D.5 Putting it All Together: The Household Utility and WTP Functions

Given an estimate of 6, derived as shown in Section D.4, and estimates of the y's and
6's, derived as shown in Section D.3, based on an assumed or estimated value of p, 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:
N, Nj
WTPm(kZ,kQ) = m-[mp + 0(Z?n - Z/J+ £ Tlk( Qp0lk - Qplk) + 11 8jk(QSjk - Qf]k )]1/p .
k=l j#i k=l

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-18
-------
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 ofanAWMA/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.
D-19
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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 ECU 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 ECU 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 ECU 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 ECU 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 ECU 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 Ib/mmbtu
SO, emission rate limit. It also assumes that all BART units greater than 25 MW are
'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).

2Note 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-l
-------
required to meet an emission rate limit of 0.2 Ibs/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-l 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 ECU
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-2
-------
Table E-l. Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air
Interstate Rule, 2015 (billions of 1999 dollars)3

CAIR Plus BART
Description CAIR Programg in the Non-CAIR Regiong BART Nationwide

Private costs" $3.57 $4.55 $5.19

Social benefits04*
3 percent discount rate $101+B $105 + B $49.5+B
7 percent discount rate $86.3 + B $89.5 + B $42.2 + B

Health-related benefits:
3 percent discount rate 99.3 103 48.8
7 percent discount rate 84.5 87.6 41.5

Visibility benefits 1.78 1.89 0.699

Net benefits (benefits-costs)0''
3 percent discount rate $97.4 + B $100 + B $44.3 + B
7 percent discount rate $82.7 + B $84.9+ 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.
8 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-3
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Table E-2. Clean Air Interstate Rule: Estimated Reduction in Health Effects
(Incidence)—2015
Health Effect
CAIR
Program1
CAIR Plus
BART in the
Non-CAIR
Region1
BART
Nation-
wide
PM-Related Endpoints:
Premature mortality13
Adult, age 30 and over
Infant, age18)d
Emergency room visits for asthma (age 18 years and
younger)
Acute bronchitis (children, aged 8-12)
Lower respiratory symptoms (children, aged 7-14)
Upper respiratory symptoms (asthmatic children, aged 9-18)
Asthma exacerbation (asthmatic children, aged 6-18)
Work loss days (adults, aged 18-65)
Minor restricted-activity days (adults, aged 18-65)
17,000
36
8,700
22,000
5,500
5,000
13,000
19,000
230,000
180,000
290,000
1,700,000
9,900,000
17,000
38
9,100
23,000
5,700
5,200
13,000
20,000
240,000
190,000
310,000
1,700,000
10,300,000
8,200
19
4,400
11,000
2,700
2,500
6,500
10,000
120,000
92,000
150,000
830,000
5,000,000
Ozone-Related Endpoints e
Hospital admissions—respiratory causes (adult, 65 and older) 1,700
Hospital admissions—respiratory causes (children, under 2) 1,100
Emergency room visit for asthma (all ages) 280
Minor restricted-activity days (adults, aged 18-65) 690,000
School absence days 510,000
NE
NE
NE
NE
NE
NE
NE
NE
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.
c 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-4
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Table E-3. Estimated Monetary Value in Reductions in Incidence of Health and
Welfare Effects (in millions of 1999$)—2015abc
Health Effect
Premature mortality11
Adult >30 years
3% discount rate
7% discount rate
Child <1 year
Chronic bronchitis (adults, 26 and over)
Nonfatal acute myocardial infarctions
3% discount rate
7% discount rate
Hospital admissions for respiratory causes
Hospital admissions for cardiovascular causes
Emergency room visits for asthma
Acute bronchitis (children, aged 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthma, 9-11)
Asthma exacerbations
Work loss days
Minor restricted-activity days (MRADs)
School absence days
Worker productivity (outdoor workers, 18-65)
Recreational visibility, 8 1 Class I areas
Pollutant

PM2.5

PM2 5, O3
PM2.5
PM2 5, O3
PM2.5
PM2.5
PM2.5
PM2.5
PM2.5
PM2 5, O3
03
03
PM2.5
CAIR
Program1

$92,800
78,100
222
3,340

1,850
1,790
78.9
105
3.56
7.06
3.74
4.77
12.7
219
543
36.4
19.9
1,780
CAIR Plus BART
in the Non-CAIR
Region1

$96,300
81,100
232
3,510

1,920
1,860
43.6
82.6
3.62
7.41
3.87
4.69
13.4
209
528
NE
NE
1,890
BART
Nationwide

$45,700
38,400
116
1,690

905
876
20.6
39.3
1.79
3.68
1.91
2.30
6.56
101
256
NE
NE
699
Monetized Totale
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.
0 Ozone benefits are estimated for the final CAIR. While ozone benefits are anticipated for CAIR plus BART in the Non-
CAIR Region and BART nationwide, these ozone benefits were not estimated.
d Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure described earlier. Results
show 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses
(EPA, 2000; OMB, 2003). Adult premature mortality estimates are based 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-5
-------
E.I References
Pope, C.A., HI, R.T. Burnett, MJ. 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. Grille, 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.
E-6
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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.I 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-l.
F-l
-------
Table F-l. Sensitivity of Benefits of Premature Mortality Reductions to Alternative Assumptions (Relative to Primary
Estimate Benefits of the Final CA VR)

Description of Sensitivity Analysis
Alternative Concentration-Response Functions for PM-Related
Pope/ACS Study (2002)c
Lung Cancer
Cardiopulmonary
Krewski/Harvard Six-Cities Study
Alternative Lag Structures for PM-Related Premature Mortality
None Incidences all occur in the first year
8-year Incidences all occur in the 8th year
3% discount rate
7% discount rate
15-year Incidences all occur in the 15th year
3% discount rate
7% discount rate
Alternative 20 percent of incidences occur in 1st year, 50
Segmented percent in years 2 to 5, and 30 percent in years 6
20
3% discount rate
7% discount rate
Avoided
Scenario 1
Premature Mortality

310
65
930

400

400
400

400
400
Incidences in 2015a
Scenario 2

1,200
250
3,700

1,600

1,600
1,600

1,600
1,600
Scenario 3

1,700
360
5,200

2,300

2,300
2,300

2,300
2,300
Value (million 1999$)"
Scenario 1

$1,800
$370
$5,300

$2,500

$2,100
$1,600

$1,700
$1,000

$2,200
$1,800
Scenario 2

$7,000
$1,500
$21,000

$10,000

$8,300
$6,300

$6,700
$3,900

$8,900
$7,100
Scenario 3

$9,900
$2,000
$30,000

$15,000

$12,000
$9,100

$9,700
$5,700

$13,000
$10,000
(continued)
-------
Table F-l. Sensitivity of Benefits of Premature Mortality Reductions to Alternative Assumptions (Relative to Primary
Estimate Benefits of the Final CAVR) (continued)
Avoided Incidences in 2015a

5-Year
Distributed

Exponential

Description of Sensitivity Analysis
50 percent of incidences occur in years 1 and 2
and 50 percent in years 2 to 5
3% discount rate
7% discount rate
Incidences occur at an exponentially declining rate
following year of change in exposure
3% discount rate
7% discount rate
Scenario 1

400
400

400
400
Scenario 2

1,600
1,600

1,600
1,600
Scenario 3

2,300
2,300

2,300
2,300
Value (million 1999$)"
Scenario 1

$2,400
$2,300

$2,400
$2,200
Scenario 2

$9,700
$9,100

$9,700
$8,900
Scenario 3

$14,000
$13,000

$14,000
$13,000
Alternative Thresholds

No Threshold (base estimate)
5 ug/m3
10 ug/m3
15 ug/m3
20 ue/m3
400
400
230
0
0
1,600
1,600
1,100
130
54
2,300
2,200
1,500
170
70
$2,300
$2,300
$1,300
$0
$0
$9,200
$9,200
$6,300
$750
$310
$13,000
$13,000
$8,600
$980
$400
Incidences rounded to two significant digits.

Dollar values rounded to two significant digits.

Note that the sum of lung cancer and cardiopulmonary deaths will not be equal to the total all-cause death estimate. Some residual mortality is associated
with long-term exposures to PM25 that is not captured by the cardiopulmonary and lung cancer categories.
-------
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-ADV-
04-002, 2004, p. 18). As such, in Table F-l we provide the estimates of cardiopulmonary
and lung cancer deaths based on the Pope et al. (2002) study.

In addition, the SAB-HES noted that the ACS cohort used in Pope et al. (2002) "has
some inherent deficiencies, in particular the imprecise exposure data, and the
nonrepresentative (albeit very large) population" (EPA-SAB-COUNCIL-ADV-04-002, 2004,
p. 18). The SAB-HES suggests that while not necessarily a better study, the ACS is a
prudent choice for the primary estimate. They go on to note that "the Harvard Six-Cities C-
R functions are valid estimates on a more representative, although geographically selected,
population, and its updated analysis has not yet been published. The Six-Cities estimates
may be used in a sensitivity analysis to demonstrate that, with different but also plausible
selection criteria for C-R functions, benefits may be considerably larger than suggested by
the ACS study" (EPA-SAB-COUNCIL-ADV-04-002, 2004, p. 18). In previous advice, the
SAB has noted that "the [Harvard Six-Cities] study had better monitoring with less
measurement error than did most other studies" (EPA-SAB-COUNCIL-ADV-99-012, 1999).
The demographics of the ACS study population (i.e., largely white and middle to upper
middle-class) may also produce a downward bias in the estimated PM mortality coefficient,
because a variety of analyses indicate that the effects of PM tend to be significantly greater
among groups of lower socioeconomic status (Krewski et al., 2000), although the cause of
this difference has not been identified. The Harvard Six-Cities study also covered a broader
age category (25 and older compared to 30 and older in the ACS study). We emphasize that,
based on our understanding of the relative merits of the two datasets, the Pope et al. (2002)
ACS model based on mean PM2 5 levels in 63 cities is the most appropriate model for
analyzing the premature mortality impacts of CAVR. Thus it is used for our primary
estimate of this important health effect.
F-4
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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 SAB-
HES 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-ADV-
04-002, 2004). The SAB-HES indicated support for using "a Weibull distribution or a

F-5
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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" (EPA-
SAB-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" (EPA-
SAB-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 PM25 (EPA-COUNCIL-LTR-05-001, 2004). The distribution of deaths over
the latency period is intended to reflect the contribution of short-term exposures in the first
year, cardiopulmonary deaths in the 2- to 5-year period, and long-term lung disease and lung
cancer in the 6- to 20-year period. Furthermore, in their advisory letter, the SAB-HES
recommended that EPA include sensitivity analyses on other possible lag structures. In this
appendix, we investigate the sensitivity of premature mortality-reduction related benefits to
alternative cessation lag structures, noting that ongoing and future research may result in
changes to the lag structure used for the primary analysis.

In previous advice from the SAB-HES, they recommended an analysis of 0-, 8-, and
15-year lags, as well as variations on the proportions of mortality allocated to each segment
in the segmented lag structure (EPA-SAB-COUNCIL-ADV-00-001, 1999; EPA-COUNCIL-
LTR-05-001, 2004). The 0-year lag is representative of EPA's assumption in previous RIAs.
The 8- and 15-year lags are based on the study periods from the Pope et al. (1995) and
Dockery et al. (1993) studies, respectively.1 However, neither the Pope et al. nor Dockery et
al. studies assumed any lag structure when estimating the relative risks from PM exposure.
In fact, the Pope et al. and Dockery et al. analyses do not supporting or refute the existence
of a lag. Therefore, any lag structure applied to the avoided incidences estimated from either
of these studies will be an assumed structure. The 8- and 15-year lags implicitly assume that
all premature mortalities occur at the end of the study periods (i.e., at 8 and 15 years).
'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
-------
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 Roosli et al. (2004),
which estimates the percentage of total mortality impact in each period t as
% Mortality Reduction (t) = — - - - ,p
'
t=\
The Roosli 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-l. 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.
F-7
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F.I.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 PM25 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 PM25
standard of 15 i-ig/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 jig, 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
2The 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."

3The 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
-------
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
4The 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
-------
occurring above 10 i-ig/m3, and around 8 percent are due to changes above 15 i-ig/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
Impact on Primary Benefit
Estimate (million 2000$)

2
3
Alternative
Calculation
Treatment of
reversals in
CB

Value of
visibility
changes in all
Class I areas
Household
soiling
damage
Description of Estimate
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%).
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.
Value of decreases in expenditures on cleaning
are estimated using values derived from
Manuel et al. (1983).
Least
Stringent
+$34

+$54
+$8
Most
Expected Stringent
+$130 +$190

+$120 +$200
+$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 long-
lasting 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-10
-------
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 C AIR 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-ll
-------
Table F-3. Ranges of Elasticity Values Used to Account for Projected Real Income
Growth3
Benefit Category
Lower Sensitivity Bound
Upper Sensitivity Bound
Minor Health Effect

Severe and Chronic Health Effects

Premature Mortality

Visibility13
0.04

0.25

0.08
0.30

0.60

1.00
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
Growth3
Benefit Category
Lower Sensitivity Bound
Upper Sensitivity Bound
Minor Health Effect

Severe and Chronic Health Effects

Premature Mortality

Visibility13
1.015

1.094

1.029
1.114

1.241

1.437
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-12
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Table F-5. Sensitivity Analysis of Alternative Income Elasticities3
Benefits in Millions of 1999$

Benefit Category
Minor Health
Effect
Severe and
Chronic Health
Effects
Premature
Mortality0
Visibility13
Total Benefits0
Lower
Least
Stringent
$20

$140

$2,100

$84
$2,300
Sensitivity

Expected
$78

$530

$8,200

$240
$9,100
Bound
Most
Stringent
$110

$750

$12,000

$420
$13,000
Upper
Least
Stringent
$21

$150

$2,900

$84
$3,200
Sensitivity

Expected
$83

$580

$11,000

$240
$12,000
Bound
Most
Stringent
$120

$810

$16,000

$420
$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-13
-------
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, E.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 Paniculate Air Pollution and
Mortality. Special Report to the Health Effects Institute, Cambridge MA.

F-14
-------
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. IE, 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., HI, MJ. 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.

Roosli, M., N. Kiinzli, and C. Braun-Fahrlander. 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.
F-15
-------
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 non-
EGU 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.I 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 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.

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-l 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-l
-------
Table G-l. SO2 Emissions and Emission Reductions for BART Source Categories in 2015
Scenarios — Reductions (tons)
BART Source Category
Industrial boilers

Petroleum refineries

Kraft pulp mills

Portland cement plants

Hydrofluoric, sulfuric, and
nitric acid plants
Chemical process plants

Iron and steel mills

Coke oven batteries

Sulfur recovery plants

Primary aluminum ore
reduction plants
Lime kilns

Baseline
Emissions (tons)
420,782
420,782
199,483
199,483
119,818
119,818
116,835
116,835
96,741
96,741
47,700
47,700
23,541
23,541
9,815
9,815
59,766
59,766
47,552
47,552
9,373
9,373
Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
87,009
120,095
23,173
25,103
0
0
2,350
2,743
35,358
36,753
2,376
2,376
2,914
2,914
4,088
4,088
13,697
14,311
1,630
1,630
0
0
$3,000/ton
Scenario
124,592
148,962
23,173
33,304
0
3,196
2,350
13,383
36,753
36,753
2,376
2,376
2,914
2,914
4,088
4,088
13,697
14,311
1,630
3,260
0
0
(continued)
G-2
-------
Table G-l. SO2 Emissions and Emission Reductions for BART Source Categories in 2015
(continued)
Scenarios — Reductions (tons)
BART Source Category
Glass fiber processing plants

Municipal incinerators

Coal cleaning plants

Carbon black plants

Phosphate rock processing
plants
Secondary metal production
facilities
Total

Baseline
Emissions (tons)
2,170
2,170
284
284
1,530
1,530
41,853
41,853
21
21
9,988
9,988
1,208,088
1,208,088
Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
0
0
0
0
0
0
0
0
0
0
0
0
172,595
210,013
$3,000/ton
Scenario
0
0
0
0
0
0
0
0
0
0
0
0
211,573
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-3
-------
Table G-2. NOX Emissions and Emission Reductions for BART Source Categories in 2015
Scenarios
Baseline
BART Source Category Emissions (tons)
Industrial boilers

Petroleum refineries

Kraft pulp mills

Portland cement plants

Hydrofluoric, sulfuric, and nitric
acid plants
Chemical process plants

Iron and steel mills

Coke oven batteries

Sulfur recovery plants

Primary aluminum ore reduction
plants
Lime kilns

217,063
217,063
86,566
86,566
103,614
103,614
120,567
120,567
17,059
17,059
72,577
72,577
20,963
20,963
10,389
10,389
651
651
1,676
1,676
12,849
12,849
Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
97,074
120,151
23,173
23,173
50,221
56,466
26,659
26,659
11,283
11,283
25,922
27,568
2,034
2,038
0
5,768
0
0
70
335
4,471
4,471
$3,000/ton
Scenario
125,575
128,640
23,173
26,685
60,985
64,521
26,659
26,659
11,283
11,283
26,753
31,567
3,259
7,198
5,768
5,768
0
0
253
335
4,471
7,153
(continued)
G-4
-------
Table G-2. NOX Emissions and Emission Reductions for BART Source Categories in 2015
(continued)
Scenarios
Baseline
BART Source Category Emissions (tons)
Glass fiber processing plants

Municipal incinerators

Coal cleaning plants

Carbon black plants

Phosphate rock processing plants

Secondary metal production
facilities
Total

6,677
6,677
1,656
1,656
1,110
1,110
4,645
4,645
719
719
1,377
1,377
681,765
681,765
Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
568
851
744
744
0
511
111
111
0
0
25
34
242,355
280,163
$3,000/ton
Scenario
2,116
2,116
744
744
511
511
120
120
45
48
25
34
291,740
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-5
-------
Table G-3. Total Annualized Costs of Control for BART Source Categories in 2015
(million 1999$)
BART Source Category
Industrial boilers

Petroleum refineries

Kraft pulp mills

Portland cement plants

Hydrofluoric, sulfuric, and nitric acid plants

Chemical process plants

Iron and Steel mills

Coke oven batteries

Sulfur recovery plants

Primary aluminum ore reduction plants

Lime kilns

Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
Scenarios
$2,000/ton
Scenario
241.5
255.0
71.1
71.1
75.1
59.2
29.6
28.7
20.4
21.4
40.5
30.4
7.9
5.7
6.2
14.9
11.7
12.1
1.7
1.0
5.0
4.3

$3,000/ton
Scenario
412.1
337.4
71.1
81.2
75.1
68.5
29.6
56.6
21.4
21.4
40.5
40.1
11.0
22.7
18.7
14.9
12.1
12.1
2.2
5.0
5.0
25.4
(continued)
G-6
-------
Table G-3. Total Annualized Costs of Control for BART Source Categories in 2015
(million 1999$) (continued)
Scenarios
BART Source Category
Glass fiber processing plants

Municipal incinerators

Coal cleaning plants

Carbon black plants

Phosphate rock processing plants

Secondary metal production facilities

Total

Discount Rate
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
$2,000/ton
Scenario
0.5
1.7
1.1
0.9
0.0
0.8
0.01
0.005
0.01
0.01
0.04
0.01
$512.36
$507.23
$3,000/ton
Scenario
5.3
4.7
1.1
0.9
1.0
0.8
0.01
0.01
0.01
0.01
0.04
0.01
$706.26
$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-7
-------
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 Boilers3
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
420,782
420,782
420,782
420,782
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
333,773
300,687
296,190
271,820
2015 Emission
Reductions
87,009
120,095
124,592
148,962
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
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
217,063
217,063
217,063
217,063
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
119,989
96,912
91,488
88,423
2015 Emission
Reductions
97,074
120,151
125,575
128,640
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source
categories, both controlled and uncontrolled.
G-8
-------
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 BART-
Eligible Industrial Boilers
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

Discount
Rate
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$109.3
$133.4
$196.8
$208.3
Annualized Average
Cost-Effectiveness
($/ton)
$1,256
$1,111
$1,580
$1,398
Marginal Costs
($/ton)
—
—
$2,328
$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 coal-
fired 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-9
-------
Table G-7. 2015 Cost and Cost-Effectiveness Results for NOX Control at BART-Eligible
Industrial Boilers

Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

Discount
Rate
7%
3%
7%
3%
Total Annualized
Costs (million
1999$)
$132.2
$121.6
$215.3
$129.1

Annualized Average
Cost-Effectiveness ($/ton)
$1,360
$1,012
$1,715
$1,044

Marginal Costs
($/ton)
—
—
$2,929
$2,684
Table G-8 shows the total annualized costs for each scenario for controlling both SO2 and
NOV.
Table G-8. 2015 Cost Results for SO2 and NOX Control at BART-Eligible Industrial
Boilers
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs (million
$241.5
$255.0
$412.1
$337.4
1999$)

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-10
-------
Table G-9. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU
BART-Eligible Units at Petroleum Refineries3
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
199,483
199,483
199,483
199,483
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
176,310
174,380
176,310
166,179
2015 Emission
Reductions
23,173
25,103
23,173
33,304
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 Refineries3
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
86,566

86,566

Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
64,512
64,512
64,512
59,881
2015 Emission
Reductions
23,173
23,173
23,173
26,685
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source
categories, both controlled and uncontrolled.
Table G-ll 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-ll
-------
Table G-ll. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU
BART-Eligible Units at Petroleum Refineries
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

Discount Total Annualized
Rate Costs (million 1999$)
7%
3%
7%
3%
$28.5
$28.5
$28.5
$43.9
Annualized Average
Cost-Effectiveness
($/ton)
$1,231
$1,045
$1,231
$1,479
Marginal Costs
($/ton)
—
—
$0
$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-12
-------
Table G-12. 2015 Cost and Cost-Effectiveness Results for NOX Control at Non-EGU
BART-Eligible Units at Petroleum Refineries
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

Discount
Rate
7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$42.6
$42.6
$42.6
$52.7
Annualized Average
Cost-Effectiveness ($/ton)
$1,930
$1,930
$1,930
$1,975
Marginal Costs
($/ton)
—
—
$0
$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
$2,000/ton Scenario

$3,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs
(million 1999$)
$71.1
$71.1
$71.1
$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-13
-------
Table G-14. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU
BART-Eligible Units at Kraft Pulp Mills3

Scenarios
,000/ton Scenario

,000/ton Scenario

2015 Baseline
Emissions
119,818
119,818
119,818
119,818

Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
119,818
119,818
119,818
116,820
2015 Emission
Reductions
0
0
0
3,196
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 Mills3
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 103,614
103,614
$3,000/ton Scenario 103,614
103,614
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
53,393
47,148
42,629
36,093
2015 Emission
Reductions
50,221
56,466
60,985
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-14
-------
Table G-16. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU
BART-Eligible Units at Kraft Pulp Mills
Discount
Scenarios Rate
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
Total Annualized Costs
(million 1999$)
$0.0
$0.0
$0.0
$7.0
Annualized Average
Cost-Effectiveness
($/ton)
$0
$0
$0
$2,189
Marginal Costs
($/ton)
—
—
—
$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-15
-------
Table G-17. 2015 Cost and Cost-Effectiveness Results for NOX Control at Non-EGU
BART-Eligible Units at Kraft Pulp Mills
Discount
Scenarios Rate
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
Total Annualized
Costs (million
1999$)
$75.1
$59.2
$106.7
$61.5
Annualized Average
Cost-Effectiveness
($/ton)
$1,496
$1,048
$1,749
$1,069
Marginal Cost
($/ton)
—
—
$3,344
$4,452
Table G-18. 2015 Cost Results for SO2 and NOX Control at Non-EGU BART-Eligible Units
at Kraft Pulp Mills
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs (million
$75.1
$59.2
$75.1
$68.5
1999$)

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-16
-------
Table G-19. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU
BART-Eligible Units at Portland Cement Plants3
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
116,835
116,835
116,835
116,835
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
114,485
114,092
114,485
103,452
2015 Emission
Reductions
2,350
2,743
2,350
13,383
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 Plants3
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 120,567
120,567
$3,000/ton Scenario 120,567
120,567
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
93,908
93,908
93,908
93,908
2015 Emission
Reductions
26,659
26,659
26,659
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 cost-
effectiveness 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-17
-------
Table G-21. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU
BART-Eligible Units at Portland Cement Plants
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

Discount
Rate
7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$4.6
$3.7
$4.6
$31.6
Annualized Average
Cost-Effectiveness
($/ton)
$1,973
$1,344
$1,973
$2,362
Marginal Costs
($/ton)
—
—
—
$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
$2,000/ton
Scenario

$3,000/ton
Scenario

Discount
Rate
7%

3%
7%

3%
Total Annualized
Costs (million 1999$)
$25.0

$25.0
$25.0

$25.0
Annualized Average Cost-
Effectiveness ($/ton)
$937

$937
$937

$937
Marginal Cost
($/ton)
—

—

—
G-18
-------
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
$2,000/ton Scenario

$3,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs (million
$29.6
$28.7
$29.6
$56.6
1999$)

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-19
-------
Table G-24. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU
BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants3
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
96,741
96,741
96,741
96,741
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
61,383
60,188
60,188
60,188
2015 Emission
Reductions
35,358
36,753
36,753
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 Plants3
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
17,059
17,059
17,059
17,059
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
5,776
5,776
5,776
5,776
2015 Emission
Reductions
11,283
11,283
11,283
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
Discount
Scenarios Rate
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
Total Annualized Costs
(million 1999$)
$12.2
$14.1
$14.1
$14.1
Annualized Average
Cost-Effectiveness
($/ton)
$345
$385
$385
$385
Marginal Costs
($/ton)
—
—
$1,362

G-20
-------
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
Total Annualized
Scenarios Discount Rate Costs (million 1999$)
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
$8.2
$7.3
$8.2
$7.3
Annualized Average
Cost-Effectiveness Marginal Costs
($/ton) ($/ton)
$728 —
$647 —
$728 —
$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-21
-------
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
$2,000/ton Scenario

$3,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs (million
$20.4
$21.4
$21.4
$21.4
1999$)

Table G-29. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU
BART-Eligible Units at Chemical Process Plants3
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
47,700
47,700
47,700
47,700
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
45,324
45,324
45,324
45,324
2015 Emission
Reductions
2,376
2,376
2,376
2,376
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-22
-------
Table G-30. 2015 NOX Baseline Emissions and Emission Reductions (in tons) for Non-EGU
BART-Eligible Units at Chemical Process Plants3
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
72,577
72,577
72,577
72,577
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
46,655
45,009
45,824
41,010
2015 Emission
Reductions
25,922
27,568
26,753
31,567
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
Discount
Scenarios Rate
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
Total Annualized
Costs (million
1999$)
$2.5
$2.4
$2.5
$2.4
Annualized Average
Cost-Effectiveness
($/ton)
$1,052
$1,013
$1,052
$1,013
Marginal Costs
($/ton)
—
—
$1,052
$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 $4,348 per ton with costs at a 3 percent discount rate.
G-23
-------
Table G-32. 2015 Cost and Cost-Effectiveness Results for NOX Control at Non-EGU
BART-Eligible Units at Chemical Process Plants
Total Annualized Costs
Scenarios Discount Rate (million 1999$)
$2,000/ton Scenario

$3,000/ton Scenario

7%
3%
7%
3%
$38.0
$28.0
$38.0
$37.7
Annualized Average
Cost-Effectiveness
($/ton)
$1,466
$1,015
$1,466
$1,192
Marginal Costs
($/ton)
—
—
—
$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
$2,000/ton Scenario

$3,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs
$40.5
$30.4
$40.5
$40.1
(million 1999$)

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-24
-------
Table G-34. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU
BART-Eligible Units at Iron and Steel Mills3
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 23,541
23,541
$3,000/ton Scenario 23,541
23,541
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
20,627
20,627
20,627
20,627
2015 Emission
Reductions
2,914
2,914
2,914
2,914
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 Mills3
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
20,963
20,963
20,963
20,963
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
18,929
18,925
17,704
13,765
2015 Emission
Reductions
2,034
2,038
3,259
7,198
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-25
-------
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
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
Total Annualized
Costs (million
1999$)
$5.3
$3.4
$5.3
$3.4
Annualized Average
Cost-Effectiveness
($/ton)
$1,819
$1,165
$1,819
$1,165
Marginal Costs
($/ton)
—
—
—
—
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-26
-------
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
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
Total Annualized
Costs (million
1999$)
$2.6
$2.3
$5.8
$15.0
Annualized Average
Cost-Effectiveness
($/ton)
$1,302
$1,109
$1,770
$2,271
Marginal Costs
($/ton)
—
—
$2,953
$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
$2,000/ton Scenario

$3,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs
$7.9
$5.7
$11.0
$22.7
(million 1999$)

Table G-39. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU
BART-Eligible Units at Coke Oven Batteries
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 9,815
9,815
$3,000/ton Scenario 9,815
9,815
Discount
Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
5,727
5,727
5,727
5,727
2015 Emission
Reductions
4,088
4,088
4,088
4,088
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source
categories, both controlled and uncontrolled.
G-27
-------
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 Batteries3
Scenarios 2015 Baseline Emissions
$2,000/ton Scenario 10,389
10,389
$3,000/ton Scenario 10,389
10,389
2015 Postcontrol
Emissions
10,389
4,621
4,621
4,621
2015 Emission
Reductions
0
5,768
5,768
5,768
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
$2,000/ton Scenario

$3,000/ton Scenario

7%
3%
7%
3%
Total Annualized
Costs (million 1990$)
$6.2
$4.0
$6.2
$4.0
Annualized Average
Cost-Effectiveness
($/ton)
$1,517
$1,074
$1,517
$1,074
Marginal Costs
($/ton)
—
—
$1,517
$1,074
G-28
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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
$2,000/ton Scenario

$3,000/ton Scenario

7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$0.0
$10.9
$12.5
$10.9
Annualized Average
Cost-Effectiveness
($/ton)
$0
$1,898
$2,167
$1,898
Marginal
Costs ($/ton)
—
—
$2,167
—
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
$2,000/ton Scenario

$3,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs (million
$6.2
$14.9
$18.7
$14.9
1999$)

G-29
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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 Plants3
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 59,766
59,766
$3,000/ton Scenario 59,766
59,766
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
46,069
45,455
46,069
45,455
2015 Emission
Reductions
13,697
14,311
13,697
14,311
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.

Table G-46 shows the total annualized costs for controlling SO2 from these sources.
G-30
-------
Table G-45. 2015 Cost and Cost-Effectiveness Results for SO2 Control at Non-EGU
BART-Eligible Units at Sulfur Recovery Plants
Discount
Scenarios Rate
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
Total Annualized
Costs (million
1999$)
$11.6
$12.2
$11.6
$12.2
Annualized Average
Cost-Effectiveness
($/ton)
$847
$849
$847
$849
Marginal Costs
($/ton)
—
—
$847
$849
Table G-46. 2015 Cost Results for SO2 Control at Non-EGU BART-Eligible Units at Sulfur
Recovery Plants
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs (million
$11.7
$12.1
$12.1
$12.1
1999$)

G.ll 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-31
-------
Table G-47. 2015 SO2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU
BART-Eligible Units at Primary Aluminum Ore Reduction Plants3
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 47,552
47,552
$3,000/ton Scenario 47,552
47,552
Discount
Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
45,922
45,922
45,922
44,292
2015 Emission
Reductions
1,630
1,630
1,630
3,260
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 Plants3
Scenario
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
1,676
1,676
1,676
1,676
2015 Postcontrol
Emissions
1,606
1,341
1,423
1,341
2015 Emission
Reductions
70
335
253
335
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-32
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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
$2,000/ton Scenario

$3,000/ton Scenario

Discount
Rate
7%
3%
7%
3%
Total Annualized Costs
(million 1999$)
$1.6
$1.0
$1.6
$4.5
Annualized Average
Cost-Effectiveness ($/ton)
$982
$590
$982
$1,381
Marginal
Costs ($/ton)
—
—

$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
$2,000/ton Scenario

$3,000/ton Scenario

Discount
Rate
7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$0.1
$0.0
$0.6
$0.5
Annualized Average
Cost-Effectiveness ($/ton)
$1,114
$509
$2,411
$1,614
Marginal Costs
($/ton)
—
—
$2,823
$1,764
Table G-51 shows the total annualized costs for controlling both SO, and NOY.
G-33
-------
Table G-51. 2015 Cost Results for SO2 and NOX Control at Non-EGU BART-Eligible Units
at Primary Aluminum Ore Reduction Plants

Scenarios
,000/ton Scenario

,000/ton Scenario

Discount Rate
7%
3%
7%
3%
Total Annualized Costs (million
$1.7
$1.0
$2.2
$5.0
1999$)

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
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 12,849
12,849
$3,000/ton Scenario 12,849
12,849
Discount
Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
8,378
8,378
8,378
5,696
2015 Emission
Reductions
4,471
4,471
4,471
7,153
The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source category,
both controlled and uncontrolled.
G-34
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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
$2,000/ton Scenario

$3,000/ton Scenario

Discount Total Annualized
Rate Costs (million 1999$)
7%
3%
7%
3%
$5.0
$4.3
$5.0
$25.4
Annualized Average
Cost-Effectiveness
($/ton)
$1,118
$953
$1,118
$3,552
Marginal
Costs ($/ton)
—
—
—
$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
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 6,677
6,677
$3,000/ton Scenario 6,677
6,677
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
6,109
5,826
4,561
4,561
2015 Emission
Reductions
568
851
2,116
2,116
The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled
and uncontrolled.
G-35
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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
$2,000/ton Scenario

$3,000/ton Scenario

Discount
Rate
7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$0.5
$1.7
$5.3
$4.7
Annualized Average Cost-
Effectiveness ($/ton)
$937
$1,972
$2,505
$2,244
Marginal
Costs ($/ton)
—
—
$3,101
$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
Incinerators3
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 1,656
1,656
$3,000/ton Scenario 1,656
1,656
Discount
Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
912
912
912
912
2015 Emission
Reductions
744
744
744
744
The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled
and uncontrolled.
G-36
-------
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
$2,000/ton Scenario

$3,000/ton Scenario

Discount Total Annualized
Rate Costs (million 1999$)
7%
3%
7%
3%
$1.1
$0.9
$1.1
$0.9
Annualized Average
Cost-Effectiveness
($/ton)
$1,478
$1,207
$1,478
$1,207
Marginal Costs
($/ton)
—

$1,478
$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-37
-------
Table G-58. 2015 NOX Emission Reductions (in tons) for BART-Eligible Units at Coal
Cleaning Plants
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
1,110
1,110
1,110
1,110
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
1,110
599
599
599
2015 Emission
Reductions
0
511
511
511
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
$2,000/ton Scenario

$3,000/ton Scenario

7%
3%
7%
3%
Total Annualized
Costs (million 1999$)
$0.0
$0.8
$1.0
$0.8
Annualized Average
Cost-Effectiveness
($/ton)
$0
$1,534
$1,900
$1,534
Marginal Costs
($/ton)
—
—
$1,900
—
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-38
-------
Table G-60. 2015 NOX Emission Reductions (in tons) for BART-Eligible Units at Carbon
Black Plants
2015 Baseline
Scenarios Emissions
$2,000/ton Scenario 4,645
4,645
$3,000/ton Scenario 4,645
4,645
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
4,534
4,534
4,525
4,525
2015 Emission
Reductions
111
111
120
120
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
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
Total Annualized
Costs (million
1999$)
$0.0
$0.0
$0.0
$0.0
Annualized Average
Cost-Effectiveness
($/ton)
$1,608
$1,495
$1,608
$1,495
Marginal Costs
($/ton)
—
—
—
—
G-39
-------
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
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
1,377
1,377
1,377
1,377
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
1,352
1,343
1,352
1,343
2015 Emission
Reductions
25
34
25
34
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-40
-------
Table G-63. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at
Secondary Metal Processing Facilities
Discount
Scenarios Rate
$2,000/ton Scenario 7%
3%
$3,000/ton Scenario 7%
3%
Total Annualized
Costs (million
1999$)
$0.0
$0.0
$0.0
$0.0
Annualized Average
Cost-Effectiveness
($/ton)
$760
$511
$760
$511
Marginal Cost
($/ton)
—
—
—
—
Table G-64. 2015 NOX Emission Reductions (in tons) for BART-Eligible Units at
Phosphate Rock Ore Processing Facilities
Scenarios
$2,000/ton Scenario

$3,000/ton Scenario

2015 Baseline
Emissions
719
719
719
719
Discount Rate
7%
3%
7%
3%
2015 Postcontrol
Emissions
689
689
689
689
2015 Emission
Reductions
30
30
30
30
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 cost-
effectiveness is $760 per ton with costs at a either a 3 or 7 percent discount rate. The marginal
costs between the scenarios are zero since there are no additional reductions with increasing
stringency.
G-41
-------
Table G-65. 2015 Cost and Cost-Effectiveness Results for BART-Eligible Units at
Phosphate Rock Ore Processing Facilities
Scenarios
Discount Rate
Total Annualized
Costs (million
1999$)
Annualized Average
Cost-Effectiveness
($/ton)
Marginal Cost
($/ton)
$2,000/ton Scenario
$3,000/ton Scenario
7%

3%
$0.0
$0.0
0.0
0.0
$760

$760

760

760
G-42
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-05-004
Environmental Protection Air Quality Strategies and Standards Division June 2005
Agency Research Triangle Park, NC
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