# Regulatory Impact Analysis for the Final Clean Air Visibility Rule or the Guidelines for Best Available Retrofit Technology (BART) Determinations Under the Regional Haze RegulationsEPA-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 Divisioniii 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4iv 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 Vi sibility 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 Reflectin g 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 Endpoi nts 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-584.1.6.2 Agricultural, Forestry, and Other VegetationRelated Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-63 4.1.6.3 Benefits from Reductions in Materials Damage . . . . 4-65 4.1.6.4 Benefits from Redu ced 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-76v 5. QUALITATIVE ASSESSMENT OF NONMONETIZED BENEFITS . . . . . . 5-1
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.2 Atmospheric Deposition of Su lfur 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 . . . . . . . . . . . . . . . . . 6-23vi 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-51vii 6.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-52 7. COST, ECONOMIC, AND ENERGY IMPACTS . . . . . . . . . . . . . . . . . . . . . . 7-1 7.1 Modeling Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1 7.2 Projected SO 2 and NO x 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 Technol ogies Employed in These Analyses 8-16 8.4.1 SO 2 Emissions Control Technologies . . . . . . . . . . . . . . . . . . . 8-16 8.4.1.1 SO 2 Control Technology for Non-EGU Sources . . . 8-16 8.4.2 NO x Emissions Control Technologies . . . . . . . . . . . . . . . . . . . 8-17 8.4.2.1 NO x Control Technology for Non-EGU Sources . . . 8-17 8.4.2.2 NO x 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, Sulfur ic, and Nitric Ac id 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 . . . . . . . . . . . . . . . . . . . . . . . 8-44viii 8.5.9 Results for Sulfur Recovery Plants . . . . . . . . . . . . . . . . . . . . . 8-47 8.5.10 Results for Primary Alu minum 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 Producti on 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: Consulta tion 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 Transf er and Advancement Act . . . . . . . . . . . . . 9-10
9.9 Executive Order 12898: Federal Acti ons to Address Environmental Justice in Minority Populations and Low-Income Populations . . . . . . 9-10 9.10 Congressional Review Act . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-11
9.11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-11ix 10. COMPARISON OF BENEFITS AND COSTS . . . . . . . . . . . . . . . . . . . . . . . . 10-1
10.1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-3 Appendix A: BART Industry-Sector Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 Appendix B: Cost and Economic Impact S upplemental Information and Sensitivity Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1 Appendix C: Additional Technical Inform ation Supporting the Benefits Analysis . . . . . . . . C-1 Appendix D: Visibility Benefits Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1 Appendix E: Benefits and Costs of the Clean Air Interstate Rule, the Clean Air Visibility Rule, and the Clean Air Interstate Rule plus the Clean Air Visibility Rule . . . . E-1 Appendix F: Sensitivity Analyses of Some Key Parameters in the Benefits Analysis . . . . . . F-1 Appendix G: Results for Two Additional Scen arios Applied to BART Non-EGU Source Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G-1x LIST OF FIGURES
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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 SO 2 and NO x 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10xi LIST OF TABLES

Number Page

1-1 Summary of Annual Be nefits, 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 Nonmone tized 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 Emi ssions, 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 Evalua tion 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 PM 2.5 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 Calculat e Total Monetized H ealth 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 Popul ation Prevalence Rates for Use in Impact Functions, General Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-44 4-10 Asthma Prevalence Rates Used to Estimate Asthmatic Populations in Impact Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-46xii 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 Usedin 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 Peri od (in 2000$) of a Nonfatal Myocardial Infarction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-57 4-15 Clean Air Visibility Rule: Estimated Reduction in Incidence of Adverse Health Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-68 4-16 Estimated Monetary Value of Reductions in Incidence of Health and Welfare Effects Associated w ith the CAVR (millions of 1999$) . . . . . . . . . . 4-69 4-17 Results of Illustrative Application of Pilot Expert E licitation: 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 Indus try Retail Sales in 2003 (Billion kWh) . . . . . . 6-4 6-4 Electricity Net Gene ration in 2003 (Billion kWh) . . . . . . . . . . . . . . . . . . . . . . . 6-4 6-5 Costs of Production for the Cement Industry: 1997–2001 . . . . . . . . . . . . . . . 6-13 6-6 Capacity Utiliza tion Rates: 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15 6-7 Cement Market Statistics 1998–2002 (10 3 Metric Tons, Unless Otherwise Noted) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-16 6-8 Inputs for the Industrial Organi c Chemicals Industry (NAICS 3251), 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-19 6-9 Historical Size of Establishments a nd 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 Ra tios for the Industrial Organic Chemicals Industry (NAICS 3251), 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-22 6-12 Value of Shipments for the Indus trial Organic Chemicals, N.E.C. Industry (SIC 2869/NAICS 3251), 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . 6-22 6-13 Costs of Production for the Crude Pe troleum 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, Annua l Production, and Imports, 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-27 6-16 Inputs for the Paper and Allie d Products Industry (NAICS 322), 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-30 6-17 Measures of Market Concentr ation for Paper a nd Allied Products Markets, 1997 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-30xiii 6-18 Size of Establishments and Va lue 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 Petrol eum Products Produced by U.S. Refineries . . . . . . . . . . . . . . . 6-36 6-22 Petroleum Refinery Costs of Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-36 6-23 Adjusted Sales of Distilla te 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, 200 3 . . . . . . . . . . . . . . . . . . . . . . 6-41 6-26 Sales of Dis tillate 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 Con centration for the Primary Metal Manufacturing Industry (NAICS 331) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-50 6-29 Size of Establishments and Valu e of Shipments for the Primary Metal Manufacturing Industry (NAICS 331) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-50 6-30 Capacity Utilization Ratios for the Primary Me tal Manufacturing Industry, 1997–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-51 6-31 Value of Shipments for the Pr imary Metal Manuf acturing 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 SO 2 and NO x 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 NE RC Region with CAIR and with BART (Mills/kWh) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9
8-1 SO 2 Emissions and Emission Reductions for BART Source Categories in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-3 8-2 NO x Emissions and Emission Reductions for BART Source Categories in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-5 8-3 Total Annualized Costs of Contro l for BART Source Categories in 2015 (million 1999$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-7 8-4 SO 2 Emissions and Emission Reductions for BART Source Categories in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-9 8-5 NO x Emissions and Emission Reductions for BART Source Categories in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-11xiv 8-6 Total Annualized Costs of Contro l for BART Source Categories in 2015 (million 1999$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-13 8-7 Available SO 2 Control Technologies for I ndustrial Boilers and Other Non-EGU Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17 8-8 Available NO x Control Technologies for Industrial Boilers . . . . . . . . . . . . . . 8-18 8-9 Available NO x Control Technologies for Other Non-EGU Source Categories Other than Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-19 8-10 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-21 8-11 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-22 8-12 2015 Cost and Cost-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-22 8-13 2015 Cost and Cost-Eff ectiveness Results for NO x Control at BART-Eligible Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-23 8-14 2015 Cost Results for SO 2 and NO x Control at BART-Eligible Industrial Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-24 8-15 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Petroleum Refineries . . . . . . . . . . . . . . . . 8-24 8-16 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Petroleum Refineries . . . . . . . . . . . . . . . . 8-25 8-17 2015 Cost and Cost-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Petroleum Refineries . . . . . . . . . . . . . . . . . . . . . . . . . 8-26 8-18 2015 Cost and Cost-Eff ectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Petroleum Refineries . . . . . . . . . . . . . . . . . . . . . . . . . 8-26 8-19 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Petroleum Refineries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-27 8-20 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . 8-28 8-21 2015 NO x 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-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-29 8-23. 2015 Cost and Cost-Eff ectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-30 8-24 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Kraft Pulp Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-30 8-25 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Portland Cement Plants . . . . . . . . . . . . . . 8-31 8-26 2015 NO x 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-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Portland Cement Plants . . . . . . . . . . . . . . . . . . . . . . . 8-32 8-28 2015 Cost and Cost-Eff ectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Portland Cement Plants . . . . . . . . . . . . . . . . . . . . . . . 8-33xv 8-29 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Portland Cement Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-33 8-30 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Hy drofluoric, Sulfur ic, and Nitric Acid Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-34 8-31 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Hy drofluoric, Sulfur ic, and Nitric Acid Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-35 8-32 2015 Cost and Cost-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants . . . . . 8-35 8-33 2015 Cost and Cost-Eff ectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants . . . . . 8-36 8-34 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants . . . . . . . . . . . . . . . . . . 8-37 8-35 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Chemical Process Plants . . . . . . . . . . . . . 8-37 8-36 2015 NO x 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-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Chemical Process Plants . . . . . . . . . . . . . . . . . . . . . . 8-39 8-38 2015 Cost and Cost-Eff ectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Chemical Process Plants . . . . . . . . . . . . . . . . . . . . . . 8-40 8-39 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Chemical Process Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-40 8-40 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Iron and Steel Mills . . . . . . . . . . . . . . . . . 8-41 8-41 2015 NO x 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-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Iron and Steel Mills . . . . . . . . . . . . . . . . . . . . . . . . . . 8-42 8-43 2015 Cost and Cost-Eff ectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Iron and Steel Mills . . . . . . . . . . . . . . . . . . . . . . . . . . 8-43 8-44 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Iron and Steel Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-43 8-45 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Coke Oven Batteries . . . . . . . . . . . . . . . . 8-44 8-46 2015 NO x 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-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Coke Oven Batteries . . . . . . . . . . . . . . . . . . . . . . . . . 8-45 8-48 2015 Cost and Cost-Eff ectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Coke Oven Batteries . . . . . . . . . . . . . . . . . . . . . . . . . 8-46 8-49 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Coke Oven Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-47xvi 8-50 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Sulfur Recovery Plants . . . . . . . . . . . . . . 8-47 8-51 2015 NO x 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-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Sulfur Recovery Plants . . . . . . . . . . . . . . . . . . . . . . . 8-49 8-53 2015 Cost and Cost-Eff ectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Sulfur Recovery Plants . . . . . . . . . . . . . . . . . . . . . . . 8-50 8-54 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Sulfur Recovery Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-50 8-55 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-51 8-56 2015 NO x 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-Eff ectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants . . . . . . . . 8-52 8-58 2015 Cost and Cost-Eff ectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants . . . . . . . . 8-53 8-59 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants . . . . . . . . . . . . . . . . . . . . . 8-54 8-60 2015 NO x Emission Reductions (in tons) for BART-Eligible Lime Kilns . . . 8-54 8-61 2015 Cost and Cost-Effec tiveness Results for BART-Eligible Lime Kilns . . 8-55 8-62 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Glass Fiber Processing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-56 8-63 2015 Cost and Cost-Effectiveness Re sults for BART-Eligible Units at Glass Fiber Processing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-56 8-64 2015 NO x Emission Reductions (in tons) for BART-Eligible Municipal Incinerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-57 8-65 2007 Cost and Cost-Effectiveness Re sults for BART-Eligible Municipal Incinerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-58 8-66 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Coal Cleaning Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-58 8-67 2015 Cost and Cost-Effec tiveness Results for BART-Eligible Units at Coal Cleaning Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-59 8-68 2015 NO x Emission Reductions (in tons) fo r BART-Eligible Units at Carbon Black Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-60 8-69 2015 Cost and Cost-Effectiveness Re sults for BART-Eligible Units at Carbon Black Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-60 8-70 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Phosphate Rock Processing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-61 8-71 2015 Cost and Cost-Effectiveness Re sults for BART-Eligible Units at Phosphate Rock Processing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-62xvii 8-72 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Secondary Metal Producti on Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-62 9-1 Potentially Regulated EGU Categories and Entities . . . . . . . . . . . . . . . . . . . . . 9-2 9-2 Examples of Potentially Regulated Non-EGU Categories and Entities . . . . . . . 9-2 10-1 Summary of Annual Bene fits, Costs, and Net Benefits of the Clean Air Visibility Rule—2015 (billions of 1999 dollars) . . . . . . . . . . . . . . . . . . . . . . . 10-21-1 CHAPTER 1 EXECUTIVE SUMMARY Synopsis EPA has estimated the benefits and costs of the Clean Air Visibility Rule or BART rule and finds that the rule results in estimated annual ne t benefits ranging from $1.9 to $12.0 billion in 2015. These alternate net bene fit 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 quan tified 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 southeaste rn 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. U pon consideration of the uncer tainties 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. E nvironmental Protection Agency (EPA) proposed guidelines for implementing the best available retr ofit technology (BART) requirements under the Regional Ha ze Rule. The proposed guide lines were intended to ease implementation of the Regi onal 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. Circ uit 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 Ru le relevant to the BART guidelines on March 5, 2004. The repropos al reflects both our review of the public comments and our response to the court ruling.1-2 This document presents estimates of the health and welfare benefits and the estimated costs of the BART program referred to officially as the Clean Air Visibility Rule (CAVR) in 2015. CAVR and BART are used interchangeably inthis 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 pow er sector in the CAIR region where annual sulfur dioxide (SO 2 ) and nitrogen oxide (NO x ) controls are required. CAIR, as promulgated, will affect a 28-State and the District of Colu mbia (DC) region in the eastern United States,and the BART rule is applicable nationwide. This rule is applicable to the electricgenerating sector (EGU) and 25 other source categories (non-EGU) sector.
1.2 Results A comparison of the benefits and costs of th e 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 SO 2 and NO x controls for New Jersey and Dela ware. The modeling used to provide CAIR baseline estimates also assumes annual SO 2 and NO x controls for Arkansas that are not a part of the complete CAIR program resulting in an understate ment of the re ported 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 fo r three different regulatory scenarios that provide information about the actions St ates may require to meet potential BART requirements for affected BART-eligible EGU and non-EGU sources. These scenarios should be viewed as illustrative, because St ates will make the ultimate decisions on the BART-eligible sources to contro l 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, a nd net benefits that may result from this rule. For more details of the alternative control s cenarios assumptions see Chapters 2, 7, and 8 of this document. We believe State actions fo r 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, a nd other relevant analysis assumptions are discussed in Chapter 2 of this report.1-3 Table 1-1. Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air Visibility Rule—2015 a(billions of 1999 dollars)
Description Scenario 1 Scenario 2 Scenario 3 Social costs b 3 percent discount rate $0.4 $1.4 $2.3 7 percent discount rate $0.3 $1.5 $2.9 Social benefits c,d,e 3 percent discount rate $2.6 + B $10.1 + B $14.3 + B 7 percent discount rate $2.2 + B $8.6 + B $12.2 + B Health-related benefits:

3 percent discount rate $2.5 $9.8 $13.9 7 percent discount rate $2.1 $8.4 $11.8 Visibility benefits $0.08 $0.24 $0.42 Net benefits (benefits-costs ) e,f 3 percent discount rate $2.2 + B $8.7 + B $12.0 + B 7 percent discount rate $1.9 + B $7.1 + B $9.3 + B

a All estimates are rounded to two significant digits fo r ease of presentation and computation. A complete

CAIR program that includes the CAIR promulgated rule and the proposal to include a nnual SO 2 and NO x controls for New Jersey and Delaware is assumed to because implemented in the baseline for the BART analysis. Annual SO 2 and NO xcontrols for Arkansas are included in the modeling used to develop these estimates resulting in a minimal overstatement of the benefits and costs for the complete CAIR program and potentially a minimal understatement of the benefits and costs for BART. The impact of the recently promulgated CAMR was not been considered in the baseline for BART.

b Note that costs are the annualized total costs of reducing pollutants including NO xand SO 2 for the EGU and

non-EGU source categories nationwide in 2015. The discount rate used to conduct the analysis impacts the control strategies chosen for the non-EGU source categor y resulting in greater level of controls under the 3 percent discount rate for Scenario 1.

c As this table indicates, total benefits are driven primarily by particulate matter (PM)-related health benefits.

The reduction in premature fatalities each year accounts for over 90 percent of total monetized benefits. Benefits in this table are nationwide (with the exception of visibility) and are associated with NO xand SO 2 reductions. Visibility benefits represent benefits in Cl ass 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 an d 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-recomme nded 20-year segmented lag structure described in

Chapter 4. Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses (EPA, 2000; OMB, 2003).

f Net benefits are rounded to the nearest $100 million. Columnar totals may not sum due to rounding.1 Although well over 90 percent of the expected benefits of this rule are derived from reductions in SO 2 and NO x , 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 th e rule, it is a potential ancillary benefit of installing certain SO 2 control technologies.

2 These estimates account for growthin the public’s willingness to pa y for reductions in health and environmental risks and account for growth in real gross domestic product (GDP) per capita between the present and 2015. Benefit estimates reflect the use of 3 percent and 7 percent discount rates consistent with EPA and the Office of Management and Budget (OMB) guidelines for preparing economic analyses (EPA, 2000; OMB, 2003).
1-4 1.2.1 Health Benefits CAVR is expected to yield significant hea lth benefits by reduc ing emissions of two key contributors to fine particle and ozone formation. SO 2 contributes to the formation of fine particle pollution (PM 2.5 ), and NO x contributes to the formation of both PM 2.5 and ground-level ozone. 1 Our analyses suggest CAVR would yield total benefits in 2015 ranging from $2.6 to $14.3 billion (based on a 3 percent discount rate) and from $2.2 to $12.2 billion (based on a 7 percent discount rate). For Scenario 2, th ese 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 activ ities because of respiratory symptoms exacerbated by PM 2.5 pollution. 2
Because of schedule and resource limitations, EPA did not conduct a quantitative analysis of benefits from reductions (and potential disbenefits from increases) in groundlevel ozone as a result of precu rsor emissions reductions project ed for BART. However, it is unlikely that net benefits resulting from ozone reductions would have a significant impact on any conclusions reached regarding the overall benefits for this rulemaking. We also estimate substantial additional health improvements for children from reductions in upper and lower resp iratory illnesses, acute bronchitis, and asth ma attacks. See Table 1-2 for a list of the annua l reduction in health effects expected in 2015 and Table 1-3 for the estimated value of those reductions.1-5 Table 1-2. Clean Air Visibility Rule: Esti mated Reduction in Incidence of Adverse Health Effects in 2015 a,b

Incidence Reduction

Health Effect Scenario 1 Scenario 2 Scenario 3 PM-Related Endpoints: Premature mortality c Adult, age 30 and over Infant, age <1 year 400 1 1,600 4 2,300 5 Chronic bronchitis (adult, age 26 and over) 230 890 1,300
Nonfatal myocardial infarction (adults, age 18 and older) 570 2,200 3,000
Hospital admissions—respiratory (all ages) d 140 510 720
Hospital admissions—cardiovascular (adults, age >18) e 120 450 640
Emergency room visits for asthma (age 18 years and younger) 370 1,300 1,800
Acute bronchitis (children, age 8–12) 550 2,100 3,000 Lower respiratory symptoms (children, age 7–14) 6,600 25,000 36,000
Upper respiratory symptoms (asthmatic children, age 9–18) 5,000 19,000 27,000
Asthma exacerbation (asthmatic children, age 6–18) 8,100 31,000 44,000
Work loss days (adults, age 18–65) 44,000 170,000 240,000 Minor restricted-activity days (MRADs) (adults, age 18–65) 260,000 1,000,000 1,400,000

a Incidences are rounded to two significant digits. These estimates represent benefits from CAVR nationwide. The modeling used to derive these incidence estimates assumes the final CAIR program in the baseline including the CAIR promulgated rule and the proposal to include SO 2 and annual NO x controls for New Jersey and Delaware. Modeling used to develop these estimates assumes annual SO 2 and NO x controls for Arkansas for CAIR resulting in a slight understatement of the reported benefits and costs for the CAVR. The r ecently promulgated CAMR has not been considered in the baseline for CAVR.
b Ozone benefits are expected for CAVR but are not estimated for this analysis.

c Adult premature mortality based on studies by Pope et al. (2002). Infant premature mort ality is based on studies by Woodruff, Grillo, and Schoendorf (1997).
d Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD), pneumonia, and asthma.

e Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure.1-6 Table 1-3. Estimated Monetary Value of Reductions in Incide nce of Health and Welfare Effects for the Clean Air Visi bility Rule in 2015 (in millions of 1999$) a,b

Effect Scenario 1 Scenario 2 Scenario 3 Health Effects: Premature mortality c,d Adult >30 years 3% discount rate 7% discount rate Child <1 year $2,330 $1,960

$6.12 $9,180 $7,730

$23.8 $13,000 $10,900 $34.2 Chronic bronchitis (adults, 26 and over) $90.5 $353 $498 Nonfatal acute myocardial infarctions 3% discount rate 7% discount rate $49.3 $45.8 $189 $176 $264 $245 Hospital admissions for respiratory causes $1.07 $4.03 $5.65 Hospital admissions for cardiovascular causes $2.6 $10.0 $14.1 Emergency room visits for asthma $0.106 $0.362 $0.51 Acute bronchitis (children, age 8–12) $0.207 $0.79 $1.12 Lower respiratory symptoms (children, 7–14) $0.109 $0.415 $0.587 Upper respiratory symptoms (asthma, 9–11) $0.137 $0.523 $0.74 Asthma exacerbations $0.367 $1.4 $1.98 Work loss days $5.56 $22.4 $31.5 Minor restricted-activity days (MRADs) $13.8 $54.1 $76.3 Welfare Effects: Recreational visibility, southeastern and southwestern Class I areas $84 $239 $416

Monetized Total e Base Estimate: 3% discount rate 7% discount rate $2,600 + B $2,200 + B $10,100 + B $8,600 + B $14,300 + B $12,200 + B

a Monetary benefits are rounded to three significant digits. 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 SO 2 and annual NO x controls for New Jersey and Delaware. Modeling used to develop the CAIR baseline estimates assumes annual SO 2 and NO x controls for Arkansas resulting in a slight understatement of the reported benefits and costs for CAVR. The recently promulgated CAMR is not considered in the baseline for CAVR. b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis year of 2015.
c Valuation assumes discounting over the Science Advisory Board (SAB)-recommended 20-year segmented lag structure described in Chapter 4. Results show 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses (EPA, 2000; OMB, 2003). d Adult premature mortality based on studies by Pope et al . (2002). Infant premature mortality based on studies by Woodruff, Grillo, and Schoendorf (1997).
e B represents the monetary value of health and welfare be nefits and disbenefits not m onetized. A detailed listing is provided in Table 1-4. Totals are rounded to the n earest 100 million, and totals may not sum due to rounding.1-7 1.2.2 Welfare Benefits
The term welfare benefits covers both environmental and so cietal 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 m onetize all welfare be nefits, EPA estimates CAVR will yield welfare be nefits of appr oximately $240 million in 2015 (1999$) for visibility improvements in southeastern and southwestern (including California) Class I (national park) areas for Scenario 2. 1.2.3 Uncertainty in the Benefits Estimates
Characterization of health-related benefits associated with PM reductions is a complex process that is subject to a variet y of potential sources of uncertainty. Key assumptions underlying the estima te of avoided premature mort ality include the following:

C Inhalation of fine partic les is causally associated with premature death at concentrations near those experienced by most Americans on a daily basis. Although biological mechanisms for this e ffect have not yet been established, the weight of the available epidemiological and experimental evidence supports an assumption of causality.

C All fine particles, regard less 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 automotiv e engines and other industrial sources. However, no clear scientific grounds ex ist for supporting differential effects estimates by particle type.

C The concentration-response (C-R) function for fine particles is approximately linear within the range of ambient concen trations under consid eration. 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 a nd those that do not meet the standard.

C The forecasts for future emissions and associated air quality modeling are valid. Although recognizing the difficulties, a ssumptions, and inhere nt 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 mort ality function to support this analysis is associated with uncertainty resulting from (a) potential of the study to incompletely capture1-8 short-term exposure-related mortality effects, (b) potential mismatch between study and analysis populations, which introduces various fo rms of bias into the results, and (c) failure to identify all key confounders a nd effects modifiers that coul d result in incorrect effects estimates relating morality to PM 2.5 exposure. EPA is researchin g methods to characterize all elements of uncertainty in the dose-response func tion for mortality. As is discussed in detail in the CAIR RIA (EPA, 2005), EPA has used tw o 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 mort ality 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 twi ce the mean benefit estimate at the 95 thpercentile and one-fourth of the mean at the 5 thpercentile, 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 5 thpercentile to a value at the 95 thpercentile that is seven times higher than the Pope et al. ( 2002)-based estimate. These results are highly dependent on the air quality scenar ios applied to the C-R functi ons of the Pope et al. (2002) study and the pilot expert elicitation. Thus, th e characterization of un certainty 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 PM 2.5 (i.e., the location of populations’ exposure relative to the changes in air quality) and may be especially sensitive to the differences in baseline PM 2.5 air quality experienced by populations prior to implementation of the CAVR. EPA is continui ng its research of methods to characterize uncertainty in total be nefits estimates and is conducting a full-scale ex pert elicitation. The full-scale expert elicitation is scheduled to be comp leted by the end of 2005. 1.3 Not All Benefits Quantified EPA was unable to quantify or monetize all of the health and environm ental benefits associated with CAVR. EPA believes these unqua ntified 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 pr ovides a list of these benefits.1-9 Table 1-4. Unquantified and Nonmonetized Effects of the Clean Air Visibility Rule
Pollutant/Effect Effects Not Included in Primary Estimates—Changes in: Ozone—Health a • Premature mortality b • Chronic respiratory damage • Premature aging of the lungs • Nonasthma respiratory emergency room visits • Increased exposure to UVb • Hospital admissions: respiratory • Emergency room visits for asthma • Minor restricted-activity days • School-loss days • Asthma attacks • Cardiovascular emergency room visits • Acute respiratory symptoms Ozone—Welfare • Yields for: – commercial forests, – fruits and vegetables, and – commercial and noncommercial crops • Damage to urban ornamental plants • Recreational demand from damaged forest aesthetics • Ecosystem functions • Increased exposure to UVb PM—Health c • Premature mortality: short-term exposures d • Low birth weight • Pulmonary function • Chronic respiratory diseases other than chronic bronchitis • Nonasthma respiratory emergency room visits • Exposure to UVb (+/-) e PM—Welfare • Visibility in many Class I areas • Residential and recreational visibility in non-Class I areas • Soiling and materials damage • Ecosystem functions • Exposure to UVb (+/-) e Nitrogen and Sulfate Deposition—Welfare • Commercial forests due to acidic sulfate and nitrate deposition • Commercial freshwater fishing due to acidic deposition • Recreation in terrestrial ecosystems due to acidic deposition • Existence values for currently healthy ecosystems • Commercial fishing, agriculture, and forests due to nitrogen deposition • Recreation in estuarine ecosystems due to nitrogen deposition • Ecosystem functions • Passive fertilization due to nitrogen deposition (continued)1-10 Table 1-4. Unquantified and Nonmonetized Effects of the Clean Air Visibility Rule (continued)
Pollutant/Effect Effects Not Included in Primary Estimates—Changes in: Mercury Health g • Incidence of neurological disorders • Incidence of learning disabilities • Incidence of developmental delays • Potential reproductive effects f • Potential cardiovascular effects f , including: – Altered blood pressure regulation f – Increased heart rate variability f – Incidence of myocardial infarction f Mercury Deposition g Welfare • Impacts on birds and mammals (e.g., reproductive effects) • Impacts to commercial, subsistence, and recreational fishing

a In addition to primary economic endpoints, a number of biological responses have been associated with ozone health

effects including increased airway responsiveness to stimuli, inflammation in the lung, acute inflammation and respiratory cell damage, and increased susceptibility to respirator y 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 th e ozone mortality epidemiology literature. EPA will consider including ozone mortality in primary benefits analyses once a peer-reviewed methodology is available.

c In addition to primary economic endpoints, a number of biological responses have been associated with PM health effects

including morphological changes and altered host defense mechanisms. The public health impact of these biological responses may be partly represented by our quantified endpoints. d While some of the effects of short-term exposures are likely to be captured in the estimates, there may be premature

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

e May result in benefits or disbenefits. See discussion in Section 5.3.4 for more details. f These are potential effects because the literature is insufficient. g Mercury emission reductions are not anticipated for BART from the EGU source category because the cap-and-trade

program promulgated for the CAMR (March 2005); however, the geographic location of mercury reductions may change as a result of this rule. EPA believes any such effects for these sources would likely be minimal. Mercury reductions are expected for the non-EGU source categories. The mercury reductions for BART from the non-EGU source categories are expected to be small compared to reductions resulting from the recently promulgated CAIR and CAMR (March 2005).
1.4 Costs and Economic Impacts The control strategies analyzed in this report represent EPA’s best approximation of the emission controls for BART-eligible sources that States may require. Ultimately, States will make the determination of those BART-eligib le sources to control and the level of costeffective controls. We recognize the uncertain ty in these estimates and present benefit and cost estimates for three scenarios. For the affected region, the projected annua l incremental private costs of CAVR to the power industry range from $253 to $896 million in 2015. These costs represent the total1-11 cost to the electricity-gener ating industry of reducing NO x and SO 2 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 estimate d using the Integrated Planning Model (IPM) and assume firms make decisions using co sts 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 (suc h as taxes) that are simply re distributions of wealth. Under Scenario 2, the social costs of this rule fo r the EGU sector are estimated to range from $119 to $688 million in 2015 and assuming a 3 or 7 percent discount rate. Average retail electricity prices are proj ected 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 proj ected 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 thatemission control costs will range from approximately $151 million to $2.2 billion in 2015 fo r 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 impleme nt controls with a maximum cost ranging from $1,000 to $10,000 per ton of SO 2 or NO x 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 a nd economic studies used to config ure the benefit and cost models. Despite these uncertainties, we believe this benefit-cost analysis provides a reasonable indication of the expected economic benef its and costs of CAVR in future years. A major source of uncertainty in the an alysis conducted for this RIA is the uncertainty surrounding the actions States ma y take to comply with BART. We have conducted a range of scenarios that represent increasing levels of stringency of controls1-12 States may require to implement BART. It is likely that the benefits and costs of BART as required by States will fall within the range of estimates presented in this document. For this analysis, such uncertainties include possible errors in measurement and projection for variables such as population grow th and baseline inciden ce 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, a nd uncertainties in expos ure 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 SO 2 and NO x 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 understate d 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 transactiona l costs and/or savings from BART on the labor supply.
1.6 References Pope, C.A., III, R.T. Burnett, M.J. T hun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mort ality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141. U.S. Environmental Protection Ag ency (EPA). September 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003.
U.S. Environmental Protection Agency (EPA). March 2005. Regulatory Impact Analysis for the Final Clean Air Interstate Rule . EPA-452/R-05-002. U.S. Office of Management and Budget (OMB ). 2003. Circular A-4 Guidance to Federal Agencies on Preparation of Regulatory Analysis.1-13 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health Perspectives 105(6):608-612.2-1 SECTION 2
INTRODUCTION AND BACKGROUND

For this rulemaking, we are developing guidelines for BART determinations under the regional haze regulations. Specifically, we are addressing the issues of: 1) the sources that must apply BART, and 2) guidance as to how a State should determine the level of control for each source subject to BART in res ponse to the court’s ruling. We are finalizing a reproposal of the BART guidelines and the sec tion of the regional haze rule relevant to the BART guidelines. This final rule reflects both our review of the public comments, and our response to the court ruling. This document presents the health and welfare benefits of the Clean Air Visibility Rule (CAVR) or BART rule and compares the benefits of this rule to the estimated costs of implementing the rule in 2015. This secti on provides background information including a discussion of the need for the proposed re gulation, 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 ha ze visibility impairment in 156 Federallyprotected parks and wilderness areas. These 156 scenic areas are called “Class I areas” in the Clean Air Act (CAA). This rule fulfilled a long-standing EPA commitment to address regional haze under the authority and requirements of sections 169A and 169B of the CAA. As required by section 169A(b)(2)(A) and 169A (g) of the CAA, we included in the final regional haze rule a require ment for BART for certain larg e stationary sources that were put in place between 1962 and 1977. We discu ssed these requirement s 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” to2-2 refer to a somewhat different population of s ources, the regional haze rule uses the term “BART-eligible source” to describe these sour ces. 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 re quires that States must consider the following factors in maki ng BART determinations: (1) the costs of compliance, (2) the energy and nonair quality e nvironmental impacts of compliance, (3) any existing pollution control technology in use at the source, (4) the remaining useful life of the source, and (5) the degree of improvement in visibility which may reasonably be anticipated to result from the use of such technology. These statutory factors for BART appear in the regional haze rule in 40 CFR 51.308(e)(1)(ii). The regional haze rule provide s States with two alternative ways to approach the requirement for BART in the CAA. Under the first approach, contained in 51.308(e)(1) of the regional haze rule, SIPs would contai n source specific emission limits for each source subject to BART. Under the second approac h, States may elect to adoptive alternative measures, such as a regional emissions trading program, in lieu of BART so long as the alternative measures achieve “more reasonable progress” than would application of sourcespecific BART emission limits. In the preamble to the 1999 regional haze rule, we discuss a number of issues relate d to both approaches. In addition, in the preamble to the regi onal haze rule (64 FR 35741, July 1, 1999) we committed to issuing further guidelines to cl arify 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 the2-3 technical analysis of possible controls in lig ht of the statutory f actors listed above (“the BART determination”). Finally, the rule discusses proposed chan ges 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 gu idelines for States to use in identifying their BART eligible sources, in identifying thos e 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 flex ibility 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 char ging more than 250 tons of refuse per day, (10) Hydrofluoric, sulfuric , and nitric acid plants, (11) Petroleum refineries, (12) Lime plants,2-4 (13) Phosphate rock processing plants, (14) Coke oven batteries, (15) Sulfur recovery plants, (16) Carbon black plants (furnace process), (17) Primary lead smelters, (18) Fuel conversion plants, (19) Sintering plants, (20) Secondary meta l 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 pr oduction facilities. Most of the source category titles are general descriptors that are inclusive of all the operations at a given plant. Some plant site s may have more than one of the categories present. Examples of this would include plan ts 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 ma y include some emissions un its meeting one of these 26 descriptions, but other emissions units that do not. 2.3 Control Scenarios The source-specific BART guidelines re quire emissions reductions from sources emitting sulfur dioxides (SO 2 ) and nitrous oxides (NO x ). The analyses conducted for this RIA include three regulatory alternative s cenarios 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 brie f discussion of the these alternatives for the electric generating units (EGUs) and all othe r sources follows. More details of the1 These levels are commonly achie vable by flue gas desulfuriza tion controls (“scrubbers”).
2-5 alternative control scenarios and associated control costs are discussed in chapters 7 and 8 of this report. 2.3.1 Electric Generating Units In the revised BART guidelines, we have included presumptive control levels for SO 2 and NO x emissions from coal-fired electric generating units greater than 200 megawatts (MW) in capacity at plants greater than 750 MW in capacity. Given the similarities of these units to other BART-eligible coal-fired units greater than 200 MW at plants 750 MW or less, EPA’s guidance suggests that states control such units at similar levels for BART. The guidelines would require 750 MW power plants to meet specific control levels of either 95 percent control or controls of 0.15 lbs/MMB tu, 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 acc ount 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 te st for the BART determination. In our analysis we have assumed that no add itional controls will occur wher e 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 t echnology will continue to improve and control costs will continue to decline.
For NO x , 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 curre ntly using SCR or SNCR for part of the year, States should presume thatthe use of those same controls year-round is highly costeffective. For other sources, the guidelines establish presumptive e mission levels that vary depending largely upon boiler type and fuel burned. For coal-fired cyclone units with a size greater than 200 MW, our analysis assumes these units will install SCR. For all other coalfired units, our analysis assumed these units will install current combustion control2-6 technology. In addition, we assume no additional controls for oil and/or gas-fired steam units. We present alternative regulatory scenarios. Scenario 2 represents our application of the presumptive limits described above to all BART eligibility EGUs greater than 200 MW. For Scenario 1, we assume that only 200 MW BART-eligible EGUs located at facilities above 750 MW capacity will comply with the SO 2 requirements and NO x controls. In this scenario, no facilities less than 750 MW capacity ar e assumed to install BART controls. For Scenario 1, we assume that units with existin g SCRs will operate thos e SCR units year round annually. In contrast in Scenario 3, we analyzed SO 2 controls equivalent to 95 percent reductions or 0.1 lbs per MMBtu on a ll previously uncontrolled units. NO x 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 th is 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) (Mar ch 2005) have met the requireme nts 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 a ssuming no additional SO 2 controls for sources located in States of Arizona, Ut ah, 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 an alysis 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 fo r 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 th at the costs and benef its 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 sour ce 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 SO 2 and NO x emission control technologies available for these source2-7 categories and estimated the associated costs of control using AirControlNET. The control scenarios evaluated assume maximum control measure cost caps of $1,000 per ton (Scenario 1), $4,000 per ton (Scenario 2), and $10,000 per ton (Scenario 3). The EPA also conducted a cost analysis for $2,000 per ton and $3,000 per t on, and the results of this analysis are presented in Appendix G of this document. The analysis consists of applying SO 2 and NO x 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 SO 2 control, and varying types of technologies for NO x control. Scrubbers are the most common type of SO 2 control for most non-EGU sources for each s cenario, while combustion controls such as low NO x burners (LNB) and post-combustion cont rols such as selective noncatalytic reduction (SNCR) and se lective 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 comm only 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 categor ies, because there are no available control measures for these sources orthere are no sources in these categories included in the nonEGU emissions data utilized in these analyses. The marginal costs of these alternative regulatory scenarios are presented along with the results of these analyses in Chapter 8 of this report. All of these results are estimated using a nationwide database of BART-eligible non-EGU sources that is based on inform ation 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 s ource database is ava ilable 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 SO 2 and NO x . 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 imp lement the ozone and PM 2.5 NAAQS standards nor the recently promulgated Clean Air Mercury Rule in the baseline for this analysis.2-8 In the analysis, the controls and reductions are assumed to be required in 2015, a date that is generally consistent with the expected timing of the rule. States must submit SIPs relevant to the BART requireme nts in January 2008. After approva l of the SIP, there is a 5 year compliance date. Thus, controls are likel y 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 es timates of the benefits and costs of CAVR in 2015 rather than the net pr esent value of a stream of benefits and costs in these particular years of analysis. 2.5 Organization of this Report
This document describes the h ealth and welfare benefits of the proposed rule. The document is organized as follows:

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

C Chapter 4, Benefits Analysis and Resu lts, describes the me thodology and results of the benefits analysis.

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

C Chapter 6, Profile of Pote ntially Affected Industrie s, describes the major industries that may be a ffected by this rule.

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

C Chapter 8, Results of Cost, Emission Re ductions, and Economic Impact Analysis for the Non-Electricity Generating Sector s, describes the costs of the rule to affected industry sectors and related economic impacts.

C Chapter 9, Executive Order and Statut ory Requirements, describes impact analyses conducted to meet executiv e order and statutory requirements.

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

C Appendix A, BART Industry-Sector Impacts

C Appendix B, Cost and Economic Imp act Supplemental Information and Sensitivity Analyses2-9 C Appendix C, Additional Tec hnical Information Supporting the Benefits Analysis

C Appendix D, Visibility Benefits Methodology

C Appendix E, Benefits and Costs of the Cl ean Air Interstate Rule, the Clean Air Visibility Rule, and the Clean Air Interstate Rule Plus the Clean Air Visibility Rule

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

C Appendix G, Additional Control Scen arios for Non-EGU Source Categories3-1 SECTION 3 EMISSIONS AND AIR QUALITY IMPACTS

This chapter summarizes the emissions inventories and air quality modeling that serve as the inputs to the benefits analysis of this rule as detailed in Chapter 4. EPA uses sophisticated photochemi cal 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 ambien t 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 th e baseline emissions inventories and the emissions reductions that were modeled for this rule. S ection 3.2 summarizes the methods for and results of estimating air quality for th e 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 Es timated Emissions Reductions The emission sources and the basis for curre nt 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 categor y 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 Inve ntory (NEI). Data necessary to identify potentially affected BART eligible non-EGU sour ces were provided by the RPOs in response to an Information Collection Request. More details concerning th e development of the emissions inventory data ma y 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.3-2 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 Power industry EGUs 2001 Data from the Acid Rain Trading Program IPM

Non-EGU Non-utility point, including point source fugitive dust 2001 NEI Baseline including CAIR control case: (1) Department of Energy (DOE) fuel use projections, (2) Regional Economic Model, Inc. (REMI) Policy Insight ® model, (3) decreases to REMI results based on trade associations, Bureau of Labor Statistics (BLS) projections and Bureau of Economic Analysis (BEA) historical growth from 1987 to 2002, (4) control assumptions BART control cases: All of the above plus AirControlNET controls applied to BART-eligible units Average Fire Wildfire, prescribed burning Same as future year Average fires from 1996 through 2002 (based on state total acres burned), with the same emissions rates and country distributions of emissions as in the 2001 NEI Average Fire Agricultural burning, open burning 2001 NEI 2001 NEI

Agriculture Livestock NH 3 2002 preliminary NEI c 2015 emissions estimated with the same approach as was used for the 2002 preliminary NEI c Agriculture Fertilizer NH 3 2001 NEI 2001 NEI (continued)3-3 Table 3-1. Emissions Sources and Basis for Current and Future-Year Inventories a,b (continued)

Sector or

Source Emissions Source 2001 Base Year Future-Year Base Case Projections
Area All other stationary area sources,including areasource fugitive dust 1999 NEI, version 3 grown to 2001 (1) DOE fuel use projections, (2) REMI Policy Insight Model, (3) decreases to REMI results based on trade associations, BLS projections and BEA historical growth from 1987–2002 On-road Highway vehicles Mobile6.2 model Projected vehicle miles traveled same as CAIR proposed and final rule, emissions from MOBILE6.2 model Nonroad Locomotives, commercial marine vessels, and aircraft 2001 NEI; CMV adjusted to new national totals from Office of Transportation Air Quality (OTAQ) Grown based on national totals from OTAQ, using state/county distribution of emissions from the 2001 NEI

Nonroad All other nonroad vehicles NONROAD 2004 model NONROAD 2004 model

a This table documents only the sources of data for the U.S. inventory. The sources of data used for Canada and Mexico are explained in the emissions inventory TSD and were held constant from the base year to the future years.

b All fugitive dust emissions were adjusted downward using county-specific transportable fractions needed as part of the current state of the art in air quality modeling.

c ftp://ftp.epa.gov/EmisInventory/prelim2002nei/non point/documentation/nh3inventorydraft_jan2004.pdf.3-4 The emissions inventories used for the BA RT 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 base line prior to CAIR emission controls, and 2015 baseline including the 2015 CAIR emission controls (CAIR control case). The CAIR control case provides the emissions inventor y baseline for the BART analysis conducted. The CAIR TSD is available as follows: http://www.epa.gov/interstateairquality/ pdfs/finaltech01.pdf Table 3-2 summarizes the 2001 baseline, 2015 CAIR control case (2015 base case for BART), and three 2015 BART control case scenarios of NO Xand SO 2 emissions. Table 3-3 shows the change in NO Xand SO 2 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 aff ected by the BART guidelines. For details on EPA’s IPM results including emission reduc tions 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 inventor ies 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 a nd results of estimating air quality for the 2015 base case and control scenarios for the pur poses of the benefits analysis. EPA has focused on the health, welfare, and ecological effects that have been linked to air quality changes. These air quality changes include the following: 1. Ambient particulate matter (PM 2.5 ), as estimated using a national-scale applications of the C ommunity M ulti-Scale A ir Q uality (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 ch anges 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.3-5 Table 3-2. Summary of Modeled Baseline Emissions, CAIR Control Case, and BART Control Strategies
Pollutant Emissions (tons) Source NO X SO 2 2001 Baseline a EGUs 4,937,398 10,901,127 Non-EGUs 2,942,618 2,958,692 Average Fire 238,931 49,108 Area 1,462,276 1,295,146 On-road 8,064,067 271,026 Nonroad 4,050,655 433,250 Total, All Sources 21,695,945 15,908,349 2015 Base CAIR Control Case a EGUs 2,172,839 5,111,436 Non-EGUs 3,183,499 3,422,915 Average Fire 238,931 49,108 Area 1,702,154 1,480,348 On-road 3,152,562 30,824 Nonroad 2,912,382 232,627 Total, All Sources 13,362,365 10,327,258 2015 BART, Scenario 1 b EGUs 2,057,220 5,057,652 Non-EGUs 3,011,944 3,338,983 Total, All Sources 13,075,193 10,189,543 2015 BART, Scenario 2 b EGUs 1,963,753 4,991,850 Non-EGUs 2,807,124 3,152,277 Total, All Sources 12,776,906 9,937,034 2015 BART, Scenario 3 b EGUs 1,740,551 4,958,975 Non-EGUs 2,779,816 3,043,903 Total, All Sources 12,526,397 9,795,785

a The “ag” sector does not have emissions of NO xand SO 2 .
b With the exception of EGUs and non-EGUs, all other s ectors are the same as the CAIR control case (CAVR baseline) for 2015.3-6 Table 3-3. Summary of Modeled Emi ssions Changes for the BART Rule: 2015 a

Pollutant Item NO X SO 2 2015 Emission Reductions with the BART, Scenario 1 Total Ton Reductions 287,172 137,715 Percentage of EGU Emission Reductions 5.3% 1.1% Percentage of Non-EGU Emission Reductions 5.4% 2.5% Percent Reduction of All Manmade Emissions 2.1% 1.3% 2015 Emission Reductions with BART, Scenario 2 Total Tons Reduction 585,459 390,224 Percentage Reduction of EGU Emissions 9.6% 2.3% Percentage of Non-EGU Emission Reduction 11.8% 7.9% Percentage Reduction of All Manmade Emissions 4.4% 3.8% 2015 Emission Reductions with BART, Scenario 3 Total Tons Reduction 835,968 531,473 Percentage Reduction of EGU Emissions 19.9% 3.0% Percentage of Non-EGU Emission Reduction 12.7% 11.1% Percentage Reduction of All Manmade Emissions 6.3% 5.1%

a Note that the emission changes and percentage changes reported are nationwide estimates.

3.2.1 PM Air Quality Estimates We use the emissions inputs summarized above with a national-scale application of the CMAQ modeling system to estimate PM ai r quality in the contiguous United States. CMAQ is a three-dimensional grid-based Eulerian air quality model designed to estimate annual particulate con centrations and deposition over larg e spatial scales (e.g., over the contiguous United States). Consideration of the different processe s that affect primary (directly emitted) and s econdary (formed by atmospheric pro cesses) PM at the regional scale in different locations is fundamental to understanding and asse ssing the effects of pollution1 Given that a large percentage of PM 2.5 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., SO 2 ) from a multitude of widely dispersed sources coupled with chemical and physical processes that are best addressed using an air quality model that employs a Eulerian grid model design.
3-7 control measures that affect PM, ozone, and deposition of pollutants to the surface. 1 Because it accounts for spatial and tempor al variations as well as di fferences in the reactivity of emissions, CMAQ is useful for evaluating the imp acts 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 y ear projection, and thr ee 2015 projections with varying levels of e missions controls. The CMAQ version 4.3 was employed for th is BART modeling analysis (Byun and Schere, 2004). This version re flects updates in a number of areas to improve performance and address comments from the peer review, incl uding (1) the formation of nitrates based on updated gaseous/heterogeneous chemistry and a current i norganic nitrate partitioning module, (2) a state-o f-the-science secondary organic aeros ol (SOA) module that includes a more comprehensive gas-particle partitioning algorithm from both anthropogenic and biogenic SOA, (3) an in-cloud sulfate chemis try that accounts for the nonlinear sensitivity of sulfate formation to varying pH, and (4) th e updated CB-IV gas-phase chemistry mechanism and aqueous chemistry mechanism that provi de 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 inform ation pertaining to the mode ling domain and simulation period. These include hourly emissions es timates and meteor ological 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 discu ssed below, we use the model predictions in a relative sense by first determin ing 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 m easurements to project concen trations 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 followi ng sections provide a more detailed discussion of each of the steps in this evaluation and a summary of the results.3-8 Figure 3-1. CMAQ Modeling Domain 3.2.1.1 Modeling Domain As shown in Figure 3-1, the modeling dom ain 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 modelin g domain is segmented into rectangular blocks referred to as grid ce lls. 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 domai n contains 14 vertical layers with the top of the modeling domain at about 16,200 meters, or 100 mb. Within the domain each vertical layer has 16,576 grid cells.3-9 3.2.1.2 Simulation Periods For use in this benefits analysis, the simulation periods modeled by CMAQ included separate full-year app lication for each of the five emi ssions 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 thatcontain information pertaining to the modeling domain and simulation period. These include gridded, hourly emissions estimates and meteorological data and initial and boundary conditions. Separate emissi ons 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 app lication and remained unchanged for each future-year modeling scenario. CMAQ requires detailed emissi ons inventories containi ng temporally allocated emissions for each grid cell in the modeling domain for each species being simulated. The previously described annual e mission inventories were prepro cessed into model-ready inputs through the emissions preprocessing system. De tails of the preprocessing of emissions are provided in the Clean Air Interstate Rule Emissions Inventory Technical Support Document (EPA, 2005a). Meteorological inputs re flecting 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), temp erature, moisture, vertical diffusion rates, and rainfall rates fo r each grid cell in each vertical layer. The lateral boundary and initial species c oncentrations are provided by a threedimensional global atmospheric chemistry and transport model (GEOS-CHEM). The lateral boundary species concentrations varied with height and time (eve ry 3 hours). Terrain elevations and land use informa tion 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 m odel 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 PM 2.5 and its related speciated components (e.g., sulfate, nitrate, elem ental carbon, organic carbon), and deposition of ammonium, nitrate, and sulfate for 2001 was pe rformed to estimate the ability of the CMAQ modeling system to replicate base-year c oncentrations. This evaluation principally3-10 comprises statistical assessments of model versus observed pairs that were paired in time and space on a daily or weekly basis, depending on the sampling period of measured data. The statistics are presented separately for the entire domain, the East, and the West (using the 100 thmeridian 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 Perfo rmance Evaluation for 2001: Updated March 2005” (EPA, 2005c).
For PM 2.5 species, this evaluation includes comp arisons of model predictions to the corresponding measurements from the C lean A ir S tatus and T rends Network (CASTNet) and the S peciation T rend N etwork (STN) in addition to measurements from the Interagency M onitoring of PRO tected V isual E nvironments (IMPROVE). The CASTNet dry deposition monitoring network contained a total of 79 sites in 2001, witha 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 concentrati on data collected by CASTNet ar e 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. Ther efore, we chose to use the total nitrate data and not to use the particulate n itrate data in this evaluation. The EPA STN network began operation in1999 to provide nationally consistent speciated PM 2.5 data for the assessment of trends at representative sites in urban areas. STN reports mass concen trations and PM 2.5 constituents, including sulfate, nitrate, ammonium, and elemental and organic carbon. Most STN site s collect data on a frequency of 1 in every 3 days, (some supplemental site s are collected 1 in every 6 days). For the 2001 analysis, CMAQ predictions were evaluated against 133 STN sites (105 sites in the East and 28 sites in the West).3-11 The IMPROVE network is a cooperative visi bility 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 nationa l parks, national wilder ness areas, and other protected pristine areas. Approximately 134 IMPROVE rural/remote sites had complete annual PM 2.5 mass and/or PM 2.5 species data for 2001. Eighty-six sites were in the West, and 48 sites were in the East. IMPROVE data are collected once in every 3 days. The principal evaluation statistics used to evaluate CMAQ performance are the fractional bias and frac tional error. Fractiona l bias is defined as:

Fractional bias is a useful model performance indicator because it has the advantage of equally weighting positive and negative bias estimates. Fractional error is similar to fractional bias except the absolute value of the difference is used so that the error is always positive. Fractional error is defined as:

These metrics were calculated annually for all IMPROVE , CASTNet, STN, and N ational A tmospheric D eP osition (NADP) sites for the East and West individually. Currently, there are no universally accepted performance criteria for judging the adequacy of PM 2.5 model performance. However, perfo rmance 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 CM AQ 2001 model performance results to the range of performance found in other recent regional PM 2.5 model applications by other groups. These modeling studies represent a broad rang e of modeling analyses that cover various models, model configur ations, domains, years and/or epis odes, chemical mechanisms, and aerosol modules. The fractional bias and fractional error statis tics 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 that3-12 Table 3-4. Model Performance Statistics for BART CMAQ 2001

CAIR CMAQ 2001 Annual Fractional Bias (%) Fractional Error (%)

PM 2.5 Total Mass STN East –12 44 West –51 64 IMPROVE East 8 43 West 14 57

Sulfate STN East 8 45 West –32 52 IMPROVE East 7 40 West –2 49 CASTNet East –2 24 West –35 50

Nitrate STN East –12 86 West –92 115 IMPROVE East –32 107 West –44 115 Total Nitrate (NO 3 + HNO 3 ) CASTNet East 16 81 West –60 105

Elemental Carbon STN East 32 63 West –20 67 IMPROVE East –23 51 West –13 66

Organic Carbon STN East –3 75 West –31 66 IMPROVE East –10 52 West 51 76

sulfate (especially in the summer) accounts for th e vast majority of vi sibility degradation in the East. Model performance statistics for summe r average sulfate is provided in Table 3-5. The general range of model performance for PM 2.5 species compares favorably to fractional bias and fractional error statistics from the better performin g model applications found by others in the modeling community, as follows:

C summer sulfate is in the range of –10 pe rcent to +30 percent for fractional bias and 35 percent to 50 percent for fractional error and

C winter nitrate is in the range of +50 per cent to +70 percent for fractional bias and 85 percent to 105 percent for fractional error.3-13 Table 3-5. Selected Perf ormance Evaluation Statis tics from the CMAQ 2001 Simulation

Eastern United States CMAQ 2001 Fractional Bias (%) Fractional Error (%)

Sulfate (Summer) STN 14 44 IMPROVE 10 42 CASTNet 3 22

Thus, CMAQ is considered appropriate for us e in projecting changes in future year PM 2.5 concentrations and the resultant health/economic benefits due to the emissions reductions. 3.2.1.5 Converting CMAQ Outputs to Benefits Inputs CMAQ generates predictions of hourly PM species concentrations for every grid. The species include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a primary fine fr action (corresponding to PM less than 2.5 microns in diameter), and several secondary par ticles (e.g., sulfates, nitr ates, and organics). PM 2.5 is calculated as the sum of the primary fine frac tion and all of the secondarily formed particles. Future-year estimates of PM 2.5 were calculated using relative reduc tion factors (RRFs) applied to 2002 ambient PM 2.5 and PM 2.5 species concentrations. A gridded field of PM 2.5 concentrations was created by interpolating Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM 2.5 species concentrations were created by interpolating EPA speciation network (ESPN) ambient data and IMPROVE data. The ambient data were interpolated to the CMAQ 36 km grid. The procedures for determining the RRFs are similar to those in EPA’s draft guidance for modeling the PM 2.5 standard (EPA, 2001). This guidance has undergone extensive peer review and is anticipated to be finalized this year. The guidance recommends that model predictions be used in a relative sense to estimate changes expected to occur in each major PM 2.5 species. The procedure for calculating future-year PM 2.5 design values is called the “Speciated Modeled Attainment Te st (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 revise d. Full documentation of the revised SMAT methodology is contained in “Pro cedures for Estimating Future PM 2.5 Values for the CAIR3-14 Final Rule by Application of the (Revised) Speciated Modeled Atta inment Test (SMAT)- Updated” (EPA, 2004).
The revised SMAT uses an FRM mass c onstruction methodology that results in reduced nitrates (relative to the amount measur ed 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 PM 2.5 and its noncarbon components. This characterization of PM 2.5 mass also reflects crustal material and other minor constituents. The resulting characterization provides a comp lete mass balance. It doe s not have any unknown mass that is sometimes presented as the difference between measured PM 2.5 mass and the characterized chemical components derived from routine sp eciation measurements . The revised SMAT methodology uses the following PM 2.5 species components: sulf ates, nitrates, ammonium, organic carbon mass, elemental carbon, crustal, water, and blank mass (a fixed value of 0.5 ug/m 3 ). In each grid cell, the PM 2.5 component species mass adds up to the interpolated PM 2.5 mass. For the purposes of projecting future PM 2.5 concentrations for input to the benefits calculations, we applied the SMAT procedur e using the base-year 2001 modeling scenario and each of the future-year scenarios. In our application of SMAT we used temporally scaled speciated PM 2.5 monitor data from 2002 as the set of base-year measured concentrations. Temporal s caling is based on ratios of m odel-predicted future case PM 2.5 species concentrations to the corresponding model-predicted 2001 con centrations. Output files from this process include both quarterly and annual mean PM 2.5 mass concentrations, which are then manipulated within SAS to produce a BenMAP input file containing 364 daily values (created by replicating the quarter ly mean values for each day of the appropriate season). The SMAT procedures as documented for us e in CAIR are applicable for projecting future nonattainment coun ties and downwind receptor areas for the transport analysis. Those procedures are the same as those performed fo r the BART PM benefits analysis with the following exceptions: 1) The BART benefits analysis uses interpolated PM 2.5 data that cover all of the grid cells in the modeling domain (cove ring the entire country), whereas the CAIR nonattainment analysis is performed at each ambient monitoring site in the East using measured PM 2.5 data (only the species data are interpolated).3-15 2) The benefits analysis is anchored by the interpolated PM 2.5 data from the single year of 2002, whereas the nonattainment anal ysis uses a 5-year weighted average (1999–2003) of PM 2.5 design values at each monitoring site. 3.2.1.6 PM Air Quality Results Table 3-6 summarizes the projected PM 2.5 concentrations for the 2015 base case and changes associated with the rule. The table includes the annual mean concentration averaged across all model grid cells in the nation, alongwith the average change between base and control concentrations. We also provide the population-weighted averag e that better reflects the baseline levels and predic ted 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 s hown, the average annual mean concentration of PM 2.5 across populated eastern U.S. grid cells declines by roughly 1.28 percent (or 0.12 μg/m 3 ) in 2015. The population-weighted averag e mean concentration declined by 0.94 percent (or 0.10 μg/m 3 ) 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, a lmost 35.4 percent of the U.S. population is predicted to experience reductions of greater than 0.1 μg/m 3 . Furthermore, over 8 percent of this population will benefit from reductions in annual mean PM 2.5 concentrations of greater than 0.2 μg/m 3

. 3.2.2 Visibility Degradation Estimates Visibility degradation is ofte n directly proportional to decr eases in light transmittal in the atmosphere. Scattering and absorpti on 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 th e scattering and absorption of light by both particles and ga ses and accounts for the higher extinction efficiency of fine particles compared to coarse particles. Fine particles with significant light-extinction efficiencies include sulfat es, nitrates, organic carbon, el emental carbon (soot), and soil (Sisler, 1996).3-16 Table 3-6. Summary of Base Case PM Ai r Quality and Changes Due to Clean Air Visibility Rule in 2015

Scenario 1 Scenario 2 Scenario 3

Region PM 2.5 (μg/m 3 ) Base Case Change a Percent Change Change a Percent Change Change a Percent ChangeEast Average b Annual Mean 9.39 –0.04 –0.43 –0.12 –1.28 –0.16 –1.70
PopulationWeighted Average Annual Mean c 10.64 –0.03 –0.28 –0.10 –0.94 –0.14 –1.32West Average Annual Mean 6.04 –0.02 –0.33 –0.04 –0.66 –0.06 –0.99
PopulationWeighted Average Annual Mean c 12.46 –0.01 –0.08 –0.07 –0.56 –0.10 –0.80

a The change is defined as the control case value minus the base case value.

b Calculated as the average across all grid cells in the U.S. portion of the region.

c Calculated by summing the product of the population and the projected a nnual mean PM concentration for each grid cell then dividing this sum by the total regional population. Table 3-7. Distribution of PM 2.5 Air Quality Improvements Over Population Due to Clean Air Visibility Rule in 2015

Range of Change in Annual Mean PM 2.5

Concentrations (μg/m 3 ) a Scenario 1 Scenario 2 Scenario 3 Number (millions) Percent (%) Number (millions) Percent (%) Number (millions) Percent (%)

# 0.1 301.7 95.2% 204.6 64.6% 130.8 41.3% 0.11 – 0.20 9.9 3.1% 85.6 27.0% 136.3 43.0% 0.21 – 0.30 5.2 1.6% 19.6 6.2% 35.5 11.1% 0.31 – 0.40 0.0 0.0% 5.1 1.6% 9.2 2.9% 0.41 – 0.50 0.0 0.0% 1.9 0.6% 2.7 0.8% 0.50 – 0.60 0.0 0.0% 0.0 0.0% 2.4 0.8%

a The change is defined as the control case value minus the base case value.

b Population counts and percentages are for the continental U.S. population.2 There are 81 IMPROVE sites with complete data for 2001. Many of these sites collect data that are “representative” of other nearby unmonitored Class I areas. A total of 116 Class I areas are represented by the 81 sites. The matching of sites to monitors is taken from “Guidance for Tracking Progress Under the Regional Haze Rule.”
3-17 Based on the light-extinction coefficient, we also calculated a unitless visibility index, called a “deciview,” that is used in the valuation of visibility. The deciview metric provides a scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the average person can generally perceive a change of one deciview. The higher the deciview value, the worse the visibility. Thus, an improvement in visibility is a decrease in deciview value. 3.2.2.1 Procedures for Estimating Visibility Degradation The impacts of the BART emissions reducti ons were examined in terms of the projected improvement in annual average visibility as well as projected visibility improvement on the 20 percen t 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 IM PROVE monitors with complete data. 2 Currently 110 IMPROVE monitoring sites (representing all 156 Class I areas) are collecting ambient PM 2.5 data at Class I areas, but only 81 of th ese sites have comp lete 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 PM 2.5 and Regional Haze modeling guidance r ecommends the calculation of future-year changes in visibility in a similar manner to the calculation of changes in PM 2.5 . We generally followed the procedures in the guidance. In calculating visibility impairment, the extinction coefficient and deciview values are made up of individual component species (e.g., sulfate, nitrat e, 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 d eciviews and then averaged across all monitored days to get an annual average as well as the 20 percent be st 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. Additi onally, subtracting the future BART control case deciview values from the future base case deciview values gives an estimate of the visibility3-18 benefits in Class I areas from the BART scenario. Additional details on the visibility calculation methodology can be found in the CAIR “Better-than-BART” TSD (EPA, 2005d). As explained above (Section 3.2.1.5), when calculating future-year PM 2.5 concentrations for BART, we have updated the SMAT procedures to use PM 2.5 component species that emulate the FRM measurements. This is thought to be the most accurate estimate of the PM 2.5 species fractions at FRM sites for the purpose of calculating PM-related health benefits. For visibility calculations, we are continuing to use the IMPROVE program species definitions and visibility formulas that are recommended in the draft modeling guidance. Each IMPROVE site has measurements of PM 2.5 species; therefore, we do not need to estimate the species fractions in the same way that we did for FRM sites (using interpolation techniques and other assumptions concerning volatilization of species). Therefore, the methodology for calculating PM 2.5 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 park s in the United States, including the Great Smokey Mountains and Shenandoah, are expectedto 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 per cent), and Shenandoah National Park is expected to see improvement s of over 0.24 deciviews (1.02 percent). It is important to note that the deciview changes reported are estimated with CAIR in the baseline. This means visibility improvements for CAIR controls that meet BART requirements are not reflected in the deciview impacts shown in this table.3-19 Table 3-8. Summary of Deciview Visibilit y Impacts at Class I Areas in the Nation a,b Scenario 2

Federal Class I Area 2015 Change in Average of 20% Worst Days Percent Change in Average of 20% Worst Days Change in Annual Average Percent Change in Annual Average Acadia, ME 0.09 0.41 0.05 0.39 Aqua Tibia, CA* 0.20 0.87 0.14 0.79 Alpine Lakes, WA 0.10 0.58 0.10 0.84 Anaconda- Pintler, MT 0.04 0.30 0.03 0.36 Arches, UT* 0.00 0.00 0.03 0.40 Badlands, SD 0.32 1.87 0.21 1.77 Bandelier, NM* 0.08 0.66 0.04 0.50 Big Bend, TX 0.04 0.22 0.05 0.40 Black Canyon of the Gunnison, CO* 0.06 0.61 0.06 0.80 Bob Marshall, MT 0.02 0.13 0.02 0.20 Boundary Waters Canoe Area, MN 0.20 1.04 0.09 0.77 Bridger, WY 0.06 0.56 0.04 0.62 Brigantine, NJ 0.11 0.43 0.06 0.30 Bryce Canyon, UT* 0.06 0.49 0.05 0.61 Cabinet Mountains, MT 0.03 0.23 0.03 0.35 Caney Creek, AR 0.19 0.78 0.27 1.46 Canyonlands, UT* 0.03 0.29 0.04 0.53 Cape Romain, SC 0.14 0.61 0.11 0.61 Caribou, CA* 0.06 0.46 0.04 0.55 Carlsbad Cavern, NM* 0.06 0.36 0.05 0.44 Chassahowitzka, FL* 0.10 0.44 0.07 0.37 Chiricahua, AZ* 0.08 0.59 0.04 0.47 Craters of the Moon, ID 0.07 0.51 0.04 0.43 Desolation, CA* 0.06 0.41 0.04 0.48 Dolly Sods, WV* 0.16 0.69 0.10 0.58 Dome Land, CA* 0.06 0.33 0.03 0.25 Eagle Cap, OR 0.09 0.49 0.05 0.44 Eagles Nest, CO* 0.07 0.67 0.05 0.73 Emigrant, CA* 0.05 0.30 0.02 0.21 Everglades, FL* –0.01 –0.05 0.02 0.11 Fitzpatrick, WY 0.06 0.56 0.04 0.62 (continued)3-20 Table 3-8. Summary of Deciview Visibility Impacts at Class I Areas in the Nation a,b Scenario 2 (continued)

Federal Class I Area 2015 Change in Average of 20% Worst Days Percent Change in Average of 20% Worst Days Change in Annual Average Percent Change in Annual Average Flat Tops, CO* –0.03 –0.28 0.02 0.32 Galiuro, AZ* 0.09 0.67 0.04 0.47 Gates of the Mountains, MT 0.02 0.19 0.02 0.32 Gila, NM* 0.08 0.67 0.03 0.40 Glacier, MT 0.02 0.13 0.02 0.18 Glacier Peak, WA 0.05 0.38 0.04 0.55 Grand Teton, WY 0.05 0.36 0.03 0.44 Great Gulf, NH 0.04 0.20 –0.04 –0.30 Great Sand Dunes, CO* 0.07 0.62 0.07 0.80 Great Smokey Mountains, TN* 0.15 0.59 0.11 0.56 Guadalupe Mountains, TX 0.08 0.44 0.05 0.43 Hells Canyon, OR 0.06 0.31 0.03 0.30 Isle Royale, MI 0.23 1.06 0.10 0.77 James River Face, VA* 0.15 0.62 0.08 0.42 Jarbidge. MV* 0.02 0.19 0.05 0.66 Joshua Tree, CA* 0.06 0.32 0.05 0.40 Joyce Kilmer—Slickrock, NC* 0.15 0.59 0.11 0.56 Kalmiopsis, OR 0.05 0.36 0.02 0.25 Kings Canyon, CA* 0.05 0.22 0.03 0.18 La Garita, CO* 0.04 0.42 0.04 0.58 Lassen Volcanic, CA* 0.06 0.45 0.04 0.53 Lava Beds, CA* 0.05 0.34 0.03 0.33 Linville Gorge, NC* 0.25 1.01 0.15 0.84 Lostwood, ND 0.21 1.08 0.09 0.64 Lye Brook, VT 0.14 0.62 0.06 0.44 Mammoth Cave, KY* 0.13 0.49 0.11 0.52 Marble Mountain, CA* 0.04 0.26 0.02 0.18 Maroon Bells- Snowmass, CO* 0.08 0.81 0.05 0.71 Mazatzal, AZ* 0.04 0.35 0.03 0.26 Medicine Lake, MT 0.10 0.55 0.06 0.45 Mesa Verde, CO* 0.08 0.70 0.05 0.59 Mingo, MO 0.14 0.52 0.16 0.78 Mission Mountains, MT 0.01 0.08 0.01 0.17 Mokelumne, CA* 0.05 0.39 0.03 0.37 (continued)3-21 Table 3-8. Summary of Deciview Visibilit y Impacts at Class I Areas in the Nation a,b Scenario 2 (continued)

Federal Class I Area 2015 Change in Average of 20% Worst Days Percent Change in Average of 20% Worst Days Change in Annual Average Percent Change in Annual Average Moosehorn, ME 0.06 0.29 0.04 0.27 Mount Hood, OR 0.07 0.55 0.05 0.55 Mount Jefferson, OR 0.05 0.40 0.03 0.38 Mount Ranier, WA 0.09 0.50 0.06 0.48 Mount Washington, OR 0.05 0.35 0.03 0.33 Mount Zirkel, CO* –0.03 –0.25 0.04 0.53 Noth Cascades, WA 0.04 0.30 0.04 0.53 Okefenokee, GA* 0.17 0.69 0.14 0.73 Otter Creek, WV 0.19 0.81 0.11 0.64 Pasauten. WA 0.07 0.44 0.05 0.65 Petrified Forest, AZ* 0.08 0.70 0.06 0.65 Pine Mountain, AZ 0.04 0.28 0.04 0.38 Presidential Range—Dry, NH* 0.11 0.55 0.05 0.35 Rawah, CO* 0.09 0.82 0.08 1.05 Red Rock Lakes, WY 0.04 0.31 0.03 0.42 Redwood, CA* 0.06 0.40 0.03 0.25 Rocky Mountain, CO* 0.07 0.49 0.06 0.67 Roosevelt Campobello, ME 0.08 0.39 0.06 0.40 Salt Creek, NM* 0.09 0.52 0.09 0.73 San Gorgonio, CA* 0.08 0.38 0.06 0.43 San Jacinto, CA * 0.11 0.53 0.10 0.69 San Pedro Parks, NM* 0.05 0.46 0.05 0.62 Sawtooth, ID 0.02 0.16 0.02 0.23 Scapegoat, MT 0.01 0.08 0.02 0.17 Selway—Bitterroot, MT 0.04 0.31 0.03 0.36 Seney, MI 0.20 0.85 0.10 0.72 Sequoia, CA* 0.07 0.30 0.05 0.28 Shenandoah, VA* 0.24 1.02 0.15 0.85 Sierra Ancha, AZ* 0.04 0.33 0.03 0.26 Sipsey, AL* 0.08 0.30 0.08 0.41 South Warner, CA* 0.04 0.32 0.03 0.37 Strawberry Mountain, OR 0.08 0.45 0.03 0.31 Superstition, AZ* 0.04 0.42 0.04 0.39 Swanquarter, NC* 0.09 0.77 0.09 0.50 (continued)3-22 Table 3-8. Summary of Deciview Visibilit y Impacts at Class I Areas in the Nation a,b Scenario 2 (continued)

Federal Class I Area 2015 Change in Average of 20% Worst Days Percent Change in Average of 20% Worst Days Change in Annual Average Percent Change in Annual Average Sycamore Canyon, AZ* 0.02 0.15 0.02 0.17 Teton, WY 0.04 0.36 0.03 0.42 Theodore Roosevelt, ND 0.17 1.00 0.10 0.82 Thousand Lakes, CA* 0.06 0.45 0.04 0.53 Three Sisters, OR 0.05 0.35 0.03 0.33 UL Bend, MT 0.04 0.26 0.03 0.27 Upper Buffalo, AR 0.20 0.86 0.25 1.39 Voyageurs, MN 0.08 0.49 –0.11 –0.99 Weminuche, CO* 0.06 0.56 0.05 0.65 West Elk, CO* 0.03 0.25 0.03 0.53 Wind Cave, SD 0.21 1.38 0.13 1.31 Wolf Island, GA* 0.16 0.65 0.10 0.51 Yellowstone, WY 0.04 0.35 0.03 0.42 Yolla Bolly—Middle Eel, CA* 0.04 0.21 0.03 0.29 Yosemite, CA* 0.04 0.26 0.01 0.12 Zion, UT* 0.04 0.33 0.05 0.54

a The change is defined as the base case (includes CAIR controls) value minus the control case value for Scenario 2. This means the visibility changes from CA IR controls that meet the BART requirements are not reflected in the deciview impacts shown on this table.

b The percentage change is the “Change” divided by the “Base Case” and then multiplied by 100 to convert the value to a percentage. * Visibility benefits were monetized for this park. Negative values indicate visibility degradation.3-23 3.3 References Byun, D., and K.L. Schere. March 2004. “Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System.” Submitted to the Journal of Applied Mechanics Reviews

. Sisler, J.F. July 1996. Spatial and Seasonal Patterns and L ong Term Variability of the Composition of the Haze in the United States: An Analysis of Data from the IMPROVE Network . Fort Collins, CO: Cooperative Institute for Research in the Atmosphere, Colorado State University. U.S. Environmental Protection Agency (EPA ). 2001. Draft Guidance for Demonstrating Attainment of the Air Quality Goals for PM 2.5 and Regional Haze; Draft 1.1, Office of Air Quality Planning and Standards, Research Triangle Park, NC. U.S. Environmental Protection Agency (EPA ). 2004. “Procedures for Estimating Future PM 2.5 Values for the CAIR Final Rule by Application of the (Revised) Speciated Modeled Attainment Test (SMAT)-Updated”, Office of Air Quality Planning and Standards, Research Triangle Park, NC. U.S. Environmental Protection Agency (EPA). 2005a. Clean Air Interstate Rule Emission Inventory Technical Support Document . Office of Air Quality Planning and Standards. Research Triangle Park, NC. U.S. Environmental Protection Agency (EPA). 2005b. Clean Air Interstate Rule Air Quality Modeling Technical Support . Office of Air Quality Planning and Standards. Research Triangle Park, N.C. U.S. Environmental Protection Agency (EPA). 2005c. CMAQ Model Performance Evaluation for 2001: Updated March 2005. Office of Air Quality Planning and Standards. ResearchTriangle Park, N.C. U.S. Environmental Protection Agency (EPA). 2005d. Demonstration that CAIR Satisfies the “Better-than-BART” Test As Proposedin the Guidelines for Making BART Determinations. Office of Air Quality Planning and Standards. Research Triangle Park, NC.1 Note that CAIR as promulgated does not include utilitie s 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 estab lishing the baseline for CAVR would tend to understate the emission reductions and benefits estimated for CAVR. In addition, the impacts of the recently promulgated CAMR were not considered in the baseline.
4-1 CHAPTER 4
BENEFITS ANALYSIS AND RESULTS

This chapter reports EPA’s analysis of a subset of the public health and welfare impacts and associated monetized benefits to society of CAVR. EPA is required by Executive Order (E.O.) 12866 to estimate the be nefits 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 NO xand SO 2 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 consiste nt with the benefits analysis performed for the recen t 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 direct s States and Tribes to establish SO 2 and NO x controls for BART-eligible sources. These controls are expected to impact primarily EGU sources outside of th e 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 SO 2 and NO x , 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. 12 Short-term exposure to ambient ozone has also been linke d to premature death. EPA is currently evaluating the epidemiological literature examining the relationship between ozone and premature mortality, sponsoring three independent meta-analyses of the literature. EPA will consider including ozone mortality in primary benefits analyses once a peer-reviewed methodology is available.
4-2 2) Air quality modeling for 2015 to determine changes in visibility and in ambient concentrations of PM, re flecting baseline and postcontrol emissions inventories. 3) A benefits analysis to determine the ch anges 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 NO x and SO 2 from BART-eligible sources and the resulting impact on visibility and on ambient concentrations of ozone and PM. Potential human health effects associated with PM 2.5 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-termozone 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. Alt hough methods exist for quantifying the benefits associated with many of these human health a nd 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 be nefits associated with CAVR for 2015. Table 4-2 lists the full complement of human hea lth 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 limitati ons, EPA did not conduct a quantitative analysis of benefits from reductions (and potential disbenefits from increases) in groundlevel ozone as a result of precu rsor emissions reductions project ed for BART. However, it is unlikely that net benefits resulting from ozone reductions would have a significant impact on any conclusions reached regarding the overall benefits for this rulemaking.3 The term “impact function” as used here refers to the combination of (a) an effect estimate obtained from the epidemiological literature, (b) the baseline incidence estimate for the health effect of interest in the modeled population, (c) the size of that modeled population, and (d) the change in the ambient air pollution metric of interest. These elements are combined in the impact function to generate estimates of changes in incidence of the health effect. The impact function is distinct from the C-R function, which strictly refers to the estimated equation from the epidemio logical study relating incidence of the health effect and ambient pollution. We refer to the specific value of the relativ e risk or estimated coeffici ents 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 functio n” rather than C-R function because “impact function” includes all key input parameters used in the incidence calculation.
4-3 Table 4-1. Estimated Monetized Benefits of the Final CAVR

Total Benefits a, b (billio ns 1999$) Scenario 1 Scenario 2 Scenario 3 Using a 3% discount rate $2.6 + B $10.1 + B $14.3 + B Using a 7% discount rate $2.2 + B $8.6+ B $12.2 + B

a For notational purposes, unquantified benefits are indicated with a “B” to represent the sum of additional monetary benefits and disbenefits. A detailed listing of unquantified health and welfare effects is provided in Table 4-2.

b Results reflect the use of two different discount rates: 3 and 7 percent, which are recommended by EPA’s Guidelines for Preparing Economic Analyses (EPA, 2000b) and OMB Circular A-4 (OMB, 2003). Results are rounded to three significant digits for ease of presentation and computation.

Figure 4-1 illustrates the major steps in the benefits analysis. Given baseline and postcontrol emissions inventorie s 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 ambi ent 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 functions 3 to generate changes in the incidence of health effects, or changes in other exposure metrics are input to dose-respons e functions to generate changes in welfare effects. The resulting effects changes are then assigned monetary values,4-4Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAVRPollutant/EffectQuantified and Monetized inBase EstimatesaQuantified and/or MonetizedEffects in Sensitivity Analyses Unquantified Effects—Changes in:Ozone/HealthbChronic respiratory damagePremature aging of the lungsNonasthma respiratory emergency room visitsIncreased exposure to UVbHospital admissions: respiratoryEmergency room visits for asthmaMinor restricted-activity daysSchool loss daysPremature mortality: short-term exposurescAsthma attacksCardiovascular emergency room visitsAcute respiratory symptomsOzone/WelfareDecreased outdoor worker productivityYields for:– Commercial forests– Fruits and vegetables, and– Other commercial and noncommercial cropsDamage to urban ornamental plantsRecreational demand from damaged forest aestheticsEcosystem functionsIncreased exposure to UVb(continued)4-5Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAVR (continued)Pollutant/EffectQuantified and Monetized inBase EstimatesaQuantified and/or MonetizedEffects in Sensitivity Analyses Unquantified Effects—Changes inPM/HealthdPremature mortality based oncohort study estimateseBronchitis: chronic and acuteHospital admissions: respiratoryand cardiovascularEmergency room visits for asthmaNonfatal heart attacks (myocardialinfarction)Lower and upper respiratory illnessMinor restricted-activity daysWork loss daysAsthma exacerbations (asthmaticpopulation)Respiratory symptoms (asthmaticpopulation)Infant mortalityPremature mortality: short termexposuresfSubchronic bronchitis casesLow birth weightPulmonary functionChronic respiratory diseases other than chronic bronchitisNonasthma respiratory emergency room visitsUVb exposure (+/-)gPM/WelfareVisibility in Southeastern Class IareasVisibility in western U.S. Class IareasVisibility in northeastern andMidwestern Class I areasHousehold soilingVisibility in residential and non-Class I areasUVb exposure (+/-)g(continued)4-6Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAVR (continued)Pollutant/EffectQuantified and Monetized inBase EstimatesaQuantified and/or MonetizedEffects in Sensitivity Analyses Unquantified Effects—Changes in:Nitrogen andSulfateDeposition/WelfareCommercial forests due to acidic sulfate and nitratedepositionCommercial freshwater fishing due to acidic depositionRecreation in terrestrial ecosystems due to acidicdepositionCommercial fishing, agriculture, and forests due tonitrogen depositionRecreation in estuarine ecosystems due to nitrogendepositionEcosystem functionsPassive fertilizationSO2/HealthHospital admissions for respiratory and cardiac diseasesRespiratory symptoms in asthmaticsNOx/HealthLung irritationLowered resistance to respiratory infectionHospital admissions for respiratory and cardiac diseases(continued)4-7Table 4-2. Human Health and Welfare Effects of Pollutants Affected by the Final CAVR (continued)Pollutant/EffectQuantified and Monetizedin Base EstimatesaQuantified and/or MonetizedEffects in Sensitivity Analyses Unquantified EffectsMercury HealthiIncidences of neurological disordersIncidences of learning disabilitiesIncidences in developmental delaysPotential cardiovascular effectsh, including:– Altered blood pressure regulationh– Increased heart rate variabilityh– Incidences of myocardial infarctionhPotential reproductive effectshMercury DepositionWelfareiImpact on birds and mammals (e.g., reproductive effects)Impacts to commercial, subsistence, and recreational fishingaPrimary quantified and monetized effects are those included whendetermining the primary estimate of total monetized benefits of CAVR. See below fora more complete discussion of the benefit estimates.bIn addition to primary economic endpoints, anumber of biological responses have beenassociated with ozone health, including increased airwayresponsiveness 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.cRecent evidence suggeststhat short-term exposures to ozone may have a significant effect on daily mortality rates, independentof exposure to PM.dIn addition to primary economic endpoints, a number of biological responses have been associated with PM health effects, including morphologicalchanges and altered host defense mechanisms. The public healthimpact of these biological responses may be partly representedby our quantifiedendpoints.eCohort estimates are designed to examine the effects of long-termexposures to ambient pollution, but relative risk estimates may also incorporate someeffects due to short-term exposures (see Kunzli et al. [2001] for a discussion of this issue).fWhile some of the effects of short-term exposure are likely to be captured by the cohort estimates, there may be additional premature mortality fromshort-term PM exposure not captured in the cohortestimates included in the primary analysis.gMay result in benefits or disbenefits. See Section 5.3.4 for more details.hThese are potential effects because the literature is insufficient.iMercury emission reductions are not anticipated for BART fromthe EGU source category because of the cap and trade program promulgated for theCAMR (March 2005); however, the geographic location of mercury reductions may change as a result of this rule. EPA believes any such effects forthese sources would be minimal. Mercury reductions are expected for the non-EGU source categories. The mercury reductions forBART from the nonEGU source categories is expected to be small in comparison to reductions resulting from the recently promulgated CAIR and CAMR(March 2005).4-8 Emissions inventories (2001 CEM, 1996 NEI, MOBILE 5b and 6 PART5 model, NONROAD2002)

Air quality monitoring data AIRS (ozone), FRM (total PM), STN (speciated PM) Model baseline and post-control ambient air quality (REMSAD, CAM-X)

Model population exposure to changes in ambient concentrations

Estimate expected changes in

human health outcomes

Estimate monetary value of changes in human health

Adjust monetary values for growth in real income to year of analysis

Sum health and welfare monetary values to obtain total monetary benefits Concentration response functions

Incidence and prevalence rates for

health endpoints

Population and demographic data (with

growth projections)

Valuation functions Interpolation of projected air concentration surfaces (base and control)

Estimate expected changes in welfare (visibility)

Estimate monetary value of changes in welfare effects Valuation functions •BenMAP-derived (ozone) •SMAT-derived (PM2.5) BenMAP integrated

model

Income elasticities

GDP projections PROCESSES INPUTS INPUTS

Emissions inventories (2001 CEM, 1996 NEI, MOBILE 5b and 6 PART5 model, NONROAD2002)

Air quality monitoring data AIRS (ozone), FRM (total PM), STN (speciated PM) Model baseline and post-control ambient air quality (REMSAD, CAM-X)

Model population exposure to changes in ambient concentrations

Estimate expected changes in

human health outcomes

Estimate monetary value of changes in human health

Adjust monetary values for growth in real income to year of analysis

Sum health and welfare monetary values to obtain total monetary benefits Concentration response functions

Incidence and prevalence rates for

health endpoints

Population and demographic data (with

growth projections)

Valuation functions Interpolation of projected air concentration surfaces (base and control)

Estimate expected changes in welfare (visibility)

Estimate monetary value of changes in welfare effects Valuation functions •BenMAP-derived (ozone) •SMAT-derived (PM2.5) BenMAP integrated

model

Income elasticities

GDP projections PROCESSES INPUTS INPUTS

Figure 4-1. Key Steps in Air Qualit y Modeling-Based Be nefits 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 hea lth 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 becau se Arkansas, New Jersey, and Delaware were included in the CAIR region when IPM m odeling 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. T hus, the analysis presented reflects EPA’s best estimate of the benefits of CAVR, assuming that New Jersey and Delaware become a part of4 Expert elicitation is a formal, highly structured and well-documented process whereby expert judgments, usually of multiple experts, are obtained (Ayyub, 2002).
4-9 the CAIR region for PM 2.5 as well as ozone, but the benef its are slightly understated because of use of modeling that includes Arkansas in the CAIR region for SO 2 and annual NO x controls. In addition, the recently promulgated CAMR is expected to affect the emissions of SO 2 from EGUs in both the CAIR region and thr oughout the rest of the United States. The base case for this analysis only included CA IR 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 effect s 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 reviewe d, was a pilot expert elicitation designed tocharacterize uncertainty in the estimation of PM-related mortality resulting from both shortterm and long-term exposure. 4 The peer review of the pilo t expert elicitation was generally favorable. We provide a deta iled 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 estima te for the mortality category include the following: 1) Inhalation of fine partic les 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, rega rdless 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 i ndustrial sources. However, no clear scientific grounds ex ist for supporting differential effects estimates by particle type.4-10 3) The C-R function for fine particles is approximately linear within the range of ambient concentrations under consideration. Thus, the estimates include health benefits from reducing fine particles in ar eas with varied concentrations of PM, including both regions that are in attainme nt 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, a ssumptions, and inhere nt 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 quantifie d and monetized benefits summarized above, a number of additional categories are not currently amenab le to quantification or valuation. These include reduced acid and par ticulate deposition damage to cultural monuments and other materials, reduced ozone effects on forested ecosystems, and environm ental benefits due to reductions of impacts of acidification in lake s 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 wh ich appropriate health impact functions are not available or which do not provide easily interpretable out comes (i.e., changes in forced expiratory volume [FEV1]). As a result, monetized benefits generated for the primary estimate may underestimate the total benefits attributab le to the final regulatory scenario.
Because of schedule and resource limitati ons, EPA did not conduct a quantitative analysis of benefits from reductions (and potential disbenefits from increases) in groundlevel ozone as a result of precu rsor emissions reductions project ed for BART. However, it is unlikely that net benefits resulting from ozone reductions would have a significant impact on any conclusions reached regarding the overall benefits for this rulemaking. Benefits estimates for the final CAVR were generated using BenMAP, a computer program developed by EPA that integrates a number of the modeling elements used in previous RIAs (e.g., interpol ation functions, population projec tions, health impact functions, valuation functions, analysis and pooling methods) to translat e modeled air concentration estimates into health effects incidence estimates and monetized benefits estimates. BenMAP provides estimates of both the mean impacts and the distribution of impacts (more information on BenMAP can be found at http://www.epa.gov/ttn/ecas/ benmodels.html).4-11 In general, this chapter is organized ar ound the steps illustrated in Figure 4-1. In Section 4.1, we provide an overvie w of the data and methods thatwere used to quantify and value health and welfare endpoi nts and discuss how we inco rporate 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 ai r 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 effect s 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 preferen ce structure and does not account for potential income or substitution effects (i.e., addinga 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 dama ge-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 ai r 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 le vels of air pollution and specifically to those linked to PM. There may be other, indirecthealth impacts associated with implementing emissions controls, such as occupational health impacts for coal miners. These impacts may be positive or negative, but in general, for this set of control alternatives, they are expected to be small relative to the direct air pollution-related impacts.5 For many goods, WTP can be observed by examining actual market transactions. For example, if a gallon of bottled drinking water sells for $1, it can be observed that at least some people are willing to pay $1 for such water. For goods not exchanged in the market, such as most environmental “goods,” valuation is not as straightforward. Nevertheless, a value may be inferred from observed behavior, such as sales and prices of products that result in similar effects or risk redu ctions (e.g., nontoxic cleaners or bike helmets). Alternatively, surveys can be used in an attempt to directly elicit WTP for an environmental improvement.

6 In general, economists tend to view an individual’s WTP for an improvement in environmental quality as the appropriate measure of the value of a risk redu ction. An individual’s willingness to accept (WTA) compensation for not receiving the improvement is al so a valid measure. However, WTP is generally considered to be a more readily available and conservative measure of benefits.
4-12 The welfare impacts analysis is limited to changes in the environment that have a direct impact on human welfare. For this analysis, we are limited by the available data to examine impacts of changes in visibility. We also provide qualitative discussions of the impact of changes in other environmental and ecological effects, for example, changes in deposition of nitrogen and sulfur to terrestrial and aquatic eco systems, 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 outco mes or their values for this analysis. Thus, similar to Kunzli et al. (2000) and other recen t health impact analyses, our estimates are based on the best available methods of benefits transfer. Benef its transfer is the science and art of adapting primary research from similar c ontexts to obtain the mo st accurate measure of benefits for the environmental quality change under analysis. Adjustments are made for the level of environmenta l quality change, the sociodemogra phic and economic characteristics of the affected population, and other factors to imp rove the accuracy and robustness of benefits estimates. 4.1.1 Valuation Concepts In valuing health impacts, we note that re ductions in ambient concentrations of air pollution generally lower the risk of future adve rse health effects by a fairly small amount for a large population. The appropriate ec onomic 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 populati on 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 generally4-13 understate the true value of reduc tions in risk of a health e ffect, because they do not include the value of avoided pain and suffering from the health effect (Harrington and Portney, 1987; Berger et al., 1987).
One distinction in environmental benefits estimation is between use values and nonuse values. Although no general agreemen t 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 chan ges in product prices, quality, and availability; changes in the quality of outdoor recreation and outdoor aesthetics; change s 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 indivi dual is willing to pay for reasons that do not relate to the direct use or enjoyment of any environmental bene fit but might relate to existence values and bequest valu es. Nonuse values are not trad ed, directly or indirectly, in markets. For this reason, measuring nonuse va lues 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 th e 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 technique s cannot be used. Three main nonmarket valuation methods are used to develop values for endpoints considered in this analysis: stated preference (or contingent va luation [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 amountof 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-ba sed valuation. Welldesigned and well-executed stated preference studies are valid for estimating the benefits of7 Concerns about the reliability of value estimates from CV studies arose because research has shown that bias can be introduced easily into these studies if they ar e not carefully conducted. Accurately measuring WTP for avoided health and welfare losses depends on the reliability and validity of the data collected. There are several issues to consider when evaluating study quality, including but not limited to (1) whether the sample estimates of WTP are representative of the population WTP; (2) whether the good to be valued is comprehended and accepted by the respondent; (3) whether the WTP elicitation format is designed to minimize strategic responses; (4) whether WTP is sensitive to respondent familiarity with the good, to the size of the change in the good, and to income; (5) whether the estimates of WTP are broadly consistent with other estimates of WTP for similar goods; and (6) the extent to which WTP responses are consistent with established economic principles.

8 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent change in income.
4-14 air quality regulations. 7 Stated preference valuation studi es form the basis for valuing a number of health and welfare e ndpoints, 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 techni que 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 o ccupation, precautionary behavior, and other activities associated with changes in the risk of death. By examining these risk changes and the other characteristics of pe ople’s choices, it is possible to infer information about the monetary values associated with chan ges 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 freque ntly as levels of PM incr ease, then the appropriately calculated increment of these costs is a reasonable lower-bound estimate (under most conditions) of true economic be nefits 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 Natio nal Income Growth Over Time Our analysis accounts for expected grow th in real income over time. Economic theory argues that WTP for most goods (suc h as environmental prot ection) will increase if real incomes increase. There is substantia l empirical evidence that the income elasticity8 of WTP for health risk reductions is positive, althoug h there is uncertainty about its exact value.4-15 Thus, as real income increases, the WTP fo r 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 fo r which benefits are estimate d, and through differences in income between study populations and the a ffected populations at a particular time. Empirical evidence of the effect of real inco me 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 diffe rences “because of the sensitivity of making such distinctions, and because of insufficien t evidence available at present” (EPA-SABEEAC-00-013, 2000). A recent advisory by anothe r committee associated with the SAB, the Advisory Council on Clean Air Compliance An alysis, 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 r eal income. The same increase should be assumed for the WTP for serious nonfatal healtheffects (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 proposedadjustments for aggregate income growth as part of the primary analysis” (E PA-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 usin g the methods described below. Based on a review of the available income elasticity literature, we adjusted the valuation of human health be nefits 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 Ne umann (1999). An abbr eviated description of the procedure we used to account for WTP fo r 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. As4-16 such, we use different elasticity estimates to adjust the WTP for minor health effects, severe and chronic health effects, and premature mortality. Note th at 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 restric tions 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 Valu es Used to Account for Projected Real Income Growth a

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, projec tions of real gross domestic product (GDP) and populations from 1990 to 2015 are needed to ad just benefits to reflect real per capita income growth. For consistency with the emi ssions and benefits mode ling, 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 app lication of a cohortcomponent model applied to 1990 U.S. Census data projections (U.S . Bureau of Census, 2000). For the years between 2000 and 2015, we applied growth rate s based on the U.S. Census Bureau projections to the U.S. Ce nsus estimate of nati onal population in 2000. We used projections of real GDP provided in Kleckner and Neumann (1999) for the years 19909 U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at http:// www.bea.doc.gov/bea/dn/ 0897nip2/ tab2a.htm.) and U.S. Bureau of Economic Analysis, Economics and Budget Outlook. Note that projections for 2007 to 2010 are based on average GDP growth rates between 1999 and 2007.

10 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the Bureau of Economic Analysis projections.
4-17 to 2010. 9 We used projections of real GDP (i n 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 describe d above, we calculated WTP adjus tment factors for each of the elasticity estimates listed in Ta ble 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 b ecause of a lack of data on the dependence of COI and income, and a lack of data on project ed growth in average wages, no adjustments are made to benefits base d on the COI approach or to work-loss days and worker productivity. This assumption leads us to underpre dict 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 produc tion would also be higher). Table 4-4. Adjustment Factors Used to Account for Projected Real Income Growth a
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.4-18 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, ma ny 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 es timates, 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 humanbehavior). Each of these inputs may be uncertain and, de pending on its role in the be nefits 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 propa gated through the entire analysis. When compounded with uncertainty in later stages, small uncertainties in emission levels can lead to large impacts on total benefits. Some key sources of uncertainty in each stage of the benefits analysis are the following:

C gaps in scientific data and inquiry;

C variability in estimated relationships, such as epidemiological effect estimates, introduced through differences in st udy design and statistical modeling;

C errors in measurement and projection for variables such as population growth rates;

C errors due to misspecification of model st ructures, including the use of surrogate variables, such as using PM 10 when PM 2.5 is not available, excluded variables, and simplification of complex functions; and

C biases due to omissions or other research limitations. Some of the key uncertainties in the benef its 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 be nefits estimates. In response to these comments, EPA has initia ted the development of a comprehensive methodology for characterizing the aggregat e impact of uncertainty in key modeling elements on both health incidence and benefits estimates. For this analysis of the final4-19 Table 4-5. Primary Sources of Uncertainty in the Benefits Analysis

1. Uncertainties Associated with Impact Functions

S The value of the ozone or PM effect estimate in each impact function.

S Application of a single impact function to pollutant changes and populations in all locations.

S Similarity of future-year impact functions to current impact functions.

S Correct functional form of each impact function.

S Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source epidemiological study.

S Application of impact functions only to those s ubpopulations matching the original study population. 2. Uncertainties Associated with Ozone and PM Concentrations

S Responsiveness of the models to changes in precursor emissions resulting from the control policy.

S Projections of future levels of precursor emissions, especially ammonia and crustal materials.

S Model chemistry for the formation of ambient nitrate concentrations.

S Lack of ozone monitors in rural areas requires extrapolation of observed ozone data from urban to rural areas.

S Use of separate air quality models for ozone and PM does not allow for a fully integrated analysis of pollutants and their interactions.

S Full ozone season air quality distribu tions are extrapolated from a limited number of simulation days. 3. Uncertainties Associated with PM Mortality Risk

S Limited scientific literature supporting a direct biolog ical mechanism for observed epidemiological evidence.

S Direct causal agents within the complex mixture of PM have not been identified.

S The extent to which adverse health effects are associated with low-level exposures that occur many times in the year versus peak exposures.

S The extent to which effects reported in the long-term exposure studies are associated with historically higher levels of PM rather than the levels occurring during the period of study.

S Reliability of the limited ambient PM 2.5 monitoring data in reflecting actual PM 2.5 exposures. 4. Uncertainties Associated with Possible Lagged Effects

S The portion of the PM-related long-term exposure mortality effects associated with changes in annual PM levels that would occur in a single year is uncertain as well as the portion that might occur in subsequent years. 5. Uncertainties Associated with Baseline In cidence Rates

S Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore may not accurately represent the actual location-specific rates.

S Current baseline incidence rates may not approximate well baseline incidence rates in 2015.

S Projected population and demographics may not repr esent well future-year population and demographics. 6. Uncertainties Associated with Economic Valuation

S Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and therefore have uncertainty surrounding them.

S Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates because of differences in income or other factors. 7. Uncertainties Associated with Aggregation of Monetized Benefits

S Health and welfare benefits estimates are limited to the available impact functions. Thus, unquantified or unmonetized benefits are not included.4-20 CAVR, EPA has developed a limited probabilistic simulation approach based on Monte Carlo methods to propagate the impact of a limite d set of sources of uncertainty through the modeling framework. Issues such as corre lation between input parameters and the identification of reasonable upper and lower bounds for input distribu tions 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. Th is expert elicitation was aimed at evaluating uncertainty in both the form of the mortality impact function (e.g., threshold versus linear models) and the fit of a specific model to the data (e.g., confidence bounds for specificpercentiles of the mortality effect estimates). Additional issues, such as the ability of longterm cohort studies to capture premature mortality resulting from short-term peak PM exposures, were also addressed in the expertelicitation. In collabor ation with OMB, EPA completed a pilot expert elicitation that is used in the ancillary uncertainty analysis for CAVR (as discussed in Secti on 4.3). Based on our experience during the pilot, EPA plans to conduct a full-scale expert elic itation in 2005 that will provide a more robust characterization of the uncertainty in the pr emature mortality function. For the final CAVR, EPA addressed key sour ces of uncertainty through Monte Carlo propagation of uncertainty in the C-R functions and econo mic valuation functions and through a series of sensitivity analyses examining the impact of alternate assumptions on the benefits estimates that are gene rated. It should be noted th at the Monte Carlo-generated distributions of benefits reflect only some of the uncertainties in the input parameters. Uncertainties associated with emissions, ai r quality modeling, popul ations, and baseline health effect incidence rates are not represente d in the distributions of benefits for CAVR. Our point estimate of total be nefits is uncertain because of the uncertainty in model elements discussed above (see Table 4-5). Un certainty 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 bene fits of the rule. In considering the monetized benefits es timates, 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 liste d in Table 4-1. As previously discussed, ozone benefits are4-21 anticipated for this rule but were not estimates for this analysis. For many health and welfare effects, such as changes in ecosystem func tions and PM-related mate rials damage, reliable impact functions and/or valuation functions are not currently availa ble. 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 ar e qualitatively discussed in the hea lth and welfare effects sections. In addition to unquantifie d benefits, there may also be envi ronmental costs (disbenefits) that we are unable to quantify. These endpoints are qualitatively discussed in the health and welfare effects sections as well. The net eff ect 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 es timate the magnitude of these unquantified and unmonetized benefits, specifi c categories merit further disc ussion. EPA believes there is considerable value to the public for the benef it categories that could not be monetized. With regard to unmonetized PM-relate d health benefit categories lis ted 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 bene fit 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 southwesternUnited States—are included in the monetary benefits estimates we pr oject for this rule. We know thatadditional visib ility benefits will occur in other parks in the country and in ur ban areas. Those bene fits are described in Chapter 3, and an analysis of the potential dollar value of the be nefits is included in Appendix F of this report. We believe the benefits associat ed with these nonhealth benefit categories are likely significant. For example, we are able to quantify significant visibility improvements in Class I areas in the Northeas t, 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 annua l benefits of $242 million beginning in 2015 for visibility improve ments. The value of visibility benefits in areas where we were unable to monetize be nefits could also be substantial. Annual visibility benefits are es timated to be a pproximately $84 million and $416 million for Scenarios 1 and 3, respectively.4-22 We conduct supplemental analys es 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 acidi fication (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 hea lth impacts depend on the demographic characteristics of the population, including age, location, and income. We use projections based on economic forecasting mode ls developed by Woods and Poole, Inc. The Woods and Poole (WP) database contains county-level projections of popul ation by age, sex, and race out to 2025. Projections in each county are de termined simultaneously with every other county in the United States to take into acc ount patterns of economic growth and migration. The sum of growth in county-level populati ons is constrained to equal a previously determined national popul ation 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 variable s 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” a pproach, which relies on linking industrialsector production of nonlocally consumed production items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export-based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector. Third, population is projected for each economic area based on net migration rates de rived from employment opportun ities and following a cohortcomponent method based on fertility and mortality in each area. Fourth, employment and population projections are repeated for countie s, using the economic region totals as bounds. The age, sex, and race distribu tions for each region or county are determined by aging the4-23 population by single year of age by sex and race for each year through 2015 based on historical rates of mortality, fertility, and migration. The WP projections of county-level popul ation are based on historical population data from 1969 through 1999 and do not include the 2000 Census results. Given the availability of detaile d 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-specif ic 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 environmen tal 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. popul ations. 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 benef its of reducing ambient c oncentrations 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 illustra ted in Figure 4-1, quantification of health impacts requires several inputs, including epid emiological effect es timates (concentrationresponse functions), baseline incidence a nd prevalence rates, potentially affected populations, and estimates of changes in ambien t 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 input s 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 Ep idemiological Effect Estimates
The PM health effects we quantified include prematur e mortality, nonfatal heart attacks, CB, acute bronchitis, upper and lower respiratory symp toms, asthma exacerbations, and days of work lost. We relied on the published scientific literature to ascertain the relationship between potential PM exposure (m easured by ambient concentrations) and4-24 adverse human health effects. We evaluated studies using the selection criteria summarized in Table 4-6. These criteria include considera tion 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 popul ation, among other considerations. The selection of C-R functions for th e 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 admissi ons for specific resp iratory diseases), uncertainties in applying eff ect relationships based on clin ical studies to the affected population, or a lack of an esta blished 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 hea lth 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 th an 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 consiste nt 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 polluta nt and a given healthendpoint have been selected, they are quantitatively combined or pool ed to derive a more robust estimate of the relationship. The BenMAP User’s Manual pr ovides details of the procedures used to combine multiple impact functions (Abt Associ ates, 2003). In general, we used fixed or random effects models to pool estimates from diffe rent studies of the same endpoint. Fixed effects pooling simply weights each study’s es timate 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 betw een-study variability, due, for example, to4-25 Table 4-6. Summary of Consideration s Used in Select ing C-R Functions

Consideration Comments Peer-Reviewed Research Peer-reviewed research is preferred to research that has not undergone the peer-review process. Study Type Among studies that consider chronic exposure (e.g., over a year or longer), prospective cohort studies are preferred over cross-s ectional studies because they control for important individual-level confounding variables that cannot be controlled for in cross-sectional studies. Study Period Studies examining a relatively longer period of time (and therefore having more data) are preferred, because they have greater statisti cal power to detect effects. More recent studies are also preferred because of possible changes in pollution mixes, medical care, and lifestyle over time. However, when there are only a few studies available, studies from all years will be included. Population Attributes The most technically appropriate measures of benefits would be based on impact functions that cover the entire sensitive popula tion but allow for heterogeneity across age or other relevant demographic factors. In the absence of effect estimates specific to age, sex, preexisting condition status, or other relevant factors, it may be appropriate to select effect estimates that cover the broadest population to match with the desired outcome of the analysis, which is total national-level health impacts. Study Size Studies examining a relatively large sample are preferred because they generally have more power to detect small magnitude effects. A large sample can be obtained in several ways, either through a large population or thro ugh repeated observations on a smaller population (e.g., thro ugh a symptom diary recorded for a panel of asthmatic children). Study Location U.S. studies are more desirable than non-U.S. studies because of potential differences in pollution characteristics, exposure patterns, medical care system, population behavior, and lifestyle. Pollutants Included in Model When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is important to use properly specified impact functions that include both pollutants. Using single-pollutant models in cases wher e both pollutants are expected to affect a health outcome can lead to double-counting when pollutants are correlated. Measure of PM For this analysis, impact functions based on PM 2.5 are preferred to PM 10 because CAVR will regulate emissions of PM 2.5 precursors, and air quality modeling was conducted for this size fraction of PM. Where PM 2.5 functions are not available, PM 10 functions are used as surrogates, recognizing that there will be potential downward (upward) biases if the fine fraction of PM 10 is more (less) toxic than the coarse fraction. Economically Valuable Health Effects Some health effects, such as forced expiratory volume and other technical measurements of lung function, are difficult to value in monetary terms. These health effects are not quantified in this analysis. Nonoverlapping Endpoints Although the benefits associated with each indivi dual health en dpoint 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.11 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.
4-26 differences in population susceptibility. We used the fixed effects model as our null hypothesis and then determined whether the data suggest that we should reject this null hypothesis, in which case we w ould 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 applie s to both impact functions defined by a single effect estimate and those defi ned by a pooling of multiple e ffect estimates. Although the effect estimate may, in fact, va ry from one location to another (e.g., because of differences in population susceptibilities or differences in the composition of PM), lo cation-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 expos ures 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 uncertain ties remain to be addre ssed by continued research (NRC, 1998), a substantial body of published scientific literature documents the correlation between elevated PM concentrations and incr eased mortality rates. Time-series methods relate short-term (often day-to-day) change s in PM concentrations and changes in daily mortality rates up to se veral days after a period of elev ated PM concentrations. Cohort methods examine the potential relationship be tween community-level PM exposures over multiple years (i.e., long-term exposures) and community-level annual mortality rates. Researchers have found statisti cally significant associations between PM and premature mortality using both types of studies. In ge neral, the risk estimates based on the cohort studies are larger than those derived from time-s eries studies. Cohort analyses are thought to4-27 Table 4-7. Endpoints and Studies Used to Calculate Total Monetized Health Benefits

Endpoint Pollutant Study Study Population Premature Mortality Premature mortality—cohort study, allcause PM 2.5 (annual mean) Pope et al. (2002) >29 years

Premature mortality —all-cause PM 2.5 (annual mean) Woodruff et al. (1997) Infant (<1 year)

Chronic Illness Chronic bronchitis PM 2.5 (annual mean) Abbey et al. (1995) >26 years

Nonfatal heart attacks PM 2.5 (daily) Peters et al. (2001) Adults
Hospital Admissions Respiratory PM 2.5 (daily) Pooled estimate: Moolgavkar (2003)—ICD 490-496 (COPD) Ito (2003)—ICD 490-496 (COPD) >64 years

PM 2.5 (daily) Moolgavkar (2000)—ICD 490-496 (COPD) 20–64 years

PM 2.5 (daily) Ito (2003)—ICD 480-486 (pneumonia) >64 years

PM 2.5 (daily) Sheppard (2003)—ICD 493 (asthma) <65 years

Cardiovascular PM 2.5 (daily) Pooled estimate: Moolgavkar (2003)—ICD 390-429 (all cardiovascular) Ito (2003)—ICD 410-414, 427-428 (ischemic heart disease, dysrhythmia, heart failure) >64 years

PM 2.5 (daily) Moolgavkar (2000)—ICD 390-429 (all cardiovascular) 20–64 years

Asthma-related ER visits PM 2.5 (daily) Norris et al. (1999) 0–18 years (continued)4-28 Table 4-7. Endpoints and Studies Used to Calculate Total Monetized Health Benefits (continued)

Endpoint Pollutant Study Study Population Other Health Endpoints Acute bronchitis PM 2.5 (annual mean) Dockery et al. (1996) 8–12 years

Upper respiratory symptoms PM 10 (daily) Pope et al. (1991) Asthmatics, 9–11 years Lower respiratory symptoms PM 2.5 (daily) Schwartz and Neas (2000) 7–14 years

Asthma exacerbations PM 2.5 (daily) Pooled estimate: Ostro et al. (2001) (co ugh, wheeze and shortness of breath) Vedal et al. (1998) (co ugh) 6–18 years a

Work loss days PM 2.5 (daily) Ostro (1987) 18–65 years

MRADs PM 2.5 (daily) Ostro and Rothschild (1989) 18–65 years

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) study. Based on advice from the SAB-HES, we extended the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age group.
better capture the full public health impact of exposure to air pollution over time, because they capture the effects of long-term exposur es and possibly some co mponent of short-term exposures (Kunzli et al., 2001; NRC, 2002). Th is section discusses some of the issues surrounding the estimation of premature mortality. Over a dozen studies have found significan t associations between various measures of long-term exposure to PM and elevated rate s of annual mortality, be ginning 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 alte rnative model specifications so metimes raised questions about causal relationships (e.g., Lipfert, Morris, a nd Wyzga [1989]). These early “cross-sectional” studies (e.g., Lave and Seskin [1977]; Ozkayn ak 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,12 EPA recognizes that the ACS cohort also is not representative of the demographic mix in the general population. The ACS cohort is almost entirely white and has higher income and education levels relative to the general population. EPA’s approach to this problem is to match populations based on the potential for demographic characteristics to modify the effect of air pollution on mortality risk. Thus, for the various ACS-based models, we are careful to apply the effect estimate only to ages matching those in the original studies, because age has a potentially large modifying impact on the effect estimate, especially when younger individuals are excluded from the study population. For the Lipfert analysis, the applied population should be limited to that matching the sample used in the analysis. This sample was all male, veterans, and diagnosed hypertensive. There are also a number of differences between the composition of the sample and the general population, including a higher percentage of African Americans (35 percent) and a much higher percentage of smokers (81 percent former smokers, 57 percent current smokers) than the general population (12 percent African American, 24 percent current smokers).
4-29 smoking, and diet. More recently, several st udies 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 signif icant improvement over the earlier work because they include individual-level info rmation with respect to healthstatus and residence. The most extensive analyses have been based on data from two prospec tive 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 adu lt male veterans diagnos ed with hypertension has been examined (Lipfert et al., 2000). Th e characteristics of this group differ from the cohorts in the Six-Cities, ACS, and 7th Day A dventist studies with respect to income, race, health status, and smoking status. Unlike pr evious 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 ve teran’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 sc ientific experts commi ssioned 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 reanalyses4-30 confirmed and expanded those of the original investigators. This intensive independent reanalysis effort was occasioned both by the im portance of the original findings and concerns that the underlying individual health effect s information has neve r 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 ex tension 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 c ohort studies in estimating mortality risk reduction (EPA-SAB-COUNCIL-ADV-99-005, 1999) . This recommendation has been confirmed by a recent report fro m the National Resear ch 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). Mo re 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 explaine d 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 r eanalysis of the ACS cohort data (Pope et al., 2002) provides additional refi nements 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 PM 2.5 following implementation of the PM 2.5 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 in cluded as part of the sensitiv ity analysis (SAB-HES, 2004).4-31 The SAB-HES also recommended using the estimated relative risks from the Pope et al. (2002) study based on the average exposure to PM 2.5 , measured by the average of two PM 2.5 measurements, over the periods 1979–1983 and 1999–2000. In addition to relative risks for all-cause mortality, the Pope et al. (2002) study provides relative risks for cardiopulmonary, lung cancer, and all-other cause mortality. Because of concerns regarding the statistical reliability of the all-other cause mortality relative risk estimates, we calculated mortality impacts for the primary analysis based on the all-cause relative risk. However, we provide separate estimates of cardiopulmonary and lung cancer deaths to show how these important causes of death are affected by reductions in PM 2.5 . Recently published studies have strengthened the case for an association between PM exposure and respiratory inflamation and infection leading to premature mortality in children under 5 years of age. Specifically, the SA B-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 da ily 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 marita l 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 th e 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 polluti on 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 PM 2.5 , this analysis uses only the Abbey et al. (1995) study, because it is the only study focusing on the relationship between PM 2.5 and new incidences of CB.4-32 Nonfatal Myocardial Infarctions (heart attacks) . Nonfatal heart attacks have been linked with short-term exposures to PM 2.5 in the United States (Pet ers 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 PM 2.5 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 cardi ovascular 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 consiste nt with hospital admission and other studies showing relationships between fine particles and cardiovascular effects both within and outside the United States. Seve ral epidemiologic studies (Lia o et al., 1999; Gold et al., 2000; Magari et al., 2001) have shown that heart ra te 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 (Car thenon 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 f unctions based on the results of a number of studies. In addition, some studies have exa mined the relationship between air pollution and emergency room visits. Since most emergenc y 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 emergenc y room visits separa tely, taking account of the fraction of emergency room visits that are admitted to the hospital. The two main groups of hospital admissions es timated 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 onl y 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 PM 2.5 , we used studies by Moolgavkar (2003) and Ito (2003). Additional published studies13 Note 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) differen ce in the effect estimates for people 65 and older with cardiovascular hospital admissions between the original and reanalyzed results, we do not expect this choice to introduce much bias.
4-33 show a statistically significant relationship between PM 10 and cardiovascular hospital admissions. However, given that the control scenarios we are analyzing are expected to reduce primarily PM 2.5 , we focus on the two studies that examine PM 2.5 . Both of these studies provide an effect estimate for popula tions 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 ad missions 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 fro m reductions in myocardial in farctions when applying the impact function for cardiovascular hospital ad missions, 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 cau ses, including chronic obstructive pulmonary disease (COPD), pneumoni a, and asthma. As with cardi ovascular admissions, additional published studies show a statistically significant relationship between PM 10 and respiratory hospital admissions. We used only those focusing on PM 2.5 . Both Moolgavkar (2000) and Ito (2003) provide effect estima tes for COPD in populations over 65, allowing us to pool the impact functions for this group. Only Moolgavkar (2000) provides a se parate effect estimate for populations 20 to 64. Total COPD hospita l 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 th e population 65 and older. In addition, Sheppard (2003) provided an e ffect estimate for asthma hos pital admissions for populations under age 65. Total avoided incidences of PM-related respirat ory-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 child ren 18 and under by Norris et al. (1999). As noted earlier, there is another study by Schw artz examining a broader age group (less than 65), but the Schwartz study focused on PM 10 rather than PM 2.5 . 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 will14 See http://www.nlm.nih.gov/medlineplus/ency/article/ 000124.htm, accessed January 2002.
4-34 likely capture the majority of the impact of PM 2.5 on asthma emergency room visits in populations under 65, although ther e 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, slig ht fever, and extreme tiredness, lasting for a number of days. According to th e 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 Do ckery 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 gr eater 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 bur ning, aching, or red eyes). Research on the effects of air pollution on upper respiratory symptoms has thus focused on effects in asthmatics. Incidences of uppe r respiratory symptoms in asth matic 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 PM 2.5 were estimated using an effect estimate developed from Ostro (1987).
MRAD result when individuals reduce most usual daily activities and replace them with less strenuous activities or re st, 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 wo rk because of difficulty breathing or chest pain. The effect of PM 2.5 and ozone on MRAD was estimated using an effect estimate derived from Ostro and Rothschild (1989).15 Estimating asthma exacerbations associated with air pollu tion 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, pr ovided 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 supporti ng the incorporation of asthma exacerbation estimates, does not believe that the association between ambient air pollution, including ozone and PM, and the new onset of asthma is sufficiently strong to support inclusion of this asthma-related endpoint in the primary estimate.
4-35 For CAVR, we have followed the SAB- HES recommendations regarding asthma exacerbations in developing the primary estimate. To prevent double-counting, we focused the estimation on asthma exacerbations occurringin 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. Cons equently, if we had included an adult-specific asthma exacer bation estimate, we w ould likely double-count incidence for this endpoint. However, becau se the general populati on 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 conducte d 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 childre n in Los Angeles for 13 weeks, recording daily occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of breath, wheeze, and cough). Th is study found a statistically significant association between PM 2.5 , measured as a 12-hour average, and the daily prevalence of shortness of breath and wheeze endpoints. A lthough 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 withshortness 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 184-36 months including measurements of daily peak expiratory flow (PEF) and the tracking of respiratory symptoms (e.g., cough, phlegm, wheez e, chest tightness) through the use of daily diaries. Association between PM 10 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 endpoint s that can be monetized, we only included the cough-related effect estimate from this study in quan tifying asthma exacerbations. We employed the following pooling approach in combining estimates generated using effect estimates from the two studies to produce a si ngle asthma exacerbati on incidence estimate. First, we pooled the separate incidence estimates for shortness of breath, wheeze, and cough generated using effect estimate s from the Ostro et al. study, because each of these endpoints is aimed at capturing the same overall endpoint (asthma ex acerbations) 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; bot h 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 epidemiologi cal studies considered for modeling asthmarelated incidence is presente d 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 high er baseline incidence rate than other studies, which may lead to an overstate ment 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 atta ck.” 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. Th e size of this uncertainty depends on factors such as the number of subjects studied and the si ze 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 narro wer uncertainty bounds around the best estimate than do studies of lesse r quality. In selecting health endpoints, we4-37 Table 4-8. Studies Examining Health Impa cts in the Asthmatic Population Evaluated for Use in the Benefits Analysis
Endpoint Definition Pollutant Study Study Population Asthma Attack Indicators Shortness of breath Prevalence of shortness of breath; incidence of shortness of breath PM 2.5 Ostro et al. (2001) African-American asthmatics, 8–13
Cough Prevalence of cough; incidence of cough PM 2.5 Ostro et al. (2001) African-American asthmatics, 8–13 Wheeze Prevalence of wheeze; incidence of wheeze PM 2.5 Ostro et al. (2001) African-American asthmatics, 8–13 Asthma exacerbation $ 1 mild asthma symptom: wheeze, cough, chest tightness, shortness of breath PM 10 , PM 1.0 Yu et al. (2000) Asthmatics, 5–13

Cough Prevalence of cough PM 10 Vedal et al. (1998) Asthmatics, 6–13 Other Symptoms/Illness Endpoints Upper respiratory symptoms $ 1 of the following: runny or stuffy nose; wet cough; burning, aching, or red eyes PM 10 Pope et al. (1991) Asthmatics, 9–11

Moderate or worse asthma Probability of moderate (or worse) rating of overall asthma status PM 2.5 Ostro et al. (1991) Asthmatics, all ages

Acute bronchitis $ 1 episodes of bronchitis in the past 12 months PM 2.5 McConnell et al. (1999) Asthmatics, 9–15
Phlegm “Other than with colds, does this child usually seem congested in the chest or bring up phlegm?” PM 2.5 McConnell et al. (1999) Asthmatics, 9–15

Asthma attacks Respondent-defined asthma attack PM 2.5 , ozone Whittemore and Korn (1980) Asthmatics, all ages

generally focus on endpoints where a statistically significant relations hip 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 relati onship typically do not report identical findings; in some instances the diffe rences are substantial. These differences can exist even between equally reputable studies and may result in health effect estimates that16 Although we are not able to use region-specific effect estimates, we use region-specific baseline incidence rates where available. This allows us to take into account regional differences in health status, which can have a significant impact on estimated health benefits.
4-38 vary considerably. Across- study variation can result from two possible causes. One possibility is that studies report different estimates of the single true relationship between a given pollutant and a health effect because of differences in study design, random chance, or other factors. For example, a hypothetical study conducted in New York and one conducted in Seattle may report different C-R functions for the relationship between PM and mortality, in part because of differences between th ese 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 th e 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. Re gardless of the use of impact functions based on effect estimates from a single epidemiological study or multiple studies, each impact function was applied uniformly thr oughout 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 overestimate s 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 functi ons should be applied only to those populations with ages that strictly match the populations in the underlying epidemiological studies.4-39 However, in many cases, there is no biological reason why the observed health effect would not also occur in other populations within a reasonable range of the studied population. For example, Dockery et al. (1996) examined acute bronchitis in children aged 8 to 12. There is no biological reason to expect a very different response in children aged 6 or 14. By excluding populations outside the range in th e studies, we may be underestimating the health impact in the overall population. In res ponse to recommendations from the SAB-HES, where there appears to be a reasonable physi ological basis for expanding the age group associated with a specific e ffect estimate beyond the study population to cover the full age group (e.g., expanding from a study population of 7 to 11 year olds to the full 6- to 18-year child age group), we have done so and used those expanded incidence estimates in the primary analysis. Uncertainties in the PM Mortality Relationship . A substantial body of published scientific literature demonstrates a correlation between elevated PM concentrations and increased premature mortality. However, muchabout this relationship is still uncertain. These uncertainties include the following: Causality : Epidemiological studies are not de signed to definitively prove causation. For the analysis of the CAVR, we assume d a causal relationship between exposure to elevated PM and premature mortality, based on the consistent evidence of a correlation between PM and mortality reporte d in the substantial body of pub lished scientific literature.
Other Pollutants : PM concentrations are correlated with the concentrations of other criteria pollutants, such as ozone and CO. To th e 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 Thurst on 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 scenario s predict concentrations well a bove the range of values for which the C-R function was f itted, avoided mortality may be misestimated. Although not included in the primary analysis, the potential imp act of a health effects threshold on avoided incidences of PM-related premature mortality is explored as a key sensitivity analysis.4-40 The possible existence of an effect threshol d is a very importantscientific question and issue for policy analyses such as this one. In 1999, the EPA SAB Advisory Council for Clean Air Compliance advised EPA that there was currently no scientific basis for selecting a threshold of 15 : g/m 3 or any other specific threshold for the PM-related health effects considered in typical benefits analys es (EPA-SAB-Council-ADV-99-012, 1999). In 2000, as a part of their review of benefits methods, th e National Research Council concluded that there is no evidence for any departure from lin earity in the observed range of exposure to PM 10 or PM 2.5 , nor any indication of a threshold (N RC, 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 c oncentrations of PM. Most recently, EPA’s updated (2004) Criteria Document states, “In summary, the available evidence does not either support or refute the exis tence of thresholds for effect s of PM on mortality across the range of uncertainties in the studies.” The PM criteria document identifies the general shape of exposure-response relationshi p(s) between PM and/or othe r pollutants and observed health effects (e.g., potential indications of thresholds), as an impor tant issue and uncertainty in interpreting the overall PM epidemiology database. These recommendations are supported by th e recent literature on health effects of short- and long-term PM exposures (Danie ls 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 cer tainly does not find a distinct threshold. Recent cohort analyses by HEI (Krewski et al., 2000) and P ope et al. (2002) provide additional evidence of a quasi-linear relationship betw een long-term exposures to PM 2.5 and mortality. According to the latest draft PM crite ria 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 PM 2.5 , but a somewhat nonlinear relationship between all-cause mortality residuals and mean PM 2.5 concentrations that flattens above ~20 : g/m 3 . The confidence bands around the fitted curves are very wide, however, neither requiring a linear relationship nor precluding a nonlinear relationship if suggested by rean alyses” (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 PM 2.5 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, the4-41 potential impact of a health effects threshold on avoided incidences of PM-related premature mortality is explored as a key sensitivity analysis. Regional Differences : As discussed above, significan t 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 chan ges in annual mortality rates. For the chronic PM/mortality relationship, the length of the lag is unknown a nd 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 ba sed 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 cause s of death within the relatively broad categories analyzed in the cohort studies use d. While we may be mo re 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 cau ses. Some respiratory diseas es 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 longcessation lag, or whether air pollution is causing premature death in individuals withpreexisting heart disease, which would imply very short cessation lags.4-42 The SAB-HES provides severa l recommendations for future research that could support the development of defens ible lag structures, including the use of dis ease-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 la g 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 PM 2.5 , and 20 percent occurring evenly over the years 6 to 20 after the reduction in PM 2.5 . The distribution of deaths over th e 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 l ung 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 progressi on 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 associ ation between pollution levels and adverse health effects generally provide a direct estimate of the relationship of air quality changes to the relative risk of a health effect, rather than an estimate of the absolute number of avoided cases. For example, a typical result might be that a 10 : g/m 3 decrease in daily PM 2.5 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 genera l population) in the asse ssment location corresponding to baseline pollutant levels in that location. To derive the to tal baseline incidence per year, this rate must be multiplied by the corres ponding population number (e.g., if the baseline incidence rate is number of cases per y ear 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 sp ecific subpopulation, such as asthmatics or diabetics. In these cases, it is necessary to devel op not only baseline incidence rate s, but also prevalence rates4-43 for the defining condition (e.g., asthma). Fo r both baseline incidence and prevalence data, we use age-specific rates where available. Im pact functions are applied to individual age groups and then summed over the relevant ag e 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 rate s. 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 thes e 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 di saggregated data available. For premature mortality, county-level data are available. For hosp ital admissions, re gional rates are available. However, for all other endpoints, a single national in cidence rate is used, due to a lack of more spatially disaggreg ated 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 onl y available incidence information comes from the studies themselves; in these cases, inci dence in the study population is assumed to represent typical inciden ce at the national level. Age, cause, and county-specific mortality rates were obtained from the U.S. Centers for Disease Control and Prev ention (CDC) for the years 1996 through 1998. CDC maintains an online data repository of healthstatistics, CDC Wonder, accessible at http://wonder.cdc.gov/. The mortality rates pr ovided 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 mo re stable estimates. When estimating rates for age groups that differed from the CDC W onder groupings, we assumed that rates were uniform across all ages in the reported age gr oup. 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 base d on projections of declines in mortality rates for younger populations and increases in mortality rate s for older populations over time. We are4-44 Table 4-9. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General Population

Endpoint Parameter Rates Value Source a Mortality Daily or annual mortality rate Age-, cause-, and county-specific rate CDC Wonder (1996–1998)

Hospitalizations Daily hospitalization rate Age-, region-, and cause-specific rate 1999 NHDS public use data files b

Asthma ER Visits Daily asthma ER visit rate Age- and regionspecific visit rate 2000 NHAMCS public use data files c ; 1999 NHDS public use data files b Chronic Bronchitis Annual prevalence rate per person

C Aged 18–44

C Aged 45–64

C Aged 65 and older 0.0367 0.0505 0.0587 1999 NHIS (American Lung Association, 2002b, Table 4)

Annual incidence rate per person 0.00378 Abbey et al. (1993, Table 3)

Nonfatal Myocardial Infarction (heart attacks) Daily nonfatal myocardial infarction incidence rate per person, 18+

C Northeast

C Midwest

C South

C West 0.0000159 0.0000135 0.0000111 0.0000100 1999 NHDS public use data files b ; adjusted by 0.93 for probability of surviving after 28 days (Rosamond et al., 1999)

Asthma Exacerbations Incidence (and prevalence) amongasthmatic AfricanAmerican children

C daily wheeze

C daily cough

C daily dyspnea 0.076 (0.173) 0.067 (0.145) 0.037 (0.074) Ostro et al. (2001)

Prevalence among asthmatic children

C daily wheeze

C daily cough

C daily dyspnea 0.038 0.086 0.045 Vedal et al. (1998)

Acute Bronchitis Annual bronchitis incidence rate, children 0.043 American Lung Association ( 2002c, Table 11) (continued)4-45 Table 4-9. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General Population (continued)

Endpoint Parameter Rates Value Source a Lower Respiratory Symptoms Daily lower respiratory symptom incidence among children d 0.0012 Schwartz et al. (1994, Table 2)
Upper Respiratory Symptoms Daily upper respiratory symptom incidence among asthmatic children 0.3419 Pope et al. (1991, Table 2)

Work Loss Days Daily WLD incidence rate per person (18–65)

C Aged 18–24

C Aged 25–44

C Aged 45–64 0.00540 0.00678 0.00492 1996 HIS (Adams et al., 1999, Table 41); U.S. Bureau of the Census (2000)

MinorRestrictedActivity Days Daily MRAD incidence rate per person 0.02137 Ostro and Rothschild (1989, p. 243)

a The following abbreviations are used to describe the national surveys conducted by the National Center for Health Statistics: HIS refers to the National Health Interview Survey; NHDS—National Hospital Discharge Survey; NHAMCS—National Hospital Ambulatory Medical Care Survey.

b See ftp://ftp.cdc. gov/ pub/Health_Statistics/NCHS/Datasets/NHDS/.

c See ftp://ftp.cdc. gov/ pub/Health_Statistics/NCHS/Datasets/NHAMCS/.

d Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze. evaluating the most appropriate way to incor porate 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 as thmatic 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 ba seline incidence rates and their sources for asthma symptom endpoints. Table 4-10 lists th e prevalence rates used to determine the applicable population for asthma symptom endpoint s. Note that these reflect current asthma prevalence and assume no change in prevalence rate s in future years. As noted above, we are investigating methods for projecting asth ma prevalence rates in future years.4-46 Table 4-10. Asthma Prevalen ce Rates Used to Estimate Asthmatic Populations in Impact Functions

Population Group Asthma Prevalence Rates Value Source All Ages 0.0386 American L ung Association ( 2002a, Table 7)—based on 1999 HIS < 18 0.0527 American L ung Association ( 2002a, Table 7)—based on 1999 HIS 5–17 0.0567 American L ung Association ( 2002a, Table 7)—based on 1999 HIS 18–44 0.0371 American L ung Association ( 2002a, Table 7)—based on 1999 HIS 45–64 0.0333 American L ung Association ( 2002a, Table 7)—based on 1999 HIS 65+ 0.0221 American L ung Association ( 2002a, Table 7)—based on 1999 HIS Male, 27+ 0.021 2000 HIS public use data files a African American, 5 to 17 0.0726 American L ung Association ( 2002a, Table 9)—based on 1999 HIS African American, <18 0.0735 American L ung Association ( 2002a, Table 9)—based on 1999 HIS

a See ftp://ftp.cdc.gov/pub/Health_S tatistics/NCHS/Dat asets/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 po llution generally lower the risk of future adverse health affects by a sma ll 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 conveni ent way to use this data in a consistent framework is to convert probabilitie s to units of avoided statisti cal 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 th e 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 th is approach, the size of the affected population is automatically take n 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 inci dences of other health endpoints.4-47 For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we use the cost of treating or mitigating the effect as a primary estimate. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the health effects causing the admission. These COI estimates generally unde rstate 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. Discu ssions of the methods used to value nonfatal myocardial infarctions (heart attacks) and sc hool 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 discussi ons, unit values are presented at 1990 levels of income for consistency with previous analys es. Equivalent future-year values can be obtained from Table 4-11. COI estimates are c onverted 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 avoidingone 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-analyse s 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 $10 million upper confidence limit represents the upper end of the interquartile range from the Viscusi and Aldy (2003) meta-analysis. Because the major ity of the studies in these metaanalyses are based on datasets from the early 1990s or previous decades, we continue to assume that the VSL estima tes provided by those meta -analyses are in 1990 income equivalents. Future resear ch might provide income-adjus ted VSL values for individual studies that can be incorporated into the meta-a nalyses. This would a llow for a more reliable base-year estimate for use in adjusting VSL for aggregate changes in income over time.4-48Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$)Health EndpointCentral Estimate of Value PerStatistical IncidenceDerivation of Estimates1990 IncomeLevel2015 IncomeLevelPremature Mortality (Value of aStatistical Life)$5,500,000 $6,400,000 Point estimate is the mean of a normal distribution with a 95 percent confidenceinterval between $1 and $10 million. Confidence interval is based on two metaanalyses of the wage-risk VSL literature: $1 million represents the lower end of theinterquartile range from the Mrozek and Taylor (2002) meta-analysis and $10 millionrepresents the upper end of the interquartile range from the Viscusi and Aldy (2003)meta-analysis. The VSL represents the value of a small change in mortality riskaggregated over the affected population.Chronic Bronchitis (CB) $340,000 $400,000 Point estimate is the mean of a generated distribution of WTP to avoid a case ofpollution-related CB. WTP to avoid a case of pollution-related CB is derived byadjusting WTP (as described in Viscusi et al., [1991]) to avoid a severe case of CBfor the difference in severity and taking into account the elasticity of WTP withrespect to severity of CB.Nonfatal Myocardial Infarction (heartattack)3% discount rateAge 0–24Age 25–44Age 45–54Age 55–65Age 66 and over7% discount rateAge 0–24Age 25–44Age 45–54Age 55–65Age 66 and over$66,902$74,676$78,834$140,649$66,902$65,293$73,149$76,871$132,214$65,293$66,902$74,676$78,834$140,649$66,902$65,293$73,149$76,871$132,214$65,293Age-specific cost-of-illness values reflect lost earnings and direct medical costs overa 5-year period following a nonfatal MI. Lost earnings estimates are based onCropper and Krupnick (1990). Direct medical costs are based on simple average ofestimates from Russell et al. (1998) and Wittels et al. (1990).Lost earnings:Cropper and Krupnick (1990). Present discounted value of 5 years of lost earnings:Age of onset: at 3%at 7%25-44 $8,774 $7,85545-54 $12,932 $11,57855-65 $74,746 $66,920Direct medical expenses: An average of:1. Wittels et al. (1990) ($102,658—no discounting)2. Russell et al. (1998), 5-year period ($22,331 at 3% discount rate; $21,113 at 7%discount rate)(continued)4-49Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)Health EndpointCentral Estimate of Value PerStatistical IncidenceDerivation of Estimates1990 IncomeLevel2015 IncomeLevelHospital AdmissionsChronic Obstructive Pulmonary Disease(COPD)(ICD codes 490-492, 494-496)$12,378 $12,378 The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 codelevel information (e.g., average hospital care costs, average length of hospital stay,and weighted share of total COPD category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov).Pneumonia(ICD codes 480-487)$14,693 $14,693 The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 codelevel information (e.g., average hospital care costs, average length of hospital stay,and weighted share of total pneumonia category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov).Asthma Admissions $6,634 $6,634 The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 codelevel information (e.g., average hospital care costs, average length of hospital stay,and weighted share of total asthma category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov).All Cardiovascular(ICD codes 390-429)$18,387 $18,387 The COI estimates (lost earnings plus direct medical costs) are based on ICD-9 codelevel information (e.g., average hospital care costs, average length of hospital stay,and weighted share of total cardiovascular category illnesses) reported in Agency forHealthcare Research and Quality (2000) (www.ahrq.gov).Emergency Room Visits for Asthma $286 $286 Simple average of two unit COI values:(1) $311.55, from Smith et al. (1997) and(2) $260.67, from Stanford et al. (1999).(continued)4-50Table 4-11. Unit Values Used for Economic Valuation of Health Endpoints (1999$) (continued)Health EndpointCentral Estimate of Value PerStatistical IncidenceDerivation of Estimates1990 IncomeLevel2015 IncomeLevelRespiratory Ailments Not Requiring HospitalizationUpper Respiratory Symptoms (URS) $25 $26 Combinations of the three symptoms for which WTP estimates are available thatclosely match those listed by Pope et al. result in seven different “symptom clusters,”each describing a “type” of URS. A dollar value was derived for each type of URS,using mid-range estimates of WTP (IEc, 1994) to avoid each symptom in the clusterand assuming additivity of WTPs. The dollar value for URS is the average of thedollar values for the seven different types of URS.Lower Respiratory Symptoms (LRS) $16 $17 Combinations of the four symptoms for which WTP estimates are available thatclosely match those listed by Schwartz et al. result in 11 different “symptomclusters,” each describing a “type” of LRS. A dollar value was derived for each typeof LRS, using mid-range estimates of WTP (IEc, 1994) to avoid each symptom in thecluster and assuming additivity of WTPs. The dollar value for LRS is the average ofthe dollar values for the 11 different types of LRS.Asthma Exacerbations $42 $44 Asthma exacerbations are valued at $42 per incidence, based on the mean of averageWTP estimates for the four severity definitions of a “bad asthma day,” described inRowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP foravoidance of a “bad asthma day,” as defined by the subjects. For purposes ofvaluation, an asthma attack is assumed to be equivalent to a day in which asthma ismoderate or worse as reported in the Rowe and Chestnut (1986) study.Acute Bronchitis $360 $380 Assumes a 6-day episode, with daily value equal to the average of low and highvalues for related respiratory symptoms recommended in Neumann et al. (1994).Restricted Activity and Work/School Loss DaysWork Loss Days (WLDs) Variable(national median= )County-specific median annual wages divided by 50 (assuming 2 weeks of vacation)and then by 5—to get median daily wage. U.S. Year 2000 Census, compiled byGeolytics, Inc.Minor Restricted Activity Days (MRADs) $51 $54 Median WTP estimate to avoid one MRAD from Tolley et al. (1986).17 The choice of a discount rate, and its associated conceptu al basis, is a topic of ongoing discussion within the Federal government. EPA adopted a 3 percent discount rate for its base estimate in this case to reflect reliance on a “social rate of time preference” discounting concept. We have also calculated benefits and costs using a 7 percent rate consistent with an “opportunity cost of capital” concept to reflect the time value of resources directed to meet regulatory requirements. In this case, the benefit and cost estimates were not significantly affected by the choice of discount rate. Further discussion of this topic appears in EPA’s Guidelines for Preparing Economic Analyses (EPA, 2000b).
4-51 As indicated in the previous section on quantification of premature mortality benefits, we assumed for this analysis that some of the incidences of premature mortality related to PM exposures occur in a distri buted fashion over the 20 years following exposure. To take this into account in the valua tion of reductions in premature mortality, we applied an annual 3 percent discount rate to the value of prem ature mortality occurring in future years. 17 The economics literature concerning the a ppropriate 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 m onetary 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 SA B, 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 (E PA-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 addr essed 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 adjustments18 This conclusion was based on a assessment of uncertainty based on statistical error in epidemiological effect estimates and economic valuation estimates. Additional sources of model error such as those examined in the pilot PM mortality expert elicitation may result in different conclusions about the relative contribution of sources of uncertainty.
4-52 Table 4-12. Expected Impact on Estimated Benefits of Premature Mortality Reductions of Differences Between Factors Used in Developing Applied VSL and Theoretically Appropriate VSL

Attribute Expected Direction of Bias Age Uncertain, perhaps overestimate Life Expectancy/Health Status Uncertain, perhaps overestimate Attitudes Toward Risk Underestimate Income Uncertain Voluntary vs. Involuntary Uncertain, perhaps underestimate Catastrophic vs. Protracted Death Uncertain, perhaps underestimate

to the VSL can be made is the timing of the risk” (EPA-SAB-EEAC-00-013, EPA, 2000). In developing our primary estimate of the benefits of premature mortality reductions, we have followed this advice and discounted over the lag period between exposure and premature mortality. Uncertainties Specific to Premature Mortality Valuation . The economic benefits associated with premature mortality are the largest category of monetized benefits of the final CAVR. In addition, in prior analyses , EPA has identified va luation of mortality benefits as the largest contribut or 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 va luation. 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,4-53 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 chan ces 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 va lue, 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 shif t 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 depe nd on such characteristics as age, health state, and the current age to which the individual is likely to survive. Although a survival curve approach provi des a theoretically preferred method for valuing the benefits of reduced risk of premature mortality associated with reducing air pollution, the approach requires a great deal of data to implement. The economic valuation literature does not yet include good estimates of the value of this risk reduction commodity. As a result, in this study we value avoided premature mortality risk using the VSL approach. Other uncertainties specific to premature mortality valuation include the following:

C Across-study variation: There is consid erable uncertainty as to whether the available literature on VSL provides adequate estimates of the VSL saved by air pollution reduction. Although there is cons iderable variation in the analytical designs and data used in the existing litera ture, 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 ri sky occupations, using a wage-hedonic approach. Certain characteristics of both the population affected and the mortality risk facing that population ar e 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 th ey 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.4-54 C Level of risk reduction: The transferability of estimates of the VSL from the wage-risk studies to the context of th e 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 doe s 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 valuedis within the range of risks evaluated in the underlying studies (Rowlatt et al., 1998).

C Voluntariness of risks evaluated: Alt hough job-related morta lity risks may differ in several ways from air pollution-related mortality risks, the most important difference may be that job-related ri sks are incurred voluntarily, or generally assumed to be, whereas air pollution-re lated risks are incurred involuntarily. Some evidence suggests that people will pay more to reduce involuntarily incurred risks than risks incurred voluntarily. If this is the case, WTP estimates based on wage-risk studies may understate WTP to reduce involuntarily incurred air pollution-related mortality risks.

C Sudden versus protracted death: A final important diffe rence related to the nature of the risk may be that some workpl ace 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 th an the WTP to avoid a risk (of identical magnitude) of sudden death. To the extent that the mortality risks addressed in this assessment are associated with longer periods of illness or greater pain and suffering than are the risks addressed in the valuation literature, the WTP measurements employed in the present analysis would reflect a downward bias.

C Self-selection and skill in avoiding risk : Recent research (Shogren and Stamland, 2002) suggests that VSL estimates based on hedonic wage studies may overstate the average value of a risk reduction. This is based on the fact that the risk-wage trade-off revealed in hedoni c studies reflects the pr eferences of the marginal worker (i.e., that worker who demands the highest compensation for his risk reduction). This worker must have either higher ris k, lower risk tolerance, or both. However, the risk estimate used in hedonic studies is generally based on average risk, so the VSL may be upwardl y biased because the wage differential and risk measures do not match.4-55 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 fro m Viscusi et al. (1991). The Viscusi et al. study, however, describes a seve re 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-re lated CB is not as se vere. The adjustment is made by applying the elasticity of WTP w ith respect to severity reported in the Krupnick and Cropper (1992) study. Details of this adjustment pro cedure are provided in the Benefits TSD for the Nonroad Diesel rule making (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 Vi scusi et al.); and the elasticity of WTP with respect to severity of the illn ess. Based on assumptions about the distributions of each of these three uncertain components, we derive a distribution of WTP to avoid a pollutionrelated case of CB by statistical uncertainty an alysis techniques. Th e 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 My ocardial Infarctions (Heart Attacks)

. The Agency has recently incorporated into its analyses the impact of air pollution on the expected number of nonfatal h eart attacks, although it has ex amined 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 att acks. 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 associat ed with a myocardial infarction extend beyond the initial event itself, we consider costs incurred over several years. Using age-specific annual lost earnings estimate d by Cropper and Krupnick ( 1990) and a 3 percent discount rate, we estimated a present di scounted value in lost earning s (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 $74,746 for someone between the ages of 55 and 65. The corresponding age-specific estima tes of lost earnings (in 2000$) using a 7 percent discount rate ar e $7,855, $11,578, and $66,920, respectivel y. Cropper and Krupnick (1990) do not provide lost ear nings estimates for populations under 25 or over 65. As such, we do not include lost earnings in th e cost estimates for these age groups.4-56 We found three possible sources in the litera ture of estimates of the direct medical costs of myocardial infarction:

C Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 5 years to be $51,211 (i n 1986$) 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 $109,474 in y ear 2000$. This estimated cost is based on a medical cost model, which incor porated therapeutic options, projected outcomes, and prices (using “knowledgeabl e 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 hospitaliz ation 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 av erage length of stay for ICD code 410 (myocardial infarction) in the year- 2000 Agency for Healthcare Research and Quality (AHRQ) HCUP databa se 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 therefor e substantially shorter than it would be if they had not died.

C Eisenstein et al. (2001) estimated 10-year costs of $44,663 in 1997$, or $49,651 in 2000$ for myocardial infarction patie nts, using statistical prediction (regression) models to estimate inpatient costs. Only inpatient costs (physician fees and hospital co sts) were included.

C Russell et al. (1998) estimated first-year direct medical costs of treating nonfatal myocardial infarction of $15,540 (in 1995$) and $1,051 annually thereafter. Converting to year 2000$, that would be $23,353 for a 5-year period (without discounting) or $29,568 for a 10-year period. In summary, the three different studies pr ovided significantly different values (see Table 4-13). As noted above, the estimates from these thr ee studies are substantially different, and we have not adequately resolved the sources of differen ces in the estimates. Because the wage-related opportunity cost estimates from Cropper and Krupni ck (1990) cover a 5-year4-57 Table 4-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks

Study Direct Medical Costs (2000$) Over an x-Year Period, for x =

Wittels et al. (1990) $109,474 a 5 Russell et al. (1998) $22,331 b 5 Eisenstein et al. (2001) $49,651 b 10 Russell et al. (1998) $27,242 b 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 $65,902, and added it to the 5-y ear opportunity cost estimate. The resulting estimates are given in Table 4-14.
Table 4-14. Estimated Costs Over a 5-Year Period (in 2000$) of a Nonfatal Myocardial Infarction

Age Group Opportunity Cost Medical Cost a Total Cost 0–24 $0 $65,902 $65,902 25–44 $8,774 b $65,902 $74,676 45–54 $12,253 b $65,902 $78,834 55–65 $70,619 b $65,902 $140,649 > 65 $0 $65,902 $65,902

a An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

b From Cropper and Krupnick (1990), using a 3 percent discount rate.19 A 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 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 damage s 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 ecologi cal 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 cau sed 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 measurem ent of the economic be nefits of improved visibility. It is difficult to quantitatively define a visibility endpoint that can be used for valuation. Increases in PM concentrations cau se increases in light extinction, a measure of how much the components of the atmosphere ab sorb 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 measure d. 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 sm all but perceptible s cenic 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. 19assumption 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.

20 The Clean Air Act designates 156 national parks and wilderness areas as Class I areas for visibility protection.

21 For details of the visibility estimates discussed in th is chapter, please refer to the Benefits TSD for the Nonroad Diesel rulemaking (Abt Associates, 2003).
4-59 EPA considers benefits from two categories of visibility changes: residential visibility and recreational visibility. In both cas es economic benefits ar e believed to consist of use values and nonuse values. Use values incl ude the aesthetic benef its of better visibility, improved road and air safety , and enhanced recreation in activities like hunting and birdwatching. Nonuse values are based on pe ople’s beliefs that the environment ought to exist free of human-induced haze. Nonuse valu es may be more impor tant for recreational areas, particularly nati onal 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 lis ted 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 be tween recreational and residential benefits is that only those people liv ing in residential areas are assumed to receive benefits from residentia l visibility, while all households in the United States are assumed to derive some benefit fro m improvements in Class I areas. Va lues 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 resi dential visibility conduc ted in 1990 (McClelland et al., 1993) and the other is a 1988 survey on recr eational visibility valu e (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 me thods 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 cont roversy and significan t development of both theoretical and empirical knowledg e about how to conduct CV surveys in the past decade. In EPA’s judgment, the Chestnut and Rowe study c ontains many of the elements of a valid CV study and is sufficiently reliable to serve as the basis for monetary estimates of the benefits22 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 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 re gulatory benefit-cost analysis , although it does provide useful estimates on the order of magnit ude of residential visibilitybenefits (EPA-SAB-COUNCILADV-00-002, 1999). Residential visi bility benefits are not calculated for this analysis. The Chestnut and Rowe study measured the demand for visibility in Class I areas managed by the National Park Service (NPS) in three br oad regions of the country: California, the Southwest, and the Southeast. Respondents in five St ates 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 leve ls in the specified recreational areas. The visib ility levels in these photogra phs 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 inco me 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 tran sfer 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 (Che stnut and Rowe, 1990a; 1990b), although representing the best available estimates, has a number of limitations. These include the following:

C The age of the study (late 1980s) will increase the uncertainty about the correspondence of the estimated values to those that might be provided by current or future populations.4-61 C The survey focused only on populations in fi ve States, so the application of the estimated values to populations outside those States requires that preferences of populations in the five surveyed States be similar to those of nonsurveyed States.

C There is an inherent difficulty in separating values expressed for visibility improvements from an overall value for improved air quality. The Chestnut and Rowe study attempted to control for th is by informing respondents that “other households are being asked about visi bility, human health, and vegetation protections in urban areas and at national parks in othe r regions.” However, most of the respondents did not feel that they were able to segregate visibility at national parks entirely from residential visibility and health effects.

C It is not clear exactly what visib ility improvements the respondents to the Chestnut and Rowe survey were valuing. For the purpose of the benefits analysis for this rule, EPA assumed that res pondents 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 mo st relevant metric for policy analysis.

C The WTP question asked about changes in average visibility. However, the survey respondents were shown photog raphs of only summe rtime conditions, when visibility is generally at its worst. It is possible that the respondents believed those visibility c onditions held year-round, in which case they would have been valuing much larger overall improvements in visibility than what otherwise would be the case.

C The survey did not include reminders of possible substitutes (e.g., visibility at other parks) or budget constraints. Th ese reminders are considered to be best practice for stated preference surveys.

C The Chestnut and Rowe survey focuse d on visibility improvements in and around national parks and wilderness areas. The survey also focused on visibility improvements of nati onal parks in the southwest United States. Given that national parks and wilderne ss 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, wit hout introducing additional uncertainty. In general, the survey design and implementa tion reflect the period in which the survey was conducted. Since that time, many improvements to the stat ed preference methodology have been developed. As future survey efforts are completed, EPA will incorporate values for visibility improvements reflecting the improved survey designs.4-62 The estimated relationship from the Chestnut and Rowe study is only directly applicable to the populations represented by survey respondents. EPA used benefits transfer methodology to extrapolate these results to th e 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 th is 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 fo r developing calibra ted WTP functions is based on the approach develope d by Smith et al. (2002). Av ailable evidence indicates that households are willing to pay more for a gi ven visibility improvement as their income increases (Chestnut, 1997). The benefits estimates he re 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 S outhwestern 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 Cla ss I area as well as values for all households in the United States living outside the State cont aining the Class I area, and the value accounts for growth in real income.
We know that additional visi bility benefits will occur in other parks in the country and in urban areas. Those bene fits are described in Chapte r 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 bene fits estimates under the final CAVR are presented in Figure 4-2. This fi gure 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 c hoose the functional form and key parameters of the estimating equation for WTP for the affectedpopulation 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.4-63 Figure 4-2. CAVR Final Rule Visibility Im provements 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 vegetati on, and ecosystems more than any other air pollutant (EPA, 1996a, page 5-11). Change s in ground-level ozone resulting from the control scenarios are expected to affect crop and forest yiel ds throughout the affected area. Well-developed techniques exist to provide monetary estimates of these benefits to agricultural producers and to consumers. Thes e 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 predic ted changes in market prices and production costs. Models also exist to measure benef its to silvicultural pr oducers and consumers. However, these models have not been adap ted for use in analyzing ozone-related forest4-64 impacts. Because of resource limitations, we ar e unable to provide ag ricultural 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 ex posed 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 Networ k (NCLAN), examined 15 species and numerous cultivars. The NCL AN results show thatseveral 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 be nefit expected to accrue as a result of reductions in ambient ozone con centrations 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) repres ent 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 a nd economic damage functions for the potential range of effects relevant to these type s 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 theref ore 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.4-65 The CAVR program, by reducing NO xemissions, 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 fertiliz ation. 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 e ffects. 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 atmosphe ric deposition of nitrogenrepresent 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 assessmen ts of marginal changes in any one stressor or nutrient in forest ecosystems. However, reductions in the deposition of nitrogencould 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 ar e expected to produce economic benefits in the form of reduced materials damage. There are two important categor ies of these benefits. Household soiling refers to the accumulation of dirt, dust, and ash on exposed surfaces. Criteria pollutants also have corrosive e ffects on commercial/industrial buildings and structures of cultural and historical signifi cance. The effects on historic buildings and outdoor works of art are of pa rticular concern because of the uniqueness and irreplaceability of many of these objects.4-66 Previous EPA benefits analyses have been able to provide quantitative estimates of household soiling damage. Consis tent with SAB advice, we de termined that the existing data (based on consumer expenditures from the early 1970s) are too out of date to provide a reliable estimate of curre nt household soiling damage s (EPA-SAB-COUNCIL-ADV-98-003, 1998). EPA is unable to estimate any benefits to commercial and i ndustrial entities fromreduced materials damage. Nor is EPA able to estimate the benefits of reductions in PMrelated damage to histor ic buildings and outdoor works of ar t. 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 unders tood and difficult to measure. The reductions in NO xcaused by the final rule could produce si gnificant 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) con centrations 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 stru cture (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 estimate s of the value of environmen tal 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 belie ves that progress can be made in estimating WTP measures for ecosystem functions. These estimates would be superior to avoided cost estimates in placi ng 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 defi nable 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 m odelers, natural scientists, and economists.4-67 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 hea lth 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 e ffects could be quantified or monetized. The monetized value of these unquantified effects is repr esented by adding an unknown “B” to the aggregate total. The estimate of total monetized hea lth benefits is thus equal to the subset of monetized PM- and ozone -related health and welf are benefits plus B, the sum of the nonmonetized h ealth and welfare benefits. Total monetized benefits are dominated by benefits of mortality risk reductions. The primary analysis estimate projects that the fi nal rule will result in 1,600 avoided premature deaths annually for the Scenario 2 in 2015. No te 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 monetiz ed 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 tota l benefits, in part because we are unable to quantify most of the nonhealth benefits. These unquan tified benefits may be substantial and could exceed the costs of the ru le, although the magnitude of these benefits is highly uncertain. The monetized benefit associated with reducti ons in the risk of premature mortality, which accounts for $9.2 billion in 2015 fo r 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 orde r of magnitude lower than for premature mortality. Hospital admissions for respiratory and cardiovascular causes, visibility, MRADs, work loss days, school abse nce days, and worker productivity account for the majority of the remaining benefits. The remaining cate gories 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 tabl e to the monetary benefits4-68 Table 4-15. Clean Air Visibilit y Rule: Estimated Reduction in Incidence of Adverse Health Effects a

Incidence Reduction Health Effect Scenario 1 Scenario 2 Scenario 3 Premature Mortality b,c Adult, age 30 and over Infant, age <1 year 400 1 1,600 4 2,300 5 Chronic bronchitis (adult, age 26 and over) 230 890 1,300 Nonfatal myocardial infarction (adults, age 18 and older) 570 2,200 3,000 Hospital admissions—respiratory (all ages) d 140 510 720 Hospital admissions—cardiovascular (adults, age >18) e 120 450 640 Emergency room visits for asthma (age 18 years and younger) 370 1,300 1,800 Acute bronchitis (children, age 8–12) 550 2,100 3,000 Lower respiratory symptoms (children, age 7–14) 6,600 25,000 36,000 Upper respiratory symptoms (asthmatic children, age 9–18) 5,000 19,000 27,000 Asthma exacerbation (asthmatic children, age 6–18) 8,100 31,000 44,000 Work loss days (adults, age 18–65) 44,000 170,000 240,000 Minor restricted-activity days (adults, age 18–65) 260,000 1,000,000 1,400,000

a Incidences are rounded to two significant digits. Th ese estimates represent benefits for CAVR Nationwide for the final CAVR program relative to a baseline with CAIR inclusive of the proposal to include SO 2 and annual NO xcontrols for New Jersey and Delaware. Note these estimates may be slightly understated due to the inclusion in CAIR of SO 2 and annual NO xcontrols for Arkansas. The baseline used to estimates these benefits does not consider the recently promulgated CAMR.

c PM premature mortality impacts for adults are based on application of the effect estimate derived from the Pope et al. (2002) cohort study. Infant prematur e mortality based upon studies by Woodruff et al., 1997.
d Respiratory hospital admissions for PM include admissions for COPD, pneumonia, and asthma.

e Cardiovascular hospital admissions for PM include tota l cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure.4-69 Table 4-16. Estimated Monetary Value of Reductions in Incide nce 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 c,d Adult, age 30 and over 3% discount rate 7% discount rate Infant, < 1 year $2,330 $1,960

$6.12 $9,180 $7,730

$23.8 $13,000 $10,900 $34.2 Chronic bronchitis (adults, 26 and over) 90.5 353 498 Nonfatal acute myocardial infarctions 3% discount rate 7% discount rate $49.3 $45.8 $189 $176 $264 $245 Hospital admissions for respiratory causes 1.07 4.03 5.65 Hospital admissions for cardiovascular causes 2.6 10 14.1 Emergency room visits for asthma 0.106 0.362 0.51 Acute bronchitis (children, age 8–12) 0.207 0.79 1.12 Lower respiratory symptoms (children, 7–14) 0.109 0.415 0.587 Upper respiratory symptoms (asthma, 9–11) 0.137 0.523 0.74 Asthma exacerbations 0.367 1.4 1.98 Work loss days 5.56 22.4 31.5 Minor restricted-activity days (MRADs) 13.8 54.1 76.3 Welfare Effects: Recreational visibility, 81 Class I areas $84 $239 $416 Monetized Total e Base Estimate: 3% discount rate 7% discount rate $2,600 + B $2,200 + B $10,100 + B $8,600 + B $14,300 + B $12,200 + B

a Monetary benefits are rounded to three significant dig its for ease of presentation and computation. Benefits in this table are nationwide (with the exception of visibility) and are associated with NO xand SO 2 reductions. Visibility benefits relate to Class I areas in the southeastern and southwestern United States. These estimates represent benefits for CAVR Nationwid e for the final CAVR program relative to a baseline with CAIR inclusive of the proposal to include SO 2 and annual NO xcontrols for New Jersey and Delaware. Note these estimates may be slightly underst ated due to the inclusion in CAIR of SO 2 and annual NO x controls for Arkansas. Note also that the recently promulgated CAMR was not considered in the baseline used to develop these estimates. b Monetary benefits adjusted to account for growth in real GDP per capita between 1990and 2015.

c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure described earlier. Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses (EPA, 2000b; OMB, 2003). d Adult premature mortality estimates based upon studie s by Pope et al., 2002. Infant premature mortality based upon Woodruff et al., 1997.

e B represents the monetary value of health and welfare benefits and disbenefits not monetized. A detailed listing is provided in Table 4-2. Column totals are rounded to the nearest 100 m illion dollars, and totals may not sum due to rounding.4-70 table reveals that there is not always a close correspondence between the number of incidences avoided for a given endpoint and the monetary value associated with that endpoint. For example, there are almost 100 times more work loss days than premature mortalities, yet work loss days account for onl y a very small fracti on 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 a ssociated with PM reductions is a complex process which is subject to a variety of pot ential sources of uncertainty. Key assumptions underlying the estimate of avoided premat ure mortality include the following:

C Inhalation of fine partic les is causally associated with premature death at concentrations near those experienced by most Americans on a daily basis. Although biological mechanisms for this e ffect have not yet been established, the weight of the available epidemiological and experimental evidence supports an assumption of causality.

C All fine particles, regard less 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 automotiv e engines and other industrial sources. However, no clear scientific grounds ex ist for supporting differential effects estimates by particle type.

C The C-R function for fine particles is approximately linear within the range of ambient concentrations under consideration. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM including both regions that are in attainme nt with the fine particle standards and those that do not meet the standard.

C The forecasts for future emissions and associated air quality modeling are valid. Although recognizing the difficulties, a ssumptions, and inhere nt 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 morta lity function to support this analysis is associated with uncertainty resulting from: (a) potential of the study to incompletely capture4-71 short-term exposure-related mortality effects, (b) potential mis-match between study and analysis populations which introduces various form s of bias into the results, and (c) failure to identify all key confounders and effects modifiers, which c ould result in incorrect effects estimates relating morality to PM 2.5 exposure. EPA is researchin g methods to characterize all elements of uncertainty in the dose-response func tion for mortality. As is discussed in detail in the CAIR RIA (EPA, 2005), EPA has used tw o 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 p ilot expert elicitation conducted in 2004 to investigate other uncertainties in the mort ality estimate. In the CAIR benefit analysis, the statistical uncertainty fro m the standard error of the Pope et al., 2002 study was twice the mean benefit estimate at the 95 thpercentile and one-fourth of the mean at the 5 thpercentile, 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 c onfidence intervals from the pilot elicitation applied to the CAIR benefit analysis ranged in value from zero at the 5 thpercentile to a value at the 95 thpercentile that is seven times higher than the Pope et al., 2002-based estimate. These results are hi ghly dependent on the air quality scenarios applied to the concentration-re sponse functions of the Pope et all, 2002 study and the pilot expert elicitation. Thus, the ch aracterization of uncertainty di scussed in the CAIR RIA could differ greatly from what would be observed for CAVR due to differences in populationweighted changes in concentrations of PM 2.5 (i.e., the location of populations exposure relative to the changes in air quality), and may be especially sensitive to the differences in baseline PM 2.5 air quality experienced by populations pr ior to implementation of the CAVR. Table 4-17 shows the mean estimate and estimated 5 thand 95 thpercentiles of premature deaths avoided for our primary estimate based on the Pope etal. (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 deaths4-72Table 4-17. Results of Illustrative Application of Pilot Expert Elicitation: Annual Reductions in Premature Mortality in2015 Associated with theClean Air Visibility RuleSource ofMortalityEstimateScenario 1 Scenario 2 Scenario 35thPercentile Mean95thPercentile5thPercentile Mean95thPercentile5thPercentile Mean95thPercentilePope et al.(2002)170 405 640 680 1,600 2,500 960 2,300 3,500Expert A 0 330 630 0 1,300 2,500 0 1,800 3,500Expert B 0 100 440 0 490 2,200 0 670 3,000Expert C 0 65 220 0 310 950 0 420 1,300Expert D 0 280 840 0 1,100 3,300 0 1,600 4,700Expert E 0 550 1,300 0 2,200 5,200 0 3,100 7,3004-73 avoided in 2015 ranges from a pproximately 310 (based on the judgments of Expert C) to 2,200 (based on the judgments of Expert E) . The statistical uncertainty bounds (5 thto 95 th 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 character ize uncertainty in total benefits estimates, and is conducting a full-scale expert elicitation. The full-scale expe rt 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 NO xand SO 2 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 mortalitie s would be avoided when the emissions reductions from the regulation are fully rea lized 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 th e 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 atmospheri c conditions and sourcelevel emissions, as well as popul ation, health baselines, inco mes, 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 estima tes 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 eval uate new methods and mode ls and select those most appropriate for estimating th e health benefits of reductions in air pollution. It is important to continue improving bene fits 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 anal yses (Guo et al., 1999; Ibald-4-74 Mulli et al., 2001). Enhanced collaboration be tween air quality modelers, epidemiologists, toxicologists, and economists should result in a more tightly integrated analytical framework for measuring health benef its 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 interv entions 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 meas ure 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 provi de a useful framework for evaluation: nonhealth benefits are substantial, but the majority of quantified benefits come from health effects. EPA included in the CAIR RIA (EPA, 2005) a preliminary and experimental application of one type of CEA—a modified quality-adjusted life-years (QALYs) approach. For CAIR, we concluded that the direct usefulness of cost-effectiveness analysis is mitigated by the lack of rule alternatives to compare relative effectiveness, but that comparisons could still be made to other be nchmarks bearing in mind methodological differences. QALYs were developed to evaluate th e effectiveness of individual medical treatments, and EPA is still evaluating the a ppropriate methods for CEA for environmental regulations. Agency concerns with the st andard QALY methodology include the treatment of people with fewer years to live (the elderl y); 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 benef its, 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 effectiven ess 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.4-75 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 PM 2.5 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 ag e distribution of the mortality and chronic disease reductions is not expected to di ffer 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 inciden ces of mortality, chronic bronchitis, and nonfatal heart attacks are 1,600, 890, and 2,200, respec tively. These are roughly 10 percent of the corresponding incidences in the CA IR 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 th e case because both rules are analyzed for a 2015 population), then Scenario 2 would result in roughly 25,000 MI LY. The costs of Scenario 2 are estimated at $1.4 billion (using a 3% discount rate). Costs of illness for chronic bronchitis and nonfatal heart attacks are expected to be $270 milli on, 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. Di viding 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 in terventions of $31,000 ( 2002$) reported in the Harvard Cost Utility Databa se (http://www.hsph.harvard.edu/ cearegistry/index.html). These results are suggestive, but should be interpreted withcaution 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. S econd, if the CAVR confidence intervals on the number of MILY are proportional to those in CA IR, it is less clear that the net cost per MILY will be less than the $50,000 cost-effectiv eness benchmark. Fina lly, by construction MILY will generally be greater than standard QALYs for a given reduction in incidence, which will bias cost-effectiveness comparisons to common QALY benchmarks. Even with these caveats, the results indicate that CAVR as likely to be cost-effective and to compare favorably with other health interventions.4-76 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 Epidemiology3(2):181-202. Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. 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Shapiro. 2000. “Effects of Ambient Air Pollution on Symptoms of Asthma in Seattle-Area Children Enrolled in the CAMP Study.” Environmental Health Perspect ives 108(12):1209-1214.5-1 CHAPTER 5 QUALITATIVE ASSESSMENT OF NONMONETIZED BENEFITS

5.1 Introduction In addition to the enumerated human health and welfare benefits resulting from reductions in ambient levels of PM and ozone, BART will result in benefits that we are not able to monetize. This chapter discusses welfar e benefits associated with reduced sulfur and nitrogen deposition that affects acidificati on of ecosystems and eutrophication in water bodies. Other welfare benefits including poten tial visibility improvements, agricultural yield increases, forestry production increases, reducti ons in soiling and mate rials damage, mercury health and welfare benefits, a nd other welfare categories are di scussed in Chapter 4 of this report. 5.2 Atmospheric Deposition of Sulfur and Nitrogen—Impacts on Aquatic, Forest, and Coastal Ecosystems Reductions in atmospheric depos ition 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 SO 2 and NO x react in the atmosphere (with water, oxyg en, and oxidants) to form various acidic compounds. These acidic compounds fall to Earth in either a wet fo rm (rain, snow, and fog) or a dry form (gases and particles). Prev ailing winds transport the acidic compounds hundreds of miles, often across state and national borders . Acidic compounds (includi ng small particles such as sulfates and nitrates) cause many negative environmental effects. These pollutants

C acidify lakes and streams,

C harm sensitive forests, and

C harm sensitive coastal ecosystems. The effect of atmospheric deposition of acids on freshwater and fore st ecosystems depends largely on the ecosystem’s ability to neutraliz e the acid (Driscoll et al., 2001). This is referred to as an ecosystem’s acid neutralizin g capacity (ANC). Acid neutralization occurs when positively charged ions such as calcium, potassium, sodium, and magnesium,5-2 collectively known as base cati ons, are released. As water moves through a watershed, two important chemical processes actto neutralize acids. The fi rst 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 min eral 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, b ecause 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 weatheri ng (Driscoll et al., 2001). BART is expected to reduce atmospheric deposition of nitrog en and sulfur and to reduce the total nitrogen and sulfur loads. Soils, forests, surface waters and aquatic biot a (fish, algae, and the rest), and coastal ecosystems share water, nutrients, and othe r essential ecosystem components and are inextricably linked by the chemical processes described above. For ex ample, the same base cations that help to neutralize acidity in la kes and streams are also essential nutrients in forest soils, meaning that cation depleti on both increases freshwater acidification and decreases forest productivity. Similarly, the same nitrogen atom that contributes to stream acidification can ultimately contribute to coastal eutrophicati on as it travels downstream to an estuarine environment. Therefore, to understand the full eff ects of atmospheric deposition, it is necessary to recognize the in teractions between all of these systems. 5.2.1 Freshwater Acidification Acid deposition causes acidification of surf ace waters. In the 1980s, acid rain was found to be the dominant cause of acidification in 75 percent of acidi c lakes and 50 percent of acidic streams. Areas especially sensitive to acidification include portions of the Northeast (particularly the Adirondack and Catskill Mountains, portions of New England, and streams in the mid-Appalachian highlands) and Southeastern streams. Some highelevation Western lakes, particularly in the Rocky M ountains, have become acidic, especially during snowmelt. However, a lthough many Western lakes and streams are sensitive to acidification, they ar e not subject to continuously high levels of acid deposition and so have not become chr onically acidified (NAPAP, 1990). ANC, a key indicator of the ability of the water and watershed soil to neutralize the acid deposition it receives, depends largely on the watershed’s physical characteristics:5-3 geology, soils, and size. Waters that are sensitive to acidification tend to be located in small watersheds that have few alka line 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 lowe r 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 aci dity. Acidification can also decrease fish population density and individual fish size (U.S. De partment of the Interior, 2003).
Recent watershed mass balance studies in the Northeast reveal that loss of sulfate from the watershed exceeds atmospheric sulf ur deposition (Driscoll et al., 2001). This suggests that these soils have b ecome saturated with sulfur, m eaning 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 in to the surface waters as sulfate. Scientists now expect that the release of sulfate that previously accumu lated in waters heds will delay the recovery of surface waters in the Northeast that is anticipated in response to the recent SO 2 emission controls (Driscoll et al., 2001). A major study of the ecological response to acidification is taki ng place in the Bear Brook Watershed in Maine. Established in 1986 as part of the EPA’s Watershed Manipulation Project, the project has found thatexperimental additions of sulfur and nitrogen to the watershed increased the concentr ations of both sulfate and nitrate in the West Bear Brook stream. Stream water concentra tions of several othe r ions, including base cations, aluminum, and ANC, changed substantially as well (Norton et al., 1999). During the first year of treatment, 94 per cent of the nitrogen added expe rimentally to the Bear Brook watershed was retained, while the remainder leached into streams as nitrate. Nitrogen retention decreased to about 82 percent insubsequent years (Kahl et al., 1993; 1999). Although the forest ecosystem continued to accu mulate nitrogen, nitrate leaching into the stream continued at elevated le vels throughout the length of the experiment. This nitrate contributed to both episodic and chronic acidifi cation of the stream. This and other similar studies have allowed sc ientists to quantify acidification a nd recovery relationships in eastern watersheds in much more de tail 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 Appalach ian Mountains was extensively investigated in5-4 the early 1990s (e.g., Church et al. [1992]; He rlihy et al. [1993]; Webb et al. [1994]; van Sickle and Church [1995]). A more recent a ssessment of the southern Appalachian region from West Virginia to Alabama identified watersheds that are sensitive to acid deposition using geologic bedrock and the associated buffe ring capacity of soils to neutralize acid. The assessment found that appr oximately 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). A nother study estimated that 18 percent of potential brook trout streams in the mid-Appa lachian Mountains ar e too acidic for brook trout survival (Herlihy et al., 1996). Perh aps 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). Tr end analyses of thes e streams indicate that few long-term sampling sites are recovering from acidifica tion, most are continuing to acidify, and the continuing acidifi cation is at levels that are bi ologically significant for brook trout populations (Webb et al., 2000). 5.2.2 Forest Ecosystems
Reductions in sulfur and n itrogen deposition under BART are expected to reduce the effects of acid deposition on forests. Our cu rrent 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, magnesiu m, potassium, and others) from soils in the northeastern and southeastern United States are at least partia lly 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 neutralizati on 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 indi cates it also leads to aluminum mobilization (Lawren ce et al., 1995), which can have harmful effects on fish (U.S. Department of th e Interior, 2003). The plant physiological processes affected by reduced calcium availability include cell wall structure and growth, carbohydrate me tabolism, stomatal regulation, resistance to plant pathogens, and tolerance of low temper atures (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 of5-5 calcium as an indicator of forest ecosystem f unction 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 stre ss. 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 decreas ed root uptake of cal cium due to calcium depletion from the soil and aluminum mobiliza tion (Smith and Shortle, 2001; Shortle et al., 1997; Lawrence et al., 1997). Acid depositi on 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 chan ges 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 ecosy stems (DeHayes et al., 1999). Red spruce must also expend more metabolic energy to acqui re calcium from soils in areas with low calcium/aluminum ratios, resulting in slow er tree growth (Smith and Shortle, 2001).
Losses of calcium from forest soils a nd 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, oa k) soils, as indicated by l ong-term data on watershed mass balances (Likens et al., 1996; Mitchell et al ., 1996), plot- and waters hed-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 solu tion 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 n itrogen saturated (Adams et al., 1997). Recent studies of the decline of sugar maples in th e 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 nativ e defoliating insects (Horsley et al., 2000). The chemical composition of leaves and needles may also be altered by aci d deposition, resulting in changes in organic matter turnover and nutrient cycling.5-6 5.2.3 Coastal Ecosystems Since 1990, a large amount of research has been conducted on the impact of nitrogen deposition to coastal waters. It is now known th at nitrogen deposition is a significant source of nitrogen to many estu aries (Valigura et al., 2001; Howarth, 1998). The amount of nitrogen entering estuaries due to atmosphe ric deposition varies widely, depending on the size and location of the estuarine watershed a nd other sources of nitrogen in the watershed. For a handful of estuaries, atmospheric de position 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 per cent 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 nitrog en, 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 cau se algal blooms, low levels of oxygen in the water column, fish and shellf ish kills, and cascading population changes up the food chain. Many of the most highly eutrophi c 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 pa rticulate matter on radiative transfer in the atmosphere can also lead to effects of uncertain magnitudeand direction on the pene tration of ultraviolet light and climate. Gr ound level ozone makes up a small percentage of total atmospheric ozone (including the stratosphe ric layer) that attenuates penetr ation of UVb radiation to the ground. EPA’s past evaluation of the informati on 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 th e course of a year (EPA, 2005a). EPA’s most5-7 recent provisional assessment of the currently available information indicates that potential but unquantifiable benefits ma y also arise from reducing oz one-related atte nuation 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 uncer tain to quantify (EPA, 2004). Ozone is a greenhouse gas, and sulfates and nitrates can re duce the amount of solar radia tion reaching the earth, but EPA believes that we are unable to quantify any net climate-related disbenefit or benefit associated with the combined ozone and PM reductions in this rule.
5.3 References Adams, M.B., T.R. Angradi, and J.N. Koche nderfer. 1997. “Stream Water and Soil Solution Responses to 5 Years of Nitrogen and Sulf ur Additions at the Fernow Experimental Forest, West Virginia.” Forest Ecology and Management 95:79-91. Bricker, S.B., C.G. Clement, D.E. Pirh alla, S.P. Orlando, and D.R.G. Farrow. 1999. National Estuarine Eutrophication Assessment: Effects of Nutrient Enrichment in the Nation’s Estuaries . National Oceanic and Atmos pheric Administration, National Ocean Service, Special Projects Office and the National Centers for Coastal Ocean Science. Silver Spring, Maryland. Church, M.R., P.W. Shaffer, K.W. Thornton, D.L. Cassell, C.I. Liff, M.G. Johnson, D.A. Lammers, J.J. Lee, G.R. Holdren, J.S. Kern, L.H. Liegel, S.M. Pierson, D.L. Stevens, B.P. Rochelle, and R.S. Turner. 1992. Direct/Delayed Res ponse Project: Future Effects of Long-Term Sulfur Deposition on St ream Chemistry in the Mid-Appalachian Region of the Eastern United States . U.S. Environmental Agency, EPA/600/R92/186, Washington, DC. 384 pp. DeHayes, D.H., P.G. Schaberg, G.J. Hawl ey, and G.R. Strimbeck. 1999. “Acid Rain Impacts Calcium Nutrition and Forest Health: Alteration of Membrane-Associated Calcium Leads to Membrane Destabilization and Foliar Injury in Red Spruce.” Bioscience 49:789-800. Driscoll, C.T., G. Lawrence, A. Bulger, T. Bu tler, C. Cronan, C. Eagar, K.F. Lambert, G.E. Likens, J. Stoddard, and K. Weathers. 2001. “Acid Deposition in the Northeastern U.S.: Sources and Inputs, Ecosystem E ffects, and Manageme nt Strategies.” Bioscience 51:180-198.5-8 Herlihy, A.T., P.R. Kaufmann, M.R. Church, P.J. Wigington, Jr., J.R. Webb, and M.J. Sale. 1993. “The Effects of Acid Deposition on St reams in the Appalachian Mountain and Piedmont Region of the Mid- Atlantic United States.” Water Resources Research 29:2687-2703. Herlihy, A.T., P.R. Kaufma nn, J.L. Stoddard, K.N. Eshleman, and A.J. Bulger. 1996. Effects of Acidic Deposition on Aquatic Re sources in the Southern Appalachians with a Special Focus on Class I Wilderness Ar eas. The Southern Appalachian Mountain Initiative (SAMI)

. Horsley, S.B., R.P. Long, S.W. Bailey, R. A. Hallett, and T.J. Hall. 2000. “Factors Associated with the Decline Disease of Sugar Maple on the Allegheny Plateau.” Canadian Journal of Forest Research 30:1365-1378. Howarth, Robert. 1998. “An Assessment of Human Influences on Fluxes of Nitrogen from the Terrestrial Landscape to the Estuaries and Continental Shelves of the North Atlantic Ocean.” Nutrient Cycling in Agroecosystems 52(2/3):213-223. Huntington, T.G., R.P. Hooper, C.E. Johns on, B.T. Aulenbach, R. Cappellato, and A.E. Blum. 2000. “Calcium Depletion in a Southeastern United States Forest Ecosystem.” Soil Science Society of America Journal 64:1845-1858. Kahl, J., S. Norton, I. Fernandez, L. Rusta d, and M. Handley. 1999. “Nitrogen and Sulfur Input-Output Budgets in the Experimental and Reference Watersheds, Bear Brook Watershed, Maine (BBWM).” Environmental Monitoring and Assessment 55:113-131. Kahl, J.S., S.A. Norton, I.J. Fernandez, K.J. Na delhoffer, C.T. Driscoll, and J.D. Aber. 1993. “Experimental Inducement of Nitrogen Saturation at the Watershed Scale.” Environmental Science and Technology 27:565-568. Lawrence, G.B., M.B. David, and W.C. Shortle. 1995. “A New Mechanism for Calcium Loss in Forest-Floor Soils.” Nature 378:162-165. Lawrence, G.W., M.B. David, S.W. Bailey, and W.C. Shortle. 1997. “Assessment of Calcium Status in Soils of Red Spruce Forests in the Northeastern United States.” Biogeochemistry 38:19-39.5-9 Lawrence, G.B., M.B. David, G.M. Lovett, P. S. Murdoch, D.A. Burns, J.L. Stoddard, B.P. Baldigo, J.H. Porter, and A.W. Thomps on. 1999. “Soil Calcium Status and the Response of Stream Chemistry to Changing Acidic Deposition Ra tes in the Catskill Mountains of New York.” Ecological Applications 9:1059-1072. Likens, G.E., C.T. Driscoll, and D.C. Bus o. 1996. “Long-Term Effects of Acid Rain: Responses and Recovery of a Forest Ecosystem.” Science 272:244-246. MacDonald, N.W., A.J. Burton, H.O. Liechty, J. A. Whitter, K.S. Pregitzer, G.D. Mroz, and D.D. Richter. 1992. “Ion Leaching in Fore st Ecosystems along a Great Lakes Air Pollution Gradient.” Journal of Environmental Quality 21:614-623. McLaughlin S.B. and R. Wimmer. 1999. “T ansley Review No. 104, Calcium Physiology and Terrestrial Ecos ystem Processes.” New Phytologist 142:373-417. Mitchell, M.J., M.B. David, I.J. Fernandez, R. D. Fuller, K. Nadelhoffer, L.E. Rustad, and A.C. Stam. 1994. “Response of Buried Mineral Soil Bags to Experimental Acidification of Forest Ecosystem.” Soil Science Society of America Journal 58:556-563. Mitchell, M.J., C.T. Driscoll, J.S. Kahl, G. E. Likens, P.S. Murdoc h, and L.H. Pardo. 1996. “Climate Control on Nitrate Loss from Forested Watersheds in the Northeast United States.” Environmental Science and Technology 30:2609-2612. National Acid Precipitation Asse ssment Program (NAPAP). 1991. 1990 Integrated Assessment Report . Washington, DC: National Acid Precipitation Assessment Program Office of the Director. Norton, S.A., J.S. Kahl, I.J. Fernandez, L.E. Rustad, J.P. Schofield, and T.A. Haines. 1994. “Response of the West Bear Brook Waters hed, Maine, USA, to the addition of (NH4)2SO4: 3-Year Results.” Forest and Ecology Management 68:61-73. Norton, S.A., J.S. Kahl, I.J. Fernandez, T.A. Ha ines, L.E. Rustad, S. Nodvin, J.P. Scofield, T. Strickland, H. Erickson, P. Wiggington, and J. Lee. 1999. “The Bear Brook Watershed, Maine, (BBWP) USA.” Environmental Monitoring and Assessment 55:7-51. Rustad, L.E., I.J. Fernandez, M.B. David, M.J. Mitchell, K.J. Nadelho ffer, and R.D. Fuller. 1996. “Experimental Soil Acid ification and Recovery atthe Bear Brook Watershed in Maine.” Soil Science of America Journal 60:1933-1943.5-10 Schaberg, P.G., D.H. DeHayes, G.J. Hawley, G.R. Strimbeck, J.R. Cumming, P.F. Murakami, and C.H. Borer. 2000. “Acid Mist, Soil Ca and Al Alter the Mineral Nutrition and Physiology of Red Spruce.” Tree Physiology 20:73-85. Smith, K.T. and W.C. Shortle. 2001. “Conser vation of Element Concentration in Xylem Sap of Red Spruce.” Trees 15:148-153. Shortle, W.C. and K.T. Smith. 1988. “Alumi num-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 Indicator s of Stress in Healthy Red Spruce.” Journal of Environmental Quality 26:871-876. U.S. Department of the Inte rior, National Park Service. 2003. Assessment of Air Quality and Related Values in Shenandoah National Pa rk. Technical Report NPS/NERCHAL/NRTR-03/090. Phila delphia, PA: National Park Service, Northeast Region. http://www.nps.gov/shen/air_quality.htm. U.S. Environmental Protection Agency (EPA). 2004. Air Quality Criteria for Particulate Matter (October 2004). <http://cfpub.e pa.gov/ncea/cfm/recordisplay.cfm? deid=87903>. U.S. Environmental Protection Agency (EPA). 2005a. Air Quality Criteria for Ozone and Related Photochemical Oxidants (F irst External Review Draft). <http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=114523>. U.S. Environmental Protection Agency (EPA). 2005b. Draft Air Quality Criteria for Ozone and Related Photoc hemical Oxidants E-Docket No. ORD-2004-0015 [Federal Register: January 31, 2005 (Volume 70, Number 19)] <http://www.epa.gov/fedrgstr/EPA-A IR/2005/January/Day-31/a1720.htm>. Valigura, R.A., R.B. Alexander, M.S. Castro, T.P. Meyers, H.W. Paerl, P.E. Stacy, and R.E. Turner. 2001. Nitrogen Loading in Coastal Water Bodies: An Atmospheric Perspective . Washington, DC: American Geophysical Union. Van Sickle, J. and M.R. Church. 1995. Methods for Estimating the Relative Effects of Sulfur and Nitrogen Deposition on Surface Water Chemistry . EPA/600/R-95/172. Washington, DC: U.S. Environmental Protection Agency.5-11 Webb, J.R., F.A. Deviney, Jr., B.J. Cosby, A.J. Bulger, and J.N. Galloway. 2000. Change in Acid- Base Status in Streams in the Shenandoah National Park and the Mountains of Virginia. American Geophysical Union, Biochemical Studies of the Shenandoah National Park. h ttp://www.nps.gov/shen/air_quality.htm. Webb, J.R., F.A. Deviney, J.N. Galloway, C.A. Rinehart, P.A Thompson, and S. Wilson. 1994. The Acid-Base Status of Native Brook Trout Streams in the Mountains of Virginia. A Regional Assessment Based on the Virginia Trout Stream Sensitivity Study . Charlottesville, VA: Univ. of Virginia. http://www.nps.gov/shen/ air_quality.htm.6-1 CHAPTER 6
PROFILE OF POTENTIALLY AFFECTED INDUSTRY SECTORS

Potential sources affected by the BART ru lemaking 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-fu el-fired steamelectric 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; munici pal incinerators; hydrofluoric , sulfuric, and nitric acid plants; petroleum refineries; lime plants; phos phate rock processingplants; coke oven batteries; sulfur recovery plan ts; carbon black plants; primary l ead smelters; fuel conversion plants; sintering plants; secondary metal pr oduction facilities; chemi cal process plants; taconite ore processing fac ilities; 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 pr eceding 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, tran smission, and distribution.
6.1.1 Generation Electricity generation is the first process inthe delivery of electricity to consumers. The process of generating electricity, in most cases, involves creating heat to rotate turbines which, in turn, create electricity. The power sector consists of over 16,000 generating units, consisting of fossil-fuel fired units, nuclear units, and hydroelectric and renewable sources dispersed throughout the country (see Table 6-2).6-2 Table 6-1. Examples of Affected Source Categories

Source Category Name SIC NAICS Industry Profile Subsection Fossil Fuel-Fired Steam Electric Plants (>250 MMBTU heat input per hour) 4911 221111, 221112, 221113, 221119, 221121, 221122 6.1 Power Sector
Fossil Fuel-Fired Industrial Boilers (>250 MMBTU heat input per hour) NA NA NA

Petroleum Refineries 2911 324110 6.6 Petroleum Refining Industry Kraft Pulp Mills 2611, 2621, 2631 322110, 322121, 322122, 322130, 322121, 322122, 322130 6.5 Paper and Allied Products Portland Cement Plants 3241 327310 6.2 Cement Iron and Steel Mill Plants 3312 331111, 331221 6.7 Primary Metal Manufacturing Hydrofluoric, Sulfuric, and Nitric Acid Plants 2819 211112 6.4 Crude Petroleum and

Natural Gas Coke Oven Batteries 3312 331111, 331221 6.7 Primary Metal Manufacturing Sulfur Recovery Plants 2819 325131, 325188, 325998, 331311 6.3 Industrial Organic Chemicals Primary Lead Smelters 3339 331419 6.7 Primary Metal Manufacturing Primary Copper Smelters 3331 331411 6.7 Primary Metal Manufacturing Primary Zinc Smelters 33xx 331419 6.7 Primary Metal Manufacturing Primary Aluminum Ore Reduction Plants 3334 331312 6.7 Primary Metal Manufacturing Municipal Incinerators (>250 tons refuse per day) 4953 562211, 562212, 562213, 562219, 562920 NA

Lime Plants 3274 327410 NA Phosphate Rock Processing Plants 1429 212319 NA (continued)6-3 Table 6-1. Examples of Affected Source Categories (continued)

Source Category Name SIC NAICS Industry Profile Subsection Carbon Black Plants (furnace process) 2895 325182 6.3 Industrial Organic Chemicals Fuel Conversion Plants NA NA NA Sintering Plants NA NA NA Secondary Metal Production Facilitie s 3341 331314, 331423, 331492 6.7 Primary Metal Manufacturing Chemical Process Plants 28xx 325 6.3 Industrial Organic Chemicals Petroleum Storage and Transfer Facilities (capacity > 300,000 barrels) 5171, 5172 424710, 454311, 454312, 424720, 425110, 425120 6.6 Petroleum Refining Industry Taconite Ore Processing Plants 3295 212324, 212325, 212393, 212399, 327992 NA
Glass Fiber Processing Plants 32xx 327212 NA Charcoal Production Facilities 2819 211112, 325131, 325188, 325998, 331311 6.3 Industrial Organic Chemicals Coal Cleaning Plants (thermal dryers) 2999 324199 NA

Table 6-2. Existing Electricity Gen erating Capacity by Energy Source, 2002
Energy Source Number of Generators Ge nerator Nameplat e Capacity (MW) Coal 1,566 338,199 Petroleum 3,076 43,206 Natural Gas 2,890 194,968 Dual Fired 2,974 180,174 Other Gases 104 2,210 Nuclear 104 104,933 Hydroelectric 4,157 96,343 Other Renewables 1,501 18,797 Other 41 756 Total 16,413 979,585 Source: EIA6-4 These electric-generating sour ces provide electricity for commercial, industrial, and residential uses, each of which consumes roughly one-third of the total electricity produced (see Table 6-3). Table 6-3. Total U.S. Electric Power Industry Retail Sales in 2003 (Billion kWh)

N % Residential 1,280 37% Commercial 1,119 32% Industrial 991 28% Other 109 3% All Sectors 3,500 100% Source: EIA In 2003, electric-generating sources produ ced 3,848 billion kWh to meet electricity demand. Roughly 70 percent of this electri city was produced through the combustion of fossil fuels, primarily coal and natural gas, with coal accounting for more than half of the total (see Table 6-4). Table 6-4. Electricity Net Generation in 2003 (Billion kWh)

N % Coal 1,970 51% Petroleum 118 3% Natural Gas 629 16% Other Gases 11 0.3% Nuclear 764 20% Hydroelectric 275 7% Other 81 2% Total 3,848 100% Source: EIA Note: Retail sales and net generation may not correspond exactly because net generation data may include net exported electricity and loss of electricity.6-5 Coal-fired generating units typically suppl y “base-load” electri city, 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 b een traditionally character ized as a collection of independently operated netw orks 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 ope rating transmission of electricity. These responsibilities typically included system planning and expanding, maintaining power quality and stab ility, and responding to failures.
6.1.3 Distribution Distribution of electricity involves networks of smalle r 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 transmi ssion and distribution system is the classic example of a natural monopoly because it is not pr actical 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 c onsidered differently than generation in current efforts to restructure th e industry. Transmission has ge nerally 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 restru cturing 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 in to separate economic6-6 entities in many State efforts. Transmissions and distribution remain price regulated throughout the country based on the cost of service. 6.1.4 Deregulation and Restructuring The ongoing process of deregulation of wholesale and retail electric markets is changing the structure of the electric power industry. In a ddition to reorganizing asset management between companie s, deregulation is aimed at the functional unbundling of generation, transmission, distributi on, and ancillary services the power sector has historically provided to competition in the ge neration segment of the industry. Beginning in the 1970s, government polic y shifted against traditional regulatory approaches and in favor of deregulation for many impor tant industries, including transportation, communications, and energy, which were all thoug ht to be natural monopolies (prior to 1970) that warranted governmental control of pric ing. Some of the primary drivers for deregulation of electric pow er 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 restru cturing in the electri c power industry slowed significantly in response to market volatility and financial turm oil associated with bankruptcy filings of key energy companies in California. By the end of 2001, restructuring had either been delayed or susp ended in eight states that pr eviously 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 fo rm of proposed legisla tion and proposed Federal Energy Regulatory Commission (FERC) actions a imed at reviving restru cturing. For states that have not begun restructuring efforts, it is unclear when and at what pace these efforts will proceed.6-7 Figure 6-1. Status of State Electricity Industry Restructuring Activities (as of February 2003)

6.1.5 Pollution and EPA Regulation of Emissions The burning of fossil fuels, which gene rates about 70 percen t of our electricity nationwide, results in air emissions of SO 2 and NO x , important precursors in the formation of fine particles and ozone (NO xonly) and to visibility impairment. In 2003, the power sector accounted for 67 percent of total nationwide SO 2 emissions and 22 percent of total nationwide NO xemissions (see Figure 6-2). Different types of fossil fuel-fired units vary widely in their air emissions levels for SO 2 and NO x , particularly when uncontro lled. For coal-fired units, NO xemission rates can vary from under 0.05 lbs/mmBtu (for a unit w ith selective catalytic reduction for NO x removal) to over 1 lb/mmBtu for an uncontrolled cyclone boiler. NO xemissions from6-8 Sulfur Dioxide Nitrogen Oxides

Power Sector (22%)

Power Sector (67%)

Figure 6-2. Emissions of SO 2 and NO x from the Power Sector (2003)

coal-fired power plants are formed during co mbustion and are a result of both nitrogen in coal and nitrogen in the air. SO 2 emission rates can vary from under 0.1 lbs/mmBtu (for some units with flue ga s desulfurization for SO 2 removal) to over 5 lbs/mmBtu for units burning higher sulfur coal. For an uncontrolled coal plant, SO 2 emissions are directly related to the amount of sulfur in the coal. Oil- and gas-fired units also have a wide range of NO xemissions depending on both the plant type and the controls installed. Units with selective catalytic reduction (SCR) can have emission rates under 0.01 lbs/mmBtu, while completely uncontrolled units can have emission rates in excess of 0.5 lbs/mmBt u. Gas-fired units have very little SO 2 emissions. NO xemission rates on oil-fired units can range from under 0.1 lbs/mmBtu (for units with new combustion controls) to over 0.6 lbs/mmBtu for units without combustion controls. SO 2 emissions for oil-fired units can range from under 0.1 lbs/mmBtu for units burning low sulfur distillate oil to over 2 lbs/mmBtu for units burning high sulfur residual oil. 6.1.6 Pollution Control Technologies There are two primary options for reducing SO 2 emissions from coal-burning power plants. Units may switch from higher to lowe r sulfur coal, or they may use flue gas desulfurization (FGD, commonly referred to as scrubbers). Accordin g to data submitted to EPA for compliance with the Title IV Acid Rain Program, the SO 2 emission rates for coal-fired units varied from under 0.4 lbs/mmBtu to over 5 lbs/mmBtu depending on the type of coal combusted.6-9 It is generally easier to switch to a coal within the same rank (e.g., bituminous or subbituminous) because these coals will have simila r heat contents and other characteristics. Switching completely to subbituminous coal (w hich 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 w ith much more limited modifications. The two most commonly used scrubber type s are wet scrubbers and spray dryers. Wet scrubbers can use a variety of sorbents to capture SO 2 including limestone and magnesium-enhanced lime. The choice of sorbent can affect the performance, size, and capital and operating costs of th e scrubber. New wet scrubbers typically achieve at least 95 percent SO 2 removal. Spray dryers can achieve over 90 percent removal. One method of reducing NO xemissions is using combustion controls (such as low NO xburners and over-fired air). Combustion controls reduce NO xby ensuring that the combustion of coal occurs under cond itions under which less formation of NO xoccurs. Postcombustion controls reduce NO xby removing the NO xafter it has been formed. The most common postcombustion control is S CR. SCR systems inject ammonia (NH 3 ), which combines with the NO xin the flue gas, to form nitrog en and water and uses a catalyst to enhance the reaction. These systems can reduce NO xby 90 percent and achieve emission rates of around 0.06 lbs/mmBtu. Selective noncatalytic reduction also removes NO xby injecting ammonia, but no catalyst is used. These systems can reduce NO xby up to 40 percent. For more detail on the cost and performan ce 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 SO 2 and NO xthroughout the contiguous United States for the entire pow er system. Documentation for IPM can be found at www.epa.gov/ai rmarkets/epa-ipm. 6.1.7 Regulation of the Power Sector At the federal level, effort s to reduce emissions of SO 2 and NO xhave been occurring since 1970. Policy makers have recognized the need to address these harmful emissions, and incremental steps have been ta ken to ensure that the country meets air quality standards. Federal regulation of SO 2 and NO xemissions at power plants began with the 1970 CAA. The Act required the Agency to de velop performance standards for a number of6-10 source categories including co al-fired power plants. The first New Source Performance Standards (NSPS) for power plants (subpart D) required new units to limit SO 2 emissions either by using scrubbers or by using low-sulfur coal. NO xwas required to be limited through the use of low NO xburners. A new NSPS (subpart Da), promulgated in 1978, tightened the standards for SO 2 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 SO 2 and NO xemissions. The SO 2 program sets a permanent cap on the total amount of SO 2 that can be emitted by electric power plants in the contiguous United States at about one -half of the amount of SO 2 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. Th e program uses a more traditional approach to NO xemission limitations for certain coal-fired electric utility boilers, with the objective of achieving a 2 million ton reduction from projected NO xemission levels that would have been emitted in 2000 without impleme ntation of Title IV. The Acid Rain Program comprises two phases for SO 2 and NO x . Phase I applied primarily to the largest coal-fired electri c generation sources from 1995 through 1999 for SO 2 and from 1996 through 1999 for NO x . Phase II for both pollutants began in 2000. For SO 2 , it applies to thousands of combustion units generating electricity nationwide; for NO xit generally applies to affected units that burned coal during 1990 through 1995. The Acid Rain Program has led to the in stallation of a number of scr ubbers on existing coal-fired units as well as significant fuel switching to lower-sulfur coals. Under the NO xprovisions of Title IV, most existing coal-fired units were required to install low NO xburners. The CAAA also placed much gr eater emphasis on controlling NO xto reduce ozone nonattainment. This has led to th e formation of several regional NO xtrading programs and an intrastate NO xtrading program in Texas. The Ozone Transport Commission (a group of northeast states) created an interstate NO xtrading program that began in 1999. In 1998, EPA promulgated regulations (the NO xSIP Call) that required 21 st ates in the eastern United States and the District of Columbia to reduce NO xemissions 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 in stallation of significant amounts of SCR.6-11 In addition to federal programs to reduce emissions of SO 2 and NO x , several states have also taken action. Seve ral 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 re sembled the well-known building stone quarried on the Isle of Portland inthe English Channel in color and texture. Portland cement is used predominantly in th e production of concrete . In 2002, the United States produced 85 million metric tons of Por tland cement, while U.S. producers shipped 113 million metric tons. The tota l value of Portland cement sh ipments 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 manufactur ing process consists of

C quarrying and crushing the raw materials,

C grinding the carefully proportioned mate rials to a high degree of fineness,

C firing the raw materials mixture in a rotary kiln to produce clinker, and

C grinding the resulting clinker to a fine powder and mixing with gypsum to produce cement. There are basically two distinct methods of blending the raw mixture: the wet process and the dry process. In the wet proce ss, 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 grindi ng and blending are done with dry materials that are fed di rectly into the kiln to be cal cined into clinker. In 2001, wet process kilns produced 18.5 percen t of clinker produced in the United States, dry process kilns produced 75.2 percent of clinker, and ac tive kilns using both processes accounted for the remaining 6.5 percent (van Oss, 2002).6-12 6.2.1.2 Types of Output Although five basic types of Portland cem ent are produced in the United States, Types I and II cement are the most common ty pe of cement shipped from U.S. plants, comprising over 85 percent of total Portla nd cement production in 2001 (van Oss, 2002). A brief description of each type is provided below. Type I: Regular Portland cements are th e 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 a nd sulfate-resisting Portland cements are intended for use when moderate heat of hydration is required or for general concrete construction exposed to moderate sulfate action. Type III: High early strength cements are made from raw materials with a lime-tosilica ratio higher than that of Type I cemen t and are ground finer than Type I cements. They contain a higher proportion of tricalcium silicate thanregular Portland cements. Type IV: Low-heat Portland cements c ontain a lower percentage of tricalcium silicate and tricalcium alumin ate than Type I, thus lo wering the heat evolution. Consequently, the percentage of tetracalcium aluminoferrite is increased. Type IV cements are produced to attain a low heat of hydration. Type V: Sulfate-resisting Portland cemen ts are those that, by their composition or processing, resist sulfates be tter than the other four types. The use of additives, or admixtures, a llows 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 and6-13 accounts for approximately 20 per cent of production costs (see Table 6-5). Fuel is largely consumed by the kilns, and inputs include coal, coke, petroleum coke, and oil. Electricity is consumed by the auxiliary equipment, and in 2001 plants used approximately 144 kilowatthours of electricity per ton of cement produced (van Oss, 2002). Raw materials serve as the kiln feed and account for 60 percent of material costs reported by the Bureau of the Census. The USGS estimates appr oximately 1.7 metric t ons 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 seve ral plants completed capacity upgrades. Table 6-5. Costs of Production for the Cement Industry: 1997–2001

Year Labor Fuel, Electricity, and Materials ($10 6 ) Capital Expenditures ($10 6 ) Quantity (10 3 ) Payroll ($10 6 ) 1997 16.9 734.2 2,475.8 505.6 1998 17.2 756.3 2,528.9 525.4 1999 17.3 815.7 2,526.6 691.2 2000 17.2 829.5 2,589.7 1,108.0 2001 17.2 860.5 2,724.5 1,430.0
Source: U.S. Department of Commerce, Bureau of the Census. 1997. 1997 Economic Census. Manufacturing Industry Series. Cement Manufacturing . Washington, DC: Government Printing Office. 6.2.2 The Demand Side Concrete and reinforced conc rete are used extensively in almost all construction applications including homes, public buildings, roads, industrial plants, dams, bridges, and many other structures. Therefore, the dema nd for Portland cement is a derived demand, and the rate of growth in demand for Portland cem ent largely depends on the rate of growth in construction activities. Cement competes with other cons truction productions such as glass, aluminum, steel, and asphalt. The USGS reports that r eady-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 require s assessing barriers to entry and developing a measure of concentr ation within each market, both of which should6-14 reflect the ability of firms to raise prices above the competitive level. Markets with barriers to entry (e.g., licenses, legal restrictions, or high fixed costs) ar e generally expected to be less competitive than those without such barriers. In addition, less concentrated markets are predicted to be more compe titive and should result in a low value of the concentration measure, while a higher valu e should indicate a higher pric e-cost margin or a higher likelihood of noncompetitiv e 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, lo cation-specific fixed costs and substantial returns to scale that act as a ba rrier to entry. The cap ital investment required for the production of cement involves using large rotary kilns that are not readily movable or transferable to other uses. Because the min imum efficient scale of cement operations is a significant share of local demand, each regiona l Portland cement market can sustain only a small number of firms that are able to earn pos itive 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 c onsumed domestically is imported into the nation each year (approximately 20 percent in 2003), imports tend to fill the gap between domestic production and fl uctuating demand. However, the co st 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 th e Herfindahl-Hirschman index (HHI) is only 466. However, closer examination of the re gional nature of the cement industry suggests these markets are concentrated and increases the likelihood thatfirms 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 produc t, buyers are prevented from distinguishing between the product of sellers in the market so that the ge ographic boundaries of each market are solely determined by the costs of tran sporting the Portland cement. A study of 256-15 regional cement markets in the United States over an 8-year period found that all of the regional markets were in fact con centrated (Iwand and Rosenbaum, 1991). In 2001, approximately 40 comp anies 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 Alab ama are the largest cem ent-producing states, accounting for approximately 50 percent of U.S. production. Cement plants have operate d at nearly full “practicable” capacity over the past 5 years with utilization rates near 90 percent (see Table 6-6). A lthough the utilization rate for 2001 fell to 80 percent, this is primarily the re sult of new cement capacity added late in the year and temporary reductions in production resulting from te chnical problem s 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 fro m 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 me tric tons over the past 3 years. The United States has relied on fore ign imports from foreign countries to meet demand with approximate ly 20 percent of consumed cement being provided from outside the United States. Canada has beena traditional source of imports , but Asian countries such as Thailand and China have recently become important sources of cement. In contrast, little6-16 Table 6-7. Cement Market Statistics 1998–2002 (10 3 Metric Tons, Unless Otherwise Noted)

1998 1999 2000 2001 2002 Production: Portland and masonry cement 83,931 85,952 87,846 88,900 89,000 Clinker 74,523 76,003 78,138 78,451 82,000 Shipments to final customers, includes exports 103,696 108,862 110,048 113,136 110,000 Imports of hydraulic cement for consumption 19,878 24,578 24,561 23,591 22,500 Imports of clinker for consumption 3,905 4,164 3,673 1,884 1,660 Exports of hydraulic cement and clinker 743 694 738 746 900 Consumption, apparent 103,457 108,862 110,470 112,710 110,000 Price, average mill value, dollars per ton 76.46 78.27 78.56 76.50 77.00 Stocks, mill, year end 5,393 6,367 7,566 6,600 7,600 Employment, mine and mill, number 17,900 18,000 18,000 18,000 18,000 Net import reliance as a percentage of apparent consumption 19 21 20 21 19

Source: van Oss, H. 2003. Mineral Commodity Summa ries: Cement. http://minerals.usgs.gov/minerals/ pubs/commodity/cement/170303.pdf. Last updated January 2003.
cement is exported (less than 1 million metric t ons) 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 signifi cant 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 Corpor ation, and a Mexican cement firm purchased Decotah Cement from the state of South Dakot a. 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 emi ssion reduction strategies. Finally, the USGS reports that the use of natural and synthetic substitutes for Portland cement are growing6-17 outside of the United States. These material s have some performance advantages in some applications and can lower energy and related envir onmental costs. 6.3 Industrial Organic Chemicals The industrial organic chemicals (not el sewhere classified) industry (NAICS 3251) produces organic chemicals for end-use appli cations and for inputs into numerous other chemical manufacturing industries. In nominal terms, it was the single largest segment of the $419 billion chemical manufacturing indus try (NAICS 325) in 1997, accounting for approximately 27 percent of the industry’s shipments. All organic chemicals are, by definiti on, 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 cust om markets, using batc h processes to produce a diverse range of chemicals. Specialty chemi cals generally require mo re 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 industria l 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 ge neral description of th e 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 interm ediates (EPA, 2002b). The typical chemical synthesis processes incorporate multiple feedstocks in a series of chemical reactions. Commodity chemicals are produced in a continuous react or, and specialty chemicals are produced in batches. Specialty chemi cals 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 proce ss. Reactions usually take place at high temperatures, with one or tw o additional components being intermittently added. As the6-18 production advances, by-products are removed using separati on, 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 or ganic chemicals comprise nine general categories of products:

C aliphitic and other acyclic organic ch emicals (ethylene); acetic, chloroaceptic, adipic, formic, oxalic, and tartaric acids and their metallic salts; chloral, formaldehyde, and methylamine;

C solvents (ethyl alcohol etc.); methano l; amyl, butyl, and ethyl acetates; ethers; acetone, carbon disulfide, and chlorinated solvents;

C polyhydric alcohols (e.g., synthetic glycerin);

C synthetic perfume and fl avoring materials (e.g., citral, methyl, oinone);

C rubber processing chemicals, both accel erators and antioxidants (cyclic and acyclic);

C cyclic and acyclic plastic izers (e.g., phosphoric acid);

C synthetic tanning agents;

C chemical warfare gases; and

C esters, amines, etc., of polyhydric al cohols 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 e quipment used (EPA, 2002b). Fr equently, 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 regulate d because it emits chemicals through many types of media, including discharges to air, land, and water, and because of the volume and composition of these emissions.6-19 6.3.1.4 Production Costs Of all the factors of production, employ ment 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. Evenwith the drop in employment, payroll was $300 million more in 2001 than in 1997. The cost of materials fluctuated between $29 and $36 billion for these years, and new capital investment averaged $109 billion a year. Table 6-8. Inputs for the Industrial Organic Chemicals Industry (NAICS 3251), 1997–2001

Year Labor Materials ($10 6 ) Capital Expenditures ($10 6 ) Quantity (10 3 ) Payroll ($10 6 ) 1997 200.8 10,290.9 59,632.4 113,356.9 1998 202.2 10,498.4 53,294.7 106,695.1 1999 196.6 10,504.8 58,090.9 106,288.5 2000 190.8 10,530.8 69,948.1 115,707.6 2001 183.2 10,582.4 66,020.6 104,430.2 Source: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001

. Washington, DC: Government Printing Office.

6.3.2 The Demand Side Industrial organic chemicals are component s of many chemical products. Most of the chemical sectors (classi fied under NAICS 325) are downstream users of organic chemicals. These sectors either purchase comm odity chemicals or enter into contracts with industrial organic chemical producers to obtai n 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).6-20 6.3.3 Organization of the Industry: Mark et Concentration, Plants, and Firms The industrial organic chemicals industr y 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 ship ment 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 Ec onomic 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 petrochemi cal manufacturing industry, and NAICS 325199 represents manufacturing of all other basic organic chemicals. Table 6-9. Historical Size of Establishments and Value of Shipments for the Industrial Organic Chemicals Industry (SIC 2869/NAICS 3251)

1987 1992 Number of Employees in Establishment Number of Facilities Value of Shipments (1992 $10 6 ) Number of Facilities Value of Shipments (1992 $10 6 ) 1 to 4 employees 97 552.8 100 102.6 5 to 9 employees 80 200.9 80 208.7 10 to 19 employees 91 484.7 97 533.9 20 to 49 employees 137 1,749.9 125 1,701.5 50 to 99 employees 99 2556.3 106 3,460.9 100 to 249 employees 110 10,361.2 111 8,855.9 250 to 499 employees 41 17,156.9 41 9,971.1 500 to 999 employees 27 9,615.5 30 13,755.0 1,000 to 2,499 employees 11 9,184.6 10 9,051.0 2,500 or more employees 6 7,156.9 5 6,613.5 Total 699 59,019.7 705 54,254.1 Sources: U.S. Department of Commerce, Bureau of the Census. 1995. 1992 Census of Manufactures, Industry Series: Industrial Organic Chemicals . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 1990. 1987 Census of Manufactures, Industry Series, Industrial Organic Chemicals . Washington, DC: Government Printing Office.6-21 Table 6-10. 1997 Size of Establishments, Va lue of Shipments, and Payroll for the Industrial Organic Chemicals Industry (NAICS 3251)

NAICS 325110 NAICS 325199
Number of Employees in Establishment Number of Facilities Value of Shipments ($10 6 ) Number of Facilities Value of Shipments ($10 6 ) 1 to 4 employees 4 3.3 111 178.4 5 to 9 employees 2 D 60 321.2 10 to 19 employees 5 26.0 80 844.9 20 to 49 employees 5 101.1 136 2,211.7 50 to 99 employees 10 866.9 100 4,364.9 100 to 249 employees 13 2,669.1 118 10,905.4 250 to 499 employees 9 5,211.0 33 7,592.2 500 to 999 employees 5 D 25 12,695.8 1,000 to 2,499 employees 1 D 12 D 2,500 or more employees — — 1 D Total 54 20,534.8 676 53,542.4

D = undisclosed Source: U.S. Department of Commerce, Bureau of the Census. 1999a. 1997 Economic Census. Manufacturing Industry Series. All Other Basic Organic Chemical Manufacturing . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 1999b. 1997 Economic Census. Manufacturing Industry Series. Petrochemical Manufacturing . Washington, DC: Government Printing Office.
6.3.3.1 Capacity Utilization The capacity utilization ratio for the indus try averaged 78 over the 5-year period presented. The varying capacity utilization ratios within this industry reflect changes in production volumes and new pr oduction facilities and capacities going on- and off-line between 1997 and 2001 (see Table 6-11).6-22 Table 6-11. Capacity Utilization Ratios fo r 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 ship ments rose in 2000 to a high of $115.7 billion before declining in 2001, as seenin Table 6-12. Between 1997 and 2001, the industry’s shipments fell 7.9 percent to $104.4 b illion. This decrease largely reflects the downturn in petrochemical produc tion 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 ($10 6 ) 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 indus try is expected to expand through 2004 at an annual rate of 3 percent (Saftlas, 1999). U.S. produ cers 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 industr ial organic chemicals, but th is surplus will decrease with increased global competition.6-23 6.4 Crude Petroleum and Natural Gas The crude petroleum and natural gas industry encompasses the oil and gas extraction process from the exploration for oil and natural gas deposits thr ough 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 c ontiguous 48 U.S., Alaska, and at offshore facilities. There are four major stages inoil 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 developm ent 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 fiel ds are equipped with a pad and roads; ships, floating structures, or a fixed platform are procured for o ffshore 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 pe rcent); feedstocks for the petrochemical industry (3 percent); and other le sser products (EPA, 2000). Natural gas is produced from either oil we lls (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 al so components. Propane and butane, the heavier components of natural gas, exist as liquids when cooled and compressed. These6-24 latter two components are usua lly separated and pr ocessed 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 trans ported 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 th e 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 individua l hydrocarbons during processing is possible because of the differing physical proper ties 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 pi pes 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 downsiz ing 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 (includingfuel and electricity) accounted for approximately 60 pe rcent; the vast majority of these expenditures are for materials and supplies. Ca pital expenditures accounted fo r the remaining 32 percent.
6.4.2 Demand-Side Characteristics Crude oil, or unrefined petroleum, is a co mplex 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, synthe tic rubber and fibers, soaps and cleansing agents, waxes, jellies, and fertilizers. Petroleum products also fuel the engines of automobiles, airplanes, ships, tract ors, trucks, and rocket s. Other applications include fuel for electric power generation, lubricants fo r machines, heating, and asphalt (EPA, 1995c).6-25 Table 6-13. Costs of Production for the Crude Petroleum and Natural Gas Industry: 1997

NAICS Labor Fuel, Electricity, and Materials ($10 6 ) Capital Expenditures ($10 6 ) Quantity (10 3 ) Payroll ($10 6 ) 211 111.0 5,511.0 42,268.0 21,784.0 213111 53.0 1,901.0 3,796.0 2,205.0 213112 106.0 3,622.0 3,093.0 1,161.0

Natural gas is used by residential, commerc ial, industrial, and electric utility users. Total consumption of natural gas in the Un ited 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 producer s 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 ge nerating 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 produc ts 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 re ferred to as fuel switching. The extent to which consumers change th eir fuel usage depends on such factors as the availability of a lternative fuels and the capital requi rements 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 c onsumer 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 consum ers will switch fuels as they repl ace existing capital with new capital6-26 capable of using relatively cheaper fuels. For example, if the price of natural gas were to significantly increase relative to the price of electricity for residential consumers, most consumers would be unlikely to replace their natural gas furnaces immediately. However, if the natural gases price consistently remained higher compared to electricity prices over time, residential consumers would be more likely to replace their natural gas furnaces with electric heat pumps as their existing furnaces wear out. 6.4.3 Organization of the Industry: Mark et Concentration, Plants, and Firms Many oil and gas firms are merging to remain competitive in both the global and domestic marketplaces. By merging with thei r 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, Califor nia, 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 pe rcent of U.S. crude oil and natural gas output, the domestic oil and gas industry is dominated by 24 integrated petroleum and natural gas re finers 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 Ga s Establishment and Company Statistics: 1997

NAICS Description Number of Companies Number of Establishments 211 Oil and Gas Extraction 6,859 8,312 213111 and 213112 SupportActivities Mining 2,745 8,694
Source: U.S. Department of Commerce, Bureau of the Census. 1999c. 1997 Economic Census, Mining Industry Series , Crude Petroleum and Natural Gas Extraction . Washington, DC: U.S. Department of Commerce.6-27 6.4.4 Markets and Trends U.S. annual oil and gas producti on is a small percentage of total U.S. reserves. In 2001, oil producers extracted approximately 1.2 pe rcent of the nation’ s crude oil reserves (see Table 6-15). A slightly larger percenta ge of NGLs was extracted (1.4 percent), and a larger percentage of natural gas was extract ed (3.0 percent). The United States produces approximately 38 percent ( 2,118 million barrels) of its annua l 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 origin ates in Canadian fields in the Rocky Mountains and off the Coast of Nova Scotia and New Brunswick. Table 6-15. Estimated U.S. Oil and Gas Reserves, Annual Production, and Imports, 2001

Category Reserves Annual Production Imports Crude Oil (10 6 barrels) 174,820 2,118 3,405 Natural Gas (10 9 cubic feet) 1,430,630 19,702 3,977 Natural Gas Liquids (10 6 barrels) 23,570 682 1
Sources: U.S. Department of Energy, Energy Information Administration. 2002c. U.S. Crude Oil, Natural Gas, and Natural Gas Liquids Reserves 2001 Annual Report . Washington, DC: U.S. Department of Energy. U.S. Department of Energy, Energy Information Administration. 2002b. Petroleum Supply Annual 2001, Volume I. Washington, DC: U.S. Department of Energy. U.S. Department of Energy, Energy Information Administration. 2003c. Natural Gas Annual 2001

. Washington, DC: U.S. Department of Energy.
Between 1990 and 1998, crude oil consumpti on increased 1.4 percent per year, and natural gas consumption increased 2.0 percen t per year. The increase in natural gas consumption came mostly at the expense of coal consumption (EPA, 1999b). The Energy Information Administration (EIA) anticipates that natural gas consumption will continue to grow, but only at an average annual growth rate of 0.3 percent (not including consumption by electricity generators) through the year 2020. They also expect crude oil consumption to grow at an annual rate of less than 1 percent over the same period (DOE, 2003a).6-28 6.5 Paper and Allied Products The paper and allied produc ts industry (NAICS 322) is one of the largest manufacturing industries in the United Stat es. In 2001, the industry shipped nearly $156 billion in paper commodities. The indus try produces a wide range of wood pulp, primary paper products, and pa perboard products such as printing and writing papers, industrial papers, tissues, c ontainer board, and boxboard. The industry also includes manufacturers that “convert” pr imary paper and paperboard into finished products like envelopes, packaging, and shi pping containers (EPA, 2002c). 6.5.1 The Supply Side: Production and Costs 6.5.1.1 Production Process The manufacturing paper and allied products industry is capital- and resourceintensive, consuming large amounts of pulp w ood and water in the manufacturing process. Approximately half of all paper and allie d products establishments are integrated facilities, meaning that they produce both pulp and pape r on-site. The remain ing half produce only paper products; few facilities produce only pulp (EPA, 2002c). The paper and paperboard ma nufacturing process can be di vided into three general steps: pulp making, pulp processing, and paper/paperboard production. Paper and paperboard are manufactured using what is e ssentially the same pro cess. The principal difference between the two products is that paperboard is thicke r than paper’s 0.3 mm. Producers manufacture pulp mixtures by us ing 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 cellu lose 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 br ightness. 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. Ne xt, 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 rolle rs. The paper is stored on large rolls before being shipped6-29 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 produc ts industry’s output ranges from writing papers to containers and packaging. Paper products include printing and wr iting papers; paperboard boxes; corrugated and solid fiber boxes; fibe r cans, drums, and s imilar products; sanitary food containers; building paper; packaging; bags; sanitary pape r napkins; envelopes; stationary products; and othe r converted paper products. 6.5.1.3 Major By-Products and Co-Products The paper and allied products i ndustry 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 e quivalent amount of waste wate r discharged per ton of pulp ranges from 14 to 140 kg (EPA, 2002c). Mo st 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 pape r and allied products industry are listed in Table 6-16. From 1997 to 2001, industry payroll generally ranged from approximately $22 to 23 billion. Employ ment 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 produc t types, applications, and low cost due to ready access to raw materials. Manufactur ers 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 ne wsprint for personal consumption and for the printing and publishi ng industry or durable for conversion into shipping cartons, drums, or sanitary boxes . Inputs in the paper production process are readily available in the United States because one-third of the country is forested, and facilities generally have ready access to waterways.6-30 Table 6-16. Inputs for the Paper and A llied Products Industry (NAICS 322), 1997–2001

Labor Materials ($10 6 ) Total Capital Expenditures ($10 6 ) Year Quantity (10 3 ) Payroll ($10 6 ) 1997 574.3 22,312.0 80,189.5 8,595.1 1998 572.4 22,529.5 82,419.4 8,546.7 1999 560.7 22,837.4 82,720.9 7,081.1 2000 548.3 22,680.1 87,346.6 7,383.5 2001 530.2 22,188.3 82,823.0 6,797.4
Sources: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures 2001

. Washington, DC: Government Printing Office.
The paper and allied products industry is an integral part of the U.S. economy; nearly every industry and service sector relie s 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 san itary 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: Mark et 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 fi rms’ combined sales we re 18.5 percent of the industry’s total sales. This i ndustry’s unconcentrated nature is also indicated by its HHI of 173.3.
Table 6-17. Measures of Ma rket Concentration for Paper and Allied Products Markets, 1997

NAICS Description CR4 CR8 HHI Number of Companies Number of Facilities 322 Paper Manufacturing 18.5 31.1 173.3 3,808 5,868
Source: U.S. Department of Commerce, Bureau of the Census. 2001. 1997 Economic Census, Manufacturing Subject Series, Concentration Ratios in Manufacturing . Washington, DC: Government Printing Office.6-31 In 1997, 3,808 companies produced paper and allied products and operated 5,868 facilities. By way of comparison, 4,264 comp anies controlled 6,416 f acilities 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 Va lue of Shipments for the Paper and Allied Products Industry (NAICS 322)

1987 1992 1997

Number of Employees in Establishment Number of Facilities Value of Shipments

($10 6 ) Number of Facilities Value of Shipments

($10 6 ) Number of Facilities Value of Shipments

($10 6 ) 1 to 4 employees 729 640.6 786 216 687 D 4 to 9 employees 531 D 565 483 500 605.2 10 to 19 employees 888 1,563.4 816 1,456.5 706 1,672.7 20 to 49 employees 1,433 18,328.6 1,389 6,366.6 1,292 7,345.4 50 to 99 employees 1,018 D 1,088 12,811.5 1,033 14,686.8 100 to 249 employees 1,176 32,141.7 1,253 35,114.0 1,193 40,366.0 250 to 499 employees 308 24,221.1 298 22,281.2 265 23,940.2 500 to 999 employees 145 28,129.1 159 31,356.5 131 32,060.7 1,000 to 2,499 employees 63 24,903.1 62 23,115.4 59 26,780.6 2,500 or more employees 1 D 2 D Total 1,732 129,927.8 6, 416 133,200.7 5,868 150,295.9

D = undisclosed Sources: U.S. Department of Commerce, Bureau of the Census. 1991. 1987 Census of Manufactures, Subject Series, General Summary . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of Manufactures, Subject Series: General Summary . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 2002a. 1997 Economic Census, Manufacturing Subject Series, General Summary . Washington, DC: Government Printing Office.6-32 Capacity utilization measures are used to track a variety of economic conditions, specific to the path of the business cycle and employment and in flationary trends. Table 6-19 presents the trend in capacity utilization for the pa per and allied products industry. The varying capacities reflect change s in the industry and the economy as a whole. The average capacity utilization ratio for th e paper and allied pr oducts industry between 1997 and 2001 was approximately 81, with capacity declining in recent years. Table 6-19. Capacity Utilization Ratios fo r 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 de mand (EPA, 2002c). Industry pe rformance 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 th e Paper and Allied Products Industry (NAICS 322), 1997–2001
Year Value of Shipments ($10 6 ) 1997 150,295.9 1998 154,984.2 1999 156,914.9 2000 165,297.4 2001 155,846.0
Source: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001

. Washington, DC: Government Printing Office.6-33 The Department of Commerce projects thatshipments of pape r and allied products will increase through 2004 by an annual aver age of 2.1 percent (Stanley, 1999). Because nearly all of the industry’s pr oducts are consumer related, ship ments 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 Ca nada, is the largest exporter of paper products. U.S. exports and imports are both ex pected 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 per cent of the manufacturing sector’s value of shipments. In this section, we describe th e current refinery process, raw materials used, uses and consumers, industry organization, and market s 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 fi nished 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 f acilities. 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 thr ough 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 th e 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, as phalt, and petroleum coke.6-34 The middle fractions, however, are not suitable for sale immediately after distillation. They are treated via one of seve ral 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 pur ification process, called hydrotreating, helps remove chemically bound sulfur from petr oleum 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 hydrocrack er, 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, in cluding hydrogen generation, catalyst handing and regeneration, sulfur recovery , wastewater treatment, and blending and storage tanks. Refining, like most continuous chemical pr ocesses, has high fixed costs from the complex and expensive capital equipment in stalled. In addition, shutdowns are very expensive, because they create large amount s of off-specification product that must be recycled and reprocessed prior to sale. As a result, refineries attempt to ope rate 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 utilizati on 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 residua l 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 frac tions through existing hydrotreater s, and operate these units under more severe conditions to redu ce levels of chemically bound sulf ur in finished products. As has been documented in economic impact analyses for the gasoline and highway diesel rules,6-35 these changes will require capital for equipment, new piping, and in-process storage, increased use of catalyst and hydroge n, and different ope rating strategies.
The addition of lower sulfur limits for nonroa d diesel fuel will result in additional costs similar in nature to those required fo r 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 le vel. 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 beco me more difficult to sell off spec products if errors or equipment failu res occur during operation. 6.6.1.3 Types of Products The major products made at petroleum re fineries are unbranded commodities, which must meet established specifications for fuel value, density, vapor pr essure, sulfur content, and several other important ch aracteristics. These products are transported through a distribution network to wholesal ers 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 produc ts 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 acc ounted 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 th e remainder split between nonroad diesel and heating oil. Jet fuel, a fraction slightly heavier than gasoline, is the third most important product, witha 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 pe rcent of the total value of sh ipments, varying from year to6-36 Table 6-21. Types of Petroleum Pro ducts Produced by U.S. Refineries

Products Total Produced (thousand barrels) Percentage of Total Liquified refinery gases 243,322 3.9% Finished motor gasoline 2,928,050 46.4% Finished aviation 6,522 0.1% Jet fuel 558,319 8.8% Kerosene 26,679 0.4% Distillate fuel oil 1,348,525 21.4% Residual fuel oil 263,017 4.2% Naphtha for feedstock 60,729 1.0% Other oils for feedstock 61,677 1.0% Special naphthas 18,334 0.3% Lubricants 63,961 1.0% Waxes 6,523 0.1% Petroleum coke 280,077 4.4% Asphalt and road oil 177,189 2.8% Still gas 244,432 3.9% Miscellaneous 21,644 0.3% Total 6,309,000 100.0%

Source: U.S. Department of Energy, Energy Information Administration. 2002b . Petroleum Supply Annual 2001 Volume 1 . Washington, DC: U.S. Department of Energy. Table 6-22. Petroleum Refinery Costs of Production

Petroleum Refinery Costs of Production Cost ($10 6 ) Cost as Percent of Product Value (%) Cost of materials $158,733 79.4% Cost of labor 4,233 2.1% Capital expenditures 6,817 3.4%
Source: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001

. Washington, DC: Government Printing Office.6-37 Petroleum Administration Defense Districts (PADDs)

Figure 6-3. PADD Districts of the United States year as crude petroleum prices change. Labor and capital expend itures tend to be more stable, accounting for about 2 to 3 per cent of the value of shipments in 2001.
6.6.2 The Demand Side This section describes the de mand side of the market fo r refined petroleum products, with a focus on the distillate fuel oil industry. It discusses the pr imary consumer markets identified and their distribution by end use and petroleum administr ation defense district (PADD) (see Figure 6-3). This section will al so consider substituti on possibilities available in each of these markets and the feasibility and costs of these substitutions.6-38 6.6.2.1 Uses and Consumers As Table 6-23 shows, highway diesel usag e of 33.1 billion gallons represents the bulk of distillate fuel usage (58 percent) in 2000. Re sidential 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 indus trial, farm, and off-highway dies el (construction) usage. In 2000, these markets consumed about 13 per cent of total U.S. distillate fuels. Table 6-23. Adjusted Sales of Di stillate Fuel Oil by End Use (2000)
End Use 2000 Usage (thousa nd gallons ) Percent Share (%) Residential 6,204,449 10.8% Commercial 3,372,596 5.9% Industrial 2,149,386 3.8% Oil company 684,620 1.2% Farm 3,168,409 5.5% Electric u tility 793,162 1.4% Railroad 3,070,766 5.4% Vessel bunking 2,080,599 3.6% On-highway diesel 33,129,664 57.9% Military 233,210 0.4% Off-highway diesel 2,330,370 4.1% Total 57,217,231 100.0%
Source: U.S. Department of Energy, Energy Information Administration. 2001. Fuel Oil and Kerosene Sales 2000 . Washington, DC: U.S. Department of Energy.
To determine the regional distribution of di stillate fuel usage, 2000 sales of distillate fuel are categorized by PADDs. As shown by Table 6-24, PADD I (the East Coast) consumes the greatest amount of distillate fuel oil at 20.9 billion gallons. However, residential, locomotive, and vessel bunking consumers account for 6.4 billion gallons of the distillate consumed, which means that at least one-third of the distillate consumed in PADD I is due to fuel oil and not to diesel fuel consumption.6-39 Table 6-24. Adjusted Sales of Distilla te Fuel Oil by End Use and by PADD

Enduse PADD (Thousand Gallons) I II III IV V Residential 5,399,194 628,414 1,117 38,761 136,962 Commercial 2,141,784 568,089 346,578 102,905 213,240 Industrial 649,726 600,800 420,400 241,146 237,313 Oil company 19,101 41,727 560,905 29,245 33,643 Farm 432,535 1,611,956 552,104 220,437 351,377 Electric utility 304,717 133,971 194,786 8,492 151,196 Railroad 499,787 1,232,993 686,342 344,586 307,059 Vessel bunking 490,150 301,356 1,033,333 173 255,586 On-highway diesel 10,228,244 11,140,616 5,643,703 1,474,611 4,642,490 Military 70, 801 36,100 9,250 4,163 112,895 Off-highway diesel 669,923 608,307 516,989 180,094 355,056 Total 20,905,962 16,904,329 9,965,507 2,644,613 6,796,817
Source: U.S. Department of Energy, Energy Information Administration. 2001. Fuel Oil and Kerosene Sales 2000 . Washington, DC: U.S. Department of Energy.
6.6.2.2 Substitution Possibilities in Consumption For engines and other combustion devices de signed 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 in creased maintenance and poorer performance. With the consideration of more string ent nonroad fuel and emission regulations, substitution will become less likely. Switchingfrom 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 o il will rapidly degrade the performance of the exhaust system of the affected engine, with negative consequences for maintenance and repair costs.6-40 6.6.3 Industry Organization: Market Concentration, Plants, and Firms To determine the ultimate e ffects of the EPA regulation, it is important to have a good understanding of the overa ll refinery industry structur e. The degree of industry concentration, regional pattern s of production and shipment , and the nature of the corporations involved are all impor tant aspects of this discussion. In this section, we look at market measures for the United Stat es as a whole and by PADD region.
6.6.3.1 Market Structure—Concentration There is a great deal of concern among me mbers 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 slow ly fall back to more reasonable levels has created suspicion of coordinated action or other market imperfec tions in certain regions. The importance of distance in total delivered costto various end-use markets also means that refiners incur a wide range of costs in servin g 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 litera ture that markets should be modeled as imperfectly competitive, however. Although th e FTC study cited above concluded that the extremely low supply and demand elasticities made large price movements likely and inevitable given inadequate supply or une xpected increases in demand, their economic analysis found no evidence of collusion or other anticompetitive behavi or in the summer of 2000. Furthermore, the industry is not highly concentrated on a nati onwide 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 followe d by the Department of Justice consider that there is little potential for pric ing power in an industry with an HHI below 1,000 (DOJ, 1997).
6.6.3.2 Plants and Firms As of January 2003, there were 145 operatin g petroleum refineries in the United States (Table 6-25). An additi onal four refineries were operable but idle . Nearly 40 percent of the nation’s refineries were contained in PADD III. This region also accounts for 45 to 50 percent of U.S. refinery net production of finished motor gaso line, distillate fuel oil, and6-41 Table 6-25. Number of Petrol eum Refineries by PADD, 2003

PADD Number of Facilities Percent of Total I 13 9.0 II 26 17.9 III 54 37.2 IV 16 11.0 V 36 24.8 Total 145 100.0%
Source: U.S. Department of Energy, Energy Information Administration. 2003d. Petroleum Supply Annual 2002, Volume 1 . Washington, DC: U.S. Department of Energy. residual fuel oil, which is not surprising since PADD III contains the petroleum-rich states of Texas and Louisiana. PADD I had the fewe st 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). Info rmation is not availa ble on actual production of highway diesel, nonroad diesel, and other distillate fuels for each refinery. It should be noted that PADD III has more than 50 percent of the total crude dis tillation 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 14 thin 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) ar e 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 re fineries are concentrated in6-42 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 di stillate products have been growing irregularly over the past severa l years. Table 6-26 shows that residential and commercial use of fuel oil has been dropping steadily si nce 1984, while highway diesel use has nearly doubled over the same period. Farm use of dis tillate has been flat over the 15-year period, while off-highway use, mainly for c onstruction, has increa sed by 40 percent. 6.7 Primary Metal Manufacturing The primary metal manufacturing industries (NAICS 331) includes industries involved in the primary and sec ondary smelting and refining of ferrous and nonferrous metal from ore or scrap. Primary sm elting and refining produces me tals directly from ores, while secondary processes produce me tals from scrap and process waste. Nonferrous metals produced by NAICS 331 industries include alumi num, lead, copper, and zinc. The iron and steel sector comprises establis hments involved in direct reduc tion 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 highes t geographic concentration of steel mills exists in the Great Lakes region, which is hom e 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 co st of new capital and the long lead time needed to introduce new equipment into the industr y, 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 ironmak ing as intermediate steps in steel production. The second technique is the electri c arc furnace (EAF), which uses el ectricity and scrap as inputs and does not require the intermediate steps of the (BOF) method.6-43Table 6-26. Sales of Distillate Fuel Oils toEnd Users 1984–1999 (thousands of barrels per day)YearResidentialCom-mercialIndust-rialOilCompany FarmElectricUtilityRail-roadVesselBunkeringOnHighwayDiesel MilitaryOffHighwayDieselAllOther Total1984 450 319 153 59 193 45 225 110 1,09345 45 109 44 2,8451985 471 294 169 57 216 34 209 124 1,12750 50 105 12 2,8681986 476 280 175 49 220 40 202 133 1,16950 50 111 9 2,9141987 484 279 190 58 211 42 205 145 1,18558 58 113 5 2,9761988 498 269 170 57 223 52 212 150 1,30464 64 119 4 3,1221989 489 252 167 55 209 70 213 154 1,37861 61 107 2 3,1571990 393 228 160 63 215 48 209 143 1,39351 51 116 (s) 3,0211991 391 226 152 59 214 39 197 141 1,33654 54 110 (s) 2,9211992 406 218 144 51 228 30 209 146 1,39142 42 113 (s) 2,9791993 429 218 128 50 211 38 190 133 1,48531 31 127 (s) 3,0411994 413 218 136 46 209 49 200 132 1,59434 34 130 (s) 3,1621995 416 216 132 36 211 39 208 129 1,66824 24 126 — 3,2071996 436 223 137 41 217 45 213 142 1,75424 24 134 — 3,3651997 423 210 141 41 216 42 200 137 1,86722 22 136 — 3,4351998 367 199 147 37 198 63 185 139 1,96718 18 142 — 3,4611999 381 196 142 38 189 60 182 135 2,09119 19 140 — 3,572Source: U.S. Department of Energy, Energy Information Administration. 2002a.Annual Energy Review 2001. Washington, DC: U.S. Department of Energy.6-44 For the BOF technique, the process begins with the manufacturing of coke. This starts with bituminous pulverized coal charge, which is fed into a coke oven and heated in the absence of oxygen. Volatile compounds are driven from the coal, and the resulting product is coke. After h eating, the coke is cooled with water and screened. This process generates the most environmen tal concern, with air emissions and quench water use cited as major problems. Industry experts predict that U. S. imports of coke w ill rise in the future, because of the high cost of constructing ne w environmentally frie ndly 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 oxyg en furnace, melted, and exposed to high-purity oxygen. The end product of th is process is molten steel. Production facilities using the EA F use scrap metal as the pr imary raw material. This metal is melted and refined using electric energy. The melting process prompts the oxidation of phosphorus, silicon, manganese, carbon and othe r 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 finish ed 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 c ontinuous casting process, in which the molten steel is cast directly into semifinis hed shapes (EPA, 1995a). Nonferrous metals . The process for manufacturin g 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. Tw o recovery technologies are used to produce refined metals. The first is pyrometallurgical technol ogies, 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 desi red materials into a molten bullion. This bullion is again refined to increase the purity. This process varies6-45 according to the raw ore, but the basic steps remain the same for pyrometallic processing (EPA, 1995b). Aluminum, copper, lead, and zinc are the more most widely us ed nonferrous metals in the United States. The production of each of these metals is br iefly 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 Valle y to take advantage of hydroelectric power and coal-bas ed energy. Most of the sec ondary smelters were located in Southern California and the Gr eat Lakes region near major i ndustrial centers for access to large amounts of scrap metal. Primary aluminum producers use a three-st ep 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 aque ous sodium hydroxide solution to form a slurry, which is heated and put throug h 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 re duced 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 transfe rred 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 th e 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 north ern and southern hemisphere but is primarily processed and consumed by countries in th e 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 contain6-46 copper oxide minerals and other oxidized wast e 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 c onsists of four steps: sintering, smelting, drossing, and pyrometallurgical refining. After a feedstock of lead concentrate completes these four steps, the resulting refined lead will have a purity gr eater 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 oflead scrap in the United States is leadacid batteries. Once the lead battery scrap is broken and classified by lead type, it is processed in blast furnaces or rota ry 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 refini ng is done hydrometallurgically . This process takes the zinc concentrate through four steps: calcinatin g, leaching, purification, and electrowinning. Calcining involves mixing zinc-c ontaining 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 th ird production step invol ves adding zinc dust, which causes the undesirable elemen ts to precipitate for easy filtering. The final step runs an electric current through the aqueous zinc soluti on 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-contai ning metals are melted in a sweating furnace and the molten zinc is recovered a nd refined. Recovered zinc is generally comparable to primary zinc in quality, a nd secondary zinc production will most likely increase in the future due to e nvironmental 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 produc ts (EPA, 1995a). Both the primary and6-47 secondary aluminum production pr ocesses 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 nonfe rrous metal produc tion results in similar output. Facilities may also produce nonfe rrous 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 produ ced depend on whether the BOF or EAF was used. When production uses a BOF, the pr ocess of cokemaking pr oduces coal tar, light oil, ammonia liquor, and fuel. Coal tar is typically refined and used to produce various commercial and industria l products. Additionally, the coke making process emits fine coke particles during c oke transport. Appr oximately 1 pound of par ticles per ton of coke produced is captured by pollution control equipment. Ironmaking produces residual sulfur dioxide and particulates, wh ich are most often captured in gas during the production proce ss. 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 dur ing all of the nonfe rrous metal smelting operations covered in the prev ious section. Primary and se condary aluminum manufacturing release particulates, althoug h the particulate matter gene rated during the calcining of hydrated aluminum oxide is valuable enoug h to the production pro cess 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 rem oves solid matter from molten c opper. 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 s econdary copper processing. The particulate matter emitte d from blast furnaces during lead production includes lead oxides, quartz, limestone, iron pyrite s, arsenic, and other metallic compounds. Additionally, about 7 percent of th e total sulfur in the raw ore is emitted as sulfur dioxide. Fabric filters and electrostatic precipitators are most commonly used to control emissions.6-48 Like copper production, zinc production results in the cr eation of sulfur dioxide, which is collected and made into sulfuric acid. More than 90 percent of potential sulfur dioxide emissions are generated during the roas ting process. This stage in the production process also releases particulate matte r containing zinc and lead (EPA, 1995b).
6.7.1.4 Production Costs Similar to the trend in the value of ship ments 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 una djusted 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 i nvestment experienced the largest decrease in percentage terms, with the 2001 level more than 20 per cent less than the 1997 level. The cost of materials fell by roughly 17 percent. Table 6-27. Inputs for the Primary Metal Manufacturing Industry (NAICS 331), 1997–2001

Labor Materials ($10 6 ) Total Capital Investment ($10 6 ) Year Quantity (10 3 ) Payroll ($10 6 ) 1997 605.1 23,811.2 99,343.3 6,515.8 1998 602.3 24,146.1 96,949.7 6,451.2 1999 582.8 23,722.4 90,596.0 5,942.8 2000 577.8 24,155.1 93,736.3 6,138.3 2001 532.9 22,199.3 83,301.3 5,140.2
Sources: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures 2001

. Washington, DC: Government Printing Office.

6.7.2 The Demand Side The steel industry has seen si gnificant changes over the last 30 years. Historically, the two largest steel-consuming industries have been the automotive and construction industry, so fluctuations in these industr ies affect demand for iron and steel products.6-49 Recently, however, foreign competition and technol ogical advances in other materials have reduced the market share of domestic steel pr oducers (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 aluminumshipments in 1999 (Cammarota, 1999). The leading domestic consumer of refined copper in 1998 was wire rod mills, whic h accounted for three-quarters of domestic consumption. The majority of the remaining quarter went to brass mills producing copper and coppe r alloy semi-fabricated shapes (Shaw, 1999). The construction and electronic products industries are the leadin g 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 c opper 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 gl obal lead consumption in the late 1990s. Although lead-acid batteries include batteries for household use, th e 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 protec tion (Larrabee, 1999b). Zinc compound use varies widely but is used mainly by agricu ltural, chemical, paint, pharmaceutical, and rubber sectors of the economy (EPA, 1995b). 6.7.3 Organization of the Industry: Mark et Concentration, Plants, and Firms Table 6-28 displays various measures of ma rket 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 indus try sales. The HHI value of 97.4 shows that the industry is unconcentrated.6-50 Table 6-28. 1997 Measures of Market Co ncentration for the Primary Metal Manufacturing Industry (NAICS 331)

NAICS Industry CR4 CR8 HHI Number of Companies Number of Facilities 331 Primary Metal Manufacturing 13.8 22.3 97.4 4,076 5,059 Sources: U.S. Department of Commerce, Bureau of the Census. 2001. 1997 Economic Census, Manufacturing Subject Series: Concentration Ratios in Manufacturing . Washington, DC: Government Printing Office. In the 1990s, many of the larger mills and ope rations 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 co mpany in this industr y operates 1.24 facilities.
Table 6-29. Size of Establishments and Value of Shipments for the Primary Metal Manufacturing Industry (NAICS 331)

1997
Number of Employees in Establishment Number of Facilities Value of Shipments ($10 6 ) 1 to 4 employees 859 272.9 4 to 9 employees 502 (D) 10 to 19 employees 621 (D) 20 to 49 employees 979 6,199.6 50 to 99 employees 720 11,084.9 100 to 249 employees 797 30,211.1 250 to 499 employees 363 35,126.8 500 to 999 employees 145 29,829.8 1,000 to 2,499 employees 55 25,247.2 2,500 or more employees 18 28,107.6 Total 5,059 168,117.7 (D) = undisclosed Sources: U.S. Department of Commerce, Bureau of the Census. 2002a. 1997 Economic Census, Manufacturing Subject Series, General Summary. Washington, DC: Government Printing Office.6-51 Over the 5-year period of 1997 through 2001, facilities produced at levels between 70 and 84 percent of their full production capacity (Table 6-30). The average level for the 5 years was 77.4, and it fell by 17 percent over the time period. Table 6-30. Capacity Utilization Ratios for the Primary Metal Manufacturing Industry, 1997–2001

1997 1998 1999 2000 2001

84 78 81 74 70
Note: All values are percentages. Source: U.S. Department of Commerce, Bureau of the Census. 2003b. Current Industry Reports, Survey of Plant Capacity, 2001. Washington, DC: Government Printing Office. 6.7.4 Markets and Trends As with many other industries, the steel mill products felt the strain of the Asian financial crisis in the late 1990s. Despite dramatic acti on 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 i ndustry’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 ($10 6 ) 1997 168,118 1998 166,109 1999 156,647 2000 156,598 2001 138,245
Sources: U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures, 2001

. Washington, DC: Government Printing Office.6-52 In 1999, industry experts predicted that dom estic demand for steel would be strong through 2004, assuming moderate growth in th e 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 becau se foreign producers w ill 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 nonresiden tial building markets, which are both projected to experience growth through the first part of the 21 stcentury (Bell, 1999). Domestic aluminum production in the n ear future will likely not meet domestic demand, which will increase U.S. reliance on aluminum imports. Growth in the transportation sector, especially light vehi cles, is the driving force behind demand for aluminum products (Cammarota , 1999). Copper production is projected to decline through the early part of the 21 stcentury, but consumption should follow the growth patterns of the U.S. economy. The construction and power utilities industries repres ent the main domestic consumers of copper (Shaw, 1999). The project ed increase in lead- acid batteries should boost the lead production increase. Industry anal ysts 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 uninte rruptible power supply market s. Finally, zinc production should grow along with the economy then slow at the beginning of the 21 stcentury. Growth in sales in the galvanizing segment shoul d 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. “Nonferr ous Metals: Aluminum.” U.S. Industry and Trade Outlook 2000. United States: McGraw-Hill. pp 14-1 to 14-4. Chevron Texaco Corp. 1998. “Diese l Fuel Refining and Chemistry.” <http://www.chevron.com/prodserv/fuel s/bulletin/diesel/L2_4_1_rf.htm>. As accessed on October 20, 2003. Federal Trade Commission (FTC). 2002. Final Report of the Federal Trade Commission: Midwest Gasoline Price Investigation, March 29, 2001

. <http://www.ftc.gov/os/2001/03/mwgasrpt .htm>. As accessed October 20, 2003.6-53 Flint Hills Resources. 2002. “Refining Overview.” <http://www.fhr.com/Refining101/default. asp>. As accessed October 20, 2003. Iwand, T., and D. Rosenbaum. 1991. “Strategies in Supergames with Capacity Constraints: Some evidence from the U.S. Portland Cement Industry.” International Journal of Industrial Organization 9:497-511. Larrabee, Dave. 1999a. “Nonf errous Metals: Lead.” U.S. Industry and Trade Outlook 2000. United States: McGraw-Hill. pp 14-7 to 14-11. Larrabee, Dave. 1999b. “Nonfe rrous Metals: Zinc.” U.S. Industry and Trade Outlook 2000. United States: McGraw-Hill. pp 14-14 to 14-17. Lillis, Kevin. 1998. “Crude Pe troleum and Natural Gas.” U.S. Industry and Trade Outlook 1998. United States: McGraw-Hill.
Minerals Yearbook: Volume I .—Metals and Minerals: Cement <http://minerals.usgs.gov/minerals/pubs/commodity/cement/index.html#myb>. Last updated October 2003.
Portland Cement Associ ation. March 2005. “About PCA, T ools for Concrete Thinking.” <www.cement.org/pca/shortageQA.asp>. Saftlas, Herman. 1999. “Indus trial Organic Chemicals.” U.S. Industry and Trade Outlook 2000. United States: McGraw-Hill. pp 11-5 to 11-7. Shaw, Robert. 1999. “Nonfe rrous Metals: Copper.” U.S. Industry and Trade Outlook 2000. United States: McGraw-Hill. pp 14-4 to 14-7. Spancake, Larry. 2000. “Crude Petroleum and Natural Gas.” U.S. Industry and Trade Outlook 2000. United States: McGraw-Hill. Stanley, Gary. 1999. “Paper and Allied Products, Pulp Mills.” U.S. Industry and Trade Outlook 2000. United States: McGraw-Hill. pp 10-1 to 10-8. U.S. Department of Commerce, Bureau of the Census. 1990. 1987 Census of Manufactures, Industry Series, Industrial Organic Chemicals . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 1991. 1987 Census of Manufactures, Subject Series, General Summary. Washington, DC: Government Printing Office.6-54 U.S. Department of Commerce, Bureau of the Census. 1995. 1992 Census of Manufactures, Industry Series: Industrial Organic Chemicals. Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of Manufactures, Subject Series: General Summary . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 1997. 1997 Economic Census. Manufacturing Industry Series . Cement Manufacturing . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 1999a. 1997 Economic Census. Manufacturing Industry Series. All Othe r 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. Departme nt of Commerce. U.S. Department of Commerce, Bureau of the Census. 2001. 1997 Economic Census, Manufacturing Subject Series, Con centration Ratios in Manufacturing . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 2002a. 1997 Economic Census, Manufacturing, Subject Series: General Summary . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 2002b. “331 Primary Metal Manufacturing.” 2002 NAICS Definitions . <http://www.census.gov/epcd/naics02/ def/NDEF331.HTM#N331>. As obtained October 14, 2003. U.S. Department of Commerce, Bureau of the Census. 2003a. Annual Survey of Manufactures 2001 . Washington, DC: Government Printing Office. U.S. Department of Commerce, Bureau of the Census. 2003b. Current Industrial Reports, Survey of Plant Capacity: 2001 Washington, DC: Government Printing Office.6-55 U.S. Department of Energy, Ener gy 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, Ener gy 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 (E PA), 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 th e Nonferrous Metals Industry . Washington, DC: U.S. Environmental Protection Agency. U.S. Environmental Protection Agency (E PA), EPA Office of Compliance. 1995c. Sector Notebook Project: Profile of the Petroleum Refining Industry . Washington, DC: U.S. Environmental Protection Agency. U.S. Environmental Protection Agency (EPA). 1999a. Economic Impact Analysis of Air Pollution Regulations: Portland Cement . <http://www.epa.gov/ttnecas1/ regdata/Portland.pdf>. La st updated October 2003.6-56 U.S. Environmental Protection Agency (EPA). 1999b. Economic Impact Analysis of the Oil and Natural Gas NESHAP and the Natural Gas Transmission and Storage NESHAP

. Research Triangle Park, NC: Office of Air Quality Planning and Standards. U.S. Environmental Protection Agency (E PA), EPA Office of Compliance. 2000. Sector Notebook Project: Profile of th e Oil and Gas Extraction Industry . Washington, DC: U.S. Environmental Protection Agency. U.S. Environmental Protection Agency (E PA), 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 th e Organic Chemical Industry, 2 nd Edition

. Washington, DC: U.S. Environmental Protection Agency. U.S. Environmental Protection Agency (E PA), EPA Office of Compliance. 2002c. Sector Notebook Project: Profile of the Pulp and Paper Industry, 2 nd Edition . Washington, DC: U.S. Environmental Protection Agency. U.S. Environmental Protection Agency (EPA), EPA Office of Compliance. 2003. “Metals.” <http://www.epa.gov/compliance/assistance/ sectors/metals.html>. As obtained October 14, 2003. van Oss, H. 2002. Minerals Yearbook: Volume I. Metals and Minerals: Cement. <http://minerals.usgs.gov/minerals/pubs/comm odity/index.html#myb>. Last updated October 2003. van Oss, H. 2003. Mineral Commodity Summaries: Cement. <http://minerals.usgs.gov/ minerals/pubs/commodity/cement/170303.pdf>. Last updated January 2003.7-1 CHAPTER 7 COST, ECONOMIC, AND ENERGY IMPACTS

This chapter reports the cost, economic, and energy impact analysis performed for the final BART rule. EPA used the IPM, develope d 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 SO 2 and NO xthroughout the contiguous United States for the entire power system. Documentation for IPM can be found at www.epa.gov/airm arkets/epa-ipm. 7.1 Modeling Background The analysis presented here covers fossil-fu el-fired steam electric plants (part of theelectric power sector), which are one of the 26 stationary source categories that are BARTeligible. The analysis presents results for three scen arios of increasing stringency for BART forEGUs. In this rule, EPA is setting presumptive limits for a subset of the BART-eligible, coalfired EGUs (those greater than 200 MW at plants greater than 750 MW) and strong recommendations for another subset of BART-e ligible 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 s cenarios represent different assumptions regarding the ultimate levels of controls imposed by States on BART-eligible EGUs, and these scenarios are summarized in Table 7-1. EPA assumes that the CAIR program is in the baseline for this final BART rule analysis. EPA further assumes that States implement the required CAIR reductions through a cap-andtrade program. For CAIR SO 2 and NO x controls, EPA modeled an annual, two-phased control strategy for 26 eastern States and the Distri ct of Columbia (see Figure 7-1). For NO x , separate ozone season caps were applied to Connecticut and Massachuse tts. See Table 7-2 for total annual emissions caps under CAIR.7-2 States controlled for both SO 2 and NOx States controlled for ozone season NOx only States not covered by CAIR

Figure 7-1. CAIR Modeled Region Table 7-1. BART Scenarios Modeled in IPM

Scenario 1 Scenario 2 Scenario 3
NO x Combustion controls on all units > 200 MW at plants >750 MW Annual operation of SCR (where existing)
No controls on O/G steam Combustion controls on units > 200 MW SCR on cyclone units >200 Annual operation of SCR (where existing) No controls on O/G steam SCR on units > 100 MW Combustion controls on 25 MW < units < 100 MW Annual operation of SCR (where existing) No controls on O/G steam

SO 2 95% reduction or 0.15 lbs/MMBtu on all previously uncontrolled units > 200 MW at plants >750 MW
No controls on units with existing scrubbers
No controls on oil units No additional controls (beyond those for WRAP) on units in five 309 States: Arizona, Utah, Oregon, Wyoming, and New Mexico. 95% reduction or 0.15 lbs/MMBtu on all previously uncontrolled units > 200 MW No controls on units with existing scrubbers No controls on oil units No additional controls (beyond those for WRAP) on units in five 309 States: Arizona, Utah, Oregon, Wyoming, and New Mexico. 95% reduction or 0.10 lbs/MMBtu on all previously uncontrolled units > 100 MW No controls on units with existing scrubbers No controls on oil units No additional controls (beyond those for WRAP) on units in five 309 States: Arizona, Utah, Oregon, Wyoming, and New Mexico.

Note: For modeling, SO 2 controls would require a 95 percent reduction from the 2005 base case SO 2 rate.7-3 Table 7-2. CAIR Annual Emissions Caps (Million Tons)
2010–2014 (2009–2014 for NO x ) 2015–Th ereafter SO 2 3.6 2.5 NO x 1.5 1.3

BART-eligible units were de fined as those that were online after August 7, 1962, and under construction prior to August 7, 1977. EPA has developed an update d list of coal-fired BART-eligible coal units by more accurately determining which units with on-line dates after 1978 were BART-eligible. A desc ription 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 repres ents 491 units (ranging in size from 29 MW to 1,300 MW) totaling 218 GW of capacity, representing mo re 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 SO 2 and NO x ). Almost 17 GW of these are in the 309 WRAP States. A number of units have already installed SO 2 and NO xcontrols. 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-Fi red EGU BART-Eligible Units

Plant Size Unit Size <750 MW >750 MW Total CAIR Region <200 MW 97 97 >200 MW 61 234 295 CAIR Total 158 234 392 Non-CAIR Region <200 MW 24 24 >200 MW 22 53 75 Non-CAIR Total 46 53 99 Total 204 287 4917-4 In the analysis, controls and reductions were assumed to be requi red 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 insta lled 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 additiona l necessary controls for SO 2 (beyond their WRAP obligations) were assumed for any units withinthe five WRAP 309 States. Also, because of modeling limitations, no additional re ductions were assumed from un its with existing scrubbers, even if they were performing at less than 95 percent removal. This assumption would tend to understate the costs and emissi on reductions of the rule. These scenarios differ from the modeling done in the original BART proposal. In modeling the proposed BART, SO 2 affected units were given the choice of meeting a 0.10 lbs/mmBtu emission rate or achieving 90 percent reductions from base emissions. Affected units needed to meet a 0.2 lbs/mmBtu emission rate limit for NO x . Additionally in the proposal, EPA required controls only on BART-e ligible units greater than 250 MW. Additionally, the final CAIR rule differs fro m this modeling in th at it requires annual SO 2 and NO xreductions in 23 States and the District of Columbia and ozone season NO xreductions in 25 States and the District of Columbia. Many of the CAIR States are affected by both the annual SO 2 and NO xreduction requirements and the ozone season (May through September) NO xrequirements. 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 th e CAIR region and required these States to make annual SO 2 and NO xreductions. 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 SO 2 and NO xreductions. Arkansas is not included in the annual SO 2 and NO xrequirements either as pa rt 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 NO xcaps for Connecticut and Massachusetts and did not include ozone season NO xcaps 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 2 and NO x .1 EPA is making such a determination in this rulemaking.

2 The interpretation of BART modeled in the CAIR TSD assumed an SO 2 limit of 90 percent or 0.10 lb/mmBtu to all coal units greater than 100 MW and a NO xlimit of 0.20 lb/mmBtu on all coal units greater than 25 MW.
7-5 EPA believes that the differences between the initial IPM run and the final IPM run have limited impact on BART-projected control costs, emissions, and other impacts. Modeling the CAIR region without Arkansas, De laware, and New Jersey does not change the results presented here in any significant way, and in any event, this generally reflects th e 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 thisanalysis, that the CAIR trading program is “better than BART” and will substitute for sourcespecific BART in the CAIR-affected region with annual SO 2 and NO xreduction requirements. 1 Source-specific BART controls were thus assumed for the ozone-season CAIR States only (Massachusetts and Connecticutin this modeling) and non-CA IR 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 Guid elines for Making BART Determinations.” In that analysis, EPA assumed a more strict interpretation of BART nationwide 2 than in the final BART and compared it to a program that included CAIR together with a more strict interpretation of BART in the non-CAIR States. Consequently, this analysis does not assume BART source-specific controls for BARTeligible units in Arkansas, De laware, and New Jersey (although they are covered for seasonal NO xunder final CAIR). As noted, sources in Delaware and New Jersey, however, would be covered by the Delaware and Ne w Jersey proposal, which if it we re finalized, would have the effective impact of bringing them into the CAIR trading program.
Arkansas was included in CAIR with a 2015 SO 2 budget of 34,091 tons—corresponding to a 63,309-ton reduction in SO 2 emissions relative to Title IV allocations. Under our modeling of CAIR, however, sources in Arkansas did not put on SO 2 controls and did not make SO 2 reductions so that a ll of the required SO 2 reductions from Arkansas were accomplished in other States. Consequently, with Ar kansas not included in CAIR, SO 2 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 SO 2 emissions in 2001). BART w ould require 95 percent controls (or a 0.15 lb/mmBtu emission rate) from these s ources. 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 re ductions would take place in other CAIR States under CAIR.7-6 Applying source-by-source BART nationally—t ogether 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 atspecific BART units, which do not necessarily represent the least cost reductions in the regi on. Consequently, such an approach would only serve to shift around emissions within the CA IR region and increase total program costs. IPM has been used for evaluating the econo mic and emission impact s 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 NO xSIP Call, various New Source Review (NSR) settlements, and several State rules affecting emissions of SO 2 and NO xthat 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 mode l has been designed to reflect the industry as accurately as possible. As a result, EPA has used discount rates in IPM th at are appropriate for the various types of investments and other costs that the power s ector incurs. The discount rates used in IPM may differ from discount rates us ed in other EPA analyses done for BART, particularly the discount rates used in the benefits analysis th at 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 basi s for the discount rates used for power sector modeling. These discount rate s have undergone review by th e 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 m odel documentation, whic h provides additional information on the assumptions disc ussed here as well as all othe r assumptions and inputs to the model (www.epa.gov/ai rmarkets/epa-ipm). 7.2 Projected SO 2 and NO x Emissions and Reductions The projected emissions levels under CA IR and under the three different BART scenarios are provided in Table 7-4.7-7 Table 7-4. Projected Emissions of SO 2 and NO x with CAIR and with BART (thousand tons from units greater than 25MW)

2015

CAIR CAIR + BART (Scenario 1) CAIR + BART (Scenario 2) CAIR + BART (Scenario 3) SO 2 (annual) 4,890 4,836 4,770 4,738 NO x(annual) 2,125 2,009 1,916 1,693 Note: The emissions data presented here are EPA modeling results. “CAIR +BART” national emissions assume BART applied only in those States not included in CAIR for PM. 7.3 Projected Costs, Control Technology, and Fuel Costs For the modeled region, EPA projects that th e 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 Scenar io 3 were at $896 million in 2015, while they are $253 million in 2015 under Scenario 1.
Table 7-5. Annualized Cost of BART

Annualized Cost ($1999 millions) 2010 2015 2020 Scenario 1 $87 $253 $283 Scenario 2 $97 $436 $429 Scenario 3 $92 $896 $853 Source: Integrated Planning Model run by EPA. Note: Changes occur in 2010 as people react to the polic y announcement, in anticipation of future effects.

It should be kept in mind that the cost of electricity generation re presents roughly onethird to one-half of total electricity costs, w ith 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 resu lt in the installation of an additional 6.2 GW of flue gas desulfurizati on (scrubbers) on existing coal-fired generation capacity for SO 2 control in 2015 (relative to CAIR). For NO xcontrol, this BART scenario is also projected to result in an additional 24 GW of combustion control equipmen t and 2.4 GW of selec tive catalytic reduction technology (SCR) on cyclone-bo iler coal-fired generation capacity by 2015 (see Table 7-6).7-8 Table 7-6. National Pollution Cont rols by Technology under BART (GW)

Incremental Retrofits under BART
Scenario 1 Scenario 2 Scenario 3 Technology 2010 2015 2020 2010 2015 2020 2010 2015 2020 Combustion Controls 19.0 24.0 26.0

Scrubbers 0.1 3.4 4.3 0.0 6.2 5.8 0.2 7.7 6.9
SCR 0.1 0.2 0.3 0.4 2.4 2.6 0.3 27.2 27.3 Note: Numbers may not add due to rounding. CAIR retrof its (CAIR is included in the baseline) include existing scrubbers and SCR as well as additional retrof its for NSR settlements and various State rules. Source: Integrated Planning Model run by EPA

.
Coal-fired generation (as well as coal produc tion) 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 in crease 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 esse ntially unchanged with implementation of BART controls outside the borders of CA IR (see Table 7-7 and Figure 7-2).7-9 Figure 7-2. NERC Power Regions Table 7-7. Retail Electricity Prices byNERC Region with CAIR and with BART (Mills/kWh)

Power CAIR Scenario 2 BART Percentage Price Change Region Main States Included 2000 2015 2020 2015 2020 2015 2020 ECAR OH, MI, IN, KY, WV, PA 57.4 58.5 58.0 58.4 58.0 –0.1% 0.0% ERCOT TX 65.1 64.6 63.3 64.6 63.3 0.1% 0.0% MAAC PA, NJ, MD, DC, DE 80.4 71.7 72.8 71.7 72.8 –0.1% 0.1% MAIN IL, MR, WI 61.2 60.3 62.0 60.2 62.0 –0.1% 0.0% MAPP MN, IA, SD, ND, NE 57.4 49.6 48.0 50.3 48.7 1.5% 1.5% NY NY 104.3 88.8 88.4 88.8 88.4 0.0% 0.0% NE VT, NH, ME, MA, CT, RI 89.9 84.7 83.0 84.8 83.0 0.1% 0.0% FRCC FL 67.9 72.3 70.5 72.3 70.5 0.0% 0.0% STV VA, NC, SC, GA, AL, MS, TN, AR, LA 59.3 56.2 56.6 56.2 56.6 –0.1% –0.1%
SPP KS, OK, MR 59.3 57.5 57.0 57.9 57.4 0.7% 0.7% PNW WA, OR, ID 45.9 47.5 46.9 47.5 46.9 0.0% 0.0% RM MT, WY, CO, UT, NM, AZ, NV, ID 64.1 65.6 65.4 65.9 65.7 0.5% 0.4%

CALI CA 94.7 99.1 99.5 99.1 99.5 0.0% 0.0% NATIONAL Contiguous Lower 48 States 66.0 64.4 64.3 64.5 64.4 0.1% 0.1% Source: EPA’s Retail Electricity Price Model. 2000 prices are from EIA’s AEO 2003.7-10 States controlled for both SO 2 and NOx

States not covered by CAIR States controlled for Ozone Season NOx

Figure 7-3. Final CAIR Region
Note: Delaware and New Jersey are not included in the Final CAIR for the annual SO 2 and NO x requirements. However, EPA intends on incorporating these two States in the annual CAIR program through a separate rulemaking. See earlier discussion for more detail. 7.5 Key Differences in EPA Model Runs for Final BART Modeling As previously stated, the emissions, cost, ai r quality, and benefits analyses done for the final BART are from a modeling scenario that assumes CAIR to require annual SO 2 and NO x reductions in 26 States and the Distri ct of Columbia and ozone season NO xrequirements in Connecticut and Massachusetts (s ee Figure 7-1). This modeling differs from what would reflect final BART, in that Arkansas , Delaware, and New Jersey ar e not included in the annual SO 2 and NO xrequirements; 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 CleanAir Mercury Rule, which im poses a 38-ton mercury cap by 2010 and a 15-ton cap by 2018.

All IPM runs done in support of BART and used as part of the final BART package are in the final CAIR docket and can be found on EP A’s Web site: http://www.epa.gov/airmarkets/ epa-ipm/iaqr.html. A complete list of IPM r uns 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 becau se of the importance of these two ke y model inputs to the power sector.7-11 However, for this rule (which is not marketbased and involves sour ce-specific installation of controls) EPA believes that scenarios with hi gher gas price and dema nd 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 pr oduction (engineering) costs of BART to the power sector. In simple terms, the private complian ce costs that are presented are the annual increase in revenues required for the industry to be as we ll off after BART is implemented as before. To estimate these annualized costs, EPA uses a c onventional and widely accep ted approach that is commonplace in economic analysis of power sect or costs for estimating engineering costs in annual terms. For estimating annua lized 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 soci al cost calculations (for annualized costs) normally includes the life of capital and the social discount rate. For pur poses 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 cost s of Scenario 2 BART are $248 million in 2015 (with $119 million for Scenario 1 BART and $567 million fo r 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 qua ntified 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 re view 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 po llution controls, investme nts in new generating3 2010 Phase II cost estimate in $1995.
7-12 sources, and additional fuel expenditures. EPA believes that the cost assumptions used for BART reflect, as closely as possible, the best information available to the Agency today. Furthermore, EPA wants to identify some unquantified costs as limits to its analysis. These costs include the costs of state administr ation of the program, which we believe are modest. There also may be unquantified costs of transitioning to BART, such as the costs associated with the possible retirement of smaller or less-efficient EGUs and employment shifts as workers are retrained at the same comp any 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 (i ncluding abatement costs) while meeting load demand and other constraints (full documentation for IPM can be f ound 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 a ffecting 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 capabilitie s of pollution control technologies for SO 2 and NO x removal as well as reductions in their costs ove r time. As an example, recent cost estimates of the Acid Rain SO 2 trading program by Resources for th e 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 Ac id 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 billion 3 in 1989 were an overestimate of the costs of the program in part because of the limitation of economic modeling to predict t echnological improvement of pollution controls and other compliance options such as fuel switching. Ex post estimates of the annual cost of the Acid Rain SO 2 trading program range from $1.0 to $1.4 billion. Harrington et al. have examined cost analyses of EPA programs and found a tendenc y for predicted costs to overstate actual implementation costs in market-based programs (Harrington, Morgenstern, and Nelson, 2000). In recognition of the possibility of technologi cal improvement, EPA’s mobile source program uses adjusted engineering cost estimates of pollution control equipment and installation costs to4 See recent regulatory impact analysis for the Tier 2 regulations for passenger vehicles (1999) and Heavy-Duty Diesel Vehicle Rules (2000). There is also evidence that scrubber costs will decrease in the future because of the learning-by-doing phenomenon, as more scrubbers are installed (see Manson, Nelson, and Neumann [2002]).

5 There is also evidence that scrubber costs will decrease in the future because of the learning-by-doing phenomenon, as more scrubbers are installed (see Manson, Nelson, and Neumann [2002]).

6 The degree of substitution/curtailment depends on the price elasticity of demand for electricity.
7-13 account for this fact, which EPA has not done in this case. 4 It is expected that a cap and trade approach to BART would provide greater in centives 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 subs tantial part of the compliance costs. Data available from recent published s ources show the reported FGD costs from recent installations to be below th e 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 inco rporates State rules or regu lations adopted before March 2004 and various NSR settlements. Do cumentation for IPM can be found at www.epa.gov/airmarkets/epa-ipm. Any State or settlement acti on 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 in creased retail electricity prices would prompt end users to curta il (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 re sponse, certain unquantified negativ e costs (i.e., savings) result from the reduced resource costs of producing le ss 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 ne w 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 the7-14 price level, the real wage received by workers may be reduced, leading to a small fall in the total amount of labor supplied. This “t ax interaction effect” may resu lt 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 curren tly 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 empiri cal data available to support quantification of any such effect lead to this qualitative identification of the costs. On balance, after considerin g various unquantified costs (and savings that are possible), EPA believes that the annual private compliance co sts 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 determinati on that CAIR is better than BART differed slightly from the IPM runs used in this RIA. The IPM runs used for the final CAIR-is-betterthan-BART determination reflect the final CAIR region (New Jersey, Delaware, and Arkansas affected only by the CAIR ozone season program) . This change was not included in the IPM runs used for this RIA because of time constr aints. As discussed previously, EPA does not believe this modification would have had a significant impact on the RIA. See the CAIR-isbetter-than-BART determinati on section of this rulemaking for further discussion.
7.8 References Carlson, Curtis, Dallas R. Burt raw, Maureen Cropper, and Kare n 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 Eval uation of Tradable Permits: The U.S. SO 2 Cap-and-Trade Program.” Mass achusetts Institute of Technol ogy Center for Energy and Environmental Policy Research. Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory Cost Estimates.” Journal of Policy Analysis and Management 19(2):297-322. Manson, Nelson, and Neumann. 2002. “Assessing the Impact of Progress and Learning Curves on Clean Air Act Compliance Costs.” Industrial Economics Incorporated.8-1 SECTION 8 RESULTS OF COST, EMISSIONS REDUCTIONS, AND ECONOMIC IMPACT ANALYSES FOR NONELECTR ICITY GENERATING UNITS

This chapter presents the results of th e cost analyses for the non-EGU sources in BART source categories covered in these analyses . The cost analyses evaluate the potential impacts associated with the final BART rule , based on assumptions about how the States may implement controls for non-EGU sources. As mentioned in Chapter 3, there are 25 nonEGU source categories that are to be considered for controls associated with the BART program. Section 8.1 presents a short summary of the results of the impact analyses. Section 8.2 provides summaries of the results of these analys es across sources categories. Section 8.3 presents a description of the methodol ogical approach for th ese cost analyses, the scenarios that are analyzed, and important assu mptions and details thatunderlie 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 limita tions associated with the cost analyses. More detailed results for the scenarios cons idered 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 SO 2 and NO x emissions at non-EGU BART-eligible un its range from 83,778 to 378,169 tons of SO 2 and from 165,634 to 391,101 tons of NO x nationwide for costs annualized at a 7 percent discount rate. For costs annualized at a 3 pe rcent discount rate, the reduction of SO 2 emissions in 2015 ranges from 132,279 to 373,797 tons and the reduction of NO x emissions in 2015 ranges from 246,607 to 393,349 tons. Annualized co sts 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 $1.8 b illion (1999$) for costs estimated at a 3 percent discount rate. The cap ital costs associated with these reductions in 2015 range from $655.70 million to $14.75 billion for costs estimated ata 7 percent discount rate and from $1.9 billion to $12.73 billion.1 Analysis of non-EGU sources considers the applicatio n of controls at the source category level up to a specified average cost per ton cutoff. 8-2 8.2 Summary of Results for Nonelectricity Generating Sources In this RIA chapter, three illustrative scenarios are applied to non-EGU BARTeligible sources. Costs and emissions reductions for these sources are estimated for these annualized costs per ton scenarios for reducing SO 2 and NO x at varying levels of stringency: $1,000, $4,000, and $10,000. 1 These scenarios, called Scenarios 1, 2, and 3 respectively, are applied nationwide and are presen ted 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 resu lts 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 tabl es 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 co sts. Table 8-1 summarizes the SO 2 emission reductions for the analyses to the BART non-EGU source cat egories using a discountrate 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 SO 2 emission reductions ranging from 83,778 tons to 378,169 tons with costs at a 7 percent disc ount rate. These scenarios lead to SO 2 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 fro m the 2015 baseline. For scenario 2, the SO 2 emission reduction estimate is 269,992 tons or a 22 percent reduction from the 2015 baseline with costs at a 7 percent discount rate a nd 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 NO x emission reductions. The nationwide NO x emission reductions from applying these three scen arios range from 165,634 tons to 391,101 tons with costs at a 7 percent discount rate. These re present a reduction of 24 to 57 percent from the 2015 baseline. These scenarios lead to NO x emission reductions ranging from 246,067 to 393,349 tons with costs at a 3 percent rate. Th ese represent a reduction of 36 to 58 percent from the 2015 baseline. For scenario 2, the NO x emission reduction estimate is 361,152 tons8-3 Table 8-1. SO 2 Emissions and Emission Reductions for BART Source Categories in 2015

BART Source Category Baseline Emissions

(tons) Discount Rate Scenarios—Reductions (tons)
Scenario 1 Scenario 2 Scenario 3 Industrial boilers 420,782 7% 32,213 151,387 200,308 420,782 3% 65,783 164,438 204,217 Petroleum refineries 199,483 7% 2,097 31,319 56,462 199,483 3% 17,033 41,911 58,525 Kraft pulp mills 119,818 7% 0 10,814 28,380 119,818 3% 0 3,196 14,610 Portland cement plants 116,835 7% 0 13,383 26,716 116,835 3% 0 18,326 30,690 Hydrofluoric, sulfuric, and nitric acid plants 96,741 7% 34,140 36,753 36,753 96,741 3% 34,140 36,753 36,753 Chemical process plants 47,700 7% 0 2,376 3,571 47,700 3% 0 2,376 3,571 Iron and steel mills 23,541 7% 0 2,914 2,914 23,541 3% 0 2,914 2,914 Coke oven batteries 9,815 7% 0 4,088 6,107 9,815 3% 0 3,724 5,564 Sulfur recovery plants 59,766 7% 13,697 13,697 13,697 59,766 3% 13,693 13,693 13,693 Primary aluminum ore reduction plants 47,552 7% 1,630 3,260 3,260 47,552 3% 1,630 3,260 3,260 Lime kilns 9,373 7% 0 0 0 9,373 3% 0 0 0 (continued)8-4 Table 8-1. SO 2 Emissions and Emission Reductions for BART Source Categories in 2015 (continued)

BART Source Category Baseline Emissions

(tons) Discount Rate Scenarios—Reductions (tons)
Scenario 1 Scenario 2 Scenario 3 Glass fiber processing plants 2,170 7% 0 0 0 2,170 3% 0 0 0 Municipal incinerators 284 7% 0 0 0
284 3% 0 0 0 Coal cleaning plants 1,530 7% 0 0 0 1,530 3% 0 0 0 Carbon black plants 41,853 7% 0 0 0 41,853 3% 0 0 0 Phosphate rock processing plants 21 7% 0 0 0
21 3% 0 0 0 Secondary metal production facilities 9,988 7% 0 0 0 9,988 3% 0 0 0
Total 1,208,088 7% 83,778 236,992 378,169 1,208,088 3% 132,279 290,591 373,797

or a 53 percent reduction from the 2015 baseline w ith 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 cost s associated with the three non-EGU scenarios. In total, the three scenarios app lied 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 pe rcent 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 fro m $1.9 billion to $12.73 billion for costs estimated at a 3 percent discount rate. More de tailed capital cost information for these scenarios can be found in8-5 Table 8-2. NO x Emissions and Emission Reductions for BART Source Categories in 2015

BART Source Category Baseline Emissions

(tons) Discount Rate Scenarios—Reductions (tons)
Scenario 1 Scenario 2 Scenario 3 Industrial boilers 217,063 7% 67,325 130,456 130,522 217,063 3% 111,939 130,424 130,505 Petroleum refineries 86,566 7% 4,035 47,556 51,316 86,566 3% 4,579 51,259 53,005 Kraft pulp mills 103,614 7% 35,249 61,418 65,776 103,614 3% 53,729 65,792 65,797 Portland cement plants 120,567 7% 19,276 54,601 71,921 120,567 3% 30,903 71,921 71,921 Hydrofluoric, sulfuric, and nitric acid plants 17,059 7% 11,276 11,283 11,283 17,059 3% 11,276 11,283 11,283 Chemical process plants 72,577 7% 23,970 34,636 34,801 72,577 3% 27,142 34,801 34,801 Iron and steel mills 20,963 7% 1,036 6,997 8,507 20,963 3% 1,036 8,500 8,673 Coke oven batteries 10,389 7% 0 5,768 5,768 10,389 3% 0 5,768 5,768 Sulfur recovery plants 651 7% 135 141 141
651 3% 135 141 141 Primary aluminum ore reduction plants 1,676 7% 50 253 255 1,676 3% 50 253 255 Lime kilns 12,849 7% 2,683 4,471 7,153 12,849 3% 4,471 7,153 7,153 (continued)8-6 Table 8-2. NO x Emissions and Emission Reductions for BART Source Categories in 2015 (continued)

BART Source Category Baseline Emissions

(tons) Discount Rate Scenarios—Reductions (tons)
Scenario 1 Scenario 2 Scenario 3 Glass fiber processing plants 6,677 7% 568 2,116 2,198 6,677 3% 775 2,116 2,198 Municipal incinerators 1,656 7% 0 744 744 1,656 3% 0 744 744 Coal cleaning plants 1,110 7% 0 511 511 1,110 3% 0 511 511 Carbon black plants 4,645 7% 7 120 120 4,645 3% 7 120 120 Phosphate rock processing plants 719 7% 0 45 48 719 3% 0 48 48 Secondary metal production facilities 1,377 7% 25 34 35 1,377 3% 25 34 35 Total 681,765 7% 165,634 361,152 391,101 681,765 3% 246,067 390,871 393,349

Appendix B. In addition, Appendix B contains sensitivity analyses that provide capital cost estimates based on a 10 percent ra te for annualizing costs, and also other sensitivity analyses in that appendix examine the effects of varia tion 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 th at a lower discount rate would lead to more application of these measures to reduce SO 2 and NO x and vice versa. More s ources 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 lower8-7 Table 8-3. Total Annualized Costs of Co ntrol for BART Source Categories in 2015 (million 1999$)

BART Source Category Discount Rate Scenarios Scenario 1 Scenario 2 Scenario 3
Industrial boilers 7% 74.6 527.6 845.4
3% 135.0 401.8 643.3
Petroleum refineries 7% 4.2 180.2 428.6 3% 15.3 214.8 394.8 Kraft pulp mills 7% 20.7 141.7 313.3
3% 45.6 106.3 168.5 Portland cement plants 7% 2.9 174.8 409.5
3% 20.8 269.0 346.1 Hydrofluoric, sulfuric, and nitric acid plants 7% 18.0 23.4 23.4
3% 16.4 21.4 21.4 Chemical process plants 7% 14.4 70.8 87.8
3% 21.1 54.4 77.1 Iron and Steel mills 7% 0.6 23.5 33.2
3% 0.4 22.7 32.4
Coke oven batteries 7% 0.0 18.7 33.8 3% 0.0 14.9 25.0 Sulfur Recovery plants 7% 11.7 12.1 12.1
3% 11.7 12.1 12.1 Primary aluminum ore reduction plants 7% 1.7 7.76 7.82
3% 1.0 5.0 5.0 Lime kilns 7% 2.0 5.0 31.8
3% 4.3 25.4 25.4 (continued)8-8 Table 8-3. Total Annualized Costs of Co ntrol for BART Source Categories in 2015 (million 1999$) (continued)

BART Source Category Discount Rate Scenarios Scenario 1 Scenario 2 Scenario 3 Glass fiber processing plants 7% 0.5 5.3 7.8
3% 0.7 4.8 6.8 Municipal incinerators 7% 0.0 1.1 1.1
3% 0.0 0.9 0.9 Coal cleaning plants 7% 0.0 1.0 1.0
3% 0.0 0.8 0.8 Carbon black plants 7% 0.01 0.2 0.2
3% 0.005 0.1 0.1 Phosphate rock processing plants 7% 0.0 0.1 0.2
3% 0.0 0.2 0.2 Secondary metal production facilities 7% 0.02 0.04 0.1
3% 0.0 0.0 0.0
Total 7% $151.43 $1,193.24 $2,237.24 3% $272.34 $1,157.09 $1,770.21

discount rate leads to more sources having available controls under that scenario. At scenario 2, the annualized costs and reductions are relatively close because the controls available to sources are about the same. Atscenario 3, the available controls are about identical regardless of the discount rate, but th e costs are lower for the 3 percent discount rate because of the lower capital costs overall. Tables 8-4 through 8-6 summarize th e results for the $2,000/ton and $3,000/ton nonEGU scenarios in the same way as for Scenarios 1 through 3. Table 8-4 summarizes the SO 2 emission reductions from applying these tw o BART non-EGU source categories using a discount rate of 7 percent and al so showing results using an disc ount rate of 3 percent. In8-9 Table 8-4. SO 2 Emissions and Emission Reductions for BART Source Categories in 2015

BART Source Category Baseline Emissions (tons) Discount Rate Scenarios—Reductions (tons) $2,000/ton Scenario $3,000/ton Scenario Industrial boilers 420,782 7% 87,009 124,592 420,782 3% 120,095 148,962 Petroleum refineries 199,483 7% 23,173 23,173 199,483 3% 25,103 33,304 Kraft pulp mills 119,818 7% 0 0 119,818 3% 0 3,196 Portland cement plants 116,835 7% 2,350 2,350 116,835 3% 2,743 13,383 Hydrofluoric, sulfuric, and nitric acid plants 96,741 7% 35,358 36,753 96,741 3% 36,753 36,753 Chemical process plants 47,700 7% 2,376 2,376 47,700 3% 2,376 2,376 Iron and steel mills 23,541 7% 2,914 2,914 23,541 3% 2,914 2,914 Coke oven batteries 9,815 7% 4,088 4,088 9,815 3% 4,088 4,088 Sulfur recovery plants 59,766 7% 13,697 13,697 59,766 3% 14,311 14,311 Primary aluminum ore reduction plants 47,552 7% 1,630 1,630 47,552 3% 1,630 3,260 Lime kilns 9,373 7% 0 0 9,373 3% 0 0 (continued)8-10 Table 8-4. SO 2 Emissions and Emission Reductions for BART Source Categories in 2015 (continued)

BART Source Category Baseline Emissions (tons) Discount Rate Scenarios—Reductions (tons) $2,000/ton Scenario $3,000/ton Scenario Glass fiber processing plants 2,170 7% 0 0 2,170 3% 0 0 Municipal incinerators 284 7% 0 0 284 3% 0 0 Coal cleaning plants 1,530 7% 0 0 1,530 3% 0 0 Carbon black plants 41,853 7% 0 0 41,853 3% 0 0 Phosphate rock processing plants 21 7% 0 0 21 3% 0 0 Secondary metal production facilities 9,988 7% 0 0 9,988 3% 0 0 Total 1,208,088 7% 172,595 211,573 1,208,088 3% 210,013 262,547

total, the scenarios applied in this analysis lead to nationwide SO 2 emission reductions ranging from 172,595 tons to 211,573 tons with cost s at a 7 percent discount rate. These estimates represent a reduction of 14 to 18 percent from the 2015 baseline. These scenarios lead to SO 2 emission reductions ranging from 210,013 to 262,547 tons with costs at a 3 percent rate. These estimate s represent a reduction of 17 to 22 percent from the 2015 baseline.
Table 8-5 summarizes the NO x emission reductions for these two non-EGU scenarios. The nationwide NO x emission reductions from applying these three scenarios range from 242,355 tons to 291,740 tons with costs at a 7 pe rcent discount rate. These represent a reduction of 36 to 43 percent from the 2015 ba seline. These scenarios lead to NO x emission reductions ranging from 280,163 to 313,382 tons with costs at a 3 percent rate. These represent a reduction of 41 to 46 percent from the 2015 baseline.8-11 Table 8-5. NO x Emissions and Emission Reductions for BART Source Categories in 2015

BART Source Category Baseline Emissions (tons) Discount Rate Scenarios $2,000/ton Scenario $3,000/ton Scenario Industrial boilers 217,063 7% 97,074 125,575 217,063 3% 120,151 128,640 Petroleum refineries 86,566 7% 23,173 23,173 86,566 3% 23,173 26,685 Kraft pulp mills 103,614 7% 50,221 60,985 103,614 3% 56,466 64,521 Portland cement plants 120,567 7% 26,659 26,659 120,567 3% 26,659 26,659 Hydrofluoric, sulfuric, and nitric acid plants 17,059 7% 11,283 11,283 17,059 3% 11,283 11,283 Chemical process plants 72,577 7% 25,922 26,753 72,577 3% 27,568 31,567 Iron and steel mills 20,963 7% 2,034 3,259 20,963 3% 2,038 7,198 Coke oven batteries 10,389 7% 0 5,768 10,389 3% 5,768 5,768 Sulfur recovery plants 651 7% 0 0 651 3% 0 0 Primary aluminum ore reduction plants 1,676 7% 70 253 1,676 3% 335 335 Lime kilns 12,849 7% 4,471 4,471 12,849 3% 4,471 7,153 (continued)8-12 Table 8-5. NO x Emissions and Emission Reductions for BART Source Categories in 2015 (continued)

BART Source Category Baseline Emissions (tons) Discount Rate Scenarios $2,000/ton Scenario $3,000/ton Scenario Glass fiber processing plants 6,677 7% 568 2,116 6,677 3% 851 2,116 Municipal incinerators 1,656 7% 744 744 1,656 3% 744 744 Coal cleaning plants 1,110 7% 0 511 1,110 3% 511 511 Carbon black plants 4,645 7% 111 120 4,645 3% 111 120 Phosphate rock processing plants 719 7% 0 45 719 3% 0 48 Secondary metal production facilities 1,377 7% 25 25

1,377 3% 34 34 Total 681,765 7% 242,355 291,740 681,765 3% 280,163 313,382

Table 8-6 summarizes the annualized cost s associated with these two non-EGU scenarios. In total, the two scenarios app lied 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 cost s at a 3 percent discount rate. Given the highly capital-intensive nature of the control measures included in these analyses, it is not unreasonable th at a lower discount rate would lead to more application of these measures to reduce SO 2 and NO x and vice versa. More s ources 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 $2,000/ton scenario, the emission reductions are higher with a 3 percent discount rate than a 7 percen t discount rate because the lower discount rate8-13 Table 8-6. Total Annualized Costs of Co ntrol for BART Source Categories in 2015 (million 1999$)

BART Source Category Discount Rate Scenarios $2,000/ton Scenario $3,000/ton Scenario
Industrial boilers 7% 241.5 412.1 3% 255.0 337.4
Petroleum refineries 7% 71.1 71.1
3% 71.1 81.2 Kraft pulp mills 7% 75.1 75.1 3% 59.2 68.5 Portland cement plants 7% 29.6 29.6 3% 28.7 56.6 Hydrofluoric, sulfuric, and nitric acid plants 7% 20.4 21.4
3% 21.4 21.4 Chemical process plants 7% 40.5 40.5
3% 30.4 40.1 Iron and Steel mills 7% 7.9 11.0 3% 5.7 22.7
Coke oven batteries 7% 6.2 18.7
3% 14.9 14.9 Sulfur Recovery plants 7% 11.7 12.1
3% 12.1 12.1 Primary aluminum ore reduction plants 7% 1.7 2.2 3% 1.0 5.0
Lime kilns 7% 5.0 5.0 3% 4.3 25.4 (continued)8-14 Table 8-6. Total Annualized Costs of Co ntrol for BART Source Categories in 2015 (million 1999$) (continued)

BART Source Category Discount Rate Scenarios $2,000/ton Scenario $3,000/ton Scenario Glass fiber processing plants 7% 0.5 5.3
3% 1.7 4.7 Municipal incinerators 7% 1.1 1.1
3% 0.9 0.9 Coal cleaning plants 7% 0.0 1.0
3% 0.8 0.8 Carbon black plants 7% 0.01 0.01 3% 0.005 0.01 Phosphate rock processing plants 7% 0.01 0.01
3% 0.01 0.01 Secondary metal production facilities 7% 0.04 0.04
3% 0.01 0.01
Total 7% $512.36 $706.26 3% $507.23 $691.73

leads to more sources having available contro ls under that scenario and the costs are fairly close. At $3,000/ton scenario, the annualized co sts 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 de vices 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 ra tes, and energy rates are variedto determine the sensitivity of the costs to such variation. We present such results in Appendix B. Costs from monitoring,2 For more information on the emissions and control measures within AirControlNET, go to www.epa.gov /ttn/ecas/AirControlNET.htm. Documentation for emissions data and control measures can be found at this Web site. 8-15 record keeping, and reporting are not included in this cost analysis because these costs are accounted for in the Regional Haze ICR. Two types of costs will be incurred in association with the addition of control technologies: a one-time capital cost for ne w equipment installati on and increased annual operating and maintenance costs. In general, economies of scale exist for pollution control technologies for both capital cost s and operating and ma intenance 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 categ ory, EPA’s estimates of emi ssions 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 a nd for varying geographic scales (nationally, regionally, and locally). 2 For this analysis, we applie d in AirControlNET control measures up to the given average annualized cost per ton cutoff for SO 2 and NO x to the BART-eligible units within the non-EGU dataset described in Chapter 3. Thes e analyses are calculated for control measures applied to the 2015 emissions inventory, which is a product of growing the emissions from the non-EGU datase t, a database with 2001 emissi ons 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 estimate d at a 3 percent and 7 percent discount rate for purposes of annualizing capital costs cons istent with EPA and OMB guidelines for preparing economic analyses (EPA, 2000; OM B, 2003). Equipment lives and control efficiencies for each control technology are taken from the AirControlNET control measures documentation report. Annual costs are estimated in 1999 dollars. All costs are converted from the original source year to 1999 dollars using the gross domestic product (GDP) price deflator.3 The terms “ICI boiler” and “industrial boiler” are used interchangeably in this RIA. 8-16 8.4 Types of Emissions Control Techno logies Employed in These Analyses A number of technologies are co mmonly employed to reduce SO 2 and NO x emissions from the non-EGU source categories. This sec tion of the chapter covers many of the technologies employed in reducing these pollutants.
8.4.1 SO 2 Emissions Control Technologies
This section describes available tec hnologies for controlling emissions of SO 2 for industrial, commercial, a nd institutional (ICI) boilers 3 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 pe rformance. While all controls presented in this analysis are consider ed generally technically feasible for each class of sources, source-specific cas es may exist where a control technology is in fact not technically feasible. In their response to the BART rule, States shoul d consider case-specific feasibility when establishing control requirements.
8.4.1.1 SO 2 Control Technology for Non-EGU Sources For industrial boilers, FGD scrubbers are th e only technology available in our data. This is not to say that other technologies ar e not available or thata 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 cont ent by weight) could not be employed to achieve SO 2 reductions, but data for such technologies or technique were not availa ble for this analysis. FGD scrubbers are also used on units at petroleu m refineries, kraft pulp mills, and Portland cement kilns. For other BART source categories, othe r types of control tec hnologies were available that are more specific to the s ources controlled. Table 8-7 lists these technologies. For more information on these technolog ies, please refer to the Ai rControlNET 4.0 control measures documentation report.8-17 Table 8-7. Available SO 2 Control Technologies for Industrial Boilers and Other NonEGU Sources
Source Type/Fuel Type Available Control Technology ICI boilers—all fuel types, kraft pulp mills, Portland cement plants (all fuel types) FGD scrubbers

Hydrofluoric, sulfuric, and nitric acid Increase percentage sulfur conversion to meet sulfuric acid NSPS (99.7% reduction) Sulfur recovery plants Sulfur recovery and/or tail gas treatment Coke oven batteries Vacuum carbonate + sulfur recovery plant
Source: AirControlNET control measures documentation report.

8.4.2 NOx Emissions Control Technologies
This section describes available tec hnologies for controlling emissions of NO x for ICI boilers and other non-EGU sour ces. In general, low-NO x 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 t echnology 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 wh en establishing control requirements.
8.4.2.1 NOxControl Technology for Non-EGU Sources Several types of cont rol technologies are considered for industrial boilers: SCR, selective noncatalytic reducti on (SNCR), natural gas reburn (NGR), coal reburn, and lowNO x burners. As stated above, the control t echnology 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 NO x emissions are expected to be of greater importance than thermal NO x emissions. When circumstan ces suggest that combustion controls do not make sense as a control tec hnology (e.g., sintering processes, coke oven batteries, sulfur recovery plants), SNCR or SCR may be an appropriate choice.8-18 Table 8-8 lists the control technologies av ailable for industrial boilers and other nonEGU sources by type of fuel. For more inform ation on these technologies, please refer to the AirControlNET 4.0 control meas ures documentation report. Table 8-8. Available NO x Control Technologies for Industrial Boilers
Source Type/Fuel Type Available Control Technology ICI boilers—coal/wall SNCR, LNB, SCR ICI boilers—coal/FBC (fluidized bed combustor) SNCR—urea based ICI boilers—coal/s toker SNCR—urea based ICI boilers—coal/cyclone SNCR, Coal Reburn, NGR, SCR ICI boilers—residual oil LNB, SNCR, LNB + FGR, SCR ICI boilers—distillate oil LNB, SNCR, LNB + FGR, SCR ICI boilers—natural gas LNB, SNCR, LNB + FGR, OT + WI, SCR ICI boilers—process gas LNB, LNB + FGR, OT + WI, SCR ICI boilers—coke SNCR, LNB, SCR ICI boilers—LPG (liquid petroleum gas) LNB, SNCR, LNB + FGR, SCR
Source: AirControlNET control measures documentation report.

8.4.2.2 NOxControl Technology for Othe r Non-EGU BART Source Categories Other non-EGU source categories covered in the analysis include petroleum refineries, kraft pulp mills, and cement kilns. NO x control technologies available for petroleum refineries, particular ly process heaters at these plants, include LNB, SNCR, FGR, and SCR along with combinations of these technologies. NO x control technologies available for kraft pulp mills include those available to industrial boilers, na mely LNB, SCR, SNCR, along with water injection (WI). NO x control technologies available for cement kilns include those available to industrial boilers, namely LNB, SCR, a nd 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 tec hnologies available for these categ ories. For more information on these technologies, please refer to the AirControlNET 4.0 control measures documentation report.8-19 Table 8-9. Available NO x Control Technologies for Other Non-EGU Source Categories Other than Industrial Boilers
Source Type/Fuel Type Available Control Technology Petroleum refineries (process heaters—process gas, distillate oil) LNB + FGR, SNCR, LNB + SNCR, SCR, LNB + SCR Kraft pulp mills LNB, SNCR, SCR, LNB + SNCR, SCR + WI Cement manufacturing—dry MKF, LNB, SNCR—urea based, SNCR—ammonia based, SCR Cement manufacturing—wet MKF, LNB, SCR, biosolid injection In-process; bituminous coal; cement kiln s SNCR—ur ea based Chemical process plants L NB, SNCR, SCR, SCR + WI Lime kilns SCR Iron and steel mills LNB, SNCR, LNB + SNCR, LNB + FGR, SCR
Source: AirControlNET control measures documentation report.

8.5 Listing of Affected Source Categories and Results for Each We present below results for each BART s ource category affected in the analyses. Results presented here reflect th e use of 7 percent and 3 percent discount rates as part of the control strategy analysis for each scenari o. There are no impacts for 8 of the 25 non-EGU source categories because there are no control measures available to reduce SO 2 and NO x from these categories within AirControlN ET. For seven source categories only NO x reductions take place in these analyses becau se 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 SO 2 and NO x reductions take place in these analyses. The first 10 source categories for which impacts are presented are those for which both SO 2 and NO x reductions take place in these analyses. These BART source categories are

C industrial boilers (250 mmBTU/hr h eat input capacity and greater);

C petroleum refineries;

C kraft pulp mills;

C Portland cement plants;8-20 C hydrofluoric, sulfur ic, and nitric acid;

C chemical process plants;

C iron and steel mills;

C coke oven batteries;

C sulfur recovery plants; and

C primary aluminum ore reductions. For seven source categories, only NO x reductions take place:

C lime kilns,

C glass fiber processing plants,

C municipal incinerators (250 tons refuse burn capacity or greater),

C coal cleaning plants (thermal dryers),

C carbon black plants (furnace process),

C phosphate rock processing plants, and

C secondary metal production facilities. Finally, for eight source categories there are no impacts estimated because there are no BART-eligible units in the non-EGU dataset or no controls available to reduce emissions from BART-eligible units:

C primary lead smelters,

C primary copper smelters,

C primary zinc smelters,

C fuel conversion plants,

C sintering plants,

C charcoal production facilities,

C taconite ore processing plants, and

C petroleum storage and transfer facilities.8-21 8.5.1 Results for Industrial Boilers
Table 8-10 shows the SO 2 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 w ith heat input design capacities of 250 mmBtu/hr or greater and are fossil fueled. The table i ndicates 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 percen t for costs at a 3 percent discount rate. Table 8-10. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU Industrial Boilers a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 420,782 7% 388,569 32,213 420,782 3% 354,999 65,783 Scenario 2 420,782 7% 269,395 151,387 420,782 3% 256,344 164,438 Scenario 3 420,782 7% 220,474 200,308 420,782 3% 216,565 204,217

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-11 presents the NO x baseline emissions and reductions for each scenario. The table indicates that the scenarios achieve incremental reductions from the 2015 baseline, ranging from 32 percent to 60 percent for costs at a 7 percent discount rate and from 52 to 60 percent for costs at a 3 percent discount rate. Table 8-12 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 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 annua lized cost-effectiveness results range from $807 to $3,047 per ton with costs at a 7 per cent rate and from $727 to $2,471 per ton with costs at a 3 percent rate. In addition, th e marginal costs range from $2,250 to $6,463 per ton with costs at a 7 percent discount rate a nd from $2,202 to $6,023 per ton with costs at a 3 percent discount rate.8-22 Table 8-11. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU Industrial Boilers a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 217,063 7% 149,738 67,325 217,063 3% 105,124 111,939 Scenario 2 217,063 7% 86,607 130,456 217,063 3% 86,639 130,424 Scenario 3 217,063 7% 86,541 130,522 217,063 3% 86,558 130,505

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-12. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Industrial Boilers

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $26.0 $807 — 3% $47.8 $727 — Scenario 2 7% $294.1 $1,943 $2,250 3% $265.0 $1,612 $2,202 Scenario 3 7% $610.3 $3,047 $6,463 3% $504.6 $2,471 $6,023

The costs and emission reductions reflect FGD scrubbers app lied to all of these units. The average and marginal costs rise as a result of FGD scrubbers being applied to more coalfired units with lower sulfur c ontents and to oil-fired units th at have lower sulfur contents than coal-fired units.8-23 Table 8-13 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for NO x 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 w ith costs at a 3 percent rate. The accompanying annualized average cost-effec tiveness 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 rang e from $2,929 to $24,242 per ton with costs at a 7 percent rate and from $2,684 to $22,840 per t on with costs at a 3 percent rate. Table 8-13. 2015 Cost and Cost-Effectiveness Results for NO xControl at BARTEligible Industrial Boilers

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $48.6 $722 — 3% $87.2 $779 — Scenario 2 7% $233.5 $1,790 $2,929 3% $136.8 $1,049 $2,684 Scenario 3 7% $235.1 $1,801 $24,242 3% $138.7 $1,063 $22,840

The average and marginal costs increase as the scenarios become more stringent as a result of additional appli cation of SCR. SCR is the most expensive NO x 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 cost s for each scenario for controlling both SO 2 and NO x . 8.5.2 Results for Petroleum Refineries
Table 8-15 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 1 percent to 28 pe rcent for costs estimated at a 7 percent discount rate and from 9 percen t to 29 percent for costs es timated at a 3 percent discount rate.8-24 Table 8-14. 2015 Cost Results for SO 2 and NO x Control at BART-Eligible Industrial Boilers

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) Scenario 1 7% $74.6 3% $135.0 Scenario 2 7% $527.6 3% $401.8 Scenario 3 7% $845.4 3% $643.3

Table 8-15. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Petroleum Refineries a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 199,483 7% 197,386 2,097 199,483 3% 182,450 17,033 Scenario 2 199,483 7% 168,164 31,319 199,483 3% 157,572 41,911 Scenario 3 199,483 7% 143,021 56,462 199,483 3% 140,958 58,525

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-16 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios 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 es timated at a 3 per cent discount rate.8-25 Table 8-16. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Petroleum Refineries a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 86,566 7% 82,531 4,035 3% 81,987 4,579 Scenario 2 86,566 7% 39,010 47,556 3% 35,307 51,259 Scenario 3 86,566 7% 35,250 51,316 3% 33,561 53,005

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-17 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 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 av erage cost-effectiveness results range from $906 to $3,958 per ton with costs at a 7 per cent 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 percen t 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 app lied to all of these units. The average and marginal costs rise as a resu lt of FGD scrubbers bein g applied to units such as fluid catalytic cracking units (FCCUs) with lowe r sulfur contents. As sulfur content of the fuel for a unit decreases, the cost per ton of control increases and vice versa. Table 8-18 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for NO x 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 av erage cost-effectiveness results range from $570 to $3,997 per ton with costs at a 7 per cent discount rate and from $595 to $4,294 per8-26 Table 8-17. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Petroleum Refineries

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $1.9 $906 — 3% $12.5 $736 — Scenario 2 7% $54.0 $1,724 $1,783 3% $72.7 $1,734 $2,417 Scenario 3 7% $223.5 $3,958 $6,741 3% $167.3 $2,858 $5,692

Table 8-18. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Petroleum Refineries

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $2.3 $570 — 3% $2.7 $595 — Scenario 2 7% $126.2 $2,654 $2,917 3% $142.1 $2,772 $2,986 Scenario 3 7% $205.1 $3,997 $20,984 3% $227.6 $4,294 $48,969

ton with costs at a 3 percent discount rate. In addition, the marginal costs range from $2,917 to $20,984 per ton with costs at a 7 percen t 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 re sult of additional pro cess heaters having to apply LNB + SNCR. In most cases, the aver age cost per ton of control is between $4,000 and $5,000 per ton.8-27 Table 8-19 shows the total annualiz ed costs for controlling both SO 2 and NO x . Table 8-19. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Petroleum Refineries

Scenarios Discount Rate Total Annualized Costs (million 1999$) Scenario 1 7% $4.2 3% $15.3 Scenario 2 7% $180.2 3% $214.8 Scenario 3 7% $428.6 3% $394.8

8.5.3 Kraft Pulp Mills
Table 8-20 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 0 to 24 percent for co sts 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 NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 34 percen t to 63 percent for costs at a 7 percent discount rate and from 52 to 63 percent for costs at a 3 percent discount rate. Table 8-22 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 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 co sts at a 3 percent discount rate. The accompanying annualized average cost-effectiv eness results range from $0 to $5,691 per ton with costs at a 7 percent discount rate and fro m $0 to $4,720 per ton with costs at a 3 percent discount rate. In addition, the marginal co sts range from $3,098 to $7,287 per ton with costs at a 7 percent discount rate and from $2,189 to $5,432 per ton with costs at a 3 percent discount rate.8-28 Table 8-20. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Unit s at Kraft Pulp Mills a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 119,818 7% 119,818 0 119,818 3% 119,818 0 Scenario 2 119,818 7% 109,005 10,814 119,818 3% 116,820 3,196 Scenario 3 119,818 7% 91,488 28,330 119,818 3% 105,208 14,610

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-21. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Unit s at Kraft Pulp Mills a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 103,614 7% 68,365 35,249 103,614 3% 49,885 53,729 Scenario 2 103,614 7% 42,196 61,418 103,614 3% 37,822 65,792 Scenario 3 103,614 7% 37,838 65,776 103,614 3% 37,827 65,797

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.8-29 Table 8-22. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Kraft Pulp Mills

Scenarios Discoun t Rate Total Annualized Costs (million 1999$) AnnualizedAverage CostEffectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.0 $0 — 3% $0.0 $0 — Scenario 2 7% $33.5 $3,098 $3,098 3% $7.0 $2,189 $2,189 Scenario 3 7% $161.5 $5,691 $7,287 3% $69.0 $4,720 $5,432

The costs and emission reductions reflect FGD scrubbers app lied to all of these units. The average and marginal costs rise as a resu lt of FGD scrubbers bein g applied to units for which the application is more expensive ($6,000 to $10,000 per ton). Table 8-23 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for NO x control. The annualized control costs range from $20.7 million to $151.8 million with costs at a 7 percent discount rate and fro m $45.6 million to $99.8 million with costs at a 3 percent rate. The accompanying annualized average cost-e ffectiveness results range from $587 to $2,308 per ton with costs at a 7 percent discount ra te 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 a nd from $4,452 to $35,800 per ton with costs at a 3 percent discount rate.
The costs and emission reductions reflect greater applications of SCR as the cost-perton cap rises, particularly for sulfite pulping recovery furnaces.
Table 8-24 shows the total annualized cost s of each scenario for controlling both SO 2 and NO x .8-30 Table 8-23. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Kraft Pulp Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Cost ($/ton) Scenario 1 7% $20.7 $587 — 3% $45.6 $849 — Scenario 2 7% $108.2 $1,762 $3,344 3% $99.3 $1,510 $4,452 Scenario 3 7% $151.8 $2,308 $10,005 3% $99.5 $1,512 $35,800

Table 8-24. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Kraft Pulp Mills

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) Scenario 1 7% $20.7 3% $45.6 Scenario 2 7% $141.7 3% $106.3 Scenario 3 7% $313.3 3% $168.5

8.5.4 Results for Portland Cement Plants
Table 8-25 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 0 percen t to 23 percent for costs at a 7 percent discount rate and from 0 to 26 percent for costs at a 3 percent discount rate.8-31 Table 8-25. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Portland Cement Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 116,835 7% 116,835 0 116,835 3% 116,835 0 Scenario 2 116,835 7% 103,452 13,383 116,835 3% 98,509 18,326 Scenario 3 116,835 7% 90,119 26,716 116,835 3% 86,145 30,690

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-26 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 16 percen t 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 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Portland Cement Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 120,567 7% 101,289 19,276 120,567 3% 89,664 30,903 Scenario 2 120,567 7% 65,966 54,601 120,567 3% 48,646 71,921 Scenario 3 120,567 7% 48,646 71,921 120,567 3% 48,646 71,921

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.8-32 Table 8-27 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 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 co sts at a 3 percent discount rate. The accompanying annualized average cost-effectiv eness results range from $0 to $5,031 per ton with costs at a 7 percent discount rate and fro m $0 to $4,131 per ton with costs at a 3 percent discount rate. In addition, the marginal co sts 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 SO 2 Control at Non-EGU BART-Eligible Units at Portland Cement Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.0 $0 — 3% $0.0 $0 — Scenario 2 7% $43.5 $3,250 $3,250 3% $49.7 $2,710 $2,710 Scenario 3 7% $134.4 $5,031 $6,818 3% $126.8 $4,131 $6,816

The costs and emission reductions reflect FGD scrubbers app lied to all of these units. The average and marginal costs rise as a resu lt of FGD scrubbers bein g applied to more units with lower sulfur content fuels. Table 8-28 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for NO x 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 av erage cost-effectiveness results range from $150 to $3,825 per ton with costs at a 7 per cent 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 disc ount rate and are $4,839 per ton with costs at a8-33 Table 8-28. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Portland Cement Plants

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

3 percent discount rate betw een the $1,000/ton and the $4,000/ton scenarios. There is no marginal costs between the $4,000/ton and $10,000/t on scenarios with costs at the 3 percent discount rate because there is no diffe rence 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 cost s of each scenario for controlling both SO 2 and NO x . Table 8-29. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Portland Cement Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) Scenario 1 7% $2.9 3% $20.8 Scenario 2 7% $174.8 3% $269.0 Scenario 3 7% $409.5 3% $346.18-34 8.5.5 Results for Hydrofluoric, Sulfu ric, and Nitric Acid Plants
Table 8-30 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 35 to 38 percent for co sts at a 7 percent discount rate and the same for costs at a 3 percent discount rate. Table 8-30. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 96,741 7% 62,601 34,140 96,741 3% 62,601 34,140 Scenario 2 96,741 7% 60,188 36,753 96,741 3% 60,188 36,753 Scenario 3 96,741 7% 60,188 36,753 96,741 3% 60,188 36,753

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-31 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline of about 66 percent. The degree of impact varies little between scenarios and the discount rate of the costs. Table 8-32 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 control. The annualized control costs range from $9.8 million to $15.2 million with costs at a 7 percent discount rate a nd from $9.1 million to $14.1 million with costs at a 3 percent discount rate. The accompanying annualized av erage cost-effectiveness results range from $287 to $413 per ton with costs at a 7 percen t 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 di scount rate and $1,914 per ton with costs at a 3 percent discount rate. In this case, there are no controls between $4,000/ton and $10,000/ton; thus, there are no marginal costs between these two scenarios.8-35 Table 8-31. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 17,059 7% 5,783 11,276 17,059 3% 5,783 11,276 Scenario 2 17,059 7% 5,776 11,283 17,059 3% 5,776 11,283 Scenario 3 17,059 7% 5,776 11,283 17,059 3% 5,776 11,283

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-32. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Hydrofluoric , Sulfuric, and Nitric Acid Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) AnnualizedAverage CostEffectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $9.8 $287 — 3% $9.1 $268 — Scenario 2 7% $15.2 $413 $2,067 3% $14.1 $385 $1,914 Scenario 3 7% $15.2 $413 N/A 3% $14.1 $385 N/A8-36 The costs and emission reductions are flat between the scenarios because there is only one control technique available to reduce SO 2 emissions from these sources—increase sulfur conversion to meet the sulfuric acid NSPS (99.7 percent control). Table 8-33 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for NO x 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 av erage cost-effectiveness results range from $726 to $728 per ton with costs at a 7 percen t 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 di scount 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 NO x Control at Non-EGU BART-Eligible Units at Hydrofluoric , Sulfuric, and Nitric Acid Plants

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

The costs and emission reductions are flat between the scenarios because there is only one control technique available to reduce NO x emissions from these sources—SNCR applied to nitric acid manufacturing sources.
Table 8-34 shows the total annualized cost s of each scenario for controlling both SO 2 and NO x .8-37 Table 8-34. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Hydrofluoric, Sulfuri c, and Nitric Acid Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) Scenario 1 7% $18.0 3% $16.4 Scenario 2 7% $23.4 3% $21.4 Scenario 3 7% $23.4 3% $21.4

8.5.6 Results for Chemical Process Plants
Table 8-35 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 0 percent to 7 percen t for costs at either a 7 or a 3 percent discount rate. Table 8-35. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Chemical Process Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 47,700 7% 47,700 0 47,700 3% 47,700 0 Scenario 2 47,700 7% 45,324 2,376 47,700 3% 45,324 2,376 Scenario 3 47,700 7% 44,129 3,571 47,700 3% 44,129 3,571

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.8-38 Table 8-36 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 33 percen t 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 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Chemical Process Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 72,577 7% 48,607 23,970 72,577 3% 45,435 27,142 Scenario 2 72,577 7% 37,941 34,636 72,577 3% 37,776 34,801 Scenario 3 72,577 7% 37,776 34,801 72,577 3% 37,385 35,192

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-37 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 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 co sts at a 3 percent discount rate. The accompanying annualized average cost-effectiv eness results range from $0 to $3,836 per ton with costs at a 7 percent discount rate and fro m $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 pe r 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: increas e sulfur percentage conversion to meet the sulfuric acid NSPS (99.7 percent control) and FGD scrubbers.8-39 Table 8-37. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Chemical Process Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.0 $0 — 3% $0.0 $0 — Scenario 2 7% $2.5 $1,052 $1,052 3% $2.4 $1,013 $1,013 Scenario 3 7% $13.7 $3,836 $9,372 3% $10.2 $2,850 $6,527

Table 8-38 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for NO x 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 av erage cost-effectiveness results range from $601 to $2,129 per ton with costs at a 7 per cent 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 ata 3 percent discount rate.
The costs and emission reductions reflect th e application of many different types of NO x 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 cost s of each scenario for controlling both SO 2 and NO x .8-40 Table 8-38. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Chemical Process Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $14.4 $601 — 3% $21.1 $776 — Scenario 2 7% $68.2 $1,969 $5,030 3% $54.4 $1,563 $4,348 Scenario 3 7% $74.1 $2,129 $35,758 3% $77.1 $2,191 $58,082

Table 8-39. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Chemical Process Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) Scenario 1 7% $14.4 3% $21.1 Scenario 2 7% $70.8 3% $56.8 Scenario 3 7% $87.8 3% $87.3

8.5.7 Results for Iron and Steel Mills
Table 8-40 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 0 to 12 percent for cost s at either a 7 or 3 percent discount rate.8-41 Table 8-40. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Iron and Steel Mills a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 23,541 7% 23,541 0 23,541 3% 23,541 0 Scenario 2 23,541 7% 20,627 2,914 23,541 3% 20,627 2,914 Scenario 3 23,541 7% 20,627 2,914 23,541 3% 20,627 2,914

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-41 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 5 to 41 percent at cost s of either a 7 or 3 percent discount rate. Table 8-41. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Iron and Steel Mills a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 20,963 7% 19,927 1,036 20,963 3% 19,927 1,036 Scenario 2 20,963 7% 13,966 6,997 20,963 3% 12,463 8,500 Scenario 3 20,963 7% 12,456 8,507 20,963 3% 12,290 8,673

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.8-42 Table 8-42 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 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-effectiv eness results range from $0 to $1,819 per ton with costs at a 7 percent discount rate and fro m $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 ar e no marginal costs between these two scenarios. Table 8-42. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Iron and Steel Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.0 $0 — 3% $0.0 $0 — Scenario 2 7% $5.3 $1,819 $1,819 3% $3.4 $1,165 $1,165 Scenario 3 7% $5.3 $1,819 N/A 3% $3.4 $1,165 N/A

The costs and emission reductions are flat between the scenarios because the two controls available both have similar averag e cost-per-ton estimates below $4,000: sulfuric acid plant and increase sulfur conversion to meet the sulfuric acid NSPS (99.7 percent control). Table 8-43 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for NO x 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 av erage cost-effectiveness results range from $579 to $3,280 per ton with costs at a 7 per cent discount rate and from $431 to $3,343 per8-43 Table 8-43. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Iron and Steel Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.6 $579 — 3% $0.4 $431 — Scenario 2 7% $18.2 $2,601 $2,953 3% $19.3 $2,271 $2,532 Scenario 3 7% $27.9 $3,280 $6,424 3% $29.0 $3,343 $56,052

ton with costs at a 3 percent discount rate . The marginal costs range from $2,953 to $6,424 per ton with costs at a 7 percent discount rate and from $2,532 to $56,052 per ton with costs at a 3 percent discount rate.
The costs and emission reductions reflect a rise in the costs of control due to additional applications of LN B + either SNCR or SCR.
Table 8-44 shows the total annualiz ed costs for controlling both SO 2 and NO x . Table 8-44. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Iron and Steel Mills

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) Scenario 1 7% $0.6 3% $0.4 Scenario 2 7% $23.5 3% $22.7 Scenario 3 7% $33.2 3% $32.48-44 8.5.8 Results for Coke Oven Batteries
Table 8-45 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 0 to 62 percent for co sts at a 7 percent discount rate and from 0 to 57 percent for costs at a 3 percent discount rate. Table 8-45. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Coke Oven Batteries

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 9,815 7% 9,815 0 9,815 3% 9,815 0 Scenario 2 9,815 7% 5,727 4,088 9,815 3% 6,091 3,724 Scenario 3 9,815 7% 3,708 6,107 9,815 3% 4,251 5,564

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-46 shows the emissions reductions achieved in the analyses for each scenario for NO x control. The table indicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 0 to 56 percent fo r costs at a 7 or 3 percent discount rate. Table 8-47 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 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 co sts at a 3 percent discount rate. The accompanying annualized average cost-effectiv eness results range from $0 to $3,488 per ton with costs at a 7 percent discount rate and fro m $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 pe r ton with costs at a 3 percent discount rate.8-45 Table 8-46. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Coke Oven Batteries a

Scenarios 2015 Baseline Emissions 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 10,389 10,389 0 10,389 10,389 0 Scenario 2 10,389 4,621 5,768 10,389 4,621 5,768 Scenario 3 10,389 4,621 5,768 10,389 4,621 5,768

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-47. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Coke Oven Batteries

Scenarios Discount Rate Total Annualized Costs (million 1990$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.0 $0 — 3% $0.0 $0 — Scenario 2 7% $6.2 $1,517 $1,517 3% $4.0 $1,074 $1,074 Scenario 3 7% $21.3 $3,488 $7,479 3% $14.1 $2,529 $5,4898-46 The costs and emission reductions reflect application of only one control—vacuum carbonate plus a sulfur recovery plant but also differing SO 2 emissions levels at the affected coke oven batteries. Table 8-48 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for NO x 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 co sts at a 3 percent discount rate. The accompanying annualized average cost-effectiv eness results range from $0 to $2,167 per ton with costs at a 7 percent discount rate and fro m $0 to $1,898 per ton with costs at a 3 percent discount rate. In addition, the marginal cost s are $2,167 per ton with costs at a 7 percent discount rate and $1,898 per ton ata 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 NO x Control at Non-EGU BART-Eligible Units at Coke Oven Batteries

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

The costs and emission reductions are flat between the scenarios because there is only one NO x control available for these sources—SNCR.
Table 8-49 shows the total annualiz ed costs for controlling both SO 2 and NO x .8-47 Table 8-49. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Coke Oven Batteries

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) Scenario 1 7% $0.0 3% $0.0 Scenario 2 7% $18.7 3% $14.9 Scenario 3 7% $33.8 3% $25.0

8.5.9 Results for Sulfur Recovery Plants
Table 8-50 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios 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 b ecause of the few controls available between $1,000 and $10,000 per ton average cost.
Table 8-50. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Sulfur Recovery Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 59,766 7% 46,069 13,697 59,766 3% 46,073 13,693 Scenario 2 59,766 7% 46,069 13,697 59,766 3% 46,073 13,693 Scenario 3 59,766 7% 46,069 13,697 59,766 3% 46,073 13,693

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled

.8-48 Table 8-51 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 21 to 22 percent for cost s 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 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Sulfur Recovery Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions
Scenario 1 651 7% 516 135
651 3% 516 135 Scenario 2 651 7% 510 141
651 3% 510 141 Scenario 3 651 7% 510 141
651 3% 510 141

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-52 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 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-effec tiveness results are $847 per ton with costs at a 7 percent discount rate a nd $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 ava ilable 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 SO 2 emissions from these sources—sulfur recovery and/or tail gas treatment.8-49 Table 8-52. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Sulfur Recovery Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $11.6 $847 — 3% $11.6 $849 — Scenario 2 7% $11.6 $847 $847 3% $11.6 $849 $849 Scenario 3 7% $11.6 $847 N/A 3% $11.6 $849 N/A

Table 8-53 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for NO x 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 av erage cost-effectiveness results range from $519 to $3,652 per ton with costs at a 7 per cent 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 annualiz ed costs for controlling both SO 2 and NO x . 8.5.10 Results for Primary Aluminum Ore Reduction Plants
Table 8-55 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 3 to 7 percent for costs at a 7 or 3 percent discount rate.8-50 Table 8-53. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Sulfur Recovery Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.1 $519 — 3% $0.1 $400 — Scenario 2 7% $0.5 $3,652 $74,167 3% $0.5 $3,315 $68,833 Scenario 3 7% $0.5 $3,652 N/A 3% $0.5 $3,315 N/A

Table 8-54. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Sulfur Recovery Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) Scenario 1 7% $11.7 3% $11.7 Scenario 2 7% $12.1 3% $12.1 Scenario 3 7% $12.1 3% $12.18-51 Table 8-55. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Primar y Aluminum Ore Reduction Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 47,552 7% 45,922 1,630 47,552 3% 45,922 1,630 Scenario 2 47,552 7% 44,292 3,260 47,552 3% 44,292 3,260 Scenario 3 47,552 7% 44,292 3,260 47,552 3% 44,292 3,260

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table 8-56 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios 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 NO x Baseline Emissions and Emission Reductions (in tons) for NonEGU BART-Eligible Units at Primar y Aluminum Ore Reduction Plants a

Scenario 2015 Baseline Emissions 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 1,676 1,626 50 1,676 1,626 50 Scenario 2 1,676 1,423 253 1,676 1,421 255 Scenario 3 1,676 1,421 255 1,676 1,421 255

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.8-52 Table 8-57 shows the annualized costs, resulting annualized average costeffectiveness for each scenario, and marg inal costs between each scenario for SO 2 control. The annualized control costs range from $1.6 milli on to $7.2 million with costs at a 7 percent discount rate and from $1.0 million to $4.5 million w ith costs at a 3 percent discount rate. The accompanying annualized average cost-e ffectiveness results range from $982 to $2,209 per ton with costs at a 7 percent discount ra te and from $590 to $1,383 per ton with costs at a 3 percent discount rate. The marginal cost s are $3,436 per ton with costs at a 7 percent discount rate and $1,383 per ton with costs ata 3 percent discount rate. There are no available controls between $4,000/ton and $10,000/t on, so there are no other marginal costs. Table 8-57. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants

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

The costs and emission reductions are flat between the scenarios because there is only one control technique available to reduce SO 2 emissions from these sources—a sulfuric acid plant. Table 8-58 shows the annualized costs, resulting annualized average costeffectiveness for each scenarios, and marg inal costs between each scenario for NO x 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 av erage cost-effectiveness results range from $740 to $2,639 per ton with costs at a 7 per cent 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 percen t discount rate. With costs at a 3 percent discount rate, the8-53 Table 8-58. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.0 $740 — 3% $0.0 $509 — Scenario 2 7% $0.6 $2,411 $2,823 3% $0.4 $1,764 $1,764 Scenario 3 7% $0.7 $2,639 $30,500 3% $0.4 $1,764 N/A

marginal costs are $1,764 per ton between th e $1,000/ton and $4,000/ton scenarios, but there is no marginal cost between the $4,000/ton and $10,000/ton scenarios because the impacts are the same.
The costs and NO x 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 scenar io at the 7 percent discount rate. Table 8-59 shows the total annualiz ed costs for controlling both SO 2 and NO x . The next seven BART source categories only have NO x controls applied to their affected units because there are no SO 2 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 NO x , not SO 2 . 8.5.11 Results for Lime Kilns
Table 8-60 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 21 to 56 percent for costs at a 7 or 3 percent discount rate. Table 8-61 shows the annualized costs, resulting annualized average costeffectiveness, and marginal costs for each s cenario. 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 percen t discount rate. The annualized average8-54 Table 8-59. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) Scenario 1 7% $1.7 3% $1.0 Scenario 2 7% $7.8 3% $5.0 Scenario 3 7% $7.8 a 3% $5.0

a The annual costs for Scenario 3 are actually $61,000 higher than for Scenario 2.

Table 8-60. 2015 NO x Emission Reductions (in tons) for BART-Eligible Lime Kilns

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 12,849 7% 10,166 2,683 12,849 3% 8,378 4,471 Scenario 2 12,849 7% 8,378 4,471 12,849 3% 5,696 7,153 Scenario 3 12,849 7% 5,696 7,153 12,849 3% 5,696 7,153

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source category, both controlled and uncontrolled.8-55 Table 8-61. 2015 Cost and Cost-Effectivene ss Results for BART-Eligible Lime Kilns

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $2.0 $745 — 3% $4.3 $953 — Scenario 2 7% $5.0 $1,118 $1,678 3% $25.4 $3,552 $8,858 Scenario 3 7% $31.8 $4,446 $9,993 3% $25.4 $3,552 N/A

cost-effectiveness ranges from $745 to $4,446 per t on 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 s cenario 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 applicati ons of LNB at $1,000/ton, SNCR at $4,000/ton, and SCR at $10,000/ton. 8.5.12 Results for Glass Fiber Processing Plants
Table 8-62 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 9 to 33 percent for co sts at a 7 percent discount rate and from 12 to 33 percent for costs at a 3 percent discount rate.
Table 8-63 shows the annualized cost , resulting annualized average costeffectiveness, and marginal costs for each s cenario. 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 ra te. 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 w ith costs at a 3 percent discount rate. Marginal costs are $3,101 per ton for reach ing 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 and8-56 Table 8-62. 2015 NO x Emission Reductions (in tons) fo r BART-Eligible Units at Glass Fiber Processing Plants

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 6,677 7% 6,109 568 6,677 3% 5,902 775 Scenario 2 6,677 7% 4,561 2,116 6,677 3% 4,561 2,116 Scenario 3 6,677 7% 4,479 2,198 6,677 3% 4,479 2,198

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled and uncontrolled.

Table 8-63. 2015 Cost and Cost-Effectiveness Results for BART-Eligib le Units at Glass Fiber Processing Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.5 $937 — 3% $0.7 $880 — Scenario 2 7% $5.3 $2,505 $3,101 3% $4.8 $2,244 $3,057 Scenario 3 7% $7.8 $3,549 $30,488 3% $6.8 $3,108 $25,4028-57 $3,057 per ton for reaching the $4,000/ton scenario and $25,402 per ton between the $4,000/ton and $10,000/ton scenario with costs ata 3 percent discount rate. The impacts reflect application of LNB at $1,000/ton a nd $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 NO x 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 NO x Emission Reductions (in tons) for BART-Eligible Municipal Incinerators a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 1,656 7% 1,656 0 1,656 3% 1,656 0 Scenario 2 1,656 7% 912 744 1,656 3% 912 744 Scenario 3 1,656 7% 912 744 1,656 3% 912 744

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled and uncontrolled.

Table 8-65 shows the annualized costs, a nnualized average cost-effectiveness, and marginal costs for each scenario. The total a nnualized costs for these scenarios range from $0 to $1.1 million with costs at a 7 percent disc ount 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 ra te and $1,207 per ton with costs at a 3 percent discount rate. The marginal costs are $1,478 per ton for reachin g the $4,000/ton scenario with costs at the 7 percent discount rate and $1,207 pe r ton at the 3 percent discountrate. Because there are no other controls between $4,000/ton and $10,000/t on, there are no additional reductions and hence no marginal costs between these two scen arios. The only available control measure for this source is SNCR.8-58 Table 8-65. 2007 Cost and Cost-Effectivene ss Results for BART-Eligible Municipal Incinerators

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.0 $0 — 3% $0.0 $0 — Scenario 2 7% $1.1 $1,478 $1,478 3% $0.9 $1,207 $1,207 Scenario 3 7% $1.1 $1,478 N/A 3% $0.9 $1,207 N/A

8.5.14 Results for Coal Cleaning Plants
Table 8-66 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 0 to 46 percent for co sts at a 7 or a 3 percent discount rate. Table 8-66. 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Coal Cleaning Plants

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 1,110 7% 1,110 0 1,110 3% 1,110 0 Scenario 2 1,110 7% 599 511 1,110 3% 599 511 Scenario 3 1,110 7% 599 511 1,110 3% 599 511

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled and uncontrolled.8-59 Table 8-67 shows the annualized costs, a nnualized 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 a nnualized 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 di scount rate and $1,534 per ton with costs at a 3 percent discount rate. Because there are no other contro ls 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-Elig ible Units at Coal Cleaning Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.0 $0 — 3% $0.0 $0 — Scenario 2 7% $1.0 $1,900 $1,900 3% $0.8 $1,534 $1,534 Scenario 3 7% $1.0 $1,900 N/A 3% $0.8 $1,534 N/A

8.5.15 Results for Carbon Black Plants
Table 8-68 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 0.2 to 2 percent for co sts at a 7 or a 3 percent discount rate.
Table 8-69 shows the annualized cost , resulting annualized average costeffectiveness, and marginal costs for each s cenario. 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 ra te. The annualized average cost-effectiveness ranges from $957 to $1,608 per ton with costs at a 7 percent8-60 Table 8-68. 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Carbon Black Plants

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 4,645 7% 4,638 7 4,645 3% 4,638 7 Scenario 2 4,645 7% 4,525 120 4,645 3% 4,525 120 Scenario 3 4,645 7% 4,525 120 4,645 3% 4,525 120

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled and uncontrolled.

Table 8-69. 2015 Cost and Cost-Effectiven ess Results for BART -Eligible Units at Carbon Black Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.1 $957 — 3% $0.0 $830 — Scenario 2 7% $0.2 $1,608 $1,646 3% $0.1 $1,237 $1,237 Scenario 3 7% $0.2 $1,608 N/A 3% $0.1 $1,237 N/A

discount rate and from $830 to $1,237 per ton with costs at a 3 percent discount rate. The marginal costs between the $1,000/ton and th e $4,000/ton scenarios are $1,646/ton with costs at a 7 percent discount rate and $1,237/ton with costs at a 3 percent discount rate. Because there are no other controls between $4,000 /ton and $10,000/ton, there are no additional reductions and hence no marginal cost s between these two scenarios. NO x controls available8-61 to these sources are SNCR and SCR, and the cost per ton for these controls is fairly similar for these sources in this analysis. 8.5.16 Results for Phosphate Rock Processing Plants
Table 8-70 shows the NO x emissions reductions achieved in the analyses for each scenario. The table indicates that the scen arios achieve incremental reductions from the 2015 baseline ranging from 0 to 7 percent for cost s at either a 7 or 3 percent discount rate. Table 8-70. 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Phosphate Rock Processing Plants

Scenarios 2015 Baseline Emissions 2015 Postcontrol Emissions 2015 Emission Reductions
Scenario 1 719 719 0
719 719 0 Scenario 2 719 674 45 719 671 48 Scenario 3 719 671 48 719 671 48

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled and uncontrolled.

Table 8-71 shows the annualized cost , resulting annualized average costeffectiveness, and marginal costs for each s cenario. 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 ata 3 percent discount rate. The marginal costs between the $1,000/ton and the $4,000/ton scenarios are $1,978 per ton, while the marginal costs between the $4,000/ton and the $10,000/ton scenarios are $44,667 per ton with costs at a 7 percen t discount rate. With costs at a 3 percent discount rate, the marginal costs between the $1,000/ton and the $4,000/ton scenarios are $3,221 per ton, and there is no marginal cost between the $4,000 /ton and the $10,000/ton scenario because the impacts are the same for each scenario. The only available NO x control for this source is LNB + SNCR.8-62 Table 8-71. 2015 Cost and Cost-Effectiven ess Results for BART -Eligible Units at Phosphate Rock Processing Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) AnnualizedAverage CostEffectiveness ($/ton) Marginal Costs ($/ton) Scenario 1 7% $0.0 $0 — 3% $0.0 $0 — Scenario 2 7% $0.1 $1,978 $1,978 3% $0.2 $3,221 $3,221 Scenario 3 7% $0.2 $4,646 $44,667 3% $0.2 $3,221 N/A

8.5.17 Results for Secondary Metal Produc tion Facilities
Table 8-72 shows the NO x emissions reductions achieved in the analyses for each scenarios. The table indicates that the s cenarios achieve incremental reductions from the 2015 baseline ranging from 2 to 3 percent for cost s at either a 7 or 3 percent discount rate. Table 8-72. 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Secondary Metal Production Facilities

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions Scenario 1 1,377 7% 1,352 25 1,377 3% 1,352 25 Scenario 2 1,377 7% 1,343 34 1,377 3% 1,342 35 Scenario 3 1,377 7% 1,342 35 1,377 3% 1,342 35

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source category, both controlled and uncontrolled.8-63 Table 8-73 shows the annualized cost , resulting annualized average costeffectiveness, and marginal costs for each s cenario. The total annualized costs for these scenarios range from $0.01 million to $0.12 million w ith costs at a 7 percent discount rate and from $0.01 to $0.04 million with costs at a 3 percent discount ra te. 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 ma rginal costs between the $1,000/ton and the $4,000/ton scenarios are $2,000 per ton and the marginal costs between the $4,000/ton and the $10,000/ton scenarios are $9,000 per ton. With costs at a 3 percent discount rate, the marginal costs between the $1,000/ton and th e $4,000/ton scenarios are $3,100/ton, and there are no marginal costs between the $4,000/t on and the $10,000/ton scenarios because the impacts are the same. Available NO x controls are LNB and the more expensive LNB + SNCR. Table 8-73. 2015 Cost and Cost-Effectiven ess Results for BART -Eligible Units at Secondary Metal Processing Facilities

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

8.6 Caveats and Limitations of the Analyses A number of caveats and limitations are associated with these analyses.

C As noted above in Section 8.5, for a large number of non-EGU BART source categories, no SO 2 or NO x controls exist. There are no impacts for 8 of the 25 non-EGU source categories because no contro l measures are available to reduce SO 2 and NO x from these categories within AirControlNET or any other documentation that has been found in the c ourse of completing this analysis. For8-64 seven source categories only NO x reductions take place in these analyses because no control measures are available with in AirControlNET or no controls are available at $10,000/ton or below. Finally , there are a total 10 source categories for which both SO 2 and NO x reductions take place in these analyses.

C Control programs implemented as co mmand-and-control re gulation, as this analysis models controls to affected non-EGU sources, will lead to less-induced technological innovation when compared to a market incentives-based approach (e.g., a cap-and-trade program).

C The technologies applied in these anal yses 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 NO x control at high levels (90 percent).

C Fuel switching is not considered as a way for BART-eligible units, especially industrial boilers, to meet potential BART requirements in SIPs. Fuel switching can consist of coal-fired sources switching from high- to low-sulfur coals (e.g., Powder River Basin coals). Many power pl ants have used this technique to meet SO 2 requirements imposed by the Acid Rain Program and by va rious regulations, but industrial sources have used it less frequently.

C There is a considerable range of equipmen t 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 underestimate d the actual equipment life from use of these devices, we overestimate the a nnual costs of these controls and vice versa.

C Labor and energy rates and other parameters to the cost estimates are estimated based on nationwide rates inst ead of regional and local rates. Using nationwide parameter estimates introduces some uncertainty in these estimates at a sourcespecific level.

C The emission reductions and controls that will be imposed on petroleum refineries as a result of various New Source Review settlements are not included in our regulatory baseline. Thus, this analysis is likely to overestimate costs of BART compliance to many BART-eligible units at petroleum refineries.

C EPA wants to identify some unquantified co sts as limits to its analysis. These costs include the costs of State administr ation of the progra m, which we believe are modest. There also may be unquantifie d costs of transitioning to BART, such as the costs associated with the possible retirement of smaller or less-efficient8-65 non-EGU units and employment shifts as workers are retrained at the same company or reemployed elsewhere in the economy.

C Recent research suggests that the total social costs of a new regulation may be affected by interactions between the ne w 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 in teraction effect, recent research also argues for caution in making prior assu mptions 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 Versi on 4.0 Control Measure Documentation Report. Prepared for th e 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 Ag ency (EPA). September 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. U.S. Office of Management and Budget. Oc tober 29, 1992. Circular A-94. “Guidelines and Discount Rates for Benefit-Co st Analysis of Federal Programs.” Available at <http://www.whitehouse.gov/omb/ circulars/a094/a094.html>. U.S. Office of Management and Budget (OMB ). September 17, 2003. Circular A-4. “New Guidelines for the Conduct of Regu latory Analysis.” Available at <http://www.whitehouse.gov/omb/ circulars/a004/a-4.html>.9-1 SECTION 9 STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSES

Impact analysis is a general term used to describe various economic analyses that supplement estimates of the bene fits and costs of a rulemaking. These analyses are conducted to meet the statutory and administr ative 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 ec onomic impact on a substantial number of small entities” 5 U.S. C. § 605(b). Small entities incl ude small businesses, small organizations, and small gove rnmental 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 (NAI CS) 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 popula tion of less that 50,000; and (3) a small organization that is any not-for-profit en terprise which is i ndependently owned and operated and is not dominant in its field. Table 9-1 lists EGU entities potentially impacted by this proposed rule with applicable NAI CS 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 implementin g the rule may choose to regula te source categories in addition to those listed in Table 9-1 and 9-2.9-2 Table 9-1. Potentially Regula ted EGU Categories and Entities a

Category NAICS Code b Examples of Potentially Regulated Entities Industry 221112 Fossil fuel-fired electri c utility steam generating units.
Federal government 22112 c Fossil fuel-fired electric utility steam generating units owned by the Federal government.
State/local/tribal government 22112 c Fossil fuel-fired electric utility steam generating units owned by municipalities. 921150 Fossil fuel-fired electric utility steam generating units in Indian Country.

a Include NAICS categories for source categories that own and operate electric generating units only. b North American Industry Classification System.

c Federal, State, or local government-owned and opera ted establishments are cl assified according to the activity in which they are engaged.
Table 9-2. Examples of Potentially Re gulated Non-EGU Categories and Entities

BART Source Category Name NAICS Code a NAICS Description Size Standard b
Fossil Fuel-Fired Industrial Boilers (>250 MMBTU heat input per hour) Various manufacturing

industries Varied Varied

Petroleum Refineries 324110 Petroleum Refineries 1500 Employees Kraft Pulp Mills 322110 Pulp Mills 750 Employees Portland Cement Plants 327310 Cement Manufacturing 750 Employees Hydrofluoric, Sulfuric, and Nitric Acid Plants 325 Chemical Manufacturing 1000 Employees

Primary Aluminum Ore Reduction Plants 331312 Primary Aluminum Production 750 Employees

Chemical Process Plants 325 Chemical Manufacturing 1000 Employees Iron and Steel Mill Plants 3311 Iron and Steel Mill and Ferroalloy Manufacturing 1000 Employees

a North American Industry Classification System.
b Small Business Administration Size Standards. http://www.sba.gov/size/sizetable 2002.html.1 See Michigan v. EPA , 213 F.3d 663, 668-69 (D.C. Cir. 2000), cert. den. 121 S.Ct. 225, 149 L.Ed.2d 135 (2001). An agency’s certification need consider the rule’s impact only on entities subject to the rule. 9-3 (9.1) According to the SBA size standard s 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 distributi on of electric energy for sale and its total electric output for th e 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 sma ll entities. Instead, it would requi re states to develop, adopt, and submit SIP revisions that would achieve the necessary SO 2 and NO x emissions reductions, and would leave to the states th e task of determining how to obtain those reductions, including which entitie s to regulate. Moreover, b ecause affected States would have discretion to choose the sources to re gulate and how much emissions reductions each selected source would have to ach ieve, EPA could definitely not predict the effect of the rule on small entities. Although not required by th e 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 meas ures 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 hi gher prices. One drawback for this approach is that it does not consider in teraction between producers and c onsumers in a market context. Therefore, it likely overstates the impacts on firm s affected by the rule and understates the impacts on consumers. We used the fo llowing equation to compute the CSR:

where TACC = total annual compliance costs, i = indexes the number of affected units owned by company j,2 An ORIS code is a 4 digit number assigned by the Energy Information Administration (EIA) at the U.S. Department of Energy to power plants owned by utilities.

3 These include common databases such as Hoover’s (2001) and ReferenceUSA (2001). We emphasize that this is the ultimate parent company in the ownership structure. Therefore it may be different than the owner identified in Forms EIA 860 and EIA 861.

4 This approach leads to a conservative estimate of ultimate parent company nameplate capacity because all units in the EIA database could not be matched to an ultimate parent at this time. Therefore, we may under estimate the true ultimate parent name plate capacity. 9-4 n = number of affected plants, and TRj = total revenue of ultimate parent company j. 9.1.1 EGU Sector Small Business Impacts For proposal, the engineering analysis c onducted for the rulemaking identified 302 EGU units potentially affected by the rule. Using unit ORIS 2 numbers and the Energy Information Administration’s pub licly available 2002 electric ge nerator databases (Form EIA 860 and Form EIA 861), we identified utility names and nameplate capacity for affected units. EPA identified 66 ultima te parent companies in pub lically available company databases, 3 collected sales and employment info rmation, and estimate d annual electricity output rates for these companies. After identif ying 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 Amer ican Industry Classification System (NAICS) industries that EPA has traditionally used to cl assify affected entities as small businesses. The size standard for firms primarily en gaged 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 cla ssify affected parent companie s as small or large using this standard, we collected data on nameplate capacity for each ultimate parent company that could be consistently matched with the electric generator database. 4 Next, we converted this measure to megawatt hours using the following formula: Capacity Factor × NamePlate Capac ity (MW) × standard hours per year9-5 and assumed an average capacity factor of 85 percent and 8,760 standardhours 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 th e rulemaking identified over 2,000 records associated with affected non-EGU units poten tially affected by the rule. Using publicly available sales and employmentdatabases, plant names, a nd 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 remain ing 48 entities were eith er 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, EP A found that five nonEGU source category small businesses may experience a 3 percent CSR level or higher. Two may experience CSRs between 1 and 3 percent, and the remaining small company CSRs are below one percent. The medi an CSR for non-EGU source category small businesses is 0.3 percent and ranges from 0 to 20 percent. 9.2 Unfunded Mandates Reform Act (UMRA)
Title II of the Unfunded Ma ndates Reform Act of 1995 (P ublic Law 104-4) (UMRA), establishes requirements for fede ral agencies to assess the effects of their regulatory actions on state, local, and tribal governments and th e private sector. Under Section 202 of the UMRA, 2 U.S.C. 1532, EPA generally must prep are a written statement, including a costbenefit analysis, for any proposed or final rule that “includes any fe deral mandate that may result in the expenditure by state, local, and trib al 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 “fed eral intergovernmental mandate,” in turn, is defined to include a regulation that “would impose an enforceable duty upon state, local, or9-6 tribal governments,” section 421(5)(A)(i), 2 U.S.C. 658(5)(A)(i), except for, among other things, a duty that is “a condition of federal assistance,” section 421(5 )(A)(i)(I). A “federal private sector mandate” includes a regulati on that “would impose an enforceable duty upon the private sector,” with certain excepti ons, section 421(7)(A), 2 U.S.C. 658(7)(A). Before promulgating an EPA rule for wh ich 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 numbe r of regulatory altern atives and adopt the least costly, most cost-effective, or least bur densome 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 cons istent with the intergovernme ntal consultation provisions of Section 204 of the UMRA, EPA carried out consultations w ith 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, a nd the EPA has consulted with governmental entities as would be required by UMRA. Conse quently, it is not necessary for EPA to reach a conclusion as to the applicability of the UMRA requirements. 9.2.1 EGU UMRA Analysis Using unit ORIS numbers and the Energy Information Administration’s publicly available 2002 electric generato r 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 complia nce costs for affected governments are estimated to be approximately $150 million (1999$). Appr oximately 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 a ffected facility names, EPA identified 25 affected entities that were owned by states or municipalities (primarily9-7 universities). Under Scenario 2, the total annual compliance costs for the governments owning the 25 affected entities are estima ted to be approxima tely $40 million (1999$). 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 approve d the information collection requirements contained in the existing regulations [40 CFR Part 51] under the provisi ons of the Paperwork Reduction Act, 44 U.S.C. 3501 etseq . 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, retai n, or disclose or provide information to or for a federal agency. This includes the time needed to review instructions ; develop, acquire, insta ll, and use technology and systems for the purposes of collecting, va lidating, and verifying information, processing and maintaining information, and disclosing and providing information; adjust the existing ways to comply with any previously appli cable instructions a nd requirements; train personnel to be able to respond to a collection of information; search data sources; complete and review the collection of in formation; and transmit or othe rwise 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 ensu re “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 regu lations that have “subs tantial direct effects on the states, on the relationship between the na tional government and the states, or on the distribution of power and responsibilities among th e various levels of government.” Under Section 6 of Executive Order 13132, EPA may notissue a regulation that has federalism implications, that imposes substantial direct compliance costs, and that is not required by statute, unless the federal g overnment provides the funds necessary to pay the direct compliance costs incurred by state and local g overnments, or EPA consults with State and local officials early in the process of developi ng the regulation. The EPA also may not issue a regulation that has federalism implications a nd that preempts state law unless EPA consults with state and local officials early in the process of devel oping 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 FR9-8 43255, August 10, 1999), because it will not have s ubstantial direct effects on the states, nor substantially alter th e relationship or the distribution of power and re sponsibilities between the states and the federal govern ment. Nonetheless, we consulte d with a wide scope of state and local officials, including the National G overnors Association, Nati onal League of Cities, National Conference of State Legislatures, U.S. Conference of Mayors, National Association of Counties, Council of St ate Governments, Interna tional City/County Management Association, and National Asso ciation of Towns and Townsh ips, during the course of developing this rule. 9.5 Executive Order 13175: Cons ultation and Coordination with Indian Tribal Governments Executive Order 13175, entitled “Consultation a nd Coordination with Indian Tribal Governments” (65 FR 67249, N ovember 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 distribu tion of power and res ponsibilities between the federal government and Indian Tribes. The CAA and the TAR establish the relationship of the federal government and Trib es 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 Ch ildren from Environm ental Health and Safety Risks Executive Order 13045, “Protection of Child ren from Environmental Health Risks and Safety Risks” (62 FR 19885, Ap ril 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 sa fety risk that EPA has r eason to believe may have a disproportionate effect on children. If the re gulatory action meets both criteria, Section 5–501 of the order directs the Agency to evaluate the environmental hea lth 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 e nvironmental health or safety risks that may9-9 disproportionately affect child ren. The EPA believes that th e 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 Concern ing 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 Signif icantly 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. Th is rule is not a “significant energy action,” because it has less than a 1 pe rcent 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.h tml) 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 th e impact of the presumptive requirements for these sources and does not consider any possibl e additional controls for EGU sources or nonEGU sources that states may require. Althoug h states may choose to use the guidelines in establishing BART limits for non-EGUs , ultimate ly states will determine the sources subject to BART and the appr opriate level of control for such sources. We are finalizing the reproposal of the ru le 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 th em to adopt alternative measures such as a trading program in lieu of requiring the installation and opera tion 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 SO 2 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 SO 2 and NO x 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 thata different level of control is appropriate. The states will5 U.S. Environmental Protection Agency, 1998. Guidance for Incorporating Environmental Justice Concerns in EPA’s NEPA Compliance Analyses. Office of Federal Activities, Washington, DC, April, 1998. 9-10 accordingly exercise substantial intervening discretion in implementing the final rule. Additionally, we have assessed that the compliance dates for the rule will provide adequate time for EGUs to install the required emission controls. 9.8 National Technology Tr ansfer and Advancement Act Section 12(d) of the Na tional 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 me thods, sampling procedures, and business practices) that are developed or a dopted by VCS bodies. The NTTAA directs EPA to provide Congress, through OMB, explana tions when the EPA decides not to use VCS. This guidance does not involve technical st andards; thus, EPA did not consider the use of any VCS. 9.9 Executive Order 12898: Fed eral 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-In come Populations,” requires fe deral agencies to consider the impact of programs, policies, and ac tivities on minority popula tions and low-income populations. According to EPA guidance, 5 agencies are to assess whether minority or lowincome populations face risks or a rate of expos ure 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 ha s considered whether this rule may have disproportionate negative impacts on minority or lo w income populations. Negative impacts to these subpopulations that appreci ably exceed similar impacts to the general population are not expected, because the Agency expects this rule to lead to reductions in air pollution emissions and exposures generally.9-11 9.10 Congressional Review Act The Congressional Review Act, 5 U.S.C. 801 et seq., as added by the Small Business Regulatory Enforcement Fairne ss 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 Repr esentatives, 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 “m ajor rule” as defined by 5 U.S.C. 804(2).
9.11 References
Hoover’s. 2001. Hoover’s Online Data Serv ice for Dun & Bradstreet. Available at <http://www.hoovers.com/free/>.
InfoUSA. 2001. InfoUSA Sales Solutions Da tabase of Company Sales. Available at <http://www.infousa.com/>. U.S. Environmental Protection Agency (EPA). 1998. Guidance for Incorporating Environmental Justice Concerns in EPA’ s NEPA Compliance Analyses. Office of Federal Activities, Wa shington, DC, April.10-1 SECTION 10 COMPARISON OF BENEFITS AND COSTS

The estimated social costs to implement BART, as described in this document, range from approximately $0.3 to $2.9 billion annually for 2015 dependi ng on the actions taken by States and the discount rate ( 1999 dollars, 3 percent or 7 percen t discount rate). Thus, the annual net benefits (soc ial benefits minus social costs) range from $1.9 + B billion or $12.0 + B billion annually (1999 dollars, ba sed on State actions taken and discount rates of 3 percent or 7 percent) in 2015. (B represents the sum of all unquantifie d 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 PM 2.5 -, mercury-, acidifica tion-, and eutrophicationrelated impacts would likely increase the net bene fits of the rule. Table 10-1 presents a summary of the benefits, costs, a nd 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 (i ncludes the CAIR prom ulgated rule and the proposal to include annual SO 2 and NO x controls for New Jersey and Delaware) in the baseline. Annual SO 2 and NO x 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 comp lete 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 anal ysis of benefits and costs fo r BART. As with any complex analysis of this scope, a number of uncertainties are inherent in the final estimate of benefits and costs that are described fully in Chapters 4, 7, and 8 of the RIA.10-2 Table 10-1. Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air Visibility Rule—2015 a(billions of 1999 dollars)
Description Scenario 1 Scenario 2 Scenario 3 Social costs b 3 percent discount rate $0.4 $1.4 $2.3 7 percent discount rate $0.3 $1.5 $2.9 Social benefits c,d,e 3 percent discount rate $2.6 + B $10.1 + B $14.3 + B 7 percent discount rate $2.2 + B $8.6 + B $12.2 + B Health-related benefits:

3 percent discount rate $2.5 $9.8 $13.9 7 percent discount rate $2.1 $8.4 $11.8 Visibility benefits $0.08 $0.24 $0.42 Net benefits (benefits-costs ) e,f 3 percent discount rate $2.2 + B $8.7 + B $12.0 + B 7 percent discount rate $1.9 + B $7.1 + B $9.3 + B

a All estimates are rounded to two significant digits fo r ease of presentation and computation. A complete

CAIR program that includes the CAIR promulgated rule and the proposal to include a nnual SO 2 and NO x controls for New Jersey and Delaware is assumed to implemented in the baseline for the BART analysis. Annual SO 2 and NO xcontrols 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 NO xand SO 2 for the EGU and

non-EGU source categories nationwide in 2015. The discount rate used to conduct the analysis impacts the control strategies chosen for the non-EGU source categor y resulting in greater level of controls under the 3 percent discount rate for Scenario 1.

c As this table indicates, total benefits are driven prim arily 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 NO xand SO 2 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 an d monetized in this analysis. B is the sum of all

unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and monetized are listed in Table 1-4.

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

Chapter 4. Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses (EPA, 2000; OMB, 2003).

f Net benefits are rounded to the nearest $100 million. Columnar totals may not sum due to rounding.10-3 10.1 References U.S. Environmental Protection Ag ency (EPA). September 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. U.S. Office of Management and Budget (OMB ). 2003. Circular A-4 Guidance to Federal Agencies on Preparation of Regulatory Analysis.A-1 APPENDIX A
BART INDUSTRY-SECTOR IMPACTS

EPA estimates the direct costs of implementing the BART guidance to range from$0.3 to $2.9 billion in 2015 in the BART region. Given possible impacts of this guidance on electricity generators and manufactur ing industries, we belie ve it is important to gauge the extent to which the guidance might a ffect 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 altern ative implementation scenarios: “Scenario 1,” “Scenario 2,” and “Scenario 3.” We were particularly interested in learni ng how possible changes in electricity prices might affect industry sectors that are la rge electricity users and how changes in manufactured-goods prices might affect othe r businesses and households. The models we employed indicated thos e impacts would be small, even w ithout incorporating the beneficial economic effects of BART-related air quality improvements such as improved worker health and productivity. Rather, our anal yses 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 effect s, such as a reduction in acid rain damage and loweringof nitrogen deposition. By focusing only on cost-side spillover e ffects on the economy, the industry-sector impacts projected by our macroeconomic model are likely overstated, primarily because the positive market impacts of the BART guidan ce on labor availability and productivity are excluded. In this regard, an independent pa nel of experts has encouraged EPA to work toward incorporating both beneficial and co stly 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 potentia l magnitude of the ec onomy-wide effects of BART implementation costs. The model, calle d EMPAX-CGE, is currently in peer review. As with all models, this tool has its streng ths and weaknesses. The results of the CGE1 See <http://www.epa.gov/cleanair 2004/> for details.

2 See <http://www.epa.gov/airmarkt/epa-ipm/> for complete IPM documentation.

3 See <http://www.epa.gov/ttn/ecas/AirControlNET.htm> for complete AirControlNET documentation.
A-2 analysis show small impacts of the BART gu idance 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 configur ed to capture the beneficial economic consequences of the increased labor availa bility and productivity expected to result from BART-related air quality improve ments. If these labor productivity improvements were included, the small production output decreases pr ojected by the model might be partially or entirely offset. EPA continues to investigate the feasibility of incorporating labor productivity gains and other bene ficial 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 Macro economic Analysis of BART The BART guidance reduces emissions of SO 2 and NO xfrom electricity generation and combustion manufacturing emissions sources to improve air quality. 1 To complement the analysis of BART effects on electricity generation conducted using IPM 2 and the effects on specific manufacturing sector s 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 m odel 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. econom y. 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). Sec tion A.1.9 discusses a lternative methods of linking EMPAXCGE to the IPM model results for electricity and consequences for the EMPAX-CGE results. Finally, Section A.2 provi des additional informati on on the EMPAX-CGE model.4 See Section A.2 for additional details on the EMPAX-CGE model.

5 See <http://www.epa.gov/cair>.
A-3 Please note that this analysis focuses on electricity-sector and relevant manufacturing-sector impacts of the BART guidance as estimated by IPM and AirControlNET. It does not account for other economic and noneconomic effects, especially the substantial economic and health benefits associated with reduced emissions. A.1.1 Background and Summa ry of EMPAX-CGE Model 4 EMPAX was first developed in 2000 to s upport the economic analysis of EPA’s maximum achievable control technology (MAC T) rules controlling emissions from three categories of combustion sources (reciprocatin g internal combustion engines, boilers, and turbines). The initial framework consisted of a national multimarket partial-equilibrium model with linkages between ma nufacturing 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 ma jor regulations directly affect a large number of industries, substantial i ndirect impacts can also result from changes in production, input use, income, and household consumption patte rns. Consequently, EMPAX now includes economic linkages among all indus trial 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 asso ciated with environmental policies. The EMPAX-CGE model was used to investigat e macroeconomic impacts of the Clean Air Interstate Rule, predecessor to BART. 5 The dynamic version of EMPAX-CGE empl oyed 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. Ne sted constant elasticity of substitution (CES) functions are used to portray substitution possi bilities available to pr oducers and consumers.6 Static versions of EMPAX-CGE have more industries and households because they do not have to solve for multiple time periods simultaneously and, consequently, have few computational constraints on the number of industries and households.

7 EIA forecasts from the Annual Energy Outlook 2003 (AEO) (EIA, 2003) are used in this analysis.
A-4 Along with the underlying data, the nesting st ructures and associated substitution elasticities define current production tec hnologies and possible alterna tives. 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 us e of factors in fixed supply (land and inputs of primary fuels, respectively).
The economic data in this CGE model come from state-level information provided by the Minnesota IMPLAN Group, and the ener gy data come from the DOE’s Energy Information Agency (EIA). In the dynamic ve rsion of EMPAX-CGE, these data are used to define five regions within the United States, each containing 17 industr ies and four types of households classified by income. 6 The five regions have been selected to preserve important regional differences in electric ity generation technologies, and 17 industries are included that cover five important ty pes of energy (coal, crude oil, electricity from fossil and nonfossil generation, natural gas, and refi ned petroleum), the energy-inten sive industries most likely to be affected by environmen tal policies, and th e 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 natura l resources, and capital accumulation. Changes in energy use per un it of output are modeled through exogenous autonomous energy efficiency improveme nts (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 out put, 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 thatthey can substantially alter costs of environmental (and other) polic ies. The IMPLAN economic database used by EMPAX-CGE includes information on some ty pes of taxes, which have b een combined with other data sources to cover important distorti ons from capital and income taxes.8 See, for example, the analyses of energy/climate using CGE models organized by the Stanford University Energy Modeling Forum (http://www.stanford.edu/group/EMF/home/index.htm).
A-5 A.1.2 Modeling Approach for Electric ity and Manufactu ring 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 th e economy will influence producers and consumers across the United Stat es. However, although CGE models have been used extensively to analyze climate policies that limit carbon emissions from electricity production, 8 some other types of emi ssions policies are more difficult to consider. Unlike carbon dioxide, emissions of pollutants such as SO 2 , NO x , and mercury ar e 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 co al- 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 e ffects, cannot be adequately captured by the more general structure of a CGE model. In a ddition, 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 characterizati on 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 allo ws linkages between EMPAX-CGE and the IPM and AirControlNET models. IPM is a comprehensive model of electri city 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 dete rmining the least-cost methods of generating sufficient electricity to meet demands, while meetin g emissions reduction (and other) objectives. However, IPM does not fully consider howchanges in the electricity sector, or electricity prices, will affect th e 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 envi ronmental 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 veryA-6 disaggregated level of analysis, IPM can determine for EMPAX-CGE a number of electricity market outcomes needed to evaluate macroec onomic implications of policies. The linkage with IPM then allows EMPAX-CGE to take th ese 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 incl uding changes in electricity prices, fuel consumption by utilities, fuel prices, and ch anges in electricity production expenditures. EMPAX-CGE is capable of simultaneously incor porating some or all of these IPM findings, depending on the desired type and degree of linkage betweenthe two models. At the regional level, EMPAX-CGE can match changes estimated by IPM for the following variables:

C electricity prices (percentage change in retail prices)

C coal and gas consumption for electricity (percentage changes in Btus)

C coal and gas prices (percentage changes in prices)

C coal and gas expenditures ($ changes—Btus of energy input times $/MMBtu)

C capital costs ($ changes)

C fixed operating costs ($ changes)

C variable operating costs ($ changes)

AirControlNET is a PC-based relational data base tool for conduc ting control strategy and costing analysis. It overlays a detaile d control measure data base on EPA emissions inventories to compute source- and pollutant-specific emi ssion 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., NO x , SO 2 , VOC, PM 10 , PM 2.5 , NH 3 ) as well as CO and Hg from point (utility and nonutility), area, nonroad, and mobile sources as provided in EPA’s National Emi ssion Inventory (NEI). For the industries affected by the BART guid ance, AirControlNET provides estimates of cost changes by industry and region of th e 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 expresse d 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 s caling up of all inputs to the industries inA-7 EMPAX-CGE, we use Nestor and Pasurka (1995) data on purchases ma de by industries for environmental protection reasons to allocat e these additional expenditures across inputs within EMPAX-CGE. Once these expenditures are specified, the incremental costs from IPM and AirControlNET can be used to ad just 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. In itial effects will revolve around how using additional resources in electricity generation and manufacturing draws some capital, labor, and materials from other sectors of th e economy. This, in turn, may affect prices in markets supplying inputs to th ese 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 w ill have limited effects outside of electricity because mo st is used for generation). Inaddition, 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 le vels 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 ener gy 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 resu lt, 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 th e future (leading to lower wages) may cause an increase in work effort today, while labor is more productive. A lternatively, it may lead9 See Section A.1.9 for EMPAX-CGE results using other linkages to IPM.
A-8 to a decrease in work effort today because, in part, labor today is used to generate capital goods for tomorrow, which will be used to augment less-productive labor in the future. For the electricity-generation industry, the methodology used to link IPM and EMPAX-CGE for this BART analysis focuses on BART resource costs and implications for coal use by utilities. 9 IPM estimates of additional resources used by electric utilities (the capital, fixed, and variable costs) are us ed 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 co sts are applied exclusively to natural gas inputs to electricity generation in EMPAX-CG E, rather than being apportioned using the Nestor and Pasurka data). Gi ven 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 hous ehold demands. However, as shown in Figure A-1, estimates for output changes from BART are generally less than 0.05 percent.
Some industries are affected more than others. BART has little impact on electric utilities, with energy-intensive sectors of th e economy being relatively more affected than other firms because their cost s have risen more than othe r segments of the economy. However, the largest of these declines in out put (paper and allied pr oducts) is approximately two-tenths of 1 percent.A-9 -5.0% -4.0% -3.0% -2.0% -1.0% 0.0% 1.0% Transportation Services Other Manufacturing EIS: Aluminum EIS: Iron and Steel EIS: Cement EIS: Glass EIS: Chemicals EIS: Paper and Allied EIS: Food and Kindred Agriculture Petroleum Natural Gas Electricity Crude Oil Coal

Percent Change from Reference Figure A-1. BART Impacts on U.S. Domestic Output, 2015
Source: EMPAX-CGE (BART Scenario 2)

Regional effects tend to showsome variation that does not appear at the national level. Figure A-2 shows these regional re sults 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 IP M and EMPAX-CGE, compared to the western half of the country. Changes in natural gas and coal production are al so distributed unevenly across the country.10 Results for the seven components of energy-intensive sectors are aggregated in Figure A-3 for presentation purposes, although they are all included simultaneously in the EMPAX-CGE model run.
A-10 -5.0% -4.0% -3.0% -2.0% -1.0% 0.0% 1.0%NortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUS

Coal Crude Oil Electricity Natural Gas PetroleumPercent Change from Reference

Figure A-2. BART Impacts on Regional Energy Output, 2015
Source: EMPAX-CGE (BART Scenario 2)

Figure A-3 shows regional results fo r nonenergy industries in EMPAX-CGE. 10 Although these sectors show more regional variation based on diffe rences in production methods and changes in manufacturing costs, th e majority of the impacts are less than five one-hundredths of 1 percent. Energy-inten sive firms in the No rtheast experience a comparative advantage relative to other parts of the country. Impacts on other manufacturing and the services industry are unifo rmly small, however; given the size of this sector, the effect on total re venue is larger than for a ll other industries combined.A-11 -1.00% -0.75% -0.50% -0.25% 0.00% 0.25%NortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUS

Agriculture Energy-Intensive Manufacturing Other Manufacturing Services TransportationPercent Change from Reference

Figure A-3. BART Impacts on Regional Industrial Output, 2015
Source: EMPAX-CGE (BART Scenario 2)

Although the average effect on energy-int ensive industries is negative because of increased manufacturing costs, industries in so me 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 pr oportionately 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 d ecline is in paper manufacturing in the South.A-12 -2.50% -2.00% -1.50% -1.00% -0.50% 0.00% 0.50% 1.00% 1.50% 2.00% 2.50%NortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUSNortheastSouthMidwestPlainsWestUS

Food and Kindred Paper and Allied Chemicals Glass Cement Iron and Steel AluminumPercent Change from Reference

Figure A-4. BART Impacts on Regi onal 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 purchas e prices paid by households. EMPAX-CGE calculates an overall price le vel 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 manuf actured-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 consum er prices are esse ntially unchanged.A-13 -0.02% 0.00% 0.02% 0.04% 0.06% 0.08% 0.10%

2005 2010 2015 2020 2025Percent Change from Reference

Figure A-5. Change in Consumer Prices Compared to Reference Case
Source: EMPAX-CGE (BART Scenario 2) Note: Changes occur in 2005 as people react to the po licy 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 hour s of labor supplied by households (this is not the same as estimatin g jobs or employment). Empirical estimates of labor-supply elasticities are used by EMPAX- CGE to simulate how demands by firms and supply decisions by households are made, along with resulting implications for real wages. EMPAX-CGE is a full-employment model in which households choose between labor and leisure time, based on both income and substitution effects. Figure A-6 gives EMPAX-CGE’s projected impacts of BART on labor markets. The results indicate that people are choosing to work slightly more hours to offset additional costs of purchasing goods. These effects are extremely small, however, on the order of five ten-thousandths of 1 percent.A-14 -0.010% -0.008% -0.006% -0.004% -0.002% 0.000% 0.002% 0.004% 0.006% 0.008% 0.010%

2005 2010 2015 2020 2025Percent Change from Reference

Figure A-6. Change in Labor I nputs Compared to Reference Case
Source: EMPAX-CGE (BART Scenario 2) Note: Changes occur in 2005 as people react to the po licy 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 (includi ng EMPAX-CGE) will tend to estimate declines in total production in the United States, as shown inFigure 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 pe rcent (two one-hundredths of 1 percent).A-15 -0.10% -0.08% -0.06% -0.04% -0.02% 0.00% 0.02% 0.04% 0.06% 0.08% 0.10%

2005 2010 2015 2020 2025Change in GDP (Percentage Change)

Figure A-7. Change in GDP Compared to Reference Case
Source: EMPAX-CGE (BART Scenario 2) Note: Changes occur in 2005 as people react to the po licy 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 th e 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 adju st the scale of the graph to the point where the two lines do not overlap. Even these small cost s could be reversed if the CGE analyses were extended to include benefits associated with BART such as improvements in labor productivity from envir onmental improvements.A-16 $10,711 $12,401 $16,753 $19,269 $19,272 - Reference

$16,756 - Reference

$14,435 - Reference

$12,403 - Reference

$10,712 - Reference $14,432

$10,000 $12,500 $15,000 $17,500 $20,000

2005 2010 2015 2020 2025Gross Domestic Product (Billion 1999$) EMPAX Reference Case BART Policy Case

Figure A-8. U.S. Gross Domestic Product (GDP): Reference Case vs. BART
Source: EMPAX-CGE (BART Scenario 2) Note: Changes occur in 2005 as people react to the po licy 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. Seve ral potential sources of diverg ences in regional impacts exist:

C differences in IPM regional results based on regional mix es of generation technologies (coal, gas, o il, and nonfossil use), which may be averaged out at a national level;

C differences in regional production and c onsumption patterns for electricity and nonelectricity energy goods;

C differences in industrial co mposition of regional economies;

C differences in household consumption patterns; and

C differences in regional growth forecasts.A-17 -0.25% -0.20% -0.15% -0.10% -0.05% 0.00% 0.05% 0.10%

2005 2010 2015 2020 2025Change in GDP (%)

Northeast South Midwest Plains West US Figure A-9. Change in Regional GDP Compa red to Reference Case
Source: EMPAX-CGE (BART Scenario 2) Note: Changes occur in 2005 as people react to the po licy announcement, in anticipation of future effects. Figure A-9 presents the regional GDP changes estimated by EMPAX-CGE that underlie the national U.S. results above. As with other ty pes of results, northeastern States are relatively unaffected by BART for several reasons: most of the resource costs are experienced by electricity generators in th e West, the industrial composition of the West (especially California) tends to lean towards less energy-i ntensive industries like services, and production patterns for energy-intensive sect ors 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 en ergy-intensive industries such as paper and chemicals that ex perience higher impacts and, as a result, have slightly larger relative GDP declines than the U.S. average.A-18 Tables A-1 and A-2 show EMPAX-CGE estimates of changes in revenue and output quantities for 2015 and 2020 for BART Scenario 2. Table A-1. U.S. Domestic Output Changes

Variable Industry 2015 2020 Percentage Change in Revenue (%) Coal 0.07% 0.32% Crude Oil 0.00% 0.00% Electricity 0.06% 0.05% Natural Gas –0.07% –0.11% Petroleum –0.03% –0.03% Agriculture –0.04% –0.04% Energy-Intensive Manufacturing –0.03% –0.03% Other Manufacturing –0.01% –0.01% Services –0.01% –0.01% Transportation –0.02% –0.02% Percentage Change In Quantity (%) Coal 0.02% 0.10% Crude Oil 0.00% 0.00% Electricity –0.07% –0.03% Natural Gas –0.02% –0.03% Petroleum –0.09% –0.09% Agriculture –0.04% –0.04% Energy-Intensive Manufacturing –0.11% –0.11% Other Manufacturing –0.01% –0.01% Services –0.01% –0.01% Transportation –0.02% –0.02%
Source: EMPAX-CGE (BART Scenario 2)A-19 Table A-2. U.S. Domestic Energy- Intensive Sector Output Changes

Variable Industry 2015 2020 Percentage Change in Revenue (%) Food and Kindred –0.02% –0.02% Paper and Allied 0.04% 0.03% Chemicals –0.07% –0.08% Glass –0.02% –0.03% Cement –0.01% 0.00% Iron and Steel 0.01% 0.01% Aluminum 0.00% 0.00% Percentage Change In Quantity (%) Food and Kindred –0.05% –0.05% Paper and Allied –0.21% –0.22% Chemicals –0.13% –0.14% Glass –0.05% –0.05% Cement –0.02% –0.02% Iron and Steel –0.05% –0.06% Aluminum –0.03% –0.03%
Source: EMPAX-CGE (BART Scenario 2)

A.1.8 Alternative BART Scenarios The preceding results focus on BART Scenario 2. This section compares these results to those for BART Scenarios 1 and 3. Figure A-10 shows how the alternative scenarios affect industrial output. In genera l, the impacts follow a pattern similar to the stringency of the individual BART scenario. However, all impacts remain uniformly small across the three sets of results. A comparable pattens also holds for GDP changes across the three alternatives (see Figure A-11). Scenario 2 has the midrange GDP effects with Scenario 1 showing essentially no GDP impacts and Scenario 3 having a dec line in GDP of between two and four onehundredths of 1 percent.A-20 -5.0% -4.0% -3.0% -2.0% -1.0% 0.0% 1.0%Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3

Coal Crude Oil 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 OtherManufacturing ServicesTransport

Percent Change from Reference

Figure A-10. Domestic Output Impact s 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 linki ng EMPAX-CGE to the IPM results. These alternative findings are contrastedto this “Central Case” (i.e., the results presented above for Scenario 2) to demonstrate how the methodolog y 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 othe r 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 imp lications of different methods for linking macroeconomic models with the IPM results.A-21 -0.10% -0.08% -0.06% -0.04% -0.02% 0.00%

2005 2010 2015 2020 2025Change in GDP (%

BART - Scenario 2 BART - Scenario 1 BART - Scenario 3

Figure A-11. GDP Impacts of Alternative B ART Scenarios

Source: EMPAX-CGE Note: Changes occur in 2005 as people react to the po licy announcement, in anticipation of future effects.
Specifically, the three alternative approaches are as follows:

C “Central Case” —This BART Scenario 2 case from the main analysis incorporates IPM estimates of resource costs (capital costs, and fixed and variable operating costs), along with percentage chan ges 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 expe nditures are also adjusted based on IPM findings by requiring additional purchases of gas by utilities.

C “Unconstrained Case” —This BART Scenario 2 case incorporates IPM estimates of both resource costs and fuel expenditures for coal and gas into EMPAX-CGE. Unlike the “Central Case,” this case allows changes in the quantity of coal use in the electricity sector to be estimated by EMPAX-CGE, once declines in the dollar value of coal purchases from the IPM m odel have been incorporated into theA-22 model. These declines in coal pur chases are integrated using the same methodology applied to the cap ital, labor, and material inputs needed to generate electricity—in this case, by requiringfewer purchases of coal to produce electricity.

C “IPM Price & Fuel Case” —This BART Scenario 2 case places the most reliance on IPM findings. Rather than allowi ng 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 inst ead 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 cons ideration the fact that, although most resource costs of electricity policies are borne by coal-fired generation, electricity prices are typically determined by the ma rginal unit in operation. Because of this, there may not be a direct correlation be tween policy costs and implications for electricity prices, although the economy out side of the electricity industry will respond to both electricity prices a nd 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 es timated 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 methodolog ies of these two cases are subs tantially 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 additi onal resource costs are enteringelectricity production. IPM results show moderate increases in electricity prices in 2015 through 2025, leading to smaller GDP effects in the “IPM Price & Fuel Case” than in the “Central Case.”A-23 -0.10% -0.08% -0.06% -0.04% -0.02% 0.00%

2005 2010 2015 2020 2025Change in GDP (%)

EMPAX - Central Case EMPAX - Unconstrained EMPAX - IPM Price & Fuel

Figure A-12. GDP Impacts of Alternative Linkages (%)

Source: EMPAX-CGE Note: Changes occur in 2005 as people react to the po licy announcement, in anticipation of future effects.
A.2 EMPAX-CGE Model Description: General Model Structure This section provides add itional details on the EMPAX- CGE model structure, data sources, and assumptions. The version of EMP AX-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 equilib rium framework wherein households maximize utility subject to budget constraints, and fi rms maximize profits subject to technology constraints. The model structure, in which ag ents 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.11 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 fo ssil-fuel sectors in EMPAX-CGE contain equations that account for the presence of fixed inputs to production (land and fossil-fuel resources, respectively).
A-24 Utility

Leisure

Goods (16 types) Consumption Household utility is a CES function of consumption and leisure.

Consumption is a Cobb-Douglas composite of the 16 types of goods.

Each consumption good is a CES composite of foreign and domestically produced goods. Foreign Domestic Domestic goods are a CES composite of locally produced goods and goods from other regions. Local Output Regional Output

Intermediates KLE M ost producer goods use fixed proportions of intermediate inputs and a capital-labor-energy (K LE) composite.Intermediate materials inputs are the 11 types of nonenergy goods, in fixed proportion for each industry.

E n ergy V alue A d ded

Labor Capital Energy (5 Types) The K LE composite is a CES function of energy and value-added (K L).

Value added is a Cobb-Douglas composite of capital and labor. Energy is a CES composite of 5 types of fuel. The structure of this function varies across industries.

Figure A-13. General Production and Co nsumption Nesting St ructure in EMPAXCGE Nested CES functions are used to portray substitution possibilities available to producers and consumers. Figure A-13 illustrates this general framework and gives a broad characterization of the model. 11 Along with the underlying data, these nesting structures and associated substitution elasticities determine the effects that will be estimated for policies. These nesting structures and elasticities used in EMPAX-CGE are generally based on the Emissions Prediction and Policy Analysis (E PPA) 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 res pond to environmental policies.12 See Brooke et al. (1998) for a description of GAMS (http:// www.gams.com/).

13 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.

14 See Rutherford (1999) for MPSGE docum entation (http://debre u.colorado.edu).

15 See <http://www.implan.com/index.html> for a description of the Minnesota IMPLAN Group ( 2003) and its data.

16 These EIA sources include AEO 2003 , the Manufacturing Energy Consumption Survey, State Energy Data Report, State Energy Price and Expenditure Report, and various annual industry profiles (EIA, 2001; undated[a and b]).
A-25 Given this basic similarity, EMPAX-CGE has adopted a comparable structure. EMPAX-CGE is programmed in the GAMS 12 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-leve l information provided by the Minnesota IMPLAN Group 15 and energy data come from EIA. 16 Although IMPLAN data contain information on the value of energy production and consumption in dollars, these data are replaced with EIA data for several reasons. First, the policies being investigated typically focus on energy markets, making it essential to include the best possible characterization of these markets in the model. Although the IMPLAN data are developed from a variety of government data sources at the U.S. Bureau of Economic Analysis and U.S. Bureau of Labor Statistics, these data do not always agree with energy information collected by EIA directly from manufacturers and electric utilities. Second, it is necessary to have physical quantities for energy consumption in the model to portr ay effects of environm ental policies. EIA reports physical quantities, while IMPLAN doe s not. Finally, although the IMPLAN data reflect the year 2000, the initial baseline year for the model is 2005. Thus, AEO energy production and consumption, out put, and economic growth forecasts for 2005 are used to adjust the year 2000 IMPLAN data.
EMPAX-CGE combines these economic and en ergy data to create a balanced social accounting matrix (SAM) that provides a base line characterization of the economy. The17 Energy-intensive sectors industry categories are based on EIA definitions of energy-intensive manufacturers in the Assumptions for the Annual Energy Outlook 2003

.

18 Economic data and information on nonelectricity energy markets are generally available only at the state level, which necessitates an approximation of the NERC re gions that follows state boundaries. For policy analyses, these approximations include Northeast = NPCC + MAAC, Southeast = SERC + FERC, Midwest = ECAR + MAIN, Plains = MAPP + SPP + ERCOT, and West = WSCC. See <http://www.nerc.com/> for further discussion of these regions.
A-26 SAM contains data on the value of output in each sector, payments for factors of production and intermediate inputs by each sector, hous ehold income and c onsumption, government purchases, investment, and trade flows. A ba lanced SAM for the year 2005 consistent with the desired sectoral and regi onal aggregation is produced us ing procedures developed by Babiker and Rutherford (1997) and described in Rutherfo rd 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 forecas ts for the baseline model year of 2005. These data are used to define 10 regions w ithin the United States, each containing 40 industries. Regions have been selected to cap ture important differences across the country in electricity-generation technologie s, 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 categor ies included in EMPAX-CGE for policy analysis. Their focus is on maintaining as much detail in the energy-intensive sectors 17 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 becau se 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, availa bility 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 th e North American Electric Reliability Council (NERC). 18A-27 Table A-3. EMPAX-CGE Industries

EMPAX Industry NAICS Classifications Coal 2121 Crude Oil a 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 Transportation b 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 a ssumed to be perfectly co mpetitive and have production technologies that exhibit constant returns to scale, except for the agriculture and natural resource extracting sectors, which have decreasin g returns to scale because they use factors in fixed supply (land and fossil fuels, respectivel y). The electricity industry is separated into two distinct sectors: fossil -fuel generation and nonfossil gene ration. 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 m odel must equal their factor endowments plus any net transfers. Along with the underlyi ng data, the nesting structures shown in Figure A-13 and associated substitution elas ticities define current production technologies and possible alternatives.A-28 Figure A-14. Regions Defined in EMPAX-CGE for Policy Analysis

A.2.3 Utility Functions Each region in the dynamic version of EMPAX-CGE contains four representative households, classified by income, that maximiz e intertemporal utility over all time periods in the model subject to budget constrai nts, where the income groups are

C $0 to $14,999,

C $15,000 to $29,999,

C $30,000 to $49,999, and

C $50,000 and above. These representative households are endow ed 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 factorA-29 use implied by production within each region. In come from sales of these productive factors is allocated to purchases of cons umption 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 aggregat e consumption good. This composite good is then combined with leisure time to produce household utility. The elasticity of substitution between consumption goods and leisure de pends on empirical es timates of labor-supply elasticities and indicates how willing househol ds are to trade off leisure time for consumption. Over time, households consid er the discounted present value of utility received from all periods’ consum ption of goods and leisure. Following standard conventions of CGE m odels, factors of pr oduction are assumed to be intersectorally mobile with in regions, but migration of pr oductive 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 em bodied in nonfossil electricity generation is spread across the United States through capital markets.
A.2.4 Trade In EMPAX-CGE, all goods and services ar e assumed to be composite, differentiated “Armington” goods made up of locally manufactured co mmodities 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 regi ons within the United States. Finally, the domestic composite goods are aggregated withimports 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 ec onomic behavior have been included in EMPAX-CGE because theoretical and empirical literature found that taxes can substantially alter estimated policy costs. The IMPLAN economic database us ed by EMPAX-CGE includes information on taxes such as indirect business taxes (all sales and excise taxes) and social security taxes. However, IMPLAN re ports factor payments for labor and capital at19 See Babiker et al. (2001) for a discussion of how this methodology was used in the EPPA model (EPPA assumes that AEEI parameters are the same across all industries in a country, while AEEI values in EMPAX-CGE are industry specific).
A-30 their gross-of-tax values, which necessitates use of additional data sources to determine personal income and capital tax rates. Information from the TAXSIM model at the National Bureau of Economic Research (Feenberg and Coutts, 1993), along with user-cost-of-capital calculations from Fullerton and Rogers ( 1993), are used to establish tax rates. Along with these rates, distortions asso ciated with taxes are a function of labor supply decisions of households. As with othe r CGE models focused on interactions between tax and environmental policies (e.g., Bovenberg and G oulder [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. La bor 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 Dynami cs and Economic Growth There are four sources of economic growth in EMPAX-CGE: technological change from improvements in energy efficiency, grow th in the available labor supply (from both population growth and changes in labor produc tivity), increases in stocks of natural resources, and capital accumulation. Energy cons umption per unit of output tends to decline over time because of improveme nts in production technologie s 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 impr ovements in energy efficiency and conservation. Labor force and regional economic growth, el ectricity generation, changes in available natural resources, and resource prices are also based on the AEO forecasts. Savings provide the basis for capital form ation and are motivated through people’s expectations about future needs for capital. Savings and investme nt decisions made by households determine aggregate capital stoc ks in EMPAX-CGE. The IMPLAN dataset20 A steady-state growth path requires all variables in the model to grow at a constant rate over time, including labor, output, inputs to production, and consumption. If the model has been properly specified, the steady-state replication check will show that the economy remains in equilibrium in each year along this path.
A-31 provides details on the types of goods and services used to produce the investment goods underlying each region’s capital stocks. Adjus tment dynamics associat ed 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 econom y 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” po licy experiments.
A.3 References Babiker, M.H., and T.F. Rutherford. 1997. “Input-Output and General Equilibrium Estimates of Embodied CO2: A Data Set and Static Framework for Assessment.” University of Colorado at Boulder, Working Paper 97-2. Available at http://debreu.colorado.edu/ papers/gtaptext.html. Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I.S. Wing, and R.C. Hyman. 2001. “The MIT Emissions Prediction and CO2 Po licy Analysis (EPPA) Model: Revisions, Sensitivities, and Comparisons of Results.” MIT Joint Program on the Science and Policy of Global Change, Re port No. 71. Available at http://web.mit.edu/globa lchange/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 Review86(4):985-1000. Available at<http://www.aeaweb.org/aer/>. Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User’s Guide. GAMS Development Corporation. Av ailable at http://www.gams.com.A-32 Feenberg, D., and E. Coutts. 1993. “A n 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. Ava ilable at http://bookstore.brookings.edu/ book_details.asp?pr oduct%5Fid=10403. Goulder, L.H., and R.C. Williams. 2003. “T he Substantial Bias from Ignoring General Equilibrium Effects in Estimating Exce ss Burden, and a Practical Solution.” Journal of Political Economy111:898-927. Available at <http://www.journals.uchicago.edu/JPE/home.html> Minnesota IMPLAN Group. 2003. State-Level Data for 2000. Available from http://www.implan.com/index.html. Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmental Protection Industry: A Proposed Framework for Assessment. U.S. Environmental Protection Agency, Office of Policy, Planning, and Evaluation. EPA 230-R-95-001. Available at http://yosemite.epa.gov/ee/epa/ eermfile.nsf/11f680 ff78df42f585256b45007e6235/41 b8b642ab9371df852564500004b543/$FILE/EE-0217A-1.pdf. Rutherford, T.F. 1999. “Applied General E quilibrium Modeling with MPSGE as a GAMS Subsystem: An Overview of the Modeling Framework and Syntax.” Computational Economics 14(1):1-46. Also available at http://www.gams.com/solvers/mpsge/syntax.htm. Rutherford, T.F., and S.V. Paltsev. 2000. “GTA P-Energy in GAMS: The Dataset and Static Model.” University of Colorado at B oulder, Working Paper 00-2. Available at http://debreu.colorado.edu/ papers/gtaptext.html. U.S. Department of Energy, Energy Info rmation Administrati on. Undated (a). State Energy Data Report. Washington DC. Available athttp://www.eia.doe.gov/emeu/states/ _use_multistate.html. U.S. Department of Energy, Energy Info rmation Administration. Undated (b). State Energy Price and Expenditure Report. Washington DC. Available at http://www.eia.doe.gov/emeu/states/price_multistate.html.A-33 U.S. Department of Energy, Ener gy 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 In formation Administration. January 2003. Annual Energy Outlook 2003. DOE/EIA-0383(2003). Washington DC. Available at http://www.eia.doe.gov/oiaf/a rchive/aeo03/pdf/0383(2003).pdf. U.S. Department of Energy, Energy In formation Administration. January 2004. Annual Energy Outlook 2004 . DOE/EIA-0383(2003). Washington, DC. Available at http://www.eia.doe.gov/oiaf/aeo/pdf/0383 (2001).B-1 APPENDIX B COST AND ECONOMIC IMPACT SUPPLEMENTAL INFORMATION AND SENSITIVITY ANALYSES

This appendix presents supplemental info rmation concerning the cost and economic impact analyses conducted for BART. Section B.1 presents a memo containing a list of BART EGUs potentially affected by the rule, and Section B.2 contains a list of BART EGUs used in the modeling of control scenarios for this RIA. Section B.3 presents a number of non-EGU cost and economic impact sensitivity analyses. B.1 List of EGU Units Potentially Affected by BART Memo From Perrin Quarles Associates, Inc. Re: Follow-Up on Units Potentially Affected by BART July 19, 2004 Perrin Quarles Associates, Inc. 675 Peter Jefferson Parkway, Suite 200 Charlottesville, Virginia 22911 Voice: (434) 979-3700 C Fax: (434) 296-2860 Email: pqa@pqa.com MEMORANDUM
TO: Roman Kramarchuk FROM: Doran Stegura RE: Follow-Up on Units Potentially Affected by BART DATE: July 19, 2004 On March 24, 2003, PQA delivered an analysis of sources that may be subject to controls under EPA’s Proposed Guidelines fo r 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 whic h construction was star ted by August 7, 1977 and that were not in operation prior to Augu st 7, 1962. Based on EPA guidance, the originalB-2 analysis also assumed that BART-eligible units are only those that are located at a plant where the total capacity of all units w ithin 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 w ithin the specified BART timeframe. The approach and assumptions used to identify wh ether units below the 750 MW threshold could potentially be BART-eligible ar e 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 c onstruction 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 que stion, PQA reviewed the RACT/BACT/LAER Clearinghouse and other internet search info rmation 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, actua l construction on these un its 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 tha t: (1) units subject to Part 60, Subpart D cannot be excluded because this NSPS subpart applie s to sources that have starte d 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 receive d 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 conf irm whether construction began prior to August 7, 1977. Of these 61 units, 43 are located in st ates covered under the Clean Air InterState Rule (CAIR) and 18 are located in states notcovered 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 ar e outside the BART time period, and 13 units require further follow-up since th e state environmental agency was not able to provide theB-3 information needed to determine whether the unit started construction prior to August 7, 1977. Table B-1 summarizes the 61 units analyz ed by PQA. The table provides an indication of whether the unit is located in a state affected by CAIR, whether the unit has been identified as within the BART timeframe, and whether additional follow-up with the state agency for information on construction permit dates is required to determine BART eligibility. Table B-1. Potential BART Units Identified for Follow-Up Analysis

State Plant Name ORIS Code Unit ID Online NSPS CAIR In BART Timeframe? Follow-Up Needed Nameplate (MW) AL Charles R Lowman 56 2 1979 D X X 233 AL Charles R Lowman 56 3 1980 D X X 233 AZ Apache Station 160 2 1979 D X 194.7 AZ Apache Station 160 3 1979 D X 194.7 AZ Springerville 8223 1 1985 D 397 AZ Springerville 8223 2 1990 D 397 CO Pawnee 6248 1 1981 D X 500 CO Ray D Nixon 8219 1 1980 D X 207 DE Indian River 594 4 1980 D X X 442.4 FL C D McIntosh 676 3 1982 D X 334 FL Deerhaven 663 B2 1981 D X 250.75 GA McIntosh (6124) 6124 1 1979 PRE X X 177.66 IA Ames 1122 8 1982 D X 65 IA George Neal South 7343 4 1979 D X X 639.9 IA Louisa 6664 101 1983 D X 738.09 IA Ottumwa 6254 1 1981 D X X 726 IN A B Brown Generating Station 6137 1 1979 D X X 265.23 KS Nearman Creek 6064 N1 1981 D X X 261 KY East Bend 6018 2 1981 D X X 669.28 KY R D Green 6639 G1 1979 D X X 263.7 KY R D Green 6639 G2 1981 D X X 263.7 KY Trimble County 6071 1 1990 D X X 566.1 LA Dolet Hills 51 1 1986 D X 720.75 LA R S Nelson 1393 6 1982 D X X 614.6 LA Rodemacher 6190 2 1982 D X X 558 MD Brandon Shores 602 1 1984 D X X 685.08 MD Brandon Shores 602 2 1991 D X X 685.08 MI Presque Isle 1769 9 1979 D X X 90 MI Wyandotte 1866 7 1982 D X 73 MN Clay Boswell 1893 4 1980 D X X 558 (continued)B-4 Table B-1. Potential BART Units Identi fied for Follow-Up Analysis (continued)

State Plant Name ORIS Code Unit ID Online NSPS CAIR In BART Timeframe? Follow-Up Needed Nameplate (MW) MO Iatan 6065 1 1980 D X X 725.85 MO Sikeston 6768 1 1981 D X X 261 NC Elizabethtown Power 10380 UNIT1 1985 D X 35 NC Elizabethtown Power 10380 UNIT2 1985 D X 35 NC Lumberton Power 10382 UNIT1 1985 D X 35 NC Lumberton Power 10382 UNIT2 1985 D X 35 NC Mayo 6250 1A 1983 D X 735.84 NC Mayo 6250 1B 1983 D X 735.84 ND Antelope Valley 6469 B1 1984 D 435 ND Antelope Valley 6469 B2 1986 D 435 ND Coyote 8222 B1 1981 D 450 NE Gerald Whelan Energy Center 60 1 1981 D 76.3 NE Nebraska City 6096 1 1979 D X 615.87 NE Platte 59 1 1982 D 109.8 NV North Valmy 8224 1 1981 D X 254.26 OH Killen Station 6031 2 1982 D X X 666.45 OK Grand River Dam Authority 165 1 1982 D 490 OK Hugo 6772 1 1982 D 400 OR Boardman 6106 1SG 1980 D X 560.5 TX Coleto Creek 6178 1 1980 D X X 600.39 TX Gibbons Creek 6136 1 1983 D X 443.97 TX Pirkey 7902 1 1985 D X 720.75 TX San Miguel 6183 SM-1 1982 D X 410 TX Sandow 6648 4 1981 D X X 590.64 UT Hunter (Emery) 6165 1 1978 D X 446.4 UT Hunter (Emery) 6165 2 1980 D X 446.4 WI Edgewater (4050) 4050 5 1985 D X X 380 WI J P Madgett 4271 B1 1979 D X X 387 WI Pleasant Prairie 6170 1 1980 D X X 616.59 WI Pleasant Prairie 6170 2 1985 D X X 616.59 WI Weston 4078 3 1981 D X X 350.46

DLS/jljB-5 B.2 EGU Units Presumed to be BART-Eligib le for Purposes of Modeling Emissions Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions

State FACILITY_NAME UNITID Online Year Nameplate Capacity a AL Barry 4 1969 404 AL Barry 5 1971 789 AL Charles R Lowman 1 1969 66 AL Charles R Lowman 2 1979 233 AL Charles R Lowman 3 1980 233 AL Colbert 5 1965 550 AL E C Gaston 5 1974 952 AL Gorgas 10 1972 789 AL Greene County 1 1965 299 AL Greene County 2 1966 269 AL James H Miller Jr 1 1978 706 AL James H Miller Jr 2 1985 706 AL Widows Creek 8 1965 550 AR Flint Creek 1 1978 558 AR Independence 1 1983 850 AR White Bluff 1 1980 850 AR White Bluff 2 1981 850 AZ Apache Station 2 1979 195 AZ Apache Station 3 1979 195 AZ Cholla 2 1978 289 AZ Cholla 3 1980 289 AZ Cholla 4 1981 414 AZ Coronado Generating Station U1B 1979 411 AZ Coronado Generating Station U2B 1980 411 AZ Irvington 4 1967 173 AZ Navajo Generating Station 1 1974 803 AZ Navajo Generating Station 2 1975 803 AZ Navajo Generating Station 3 1976 803 CO Cherokee 3 1962 150 CO Cherokee 4 1968 350 CO Comanche (470) 1 1973 350 (continued)B-6 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a CO Comanche (470) 2 1975 350 CO Craig C1 1980 446 CO Craig C2 1979 446 CO Hayden H1 1965 190 CO Hayden H2 1976 275 CO Martin Drake 5 1962 50 CO Martin Drake 6 1968 75 CO Martin Drake 7 1974 132 CO Pawnee 1 1981 500 CO Ray D Nixon 1 1980 207 CO Valmont 5 1964 166 CT Bridgeport Harbor Station BHB3 1968 400 DE Edge Moor 4 1966 177 DE Indian River 3 1970 177 DE Indian River 4 1980 442 FL Big Bend BB01 1970 446 FL Big Bend BB02 1973 446 FL Big Bend BB03 1976 446 FL Crist Electric Generating Plant 6 1970 370 FL Crist Electric Generating Plant 7 1973 578 FL Crystal River 1 1966 441 FL Crystal River 2 1969 524 FL Crystal River 4 1982 739 FL Crystal River 5 1984 739 FL F J Gannon GB04 1963 187 FL F J Gannon GB05 1965 239 FL F J Gannon GB06 1967 414 FL Lansing Smith 1 1965 150 FL Lansing Smith 2 1967 190 GA Bowen 1BLR 1971 700 GA Bowen 2BLR 1972 700 GA Bowen 3BLR 1974 880 (continued)B-7 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a GA Bowen 4BLR 1975 880 GA Hammond 4 1970 500 GA Harllee Branch 1 1965 250 GA Harllee Branch 2 1967 319 GA Harllee Branch 3 1968 481 GA Harllee Branch 4 1969 490 GA Jack McDonough MB1 1963 245 GA Jack McDonough MB2 1964 245 GA Kraft 3 1965 104 GA McIntosh (6124) 1 1979 178 GA Mitchell 3 1964 125 GA Scherer 1 1982 818 GA Scherer 2 1984 818 GA Wansley (6052) 1 1976 865 GA Wansley (6052) 2 1978 865 GA Yates Y6BR 1974 350 GA Yates Y7BR 1974 350 IA Ames 7 1968 33 IA Burlington (IA) 1 1968 212 IA Council Bluffs 3 1978 726 IA Fair Station 2 1967 38 IA George Neal North 1 1964 147 IA George Neal North 2 1972 349 IA George Neal North 3 1975 550 IA George Neal South 4 1979 640 IA Lansing 4 1977 275 IA Milton L Kapp 2 1967 218 IA Muscatine 8 1969 75 IA Ottumwa 1 1981 726 IA Pella 6 1963 38 IA Pella 7 1973 38 IA Prairie Creek 4 1967 149 (continued)B-8 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a IA Sixth Street 2 1970 85 IA Sixth Street 4 1970 85 IA Streeter Station 7 1973 35 IL Baldwin 1 1970 623 IL Baldwin 2 1973 635 IL Baldwin 3 1975 635 IL Coffeen 01 1965 389 IL Coffeen 02 1972 617 IL Dallman 31 1968 90 IL Dallman 32 1972 90 IL Dallman 33 1978 207 IL Duck Creek 1 1976 441 IL E D Edwards 2 1968 281 IL E D Edwards 3 1972 364 IL Havana 9 1978 488 IL Joliet 29 71 1965 660 IL Joliet 29 72 1965 660 IL Joliet 29 81 1965 660 IL Joliet 29 82 1965 660 IL Kincaid 1 1967 660 IL Kincaid 2 1968 660 IL Lakeside 7 1965 38 IL Lakeside 8 1965 38 IL Marion 1 1963 33 IL Marion 2 1963 33 IL Marion 3 1963 33 IL Marion 4 1978 173 IL Newton 1 1977 617 IL Newton 2 1982 617 IL Powerton 51 1972 893 IL Powerton 52 1972 893 IL Powerton 61 1975 893 (continued)B-9 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a IL Powerton 62 1975 893 IL Waukegan 8 1962 355 IL Will County 4 1963 598 IL Wood River 5 1964 388 IN A B Brown Generating Station 1 1979 265 IN Bailly 7 1962 194 IN Bailly 8 1968 422 IN Cayuga 1 1970 531 IN Cayuga 2 1972 531 IN Dean H Mitchell 11 1970 115 IN F B Culley Generating Station 2 1966 104 IN F B Culley Generating Station 3 1973 265 IN Frank E Ratts 1SG1 1970 117 IN Frank E Ratts 2SG1 1970 117 IN Gibson 1 1976 668 IN Gibson 2 1975 668 IN Gibson 3 1978 668 IN Gibson 4 1979 668 IN Harding Street Station (EW Stout) 70 1973 471 IN Merom 1SG1 1983 540 IN Merom 2SG1 1982 540 IN Michigan City 12 1974 540 IN Petersburg 1 1967 253 IN Petersburg 2 1969 471 IN Petersburg 3 1977 574 IN R M Schahfer 14 1976 540 IN R M Schahfer 15 1979 556 IN State Line Generating Station (IN) 4 1962 389 IN Tanners Creek U4 1964 580 IN Wabash River 6 1968 387 IN Warrick 2 1964 144 IN Warrick 3 1965 144 (continued)B-10 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a IN Warrick 4 1970 323 IN Whitewater Valley 2 1973 60 KS Jeffrey Energy Center 1 1978 720 KS Jeffrey Energy Center 2 1980 720 KS La Cygne 1 1973 893 KS La Cygne 2 1977 685 KS Lawrence Energy Center 5 1971 458 KS Nearman Creek N1 1981 261 KS Quindaro 1 1965 82 KS Quindaro 2 1971 158 KS Tecumseh Energy Center 10 1962 176 KY Big Sandy BSU1 1963 281 KY Big Sandy BSU2 1969 816 KY Cane Run 4 1962 163 KY Cane Run 5 1966 209 KY Cane Run 6 1969 272 KY Coleman C1 1969 174 KY Coleman C2 1970 174 KY Coleman C3 1971 173 KY Cooper 1 1965 100 KY Cooper 2 1969 221 KY E W Brown 2 1963 180 KY E W Brown 3 1971 446 KY East Bend 2 1981 669 KY Elmer Smith 1 1964 151 KY Elmer Smith 2 1974 265 KY Ghent 1 1974 557 KY Ghent 2 1977 556 KY H L Spurlock 1 1977 305 KY H L Spurlock 2 1981 508 KY Henderson I 6 1968 32 KY HMP&L Station 2 H1 1973 180 (continued)B-11 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a KY HMP&L Station 2 H2 1974 185 KY Mill Creek 1 1972 356 KY Mill Creek 2 1974 356 KY Mill Creek 3 1978 463 KY Mill Creek 4 1982 544 KY Paradise 1 1963 704 KY Paradise 2 1963 704 KY Paradise 3 1970 1150 KY R D Green G1 1979 264 KY R D Green G2 1981 264 KY Robert Reid R1 1965 82 KY Trimble County 1 1990 566 LA Big Cajun 2 2B1 1980 559 LA Big Cajun 2 2B2 1981 559 LA R S Nelson 6 1982 615 LA Rodemacher 2 1982 558 MA Brayton Point 1 1963 241 MA Brayton Point 2 1964 241 MA Brayton Point 3 1969 643 MD Brandon Shores 1 1984 685 MD Brandon Shores 2 1991 685 MD C P Crane 2 1963 209 MD Chalk Point 1 1964 364 MD Chalk Point 2 1965 364 MD Dickerson 3 1962 196 MD Herbert a Wagner 3 1966 359 MD Morgantown 1 1970 626 MD Morgantown 2 1971 626 MI Belle River 1 1984 698 MI Belle River 2 1985 698 MI Eckert Station 4 1964 80 MI Eckert Station 5 1968 80 (continued)B-12 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a MI Eckert Station 6 1970 80 MI Erickson 1 1973 155 MI Harbor Beach 1 1968 121 MI J H Campbell 1 1962 265 MI J H Campbell 2 1967 385 MI J H Campbell 3 1980 871 MI James De Young 5 1969 29 MI Monroe 1 1971 817 MI Monroe 2 1973 823 MI Monroe 3 1973 823 MI Monroe 4 1974 817 MI Presque Isle 2 1962 38 MI Presque Isle 3 1964 54 MI Presque Isle 4 1966 58 MI Presque Isle 5 1974 90 MI Presque Isle 6 1975 90 MI Presque Isle 7 1978 90 MI Presque Isle 8 1978 90 MI Presque Isle 9 1979 90 MI St. Clair 7 1969 545 MI Trenton Channel 9A 1968 536 MN Allen S King 1 1968 598 MN Clay Boswell 3 1973 365 MN Clay Boswell 4 1980 558 MN Hoot Lake 3 1964 75 MN Northeast Station NEPP 1971 32 MN Riverside (1927) 8 1964 239 MN Sherburne County 1 1976 660 MN Sherburne County 2 1977 660 MN Silver Lake 4 1969 54 MO Asbury 1 1970 232 MO Blue Valley 3 1965 58 (continued)B-13 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a MO Columbia 7 1965 74 MO Iatan 1 1980 726 MO James River 4 1964 60 MO James River 5 1970 105 MO Labadie 1 1970 574 MO Labadie 2 1971 574 MO Labadie 3 1972 621 MO Labadie 4 1973 621 MO Lake Road 6 1970 90 MO Montrose 3 1964 188 MO New Madrid 1 1972 600 MO New Madrid 2 1977 600 MO Rush Island 1 1976 621 MO Rush Island 2 1977 621 MO Sibley 2 1962 50 MO Sibley 3 1969 419 MO Sikeston 1 1981 261 MO Sioux 1 1967 550 MO Sioux 2 1968 550 MO Southwest 1 1976 194 MO Thomas Hill MB1 1966 180 MO Thomas Hill MB2 1969 285 MS Daniel Electric Generating Plant 1 1977 500 MS Daniel Electric Generating Plant 2 1981 500 MS R D Morrow 1 1978 200 MS R D Morrow 2 1978 200 MS Watson Electric Generating Plant 4 1968 250 MS Watson Electric Generating Plant 5 1973 500 MT Colstrip 1 1975 358 MT Colstrip 2 1976 358 MT J E Corette 2 1968 191 NC Asheville 1 1964 207 (continued)B-14 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a NC Asheville 2 1971 207 NC Belews Creek 1 1974 1080 NC Belews Creek 2 1975 1080 NC Cliffside 5 1972 571 NC L V Sutton 3 1972 447 NC Lee 3 1962 252 NC Marshall 1 1965 350 NC Marshall 2 1966 350 NC Marshall 3 1969 648 NC Marshall 4 1970 648 NC Roxboro 1 1966 411 NC Roxboro 2 1968 657 NC Roxboro 3A 1973 745 NC Roxboro 3B 1973 745 NC Roxboro 4A 1980 745 NC Roxboro 4B 1980 745 ND Coal Creek 1 1979 506 ND Coal Creek 2 1981 506 ND Leland Olds 1 1966 216 ND Leland Olds 2 1975 440 ND Milton R Young B1 1970 257 ND Milton R Young B2 1977 477 ND R M Heskett B2 1963 75 ND Stanton 1 1967 172 NE Gerald Gentleman Station 1 1979 681 NE Gerald Gentleman Station 2 1982 681 NE Lon D Wright Power Plant 8 1976 92 NE Nebraska City 1 1979 616 NE North Omaha 4 1963 136 NE North Omaha 5 1968 218 NE Sheldon 1 1968 109 NH Merrimack 2 1968 346 (continued)B-15 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a NJ B L England 1 1962 136 NJ B L England 2 1964 163 NJ Hudson 2 1968 660 NM Four Corners 1 1963 190 NM Four Corners 2 1963 190 NM Four Corners 3 1964 253 NM Four Corners 4 1969 818 NM Four Corners 5 1970 818 NM San Juan 1 1976 361 NM San Juan 2 1973 350 NM San Juan 3 1979 534 NM San Juan 4 1982 534 NV Mohave 1 1971 818 NV Mohave 2 1971 818 NV North Valmy 1 1981 254 NV Reid Gardner 1 1965 114 NV Reid Gardner 2 1968 114 NV Reid Gardner 3 1976 114 NY Dynegy Danskammer 4 1967 239 NY Lovett 4 1966 180 NY Lovett 5 1969 201 NY S A Carlson 12 1963 58 OH Avon Lake Power Plant 12 1970 680 OH Bay Shore 3 1963 141 OH Bay Shore 4 1968 218 OH Cardinal 1 1967 615 OH Cardinal 2 1967 615 OH Cardinal 3 1977 650 OH Conesville 3 1962 162 OH Conesville 4 1973 842 OH Conesville 5 1976 444 OH Conesville 6 1978 444 (continued)B-16 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a OH Eastlake 5 1972 680 OH Gen J M Gavin 1 1974 1300 OH Gen J M Gavin 2 1975 1300 OH Hamilton 9 1974 51 OH J M Stuart 1 1971 610 OH J M Stuart 2 1970 610 OH J M Stuart 3 1972 610 OH J M Stuart 4 1974 610 OH Killen Station 2 1982 666 OH Lake Shore 18 1962 256 OH Miami Fort 7 1975 557 OH Miami Fort 8 1978 558 OH Muskingum River 5 1968 615 OH W H Sammis 4 1962 185 OH W H Sammis 5 1967 318 OH W H Sammis 6 1969 623 OH W H Sammis 7 1971 623 OH Walter C Beckjord 5 1962 245 OH Walter C Beckjord 6 1969 461 OK Muskogee 4 1977 572 OK Muskogee 5 1978 572 OK Northeastern 3313 1979 473 OK Northeastern 3314 1980 473 OK Sooner 1 1979 569 OK Sooner 2 1980 569 OR Boardman 1SG 1980 561 PA Bruce Mansfield 1 1976 914 PA Bruce Mansfield 2 1977 914 PA Bruce Mansfield 3 1980 914 PA Brunner Island 2 1965 405 PA Brunner Island 3 1969 790 PA Cheswick 1 1970 565 (continued)B-17 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a PA Conemaugh 1 1970 936 PA Conemaugh 2 1971 936 PA Hatfields Ferry 1 1969 576 PA Hatfields Ferry 2 1970 576 PA Hatfields Ferry 3 1971 576 PA Homer City 1 1969 660 PA Homer City 2 1969 660 PA Homer City 3 1977 692 PA Keystone 1 1967 936 PA Keystone 2 1968 936 PA Mitchell 33 1963 299 PA Montour 1 1972 823 PA Montour 2 1973 819 PA New Castle 5 1964 136 PA Portland 2 1962 255 SC Canadys Steam CAN1 1962 136 SC Canadys Steam CAN2 1964 136 SC Canadys Steam CAN3 1967 218 SC Dolphus M Grainger 1 1966 82 SC Dolphus M Grainger 2 1966 82 SC Jefferies 3 1970 173 SC Jefferies 4 1970 173 SC Wateree WAT1 1970 386 SC Wateree WAT2 1971 386 SC Williams WIL1 1973 633 SC Winyah 1 1975 315 SC Winyah 2 1977 315 SD Big Stone 1 1975 456 TN Bull Run 1 1967 950 TN Cumberland 1 1973 1300 TN Cumberland 2 1973 1300 TX Big Brown 1 1971 593 (continued)B-18 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a TX Big Brown 2 1972 593 TX Coleto Creek 1 1980 600 TX Harrington Station 061B 1976 360 TX Harrington Station 062B 1978 360 TX J T Deely 1 1977 446 TX J T Deely 2 1978 446 TX Martin Lake 1 1977 793 TX Martin Lake 2 1978 793 TX Monticello 1 1974 593 TX Monticello 2 1975 593 TX Monticello 3 1978 793 TX Sam Seymour 1 1979 615 TX Sam Seymour 2 1980 615 TX Sandow 4 1981 591 TX W A Parish WAP5 1977 734 TX W A Parish WAP6 1978 734 TX Welsh 1 1977 558 TX Welsh 2 1980 558 TX Welsh 3 1982 558 UT Hunter (Emery) 1 1978 446 UT Hunter (Emery) 2 1980 446 UT Huntington 1 1977 446 UT Huntington 2 1974 446 VA Chesapeake 4 1962 239 VA Chesterfield 5 1964 359 VA Chesterfield 6 1969 694 VA Possum Point Power Station 4 1962 239 WA Centralia BW21 1972 730 WA Centralia BW22 1973 730 WI Columbia 1 1975 512 WI Columbia 2 1978 512 WI Edgewater (4050) 4 1969 351 (continued)B-19 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a WI Edgewater (4050) 5 1985 380 WI Genoa 1 1969 346 WI J P Madgett B1 1979 387 WI Manitowoc 7 1962 79 WI Nelson Dewey 2 1962 114 WI Pleasant Prairie 1 1980 617 WI Pleasant Prairie 2 1985 617 WI Pulliam 8 1964 136 WI South Oak Creek 7 1965 318 WI South Oak Creek 8 1967 324 WI Valley (Wepco) 1 1968 136 WI Valley (Wepco) 2 1968 136 WI Valley (Wepco) 3 1969 136 WI Valley (Wepco) 4 1969 136 WI Weston 3 1981 350 WV Fort Martin 1 1967 576 WV Fort Martin 2 1968 576 WV Harrison 1 1972 684 WV Harrison 2 1973 684 WV Harrison 3 1974 684 WV John E Amos 1 1971 816 WV John E Amos 2 1972 816 WV John E Amos 3 1973 1300 WV Mitchell 1 1971 816 WV Mitchell 2 1971 816 WV Mount Storm Power Station 1 1965 570 WV Mount Storm Power Station 2 1966 570 WV Mount Storm Power Station 3 1973 522 WV Mountaineer (1301) 1 1980 1300 WV Pleasants 1 1979 684 WV Pleasants 2 1980 684 WY Dave Johnston BW43 1964 230 (continued)B-20 Table B-2. Units that were Presumed to be BART-Eligible for Purposes of Modeling Emissions (continued)
State FACILITY_NAME UNITID Online Year Nameplate Capacity a WY Dave Johnston BW44 1972 360 WY Jim Bridger BW71 1974 561 WY Jim Bridger BW72 1975 561 WY Jim Bridger BW73 1976 561 WY Jim Bridger BW74 1979 561 WY Laramie River 1 1980 570 WY Laramie River 2 1981 570 WY Laramie River 3 1982 570 WY Naughton 1 1963 163 WY Naughton 2 1968 218 WY Naughton 3 1971 326 WY Wyodak BW91 1978 362

a Nameplate capacity of gene rator connected to boiler.B-21 B.3 Non-EGU Cost and Economic Impact Sensitivity Analyses This appendix contains a number of sensitivity analyses for Scenarios 1 through 3 ($1,000/ton, $4,000/ton, and $10,000/ton) applied to the non-EGU source categories. These sensitivity analyses are the following:

C total capital costs of controlling both SO 2 and NO x in 2015 for each illustrative scenario—calculated at a 7 per cent discount rate (see Table B-3)

C total capital costs of controlling both SO 2 and NO x in 2015—calculated at a 3 percent discount rate (see Table B-4)

C total capital costs of controlling both SO 2 and NO x in 2015 for Scenario 2—calculated at a 10 percent discount rate (see Table B-5)

C total annualized costs of controlling both SO 2 and NO x in 2015 for Scenario 2—calculated at a 10 percent discount rate (see Table B-6)

C total annualized and capital costs of controlling both SO 2 and NO x for Scenario 2 in 2015 for a 25 percent increase in labor rates—calculated at a 7 percent discount rate (see Table B-7)

C total annualized and capital costs of controlling both SO 2 and NO x for Scenario 2 in 2015 for a 25 percent decrease in la bor rates—calculated at a 7 percent discount rate (see Table B-8)

C total annualized and capital costs of controlling both SO 2 and NO x for Scenario 2 in 2015 for a 25 percent increase in ener gy prices—calculated at a 7 percent discount rate (see Table B-9)

C total annualized and capital costs of controlling both SO 2 and NO x for Scenario 2 in 2015 for a 25 percent decrease in ener gy prices—calculated at a 7 percent discount rate (see Table B-10)B-22 Table B-3. Total Capital Costs of Controlling Both SO 2 and NO x for the Non-EGU BART Source Categories in 2015—7 P ercent Discount Rate (million 1999$)

BART Source Category Scenarios Scenario 1 $1,000/ton Scenario 2 $4,000/ton Scenario 3 $10,000/ton Industrial boilers $422.3 $4,132.2 $6,324.9 Petroleum refineries 22.6 1,220.9 2,968.6 Kraft pulp mills 70.7 1,131.6 2,168.2 Portland cement plants 18.4 817.6 2,092.7 Hydrofluoric, sulfuric, and nitric acid plants 34.6 42.1 42.1

Chemical process plants 58.4 392.4 503.8 Iron and steel mills 2.6 162.9 227.6
Coke oven batteries 0.0 81.0 191.3 Sulfur recovery plants 0.3 1.0 1.0 Primary aluminum ore reduction plants 14.6 62.0 62.4

Lime kilns 9.9 16.6 140.1 Glass fiber processing plants 1.2 11.8 20.1 Municipal incinerators 0.0 4.6 4.6 Coal cleaning plants 0.0 4.0 4.0 Carbon black plants 0.02 0.9 0.9 Phosphate rock processing plants 0.0 0.7 1.5 Secondary metal production facilities 0.1 0.3 0.4

Total $655.7 $8,082.7 $14,754.1B-23 Table B-4. Total Capital Costs of Controlling Both SO 2 and NO x for the Non-EGU BART Source Categories in 2015—3 P ercent Discount Rate (million 1999$)

BART Source Category Scenarios Scenario 1 $1,000/ton Scenario 2 $4,000/ton Scenario 3 $10,000/ton Industrial boilers $1,101.3 $3,547.2 $4,549.1 Petroleum refineries 164.6 1,632.2 2,999.0 Kraft pulp mills 378.4 880.9 1,299.9 Portland cement plants 69.5 1,268.6 1,844.5 Hydrofluoric, sulfuric, and nitric acid plants 27.5 33.3 33.3

Chemical process plants 158.9 317.0 530.8
Iron and steel mills 2.2 173.7 240.7 Coke oven batteries 0.0 63.9 148.0 Sulfur recovery plants 0.2 0.8 0.8 Primary aluminum ore reduction plants 47.6 47.6 47.6

Lime kilns 12.6 106.9 106.9 Glass fiber processing plants 1.9 10.1 17.0 Municipal incinerators 0.0 3.2 3.2 Coal cleaning plants 0.0 3.3 3.3 Carbon black plants 0.02 0.7 0.7 Phosphate rock processing plants 0.0 1.1 1.1 Secondary metal production facilities 0.1 0.3 0.3

Total $1,881.70 $8,090.92 $12,726.09B-24 Table B-5. Total Capital Costs of Controlling Both SO 2 and NO x for Scenario 2 ($4,000 per ton) Applied to the Non-EGU B ART Source Categories in 2015 (million 1999$)—10 Percent Discount Rate

BART Source Category Scenario 2 ($4,000 per ton) Industrial boilers $3,054.1
Petroleum refineries 862.6 Kraft pulp mills 788.4 Portland cement plants 154.1 Hydrofluoric, sulfuric, and nitric acid plants 49.3 Chemical process plants 398.7
Iron and steel mills 117.0 Coke oven batteries 95.2 Sulfur recovery plants 1.5 Primary aluminum ore reduction plants 22.4 Lime kilns 19.8 Glass fiber processing plants 13.1 Municipal incinerators 5.7 Coal cleaning plants 4.6 Carbon black plants 0.9 Phosphate rock processing plants 0.8 Secondary metal production facilities 0.3 Total $5,588.6B-25 Table B-6. Total Annualized Costs of Controlling Both SO 2 and NO x for the Scenario 2 Applied to the Non-EGU BART Source Categories in 2015—10 Percent Discount Rate (million 1999$)

BART Source Category Scenario 2 ($4,000 per ton) Industrial boilers $460.6
Petroleum refineries 137.0 Kraft pulp mills 116.7 Portland cement plants 36.0 Hydrofluoric, sulfuric, and nitric acid plants 25.3 Chemical process plants 72.7
Iron and steel mills 18.1
Coke oven batteries 22.4 Sulfur recovery plants 11.9 Primary aluminum ore reduction plants 3.1 Lime kilns 5.8 Glass fiber processing plants 5.8 Municipal incinerators 1.3 Coal cleaning plants 1.1 Carbon black plants 0.2 Phosphate rock processing plants 0.1 Secondary metal production facilities 0.05 Total $928.29B-26 Table B-7. Total Annualized and Capital Costs of Controlling Both SO 2 and NO x for Scenario 2— Applied to the BART Non-EGU Source Categories in 2015—7 Percent Discount Rate—25 Percent Labor Rate Increase (million 1999$)

BART Source Category Annualized Costs Capital Costs Industrial boilers $521.7 $4,074.1 Petroleum refineries 180.8 1,221.0 Kraft pulp mills 119.1 1,012.2 Portland cement plants 175.6 817.6 Hydrofluoric, sulfuric, and nitric acid plants 23.4 42.1 Chemical process plants 71.5 392.4 Iron and steel mills 23.5 162.9
Coke oven batteries 18.6 80.0 Sulfur recovery plants 12.2 1.0 Primary aluminum ore reduction plants 7.8 62.0
Lime kilns 5.1 16.6 Glass fiber processing plants 5.3 11.8 Municipal incinerators 1.1 4.6 Coal cleaning plants 1.0 4.0 Carbon black plants 0.2 0.9 Phosphate rock processing plants 0.1 0.7 Secondary metal production facilities 0.04 0.3 Total $1,167.13 $7,906.20B-27 Table B-8. Total Annualized and Capital Costs of Controlling Both SO 2 and NO x for Scenario 2 Applied to the Non-EGU BART Source Categories in 2015—7 Percent Discount Rate—25 Percent Labor Rate Decrease (million 1999$)

BART Source Category Annualized Costs Capital Costs Industrial boilers $525.5 $4,128.5 Petroleum refineries 179.5 1,221.0 Kraft pulp mills 118.9 1,012.2 Portland cement plants 173.9 817.6 Hydrofluoric, sulfuric, and nitric acid plants 23.3 42.1 Chemical process plants 70.1 392.4 Iron and steel mills 23.5 162.9
Coke oven batteries 18.7 81.0 Sulfur recovery plants 12.1 1.0 Primary aluminum ore reduction plants 7.8 62.0
Lime kilns 5.0 16.6 Glass fiber processing plants 5.3 11.8 Municipal incinerators 1.1 4.6 Coal cleaning plants 1.0 4.0 Carbon black plants 0.2 0.9 Phosphate rock processing plants 0.1 0.7 Secondary metal production facilities 0.04 0.3 Total $1,165.83 $7,960.59B-28 Table B-9. Total Annualized and Capital Costs of Controlling Both SO 2 and NO x for Scenario 2— Applied to the BART Non-EGU Source Categories in 2015—7 Percent Discount Rate—25 Percent Ener gy Price Increase (million 1999$)

BART Source Category Annualized Costs Capital Costs Industrial boilers $522.2 $4,074.2 Petroleum refineries 180.7 1,220.0 Kraft pulp mills 118.2 1,012.2 Portland cement plants 179.3 817.6 Hydrofluoric, sulfuric, and nitric acid plants 24.2 42.1 Chemical process plants 70.8 392.4 Iron and steel mills 23.5 162.9
Coke oven batteries 18.7 81.1 Sulfur recovery plants 12.1 1.0 Primary aluminum ore reduction plants 7.8 62.0
Lime kilns 5.0 16.6 Glass fiber processing plants 5.3 11.8 Municipal incinerators 1.1 4.6 Coal cleaning plants 1.0 4.0 Carbon black plants 0.2 0.9 Phosphate rock processing plants 0.1 0.7 Secondary metal production facilities 0.04 0.3 Total $1,171.27 $7,906.20B-29 Table B-10. Total Annualized and Ca pital Costs of Controlling Both SO 2 and NO x for Scenario 2 Applied to the BART Non-EGU Source Categories in 2015—7 Percent Discount Rate—25 Percent Ener gy Price Increase (million 1999$)

BART Source Category Annualized Costs Capital Costs Industrial boilers $25.0 $4,128.5 Petroleum refineries 179.7 1,221.0 Kraft pulp mills 118.8 1,012.2 Portland cement plants 170.2 817.6 Hydrofluoric, sulfuric, and nitric acid plants 22.6 42.1 Chemical process plants 70.7 392.4 Iron and steel mills 23.4 162.9
Coke oven batteries 18.6 81.1 Sulfur recovery plants 12.1 1.0 Primary aluminum ore reduction plants 7.8 62.0
Lime kilns 5.0 16.6 Glass fiber processing plants 5.3 11.8 Municipal incinerators 1.1 4.6 Coal cleaning plants 1.0 4.0 Carbon black plants 0.2 0.9 Phosphate rock processing plants 0.1 0.7 Secondary metal production facilities 0.04 0.3 Total $1,161.68 $7,960.59C-1 

### *

*

* #

# = Center Grid-Cell “E” 

### * = Air Pollution Monitor 

### *

*

* * APPENDIX C ADDITIONAL TECHNICAL INFORMATION SUPPORTING THE BENEFITS ANALYSIS

This appendix provides additi onal technical details about several important elements of the benefits analysis, including the spatia l interpolation method a nd health effect pooling methods. Additional details on benefits methods can be found in the BenMAP User’s Manual, available in the docke t and online at http://www.epa.gov/ttn/ecas/benmodels.html. C.1 Voronoi Neighbor Averaging In calculating the base year concentrations of PM species and ozone at model grid cells prior to scaling with model outputs, we used a spatial inte rpolation 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 th e set of neighboring monitors for grid cell E.C-2 d i , . . 1 1 20 1 20 1 16 1 14 0 27 = + +       = In particular, BenMAP identifies the neares t monitors, or “neighbors,” by drawing a polygon, or Voronoi cell, around the center of each grid cell. The polygons have the special property that the boundaries are the same distance from the two closest points.

We then chose those monitors th at share a boundary with the center of grid cell E. These are the nearest neighbors, and we used these monito rs 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 neighbor ing monitors and then calculates an inverse-distance weighted average of the metrics. The further the monitor is from the grid cell center, the smaller the weight. The weight for the monitor 20 kilometers from the center of grid cell E is calculated as follows:C-3 The weights for the other monitors w ould be calculated in a similar fashion. C.2 The Random/Fixed Effect Pooling Procedure Often more than one study has estimated a C-R function for a given pollutant-health endpoint combination. Each study provides an estimate of the pollutant coefficient, $ , in the C-R function, along with a measure of the uncer tainty of the estimate . Because uncertainty decreases as sample size increases, combining da ta sets is expected to yield more reliable estimates of $ and therefore more reliable estimates of the incidence change predicted using $ . Combining data from several comparable studies to analyze them together is often referred to as meta-analysis. For a number of reasons, including data confidentiality, it is often impractical or impossible to combine the original data sets. Combining the results of studies to produce better estimates of $ provides a second-best but still valuab le way to synthesize information (DerSimonian and Laird, 1986). This is referred to as pooling. Pooling $ s requires that all of the studies contributing estimates of $ use the same functional form for the C-R function. That is, the $ s must be measuring the same thing. It is also possible to pool the study-speci fic estimates of incidence change derived from the C-R functions, instead of pooling the underlying $ s themselves. For a variety of reasons, this is often possible when it is not feasible to pool the underlying $ s. For example,C-4 if one study is log-linear and another is linear, we could not pool the $ s because they are not different estimates of a coefficient in the same C-R function but are instead estimates of coefficients in different C-R functions. We can, however, calculate the incidence change predicted by each C-R function (for a given chan ge in pollutant concentration and, for the log-linear function, a given baseline inciden ce rate) and pool these incidence changes. BenMAP allows the pooling of incidence change s predicted by severa l studies for the same pollutant-health endpoint group combination. Italso 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 in cidence change and/or monetary benefit. To do this, BenMAP pools the study-specific dist ributions of incidence changes (or monetary benefit) to derive a pooled di stribution. This pooled distribu tion 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 gi ven to each study. Various methods can be used to assign weights to studies. Below we discuss the po ssible weighting schemes that are available in BenMAP. Subjective (User-specified) Weights BenMAP allows the user the option of sp ecifying 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 s implicity but the disadvantage of not taking into account the uncertainty of each of the estimates. Estimates w ith 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 da ta and the sample size used to obtain the estimate, two keyC-5 factors that influence the reliability of resu lts. BenMAP has two me thods 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 scheme s is first presented in terms of pooling the pollutant coefficients (the $ s), because that most closely matches the discussion of the method for pooling study results as it was orig inally presented by DerSimonian and Laird (1986). We then give an overview of the an alogous weighting process used within BenMAP to generate weights for incidence changes rather than $ s. C.3 Fixed Effects Weights
The fixed effects model assumes that ther e is a single true C-R relationship and therefore a single true value for the parameter $ that applies everywhere. Differences among $ s reported by different studies are therefore simply the result of sampling error. That is, each reported $ is an estimate of the same underlying parameter. The certainty of an estimate is reflected in its variance (the larger the variance, the less certain the estimate). Fixed effects pooling therefore weights each estimate under consider ation in proportion to the inverse of its variance. Suppose there are n studies, with the i thstudy providing an estimate $ i with variance
v i (I = 1, ..., n). Let

denote the sum of the inverse va riances. Then the weight, w i , given to the ith estimate, $ i , is

This means that estimates with small variances (i.e., estimates with relatively little uncertainty surrounding them) receive large we ights and those with large variances receive small weights.C-6 The estimate produced by pooling based on a fixed effects model, then, is just a weighted average of the estimates from the studies being considered, with the weights as defined above. That is,

The variance associated with this pooled estimate is the inverse of the sum of the inverse variances:

Table C-1 shows the relevant calculations for this pooling for three sample studies. Table C-1. Example of Fixe d Effects Model Calculations
Study $ i v i 1/v i w i w i * $ i 1 0.75 0.1225 8.16 0.016 0.012 2 1.25 0.0025 400 0.787 0.984 3 1.00 0.0100 100 0.197 0.197 Sum 3 = 508.16 3 = 1.000 3 = 1.193

The sum of weighted contributions in th e last column is the pooled estimate of $ based on the fixed effects model. This estimate (1.193) is considerably closer to the estimate from study 2 (1.25) than is the estimate (1.0) th at 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 therefor e more heavily weighted in the pooling. The variance of the pooled estimate, v fe , is the inverse of the sum of the variances, or 0.00197. (The sums of the $ i and v i are not shown, because they are of no importance. The sum of the 1/v i is S, used to calculate the weights. The sum of the weights, w i , i=1, ..., n, is 1.0, as expected).C-7 C.4 Random/Fixed Effects Weights
An alternative to the fixed effects model is the random effects model, which allows the possibility that the estimates $ i from the different studies may in fact be estimates of different parameters, rather than just different estimates of a sing le underlying parameter. In studies of the effects of PM 10 on mortality, for example, if the composition of PM 10 varies among study locations the underlying relationship between mortality and PM 10 may be different from one study location to another. For example, fi ne particles make up a greater fraction of PM 10 in Philadelphia than in El Paso. If fine particles are disproportionately responsible for mortality relative to coarse particles, then one would expect the true value of $ in Philadelphia to be great er than the true value of $ in El Paso. This would violate the assumption of the fixed effects model. The following procedure can test whether it is appropriate to ba se the pooling on the random effects model (vs. the fixed e ffects model). A test statistic, Q w , the weighted sum of squared differences of the se parate study estimates from th e pooled estimate based on the fixed effects model, is calculated as

Under the null hypothesis that there is a single underlying parameter, $ , of which all the $ i s are estimates, Q w has a chi-squared distribution with n–1 degrees of freedom. (Recall that n is the number of studies in the meta-analysis.) If Q w 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 eff ects model, and the random effects model is assumed, allowing the possibility that each study is estimating a different $ . (BenMAP uses a 5 percent one-tailed test.) The weights used in a pooling based on th e random effects model must take into account not only the within-study variances (u sed in a meta-analysi s based on the fixed effects model) but the between-study variances as well. These weights are calculated as follows: Using Q w , the between-study variance, 0 2 , isC-8 It can be shown that the denominator is always positive. Therefore, if the numerator is negative (i.e., if Q w < n–1), then 0 2 is a negative number, and it is not possible to calculate a random effects estimate. In this case, however, the small value of Q w would presumably have led to accepting the null hypothesis desc ribed above, and the me ta-analysis would be based on the fixed effects model. The re maining discussion therefore assumes that 0 2 is positive.
Given a value for 0 2 , the random effects estimate is calculated in almost the sameway as the fixed effects estimate. However, the weights now incorporate both the withinstudy variance (v i ) and the between-study variance ( 0 2 ). Whereas the weights implied by the fixed effects model used only v i , the within-study variance, the weights implied by the random effects model use v i + 0 2

. Let v i * = v i + 0 2 . Then

and

The estimate produced by pooling based on the random effects model, then, is just a weighted average of the estimates from the studies being considered, with the weights as defined above. That is,C-9 The variance associated with this random e ffects pooled estimate is, as it was for the fixed effects pooled estimate, the inverse of the sum of the inverse variances:

The weighting scheme used in a poolin g based on the random effects model is basically the same as that used if a fixed e ffects 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 fro m the same underlying population), while the random eff ects model assumes that ther e is not only sampling error associated with each study, but that there is also between-study variability—each study is estimating a different underlying $ . Therefore, the sum of the within-study variance and the between-study variance yields an overall variance estimate. C.5 Fixed Effects and Random/Fixed Effect s Weighting to Pool Incidence Change Distributions and Dollar Benefit Distributions Weights can be derived for pooling inciden ce changes predicted by different studies, using either the fixed effects or the fixed/randomeffects model, in a way that is analogous to the derivation of weights for pooling the $ s in the C-R functions. As described above, BenMAP generates a Latin hype rcube representation of the di stribution of incidence change corresponding to each C-R function selected. The means of those study-specific Latin hypercube distributions of incide nce change are used in exactly the same way as the reported $ s are used to calculate fixed effects and random effects weights described above. The variances of incidence change are used in the same way as the variances of the $ s. The formulas above for calculating fi xed effects weights, for testin g the fixed effects hypothesis, and for calculating random effects weights can a ll be used by substituting the mean incidence1 There may be a problem with transferring the fixed effects hypothesis test to “incidence change space.” The test statistic to test the fixed effects model is a chi-squared random variable. In the original paper on this pooling method, DerSimonian and Laird (1986) were discussing the pooling of estimates of parameters, which are generally normally distributed. The incidence changes predicted from a C-R function will not be normally distributed if the C-R function is not a linear function of the pollutant coefficient, which, in most cases it is not. (Most C-R functions are log-linear.) In that case, the test statistic may not be chi-square distributed. However, most log-linear C-R functions are nearly linear because their coefficients are very small. In that case the test statistic is likely to be nearly chi-square distributed. C-10 change for the ith C-R function for $ i and the variance of incidence change for the ith C-R function for v i . 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 La tin hypercube distributions are used in exactly the same way as the reported $ s are used to calculate the fixed effects and random effects weights described above. The variances of dollar benefits are used in the same way as the variances of the $ s. The formulas above for calculating fixe d effects weights, for testing the fixed effects hypothesis, and for calculating random e ffects weights can all be used by substituting the mean dollar benefit change for the ith valuation for $ i and the variance of dollar benefits for the ith valuation for v i . 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 ba sed uncertainty—that is, all uncertainty is at the national level in BenMAP, and all regi onal differences (e.g., populati on) are treated as certain. C.6 Reference DerSimonian, R. and N. Laird 1986. “Met a-analysis in Clinical Trials.” Controlled Clinical Trials 7(3):177-188.1 Hereafter referred to as Class I areas, which are defined as areas of the country such as national parks, national wilderness areas, and national monuments that have been set aside under Section 162(a) of the Clean Air Act to receive the most stringent degree of air quality protection. Class I federal lands fall under the jurisdiction of three federal agencies, the National Park Service, the Fish and Wildlife Service, and the Forest Service.
D-1 APPENDIX D VISIBILITY BENEFITS METHODOLOGY

Visibility degradation estimates used in this analysis are generated by the CMAQ model. To conduct the visibility benefits analysis, however, we need visibility data at the county level. To convert CMAQ visibility data from the square grid to the county level, we use the following rule: if a county center falls within a given CMAQ grid cell, we assign that CMAQ grid cell’s visibility values to that c ounty. Because the m odeled 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), a nd 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., th e 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 st udied (in California, the Southeast, and the Southwest) are included in the primary presen tation of benef its; 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) th at 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 that2 The constant elasticity of substitution utility function has been chosen for use in this analysis because of its flexibility when illustrating the degree of substitutability present in various economic relationships (in this case, the trade-off between income and improvements in visibility).

3 We remind the reader that, although residential and r ecreational visibility benefits estimation is discussed simultaneously in this section, benefits are calculate d and presented separately for each visibility category.
D-2 are “out-of-region.” This is pl ausible, because people are more likely to visit, be familiar with, and care about parks a nd 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 approach 2 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 H outven, and Patta nayak (1999). The presentation of this methodology is orga nized as follows. The basic utility model is presented in Section D.1. In Section D.2 we discuss the measurement of visibility, and the mapping from environmental “bad s” to environmental “goods.” In Sections D.3 and D.4 we summarize the information that is available to estimate the parameters of the model corresponding to visibility at in-region and out-of-region Class I areas, and visibility in residential areas, respectively, and we describe the methods used to estimate these parameters. Section D.5 synthesizes the results. D.1 Basic Utility Model We begin with a CES utility function in which a hous ehold derives utility from (1) “all consumption goods,” X, (2) visibility in the residential area in wh ich the household is located (“residential visibility”), 3 (3) visibility at Class I areas in the same region as the household (“in-region recreational visibility”), and (4) visibility at Class I areas outside the household’s region (“out-of-region recreational visibility”).D-3 

### UXZ Q Q ik jk ni n ik ik k N jk jk k N

j i
ik jk i j 

### = ++ +
> >∀ >∀ ≤ = = ≠ 

# ∑ ∑ ∑ ( ),

, , ,, , ,, . / ρ ρ ρ ρρ 

### θγ δ θγ δ ρ 1 1 1



### 00 0 1

m pX −⋅ ≤ 0 , There are a total of six regions being consid ered, so there are five regions for which any household is out of region. The u tility function of a household in the n thresidential area and the i thregion of the country is:

where
Z n = the level of visibility in the nth residential area; Q ik = the level of visibility at the kthin-region park (i.e., the kth park in the ith region); Q

jk

= the level of visibility at the kthpark in the j thregion ( for which the household is out of region), j ... i;
N i = the number of Class I areas in the i thregion;
N

j = the number of Class I areas in the j thregion (for which the household is out of region), j ... i; and
2 , the ( ’s and * ’s are parameters of th e utility function corresponding to the visibility levels at residential areas and at in-region and out-of-region Class I areas, respectively. In particular, the ( ik ’s are the parameters corresponding to visibility at in-region Class I areas; the * 1 ’s are the parameters corresponding to visibility at Class I areas in region 1 (California), if i ... 1; the * 2 ’s are the parameters corresponding to visibility at Class I areas in region 2 (Colorado Plateau), if i ... 2, and so forth. Because the model assumes that the relationship between residential visibility and utility is the same everywhere, there is only
one 2 . The parameter D in this CES utility function is an important determinant of the slope of the marginal WTP curve associated with any of the environmental quality variables. When D =1, the marginal WTP curve is horizontal. When D <1, it is downward sloping. The household’s budget constraint is:D-4 V mZ Q m Z Q Q

ni n n ik ik k N jk jk k N

j i i j (, ,;,,,) ( ) , / θγδρ θ γ δ ρ ρ ρ ρρ =++ +

= = ≠ 

## ∑ ∑ ∑

1 1 1

WTP Z Q m m Z Z Q Q Q Q ni n n ik ik ik k N jk jk jk k N

j i i j ( , ) [ ( ) ( ) ( )] . / ∆∆ = − + − + − + −

= = ≠ 

## ∑ ∑ ∑ ρ ρρ ρ ρ ρ ρ ρ θγ δ 01 0 1 1 0 1 1 1

WTP Q m m Q Q i ii i ( ) [ ( )] , / ∆ 1 1 01 11 1 =− + − ρ ρ ρρ γ where m is income, and p is the price of X. Without loss of generality, set p = 1. The only choice variable is X. The household maximizes its utility by choosing X=m. The indirect utility function for a household in the n thresidential area and the ith region is therefore

where Q denotes the vector of vectors, Q 1 , Q 2 , Q 3 , Q 4 , Q 5 , and Q 6 , and the unsubscripted ( and * denote vectors as well.
Given estimates of D , 2 , the ( ’s and the * ’s, the household’s utility function and the corresponding WTP functions are fully speci fied. The household’s WTP for any set of changes in the levels of visibility at in-re gion Class I areas, out-of-region Class I areas, and the household’s residential area can be shown to be:

The household’s WTP for a single visibilityimprovement will de pend on its order in the series of visibility improve ments the household is valuing. If it is the first visibility improvement to be va lued, the household’s WTP for it follows directly from the previous equation. For example, the household’s WTP for an improvement in visibility at the first inregion park, from Q i1 = Q 0i1 to Q i1 = Q 1i1 , is

if this is the first (or only) visibility change the household values. D.2 Measure of Visibility: Envi ronmental “Goods” Versus “Bads” In the above model, Q and Z are environm ental “goods.” As the level of visibility increases, utility increases. The utility func tion 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 Q 0 to a higher4 There may be more than one “good” related to a given environmental “bad.” To simplify the discussion, however, we assume only a single “good.” D-5 ∂ ∂

V B < 0 ,

Q FB = ( ),

V V mQ V mF B = = ( , ) ( , ( )) . (improved) level of visibility, Q 1 , is therefore a concave function of Q 1 , 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 environmen tal bads is to consider the “goods” with which they are associated, and to incorpor ate those goods into the utility function. In particular, if B denotes an e nvironmental “bad,” such that:

and the environmental “good,” Q, is a function of B,

then the environmental “bad” can be related to utility via the corresponding environmental “good”: 4

The relationship between Q and B, F(B), is an empirical relationship that must be estimated. There is a potential problem with this appr oach, 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 mo re than one value of the environmental good corresponding to any given value of the envir onmental bad, and it is not clear which value to use. This has been identified as a problem with translating decivi ews (an environmental “bad”) into visual range (an environmental “g ood”). 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 thisQ e PM = −

1 α β

. 5 Another example of an environmental “bad” is par ticulate matter air pollution (PM). The relationship between survival probability (Q) and the ambient PM level is generally taken to be of the form

where " denotes the mortality rate (or level) when there is no ambient PM (i.e., when PM=0). However, " is implicitly a function of all the factors other than PM that affect mortality. As these factors change (e.g., from one location to another), " will change (just as visual range changes as light angle changes). It is therefore possible to have many values of Q corresponding to a given value of PM, as the values of " vary.

6 Ideally, we would want the location-, time-, and meteorological condition-specific relationships between deciviews and visual range, which could be applied as appropriate. This is probably not feasible, however. D-6 DV 10* ln( 391 VR ) , =



### VR 391* e . 0.1DV 

### = − problem is not unique to visibility, but is a general problem when trying to translate environmental “bads” into “goods.” 5
In order to translate decivi ews (a “bad”) into visual range (a “good”), we use a relationship derived by Pitchf ord and Malm (1994) in which

where DV denotes deciview and VR denotes visual range (in kilo meters). Solving for VR as a function of DV yields

This conversion is based on specific assumpti ons characterizing the “average” conditions of those factors, such as light angle, that affect visual range. To th e extent that specific locations depart from the average conditions, the relationship will be an imperfect approximation. 6 D.3 Estimating the Parameters for Vi sibility at Class I Areas: the ( ’s and * ’s As noted in Section 2, if we consider a particular visibility change as the first or the only visibility change valued by the household, the household’s WTP for that change in visibility can be calculated, given income (m), the “shape” parameter, D , and the corresponding recreational visib ility parameter. For example, a Southeast household’s WTP for a change in visibility at in-region parks (collectively) from Q 1 = Q 01 to Q 1 = Q 11 is:D-7 γ ρ ρ

ρ ρ j j in

j j m WTP m Q Q = − −

− ( ) ( ) 0 1 WTP(DQ ) m [m g (Q Q )] 1 r 1 01 r 11 r 1/r =− + −

if this is the first (or only) visibility change the household values. Alternatively, if we have estimates of m as well as WTP 1 in and WTP 1 outof in-region and out-of-region households, respectively, fo r a given change in visibility from Q 01 to Q 11 in Southeast parks, we can solve for ( 1 and * 1 as a function of our estimates of m, WTP 1 in and WTP 1 out , for any given value of D . Generalizing, we can derive the values of ( and * for the j thregion as follows:

and

δ ρ ρ

ρ ρ j j out

j j m WTP m Q Q = − −

− ( ) ( ) . 0 1
Chestnut and Rowe (1990) and Chestnut (1997) estimated WTP (p er 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 regi on 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 imp rovements in the nati onal parks in a given region. Study subjects were shown a map of th e region, with dots indicating the locations of the parks in question. The WTP questions referre d 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 we re categorized according to whether the respondents lived in the same region as the parks in question (“in-regi on” respondents) or in a different region (“out-of-regi on” respondents). The areas for which in-region and out-ofregion WTP estimates are available from Ches tnut and Rowe (1990), and the sources of benefits transfer-based estimates that we employ in the absence of estimates, are summarized in Table D-1. In all cases, WT P refers to WTP per household.D-8 Table D-1. Available Information on WT P for Visibility Improvements in National Parks

Region of Park Region of Household In Region a Out of Region b 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 (fo r 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 hous ehold’s utility functi on will depend on where the household is located. The re gion-specific parameters associat ed 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 Regi on-Specific Recreati onal Visibility Parameters to be Estimated in Household Utility FunctionsD-9 Region of Household Region of Park Region 1 Region 2 Region 3 Region 4 Region 5 Region 6 Region 1 ( 1 a * 2 * 3 * 4 * 5 * 6 Region 2 * 1 ( 2 * 3 * 4 * 5 * 6 Region 3 * 1 * 2 ( 3 * 4 * 5 * 6 Region 4 * 1 * 2 * 3 ( 4 * 5 * 6 Region 5 * 1 * 2 * 3 * 4 ( 5 * 6 Region 6 * 1 * 2 * 3 * 4 * 5 ( 6

a The parameters arrayed in this table are region specific rather than park specific or wilderness area specific.

For example, * 1 is the parameter associated with visibility at “ Class I areas in region 1” for a household in any region other than region 1. The benefits analysis must derive Class I area-specific parameters (e.g., * 1k , for the kth Class I area in the first region).
For the three regions covered in Chestnut and Rowe (1990) (Calif ornia, 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 func tions corresponding to visibility at in-region parks ( ( 1 ); similarly, we can directly us e the out-of-region WTP estimates from the study to estimate the para meters for out-of-region parks ( * 1 ). For the other three regions not covered in the study, however , we must rely on benefits tr ansfer 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 res ponses 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 incl uded. 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 functionD-10 accurately describes household pr eferences, 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 th e same value of the associated recreational visibility parameter, given a value of D and an income, m. In prac tice, of course, this is not the case. In addressing these issues, we take a three-phase approach: (1) We estimate region-specific parameters for the region in the modeled domain covered by Chestnut and Rowe (1990) (California, the Colorado Plateau, and the Southeast)— ( 1 , ( 2 , and ( 3 and * 1 , * 2, and * 3 . (2) We infer region-specific parameters fo r those regions not covered by the Chestnut and Rowe study (the Northwes t United States, the Northern Rockies, and the rest of the United States)— ( 4 , ( 5 , and ( 6 and * 4 , * 5 , and * 6. (3) We derive park- and wilderness area-specific parameters within each region ( ( 1k and * 1k , for k=1, ..., N 1 ; ( 2k and * 2k , for k=1, ..., N 2 ; 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 sing le region-specific out-of-region parameter for each of the three regions covered in Ches tnut 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 th ird phase, we derive park- and wilderness areaspecific parameters for each region. D.3.1 Estimating Region-Speci fic Recreational Visibility Parameters for the Region Covered in the Chestnut and Ro we Study (Regions 1, 2, and 3)
Given a value of D and estimates of m and in-regi on and out-of-region WTPs for a change from Q 0 to Q 1 in a given region, the in-region parameter, ( , and the out-of-region parameter, * , for that region can be solved for. Chestnut and Rowe (1990), however, considered not just one, but three visibility ch anges in each region, each of which results in a different calibrated ( and a different calibrated * , even though in theory all the ( ’s should beD-11 

### ( ( $ ) )

WTP WTP ij j ij i 

### γ

−

= 

# ∑ 2

1 3 the same and similarly, all the * ’s should be the same. For each region, however, we must have only a single ( and a single *

. Denoting as our estimate of ( for the j thregion, based on all three visibility $ γ j changes, we chose to best predict the thr ee WTPs observed in the study for the three $ γ j visibility improvements in the j thregion. First, we calculated , i=1, 2, 3, corresponding to $ γ ji each of the three visibility improvements consider ed in the study. Then, using a grid search method beginning at the average of the thre e ’s, we chose to minimize the sum of the $ γ ji $ γ j squared differences between the WTPs we predict using and the three region-specific $ γ j WTPs observed in the study. That is, we selected to minimize: $ γ j

where WTP ij and WTP ij ( ) are the observed and the predicted WTPs for a change in $ γ j visibility in the j thregion from Q 0 = Q 0i to Q 1 = Q 1i , i=1, ..., 3. An analogous procedure was used to select an optimal * , for each of the three regions in the Chestnut and Rowe study. D.3.2 Inferring Region-Speci fic Recreational Visibility Parameters for Regions Not Covered in the Chestnut and Ro we Study (Regions 4, 5, and 6) One possible approach to estimating region-specific parameters for regions not covered by Chestnut and Rowe (1990) ( ( 4 , ( 5 , and ( 6 and * 4 , * 5 , and * 6 ) is to simply assume that households’ utility functions are the same everywhere, and that the environmental goods being valued are the same—e.g., that a change in visibility at national parks in California is the same environmental good to a Californian as a change in visibility at national parks in Minnesota is to a Minnesotan. For example, to estimate * 4 in the utility function of a California household, corresponding to visibility at national parks inthe Northwest United States, we might assume that out-of-region WTP for a given visibility ch ange 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 exampl e, that we have an estimated mean WTP of out-of-region households for a visibility change from Q 01 to Q 11 at national7 We acknowledge that reliance on visitation rates does not get at nonuse value.
D-12 δ ρ ρ
ρ ρ 4 4
04 14 = − −

− ( )

m WTP m Q Q out parks in California (region 1), denoted WTP 1 out . Suppose the mean income of the out-ofregion subjects in the study was m. We might assume that, for the same change in visibility at national parks in the Northwest United States, WTP 4 out= WTP 1 outamong out-of-region individuals with income m.
We could then derive the value of * 4 , given a value of D as follows:

where Q 04 = Q 01 and Q 14 = Q 11 , (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) a ll households have the same preference structures and (2) what is being valued in the Northwest United States (by a California household) is the same as what is being valued in the California (by all out-ofregion households). While we cannot know th e extent to which the first assumption approximates reality , the second assumption is clearly problematic. Nati onal 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 w ould 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 twi ce as many visitor-days are spent in California parks per year as in parks in the No rthwest 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 ar e more of them—i.e., that the environmental goods being valued in the two regions (“visi bility at national park s”) are not the same. A preferable way to estimate * 4 , then, might be to assume the following relationship:D-13 WTP WTP n
n out

out 4

1 4

1 =



### WTP n
n WTP out out 4 4

1 1 

### = *

WTP n
n WTP in in 4 4

1 1 = * . (income held constant), where n 1 = the average annual number of visitor-days to California parks and n 4 = the average annual number of visitor-d ays to parks in the Northwest United States. This implies that

for the same change in visibility in regi on 4 parks among out-of-region individuals with income m. If, for example, n 1 = 2n 4 , WTP 4 outwould be half of WTP 1 out . The interpretation would be the following: Califor nia 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 be ing valued (by people out of region) are not the same in all regions. The parameter * 4 is estimated as shown above, using this adjusted WTP 4 out . The same procedure is used to estimate * 5 and * 6. We estimate ( 4, ( 5 , and ( 6 in an analogous way, using the in-region WTP estimates from the transfer regions, e.g.,

D.3.3 Estimating Park- and Wilderness Area-Specific Parameters As noted above, Chestnut and Rowe ( 1990) estimated WTP for a region’s national parks collectively, rather than providing park-specific WTP estimates. The ( ’s and * ’s are therefore the parameters that would be in household utility f unctions if there were only aD-14 single park in each region, or if the many park s in a region were effec tively indistinguishable from one another. Also noted above is the f act that the Chestnut and Rowe study did not include all Class I areas in the regions it cove red, focusing primarily on national parks rather than wilderness areas. Most Class I wilderness areas were not re presented on the maps shown to study subjects. In California, for example, there are 31 Class I areas, including 6 national parks and 25 w ilderness areas. The Chestnut a nd 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 w ilderness 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 de rivation of park- and wilderness area-specific parameters depends on this. D.3.4 Derivation of Region-Specific WTP for National Parks and Wilderness Areas If study subjects were lumping all Class I areas together in their minds when giving their WTP responses, then it would be reasona ble to allocate that WTP among the specific parks and wilderness areas in the region to derive park- and wilderness area-specific ( ’s and * ’s for the region. If, on the other hand, study subjects were thinking only of the (mostly) parks shown on the map when they gave thei r WTP response, then there are two possible approaches that could be take n. One approach assumes thathouseholds would be willing to pay some additional amount for the same visibility improvementin additional Class I areas that were not shown, and that this add itional 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 w illing 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 us ing benefits transfer methods. A third possibility, then, is to s imply omit wilderness ar eas from the benefits analysis. For this analysis we calculate visibility benefits assuming that study subjects lumped all Class I areas together when stating their WTP, even if these Class I areas were not present on the map.8 This might be thought of as two assumptions: (1) that the relative frequencies of visits to the parks in a region from everyone in the world is a reasonable representation of the relative frequency of visits from people in the United States —i.e., that the parks that are most popular (receive the most visitors per year) in general are also the most popular among Americans; and (2) that the relative frequency with which Americans visit each D-15 WTP n
n WTP k in k in 1 1

1 1 = * ,

WTP n
n *WTP . 1k out 1k

1 1out = D.3.5 Derivation of Park- and Wilderness Area-Specific WTPs, Gi ven Region -Specific WTPs for National Parks and Wilderness Areas The first step in deriving park- and w ilderness area-specific parameters is theestimation of park- and wilderness area-specific WTPs. To derive park and wilderness areaspecific WTPs, we apportion the region-specific WTP to the sp ecific Class I areas in the region according to each area’s share of the region’s visitor-days. For example, if WTP 1 in and WTP 1 outdenote the mean household WTPs in the Chestnut and Rowe (1990) study among respondents who were in-region- 1 and out-of-region-1, respectively, n 1k denotes the annual average number of visitor-days to th e kth Class I area in California, and n 1 denotes the annual average number of visitor-days to all Cl ass I areas in Californi a (that are included in the benefits analysis), then we assume that

and

Using WTP j in and WTP j out , either from the Chestnut and Rowe study (for j = 1, 2, and 3) or derived by the benefits tran sfer 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 sc heme, it is a reasonabl e 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 a llocation 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 (WTP j in ) or out of that region (but in the United States) (WTP j out ) for visibility improvements at these parks. 8of their parks is a good index of their relative WTPs for visibility improvements at these parks. D-16 A possible problem with this allocation sche me is that the relative frequency of visits is an indicator of use value but not necessarily of nonuse valu e, 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 ma ny 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 allo cation scheme based on relative visitation freque ncies 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 Paramete rs, Given Park- and Wilderness Area-Specific WTPsOnce the Class I area-specific WTPs have been estimated, we could derive the parkand wilderness area-specific ( ’s and * ’s using the method used to derive region-specific ( ’s and * ’s. Recall that method involved (1) calibrating ( and * to each of the three visibility improvements in the Chestnut a nd Rowe study (producing three ( ’s and three * ’s), (2) averaging the three ( ’s and averaging the three * ’s, and finally, (3) using these average ( and * as starting points for a grid search to find the optimal ( and the optimal * —i.e., the ( and * that would allow us to reproduce, as clos ely as possible, the three in-region and three out-of-region WTPs in the study for the three visibility changes being valued. Going through this procedure for each national park and each wilderness area separately would be very time consuming, however. We therefore used a simpler approach, which produces very close approximations to the ( ’s and * ’s produced using the above approach. If:
WTP j in = the in-region WTP for the change in visibility from Q 0 to Q 1 in the j th region;
WTP jk in = the in-region WTP for the same visibility change (from Q 0 to Q 1 ) in the kthClass I area in the jth region (= s jk *WTP j in , where sjk is the ktharea’s share of visitor-days in the j thregion); m = income;

( j * = the optimal value of ( for the jth region; and9 ( j * is only approximately equal to the right-hand side because, although it is the optimal value designed to reproduce as closely as possible all three of the WTPs corresponding to the three visibility changes in the Chestnut and Rowe study, ( j * will not exactly reproduce any of these WTPs.

10 This method uses a single in-region WTP and a single out-of-region WTP per region. Although the choice of WTP will affect the resulting adjustment factors (the a jk ’s) and therefore the resulting ( jk ’s and * jk ’s, the effect is negligible. We confirmed this by usi ng each of the three in-region WTPs in California and comparing the resulting three sets of ( jk ’s and * jk ’s, which were different from each other by about one onehundredth of a percent.
D-17 γ ρ ρ

ρ ρ j j in m WTP m Q Q * ( ) ( ) ≈ − −

− 0 1

γ ρ ρ

ρ ρ jk jk in m WTP m Q Q = − −

− ( ) ( ) 0 1

γ γ

jk jk j a ≈ * , *

a m WTP m m WTP m jk jk in

j in = − −

− − ( ) ( ) . ρ ρ

ρ ρ ( jk = the value of ( jk calibrated to WTP jk in and the change from Q 0 to Q 1 ; then 9 :

and

which implies that:

where:

We use the adjustment factor, a jk , to derive ( jk from ( j *, for the k thClass I area in the j th region. We use an analogous procedure to derive *jk from *j * for the k thClass I area in the j th region (where, in this case, we use WTP j outand WTP jk outinstead of WTP j in and WTP jk in ). 10D-18 θ ρ ρ
ρ ρ = − −

− ( ) ( ) . m WTP m Z Z

0 1

WTP Z Q m m Z Z Q Q Q Q ni n n ik ik ik k N jk jk jk k N

j i i j ( , ) [ ( ) ( ) ( )] . / ∆∆ = − + − + − + −

= = ≠ 

## ∑ ∑ ∑ ρ ρρ ρ ρ ρ ρ ρ θγ δ 01 0 1 1 0 1 1 1 D.4 Estimating the Parameter for Vi sibility in Residential Areas: 2
The estimate of 2 is based on McClelland et al. (1991), in which household WTP for improvements in residen tial visibility was elic ited from respondents in Chicago and Atlanta. A notable difference between the Chestnut and Rowe study and the McClelland study is that, while the former elicited WTP responses for three different visibility changes, the latter considered only one visibility change. The estimation of 2 was therefore a much simpler procedure, involving a straightforward calibra tion to the single income and WTP in the study:

D.5 Putting it All Together: The Household Utility and WTP Functions
Given an estimate of 2 , derived as shown in Sec tion D.4, and estimates of the ( ’s and * ’s, derived as shown in Section D.3, ba sed on an assumed or estimated value of D , the utility and WTP functions for a household in any regi on are fully specified. We can therefore estimate the value to that household of visi bility 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 re sidential area for any set of changes in the levels of visibility at in-region Class I areas , out-of-region Class I areas, and the household’s residential area (given by equation (24)) is:

The national benefits associated with any suite of visibility changes is properly calculated as the sum of these household WTPs fo r 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 ot her components of the WTP f unction 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 compone nts in this way will yield slightly upward biased estimates of bene fits, because disposable income, m, is not being reduced by the WTPs for any prior visi bility improvements. That is, each visibilityD-19 improvement (e.g., visibility at Class I areas in the California) is assumed to be the first, and they cannot all be the first. The upward bi as 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 Me morandum: Methodology for Estimating Values for Changes in Visibility at National Parks. Chestnut, L.G., and R.D. Rowe. 1990. “A Ne w National Park Visib ility Value Estimates.” In Visibility and Fine Particles, Trans actions of an AWMA/EPA International Specialty Conference , C.V. Mathai, ed. Air and Wa ste 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. Environmen tal Protection Agency, Office of Policy, Planning and Evaluation. June. Pitchford, M.L., and W.C. Malm. 1994. “Dev elopment 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 Univer sity, 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, a nd S. Pattanayak. 1999. Benef its Transfer as Preference Calibration. Resources for the Fu ture Working Paper (Unnumbered). U.S. Environmental Protection Ag ency (EPA). September 2000. Guidelines for Preparing Economic Analyses . EPA 240-R-00-003.1 The modeling for the rule includes annual SO 2 and NO xcontrols for Arkansas and results in a minimal overstatement of the benefits and costs of the CAIR program (CAIR final plus the New Jersey and Delaware proposal).

2 Note that the net benefits reported in this appendix are estimated using the private costs of the respective rules rather than social costs. Thus, the net benefits shown for the CAIR program in this appendix differ somewhat from the estimates presented in the body of this report.
E-1 APPENDIX E BENEFITS AND COSTS OF THE CLEAN AIR INTERSTATE RULE, THE CLEAN AIR VISIBILITY RULE, AND THE CLEAN AIR INTERSTATE RULE PLUS THE CLEAN AIR VISIBILITY RULE

This appendix presents the benefits and co sts for the CAIR program (CAIR final rule plus the New Jersey and Delaware proposal), 1 the EGU requirements for Best Available Retrofit Technology (BART) Guidelines for th e 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 importa nt to note that the CAIR, CAIR Plus BART in the Non-CAIR Region, and BART Nationwide be nefit and costs estimates reflect controls for the EGU source category only, while the BART regulation will potentially affect 26 source categories. The analysis presented in this appendix was conducted to show the benefits and costs of th e alternative programs a ddressing the EGU sector. 2 A comparison of the BART nationwide scenario with the CAIR plus BART in the Non-CAIR Region scenario provides some information on the possible bene fits and costs for BART for EGU sources in the Non-CAIR Region. The control strategy assumptions for the BART nationwide and BART portion of the CAIR Plus BART in the Non-CAIR Region scenarios differ from the BART scenarios (Scenario 1, Scenario 2, and Scenario 3) analyzed in this RIA. Because of the differing control strategy modeling for the EGU source categ ory, the benefits and costs reported in this appendix for EGU sources will differ from these scenarios. The analysis conducted for this appendix assumes that all units greater than 100 MW that do not currently have scrubbers are required to reduce emissions from uncontrolled levels by 90 percent or meet a 0.1 lb/mmbtu SO 2 emission rate limit. It also assumes that all BART units greater than 25 MW areE-2 required to meet an emission rate limit of 0.2 lbs/mmbtu. Th is analysis was conducted to support the “Better than BART” analysis c onducted for the CAIR final rule and was a conservative (i.e., control on most units) look at controls that states might choose to require on sources not subject to presumptive BART. As Table E-1 shows, annua l net benefits for the CA IR program are $97.4 billion in 2015. This estimate compares to annual net benef its 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 Regi on, and $37.0 billion for the BART nationwide program assuming a 7 percent di scount rate. The analysis s hows that if one assumes the CAIR program exists, the incremental benefits of requiring BART controls for the EGU source category only in areas outside the CA IR region are approximately $3.2 to $4 billion (7 percent and 3 percent disc ount rate, respectively). Re lated 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 nati onwide. 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 repr esented by the letter B.E-3 Table E-1. Summary of Annual Benefits, Costs, and Net Benefits of the Clean Air Interstate Rule, 2015 (billions of 1999 dollars) a

Description CAIR Program g CAIR Plus BART in the Non-CAIR Region g BART Nationwide Private costs b $3.57 $4.55 $5.19
Social benefits c,d,e 3 percent discount rate 7 percent discount rate $101 + B $86.3 + B $105 + B $89.5 + B $49.5+ B $42.2 + B Health-related benefits: 3 percent discount rate 7 percent discount rate 99.3 84.5 103 87.6 48.8 41.5 Visibility benefits 1.78 1.89 0.699 Net benefits (benefits-costs) e,f 3 percent discount rate 7 percent discount rate $97.4 + B $82.7 + B $100 + B $84.9+ B $44.3 + B $37.0 + B

a All estimates are rounded to three significant digits for ease of presentation and computation. These annual

estimates represent the benefits and costs of these regulatory programs expected to occur in 2015. BART estimates reflect benefits and costs for controls fo r 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 NO xand SO 2 . 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 natio nal 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 bene fits in 2015. Bene fits in this table are nationwide (with the exception of ozone and visibility) and are associated with NO xand SO 2 reductions. Ozone benefits relate to the eastern United States. Visibility benefits re late 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 an d 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-recomme nded 20-year segmented lag structure described in

Chapter 4. Results reflect 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses (EPA, 2000; OMB, 2003). f Net benefits are rounded to the nearest $100 million. Columnar totals may not sum due to rounding. g CAIR costs and benefits are the estimates for the CAIR program that includes the promulgated CAIR and the

proposal to include a nnual SO 2 and NO xcontrols for New Jersey and Delaware. Modeling for CAIR assumes annual SO 2 and NO xcontrols for Arkansas that is not a part of the CAIR program. Thus, the benefits and costs reported are slightly overstated.E-4 Table E-2. Clean Air Interstate Rule: Estimated Reduction in Health Effects (Incidence)—2015

Health Effect CAIR Programf CAIR Plus BART in the Non-CAIR

Region f BARTNationwide PM-Related Endpoints: Premature mortality b

Adult, age 30 and over Infant, age <1 year 17,000 36 17,000 38 8,200 19 Chronic bronchitis (adult, age 26 and over) 8,700 9,100 4,400 Nonfatal myocardial infarction (adults, age 18 and older) 22,000 23,000 11,000 Hospital admissions—respiratory (all ages) c 5,500 5,700 2,700 Hospital admissions—cardiovascular (adults, age >18) d 5,000 5,200 2,500 Emergency room visits for asthma (age 18 years and younger) 13,000 13,000 6,500 Acute bronchitis (children, aged 8–12) 19,000 20,000 10,000 Lower respiratory symptoms (children, aged 7–14) 230,000 240,000 120,000 Upper respiratory symptoms (asthmatic children, aged 9–18) 180,000 190,000 92,000 Asthma exacerbation (asthmatic children, aged 6–18) 290,000 310,000 150,000 Work loss days (adults, aged 18–65) 1,700,000 1,700,000 830,000 Minor restricted-activity days (adults, aged 18–65) 9,900,000 10,300,000 5,000,000 Ozone-Related Endpoints e Hospital admissions—respiratory causes (adult, 65 and older) 1,700 NE NE Hospital admissions—respiratory causes (children, under 2) 1,100 NE NE Emergency room visit for asthma (all ages) 280 NE NE Minor restricted-activity days (adults, aged 18–65) 690,000 NE NE School absence days 510,000 NE NE

a Incidences are rounded to two significant digits.

. BART estimates reflect incidences for controls for the EGU source category only and a conservative (i.e, controls on mo st 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 tota l cardiovascular and subcategories for ischemic heart

disease, dysrhythmias, and heart failure.

e Although ozone benefits are expected to occur for CAIR Plus BART in the Non-CAIR Region and BART

nationwide, ozone benefits are estimated for the CAIR program only. f These health effects incidences reflect estimates for the CAIR program (the promulgated CAIR and the

proposal to include a nnual SO 2 and NO xcontrols for New Jersey and Delaware in CAIR). Modeling for CAIR assumes annual SO 2 and NO xcontrols for Arkansas that is not a part of the CAIR program. Thus, the incidence estimates reported for CAIR are slightly overstated. NE = Not estimatedE-5 Table E-3. Estimated Monetary Value in Reductions in Incide nce of Health and Welfare Effects (in millions of 1999$)—2015 a,b,c

Health Effect Pollutant CAIR Programf CAIR Plus BART in the Non-CAIR Region f BART Nationwide Premature mortality d Adult >30 years 3% discount rate 7% discount rate Child <1 year PM 2.5 $92,800 78,100 222 $96,300 81,100 232 $45,700 38,400 116 Chronic bronchitis (adults, 26 and over) PM 2.5 3,340 3,510 1,690 Nonfatal acute myocardial infarctions 3% discount rate 7% discount rate PM 2.5 1,850 1,790 1,920 1,860 905 876 Hospital admissions for respiratory causes PM 2.5 , O 3 78.9 43.6 20.6 Hospital admissions for cardiovascular causes PM 2.5 105 82.6 39.3 Emergency room visits for asthma PM 2.5 , O 3 3.56 3.62 1.79 Acute bronchitis (children, aged 8–12) PM 2.5 7.06 7.41 3.68 Lower respiratory symptoms (children, 7–14) PM 2.5 3.74 3.87 1.91 Upper respiratory symptoms (asthma, 9–11) PM 2.5 4.77 4.69 2.30 Asthma exacerbations PM 2.5 12.7 13.4 6.56 Work loss days PM 2.5 219 209 101 Minor restricted-activity days (MRADs) PM 2.5 , O 3 543 528 256 School absence days O 3 36.4 NE NE Worker productivity (outdoor workers, 18–65) O 3 19.9 NE NE Recreational visibility, 81 Class I areas PM 2.5 1,780 1,890 699 Monetized Total e Base estimate: 3% discount rate 7% discount rate $101+B $86.3+B $105+B $89.5+B $49.5+B $42.2+B

a Monetary benefits are rounded to three significant digits for ease of presentation and computation. Benefit estimates

relate to emissions reductions for the EGU source category only. Estimates represent nationwide benefits (with the exception of ozone and visibility) and are associated with NO x and SO 2 reductions. Ozone benefits represent benefits for the eastern United States. Visibility estimates relate to Cla ss I areas in the southeastern United States. BART estimates reflect benefits for controls for the EGU source category only and a conservative (i.e, controls on most units) look at controls that states might choose to require on sources not subject to presumptive BART. For these reasons, the benefits and costs of BART in this appendix differ from Scenarios 1, 2, and 3 in this RIA. b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and 2015.

c Ozone benefits are estimated for the final CAIR. While ozone benefits are anticipated for CAIR plus BART in the NonCAIR Region and BART nationwide, these ozone benefits were not estimated. d Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure described earlier. Results show 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses (EPA, 2000; OMB, 2003). Adult premature mortality estimates are based on studies by Pope et al. (2002). Infant premature mortality estimates are based upon studies by Woodruff, Gr illo, 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 SO 2 and NO x controls for New Jersey and Delaware in CAIR). Modeling for CAIR assumes annual SO 2 and NO x controls for Arkansas that is not a part of the CAIR program. Thus, the benefit estimates reported for CAIR are slightly overstated. NE = Not estimatedE-6 E.1 References Pope, C.A., III, R.T. Burnett, M.J. T hun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mort ality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141. U.S. Environmental Protection Ag ency (EPA). September 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. U.S. Office of Management and Budget (OMB ). 2003. Circular A-4 Guidance to Federal Agencies on Preparation of Regulatory Analysis. Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health Perspectives 105(6):608-612.F-1 APPENDIX F SENSITIVITY ANALYSES OF SOME KEY PARAMETERS IN THE BENEFITS ANALYSIS

The primary analysis of benefits of the Clean Air Visibility Rule (CAVR) presented in Chapter 4 is based on our current interpretation of the scientific and economic literature. That interpretation requires j udgments 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 th e 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 es timates 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 sensitiv ity 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 significan t impact on total benefits. The individual adjustments in the tables should not simply be added together because (1) there may be overlap among the alternative assumptions and (2) the joint probability among certain sets of alternative assumptions may be low. F.1 Premature Mortality—Long-Term Exposure Reduction in the risk of premature mortality is the most important PM-related health outcome in terms of contribution to dollar benefits in the analysis for this rule. There are at least three important analytical assumptions that may significantly impact the estimates of the number and valuation of avoided premature mortalities. These include selection of the C-R function, structure of the lag between reduced exposure a nd reduced mortality risk, and effect thresholds. Results of this set of sensitivity analyses are presented in Table F-1.F-2Table F-1. Sensitivity of Benefits ofPremature Mortality Reductions to Alternative Assumptions (Relative to PrimaryEstimate Benefits of the Final CAVR)Description of Sensitivity AnalysisAvoided Incidences in 2015aValue (million 1999$)bScenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3Alternative Concentration-Response Functions for PM-Related Premature MortalityPope/ACS Study (2002)cLung Cancer 310 1,200 1,700 $1,800 $7,000 $9,900Cardiopulmonary 65 250 360 $370 $1,500 $2,000Krewski/Harvard Six-Cities Study 930 3,700 5,200 $5,300 $21,000 $30,000Alternative Lag Structures for PM-Related Premature MortalityNone Incidences all occur in the first year 400 1,600 2,300 $2,500 $10,000 $15,0008-year Incidences all occur in the 8thyear3% discount rate 400 1,600 2,300 $2,100 $8,300 $12,0007% discount rate 400 1,600 2,300 $1,600 $6,300 $9,10015-year Incidences all occur in the 15thyear3% discount rate 400 1,600 2,300 $1,700 $6,700 $9,7007% discount rate 400 1,600 2,300 $1,000 $3,900 $5,700AlternativeSegmented20 percent of incidences occur in 1styear, 50percent in years 2 to 5, and 30 percent in years 6 to203% discount rate 400 1,600 2,300 $2,200 $8,900 $13,0007% discount rate 400 1,600 2,300 $1,800 $7,100 $10,000(continued)F-3Table F-1. Sensitivity of Benefits ofPremature Mortality Reductions to Alternative Assumptions (Relative to PrimaryEstimate Benefits of the Final CAVR) (continued)Description of Sensitivity AnalysisAvoided Incidences in 2015aValue (million 1999$)bScenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 35-YearDistributed50 percent of incidences occur in years 1 and 2and 50 percent in years 2 to 53% discount rate 400 1,600 2,300 $2,400 $9,700 $14,0007% discount rate 400 1,600 2,300 $2,300 $9,100 $13,000Exponential Incidences occur at an exponentially declining ratefollowing year of change in exposure3% discount rate 400 1,600 2,300 $2,400 $9,700 $14,0007% discount rate 400 1,600 2,300 $2,200 $8,900 $13,000Alternative ThresholdsNo Threshold (base estimate) 400 1,600 2,300 $2,300 $9,200 $13,0005:g/m3400 1,600 2,200 $2,300 $9,200 $13,00010:g/m3230 1,100 1,500 $1,300 $6,300 $8,60015:g/m30 130 170 $0 $750 $98020:g/m30 54 70 $0 $310 $400aIncidences rounded to two significant digits.bDollar values rounded to two significant digits.cNote that the sum of lung cancer and cardiopulmonary deaths will not be equal to the total all-cause death estimate. Some residual mortality is associatedwith long-term exposures to PM2.5that is not captured by the cardiopulmonary and lung cancer categories.F-4 F.1.1 Alternative C-R Functions Following the advice of the most recent EPA SAB-HES, we used the Pope et al. (2002) all-cause mortality mode l to derive our primary es timate of avoi ded 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 rela ted causes of death” (EPA-SAB-COUNCIL-ADV04-002, 2004, p. 18). As such, in Table F-1 we provide the estimate s of cardiopulmonary and lung cancer deaths based on the Pope et al. (2002) study. In addition, the SAB-HES noted that the AC S cohort used in Pope et al. (2002) “has some inherent deficiencies , in particular the impreci se 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 aprudent choice for the primary estimate. They go on to note that “the Harvard Six-Cities CR functions are valid estimates on a more representative, although geographically selected, population, and its updated analysis has not ye t 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 functi ons, benefits may be considerab ly 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 othe r studies” (EPA-SAB-COUNCIL-ADV-99-012, 1999). The demographics of the ACS study populati on (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 ol der in the ACS study). We emphasize that, based on our understanding of the relative merits of the two da tasets, the Pope et al. (2002) ACS model based on mean PM 2.5 levels in 63 cities is the most appropriate model for analyzing the premature mortality impacts of CAVR. Thus it is used for our primary estimate of this important health effect.F-5 F.1.2 Alternative Lag Structures Over the last ten years, there has been a continuing discussion and evolving advice regarding the timing of changes in health eff ects following changes in ambient air pollution. It has been hypothesized that some reductions in premature mortality from exposure to ambient PM 2.5 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 devel oped 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, th ey 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 hea lth benefits of improved air quality. Thus some time lag is appropriate for distributing the cumulative mortality effect of PM in the population” (EPA-SAB-COUNCIL-ADV-00-001, 1999, p. 9). In recent advice, the SABHES suggests that appropriate lag structures may be devel oped 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 l onger 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 cause s 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. So me respiratory diseases may have a long period of progre ssion, while others, such as pne umonia, have a very short duration. In the case of cardiovasc ular disease, there is an important question of whether air pollution is causing the disease, which woul d 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-HE S provides several recommendations for future research that coul d support the developmen t of defensible lag structures, including using disease-specific lag models and constructing a segmented lag distribution to combine differential lags across causes of death (EPA-SAB-COUNCIL-ADV04-002, 2004). The SAB-HES indicated support for using “a Weibull distribution or a1 Although these studies were conducted for 8 and 15 years, respectively, the choice of the duration of the study by the authors was not likely due to observations of a lag in effects but is more likely due to the expense of conducting long-term exposure studies or the amount of satisfactory data that could be collected during this time period. F-6 simpler distributional form ma de up of several segments to cover the response mechanisms outlined above, given our lack of knowledge on th e specific form of th e distributions” (EPASAB-COUNCIL-ADV-04-002, 2004, p. 24). Howeve r, they noted that “an important question to be resolved is what the relative magnitudes of these segments should be, and how many of the acute effects are assumed to be included in the cohort effect estimate” (EPASAB-COUNCIL-ADV-04-002, 2004, p. 24-25). Sin ce the publication of that report in March 2004, EPA has sought add itional clarification from this committee. In its followup advice provided in December 2004, this SAB sugge sted that until additional research has been completed, EPA should assume a segmente d 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 PM 2.5 , and 20 percent occurring evenly over the years 6 to 20 after the reduction in PM 2.5 (EPA-COUNCIL-LTR-05-001, 2004). Th e distribution of deaths over the latency period is intended to reflect the contribution of shor t-term exposures in the first year, cardiopulmonary deaths in the 2- to 5-y ear period, and long-term lung disease and lung cancer in the 6- to 20-year period. Furtherm ore, 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, theyrecommended an analysis of 0-, 8-, and 15-year lags, as well as variations on the proportions of mortality allocated to each segment in the segmented lag structure (EPA -SAB-COUNCIL-ADV-00-001, 1999; EPA-COUNCILLTR-05-001, 2004). The 0-year lag is representative of EPA’s assumption in previous RIAs. The 8- and 15-year lags are based on the study periods from the Pope et al. (1995) and Dockery et al. (1993) studies, respectively. 1 However, neither the Pope et al. nor Dockery et al. studies assumed any lag structure when estimating the relative risks from PM exposure. In fact, the Pope et al. and Do ckery et al. analyses do not s upporting or refute the existence of a lag. Therefore, any lag structure applied to the avoided incidences estimated from either of these studies will be an assumed structure. The 8- and 15-year lags implicitly assume that all premature mortalities occur at the end of the study periods (i.e., at 8 and 15 years).F-7 In addition to the simple 8- and 15-year lags, we have a dded 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 PM 2.5 , and 30 percent occurring evenly over the years 6 to 20 after the reduction in PM 2.5 . The second sensitivity analysis assumes the 5-year distributed lag structure usedin 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 rela tionship between reduction in exposure and reduction in mortality risk. This structure is based on an analysis by Röösli et al. (2004), which estimates the percenta ge of total mortality impact in each period t as

. (F.1) 

### ( ) 

# [ ] ( ) 

# [ ] % .

. 

### Mortality Reduction (t) = − +−

− +− −

−

= ∞ 

# ∑ RR e
RR e t

t

t 

### 1 11
1 11 0 5

0 5
1

The Röösli et al. (2004) analysis derives the la g 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 obser ved 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 adopti on in the primary analysis. The estimated impacts of alternative lag structures on the monetary benefits associated with reductions in PM-related premature mortality (estimated with the Pope et al. ACS impact function) are presented in Table F-1. These estimates are based on the value of statistical lives saved appro ach (i.e., $5.5 million per incidence) and are presented for both a 3 and 7 percent discount rate over the lag period.2 The advice from the 2004 SAB-HES (EPA-SAB-COUNCIL-ADV-04-002) is characterized by the following: “For the studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted the most careful work on this issue. They report that the associations between PM 2.5 and both all-cause and cardiopulmonary mortality were near linear within the relevant ranges, with no apparent threshold. Graphical analyses of these studies (Dockery et al., 1993, Figure 3, and Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels. Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, the low end of the concentrations reported in the studies.”

3 The illustrative mortality results based on the pilot expert elicitation, described in Chapter 4 and more completely in Appendix B of the CAIR RIA presents the potential implications of assuming some probability of a threshold on the benefits estimate. F-8 F.1.3 Thresholds Although the consistent advice from EPA’s SAB 2 has been to model premature mortality associated with PM exposure as a nont hreshold effect, that is, with harmful effects to exposed populations regardless of the abso lute level of ambien t PM concentrations. EPA’s most recent PM 2.5 Criteria Document concludes that“the available evidence does not either support or refute the exis tence of thresholds for the e ffects 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 rela tionship. The nature of the hypothesized relationship is that there might exist a PM c oncentration 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 PM 2.5 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 alterna tive assumptions for a PM mortality threshold. If, for example, there were no benefits of reducing PM concentrations below the PM 2.5 standard of 15 : g/m 3 , 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 approximate ly 700 annually. The recent NRC report stated that “for pollutants such as PM 10 and PM 2.5 , there is no evidence for any departure of linearity in the observed range of exposure, nor any indication of a threshold” (NRC, 2002, p. 109). At a threshold of 10 : g, approximately the 20th percentile of observed concentrations in the Pope et al. (2002) st udy, mortality impacts would be reduced by only 16 percent to approximately 14,000 annually. Another possible sensitivity analysis that we4 The pilot expert elicitation discussed in Appendix B of the CAIR RIA provides some information on the impact of applying nonlinear and threshold-based C-R functions. F-9 have not conducted at this time might examine the potential for a nonlinear relationship at lower exposure levels. 4 One important assumption that we adopted for the threshold sensitivity analysis is that no adjustments are made to the shape of the C-R function above the assumed threshold. Instead, thresholds were applied by simply assuming that any changes in ambient concentrations below the assu med 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 assumpti on 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 th e effect estimate is derived from the HEI reanalysis of the Harvard Six-C ities data (Krewski et al., 2000). Because of discounting of delayed be nefits, the lag structure may also have a large impact on monetized benefits, reducing bene fits 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 di stributed 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 use d. If no lag is assumed, bene fits are increased by around 10 percent relative to the segmente d lag with a 3 percent discount rate and 30 percent with a 7 percent discount rate. Benefits are more sensitive to assu mptions 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 PM 2.5 concentrationsF-10 occurring above 10 : g/m 3 , and around 8 percent are due to changes above 15 : g/m 3 , the current PM 2.5 standard. F.2 Alternative and Supplementary Estimates
We also examined how the value for indi vidual endpoints or to tal benefits would change if we were to make a different assumption about speci fic 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 Para meter Sensitivity Analyses

Alternative Calculation Description of Estimate Impact on Primary Benefit Estimate (million 2000$) Least Stringent Expected Most Stringent 1 Treatment of reversals in CB Instead of omitting cases of CB that reverse after a period of time, they are treated as being cases with the lowest severity rating. The number of avoided chronic CB for the least stringent Scenario 1 increases from 230 to 430 (87%). The increase for Scenario 2 is from 890 to 1,670 (87%). The increase for Scenario 3 is from 1,300 to 2,400 (85%). +$34 +$130 +$190

2 Value of visibility changes in all Class I areas Values of visibility changes at Class I areas in California, the Southwest, and the Southeast are transferred to visibility changes in Class I areas in other regions of the country. +$54 +$120 +$200

3 Household soiling damage Value of decreases in expenditures on cleaning are estimated using values derived from Manuel et al. (1983). +$8 +$33 +$47

An important assumption relate d to chronic conditions is the possible reversal in CB incidences (row 1 of Table F-2). Reversals ar e defined as those cases where an individual reported having CB at the beginning of th e study period but reported not having CB in follow-up interviews at a later point in the st udy period. Because chr onic diseases are longlasting or permanent by definition, if the dis ease 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 assignedF-11 a range of severities, one way to address this is to treat reversals as cases of CB that are at the lowest severity level. These re versals of CB thus are assigned the lowest value for CB in this sensitivity analysis, rather than omitting reversals as is the case in the primary analysis. The alternative calculation for recreational visibility (row 2 of Table F-2) is an estimate of the full value of visibility in the entire region affected by the CAIR emission reductions. The Chestnut and Rowe (1990a ) study from which the primary valuation estimates are derived only examined WTP for visibility changes in the southeastern portion of the affected region. To obtain estimates of WTP for visibility changes in the northeastern and central portion of the affected region, we ha ve 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 possib ility that a visibility improvement in parks in one region is not necessarily the same environmen tal quality good as the same visibility improvement at parks in a diffe rent 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 poten tial difference into account, we adjusted the WTP being transferred by the ratio of visitor days in the two regions.
The alternative calculation for household soilin g (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 consis tent with economic principals” (Desvousges, Johnson, and Banzhaf, 1998). However, the data used to estimate household so iling 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 be nefits 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 th e categories (minor health effects, severe and chronic health effects, premature mortality, a nd 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, whileF-12 Table F-3. Ranges of Elasticity Values Used to Account for Projected Real Income Growth a

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effect 0.04 0.30 Severe and Chronic Health Effects 0.25 0.60 Premature Mortality 0.08 1.00 Visibility b

— —

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 adjus tment factors. The re sults 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 Growth a

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effect 1.015 1.114 Severe and Chronic Health Effects 1.094 1.241 Premature Mortality 1.029 1.437 Visibility b

— —

a Based on elasticity values reported in Table C-4, U.S. Census population projections, and projections of real GDP per capita.
b No range was applied for visibility because no ranges were available in the current published literature.F-13 Table F-5. Sensitivity Analysis of Alternative Income Elasticities a

Benefit Category Benefits in Millions of 1999$ Lower Sensitivity Bound Upper Sensitivity Bound
Least Stringent Expected Most Stringent Least Stringent Expected Most Stringent
Minor Health Effect $20 $78 $110 $21 $83 $120

Severe and Chronic Health Effects $140 $530 $750 $150 $580 $810

Premature Mortality c $2,100 $8,200 $12,000 $2,900 $11,000 $16,000

Visibility b $84 $240 $420 $84 $240 $420
Total Benefits c $2,300 $9,100 $13,000 $3,200 $12,000 $18,000

a All estimates rounded to two significant digits.
b No range was applied for visibility because no ranges were available in the current published literature.

c Assuming a 3 percent discount rate for mortality benefits.
Consistent with the impact of mortality on total benefits, the adjustment factor for mortality has the largest impact on total benefits. The value of mortality in 2015 ranges from 90 percent to 130 percent of the primary es timate based on the lo wer and upper sensitivity bounds on the income adjustment factor. The e ffect on the value of minor and chronic health effects is much less pronounced, ranging fro m 98 percent to 105 percent of the primary estimate for minor effects and from 93 per cent to 106 percent for chronic effects.
F.4 References Chestnut, L.G. 1997. “Draft Memorandu m: 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. Environmen tal Protection Agency, Resear ch Triangle Park, NC and Air Quality Management Division, Na tional Park Service, Denver, CO.F-14 Chestnut, L.G., and R.D. Rowe. 1990b. “A Ne w National Park Visib ility 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, Ir eland: An Intervention Study.” Lancet Oct 19;360(9341):1210-4. Desvousges, W.H., F.R. Johnson, and H.S. Banzhaf. 1998. Environmental Policy Analysis With Limited Information: Principles and Applications of the Transfer Method (New Horizons in Environmental Economics.) Edward Elgar Pub: London. Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, and F.E. Speizer. 1993. “An Association be tween 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 Co sts and Benefits (1999): Advisory by the Health and Ecological Effects Subcommitt ee 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—Ben efits and Costs of the Clean Air Act 1990-2020: An Advisory by a Special Pane l 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: Advi sory by the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis

. Kleckner, N., and J. Neuma nn. June 3, 1999. “Recommended Approach to Adjusting WTP Estimates to Reflect Changes in Real In come.” Memorandum to Jim Democker, US EPA/OPAR. Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, and W.H. White. July 2000. Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality . Special Report to the Health Effects Institute, Cambridge MA.F-15 Manuel, E.H., R.L. Horst, K.M. Brennan, W. N. Lanen, M.C. Duff, and J.K. Tapiero. 1983. Benefits Analysis of Alternative Sec ondary 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 Academie s Press: Washington, D.C. Pope, C.A. III, J. Schwartz, and M.R. Ransom. 1992. “Daily Mortality and PM 10 Pollution in Utah Valley.” Arch Environ Health 47(3):211-217. Pope, C.A., III, M.J. T hun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and C.W. Heath, Jr. 1995. “Particulate Air Pollu tion as a Predictor of Mortality in a Prospective Study of U.S. Adults.” American Journal of Respiratory Critical Care Medicine 151:669-674. Pope, C.A., III, R.T. Burnett, M.J. T hun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mort ality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141. Röösli, M., N. Künzli, and C. Braun-Fahrlände r. August 1-4, 2004. “Use of Air Pollution ‘Intervention-Type’ Studies in Health Risk Assessment.” 16 thConference 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 Resear ch and Development. EPA/600/P-99/002bF, October 2004.G-1 APPENDIX G RESULTS FOR TWO ADDITIONAL SCENARI OS APPLIED TO BART NON-EGU SOURCE CATEGORIES

We present below results for each BART source category affected by these two nonEGU scenarios ($2,000/ton and $3,000/ton, respectiv ely). Cost and emission reductions are available for these scenarios; no benefits analyses were conducte d 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 s cenario. There are no impacts for 8 of the 25 non-EGU source categories because there are no control measures available to reduce SO 2 and NO x from these categories within AirControlNET. For seven source categories only, NO x reductions take place in these analyses because there are no control measures available within AirControlNET at or below these cost/ton levels. G.1 Summary of Results for Two Additional Non-EGU Scenarios The two scenarios presented in this appendi x are applied nationwide and are presented in detail in this appendix. These scenarios are meant to be illustrative of the potential alternatives that may be available to states as they consider what scenarios to include in their SIPs for nonEGU sources. These scenarios are also compliant with the requirement in OMB Circular A-4 to examine alternative levels of stringency as part of an RIA. This section includes several summary tables in which the emission reductions and costs for these two non-EGU scenarios applied are show n by source category and also by the discount rate for the annualized costs. Table G-1 summarizes the SO 2 emission reductions for these BART non-EGU source categories using a discount rate of 7 percent a nd also showing results using a discount rate of 3 percen t. In total, the scenarios applied in this analysis lead to nationwide SO 2 emission reductions ranging from 172,595 tons to 211,573 tons with costs at a 7 percent discount rate. These es timates represent a reduction of 14 to 18 percent from the 2015 baseline. These scenarios lead to SO 2 emission reductions ranging from 210,013 to 262,547 tons with costs at a 3 percent rate. These estimates represent a reduction of 17 to 22 percent from the 2015 baseline.G-2 Table G-1. SO 2 Emissions and Emission Reductions for BART Source Categories in 2015

BART Source Category Baseline Emissions (tons) Discount Rate Scenarios—Reductions (tons) $2,000/ton Scenario $3,000/ton Scenario Industrial boilers 420,782 7% 87,009 124,592 420,782 3% 120,095 148,962 Petroleum refineries 199,483 7% 23,173 23,173 199,483 3% 25,103 33,304 Kraft pulp mills 119,818 7% 0 0 119,818 3% 0 3,196 Portland cement plants 116,835 7% 2,350 2,350 116,835 3% 2,743 13,383 Hydrofluoric, sulfuric, and nitric acid plants 96,741 7% 35,358 36,753 96,741 3% 36,753 36,753 Chemical process plants 47,700 7% 2,376 2,376 47,700 3% 2,376 2,376 Iron and steel mills 23,541 7% 2,914 2,914 23,541 3% 2,914 2,914 Coke oven batteries 9,815 7% 4,088 4,088 9,815 3% 4,088 4,088 Sulfur recovery plants 59,766 7% 13,697 13,697 59,766 3% 14,311 14,311 Primary aluminum ore reduction plants 47,552 7% 1,630 1,630 47,552 3% 1,630 3,260 Lime kilns 9,373 7% 0 0 9,373 3% 0 0 (continued)G-3 Table G-1. SO 2 Emissions and Emission Reductions for BART Source Categories in 2015 (continued)

BART Source Category Baseline Emissions (tons) Discount Rate Scenarios—Reductions (tons) $2,000/ton Scenario $3,000/ton Scenario Glass fiber processing plants 2,170 7% 0 0 2,170 3% 0 0 Municipal incinerators 284 7% 0 0
284 3% 0 0 Coal cleaning plants 1,530 7% 0 0 1,530 3% 0 0 Carbon black plants 41,853 7% 0 0 41,853 3% 0 0 Phosphate rock processing plants 21 7% 0 0
21 3% 0 0 Secondary metal production facilities 9,988 7% 0 0 9,988 3% 0 0
Total 1,208,088 7% 172,595 211,573 1,208,088 3% 210,013 262,547

Table G-2 summarizes the NO x emission reductions. The nationwide NO x 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 repres ent a reduction of 36 to 43 percent from the 2015 baseline. These scenarios lead to NO x emission reductions ranging from 280,163 to 313,382 tons with costs at a 3 percent rate. These re present a reduction of 41 to 46 percent from the 2015 baseline.G-4 Table G-2. NO x Emissions and Emission Reductions for BART Source Categories in 2015

BART Source Category Baseline Emissions (tons) Discount Rate Scenarios $2,000/ton Scenario $3,000/ton Scenario Industrial boilers 217,063 7% 97,074 125,575 217,063 3% 120,151 128,640 Petroleum refineries 86,566 7% 23,173 23,173 86,566 3% 23,173 26,685 Kraft pulp mills 103,614 7% 50,221 60,985 103,614 3% 56,466 64,521 Portland cement plants 120,567 7% 26,659 26,659 120,567 3% 26,659 26,659 Hydrofluoric, sulfuric, and nitric acid plants 17,059 7% 11,283 11,283 17,059 3% 11,283 11,283 Chemical process plants 72,577 7% 25,922 26,753 72,577 3% 27,568 31,567 Iron and steel mills 20,963 7% 2,034 3,259 20,963 3% 2,038 7,198 Coke oven batteries 10,389 7% 0 5,768 10,389 3% 5,768 5,768 Sulfur recovery plants 651 7% 0 0 651 3% 0 0 Primary aluminum ore reduction plants 1,676 7% 70 253 1,676 3% 335 335 Lime kilns 12,849 7% 4,471 4,471 12,849 3% 4,471 7,153 (continued)G-5 Table G-2. NO x Emissions and Emission Reductions for BART Source Categories in 2015 (continued)

BART Source Category Baseline Emissions (tons) Discount Rate Scenarios $2,000/ton Scenario $3,000/ton Scenario Glass fiber processing plants 6,677 7% 568 2,116 6,677 3% 851 2,116 Municipal incinerators 1,656 7% 744 744 1,656 3% 744 744 Coal cleaning plants 1,110 7% 0 511 1,110 3% 511 511 Carbon black plants 4,645 7% 111 120 4,645 3% 111 120 Phosphate rock processing plants 719 7% 0 45 719 3% 0 48 Secondary metal production facilities 1,377 7% 25 25 1,377 3% 34 34 Total 681,765 7% 242,355 291,740 681,765 3% 280,163 313,382

Table G-3 summarizes the annualized costs a ssociated 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 per cent discount rate and $507.23 million to $691.73 million (1999$) with costs at a 3 percent discount rate.G-6 Table G-3. Total Annualized Costs of Co ntrol for BART Source Categories in 2015 (million 1999$)

BART Source Category Discount Rate Scenarios $2,000/ton Scenario $3,000/ton Scenario
Industrial boilers 7% 241.5 412.1
3% 255.0 337.4
Petroleum refineries 7% 71.1 71.1
3% 71.1 81.2 Kraft pulp mills 7% 75.1 75.1 3% 59.2 68.5 Portland cement plants 7% 29.6 29.6
3% 28.7 56.6 Hydrofluoric, sulfuric, and nitric acid plants 7% 20.4 21.4
3% 21.4 21.4 Chemical process plants 7% 40.5 40.5
3% 30.4 40.1 Iron and Steel mills 7% 7.9 11.0
3% 5.7 22.7
Coke oven batteries 7% 6.2 18.7
3% 14.9 14.9 Sulfur recovery plants 7% 11.7 12.1
3% 12.1 12.1 Primary aluminum ore reduction plants 7% 1.7 2.2
3% 1.0 5.0 Lime kilns 7% 5.0 5.0
3% 4.3 25.4 (continued)G-7 Table G-3. Total Annualized Costs of Co ntrol for BART Source Categories in 2015 (million 1999$) (continued)

BART Source Category Discount Rate Scenarios $2,000/ton Scenario $3,000/ton Scenario Glass fiber processing plants 7% 0.5 5.3
3% 1.7 4.7 Municipal incinerators 7% 1.1 1.1
3% 0.9 0.9 Coal cleaning plants 7% 0.0 1.0
3% 0.8 0.8 Carbon black plants 7% 0.01 0.01
3% 0.005 0.01 Phosphate rock processing plants 7% 0.01 0.01
3% 0.01 0.01 Secondary metal production facilities 7% 0.04 0.04
3% 0.01 0.01
Total 7% $512.36 $706.26 3% $507.23 $691.73

Given the highly capital-intensive nature of the control measures included in these analyses, it is not unreasonable thata lower discount rate would lead to more application of these measures to reduce SO 2 and NO x 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 emi ssion reductions are higher with a 3 percent discount rate than a 7 percent di scount rate because the lower disc ount rate leads to more sources having available controls under that scenario and the costs are fairly close. At $3,000/ton scenario, the annualized costs and reductions are relatively closer.G-8 G.2 Results for Industrial Boilers
Table G-4 shows the SO 2 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 give n 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 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU Industrial Boilers a

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

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.
Table G-5 presents the NO x baseline emissions and reductions for each scenario. The table indicates that th e scenarios achieve incremental reduc tions from the 2015 baseline, ranging from 45 percent to 58 percent for costs at a 7 pe rcent discount rate and from 55 to 59 percent for costs at a 3 percent discount rate. Table G-5. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU Industrial Boilers

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 217,063 7% 119,989 97,074 217,063 3% 96,912 120,151 $3,000/ton Scenario 217,063 7% 91,488 125,575 217,063 3% 88,423 128,640

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.G-9 Table G-6 shows the annualized costs, resultin g annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 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-effectiv eness 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 pe rcent rate. In addition, the marginal costs between these scenarios are $2,328 pe r ton with costs at a 7 percent discount rate and $2,595 per ton with costs ata 3 percent discount rate. Table G-6. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BARTEligible Industrial Boilers

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $109.3 $1,256 — 3% $133.4 $1,111 — $3,000/ton Scenario 7% $196.8 $1,580 $2,328 3% $208.3 $1,398 $2,595

The costs and emission reductions reflect FGD scrubbers app lied to all of these units. The average and marginal costs rise as a result of FGD scrubbers being applied to more coalfired units with lower sulfur cont ents and to oil-fired units that have lower sulfur contents than coal-fired units. Table G-7 shows the annualized costs, resultin g annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for NO x control. The annualized control costs range from $132.2 million to $215.3 million w ith costs at a 7 percent rate and from $121.6 million to $129.1 million with costs at a 3 pe rcent 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 appli cation of SCR. SCR is the most expensive NO x control device available to industrial boilers in our analysis, though they also have a high control level (80 percent).G-10 Table G-7. 2015 Cost and Cost-Effectiveness Results for NO x Control at BART-Eligible Industrial Boilers

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $132.2 $1,360 — 3% $121.6 $1,012 — $3,000/ton Scenario 7% $215.3 $1,715 $2,929 3% $129.1 $1,044 $2,684

Table G-8 shows the total annualized cost s for each scenario for controlling both SO 2 and NO x . Table G-8. 2015 Cost Results for SO 2 and NO x Control at BART-Eligible Industrial Boilers

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) $2,000/ton Scenario 7% $241.5 3% $255.0 $3,000/ton Scenario 7% $412.1 3% $337.4

G.3 Results for Petroleum Refineries
Table G-9 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline of 12 percent with cost s estimated at a 7 percent discount rate and from 13 percent to 17 percent for costs estimated ata 3 percent discount rate.G-11 Table G-9. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Petroleum Refineries a

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

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-10 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline of 27 percent for costs estimated at a 7 percent discount rate a nd from 27 to 34 percent for costs estimated at a 3 percent discount rate. Table G-10. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Petroleum Refineries a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 86,566 7% 64,512 23,173 3% 64,512 23,173 $3,000/ton Scenario 86,566 7% 64,512 23,173 3% 59,881 26,685

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-11 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 control. The annualized control costs are $28.5 million with costs at a 7 percent discount ra te 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 ata 7 percent discount ra te and $3,025 per ton with costs at a 3 percent discount rate.G-12 Table G-11. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Petroleum Refineries

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $28.5 $1,231 — 3% $28.5 $1,045 — $3,000/ton Scenario 7% $28.5 $1,231 $0 3% $43.9 $1,479 $3,025

The costs and emission reductions reflect FGD scrubbers app lied 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 su lfur 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, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for NO x 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 pe rcent discount rate and range from $1,930 to $1,975 per ton with costs at a 3 per cent 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 re sult of additional pro cess heaters having to apply LNB + SNCR.G-13 Table G-12. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Petroleum Refineries

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $42.6 $1,930 — 3% $42.6 $1,930 — $3,000/ton Scenario 7% $42.6 $1,930 $0 3% $52.7 $1,975 $2,876

Table G-13 shows the total annualiz ed costs for controlling both SO 2 and NO x . Table G-13. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Petroleum Refineries

Scenarios Discount Rate Total Annualized Costs (million 1999$) $2,000/ton Scenario 7% $71.1 3% $71.1 $3,000/ton Scenario 7% $71.1 3% $81.2

G.4 Kraft Pulp Mills
Table G-14 shows the SO 2 emissions reductions achieved in the analyses for each of these scenarios. The table indicates that th e 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 NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 48 percent to 59 percent for costs at a 7 percent discount rate and from 54 to 65 percent for costs at a 3 percent discount rate.G-14 Table G-14. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Kraft Pulp Mills a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 119,818 7% 119,818 0 119,818 3% 119,818 0 $3,000/ton Scenario 119,818 7% 119,818 0 119,818 3% 116,820 3,196

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-15. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Kraft Pulp Mills a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 103,614 7% 53,393 50,221 103,614 3% 47,148 56,466 $3,000/ton Scenario 103,614 7% 42,629 60,985 103,614 3% 36,093 67,521

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-16 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 control. The annualized control costs are $0 with costs at a 7 percen t discount rate and rang e from $0 to $7.0 million with costs at a 3 percent discount rate. The accomp anying 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 ata 3 percent discount rate. In addition, the marginal costs are zero with costs at a 7 per cent discount rate and $2,189 per ton with costs at a 3 percent discount rate.G-15 Table G-16. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Kraft Pulp Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $0.0 $0 — 3% $0.0 $0 — $3,000/ton Scenario 7% $0.0 $0 — 3% $7.0 $2,189 $2,189

The costs and emission reductions reflect FGD scrubbers app lied to all of these units. The average and marginal costs rise as a resu lt of FGD scrubbers bein g applied to units for which the application is more expensive. Table G-17 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for NO x 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 cost s at a 3 percent rate. The accompanying annualized average cost-effectiv eness results range from $1,496 to $1,749 per ton with costs at a 7 percent discount rate and fro m $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 per cent 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 cost s of each scenario for controlling both SO 2 and NO x .G-16 Table G-17. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Kraft Pulp Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Cost ($/ton) $2,000/ton Scenario 7% $75.1 $1,496 — 3% $59.2 $1,048 — $3,000/ton Scenario 7% $106.7 $1,749 $3,344 3% $61.5 $1,069 $4,452

Table G-18. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Kraft Pulp Mills

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) $2,000/ton Scenario 7% $75.1 3% $59.2 $3,000/ton Scenario 7% $75.1 3% $68.5

G.5 Results for Portland Cement Plants
Table G-19 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 2 percent for costs at a 7 pe rcent discount rate and from 2 to 11 percent for costs at a 3 percent discount rate.
Table G-20 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline of 28 percent for costs at a 7 or a 3 percent discount rate.G-17 Table G-19. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Portland Cement Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 116,835 7% 114,485 2,350 116,835 3% 114,092 2,743 $3,000/ton Scenario 116,835 7% 114,485 2,350 116,835 3% 103,452 13,383

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-20. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Portland Cement Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 120,567 7% 93,908 26,659 120,567 3% 93,908 26,659 $3,000/ton Scenario 120,567 7% 93,908 26,659 120,567 3% 93,908 26,659

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.
Table G-21 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 control. The annualized control costs are $4.6 million with costs at a 7 percent discount rate and range from $3.7 to $31.6 million with costs at a 3 percent discount rate . The accompanying annualized average costeffectiveness results are $1,973 pe r ton with costs at a 7 per cent discount rate and from $1,344 to $2,362 per ton with costs at a 3 percent discount ra te. In addition, the marginal costs are zero with costs at a 7 percent discount rate and $2,622 per ton with cost s at a 3 percent discount rate.G-18 Table G-21. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Portland Cement Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $4.6 $1,973 — 3% $3.7 $1,344 — $3,000/ton Scenario 7% $4.6 $1,973 __ 3% $31.6 $2,362 $2,622

The costs and emission reductions reflect FGD scrubbers app lied to all of these units. The average and marginal costs rise as a resu lt of FGD scrubbers bein g applied to more units with lower sulfur content fuels. Table G-22 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for NO x 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 NO x control, the marginal costs are zero. Table G-22. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Portland Cement Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average CostEffectiveness ($/ton) Marginal Cost ($/ton) $2,000/ton Scenario 7% $25.0 $937 —

3% $25.0 $937 — $3,000/ton Scenario 7% $25.0 $937 __

3% $25.0 $937 —G-19 The average and marginal costs of control increase as more SCR applications take place as the cost-per-ton cap rises. These applications take the place of less expensive but less effective controls such as mid-kiln firing.
Table G-23 shows the total annualized cost s of each scenario for controlling both SO 2 and NO x . Table G-23. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Portland Cement Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) $2,000/ton Scenario 7% $29.6 3% $28.7 $3,000/ton Scenario 7% $29.6 3% $56.6

G.6 Results for Hydrofluoric, Sul furic, and Nitric Acid Plants
Table G-24 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 35 to 38 percent for costs at a 7 percent discount ra te and the same for costs at a 3 percent discount rate.
Table G-25 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates 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, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 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 ata 3 percent discount rate. In addition, the marginal costs are $1,362 per ton with costs at a 7 percent discount rate and zero (no reductions) with costs at a 3 percent discount rate.G-20 Table G-24. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Hydrofluoric , Sulfuric, and Nitric Acid Plants a

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

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-25. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Hydrofluoric , Sulfuric, and Nitric Acid Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 17,059 7% 5,776 11,283 17,059 3% 5,776 11,283 $3,000/ton Scenario 17,059 7% 5,776 11,283 17,059 3% 5,776 11,283

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-26. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Hydrofluoric , Sulfuric, and Nitric Acid Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $12.2 $345 — 3% $14.1 $385 — $3,000/ton Scenario 7% $14.1 $385 $1,362 3% $14.1 $385 —G-21 The costs and emission reductions are flat between the scenarios because there is only one control technique available to reduce SO 2 emissions from these sources—increase sulfur conversion to meet the sulfuric acid NSPS (99.7 percent control). Table G-27 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for NO x 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-e ffectiveness results are $728 per ton with costs at a 7 percent discount ra te 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 NO x Control at Non-EGU BART-Eligible Units at Hydrofluoric , Sulfuric, and Nitric Acid Plants

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

The costs and emission reductions are flat between the scenarios because there is only one control technique available to reduce NO x emissions from these sources—SNCR applied to nitric acid manufacturing sources.
Table G-28 shows the total annualized cost s of each scenario for controlling both SO 2 and NO x . G.7 Results for Chemical Process Plants
Table G-29 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline of 7 percent for costs at eith er a 7 or a 3 percent discount rate.G-22 Table G-28. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Hydrofluoric, Sulfuric, and Nitric Acid Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) $2,000/ton Scenario 7% $20.4 3% $21.4 $3,000/ton Scenario 7% $21.4 3% $21.4

Table G-29. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Chemical Process Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 47,700 7% 45,324 2,376 47,700 3% 45,324 2,376 $3,000/ton Scenario 47,700 7% 45,324 2,376 47,700 3% 45,324 2,376

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-30 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates 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, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 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 accomp anying annualized average cost-effectiveness results are $1,052 per ton with costs at a 7 per cent discount rate and $1,013 per ton with costs at a 3 percent discount rate.G-23 Table G-30. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Chemical Process Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 72,577 7% 46,655 25,922 72,577 3% 45,009 27,568 $3,000/ton Scenario 72,577 7% 45,824 26,753 72,577 3% 41,010 31,567

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-31. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Chemical Process Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $2.5 $1,052 — 3% $2.4 $1,013 — $3,000/ton Scenario 7% $2.5 $1,052 $1,052 3% $2.4 $1,013 $1,013

The costs and emission reductions reflect a major difference in the impacts between the two available control techniques: increase sulfur percentage conve rsion to meet the sulfuric acid NSPS (99.7 percent control) and FGD scrubbers. Table G-32 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for NO x control. The annualized control costs are $38 million with costs at a 7 percent discount ra te 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 pe rcent 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 co sts at a 3 percent discount rate.G-24 Table G-32. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Chemical Process Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $38.0 $1,466 — 3% $28.0 $1,015 — $3,000/ton Scenario 7% $38.0 $1,466 — 3% $37.7 $1,192 $4,348

Table G-33 shows the total annualized cost s of each scenario for controlling both SO 2 and NO x . Table G-33. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Chemical Process Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) $2,000/ton Scenario 7% $40.5 3% $30.4 $3,000/ton Scenario 7% $40.5 3% $40.1

G.8 Results for Iron and Steel Mills
Table G-34 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline of 12 percent for costs at e ither a 7 or 3 percent discount rate.
Table G-35 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 5 to 41 percent for costs of either a 7 or 3 percent discount rate.G-25 Table G-34. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Iron and Steel Mills a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 23,541 7% 20,627 2,914 23,541 3% 20,627 2,914 $3,000/ton Scenario 23,541 7% 20,627 2,914 23,541 3% 20,627 2,914

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-35. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Iron and Steel Mills a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 20,963 7% 18,929 2,034 20,963 3% 18,925 2,038 $3,000/ton Scenario 20,963 7% 17,704 3,259 20,963 3% 13,765 7,198

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-36 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 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-e ffectiveness results are $1,819 per ton with costs at a 7 percent discount ra te and $1,165 per ton with costs at a 3 percent discount rate. Marginal costs are zero between the scenarios because there are no additional reductions from going to the $3,000 per ton scenario from the $2,000 per ton scenario.G-26 Table G-36. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Iron and Steel Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $5.3 $1,819 — 3% $3.4 $1,165 — $3,000/ton Scenario 7% $5.3 $1,819 — 3% $3.4 $1,165 —

Table G-37 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for NO x 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-effectiv eness results range from $1,302 to $1,770 per ton with costs at a 7 percent discount rate and fro m $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 annualiz ed costs for controlling both SO 2 and NO x . G.9 Results for Coke Oven Batteries
Table G-39 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 0 to 62 percent for costs at a 7 percent discount rate and from 0 to 57 percent for costs at a 3 percent discount rate.G-27 Table G-37. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Iron and Steel Mills

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $2.6 $1,302 — 3% $2.3 $1,109 — $3,000/ton Scenario 7% $5.8 $1,770 $2,953 3% $15.0 $2,271 $2,532

Table G-38. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Iron and Steel Mills

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) $2,000/ton Scenario 7% $7.9 3% $5.7 $3,000/ton Scenario 7% $11.0 3% $22.7

Table G-39. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Coke Oven Batteries

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 9,815 7% 5,727 4,088 9,815 3% 5,727 4,088 $3,000/ton Scenario 9,815 7% 5,727 4,088 9,815 3% 5,727 4,088

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.G-28 Table G-40 shows the emissions reductions achie ved in the analyses for each scenario for NO x 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 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Coke Oven Batteries a

Scenarios 2015 Baseline Emissions 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 10,389 10,389 0 10,389 4,621 5,768 $3,000/ton Scenario 10,389 4,621 5,768 10,389 4,621 5,768

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-41 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 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-e ffectiveness 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 SO 2 Control at Non-EGU BART-Eligible Units at Coke Oven Batteries

Scenarios Discount Rate Total Annualized Costs (million 1990$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $6.2 $1,517 — 3% $4.0 $1,074 — $3,000/ton Scenario 7% $6.2 $1,517 $1,517 3% $4.0 $1,074 $1,074G-29 The costs and emission reductions reflect application of only one control—vacuum carbonate plus a sulfur recovery plant but also differing SO 2 emissions levels at the affected coke oven batteries. Table G-42 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for NO x 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 discountrate. The accompanying annualized average cost-effectiveness results range from $0 to $2,167 pe r ton with costs at a 7 percent discount rate and are $1,898 per ton with costs at a 3 percent disc ount rate. In addition, the marginal costs are $2,167 per ton with costs at a 7 percent discount ra te and zero with costs at a 3 percent discount rate. Table G-42. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Coke Oven Batteries

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

Table G-43 shows the total annualiz ed costs for controlling both SO 2 and NO x . Table G-43. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Coke Oven Batteries

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) $2,000/ton Scenario 7% $6.2 3% $14.9 $3,000/ton Scenario 7% $18.7 3% $14.9G-30 G.10 Results for Sulfur Recovery Plants
Table G-44 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline of 23 percent for costs at a 7 percent disc ount rate and 24 percent for costs at a 3 percent discount rate. The emission re ductions are the same for each scenario because of the few controls available under these scenarios. Table G-44. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Sulfur Recovery Plants a

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

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled

.

There are no reductions of NO x from sulfur recovery units unde r either of these scenarios. Table G-45 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 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 ar e $847 per ton with costs at a 7 percent discount rate and $849 per ton with costs at a 3 percent discount rate. The costs and emission reductions are flat between the scenarios because there is only one control technique available to reduce SO 2 emissions from these sources—sulfur recovery and/or tail gas treatment.
Table G-46 shows the total annua lized costs for controlling SO 2 from these sources.G-31 Table G-45. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Sulfur Recovery Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $11.6 $847 — 3% $12.2 $849 — $3,000/ton Scenario 7% $11.6 $847 $847 3% $12.2 $849 $849

Table G-46. 2015 Cost Results for SO 2 Control at Non-EGU BART-Eligible Units at Sulfur Recovery Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) $2,000/ton Scenario 7% $11.7 3% $12.1 $3,000/ton Scenario 7% $12.1 3% $12.1

G.11 Results for Primary Aluminum Ore Reduction Plants
Table G-47 shows the SO 2 emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 3 to 7 percent for co sts at a 7 or 3 percent discount rate.
Table G-48 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 4 to 15 percent with costs at a 7 percent discount rate and are 20 percent with costs at a 3 percent discount rate.G-32 Table G-47. 2015 SO 2 Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 47,552 7% 45,922 1,630 47,552 3% 45,922 1,630 $3,000/ton Scenario 47,552 7% 45,922 1,630 47,552 3% 44,292 3,260

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-48. 2015 NO x Baseline Emissions and Emission Reductions (in tons) for Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants a

Scenario 2015 Baseline Emissions 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 1,676 1,606 70 1,676 1,341 335 $3,000/ton Scenario 1,676 1,423 253 1,676 1,341 335

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in these source categories, both controlled and uncontrolled.

Table G-49 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for SO 2 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 percen t discount rate. The marginal costs are zero with costs at a 7 percent discount rate and $2,147 per ton with cost s at a 3 percent discount rate.G-33 Table G-49. 2015 Cost and Cost-Effectiveness Results for SO 2 Control at Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $1.6 $982 — 3% $1.0 $590 — $3,000/ton Scenario 7% $1.6 $982 __ 3% $4.5 $1,381 $2,147

Table G-50 shows the annualized costs, resu lting annualized average cost-effectiveness for each scenario, and marginal costs between each scenario for NO x 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 percen t 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 ra te and $1,764 per ton with costs at a 3 percent discount rate. The costs and NO x emission reductions reflect LNB applications. Table G-50. 2015 Cost and Cost-Effectiveness Results for NO x Control at Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $0.1 $1,114 — 3% $0.0 $509 — $3,000/ton Scenario 7% $0.6 $2,411 $2,823 3% $0.5 $1,614 $1,764

Table G-51 shows the total annualiz ed costs for controlling both SO 2 and NO x .G-34 Table G-51. 2015 Cost Results for SO 2 and NO x Control at Non-EGU BART-Eligible Units at Primary Aluminum Ore Reduction Plants

Scenarios Discount Rate Tota l Annualized Co sts (million 1999$) $2,000/ton Scenario 7% $1.7 3% $1.0 $3,000/ton Scenario 7% $2.2 3% $5.0

The next seven BART source categories only have NO x controls applied to their affected units because there are no SO 2 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 NO x , not SO 2 . G.12 Results for Lime Kilns
Table G-52 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the options achieve incremental reductions from the 2015 baseline ranging from 21 to 56 percent for co sts at a 7 or 3 percent discount rate. Table G-52. 2015 NO x Emission Reductions (in tons) for BART-Eligible Lime Kilns

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 12,849 7% 8,378 4,471 12,849 3% 8,378 4,471 $3,000/ton Scenario 12,849 7% 8,378 4,471 12,849 3% 5,696 7,153

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source category, both controlled and uncontrolled.G-35 Table G-53 shows the annualized costs, resu lting annualized average cost-effectiveness, and marginal costs for each scenario. The to tal 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 annua lized average cost-effectiveness is $1,118 per ton with costs at a 7 percent discount rate and ra nges from $953 to $3,552 per ton with costs at a 3 percent discount rate. The margin al costs are zero (no additiona l reductions) with costs at a 7 percent discount rate a nd $7,867 per ton with costs at a 3 pe rcent discount rate. These impacts reflect applications of SNCR. Table G-53. 2015 Cost and Cost-Effectivene ss Results for BART-Eligible Lime Kilns

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $5.0 $1,118 — 3% $4.3 $953 — $3,000/ton Scenario 7% $5.0 $1,118 __ 3% $25.4 $3,552 $7,867

G.13 Results for Glass Fiber Processing Plants
Table G-54 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the options achieve incremental reductions from the 2015 baseline ranging from 9 to 32 percent for co sts at a 7 or 3 percent discount rate. Table G-54. 2015 NO x Emission Reductions (in tons) fo r BART-Eligible Units at Glass Fiber Processing Plants

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 6,677 7% 6,109 568 6,677 3% 5,826 851 $3,000/ton Scenario 6,677 7% 4,561 2,116 6,677 3% 4,561 2,116

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled and uncontrolled.G-36 Table G-55 shows the annualized cost, resu lting annualized average cost-effectiveness, and marginal costs for each scenario. The tota l annualized costs for these scenarios range from $0.5 million to $5.3 million with costs at a 7 per cent discount rate and from $1.7 million to $4.7 million with costs at a 3 percent discount rate. The annualized average co st-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-Eligib le Units at Glass Fiber Processing Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average CostEffectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $0.5 $937 — 3% $1.7 $1,972 — $3,000/ton Scenario 7% $5.3 $2,505 $3,101 3% $4.7 $2,244 $3,057

G.14 Results for Municipal Incinerators The analysis of municipal in cinerators (>250 tons per da y burn refuse capacity) shows the results for each scenario. Table G-56 shows the NO x 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 cost s at a 7 or 3 percent discount rate. Table G-56. 2015 NO x Emission Reductions (in tons) for BART-Eligible Municipal Incinerators a

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 1,656 7% 912 744 1,656 3% 912 744 $3,000/ton Scenario 1,656 7% 912 744 1,656 3% 912 744

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled and uncontrolled.G-37 Table G-57 shows the annualized costs, a nnualized average cost-effectiveness, and marginal costs for each scenario. The total a nnualized 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-effectivene ss 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 percen t discount rate. The only ava ilable control measure for this source is SNCR. Table G-57. 2007 Cost and Cost-Effectivene ss Results for BART-Eligible Municipal Incinerators

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $1.1 $1,478 — 3% $0.9 $1,207 $3,000/ton Scenario 7% $1.1 $1,478 $1,478 3% $0.9 $1,207 $1,207

G.15 Results for Coal Cleaning Plants
Table G-58 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 0 to 46 percent for co sts at a 7 or a 3 percent discount rate. Table G-59 shows the annualized costs, a nnualized average cost-effectiveness, and marginal costs for each scenario. The total a nnualized costs for these scenarios range from $0 to $1 million with costs at a 7 percent discount ra te and $0.8 million with costs at a 3 percent discount rate. The annualized average cost-effec tiveness is $0 to $1,900 per ton with costs at a 7 percent discount rate a nd $1,534 per ton with costs at a 3 per cent 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 ata 3 percent discount rate. Controls available to these sources are LNB and SNCR.G-38 Table G-58. 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Coal Cleaning Plants

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

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled and uncontrolled.

Table G-59. 2015 Cost and Cost-Effectiveness Results for BART-Elig ible Units at Coal Cleaning Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $0.0 $0 — 3% $0.8 $1,534 — $3,000/ton Scenario 7% $1.0 $1,900 $1,900 3% $0.8 $1,534 —

G.16 Results for Carbon Black Plants
Table G-60 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline of about 2 percent for costs at a 7 or a 3 percent discount rate.G-39 Table G-60. 2015 NO x Emission Reductions (in tons) fo r BART-Eligible Units at Carbon Black Plants

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 4,645 7% 4,534 111 4,645 3% 4,534 111 $3,000/ton Scenario 4,645 7% 4,525 120 4,645 3% 4,525 120

a The 2015 baseline emissions estimate reflects emissions from all sources in this source category, both controlled and uncontrolled.

Table G-61 shows the annualized cost, resu lting annualized average cost-effectiveness, and marginal costs for each scenario. The tota l 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 co st-effectiveness is $1,608 per ton with costs at a 7 percent discount rate a nd $1,495 per ton with costs at a 3 per cent discount rate. Marginal costs are zero since there are no reductions between the scenarios. NO x 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-Eligib le Units at Carbon Black Plants

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Costs ($/ton) $2,000/ton Scenario 7% $0.0 $1,608 — 3% $0.0 $1,495 — $3,000/ton Scenario 7% $0.0 $1,608 — 3% $0.0 $1,495 —G-40 G.17 Results for Secondary Metal Production Facilities
Table G-62 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates 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 NO x Emission Reductions (in tons) for BART-Eligible Units at Secondary Metal Production Facilities

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 1,377 7% 1,352 25 1,377 3% 1,343 34 $3,000/ton Scenario 1,377 7% 1,352 25 1,377 3% 1,343 34

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source category, both controlled and uncontrolled.

Table G-63 shows the annualized cost, resu lting annualized average cost-effectiveness, and marginal costs for each scenario. The tota l annualized costs for these scenarios range from $0.01 million to $0.04 million with costs at a 7 percent di scount rate and the sa me with costs at a 3 percent discount rate. The annualized average cost-effectiv eness 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 NO x controls are LNB and the more expensive LNB + SNCR. G.18 Results for Phosphate Rock Ore Processing Facilities
Table G-64 shows the NO x emissions reductions achieved in the analyses for each scenario. The table i ndicates that the scenarios achieve incremental reductions from the 2015 baseline ranging from 2 percent for costs at a 7 pe rcent discount rate and from 2 to 47 percent for costs at a 3 percent discount rate.G-41 Table G-63. 2015 Cost and Cost-Effectiven ess Results for BART -Eligible Units at Secondary Metal Processing Facilities

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Cost ($/ton) $2,000/ton Scenario 7% $0.0 $760 — 3% $0.0 $511 — $3,000/ton Scenario 7% $0.0 $760 — 3% $0.0 $511 —

Table G-64. 2015 NO x Emission Reductions (in tons) for BART-Eligible Units at Phosphate Rock Ore Processing Facilities

Scenarios 2015 Baseline Emissions Discount Rate 2015 Postcontrol Emissions 2015 Emission Reductions $2,000/ton Scenario 719 7% 689 30
719 3% 689 30 $3,000/ton Scenario 719 7% 689 30
719 3% 689 30

a The 2015 baseline emissions estimate reflects emissions from all BART-eligible sources in this source category, both controlled and uncontrolled.

Table G-65 shows the annualized cost, resu lting annualized average cost-effectiveness, and marginal costs for each scenario. The tota l annualized costs for these scenarios range from $0.01 million with costs at either a 7 or 3 percen t discount rate. The annualized average costeffectiveness is $760 per ton with costs at a either a 3 or 7 per cent discount rate. The marginal costs between the scenarios are zero since there are no additional reductions with increasing stringency.G-42 Table G-65. 2015 Cost and Cost-Effectiven ess Results for BART -Eligible Units at Phosphate Rock Ore Processing Facilities

Scenarios Discount Rate Total Annualized Costs (million 1999$) Annualized Average Cost-Effectiveness ($/ton) Marginal Cost ($/ton) $2,000/ton Scenario 7% $0.0 $760 — 3% $0.0 $760 — $3,000/ton Scenario 7% 0.0 760 —
3% 0.0 760 —United States Office of Air Quality Planningand Standards Publication No. EPA-452/R-05-004Environmental Protection Air Quality Strategies and Standards Division June 2005Agency Research Triangle Park, NC