### February 2011


# Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants for

Industrial, Commercial, and Institutional Boilers and Process Heaters

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards (OAQPS) Air Economics Group Risk and Benefits Group Air Quality Modeling Group Research Triangle Park, NC 27711

# Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants for

Industrial, Commercial, and Institutional Boilers and Process Heaters

February 2011

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards (OAQPS) Air Economics Group Risk and Benefits Group Air Quality Modeling Group Research Triangle Park, NC 27711iii CONTENTS

Section Page

1 Introduction ................................................................................................................ ... 1-1 1.1 Executive Summary ............................................................................................. 1-1 1.2 Organization of this Report .................................................................................. 1-3
1.3 Section 1 References ............................................................................................ 1-6

2 Industry Profiles ........................................................................................................... . 2-1 2.1 Food Manufacturing ............................................................................................. 2-1 2.1.1 Introduction .............................................................................................. 2-1 2.1.2 Supply and Demand Characteristics ........................................................ 2-4 2.1.2.1 Goods and Services Used in Food Manufacturing ..................... 2-4 2.1.2.2 Energy ........................................................................................ 2-4 2.1.2.3 Uses and Consumers .................................................................. 2-7 2.1.3 Firm and Market Characteristics .............................................................. 2-7 2.1.3.1 Location ...................................................................................... 2-7 2.1.3.2 Production Capacity and Utilization .......................................... 2-9 2.1.3.3 Employment ............................................................................... 2-9 2.1.3.4 Plants and Capacity .................................................................. 2-10 2.1.3.5 Firm Characteristics ................................................................. 2-10 2.1.3.6 Size Distribution ....................................................................... 2-12 2.1.3.7 Domestic Production ................................................................ 2-12 2.1.3.8 International Trade ................................................................... 2-12 2.1.3.9 Market Prices ............................................................................ 2-16 2.2 Wood Product Manufacturing............................................................................ 2-16 2.2.1 Introduction ............................................................................................ 2-16 2.2.2 Supply and Demand Characteristics ...................................................... 2-20 2.2.2.1 Goods and Services Used in Wood Product Manufacturing .... 2-20 2.2.2.2 Uses and Consumers ................................................................ 2-22 2.2.3 Firm and Market Characteristics ............................................................ 2-24 2.2.3.1 Location .................................................................................... 2-24 2.2.3.2 Production Capacity and Utilization ........................................ 2-25iv 2.2.3.3 Employme nt ............................................................................. 2-26 2.2.3.4 Plants and Capacity .................................................................. 2-26 2.2.3.5 Firm Characteristics ................................................................. 2-27 2.2.3.6 Size Distribution ....................................................................... 2-27 2.2.3.7 Domestic Production ................................................................ 2-28 2.2.3.8 International Trade ................................................................... 2-28 2.2.3.9 Market Prices ............................................................................ 2-31 2.3 Paper Manufacturing .......................................................................................... 2-33 2.3.1 Introduction ............................................................................................ 2-33 2.3.2 Supply and Demand Characteristics ...................................................... 2-37 2.3.2.1 Goods and Services Used in Paper Manufacturing .................. 2-37 2.3.2.2 Uses and Consumers ................................................................ 2-42 2.3.3 Firm and Market Characteristics ............................................................ 2-43 2.3.3.1 Location .................................................................................... 2-43 2.3.3.2 Production Capacity and Utilization ........................................ 2-44 2.3.3.3 Employme nt ............................................................................. 2-44 2.3.3.4 Plants and Capacity .................................................................. 2-45 2.3.3.5 Firm Characteristics ................................................................. 2-45 2.3.3.6 Size Distribution ....................................................................... 2-47 2.3.3.7 Domestic Production ................................................................ 2-48 2.3.3.8 International Trade ................................................................... 2-49 2.3.3.9 Market Prices ............................................................................ 2-49 2.4 Chemical Manufacturing ................................................................................... 2-51 2.4.1 Introduction ............................................................................................ 2-51 2.4.2 Supply and Demand Characteristics ...................................................... 2-54 2.4.2.1 Goods and Services Used in Chemical Manufacturing ............ 2-55 2.4.2.2 Uses and Consumers ................................................................ 2-55 2.4.3 Firm and Market Characteristics ............................................................ 2-58 2.4.3.1 Location .................................................................................... 2-58 2.4.3.2 Production Capacity and Utilization ........................................ 2-60 2.4.3.3 Employme nt ............................................................................. 2-60 2.4.3.4 Plants and Capacity .................................................................. 2-60 2.4.3.5 Firm Characteristics ................................................................. 2-62 2.4.3.6 Size Distribution ....................................................................... 2-64 2.4.3.7 Domestic Production ................................................................ 2-64 2.4.3.8 International Trade ................................................................... 2-64 2.4.3.9 Market Prices ............................................................................ 2-64v 2.5 Section 2 References .......................................................................................... 2-68

3 Engineering Cost Analysis ............................................................................................ 3-1 3.1 Major Sources ...................................................................................................... 3-2
3.2 Area Sources ........................................................................................................ 3-5
3.3 Section 3 References ............................................................................................ 3-6

4 Economic Impact Analysis ........................................................................................... 4-1 4.1 Partial Equilibrium Analysis (Multiple Markets) ................................................ 4-1 4.1.1 Overview .................................................................................................. 4-1 4.1.2 Economic Impact Analysis Results ......................................................... 4-2 4.1.2.1 Market-Level Results ................................................................. 4-2 4.1.2.2 Social Cost Estimates Major Source Rule .................................. 4-3 4.1.2.3 Social Cost Estimates Area Source Rule .................................... 4-5 4.1.2.4 Job Effects .................................................................................. 4-6
4.2 Section 4 References .......................................................................................... 4-14

5 Small Entity Analyses ................................................................................................... 5-1 5.1 Small Entity Screen ing Analysis ......................................................................... 5-1 5.1.1 Small Businesses ...................................................................................... 5-1 5.1.1.1 Representative Small Business Analysis Using Census Statistics of U.S. Businesses ....................................................... 5-1 5.1.1.2 Additional Small Business Analysis Using Sample of Small Businesses Identified in Combustion Facility Survey ......................................................................................... 5-4 5.1.2 Small Governmental Jurisdictions and Not-for-Profit Enterprises .......... 5-5 5.2 Final Regulatory Flexibility Anal ysis (FRFA): Major Sources ........................... 5-6 5.2.1 Need for Rule and Objectives .................................................................. 5-6 5.2.2 Summary of the Significant Issues raised by the Public Comments and Agency Assessment .......................................................................... 5-6 5.2.3 Description of and Estimate of the Number of Small entities to Which the Rules Will Apply .................................................................... 5-7 5.2.4 Description of the Projected Re porting, Record keeping and Other Compliance Requirements of the Rule .................................................... 5-9vi 5.2.5 Description of the steps the Ag ency has Taken to Minimize the Significant Economic Impact on Small Entities .................................... 5-11 5.3 Final Regulatory Flexibility Anal ysis (FRFA): Area Sources ........................... 5-12 5.3.1 Need for Rule and Objectives ................................................................ 5-12 5.3.2 Summary of the Significant Issues raised by the Public Comments and Agency Assessment ........................................................................ 5-12 5.3.3 Description of and Estimate of the Number of Small entities to Which the Rules Will Apply .................................................................. 5-13 5.3.4 Description of the Projected Re porting, Record keeping and Other Compliance Requirements of the Rule .................................................. 5-15 5.3.5 Description of the steps the Ag ency has Taken to Minimize the Significant Economic Impact on Small Entities .................................... 5-16
5.4 Section 5 References .......................................................................................... 5-16

6 Air Quality Modeling of Emissions Reductions ........................................................... 6-1 6.1 Synopsis ............................................................................................................... 6- 1 6.2 Photochemical Model Background ...................................................................... 6-2
6.3 Model Domain and Grid Resolution .................................................................... 6-3 6.4 Emissions Input Data ........................................................................................... 6-4 6.4.1 2005 Baseline Emissions ......................................................................... 6-4 6.4.2 Future Year Baseline Emissions .............................................................. 6-5 6.4.3 Future Year Sector Contribution Approach ............................................. 6-7
6.5 Model Results ...................................................................................................... 6-9 6.5.1 Impacts of Sector on To tal Mercury Deposition .................................... 6-10 6.5.2 Impacts of Sector on Future Annual PM2.5 Levels ............................... 6-10 6.5.3 Impacts of Sector on Futu re 24-hour PM2.5 Levels .............................. 6-11 6.5.4 Impacts of Sector on Future Visibility Levels ....................................... 6-11 6.5.5 Impacts of Sector on Future Ozone Levels ............................................ 6-12
6.6 Section 6 References .......................................................................................... 6-12

7 Benefits of Emissions Reductions ................................................................................ 7-1 7.1 Synopsis ............................................................................................................... 7- 1 7.2 Calculating Benefits ............................................................................................. 7-2vii 7.2.1 Methodology Improvements since Proposal ............................................ 7-4 7.2.2 Benefits Analysis Approach for PM2.5 and Ozone ................................. 7-5 7.2.3 Health Impact Analysis (HIA) ................................................................. 7-6 7.2.4 Estimating PM2.5-related Pr emature Mortality ..................................... 7-10 7.2.5 Economic Valuation of Health Impact .................................................. 7-12 7.2.6 Calculating Boiler Sector-specifi c Benefit-per-ton Estimates ............... 7-14
7.3 Health Benefits Results ...................................................................................... 7-20 7.4 Energy Disbenefits ............................................................................................. 7-34 7.4.1 Social Cost of Carbon and Greenhouse Gas Disbenefits ....................... 7-34 7.5 Unquantified or Nonmon etized Benefits ........................................................... 7-37 7.5.1 Other SO2 Benefits ................................................................................ 7-37 7.5.2 Carbon Monoxide Benefits .................................................................... 7-39 7.5.3 Visibility Benefits .................................................................................. 7-40 7.5.4 Ozone Vegetation Benefits .................................................................... 7-40 7.5.5 Direct HAP Benefits .............................................................................. 7-40 7.5.5.1 Mercury .................................................................................... 7-44 7.5.5.2 Hydrogen Chloride (HCl) ......................................................... 7-49 7.5.5.3 Chlorine Gas (Cl2) ................................................................... 7-50 7.5.5.4 Hydrogen Cyanide (HCN) ........................................................ 7-50 7.5.5.5 Hydrogen Fluoride (HF) ........................................................... 7-50 7.5.5.6 Toluene ..................................................................................... 7-50 7.5.5.7 Formaldehyde ........................................................................... 7-51 7.5.5.8 Polycyclic Aromatic Hydrocarbons (PAHs) ............................ 7-53 7.5.5.9 Acetaldehyde ............................................................................ 7-54 7.5.5.10 Nickel ....................................................................................... 7-55 7.5.5.11 Manganese ................................................................................ 7-55 7.5.5.12 Dioxins (Chlorinated di benzodioxins (CDDs) ......................... 7-56 7.5.5.13 Furans (Chlorinated dibenzofurans (CDFs)) ............................ 7-57 7.5.5.14 Other Air Toxics ....................................................................... 7-57
7.6 Limitations and Uncertainties ............................................................................ 7-57 7.6.1 Monte Carlo Analysis ............................................................................ 7-58 7.6.2 LML Assessment for PM2.5 .................................................................. 7-59 7.6.3 Alternate Concentration-response Functions for PM Mortality ............ 7-59 7.6.4 Alternate Concentration-response F unctions for Ozone Mortality ........ 7-60viii 7.6.5 Qualitative Assessment of Uncertainty and Other Analysis Limitations ............................................................................................. 7-60
7.7 Section 7 References .......................................................................................... 7-62

8 Comparison of Monetized Benefits and Costs.............................................................. 8-1
8.1 Boiler MACT ....................................................................................................... 8-1
8.2 Boiler Area Source Rule ...................................................................................... 8-1
8.3 Section 8 References ............................................................................................ 8-5 Appendixes A OAQPS Multimarket Model to Assess the Economic Impacts of Environmental Regulation ................................................................................ A- 1 1 B Detailed Economic Model Results by Sector ....................................................B- 1 1ix LIST OF FIGURES

Number Page
2-1. Distribution of Employment within Food Manufacturi ng (NAICS 311): 2006 ........... 2-3 2-2. Distribution of Total Value of Sh ipments within Food Manufacturing (NAICS 311): 2006 ....................................................................................................... 2-3 2-3. Electric Power Use Trends in Food Manufacturing (NAICS 311): 1997–2005 ........... 2-7 2-4. Establishment Concentration in Food Manufacturing (NAICS 311): 2002 ................. 2-8 2-5. Capacity Utilization Trends in Food Manufacturing (NAICS 311) ............................. 2-9 2-6. Employment Concentration in Food Manufacturing (NAICS 311): 2002 ................. 2-10 2-7. Capacity Trends in Food Manufacturing (NAICS 311) ............................................. 2-11 2-8. Industrial Production Trends in Food Manufacturing (NAICS 311) .......................... 2-15 2-9. International Trade Trends in F ood Manufacturing (NAICS 311) ............................. 2-15 2-10. Producer Price Trends in Food Manufacturing (NAICS 311) .................................... 2-16 2-11. Distribution of Value of Shipment s within Wood Product Manufacturing (NAICS 322): 2007 ..................................................................................................... 2-19 2-12. Distribution of Employme nt within Wood Product Manufacturing (NAICS 322): 2007 ................................................................................................................... 2-20 2-13. Electrical Power Use Trends in th e Wood Product Manuf acturing Industry (NAICS 321): 1997–2005 ........................................................................................... 2-24 2-14. Establishment Concentration in th e Wood Product Manuf acturing Industry (NAICS 321): 2002 ..................................................................................................... 2-26 2-15. Capacity Utilization Trends in the Wood Product Manufacturing Industry (NAICS 321) ............................................................................................................... 2-2 7 2-16. Employment Concentration in th e Wood Product Manufacturing Industry (NAICS 321): 2002 ..................................................................................................... 2-28 2-17. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321) .......... 2-29 2-18. Industrial Production Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997–2009 ........................................................................................... 2-32 2-19. International Trade Trends in th e Wood Product Manufacturing Industry (NAICS 321)].............................................................................................................. 2-32 2-20. Producer Price Trends in the Wood Pr oduct Manufacturing Industry (NAICS 321) .......................................................................................................................... ... 2-33 2-21. Distribution of Value of Shipments w ithin Paper Manufacturing (NAICS 322): 2007........................................................................................................................... .. 2-36 2-22. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007 ........ 2-36 2-23. Electrical Power Use Trends in the Pa per Manufacturing In dustry: 1997–2005 ....... 2-41 2-24. Establishment Concentration in Pape r Manufacturing Industry (NAICS 322): 2002........................................................................................................................... .. 2-43x 2-25. Capacity Utilization Trends in the Pape r Manufacturing Industry (NAICS 322) ...... 2-44 2-26. Employment Concentration in the Pa per Manufacturing Industry (NAICS 322): 2002........................................................................................................................... .. 2-45 2-27. Capacity Trends in the Paper Manu facturing Industry (NAICS 322) ........................ 2-46 2-28. Industrial Production Trends in the Paper Manufactur ing Industry (NAICS 322): 1997–2009................................................................................................................... 2- 49 2-29. International Trade Trends in the Pape r Manufacturing Industry (NAICS 322) ........ 2-50 2-30. Producer Price Trends in the Paper Ma nufacturing Industry (NAICS 222) ............... 2-50 2-31. Distribution of Employme nt within Chemical Ma nufacturing (NAICS 325): 2007........................................................................................................................... .. 2-53 2-32. Distribution of Total Value of Shipme nts within Chemical Manufacturing (NAICS 325): 2007 ..................................................................................................... 2-53 2-33. Electric Power Use Trends in Chemi cal Manufacturing (NAICS 325): 1997– 2005........................................................................................................................... .. 2-58 2-34. Establishment Concentration in Chemi cal Manufacturing (NAICS 325): 2002 ........ 2-60 2-35. Capacity Utilization Trends in Chem ical Manufacturing (NAICS 325) .................... 2-61 2-36. Employment Concentration in Chemical Manufacturing (NAICS 325): 2002 .......... 2-61 2-37. Capacity Trends in Chemical Manufacturing (NAICS 325) ...................................... 2-62 2-38. Industrial Production Trends in Chemi cal Manufacturing (NAICS 325) ................... 2-66 2-39. International Trade Trends in Chem ical Manufacturing (NAICS 325) ...................... 2-67 2-40. Producer Price Trends in Chemical Manufacturing (NAICS 325) ............................. 2-67

4-1. Market-Level Changes by Source and Option .............................................................. 4-3 4-2. Distribution of U.S. Surplus Cha nges by Sector: Major Sources ................................. 4-4 4-3. Distribution of Total Surplus Cha nges by Sector: Area Sources .................................. 4-6 4-4. Employment Impacts Using Morgenster n, Pizer, Shih (2002) (1,000 FTEs) ............. 4-10

5-1. Share of NAICS/Enterprise Employme nt Categories (<500 employees) with Sales Tests Exceeding 3% ............................................................................................ 5-4
6-1. Map of the photochemical modeling domains. The black outer box denotes the 36 km national modeling domain; the red inner box is the 12 km western U.S. grid; and the blue inner box is th e 12 km eastern U.S. grid. ......................................... 6-3 6-2. Locations of boilers in the NEI point invent ory for the future baseline (2016) ........... 6-9

7-1. Total Monetized PM 2.5 and Ozone Benefits for the Final Boiler MACT and Boiler Area Source Rule in 2014 .................................................................................. 7-2 7-2. Illustration of BenMAP Approach ................................................................................ 7-7 7-3. Data Inputs and Outputs for the BenMAP Model for a PM 2.5 Analysis ....................... 7-8 7-4. Change in Ambient PM 2.5 Levels from SO 2 Emissions from Point Source Boilers ....................................................................................................................... .. 7-17 7-5. Change in Ambient PM 2.5 Levels from PM Emissions from Point Source Boilers .... 7-18xi 7-6. Change in Ambient PM 2.5 Levels from SO 2 Emissions from Non-point Source Boilers ....................................................................................................................... .. 7-18 7-7. Change in Ambient PM 2.5 Levels from PM Emissions from Non-point Source Boilers ....................................................................................................................... .. 7-19 7-8. Breakdown of Monetized PM 2.5 Health Benefits Estimates using Mortality Function from Pope et al. (2002) ................................................................................ 7-28 7-9. Breakdown of Monetized Ozone Health Benefits Estimates using Mortality Function from Bell et al. (2004) ................................................................................. 7-29 7-10. Total Monetized PM 2.5 and Ozone Benefits Estimates for the Final Boiler MACT and Boiler Area Source Rule in 2014 ............................................................. 7-30 7-11. Breakdown of Monetized PM 2.5 Benefits Estimates by Precursor for the Final Boiler MACT in 2014 ................................................................................................. 7-31 7-12. Breakdown of Monetized PM 2.5 Benefits Estimates by Precursor for the Final Boiler Area Source Rule in 2014 ................................................................................ 7-31 7-13. Breakdown of Monetized PM 2.5 Benefits Estimates by Subcategory for the Final Boiler MACT in 2014 ................................................................................................. 7-32 7-14. Breakdown of Monetized PM 2.5 Benefits Estimates by Subcategory for the Final Boiler Area Source Rule in 2014 ................................................................................ 7-32 7-15. Percentage of Population Exposed to Baseline Air Quality Levels for Final Boiler MACT and Boiler Area Source Rule ............................................................... 7-33 7-16. Cumulative Percentage of Population E xposed to Baseline Air Quality Levels for Final Boiler MACT and Boiler Area Source Rule ................................................ 7-34 7-17. Estimated County Level Carcinogenic Ri sk from HAP exposure from outdoor sources (2002 NATA) ................................................................................................. 7-42 7-18. Estimated County Level Noncancer (Res piratory) Risk from HAP exposure from outdoor sources (2002 NATA) ........................................................................... 7-42 7-19. Percentage Reduction in Total Mercury Deposition in the Eastern U.S. .................... 7-48 7-20. Percentage Reduction in Total Mercury Deposition in the Western U.S. .................. 7-49

8-1. Net Benefits for the Fina l Major and Area Source Bo iler Rules at 3% Discount Rate .......................................................................................................................... ..... 8-4 8-2. Net Benefits for the Fina l Major and Area Source Bo iler Rules at 7% Discount Rate .......................................................................................................................... ..... 8-5xii LIST OF TABLES

Number Page
1-1. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2014 (millions of 2008$) ........................................ 1-4 1-2. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the Boiler Area Source Rule in 2014 (millions of 2008$) .................................................. 1-5

2-1. Key Statistics: Food Manuf acturing (North American Industry Classification System [NAICS] 311) ................................................................................................... 2-2 2-2. Industry Data: Food Manufact uring (NAICS 311) ....................................................... 2-2 2-3. Costs of Goods and Services Used in Food Manufacturing (NAICS 311) ($2007) ....................................................................................................................... ... 2-5 2-4. Key Goods and Services Used in Food Manufacturing (NAICS 311) ($2007, millions) ........................................................................................................... 2 -5 2-5. Energy Used in Food Manufact uring (NAICS 311) ..................................................... 2-6 2-6. Demand by Sector: Food Manufacturing (NAICS 311) ($2007, millions) .................. 2-8 2-7. Top Publicly Held U.S. Food Companies: 2007 ......................................................... 2-11 2-8. Corporate Income and Profitability fo r Food Manufacturing (NAICS 311) .............. 2-12 2-9. Small Business Size Standards: Food Manufacturing (NAICS 311) .......................... 2-13 2-10. Distribution of Economic Data by En terprise Size: Food Manufacturing (NAICS 311) ............................................................................................................... 2-1 4 2-11. Key Statistics: Wood Product Manu facturing (NAICS 321) ...................................... 2-17 2-12. Industry Data: Wood Product Ma nufacturing (NAICS 321) ...................................... 2-17 2-13. Costs of Goods and Services in Wood Product Manufacturing (NAICS 321) ($2007) ....................................................................................................................... . 2-21 2-14. Key Goods and Services Used in Wood Product Manufacturing (NAICS 321) ($2007, millions) ......................................................................................................... 2-2 2 2-15. Energy Used in Wood Product Manu facturing (NAICS 321) .................................... 2-23 2-16. Demand by Sector: Wood Product Ma nufacturing (NAICS 321) ($2007, millions) ..................................................................................................................... . 2-25 2-17. Largest U.S. Paper and Forest Products Companies: 2006 ........................................ 2-29 2-18. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 321) ...................................................................................... 2-30 2-19. Small Business Size Standards: Wood Pr oduct Manufacturing (NAICS 321) ........... 2-31 2-20. Key Statistics: Paper Manufact uring (NAICS 322) .................................................... 2-34 2-21. Industry Data: Paper Manufact uring (NAICS 322) .................................................... 2-34 2-22. Costs of Goods and Services Used in the Paper Manufacturing Industry (NAICS 322) .......................................................................................................................... ... 2-38xiii 2-23. Key Goods and Services Used in the Paper Manufacturing Industry (NAICS 322) ($millions, $2007) ................................................................................ 2-39 2-24. Energy Used in Paper Manuf acturing (NAICS 322) .................................................. 2-40 2-25. Estimated Energy Sources for the U.S. Pulp and Paper Industry ............................... 2-41 2-26. Demand by Sector: Paper Manufacturing Industry (NAICS 322) ($millions, $2007) ........................................................................................................................ . 2-42 2-27. Largest U.S. Paper and Forest Products Companies: 2006 ........................................ 2-46 2-28. Distribution of Economic Data by En terprise Size: Paper Manufacturing (NAICS 322) ............................................................................................................... 2-4 7 2-29. Small Business Size Standards: Pape r Manufacturing (NAICS 322) ......................... 2-48 2-30. Key Statistics: Chemical Ma nufacturing (NAICS 325).............................................. 2-52 2-31. Industry Data: Chemical Manuf acturing (NAICS 325) .............................................. 2-52 2-32. Key Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007, millions) ......................................................................................................... 2-5 4 2-33. Costs of Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007) ....................................................................................................................... . 2-56 2-34. Energy Used in Chemical Manufacturing (NAICS 325) ............................................ 2-57 2-35. Demand by Sector: Chemical Manufactur ing (NAICS 325) ($2007 millions) .......... 2-59 2-36. Top Chemical Producers: 2007 ................................................................................... 2-63 2-37. 2007 Corporate Income and Profitability (NAICS 325) ............................................. 2-63 2-38. Small Business Size Standards: Chemi cal Manufacturing (NAICS 325) .................. 2-65 2-39. Distribution of Economic Data by Ente rprise Size: Chemical Manufacturing (NAICS 325) ............................................................................................................... 2-6 6

3-1. Summary of Capital and Annual Costs fo r New and Existing Major Sources ............. 3-3 3-2. Summary of Annual Costs for New and Existing Area Sources .................................. 3-6

4-1. Distribution of Social Costs Majo r Sources (billio n, 2008$): 2014 ............................. 4-4 4-2. Distribution of Social Costs Area Sources (billion, 2008$): 2014 ............................... 4-5 4-3. Employment Impacts Using Morgenst ern, Pizer, Shih (2002) (FTE) .......................... 4-9 4-4. Cost Approach Jobs Impacts in FTEs ......................................................................... 4-12 4-5. Employment Im pacts .................................................................................................. 4-13 4-6. Aggregate Employment in FTEs ................................................................................ 4-13 4-7. Aggregate Employment Distri buted to Regulated Entities and Others in FTEs ........ 4-14

5-1. Affected Sectors and Size Standards ............................................................................ 5-2 5-2. Major Sources: Sales Tests Using Small Companies Identified in the Combustion Survey ....................................................................................................... 5-3 5-3. Information Collection Effort for F acilities with Combustion Units: Major Sources ....................................................................................................................... ... 5-8 5-4. Estimated Affected Facilities Using 13 State Boiler Inspec tor Inventory: Area Sources ....................................................................................................................... . 5-14xiv 6-1. Geographic Elements of Domains Us ed in Photochemical Modeling .......................... 6-4 6-2. Control Strategies and/or Growth Assump tions Included in the 2016 Projection ........ 6-6 6-3. Estimated Future Year (2016) Controlla ble Boiler Sector Emissions .......................... 6-8

7-1. Human Health and Welfare Effects of Air Pollutants Affected ................................... 7-9 7-2. Summary of Monetized Benefits Esti mates for Final Boiler MACT in 2014 (millions of 2008$) ..................................................................................................... 7-21 7-3. Summary of Monetized Benefits Estimate s for Final Boiler Area Source Rule in 2014 (millions of 2008$) ............................................................................................ 7-21 7-4. Summary of Estimated Reductions in Health Incidences from PM 2.5 for the Final Boiler MACT in 2014 (95 th percentile confidence interval) ............................. 7-22 7-5. Summary of Estimated Reductions in Health Incidences from PM 2.5 for the Final Boiler Area Source Rule in 2014 (95 th percentile confidence interval) ............ 7-23 7-6. Summary of Monetized Benefits Estimates from PM 2.5 for the Final Boiler MACT in 2014 (95 th percentile confidence interval) .................................................. 7-24 7-7. Summary of Monetized Benefits Estimates from PM 2.5 for the Final Boiler Area Source Rule in 2014 (95 th percentile confidence interval) ......................................... 7-25 7-8. Summary of Monetized Benefits Estima tes from Ozone for the Final Boiler MACT in 2014 (95 th percentile confidence interval) .................................................. 7-26 7-9. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 ) for 2014 ........................ 7-36 7-10. Monetized SCC-Derived Disbenefits of CO 2 Emission Increases in 2014 (millions of 2008$) ..................................................................................................... 7-37 7-11. Top HAPs by Mass from Boilers by Fuel Type ......................................................... 7-44

8-1. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the Boiler MACT in 2014 (millions of 2008$) ................................................................... 8-2 8-2 Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the Boiler Area Source Rule in 2014 (millions of 2008$) .................................................. 8-31-1 SECTION 1 INTRODUCTION The U.S. Environmental Protection Agency (E PA) is promulgating two rules for national emission standards for hazardous air pollutants (NESHAP) for new and existing industrial, commercial, and institutional boilers and process heaters. 1 One rule requires all major sources to meet hazardous air pollutant (HAP) emissions standards reflecting the application of the maximum achievable control t echnology (MACT). In the other rule, EPA is promulgating a NESHAP for two area source ca tegories: industrial boilers and institutional and commercial boilers. 2 The emission standards for controlling me rcury and polycyclic organic matter (POM) emissions are based on the MACT. The emission st andards for controlling other HAPs are based on EPA’s determination as to what constitutes the generally available control technology (GACT) or management practices. As part of the regulatory process, EPA is required to develop a regulatory impact analysis (RIA ). The RIA includes an economic impact analysis (EIA) and a small entity impacts analysis and doc uments the RIA methods and results.
1.1 Executive Summary The key results of the RIA are as follows:

 Engineering Cost Analysis: EPA estimates the major source NESHAP’s total annualized costs will be $1.4 billion (2008$). For the area source NESHAP, EPA estimates the total annualized costs will be $0.5 billion.

 Market Analysis: Under the major source NESHAP, the Agency’s economic model suggests the average national prices for industrial sect ors could be small (less than 0.01% higher). Average an nual domestic production may also fall by less than 0.01%. Because of higher domestic prices, imports rise by less than 0.01% per year. Market-level effects fo r the area source NESHAP are smaller when compared to the major source rule ; average price, pr oduction, and import changes are less than 0.01%.

 Social Cost Analysis: The estimated social cost of the major source rule is just under $1.5 billion (2008$). The Agency ’s economic model suggests that industries are able to pass approximate ly $0.5 billion of th e rule’s costs to consumers (e.g., higher market prices). Do mestic industries’ surplus falls by $1.4 billion, while other countries on net benef it from higher prices (a net increase in rest-of-the world [ROW] surplus of less than $0.1 billion). A dditional costs and fuel savings for new and existing major sources that are not included in the

1 On June 19, 2007, the U.S. Court of Appeals for the District of Co lumbia Circuit (DC Ci rcuit) vacated the NESHAP for industrial/commercial/institutional boilers and process heaters. This action provides EPA’s rule in response to the court’s vacatur. 2 Gas-fired boilers are not part of the area source categorie s of industrial boilers and institutional/commercial boilers.1-2 economic model represent a net benefit of $0.4 billion. 1 The estimated social cost of the area source rule is approxima tely $0.5 billion (2008$). The Agency’s economic model suggests that industrie s are able to pass approximately $0.2 billion of the rule’s costs to consumers. Domestic industries’ surplus falls by $0.3 billion and the net increase in ROW surp lus is less than $0.1 billion. Additional costs and fuel savings for unknown, exis ting, and new area sources not included in the economic model results represen t a net benefit of less than $0.1 billion.

 Employment Changes : The estimated employment changes range between

− 3100 to 6,500 empl o yees, with a central estimate of +1,700 employees for the major source NESHAP. The estimated employment changes range between

− 1,000 to 2,000 employees, with a central estimate of +500 employees for the area source NESHAP.

 Small Entity Analyses: EPA performed a scre ening analysis for impacts on small entities by comparing compliance co sts to sales/revenues (e.g., sales and revenue tests). EPA’s analys is found the tests were typi cally higher than 3% for small entities included in the screening analysis. Purs uant to section 603 of the RFA, EPA prepared an initial regulator y flexibility analysis (IRFA) for the proposed rule and convened a Small Business Advocacy Review Panel to obtain advice and recommendations of representativ es of the regulated small entities. A detailed discussion of the Panel’s ad vice and recommendations is found in the final Panel Report (Docket ID N o. EPA-HQ-OAR-2002-0058-0797). A summary of the Panel’s recommendations is also pr esented in the preamble to the proposed rule at 75 FR 32044-32045 (Jun e 4, 2010). In the p r oposed rule, EPA included provisions consistent with four of th e Panel’s recommendations. As required by section 604 of the RFA, we also prepared a final regulatory flexibility analysis (FRFA) the final rule (see Section 5).

 Benefits Analysis:

– The benefits from reducing some air pollu tants have not been monetized in this analysis, including reducing a comb ined 113,000 tons of carbon monoxide, 30,000 tons of HCl, 830 tons of HF, 2,900 pounds of mercury, 3,000 tons of other metals, and 23 grams of dioxins/furans ( TEQ) each year. We assess the benefits of these emission reductions qua litatively in this analysis.

– We have monetized the benefits from reducing PM (as a surrogate for metal HAP), as well as the co-benefits that re sult from the HAP emissions reductions (e.g., the pollution control equipment for HCl also reduces sulfur dioxide, a precursor to PM 2.5 ). Thus all monetized benefits reported reflect improvements in ambient PM 2.5 and ozone concentrations. As such, although the monetized benefits likely underestimate the total bene fits, the extent of the underestimate is unclear.

– Using a 3% discount rate, we estimate the total monetized bene fits of the Boiler MACT to be $22 billion to $54 billion in the implementation year (2014). Using a 7% discount rate, we estimate the total m onetized benefits of the Boiler MACT to

1 See additional details in Chapter 3 and Cost Appendices.1-3 be $20 billion to $49 billion in the im plementation year. Using alternate relationships between PM 2.5 and premature mortality s upplied by experts, higher and lower benefits estimates are plausible, but most of the expert-based estimates fall between these estimates.

– Using a 3% discount rate, we estimate the total monetized bene fits of the Boiler Area Source Rule to be $210 million to $520 million in the implementation year (2014). Using a 7% discount rate, we esti mate the total monetized of the Boiler Area Source Rule to be $190 million to $470 million in the implementation year.

– Using a 3% discount rate, we estimate the total monetized benefits of the combined Boiler MACT and Boiler Area S ource Rule to be $22 billion to $58 billion in the implementation year (2014) . Using a 7% discount rate, we estimate the total monetized benefits of the combined Boiler MACT and Boiler Area Source Rule to be $20 billion to $50 bill ion in the implementation year. All estimates are in 2008$.

 Net Benefits: For the Boiler MACT, the net benefits are $21 billion to $53 billion at a 3% discount rate for the benefits and $19 million to $48 billion at a 7% discount rate. For the Boiler Area S ource Rule, the net benefits are

− $280 million to $30 million at a 3% discount rate for the benefits and

− $300 million to

− $20 million at a 7% discount rate. These resu lts are shown in Tables 1-1 and 1-2.
1.2 Organization of this Report The remainder of this report supports and details the methodology a nd the results of the EIA:

 Section 2 presents the af fected industry profiles.

 Section 3 describes the e ngineering cost analysis.

 Section 4 describes the economic impact analysis.

 Section 5 describes the small entity analyses.

 Section 6 describes the air quality modeling performed by EPA.

 Section 7 presents the benefits estimates.

 Section 8 presents the net benefits.

 Appendix A describes the multimarket model used in the economic analysis.

 Appendix B provides additional economic model result tables by sector.1-4 Table 1-1. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2014 (millions of 2008$) a

3% Discount Rate 7% Discount Rate Selected Total Monetized Benefits b $22,000 to $54,000 $20,000 to $49,000 Total Social Costs 3 $1,500 1,500 Net Benefits $20,500 to $52,500 $18,500 to $47,500 Non-monetized Benefits 112,000 tons of carbon monoxide 30,000 tons of HCl 820 tons of HF 2,800 pounds of mercury 2,700 tons of other metals 23 grams of dioxins/furans (TEQ) Health effects from SO 2 exposure Ecosystem effects Visibility impairment Alternative Total Monetized Benefits b $18,000 to $43,000 $16,000 to $39,000 Total Social Costs b $1,900 $1,900 Net Benefits $16,100 to $41,100 $14,100 to $37,100 Non-monetized Benefits 112,000 tons of carbon monoxide 22,000 tons of HCl 620 tons of HF 2,400 pounds of mercury 2,600 tons of other metals 23 grams of dioxins/furans (TEQ) Health effects from SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementati on year (2014), and are rounded to two significant figures. These results

include units anticipated to come online and the lowest cost disposal assumption. b The total monetized benefits reflect the human health benefits associat ed with reducing exposure to PM 2.5 through

reductions of directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 , as well as reducing exposure to ozone through reductions of VOCs. It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, rega rdless of their chemical com position, are equally potent in causing premature mortality because the scientific evidence is not yet suffici ent to allow differentiation of effect estimates by particle type. These estimates include ener gy disbenefits associated w ith the increased emissions from additional energy usage valued at $22 million for the selected option and $37 million for the alternative option. Ozone benefits are valued at $3.6 to $15 million for both options.

c The methodology used to estimate social costs for one year in the multimarket model using surplus changes

results in the same social co sts for both discount rates.1-5 Table 1-2. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Boiler Area Source Rule in 2014 (millions of 2008$) a

3% Discount Rate 7% Discount Rate Final MACT/GACT Approach: Selected Total Monetized Benefits b $210 to $520 $190 to $470 Total Social Costs c $490 $490 Net Benefits

− $280 to $30

− $300 to

− $20 Non-monetized Benefits 1,100 tons of carbon monoxide 340 tons of HCl 8 tons of HF 90 pounds of mercury 320 tons of other metals <1 gram of dioxins/furans (TEQ) Health effects from SO 2 exposure Ecosystem effects Visibility impairment Proposed MACT Approach: Alternative Total Monetized Benefits b $200 to $490 $180 to $440 Total Social Costs c $850 $850 Net Benefits

− $650 to

− $360

− $670 to

− $410 Non-monetized Benefits 1,100 tons of carbon monoxide 340 tons of HCl 8 tons of HF 90 pounds of mercury 320 tons of other metals <1 gram of dioxins/furans (TEQ) Health effects from SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementation year (2014), an d are rounded to two significant figures. These results

include units anticipated to come online and the lowest cost disposal assumption. b The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through

reductions of directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premat ure mortality because the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. These estimates include energy disbenefits associated with the increased emi ssions from additional energy usage valued at less than $1 million.

c The methodology used to estimate social costs for one year in the multimarket model using surplus changes

results in the same social co sts for both discount rates.1-6 1.3 Section 1 References Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air Pollution and Mortality.” American Journal of Respir atory and Critical Care Medicine 173:667-672. Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortal ity, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.2-1 SECTION 2 INDUSTRY PROFILES In this section, we provide an introduction selected industries that are affected by the rules. The industries were selected based on hi gh facility population count s within 3-digit NAICs industries reported in the combusti on facility survey. The purpose is to give the reader a general understanding of economic aspects and industry tr ends to provide additional context for the economic impact analysis.
2.1 Food Manufacturing
2.1.1 Introduction Food manufacturing involves the transforma tion of raw agricultural and livestock products into processed food. Between 1997 and 2002, shipment values stagnated, falling 0.38%, while the number of employees and payr oll increased 2.71% and 7.76%, respectively (Table 2-1). This trend reversed between 2002 and 2006, as shipment values rose 4.77 % and number of employees and payroll fell 5.94% a nd 3.28% respectively (Tab le 2-1). Shipments, payroll, and employment conti nued to increase between 2006 and 2007, but there was a notable drop in the number of establishments between 2002 and 2007 (Table 2-1). As Table 2-2 shows, payroll per employee grew 4.91% from 1997 to 2002 and continued to increase, albeit at a slower rate of 2.83%, from 2002 to 2006. Be tween 2006 and 2007, the payroll per employee declined as the growth in employees outpaced th e increase in the annual payroll (Table 2-2).
The food manufacturing industry consists of nine different industry groups, each distinguished by the livestock or agricultural products used as raw materials for the processed food products as follows:

 Animal Food Manufacturing (North Amer ican Industry Classification System [NAICS] 3111)

 Grain and Oilseed Milling (NAICS 3112)

 Sugar and Confectionery Produc t Manufacturing (NAICS 3113)

 Fruit and Vegetable Preserving and Speci alty Food Manufacturing (NAICS 3114)

 Dairy Product Manufacturing (NAICS 3115)

 Animal Slaughtering and Processing (NAICS 3116)

 Seafood Product Preparation and Packaging (3117)

 Bakeries and Tortilla Ma nufacturing (NAICS 3118)2-2 Table 2-1. Key Statistics: Food Manufacturing (North American Industry Classification System [NAICS] 311)

1997 2002 2006 2007 Shipments ($2007, millions) $528,928 $526,939 $552,075 $589,550 Payroll ($2007, millions) $48,118 $51,852 $50,151 $50,467 Employees 1,466,956 1,506,781 1,417,274 1,466,683 Establishments 26,302 27,899 NA 22,055
NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (2007 NAICS Basis): 2002 and 2007.” <http://factfind er.census.gov>; (January 4, 2010).
Table 2-2. Industry Data: Food Manufacturing (NAICS 311)

Industry Data 1 997 2002 2006 2007 Total shipments ($2007, millions) $528,928 $526,939 $552,075 589,550 Shipments per establishment ($2007, thousands) $20,110 $18,887 NA $26,731 Average Shipments per employee ($2007) $360,561 $349,712 $389,533 $401,961 Average Shipments per $ of payroll ($2007) $10.99 $10.16 $11.01 $11.68 Average Annual payroll per employee ($2007) $32,800.97 $34,412.12 $35,385.46 $34,409.00 Average Employees per establishment 56 54 NA 67
NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (2007 NAICS Basis): 2002 and 2007.” <http://factfind er.census.gov>; (January 4, 2010). In 2006, Animal Slaughtering and Processing made up the largest share of both employment (33%) and the value of shipments (27%) in food manufacturing (Fi gures 2-1 and 2-2).2-3 Animal Food Manufacturing, 3% Grain and Oilseed Milling,

4% Sugar and Confectionery Product Manufacturing, 5% Fruit and Vegetable Preserving and Specialty Food Manufacturing, 12% Dairy Product Manufacturing, 9%
Animal Slaughtering and Processing, 33% Seafood Product Preparation and

Packaging, 3% Bakeries and Tortilla

Manufacturing, 20% Other Food Manufacturing, 11%

Figure 2-1. Distribution of Employment with in Food Manufacturing (NAICS 311): 2006 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008).

Animal Food Manufacturing, 6% Grain and Oilseed Milling, 10% Sugar and Confectionery Product Manufacturing, 6%

Fruit and Vegetable

Preserving and Specialty Food Manufacturing, 12%

Dairy Product Manufacturing, 14% Animal Slaughtering and Processing, 27% Seafood Product Preparation and

Packaging, 2% Bakeries and Tortilla

Manufacturing, 11% Other Food Manufacturing, 12%

Figure 2-2. Distribution of Total Value of Shipments within Food Manufacturing (NAICS 311): 2006 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008).2-4 Many major environmental regulations direc tly affect the food manufacturing industry and/or other markets that provide key goods a nd services to the indus try (e.g., energy). RTI’s multimarket model is specifically designed to an alyze these types of regulations. The model emphasizes the links among industrial sectors an d provides policy makers with new insights about the direct and indirect e ffects of a regulatory program and the distribution of costs across the U.S. economy.
2.1.2 Supply and Demand Characteristics Next, we provide a broad overview of th e supply and demand sides of the food manufacturing industry. We emphasize the economic interactions this indu stry has with other industries and people, including identifying the key goods and serv ices used by the industry and the major uses and consumers of food manufacturing products.
2.1.2.1 Goods and Services Used in Food Manufacturing In 2006, the cost of materials made up 57% of the value of shipments in food production. Total employee compensation accounted for 12% of th is value, with half of that coming from production workers’ wa ges (Table 2-3).
The top 10 industry groups supplying inputs to food production accounted for 84% of the total intermediate inputs to the industry, with the top three indust ry groups (food products, animal products, and crop products) accounting for over half of the tota l intermediate inputs (Table 2-4). Electric power generation, transmi ssion, and distribution accounted for 2% of the total intermediate inputs, wher eas boilers, tanks, and shipping containers accounted for 1%.
2.1.2.2 Energy The Department of Energy (DOE) classifies the entire food products industry as an energy-intensive industry to model within it s Industrial Demand Module (DOE, 2008). In 2002, food manufacturing accounted for 6.86% of the total fuel consumption by all manufacturing industries (NAICS 311–339) and 1 9.24% of the conventional boiler use fuel consumption by all manufacturing industries (DOE, Ener gy Information Administration, 2007a).2-5 Table 2-3. Costs of Goods and Services Us ed in Food Manufacturing (NAICS 311) ($2007)

Industry Ratios 2005 Share 2006 Share Total shipments ($millions) $563,797 100% $552,075 100% Total compensation ($millions) $64,909 12% $64,027 12%

Annual payroll $50,650 9% $50,151 9% Fringe benefits $14,259 3% $13,877 3% Total employees 1,440,283 1,417,274

Average compensation per employee $45,067 $45,176 Total production workers’ wages ($millions) $33,983 6% $33,670 6%
Total production workers 1,099,530 1,090,081 Total production hours (thousands) 2,242,558 2,198,396

Average production wages per hour $15 $15 Total cost of materials ($thousands) $315,993 56% $312,847 57%

Materials, parts, packaging $286,895 51% $284,028 51% Purchased electricity $4,513 1% $4,787 1% Purchased fuel $5,136 1% $5,398 1% Other $19,449 3% $18,634 3% Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). Table 2-4. Key Goods and Services Used in Food Manufacturing (NAICS 311) ($2007, millions)

Sector BEA Code Food Products Food products 3110 $91,518 Animal products 1120 $85,785 Crop products 1110 $43,109 Management of companies and enterprises 5500 $34,235 Wholesale trade 4200 $27,849 Converted paper products 3222 $18,782 Truck transportation 4840 $12,943 Plastics and rubber products 3260 $9,641 Electric power generation, transmission, and distribution 2211 $6,004 Boilers, tanks, and shipping containers 3324 $4,564 Total intermediate inputs T005 $400,067 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.2-6 In both 2005 and 2006, purchased electricity a nd fuel each accounted for 1% of the total value of shipments in food manuf acturing (Table 2-3). In 2002, to tal energy consumption totaled 1,116 TBTU, a 7% increase over 1998 (Table 2-5). Of this total fuel consumption, the largest share (41.72%) was consumed for indirect uses including conventional boiler use and combined heat and power (CHP) and/or cogeneration process (MECS Table 5.2). Between 1997 and 2005, while the manufacturing sector as a whole used less electricity, food manufacturing used more electricity (Figure 2-3). From 2005 to 2006, the electricity consump tion increased by nearly 9% (Table 2-5). Table 2-5. Energy Used in Food Manufacturing (NAICS 311)

Fuel Type 1998 2002 2006 Net electricity a (million kWh) 62,457 67,521 73,440 Residual fuel oil (million bbl) 2 2 4 Distillate fuel oil b (million bbl) 3 3 3 Natural gas c (billion cu ft) 553 560 618 LPG and NGL d (million bbl) 1 1 1 Coal (million short tons) 6 8 7 Coke and breeze (million short tons) * * * Other e (trillion BTU) 97 90 107 Total (trillion BTU) 1,044 1,116 1,186

a Net electricity is obtained by summing purchases, transfers in, and generation from noncombustible renewable

resources, minus quantities sold and transferred out. It does not include electri city inputs from on-site cogeneration or generation from combustible fuels because that energy has already been included as generating fuel (for example, coal). b Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels.

c Natural gas includes natural gas obtained from utilities, local distribution companies, and any other supplier(s),

such as independent gas producers, gas brokers, mark eters, and any marketing subsidiaries of utilities. d Examples of liquefied petroleum gases (LPGs) are ethane, ethylene, propane, propylene, normal butane, butylene,

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs).

e Other includes net steam (the sum of purchases, generatio n from renewables, and net transfers), and other energy

that respondents indicated was us ed to produce heat and power. * Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007a. “2002 Energy Consumption by Manufacturers—Data Tables.” Tables 3.2 and N3.2. Washington, DC: DOE <http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>. U.S. Department of Energy, Energy Information Administration. 2009a. “2006 Energy Consumption by Manufacturers—Data Tables.” Table 3.1. Washington, DC: DOE. <http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html>.2-7 90 95 100 105 110 115 120Jan‐97Jun‐97Nov‐97Apr‐98Sep‐98Feb‐99Jul‐99Dec‐99May‐00Oct‐00Mar‐01Aug‐01Jan‐02Jun‐02Nov‐02Apr‐03Sep‐03Feb‐04Jul‐04Dec‐04May‐05Oct‐05IndexValue(Jan‐97=100)

Manufacturing Food
Figure 2-3. Electric Power Use Trends in Food Manufacturing (NAICS 311): 1997–2005 Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” <http://www .federalreserve.gov/datadownload/>.
2.1.2.3 Uses and Consumers The majority of food manufacturing’s total co mmodity output (58%) is sold for personal consumption. Of the sales for intermediate use, 42% are sold back into the food manufacturing industry (Table 2-6).
2.1.3 Firm and Market Characteristics This remaining subsection describes geographi c, production, and market data. These data provide the basis for further an alysis, including regul atory flexibility analyses, and give a complete picture of the recent histori cal trends of production and pricing.
2.1.3.1 Location In 2002, California had the most food manufact uring establishments in the United States, followed by New York and Texas (see Figure 2-4). In addition, Pennsylvania, Illinois, Wisconsin, New Jersey, and Florida had ove r 1,000 establishments in their states.2-8 Table 2-6. Demand by Sector: Food Manuf acturing (NAICS 311) ($2007, millions)

Sector BEA Code Food Products Food manufacturing 3110 $91,518 Food services and drin king places 7220 $37,291 Animal production 1120 $15,870 General state and local government services S007 $15,170 Retail trade 4A00 $13,985 Beverage manufacturing 3121 $11,703 Hospitals 6220 $9,539 Educational services 6100 $4,485 Nursing and residential care facilities 6230 $4,187 Social assistance 6240 $2,277 Total intermediate use T001 $217,570 Personal consumption expenditures F010 $301,748 Exports of goods and services F040 $28,151 Imports of goods and services F050

− $33,119 Total final uses (GDP) T004 $299,470 Total commodity output T007 $517,040 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.

Establishments Fewer than 250 250 - 499 500 - 999 1,000 - 2,000 More than 2,000 Figure 2-4. Establishment Concentration in Food Manufacturing (NAICS 311): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).2-9 2.1.3.2 Production Capacity and Utilization Capacity utilization of th e food manufacturing industry did not fall off during the recession of 2001 as much as the manufacturi ng sector as a whole (Figure 2-5). Food manufacturing’s capacity utiliz ation has remained higher than manufacturing as a whole and went above 85% in the spring of 2008. The effect s of the recent economic downturn have not affected capacity utilization as sharply in the food industry rela tive to the overall manufacturing sector (Figure 2-5).

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08PercentageCapacityUtilization

Manufacturing Food
Figure 2-5. Capacity Utilization Trends in Food Manufacturing (NAICS 311) Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” <http://www.federalreserve.gov/datadownload/>.
2.1.3.3 Employment The geographic distribution of employment in food manufact uring varies substantially from the distribution of establishments. In 2002, Arkansas, ranked thirty-first in number of establishments and had the eighth most employ ees (53,844) because of its national high of 199 employees per establishment. New York, ranked second in number of establishments, had only the tenth most employees (50,012). North Caro lina and Georgia also had greater than 50,000 employees, despite having fewer than 600 establishments (Figure 2-6).2-10 Employment Fewer than 10,000 10,000 to 19,999 20,000 to 39,999 40,000 to 80,000 More than 80,000 Figure 2-6. Employment Concentration in Food Manufacturing (NAICS 311): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).
2.1.3.4 Plants and Capacity Production capacity in food manufacturing only grew 17.94% between 1997 and early 2008, a compound annual growth rate (CAGR) of 1.45% . This is substantially less than the 42.50% growth for the manufacturing industry as a whole (Figure 2-7).
2.1.3.5 Firm Characteristics In fiscal year 2007, the top eight food manuf acturing companies each had greater than $10 billion in sales. These companies, however, are global, many with a large portion of both sales and production coming from operations outside of the United States (Table 2-7). The largest U.S. food manufacturing company, Kraft Foods Inc., has 50.27% of its long-lived assets located outside of the United St ates (Kraft Foods Inc., 2008).2-11 0 20 40 60 80 100 120 140 160Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(January1997=100)

Manufacturing Food
Figure 2-7. Capacity Trends in Food Manufacturing (NAICS 311) Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Capacity.” <http://www.federalreserve.gov/datadownload/>. Table 2-7. Top Publicly Held U.S. Food Companies: 2007

Sales ($millions) % of Sales in North America Kraft Foods Inc. 37,241 57.8% Tyson Foods Inc. 26,900 90.0% General Mills Inc. 12,442 82.9% Sara Lee Corp. 12,278 53.8% ConAgra Foods Inc. 12,028 89.2%
Smithfield Foods Inc. a 11,911 86.2% Dean Foods Co. 11,822 >99.0% Kellogg Co. a 11,776 66.1% H.J. Heinz Co. 9,002 42.3% Campbell Soup Co. 7,867 69.0%

a Percentage of sales in the United States is actually percentage of sales in North America. Source: Graves, T. 2008. “Food and Nonalcoholic Beverages.” Standard and Poor’s Industry Surveys . 176(25).2-12 For the industry as a whole, the number of corporations as well as the number of corporations with net income in the food manufacturing indu stry grew between 2004 and 2005. Although the overall number of co mpanies continued to grow in 2006, the number of those with a positive net income declined along with prof it margins and total receipts (Table 2-8). Table 2-8. Corporate Income and Profitabili ty for Food Manufacturing (NAICS 311)

2004 2005 2006 Number of corporations 14,408 14,956 16,146 Number of corporations with net income 6,541 7,503 7,333 Total receipts (thousands) $502,149 ,944 $504,944,378 $484,193,319 Business receipts (thousands) $477,9 06,423 $465 ,369,666 $ 459,884,663 Before-tax profit margin 5.27% 10.09% 7.43% After-tax profit margin 3.74% 7.62% 5.11% Source: Internal Revenue Service, U.S. Department of Treasury. 2008a. “Corporation Source Book: Data File 2005.” <http://www.irs.gov/taxs tats/article/0,,id=167415,00.html>; (January 14, 2009).
2.1.3.6 Size Distribution The primary criterion for categorizing a busine ss as small is number of employees, using definitions by the SBA for regulatory flexibility analyses. The data describing size standards are provided in Table 2-9. Over 80% of the NAICS industries within the food manufacturing industry use a cutoff of 500 employees. In 2002, enterprises with fewer than 500 employees accounted for 32% of employment and 23% of r eceipts within food manuf acturing (Table 2-10).
2.1.3.7 Domestic Production Between 1997 and early 2008, overall manufactur ing production grew faster (34.88%) than the food manufacturing component (26.18%) (Figure 2-8). The food manufacturing industry has been less volatile, particularly during the recession of 2001 and the current economic downturn.
2.1.3.8 International Trade In 2006, the United States regained a trade surplus in food manufact uring it had briefly lost during 2004 to 2005 (see Figure 2-9). The trade surplus in 2007 was over $4 billion. Both exports and imports have declined since their 2008 peak as a result of the global economic recession.2-13 Table 2-9. Small Business Size Standards: Food Manufacturing (NAICS 311)
NAICS Description Employees 311111 Dog and Cat Food Manufacturing 500 311119 Other Animal Food Manufacturing 500 311211 Flour Milling 500 311212 Rice Milling 500 311213 Malt Manufacturing 500 311221 Wet Corn Milling 750 311222 Soybean Processing 500 311223 Other Oilseed Processing 1,000 311225 Fats and Oils Refining and Blending 1,000 311230 Breakfast Cereal Manufacturing 1,000 311311 Sugarcane Mills 500 311312 Cane Sugar Refining 750 311313 Beet Sugar Manufacturing 750 311320 Chocolate and Confectionery Ma nufacturing from Cacao Beans 500 311330 Confectionery Manufacturing from Purchased Chocolate 500 311340 Non-Chocolate Confectionery Manufacturing 500 311411 Frozen Fruit, Juice and Vegetable Manufacturing 500 311412 Frozen Specialty Food Manufacturing 500 311421 Fruit and Vegetable Canning3 3,500 311422 Specialty Canning 1,000 311423 Dried and Dehydrated Food Manufacturing 500 311511 Fluid Milk Manufacturing 500 311512 Creamery Butter Manufacturing 500 311513 Cheese Manufacturing 500 311514 Dry, Condensed, and Evaporated Dairy Product Manufacturing 500 311520 Ice Cream and Frozen Dessert Manufacturing 500 311611 Animal (except Poultry) Slaughtering 500 311612 Meat Processed from Carcasses 500 311613 Rendering and Meat By-product Processing 500 311615 Poultry Processing 500 311711 Seafood Canning 500 311712 Fresh and Frozen Seafood Processing 500 311811 Retail Bakeries 500 311812 Commercial Bakeries 500 311813 Frozen Cakes, Pies, and Other Pastries Manufacturing 500 311821 Cookie and Cracker Manufacturing 750 311822 Flour Mixes and Dough Manufacturing from Purchased Flour 500 (continued)2-14 Table 2-9. Small Business Size Standards: F ood Manufacturing (NAICS 311) (continued)
NAICS Description Employees 311823 Dry Pasta Manufacturing 500 311830 Tortilla Manufacturing 500 311911 Roasted Nuts and Peanut Butter Manufacturing 500 311919 Other Snack Food Manufacturing 500 311920 Coffee and Tea Manufacturing 500 311930 Flavoring Syrup and Concentrate Manufacturing 500 311941 Mayonnaise, Dressing and Other Prepared Sauce Manufacturing 500 311942 Spice and Extract Manufacturing 500 311991 Perishable Prepared Food Manufacturing 500 311999 All Other Miscellaneous Food Manufacturing 500 Source: U.S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/sizestandardstopics/size/index.html>.

Table 2-10. Distribution of Economic Data by Enterprise Size: Food Manufacturing (NAICS 311)
Enterprises with:

Variable Total 1 to 20 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees Firms 21,384 13,645 3,935 1,247 147 63 96 Establishments 25,698 13,719 4,254 1,951 370 211 319 Employment 1,443,766 85,850 156, 158 218,041 67,104 30,099 72,262 Receipts ($millions) $457,521 $12,665 $32,274 $56,661 $23,103 $10,007 $21,878 Receipts/firm ($thousands) $21,395 $928 $8,202 $45,438 $157,163 $158,835 $227,898 Receipts/establishment ($thousands) $17,804 $923 $7,587 $29,042 $62,440 $47,425 $68,584
Receipts/employment ($) $316,894 $147,523 $206,678 $259,862 $344,286 $332,457 $302,762

a Excludes Statistics of U.S. Businesses (SUSB) employment category for zero empl oyees. These entities only operated for a

fraction of the year. Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Busi nesses: U.S. Detail Employment Sizes: 2002.” <http://www.census.gov/csd/ susb/download_susb02.htm>.2-15 60 70 80 90 100 110 120Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(2002=100)

Manufacturing Food, Beverage, and Tobacco Figure 2-8. Industrial Production Trends in Food Manufacturing (NAICS 311) Source: Federal Reserve Board. 2008. “Industrial Produc tion and Capacity Utilization: Industrial Production.” <http://www.federalreserve.gov/datadownload/>.

0 10 20 30 40 50 60

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2008 YTD 2009 YTDActualDollars($billion)

U.S. Domestic Exports (FAS Value) U.S. Imports for Consumption (CIF Value) Figure 2-9. International Trade Trends in Food Manufacturing (NAICS 311) Source: U.S. International Trade Commission. 2008. “U.S. Domestic Exports” & “U.S. Imports for Consumption.” <http://dataweb.usitc.gov/s cripts/user_set.asp>.2-16 2.1.3.9 Market Prices Prices of goods in food manufacturing have mo ved generally in line wi th prices in overall manufacturing (see Figure 2-10) . Both indexes increased over 31% since between early 2003 and early 2008, a CAGR of 5.13%. This rise was followed by a marked decline in recent years along with the downward trend in prices throughout the economy.

0 20 40 60 80 100 120 140 160 180 20019901991199219931994199519961997199819992000200120022003200420052006200720082009IndexValues(December1984=Base)

Manufacturing Food
Figure 2-10. Producer Price Trends in Food Manufacturing (NAICS 311) Source: U.S. Department of Labor; Bureau of Labor Statistics. 2010. “Producer Price Indexes.” <http://www.bls.gov/pPI/Series Id: PCU311—311—Food Manufacturing & PCUOMFG–OMFG–Total Manufacturing>.
2.2 Wood Product Manufacturing
2.2.1 Introduction According to a report by Sta ndard & Poor’s (2008), a numbe r of factors are shaping the current economic environment for wood products, including, but not li mited to, the housing slump, high input costs, low prices for lumber a nd other building material s, and a weak dollar. Table 2-11 shows that revenues in this industry ar e not entirely predictable, exhibiting a drop in shipment revenue between 1997 and 2002 but a rise back to within $5 bil lion of the 1997 value in 2006 and a decline to within $14 billion of the 2006 value in 2007.2-17 Table 2-11. Key Statistics: Wood Product Manufacturing (NAICS 321)

1997 2002 2006 2007 Shipments ($2007, millions) $110,956 $102,721 $115,390 $101,879 Payroll ($2007, millions) $17,959 $18,528 $18,623 $17,439 Employees 570,034 543,459 536,094 519,651 Establishments 17,367 17,255 NA 14,862

NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” Accessed on December 27, 2009. While total payroll dropped 3% over from 1997 to 2007, annual payroll per employee rose 6.5% because of the decline in the number of employees (Table 2-12). Shipments per employee grew 10.6% from 1997 to 2006 and dropped 8.9 % from 2006 to 2007 (Table 2-12). Table 2-12. Industry Data: Wood Produc t Manufacturing (NAICS 321)

Industry Data 1 997 2002 2006 2007 Total shipments ($2007, millions) $110,956 $102,721 $115,390 $101,879 Shipments per establishment ($thousands) $25,613 $5,953 NA $6,855 Average Shipments per employee ($2007) $194,648 $189,014 $215,243 $196,053 Average Shipments per $ of payroll ($2007) $6.18 $5.54 $6.20 $5.84 Average Annual payroll per employee ($2007) $31,504 $34,093 $34,738 $33,558

Average Employees per establishment 33 31 NA 35

NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” Accessed on December 27, 2009.2-18 The U.S. Census Bureau categorizes this i ndustry’s facilities into three categories: “sawmills and wood preservation;” “veneer, plywood, and engineered wood product manufacturing;” and “other wood product manufact uring.” These are further divided into the following types of facilities as defined by the Census Bureau:

 Sawmills and Wood Preservation

– Sawmills and Wood Preservation (NAICS 32111): This industry comprises establishments primarily engaged in one or more of the following manufacturing activities: (a) sawing dimension lumber , boards, beams, timber, poles, ties, shingles, shakes, siding, and wood chips from logs or bolts; (b) sawing round wood poles, pilings, and posts and trea ting them with preservatives; and (c) treating wood sawed, planed, or shaped in other establishments with creosote or other preservatives to pr event decay and to protect against fire and insects. Sawmills may plane the rough lumber that they make with a planning machine to achieve smoothness and uniformity of size.

 Veneer, Plywood, and Engineered Wood Product Manufacturing

– Veneer, Plywood, and Engineered Wood Product Manufacturing (NAICS 32121): This industry comprises establishments pr imarily engaged in one or more of the following manufacturing activities: (a) ve neer and/or plywood, (b) engineered wood members, and (c) reconstituted w ood products. This industry includes manufacturing plywood from veneer made in the same establishment or fromveneer made in other establishments, and manufacturing plywood faced with nonwood materials, such as plastics or metal.

 Other Wood Product Manufacturing

– Millwork (NAICS 32191): This industry comprises establishments primarily engaged in manufacturing hardwood and softwood cut stock and dimension stock (i.e., shapes); wood windows and wood doors; and other millwork including wood flooring. Dimension stock or cut stock is defined as lumber and worked wood products cut or shaped to specialized si zes. These establishments generally use woodworking machinery, such as jointers, planers, lathes, and routers to shape wood.

– Wood Container and Pallet Manufact uring (NAICS 32192): This industry comprises establishments primarily engaged in manufacturing wood pallets, wood box shook, wood boxes, other wood containe rs, and wood parts for pallets and containers.

– All Other Wood Product Manuf acturing (NAICS 32199): This industry comprises establishments primarily engaged in manufacturing wood products (except establishments operating sawmills a nd wood preservation facilities; and establishments manufacturing veneer , plywood, engineered wood products, millwork, wood containers, or pallets).2-19 Figure 2-11 shows that the i ndustry proportion of the value of shipments for other wood product manufacturing (51%) was greater than th e value of shipments for sawmills and wood preservation (27%) and veneer , plywood, and engineered wood products (22%). Figure 2-12 indicates that the majority of employees in th is industry fell under ot her wood products (60%). Veneer, plywood, and engineered wood products ha d the same percentage (20%) of employees as sawmills and wood preservation (20%), even though it contributed to a lesser portion of the value of shipments.

Veneer, Plywood, & Engineered Wood Product Mfg (NAICS 3212), 22%

Sawmills & Wood Preservation (NAICS 3211),

27% Other Wood Product Mfg (NAICS 3219),

51%

Figure 2-11. Distribution of Value of Shipm ents within Wood Product Manufacturing (NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statis tics: 2007.” <http://factfinde r.census.gov>. Accessed on December 27, 2009. [S ource for 2007 numbers]2-20 Other Wood Product Mfg (NAICS 3219),

60% Sawmills & Wood Preservation (NAICS 3211), 20%

Veneer, Plywood, &

Engineered Wood Product Mfg (NAICS 3212),

20%

Figure 2-12. Distribution of Employment wi thin Wood Product Manufacturing (NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key St atistics: 2007” Release Date: 12/22/09. <http://factfinder.census.gov>. A ccessed on December 27, 2009.

2.2.2 Supply and Demand Characteristics Next, we provide a broad overview of the supply and demand sides of the wood product manufacturing industry. We emphasize the economic interactions this indu stry has with other industries and people and identify the key goods a nd services used by the industry and the major uses and consumers wood products.
2.2.2.1 Goods and Services Used in Wood Product Manufacturing In 2007, the cost of materials made up 59% of the total shipment value of goods in the wood product manufacturing industry (Table 2-13). Total compensation of employees represented 22% of the total value in 2007. Both the number of total shipments and th e number of employees in this industry decreased between 2005 a nd 2007—the former by 14% and the latter by 3%.
The top 10 industry groups supplying input s to the wood product industry accounted for 80% of the total intermediate inputs accordi ng to 2008 Bureau of Economic Analysis data (Table 2-14). The largest comes from the wood product industry itself. This is quite understandable, since the descript ions of the various industries within wood product manufacturing imply that they supply each other with products in order to add value and distribute their products to the broader market. The top five inputs ar e rounded out by forestry and logging products, wholesale trade, management of companies a nd enterprises, and truck transportation, which together make up 70% of the total cost of input.2-21 Table 2-13. Costs of Goods and Services in Wood Product Manufacturing (NAICS 321) ($2007)

Industry Ratios 2005 Share 2006 Share 2007 Share Total shipments (millions) $118,705 100% $115,390 100% $102,002 100% Total compensation (millions) $23,327 20% $23,306 20% $22,513 22% Annual payroll millions $18,884 16% $18,623 16% $17,444 17% Fringe benefits $4,442 4% $4,683 4% $5,069 5% Total employees 538,890 536,094 524,212 Average compensation per employee $43,286 $43,473 $42,947 Total production workers’ wages (millions) $13,363 11% $13,132 11% $12,086 12%

Total production workers 431,569 432,315 417,471 Total production hours (thousands) 911,332 887,613 837,074

Average production wages per hour ($2007) $15 $15 $14

Total cost of materials (thousands) $71,808 60% $69,892 61% $60,682 59%

Materials, parts, packaging $65,319 55% $63,499 55% $54,462 53% Purchased electricity $1,530 1% $1,625 1% $1,446 1% Purchased fuel $810 1% $835 1% $843 1% Other $4,149 3% $3,933 3% $3,931 4% Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment: using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” Accessed on December 27, 2009. 2.2.2.1.1 Energy. The Department of Energy (DOE) categorizes wood product manufacturing (NAICS 321) as a non-energy-intensive i ndustry. The 2008 Annual Energy Outlook predicts that the wood product industry will be one of five (out of eight) non-energyintensive industries experiencing positive average growth of delivered energy consumption between 2006 and 2030 (DOE, 2008).2-22 Table 2-14. Key Goods and Services Used in Wood Product Manufacturing (NAICS 321) ($2007, millions)

Description BEA Commodity Code Wood Products Wood products 3210 $20,989 Forestry and logging products 1130 $18,914 Wholesale trade 4200 $5,417 Management of companies and enterprises 5500 $2,853 Truck transportation 4840 $2,542 Electric power generation, transmission, and distribution 2211 $1,388 Other fabricated metal products 332B $1,310 Nonmetallic mineral products 3270 $1,110 Real estate 5310 $799 All other administrative and support services 561A $748 Architectural and structural metal products 3323 $725 Rail transportation 4820 $723 Other inputs $14,650 Total intermediate inputs T005 $72,169 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.

Table 2-15 shows that tota l energy use between 1998 a nd 2002. Figure 2-13 shows that electrical power us e decreased, since 2000.
2.2.2.2 Uses and Consumers
Table 2-16 shows that three of the top four consumers of wood products are represented by the construction sector of the economy ( NAICS 23). New residential construction, new nonresidential construction, and ma intenance and repair construc tion consume 35% of the total commodity output in this industry. The top 10 co nsumers of wood products make up 54% of the demand for wood products. Although many of the top consumers deal with construction, repair, or real estate services, other t ypes of consumers, such as food services and drinking places, rail transportation, plastics and rubber products ma nufacturing, and other, use these products.2-23 Table 2-15. Energy Used in Wood Pro duct Manufacturing (NAICS 321)

Fuel Type 1998 2002 2006 Net electricity a (million kWh) 21,170 20,985 26,723 Residual fuel oil (million bbl) * * 1 Distillate fuel oil b (million bbl) 2 2 3 Natural gas c (billion cu ft) 71 56 84 LPG and NGL d (million bbl) 1 1 1 Coal (million short tons) * * Q Coke and breeze (million short tons) — — * Other e (trillion BTU) 341 229 228 Total (trillion BTU) 504 375 445

a Net electricity is obtained by summing purchases, transfers in, and generation from noncombustible renewable

resources, minus quantities sold and transferred out. It does not include electri city inputs from on-site cogeneration or generation from combustible fuels because that energy has already been included as generating fuel (for example, coal). b Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels.

c Natural gas includes natural gas obtained from utilities, local distribution companies, and any other supplier(s),

such as independent gas producers, gas brokers, mark eters, and any marketing subsidiaries of utilities. d Examples of liquefied petroleum gases (LPGs) are ethane, ethylene, propane, propylene, normal butane, butylene,

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs).

e Other includes net steam (the sum of purchases, generatio n from renewables, and net transfers), and other energy

that respondents indicated was us ed to produce heat and power. * Estimate less than 0.5.

Q = Withheld because relative standa rd error is greater than 50%. Sources: U.S. Department of Energy, Energy Information Administration. 2007a. “2002 Energy Consumption by Manufacturers

— Data Tables.” Tables 3.2 and N3.2. <http://www.eia.doe.gov/emeu/mecs/ mecs2002/data02/shelltables.html>. Washington, DC: DOE. U.S. Department of Energy, Energy Information Administration. 2007b. “2006 Energy Consumption by Manufacturers

— Data Tables.” Tables 3.1. <http://w ww.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html>. [Source for 2006 numbers]2-24 85 90 95 100 105 110Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(Jan‐97=100)

Manufacturing Electric Power Use for NAICS 321
Figure 2-13. Electrical Power Use Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997–2005 Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” <http://www .federalreserve.gov/datadownload/>
2.2.3 Firm and Market Characteristics This section describes geographic, production, and market data. These data provide the basis for further analysis, includ ing regulatory flexibility analyses , as well as a complete picture of the recent historical trends of production and pricing.
2.2.3.1 Location As Figure 2-14 illustrates, the states w ith the largest number of wood product manufacturing establishments are disperse d throughout the country, with a significant concentration of establishments in the northeastern states. Other states with many establishments include California, Texas, and North Carolina.2-25 Table 2-16. Demand by Sector: Wood Pro duct Manufacturing (NAICS 321) ($2007, millions)

Sector BEA Code 3210 Wood Products New residential construction 2302 $19,997 New nonresidential construction 2301 $11,854 Furniture and related product manufacturing 3370 $8,197 Maintenance and repair construction 2303 $4,048 Motor vehicle body, trailer and parts manufacturing 336A $2,516 Real estate 5310 $2,335 Food services and drin king places 7220 $2,307 Other miscellaneous manufacturing 3399 $1,311 Wholesale trade 4200 $1,284 Rail transportation 4820 $1,138 Retail trade 4A00 $1,047 Plastics and rubber products manufacturing 3260 $877 General state and local government use S007 $3,116 Owner occupied dwelling S008 $11,209 Private fixed investment F020 $7,933 Exports of goods and services F040 $3,978 Total final uses (gross domestic product [GDP]) T004 $3,719 Total commodity output T007 $101,753 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.
2.2.3.2 Production Capacity and Utilization Capacity utilization of the wood product ma nufacturing industry has been experiencing capacity utilization increases and declines with more extreme fluctuations than those of all manufacturing industries combined. The declin e in wood product manufacturing is similar to total manufacturing between 1997 and 2002. Howe ver, capacity utilization in total manufacturing, which peaked in 2006, started in creasing at a faster rate than wood product manufacturing, but decreased sharply after it s peak. Wood product manufacturing experienced its own rapid decrease in cap acity utilization between 2007 and 2009, though not at the same rate as total manufacturing (Figure 2-15).2-26 Establishments by State

Less than 100
100 - 199
200 - 399
400 - 600
More than 600 Figure 2-14. Establishment Co ncentration in the Wood Product Manufacturing Industry (NAICS 321): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).
2.2.3.3 Employment California has the largest number of em ployees in the wood product manufacturing industry with over 39,000 reported in the 2002 census followed by over 32,000 in Oregon (Figure 2-16). The states with the highest number of employees do not direc tly correlate with the states with the highest number of establishments. States such as Indiana, Georgia, Arkansas, and Oregon had fewer than 600 establishments, as shown in Figure 2-14, but had more than 20,000 employees, whereas states such as Ohio and New York had fewer than 20,000 employees but more than 600 establishments.
2.2.3.4 Plants and Capacity While the capacity of the manufacturing sector has been growing c onsistently since 1997, the wood product manufacturing industry has expe rienced inconsistent growth. After a small amount of growth in capacity between 1997 and 2001, the wood product manufacturing industry’s capacity dipped between 2002 and 2005 but has been growing at a slow rate since then though it started to dip again in 2008 and 2009 (Figure 2-17).2-27 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PercentageCapacityUtilization

Manufacturing Wood Products
Figure 2-15. Capacity Utilization Trends in the Wood Product Manufacturing Industry (NAICS 321) Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” <http://www.federalreserve.gov/datadownload/>
2.2.3.5 Firm Characteristics In 2006, the top 10 paper and forest produc t companies produced over $75 billion in sales, with the top two companies—Internati onal Paper and Weyerhaeuser—generating nearly $22 billion each (Table 2-17). The top two companie s’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor’s (S&P’s) list (Benwa rt, 2006). Although these numbers do not exclusively reflect wood products , they do convey the market environment in which firms in this sector compete.
2.2.3.6 Size Distribution The primary criterion for categorizing a busin ess as small is the number of employees, using definitions by the SBA for regulatory flexibility analyses. According to SUSB reports for 2002, small companies were the recipients of the majority of receipts in 2002; 53% of receipts were generated by companies with fewer than 500 employees (Table 2-18). The number of employees in the small business cutoff is 500 employees for all subi ndustries in the wood product manufacturing industry (Table 2-19).2-28 Employemnt by State

Less than 2,000 2,000 - 4,999 5,000 - 9,999 10,000 - 20,000 More than 20,000 Figure 2-16. Employment Concentration in the Wood Product Manufacturing Industry (NAICS 321): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).
2.2.3.7 Domestic Production Similar to industry capacity rates, industry production rates for wood product manufacturing have decreased since 2006 compared to the stea dy increase in production for the manufacturing sector since 1997 (Figure 2-18). Similar to capacity utilization trends (Figure 2-16), the index shows a faster rate of decline for wood products than the entire manufacturing sector.
2.2.3.8 International Trade Since 1997, the wood product manufacturing indust ry has contributed to an increasing trade deficit (Figure 2-16). The value of impor ts has fluctuated greatly since 1997; however, exports have remained fairly constant , with seasonal changes, since 1997.2-29 100 105 110 115 120 125 130 135 140 145 150Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Wood Products
Figure 2-17. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321) Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Capacity.” <http://www.federalreserve.gov/datadownload/>. Table 2-17. Largest U.S. Paper and Forest Products Companies: 2006

Company Revenues ($millions) a International Paper 21,995 Weyerhaeuser 21,896 Smurfit-Stone 7,157 MeadWestvaco 6,530 Temple-Inland 5,558 Bowater 3,530 Grief Inc. 2,628 Louisiana-Pacific 2,235 Packaging Corp. 2,187 Plum Creek 1,627

a Includes revenues from operations other than paper and forest products in certain cases. Source: Benwart, S.J. 2006. “Paper & Forest Products.” Standard and Poor’s Industry Surveys . 176(28).2-30Table 2-18. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 321)Enterprises withVariable Total1 to 20Employeesa20 to 99Employees100 to 499Employees500 to 749Employees750 to 999Employees1,000 to 1,499EmployeesFirms15,1989,7403,280791632730Establishments17,0529,7583,4821,27116691133Employment534,01165,423132,612118,91019,78411,94418,533Receipts ($thousands)$88,649$8,204$18,276$19,717$3,192$1,902$3,118Receipts/firm ($thousands)$5,833$842$5,572$24,927$50,673$70,453$103,927Receipts/establishment($thousands)$5,199$841$5,249$15,513$19,231$20,904$23,442Receipts/employment ($)$166,006$125,393$137,818$165,814$161,363$159,262$168,231aExcludesStatistics of U.S. Businesses(SUSB) employment category for zero employees. These entities only operated for a fraction of the year.Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002.”<http://www.census.gov/csd/susb/download_susb02.htm>.2-31 Table 2-19. Small Business Size Standards: Wood Product Manufacturing (NAICS 321)

NAICS NAICS Description Employees
321113 Sawmills 500
321114 Wood Preservation 500 321211 Hardwood Veneer and Plywood Manufacturing 500 321212 Softwood Veneer and Plywood Manufacturing 500 321213 Engineered Wood Member (except Truss) Manufacturing 500 32121 Truss Manufacturing 500 321219 Reconstituted Wood Product Manufacturing 500 321911 Wood Window and Door Manufacturing 500 321912 Cut Stock, Resawing Lumber, and Planing 500 321918 Other Millwork (including Flooring) 500 321920 Wood Container and Pallet Manufacturing 500 321991 Manufactured Home (Mobile Home) Manufacturing 500 321992 Prefabricated Wood Building Manufacturing 500 321999 All Other Miscellaneous Wood Product Manufacturing 500 Source: U.S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/sizestandardstopics/size/index.html>.
2.2.3.9 Market Prices Prices of goods in the wood product manuf acturing industry have remained roughly the same since 2005. The prices for the entire manuf acturing sector increased between 2003 and 2008 but have decreased since August 2008. Producer price indices (PPIs) show that producer prices for wood products increased by 6% from 2004 to 2007 (Figure 2-20).2-32 60 70 80 90 100 110 120 130 140Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index Value (Jan-97=100)

Manufacturing Wood Products
Figure 2-18. Industrial Production Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997–2009 Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Production.” <http://www.federalreserve.gov/datadownload/>.

0 5,000 10,000 15,000 20,000 25,000 30,000Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08$1,000,000 (2007)

Wood Product Exports Wood Product Imports Figure 2-19. International Trade Trends in the Wood Product Manufacturing Industry (NAICS 321)] Source: U.S. International Trade Commission. 2008. “U.S. Domestic Exports” & “U.S. Imports for Consumption.” <http://dataweb.usitc.gov/s cripts/user_set.asp>.2-33 0 20 40 60 80 100 120 140Dec‐03Jun‐04Dec‐04Jun‐05Dec‐05Jun‐06Dec‐06Jun‐07Dec‐07Jun‐08PPI(Dec‐03=100)

Wood Products PPI All Manufacturing PPI
Figure 2-20. Producer Price Trends in the Wood Product Manufacturing Industry (NAICS 321) Source: U.S. Bureau of Labor Statistics (BLS). 2009a. “Producer Price Index.” Series ID: PCU321—321—& PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home. htm>. Accessed on January 8, 2010.
2.3 Paper Manufacturing
2.3.1 Introduction The paper manufacturing subsector is an esse ntial component of a ll business operations worldwide. Broadly speaking, pa per and paperboard are manufactured by converting timber or other recycled material into pr oducts such as printing and writing papers, newsprint, tissue, and containerboard (Benwart, 2006). Th e subsector has been experienci ng a decline in shipments as of late. From 1997 to 2007, shipments in the industr y declined 7%, and employment declined by 27% (Table 2-21). While total payroll dropped 26 % over this time, annual payroll per employee rose 2% from 1997 to 2007 because of the decline in the number of employees (Table 2-20). Shipments per employee grew 28% from 1997 to 2007, with much of that growth taking place between 2002 and 2006 (Table 2-21).2-34 Table 2-20. Key Statistics: Pap er Manufacturing (NAICS 322)

1997 2002 2006 2007 Shipments ($2007, millions) $188,496 $175,983 $174,887 $175,806 Payroll ($2007, millions) $27,983 $24,561 $21,188 $20,804 Employees 574,274 489,367 414,049 416,886 Establishments 5,868 5,495 NA 4,803

NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” Accessed on December 28, 2009. [Sou rce for 2007 numbers] Table 2-21. Industry Data: Paper Manufacturing (NAICS 322)

Industry Data 1 997 2002 2006 2007 Total shipments ($2007, millions) $188,496 $175,983 $174,887 $175,806 Shipments per establishment ($2007, thousands) $32,123 $32,026 NA $36,603 Average Shipments per employee ($2007) $328,233 $359,614 $422,381 $421,712 Average Shipments per $ of payroll ($2007) $6.74 $7.17 $8.25 $8.45 Average Annual payroll per employee ($2007) $48,727 $50,189 $51,174 $49,904 Average Employees per establishment 98 89 NA 87

NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” <http://factfinder.census.gov>. Accessed on December 28, 2009 . [Source for 2007 numbers]2-35 The U.S. Census Bureau categorizes this indu stry’s facilities into two categories: pulp, paper, and paperboard manufacturing and conve rted paper product manufacturing. These are further divided into the following types of fac ilities as defined by the Census Bureau (2001):

 Pulp, Paper, and Paperboard:

– Pulp Mills (NAICS 32211): This industr y comprises establishments primarily engaged in manufacturing pulp without manufacturing paper or paperboard. The pulp is made by separating the cellulose fibers from the other impurities in wood or other materials, such as used or recy cled rags, linters, scra p paper, and straw.

– Paper Mills (NAICS 32212): This industr y comprises establishments primarily engaged in manufacturing paper fr om pulp. These establishments may manufacture or purchase pulp. In additi on, the establishments may convert the paper they make. The activity of making pa per classifies an establishment into this industry rega rdless of the output.

– Paperboard Mills (NAICS 32213): This industry comprises establishments primarily engaged in manufacturing paper board from pulp. These establishments may manufacture or purchase pulp. In a ddition, the establishments may also convert the paperboard they make.

 Converted Paper Products:

– Paperboard Containers Manufacturing (NAICS 32221): This industry comprises establishments primarily engaged in c onverting paperboard into containers without manufacturing paperboard. These establishments use corrugating, cutting, and shaping machinery to form paperboa rd into containers. Products made by these establishments include boxes; corr ugated sheets, pads, and pallets; paper dishes; and fiber drums and reels.

– Paper Bag and Coated and Treated Pa per Manufacturing (NAICS 32222): This industry comprises establishments primar ily engaged in one or more of the following manufacturing activities: cutti ng and coating paper and paperboard; cutting and laminating paper and paperboard and other flexible materials (except plastics film to plastics film); bags or multiwall bags or sacks of paper, metal foil, coated paper, or laminates or coated comb inations of paper and foil with plastics film; laminated aluminum and other conve rted metal foils from purchased foils; and surface coating paper or paperboard.

– Stationary Product Manufacturing (NAI CS 32223): This industry comprises establishments primarily engaged in conve rting paper or paperboard into products used for writing, filing, art work, and similar applications.

– Other Converted Paper Products (NAI CS 32229): This industry comprises establishments primarily engaged in one of the following manufacturing activities:

 converting paper and paperboard into products (except containers, bags, coated and treated paper and paperboa rd, and stationery products), or2-36  converting pulp into pulp products, such as disposable diapers, or molded pulp egg cartons, food trays, and dishes. Figure 2-21 shows that the value of shipment s for converted paper products was 54% of the value of all paper products in 2007, while the value of shipments for pulp, paper, and paperboard products was 46%. Figu re 2-22 indicates that 70% of industry employees worked in the converted paper product categor y of the industry due to the la bor intensive aspects of those facilities.

PPP (3221) 46% CPP (3222) 54%

Figure 2-21. Distribution of Value of Shipm ents within Paper Manufacturing (NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder: “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” Accessed on December 28, 2009.

PPP (3221) 30%

CPP (3222) 70%

Figure 2-22. Distribution of Employment with in Paper Manufacturing (NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” <http://factfinder.census.gov>. A ccessed on December 28, 2009.2-37 2.3.2 Supply and Demand Characteristics Next, we provide a broad overview of th e supply and demand sides of the paper manufacturing industry. We emphasize the economic interactions this indu stry has with other industries and people and identify the key goods a nd services used by the industry and the major uses and consumers of paper manufacturing products.
2.3.2.1 Goods and Services Used in Paper Manufacturing In 2007, the cost of materials made up 53% of the total shipment value of goods in the paper manufacturing industry (Table 2-22). Tota l compensation of employees represented 15% of the total value in 2007, down from 17% in 2005. The total number of employees dropped by 2%, between 2005 and 2007, while shipments increased by 3% in the same period.
The top 10 industry groups supplying input s to the paper manufacturing subsector accounted for 70% of the total intermediate inputs according to 2008 Bureau of Economic Analysis (BEA) data (Table 2-23). Inputs for pulp, paper, and paperboard products are notably different from inputs for converted paper produ cts because the NAICS 3221 group represents the initial step in the paper manufact uring process; thus, its inputs include more raw resources such as wood products, forestry and logging products, natural gas, and electricity. This becomes evident when observing inputs for converted pape r products: 49% of the cost of inputs comes from pulp, paper, and paperboard products.
2.3.2.1.1 Energy

. The Department of Energy (DOE ) categorizes paper manufacturing (NAICS 322) as an energy-int ensive subsector. The 2008 Annual Energy Outlook predicts that the paper-producing subsector will be one of fo ur subsectors experiencing positive average growth of delivered energy consum ption between 2006 and 2030 (DOE, 2008).
Energy generation from the recovery boiler is often insufficient for total plant needs, so facilities augment recove ry boilers with fossil fuel–fired and wood waste–fired boilers (hogged fuel) to generate steam and often electricity. In dustry wide, the use of pulp wastes, bark, and other papermaking residues supplies 58% of the energy requirements of pulp and paper companies (EPA, 2002).
Likewise, Table 2-24 shows that total en ergy use decreased between 1998 and 2006 by 14%. Figure 2-24 indicates that total electrical power use ch anged sporadically between 2002 and 2004 but decreased consiste ntly and rapidly after 2004.2-38 Table 2-22. Costs of Goods and Services Us ed in the Paper Manufacturing Industry (NAICS 322)

Variable 2005 Share 2006 Share 2007 Share Total shipments ($2007, millions) $171,477 100% $174,887 100% $176,018 100% Total compensation ($2007, millions) $28,846 17% $27,791 16% $27,150 15%

Annual payroll $21,792 13% $21,188 12% $20,804 12% Fringe benefits $7,054 4% $6,603 4% $6,346 4% Total employees 426,748 414,049 417,367

Average compensation per employee $67,596 $67,121 $65,051 Total production workers wages ($2007,

millions) $14,965 9% $14,689 8% $14,190 8%

Total production workers 331,228 321,684 321,937 Total production hours (thousands) 716,963 691,134 680,732

Average production wages per hour $21 $21 $21 Total cost of materials ($2007, thousands) $91,897 54% $92,452 53% $94,029 53%

Materials, parts, packaging $77,494 45% $78,202 45% $79,984 45% Purchase electricity $3,788 2% $3,841 2% $3,780 2% Purchased fuel ($2007) $5,537 3% $5,509 3% $5,511 3% Other $5,078 3% $4,901 3% $4,755 3% Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” <http://factfinder. census.gov>. Accessed on December 28, 2009. [S ource for 2007 numbers]2-39 Table 2-23. Key Goods and Services Used in the Paper Manufacturing Industry (NAICS 322) ($millions, $2007)

Description BEA Code NAICS 3221 Pulp, Paper, and Paperboard NAICS 3222 Converted Paper Products Total Pulp, paper, and paperboard 3221 $4,155 $30,448 $34,603 Wholesale trade 4200 $3,916 $6,356 $10,273 Management of companies and enterprises 5500 $3,154 $3,838 $6,993 Forestry and logging products 1130 $5,389 $0 $5,389 Basic chemicals 3251 $3,734 $263 $3,997 Electric power generation, transmission, and distribution 2211 $2,690 $913 $3,603

Wood products 3210 $3,450 $33 $3,484 Converted paper products 3222 $1,415 $1,745 $3,159 Natural gas distribution 2212 $2,680 $345 $3,026 Truck transportation 4840 $1,428 $1,571 $2,999 Total intermediate inputs T005 $47,835 $62,690 $110,525 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.2-40 Table 2-24. Energy Used in Paper Manufacturing (NAICS 322)

Fuel Type 1998 2002 2006 Net electricity a (million kWh) 70,364 65,503 72,518 Residual fuel oil (million bbl) 24 16 15
Distillate fuel oil b (million bbl) 2 2 2 Natural gas c (billion cu ft) 570 490 461
LPG and NGL d (million bbl) 1 2 1 Coal (million short tons) 12 11 10 Coke and breeze (million short tons) — * —
Other e (trillion BTU) 1,476 1,276 1,303 Total (trillion BTU) 2,744 2,361 2,354

a Net electricity is obtained by summing purchases, transfers in, and generation from noncombustible renewable

resources, minus quantities sold and transferred out. It does not include electri city inputs from on-site cogeneration or generation from combustible fuels because that energy has already been included as generating fuel (for example, coal). b Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels.

c Natural gas includes natural gas obtained from utilities, local distribution companies, and any other supplier(s),

such as independent gas producers, gas brokers, mark eters, and any marketing subsidiaries of utilities. d Examples of liquefied petroleum gases (LPG) are ethane, ethylene, propane, propylene, normal butane, butylene,

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs).

e Other includes net steam (the sum of purchases, generatio n from renewables, and net transfers), and other energy

that respondents indicated was us ed to produce heat and power. * Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007a. “2002 Energy Consumption by Manufacturers—Data Tables.” Tables 3.2 and N3.2. <http://www.eia.doe.gov/emeu/mecs/mecs2002/ data02/shelltables.html>. Washington, DC: DOE. U.S. Department of Energy, Energy Information Administration. 2007b. “2006 Energy Consumption by Manufacturers—Data Tables.” Table 3.1. <http://www.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html>. Accessed on December 27, 2009. [Source fo r 2006 numbers] Over the last 25 years, the pulp and paper subsector has changed its energy generation methods from fossil fuels to a greater use of pr ocesses such as increases in the use of wood wastes in place of fuel (Table 2-25). During the 1972–1999 period, the proportion of total industry power generated from the combination of woodroom wastes, spent liquor solids, and other self-generated methods increased from about 41% to about 56%, while coal, fuel oil, and natural gas use decreased from a bout 54% to about 36% (EPA, 2002).2-41 90 92 94 96 98 100 102 104 106 108Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(Jan‐97=100)

Manufacturing Paper
Figure 2-23. Electrical Power Use Trends in the Paper Manufacturing Industry: 1997– 2005 Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” <http://www .federalreserve.gov/datadownload/>. Table 2-25. Estimated Energy Sources fo r the U.S. Pulp and Paper Industry

Energy Source 1972 1979 1990 1999
Purchased steam 5.4% 6.7% 7.3% 1.5%
Coal 9.8% 9.1% 13.7% 12.5%
Fuel oil 22.3% 19.1% 6.4% 6.3% Natural gas 21.5% 17.8% 16.4% 17.6% Other purchased energy — — — 6.7% Waste wood and wood chips (hogged fuel)

and bark 6.6% 9.2% 15.4% 13.5%

Spent liquor solids 33.7% 37.3% 39.4% 40.3% Other self-generated power 0.6% 0.8% 1.2% 1.6% Source: U.S. Environmental Protection Agency. 2002. “Profile of the Pulp and Paper Industry.” Sector Notebook Project. <http://www.epa.gov/Compliance/resour ces/publications/assistance/sectors/notebooks/ index.html>.2-42 2.3.2.2 Uses and Consumers
Products manufactured in the NAICS groups 3221 and 3222 have different, but complementary, consumer profiles. NAICS 322 1 supplies a significant portion of NAICS 3222 demand (37% of total commodity output). Bo th industries speciali ze in products with intermediate uses, with an average of 92% of sales between the two goi ng toward this purpose. NAICS 3222 has a very diverse a ssortment of subsector groups from which it receives demand. Food manufacturing makes up 21% of the demand, ma king members of this industry th e largest consumer of converted paper products (Table 2-26). Pulp, paper, and paperboard products have a large trade deficit, while converted paper products have a very small trade surplus. Table 2-26. Demand by Sector: Paper Manufactu ring Industry (NAICS 322) ($millions, $2007)

Sector BEA Code 3221 Pulp, Paper, and Paperboard 3222 Converted Paper Products Total Converted paper product manufacturing 3222 $30,448 $1,745 $32,193 Food manufacturing 3110 $638 $18,782 $19,421 Printing and related support activ ities 3230 $13,320 $3,874 $17,194 General state and local government

services S007 $6,065 $7,792 $13,857
Pulp, paper, and paperboard mills 3221 $4,155 $1,415 $5,569 Newspaper, periodical, book, and

directory publishers 5111 $4,851 $168 $5,018
Plastics and rubber products

manufacturing 3260 $1,249 $3,403 $4,651
Wholesale trade 4200 $990 $2,619 $3,609 Food services and drinking places 7220 $1,510 $2,597 $4,107 Total intermediate use T001 $76,729 $80,862 $157,591 Personal consumption expenditures F010 $11,882 $9,295 $21,177 Exports of goods and services F040 $7,724 $5,799 $13,523 Imports of goods and services F050

− $15,284

− $5,720

− $21,005 Total final uses (GDP) T004 $4,996 $9,607 $14,604 Total commodity output T007 $81,725 $90,469 $172,195 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.2-43 2.3.3 Firm and Market Characteristics This section describes geographic, production, and market data. These data provide the basis for further analysis, includi ng regulatory flexibility analyses, and give a complete picture of the recent historical trends of production and pricing.
2.3.3.1 Location As Figure 2-24 illustrates, California is home to the most paper manufacturing establishments in the United States, followed by Illinois and some borderi ng northeastern states. The location of establishments in the paper manufacturing industry va ries a great deal by subsector. Wisconsin and New York have the most pulp, paper, and paperboard establishments, while California dominates with over 500 converted paper product establis hments. Overall, the United States has 561 pulp, pape r, and paperboard establishm ents and 4,956 converted paper product establishments.

Establishments Fewer than 50 50 - 99 100 - 199 200 - 300 More than 300 Figure 2-24. Establishment Concentration in Paper Manufacturing Industry (NAICS 322): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).2-44 2.3.3.2 Production Capacity and Utilization Capacity utilization of the paper manufactur ing subsector has been experiencing a steady decline, similar to the decline of the total manufacturing sector. However, paper manufacturing has managed to use its capacity at a consistently higher rate than the average for manufacturing industries (Figure 2-25).

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PercentageCapacityUtilization

Manufacturing Paper (NAICS 322) Figure 2-25. Capacity Utilization Trends in the Paper Manufacturing Industry (NAICS 322) Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” <http://www.federalreserve.gov/datadownload/>.
2.3.3.3 Employment Wisconsin has the largest number of employ ees in the paper manufacturing subsector with over 38,008 reported in the 2002 census fo llowed by 29,379 in California (Figure 2-26). The converted paper products group has more em ployees per establishm ent, 283, than the pulp, paper, and paperboard group, 67.2-45 Employment Fewer than 3,000 3,000 - 5,999 6,000 - 11,999 12,000 - 24,000 More than 24,000 Figure 2-26. Employment Concentration in the Paper Manufacturing Industry (NAICS 322): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).
2.3.3.4 Plants and Capacity While the manufacturing sector has been growing consistently since 1997, the paper manufacturing sector has not experienced th e same amount of success in the same period. Despite a small amount of growth in capacity between 1997 and 2001, the paper manufacturing subsector’s capacity has declined to as much as 7% below 1997 capacity levels (Figure 2-27).
2.3.3.5 Firm Characteristics In 2006, the top 10 paper and forest produc t companies produced over $75 billion in sales, with the top two companies—Internati onal Paper and Weyerhaeuser—generating nearly $22 billion each (Table 2-27). The top two companie s’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor’s (S&P’s) list (Benwa rt, 2006). Although these numbers do not exclusively reflect paper product s, they do convey the market environment in which firms in this sector compete.2-46 90 100 110 120 130 140 1501997‐011997‐071998‐011998‐071999‐011999‐072000‐012000‐072001‐012001‐072002‐012002‐072003‐012003‐072004‐012004‐072005‐012005‐072006‐012006‐072007‐012007‐072008‐012008‐072009‐012009‐07IndexValue(Jan‐97=100)

Manufacturing Paper (NAICS 322)
Figure 2-27. Capacity Trends in the Pa per Manufacturing Industry (NAICS 322) Source: Federal Reserve Board. 2009. “Industrial Prod uction and Capacity Utilization: Industrial Capacity.” <http://www.federalreserve.gov/datadownload/>. Table 2-27. Largest U.S. Paper and Forest Products Companies: 2006

Company Revenues ($millions) a International Paper 21,995 Weyerhaeuser 21,896 Smurfit-Stone 7,157 MeadWestvaco 6,530 Temple-Inland 5,558
Bowater 3,530 Grief Inc. 2,628 Louisiana-Pacific 2,235 Packaging Corp. 2,187
Plum Creek 1,627

a Includes revenues from operations other than paper and forest products in certain cases. Sources: Benwart, S.J. 2006. “Paper & Forest Products. Standard and Poor’s Industry Surveys.” 176(28). U.S. and international sales data from company reports.2-47 2.3.3.6 Size Distribution The primary criterion for categorizing a busin ess as small is the number of employees, using definitions by the SBA for regulatory flexibility analyses. According to SUSB reports for 2002, large companies dominated revenue-generati ng transactions in the paper manufacturing subsector; 80% of receipts were generated by companies with 500 employees or more (Table 2-28). This was especially true in the pulp, paper, and paperboa rd group, in which large companies generated 92% of receipts. The numbe r of employees in the small business cutoff varies according to six-digit NAICS codes (Table 2-29). The cutoff for all subsectors in the pulp, paper, and paperboard group is 750 employees, while the cutoff for most converted paper product groups is 500 employees.
Table 2-28. Distribution of Economic Data by Enterprise Size: Paper Manufacturing (NAICS 322)

Enterprises with

Variable Total 1 to 20 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees
Firms 3,538 1,482 1,200 476 43 22 33
Establishments 5,546 1,488 1,271 755 83 69 138 Employment 495,990 11,325 52,334 78,402 13,293 12,496 23,283 Receipts ($millions ) $154,746 $2,218 $9,483 $17,620 $3,034 $3,951 $6,798 Receipts/firm ($thousands) $43,738 $1,497 $7,903 $37,017 $70,561 $179,577 $206,001

Receipts/establishment ($thousands) $27,902 $1,491 $7,461 $23,338 $36,556 $57,256 $49,261

Receipts/employment ($) $311,994 $195,850 $181,203 $224,742 $228,250 $316,157 $291,974

a Excludes SUSB employment category for zero employees. These entities only operated for a fraction of the year. Source: U.S. Census Bureau. 2008. “Firm Size Data fr om the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002.” <http://www.census.gov/csd/susb/download_susb02.htm>.2-48 Table 2-29. Small Business Size Standards: Paper Manufacturing (NAICS 322)

NAICS NAICS Description Employees 322110 Pulp Mills 750 322121 Paper (except Newsprint) Mills 750 322122 Newsprint Mills 750 322130 Paperboard Mills 750 322211 Corrugated and Solid Fiber Box Manufacturing 500 322212 Folding Paperboard Box Manufacturing 750 322213 Setup Paperboard Box Manufacturing 500 322214 Fiber Can, Tube, Drum, and Similar Products Manufacturing 500 322215 Non-Folding Sanitary Food Container Manufacturing 750 322221 Coated and Laminated Packaging Paper Manufacturing 500 322222 Coated and Laminated Paper Manufacturing 500 322223 Coated Paper Bag and Pouch Manufacturing 500 322224 Uncoated Paper and Multiwall Bag Manufacturing 500
322225 Laminated Aluminum Foil Manufacturing for Flexible, Packaging Uses 500

322226 Surface-Coated Paperboard Manufacturing 500 322231 Die-Cut Paper and Paperboard Office Supplies, Manufacturing 500 322232 Envelope Manufacturing 500 322233 Stationery, Tablet, and Related Product Manufacturing 500 322291 Sanitary Paper Product Manufacturing 500 322299 All Other Converted Paper Product Manufacturing 500 Source: U.S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/sizestandardstopics/size/index.html>.
2.3.3.7 Domestic Production Similar to industry capacity rates, subsect or production rates for paper manufacturing have witnessed a decreasing rate of production comp ared to the steady increase in production for the manufacturing sector since 1997 (Figure 2-28). It seems that the paper manufacturing sector was not able to return to its former levels of growth following the 2001 recession; it has experienced a downward pr oduction trend since then.2-49 Figure 2-28. Industrial Production Trends in the Paper Manufacturing Industry (NAICS 322): 1997–2009 Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Production.” <http://www.federalreserve.gov/datadownload/>.
2.3.3.8 International Trade Since 1997, paper manufacturing products, bot h pulp, paper, and paperboard products and converted paper products, have contributed to an increasing trade surplus in this sector (Figure 2-29). Imports and exports have been changing at sim ilar rates since 1999.
2.3.3.9 Market Prices Prices of goods in paper manufacturing have be en increasing at a rate consistent with all manufacturing products (Figure 2- 30). Producer price i ndices (PPIs) show th at producer prices for paper in 2007 increased by 20% since 1997, wh ile producer prices for all manufacturing goods increased by roughly 27%.2-50 0 5,000 10,000 15,000 20,000 25,000 30,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007$1,000,000(2007)

Year Paper Exports Paper Imports
Figure 2-29. International Trade Trends in the Paper Manufacturing Industry (NAICS 322) Source: U.S. International Trade Commission. 2008. “U.S. Domestic Exports” & “U.S. Imports for Consumption.” <http://dataweb.usitc.gov/s cripts/user_set.asp>.

75 85 95 105 115 125 135Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Paper (NAICS 322)
Figure 2-30. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222) Source: U.S. Bureau of Labor Statistics (BLS). 2009b. “Producer Price Index.” Series ID: PCU322–322– & PCUOMFG–OMFG–. <http://www.bls.gov/ppi/home.htm>.2-51 2.4 Chemical Manufacturing
2.4.1 Introduction The chemical manufacturing industry produ ces over 70,000 chemical substances, many of which are ubiquitous in American life. Br oadly speaking, chemical manufacturing operates by converting feedstocks into chemical products th at can serve as intermediate goods or final products such as medicine, soap, and printer ink. From 1997 to 2007, shipments in the industry grew 42%, while employment declined by 8% (T able 2-30). While tota l payroll dropped 0.6% over this time, annual payroll per employee rose 7.8% from 1997 to 2007 because of the decline in the number of employees (Table 2-31). Sh ipments per employee grew 54% from 1997 to 2007, with much of that growth taking place between 2002 and 2006 (Table 2-31).
Chemical manufacturing (NAICS 325) covers a diverse set of industry groups, which we have aggregated into the following three groups:

 Bulk Chemicals—Includes the most energy-intensive industry groups as aggregated by the Department of Ener gy (DOE). Basic Chemical Manufacturing (NAICS 3251); Resin, Rubber, and Artif icial Fibers Manufacturing (NAICS 3252); and Agricultural Chemi cal Manufacturing (NAICS 3253).

 Pharmaceutical and Medicine Manufactur ing (NAICS 3254)—Consists primarily of pharmaceutical preparation manufacturing. This industry group is the largest importer of goods within chemical manufacturing.

 Other Chemical Manufacturing: Consists of Paint, Coating, and Adhesive Manufacturing (NAICS 3255); Soap , Cleaning Compound, and Toiletry Manufacturing (NAICS 3256); and Other Chemical Product and Preparation Manufacturing (NAICS 3259). In 2007, each of these groups generated approximate ly one-third of the total employment in chemical manufacturing (Figure 2-31). The bulk chemicals group accounted for the biggest share of chemical manufacturing’s total value of shipments (Figure 2-32).2-52 Table 2-30. Key Statistics: Chemic al Manufacturing (NAICS 325)

1997 2002 2006 2007 Shipments ($2007, millions) $521,251 $531,173 $675,223 $738,303 Payroll ($2007, millions) $49,961 $51,317 $46,981 $49,648 Employees 882,645 853,224 747,134 814,024 Establishments 13474 13,475 NA 12,937
NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” <http://factfinder.census.gov>. Accessed on December 27, 2009 . [Source for 2007 numbers] Table 2-31. Industry Data: Chemical Manufacturing (NAICS 325)

Industry Data 1 997 2002 2006 2007 Total shipments ($2007, millions) $521,251 $531,173 $675,223 $738,303 Shipments per establishment ($thousands) $38,686 $39,419 NA $57,069 Shipments per employee ($2007) $590,556 $622,548 $903,750 $906,979 Shipments per $ of payroll ($2007) $10.43 $10.35 $14.37 $14.87 Annual payroll per employee ($2007) $56,603 $60,145 $62,882 $60,991 Employees per establishment 66 63 NA 63
NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” <http://factfinder.census.gov>. A ccessed on December, 27, 2009 . [Source for 2007 numbers]2-53 Bulk Chemicals, 35%

Pharmaceuticals, 31% Other Chemicals, 34%

Figure 2-31. Distribution of Employment with in Chemical Manufacturing (NAICS 325): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detaile d Statistics by Industry for the U.S.: 2007.” Release date: October 30, 2009. Acce ssed on December 27, 2009.

Bulk Chemicals, 50%

Pharmaceuticals, 26% Other Chemicals, 24%

Figure 2-32. Distribution of To tal Value of Shipments with in Chemical Manufacturing (NAICS 325): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31:EC0731I1: Manufacturing Industry Series: De tailed Statistics by Industry for U.S.: 2007.” <http://factfinder.census.gov>. A ccessed on December 27, 2009.2-54 2.4.2 Supply and Demand Characteristics Next, we provide a broad overview of the supply and demand side of the chemical manufacturing industry. We emphasize the economic interactions this indu stry has with other industries and people, including identifying the key goods and serv ices used by the industry and the major uses and consumers of chemical manufacturing products.
The top 10 industry groups supplying inputs to the chemical manuf acturing industry in 2002 accounted for 71% of the total intermedia te inputs (Table 2-32). Bulk chemicals’ production was the most energy intensive, using 79% of the chemical manufacturing inputs from petroleum and coal products, electric power generation, transmission and distribution, and natural gas distribution. Table 2-32. Key Goods and Services Used in Chemical Manufact uring (NAICS 325) ($2007, millions)

Good or Service BEA Code Bulk Chemicals Pharmaceuticals Other Chemicals Total Basic chemicals 3251 $59,495 $4,772 $14,021 $78,288 Management of companies and enterprises 5500 $15,071 $19,380 $16,396 $50,846
Pharmaceuticals and medicines 3254 $0 $25,125 $0 $25,125 Wholesale trade 4200 $9,428 $8,367 $6,077 $23,872 Scientific research and development services 5417 $6,172 $6,139 $5,554 $17,865 Petroleum and coal products 3240 $10,066 $398 $3,432 $13,896 Plastics and rubber products 3260 $2,675 $1,132 $5,556 $9,363 Resins, rubber, and artificial fibers 3252 $4,048 $0 $4,949 $8,996 Electric power generation, transmission, and

distribution 2211 $6,025 $716 $807 $7,548

Natural gas distribution 2212 $6,390 $154 $390 $6,934 Total intermediate use T005 $167,699 $82,403 $91,833 $341,935 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.2-55 2.4.2.1 Goods and Services Used in Chemical Manufacturing In 2007, the cost of materials made up 49% of chemical manufacturing’s total shipment value (Table 2-32). Total compensation to employ ees represented 9% of total shipment value, down from 10% in 2005.
2.4.2.1.1 Energy. The Department of Energy (DOE) classifies bulk chemical manufacturing as an energy-intensive i ndustry. Pharmaceuticals and other chemical manufacturing are categorized as non-energy-intensive industrie s, grouped together with other industry groups under the “Balance of Manufacturing” category (DOE, 2008).
Fuel used in chemical production can either f acilitate chemical proc esses or provide the feedstock to derive value-added chemicals. In 2007, 70% of chemical manufacturing’s energy bill was spent on fuel used as feedstocks (O’R eilly, 2008). These fuel costs represented 2% of chemical manufacturing’s total value of shipments (Table 2-33).
As a whole, chemical manufacturing use less energy over the last 10 years. According to DOE, natural gas use by the chemical manufact uring industry dropped 30% from 1998 to 2006, and electricity use fell 10% (Table 2-34). From 1 997 to 2005, when data ceased to be available, chemical manufacturing used less electricity rela tive to the manufacturin g sector as a whole (Figure 2-33).
2.4.2.2 Uses and Consumers Products manufactured in the groups bulk chemicals, pharmaceuticals, and other chemicals have very different consumer profiles. Bulk chemicals is dominated by intermediate use, representing 93% of its total commodity out put and 56% of the total intermediate use of chemical manufacturing products. Pharmaceutic als has both a high level of demand from personal consumption, accounting for 67% of the total personal consumption of chemical manufacturing products, and a larg e trade deficit (Table 2-35).2-56 Table 2-33. Costs of Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007)

Variable 2005 Share 2006 Share 2007 Share Total shipments $646,895 100% $675,223 100% $722,494 100% Total compensation (millions) $62,669 10% $61,683 9% $63,591 9% Annual payroll $48,159 7% $46,981 7% $48,780 7% Fringe benefits $14,510 2% $14,702 2% $14,811 2% Total employees 756,078 747,134 801,567 Average compensation per

employee $82,887 $82,559 $79,333
Total production workers’ wages (millions) $22,643 4% $22,231 3% $23,157 3%
Total production workers 431,502 430,880 463,802 Total production hours (thousands) 899,499 885,993 948,244

Average production wages per

hour $25 $25 $24

Total cost of materials ($thousands) $299,859 46% $318,945 47% $357,055 49%
Materials, parts, packaging $247,851 38% $260,934 39% $291,656 40% Purchase electricity $8,291 1% $8,490 1% $8,936 1% Purchased fuel $14,568 2% $13,667 2% $14,227 2% Other $29,148 5% $35,855 5% $42,236 6% Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” Accessed on December, 27, 2009.2-57 Table 2-34. Energy Used in Chemical Manufacturing (NAICS 325)

Fuel Type 1998 2002 2006 Total (trillion BTU) 3,704 3,769 3,159 Net electricity a (million kWh) 169,233 153,104 151,646 Residual fuel oil (million bbl) 8 7 4
Distillate fuel oil b (million bbl) 2 2 2 Natural gas c (billion cu ft) 1,931 1,634 1,349
LPG and NGL d (million bbl) 15 9 2 Coal (million short tons) 13 14 8 Coke and breeze (million short tons) * * *
Other e (trillion BTU) 748 1,158 1,045 Total (trillion BTU) 3,704 3,769 3,159

a Net electricity is obtained by summing purchases, transfers in, and generation from noncombustible renewable

resources, minus quantities sold and transferred out. It does not include electri city inputs from on-site cogeneration or generation from combustible fuels because that energy has already been included as generating fuel (for example, coal). b Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels.

c Natural gas includes natural gas obtained from utilities, local distribution companies, and any other supplier(s),

such as independent gas producers, gas brokers, mark eters, and any marketing subsidiaries of utilities. d Examples of liquefied petroleum gases (LPGs) are ethane, ethylene, propane, propylene, normal butane, butylene,

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs).

e Other includes net steam (the sum of purchases, generatio n from renewables, and net transfers), and other energy

that respondents indicated was us ed to produce heat and power. * Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007b. “2006 Energy Consumption by Manufacturers—Data Tables.” Table 3.1. Washin gton, DC: DOE. <http://www.eia.doe.gov/emeu/mecs/ mecs2006/2006tables.html>. [Source for 2006 numbers] U.S. Department of Energy, Energy Information Administration. 2007a. “2002 Energy Consumption by Manufacturers—Data Tables.” Tables 3.2 and N3.2. Washington, DC: DOE. <http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>.2-58 80 85 90 95 100 105 110Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(Jan‐97=100)

Manufacturing Chemical Manufacturing
Figure 2-33. Electric Power Use Trends in Chemical Manufacturing (NAICS 325): 1997–2005 Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” <http://www .federalreserve.gov/datadownload/>.
2.4.3 Firm and Market Characteristics This remaining subsection describes geographi c, production, and market data. These data provide the basis for further an alysis, including regul atory flexibility analyses, and give a complete picture of the recent histori cal trends of production and pricing.
2.4.3.1 Location In 2002, California had the most chemical manufacturing establishments in the United States, followed by Texas and Ne w Jersey (Figure 2-34). The composition of establishments in these states differs among th e different industry groups. Despite the fact that each group employed an approximately equal share of people in 2002, 54% of the total establishments were other chemicals establishmen ts, and only 13% were pharm aceutical establishments.2-59 Table 2-35. Demand by Sector: Chemical Ma nufacturing (NAICS 325) ($2007 millions)

Sector BEA Code Bulk Chemicals Pharmaceuticals Other Chemicals Total Plastics and rubber products manufacturing 3260 $39,353 $0 $3,057 $42,410 Basic chemical manufacturing 3251 $33,972 $0 $1,675 $35,647 Pharmaceutical and medicine manufacturing 3254 $4,778 $25,125 $462 $30,365

Resin, rubber, and artificial fibers manufacturing 3252 $28,249 $0 $1,076 $29,325
Ambulatory health care services 6210 $2,716 $22,900 $934 $26,550 General state and local government services S007 $7,150 $10,586 $8,807 $26,543
Hospitals 6220 $2,936 $15,390 $394 $18,720 Other chemical product and preparation manufacturing 3259 $8,021 $0 $2,680 $10,701
Textile mills 3130 $9,568 $0 $930 $10,498 Soap, cleaning compound, and toiletry manufacturing 3256 $3,886 $0 $6,289 $10,176
Total intermediate use T001 $212,996 $83,279 $82,107 $378,382 Personal consumption expenditures F010 $4,449 $123,746 $55,882 $184,077 Exports of goods and services F040 $47,121 $15,683 $13,136 $75,940 Imports of goods and services F050

− $38,732

− $67,950

− $10,906

− $117,588 Total final uses (GDP) T004 $15,733 $73,485 $58,023 $147,241 Total commodity output T007 $228,729 $156,765 $140,129 $525,623 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.2-60 Establishments Fewer than 100 100 - 249 250 - 474 475 - 750 More than 750 Figure 2-34. Establishment Co ncentration in Chemical Ma nufacturing (NAICS 325): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).
2.4.3.2 Production Capacity and Utilization Capacity utilization of the ch emical manufacturing industry ha s been broadly in line with the manufacturing sector (Figure 2-35). In th e second half of 2005, the chemical manufacturing industry’s capacity utilization fell dramatically because of the multiple hurricanes affecting the Gulf Coast states. The impact of the economic dow nturn in 2001 can be s een in the capacity utilization of both manufacturi ng and chemical manufacturing.
2.4.3.3 Employment The geographic distribution of employment in chemical manufacturing differs largely among the different groups. In California, 52% of the chemical manufacturing employment comes from the pharmaceutical industry, while 60% of the chemical manufacturing employment in the Gulf Coast states comes from bulk chemicals manufacturing (Figure 2-36).
2.4.3.4 Plants and Capacity Production capacity in chemical manufacturi ng has grown 33% since 1997. This growth, however, is 9% less than the growth rate for the manuf acturing industry as a whole (Figure 2-37).2-61 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PercentageCapacityUtilization

Manufacturing Chemical Manufacturing
Figure 2-35. Capacity Utilization Trends in Chemical Manufacturing (NAICS 325) Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” <http://www.federalreserve.gov/datadownload/>.

Employment Fewer than 5,000 5,000 - 13,999 14,000 - 29,999 30,000 - 40,000 More than 40,000 Figure 2-36. Employment Concentration in Ch emical Manufacturing (NAICS 325): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).2-62 100 110 120 130 140 150Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Chemical Manufacturing
Figure 2-37. Capacity Trends in Ch emical Manufacturing (NAICS 325) Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Capacity.” <http://www.federalreserve.gov/datadownload/>.
2.4.3.5 Firm Characteristics In 2007, the top six companies by chemical sale s had greater than $10 billion in sales. Together, their sales are greater than the next 44 highest chemi cal companies combined. These, however, are global companies, with a large portion of both sa les and production coming from operations outside of the United States (Tab le 2-36). The largest chemical manufacturing company, Dow Chemicals, has 108 out of 150 manuf acturing sites located outside of the United States (Dow Chemical Company, 2008).
In 2007, 58% of U.S. chemical manufacturi ng corporations generated net income. Including those with and without net income, chemical manufactur ers had an average before-tax profit margin of 10.24%. Profitability is highest for pharmaceutical and medicine corporations (Table 2-37).2-63 Table 2-36. Top Chemical Producers: 2007

Chemical Sales

($millions) % of Total Sales % of Sales in United States Dow Chemical 53,513 100% 35% ExxonMobil 36,826 9% 38% DuPont 29,218 100% 38% Lyondell a 16,165 57% 80% Chevron Phillips 12,534 100% 86% PPG Industries a 10,025 90% 56% Huntsman Chemical 9,651 100% 50% Praxair 9,402 100% 43.5% Air Products a 8,820 88% 51% Rohm & Haas b 7,837 88% 49%

a Percentage of sales in the United States calcu lated from total sales, not chemical sales. b Percentage of sales in the United States is actually percentage of sales in North America. Source: O’Reilly, R. 2008. “Chemicals.” Standard and Poor’s Industry Surveys. 176(28). Table 2-37. 2007 Corporate Income and Profitability (NAICS 325)

Industry Number of Corporations Number of Corporations with Net Income Total Receipts ($thousands) Business Receipts ($thousands) BeforeTax Profit

Margin After-Tax Profit Margin Basic chemical 1,244 757 $195,022,700 $178,019,490 5.07% 4.10% Resin, synthetic rubber, and artificial synthetic fibers and filaments 1,067 648 $44,692,366 $40,078,009 8.06% 6.33%

Pharmaceutical and medicine 1,034 611 $381,339,258 $317,414,432 15.63% 11.66%

Paint, coating, and adhesive 1,411 1,260 $51,778,868 $49,486,744 5.39% 4.02%

Soap, cleaning compound, and toilet preparation 1,862 463 $150,506,485 $139,836,602 9.07% 7.51%

Other chemical product and preparation 2,946 1,773 $89,014,032 $84,062,534 6.71% 5.27%

Chemical manufacturing 9,564 5,512 $912,353,710 $808,897,810 10.24% 7.89% Source: Internal Revenue Service, U.S. Department of Treasury. 2008b. “Corporation Source Book: Data File 2007.” <http://www.irs.gov/taxs tats/article/0,,id=167415,00.html>; (January, 15, 2010).2-64 2.4.3.6 Size Distribution The primary criterion for categorizing a busine ss as small is number of employees, using definitions by the SBA for regulatory flexibility analyses. The data describing size standards are provided in Table 2-38 and Tabl e 2-39. In 2002, enterprises with fewer than 500 employees accounted for 27% of employment and 15% of receipts within the chemical manufacturing industry).
2.4.3.7 Domestic Production In the late 1990s, overall manufacturing pr oduction was growing much faster than the chemical manufacturing component (Figure 2-38 ). Following the recession of 2001, however, the components have moved broadly in line with one another, except for the drop in chemical manufacturing production cause d by the hurricane season of 2005.
2.4.3.8 International Trade In the year 2000, the United States moved from having a trade surplus to a trade deficit in chemical manufacturing products (F igure 2-39). This change occurr ed because the trade deficit in pharmaceutical manufacturing, currently at $35 billion, overwhelmed the trade surplus of bulk chemicals and other chemical manufacturi ng combined, currently at $22 billion.
2.4.3.9 Market Prices Prices of goods in chemical manufacturing ha ve accelerated rapidly in the last 2 years, having outpaced overall manufacturing since 200 2 (Figure 2-40). Much of this recent acceleration seen in the industry PPI is due to the bulk chemicals segment, largely reflecting the rapid increase in fertilizer prices.2-65 Table 2-38. Small Business Size Standards: Chemical Manufactu ring (NAICS 325)
NAICS Description Employees 325110 Petrochemical Manufacturing 1,000 325120 Industrial Gas Manufacturing 1,000 325131 Inorganic Dye and Pigment Manufacturing 1,000 325132 Synthetic Organic Dye a nd Pigment Manufacturing 750 325181 Alkalies and Chlorine Manufacturing 1,000 325182 Carbon Black Manufacturing 500 325188 All Other Basic Inorganic Chemical Manufacturing 1,000 325191 Gum and Wood Chemical Manufacturing 500 325192 Cyclic Crude and Intermediate Manufacturing 750 325193 Ethyl Alcohol Manufacturing 1,000 325199 All Other Basic Organic Ch emical Manufacturing 1,000 325211 Plastics Material and Resin Manufacturing 750 325212 Synthetic Rubber Manufacturing 1,000 325221 Cellulosic Organic Fiber Manufacturing 1,000 325222 Noncellulosic Organic Fiber Manufacturing 1,000 325311 Nitrogenous Fertilizer Manufacturing 1,000 325312 Phosphatic Fertilizer Manufacturing 500 325314 Fertilizer (Mixing Only) Manufacturing 500 325320 Pesticide and Other Agricultu ral Chemical Manufacturing 500 325411 Medicinal and Botanical Manufacturing 750 325412 Pharmaceutical Preparation Manufacturing 750 325413 In-Vitro Diagnostic Substance Manufacturing 500 325414 Biological Product (except Diagnostic) Manufacturing 500 325510 Paint and Coating Manufacturing 500 325520 Adhesive Manufacturing 500 325611 Soap and Other Detergent Manufacturing 750 325612 Polish and Other Sanitation Good Manufacturing 500 325613 Surface Active Agent Manufacturing 500 325620 Toilet Preparation Manufacturing 500 325910 Printing Ink Manufacturing 500 325920 Explosives Manufacturing 750 325991 Custom Compounding of Purchased Resins 500 325992 Photographic Film, Paper, Plat e and Chemical Manufacturing 500 325998 All Other Miscellaneous Chem ical Product and Preparation Manufacturing 500

Source: U. S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/sizestandardstopics/size/index.html>.2-66 Table 2-39. Distribution of Economic Data by Enterprise Size: Chemical Manufacturing (NAICS 325)
Enterprises with

Variable Total 1 to 20 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees Firms 9,341 5,413 1,974 790 95 56 71 Establishments 13,096 5,433 2,208 1,352 250 185 276 Employment 827,430 34,838 78,090 113,326 28,025 18,119 28,338 Receipts ($millions) $468,211 $9,631 $21,394 $39,111 $12,217 $7,324 $14,762 Receipts/firm ($thousands) $50,124 $1, 779 $10,838 $49,507 $128,603 $130,779 $207,913 Receipts/establishment ($thousands) $35,752 $1,773 $9,689 $28,928 $48,869 $39,587 $53,485

Receipts/employment ($) $565,862 $276,464 $273,971 $345,117 $435,942 $404,195 $520,920

a Excludes SUSB employment category for zero employees. These entities only operated for a fraction of the year. Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Busi nesses: U.S. Detail Employment Sizes: 2002.” <http://www.census.gov/csd/ susb/download_susb02.htm>.

100 110 120 130 140Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Chemical Manufacturing
Figure 2-38. Industrial Production Trends in Chemical Manufacturing (NAICS 325) Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Production.” <http://www.federalreserve.gov/datadownload/>.2-67 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007$1,000,000(2007)

Imports Exports Figure 2-39. International Trade Trends in Chemical Manufacturing (NAICS 325) Source: U.S. International Trade Commission. 2008. “U.S. Domestic Exports” & “U.S. Imports for Consumption.” <http://dataweb.usitc.gov/s cripts/user_set.asp>.

90 100 110 120 130 140 150 160 170Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PPI(Jan‐98=100)

Manufacturing Chemical Manufacturing
Figure 2-40. Producer Price Trends in Chemical Manufacturing (NAICS 325) Source: U.S. Bureau of Labor Statistics (BLS). 2009c. Producer Price Index. Series ID: PCU325—325— &PCUOMFG—OMFG—. <http://www .bls.gov/ppi/home.htm>.2-68 2.5 Section 2 References Benwart, S.J. 2006. “Paper & Forest Products.” Standard and Poor’s Industry Surveys 176(28). Dow Chemical Company. 2008. “Transforming: The Dow Chemical Company 2007 10-K and Stockholder Summary.” <http://www.dow.com/financial/2007ann/>.
Federal Reserve Board. 2009. “Industrial Pr oduction and Capac ity Utilization.” <http://www.federalreserve.gov/datadownload/>. Graves, T. 2008. “Food and Nonalcoholic Beverages.” Standard and Poor’s Industry Surveys 176(25). Internal Revenue Service, U.S. Department of Treasury. 2008a. “Corporation Source Book: Data File 2005.” <http://www.irs.gov/taxs tats/article/0,,id=167415,00.html>. Internal Revenue Service, U.S. Department of Treasury. 2008b. “Corporat ion Source Book: Data File 2007.” <http://www.irs.gov/taxs tats/article/0,,id=167415,00.html>. Kraft Foods Inc. 2008. “Form 10-K, Period ending December 31, 2007.” <http://www.kraft.com/Investor/sec-filings-annual-report/>. O’Reilly, R. 2008. “Chemicals.” Standard and Poor’s Industry Surveys. 176(28). Standard & Poors, 2008. Standard & Poor’s ne tAdvantage [electronic resource]. New York: Standard & Poors. U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at th e Summary Level.” Table 2. Washington, DC: BEA. U.S. Bureau of Labor Statistics. 2009a. “Producer Price Index.” Series ID: PCU321—321—& PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home.htm>.
U.S. Bureau of Labor Statistics. 2009b. “Pr oducer Price Index.” Se ries ID: PCU322–322– & PCUOMFG–OMFG–. <http:// www.bls.gov/ppi/home.htm>.
U.S. Bureau of Labor Statistics. 2009c. “P roducer Price Index.” Series ID: PCU325—325— &PCUOMFG—OMFG—. <http://www .bls.gov/ppi/home.htm>.
U.S. Census Bureau. 2001. “1997 Census NAICS Definitions.” <http://www.census.gov/epcd/ec97/industry/E322.HTM#def>.
U.S. Census Bureau. 2008. “Firm Size Data from th e Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002.” <http://www.cen sus.gov/csd/susb/download_susb02.htm>.
U.S. Census Bureau. “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997” (generated by RTI Internationa l using American FactFinder). <http://factfinder.census.gov>. Accessed July 8, 2008.2-69 U.S. Census Bureau. “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (2007 NAICS Basis): 2002 and 2007” (generated by RTI Internationa l using American FactFinder). <http://factfinder.census.gov> . Accessed January 4, 2010.
U.S. Census Bureau. “Sector 00: EC0700A1: All Sectors: Geographic Area Series: EconomyWide Key Statistics: 2007” (generated by Kapur Energy and Environment using American FactFinder). <h ttp://factfinder.census.gov>. Accessed December 27, 2009. U.S. Census Bureau. “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 a nd 2005” (generated by RTI International using American FactFinder). <http://f actfinder.census.gov>. Accessed July 8, 2008.
U.S. Census Bureau. “Sector 31: EC0731I1: Manuf acturing: Industry Series : Detailed Statistics by Industry for the United States: 2007” (g enerated by Kapur Energy and Environment using American FactFinder). <http://f actfinder.census.gov>. Accessed December 27, 2009. U.S. Census Bureau. “Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for the States, Metropolitan and Micropolitan St atistical Areas, Counties, and Places: 2002” (generated by RTI Internationa l using American FactFinder). <http://factfinder.census. gov>. Accessed July 23, 2008. U.S. Department of Energy, Energy Info rmation Administration. 2007a. “2002 Energy Consumption by Manufacturers—Data Tables” (Tables 3.2 and N3.2). <http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>. U.S. Department of Energy, Energy In formation Administration. 2007b. “2006 Energy Consumption by Manufacturers—Data Tables” (Table 3.1). <http://www.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html>. U.S. Department of Energy, Energy Informa tion Administration. 2008. “Assumptions to the Annual Energy Outlook 2008: With Projections for 2030.” <http://www.eia.doe.gov/oiaf/aeo/assumption/>. U.S. Department of Labor, Bureau of Labor Statistics. 2010. “Producer Price Indexes.” <http://www.bls.gov/pPI/Series Id : PCU311—311—Food Manufacturing & PCUOMFG–OMFG–Total Manufacturing>. U.S. Environmental Protection Agency. 2002. “Prof ile of the Pulp and Paper Industry. Sector Notebook Project.” <http://www.epa.gov/Complia nce/resources/publica tions/assistance/ sectors/notebooks/index.html>.
U.S. International Trade Commission. 2008. “U.S . Domestic Exports” and “U.S. Imports for Consumption.” <http:/ /dataweb.usitc.gov/scri pts/user_set.asp>.2-70 U.S. Small Business Administration. 2008. “Table of Small Business Size Standards Matched to North American Industry Classification Sy stem Codes.” <http://www.sba.gov/services/ contractingopportunitie s/sizestandardstopics /size/index.html>.3-1 SECTION 3 ENGINEERING COST ANALYSIS We provide an overview of the engineering cost analysis used to estimate the additional private expenditures industry may make in order to comply with the rule. A detailed discussion of the methodology used to estimate cost im pacts is presented in Appendices C and D.
Several provisions in the rule and the data us ed to generate regulatory impacts analysis between proposal and the final rule. As a result, the costs of the RIA analysis decreased from the proposal to final analysis.
The major regulatory provisions resulting in a reduction in the cost estimates were:

 Consolidating the biomass and coal s ubcategories into a single solid fuel subcategory for fuel-based HAP and HAP surrogates (Hg, HCl, PM). This provision has reduced the number of biom ass units expected to require HCl control in order to meet the emission limits.

 Creating a hybrid grate/suspension cate gory for combustion based HAP and HAP surrogates to address boilers that expre ss unique design features to handle the combustion of fuels with a very high mois ture content. This increased the CO emission level for this subcategory which resulting in less units needing CO catalyst or advanced combustion cont rols in order to meet the limit.

 Creating a limited use subcategory. This s ubcategory removes the control, testing and monitoring costs for 134 large boilers fi ring solid and liquid fuels that operate less than 876 hours per year.

 Creating a fuel gas specification to allow additional gaseous fuels to qualify for the gas 1 subcategory. Based on the data in the record, most fuels are expected to meet the mercury specification and will qualify for a work practice standard instead of meeting emission limits. The num ber of gas 2 units estimated to meet emission limits at proposal was and this has been reduced to an estimated 71 units in the final rule, compared with an estimated 199 gas 2 units in the proposed analysis.

 Removing the CO CEMS monitoring requi rement for units with a heat input capacity. Although this change in monitoring is offset in part by O 2 parameter monitoring requirement.

 Removing duplicative monitoring requirements for PM CEMS, opacity monitoring and bag leak detection monitors. The proposal had included requirements to measure more than one of these parameters and this duplication has been removed from the final rule.3-2  Reducing testing frequency for dioxin/fura n emissions. The final rule requires a one-time initial test instead of annual testing.
The major data analysis elements re sulting in a reduction in cost were:

 As discussed in detail in the “Handli ng and Processing of Corrections and New Data in the EPA ICR Databases” memora ndum, several new data submissions and corrections to existing entries were re ceived from the public comment process. These new data incorporated into the revised MACT floor, baseline emission, and emission reduction analysis.

 The criteria for considering data in th e MACT floor analysis were revised to reduce the low bias impact from tests conducted during co-firing configurations. In the final rule, the test had to be fired with at least 90 percent of its heat input belonging to the same subcategory for exis ting sources and 100 pe rcent of its heat input belonging to the same subcategory for new sources. At proposal, data were considered relevant for consideration in the MACT floor analysis if at least 10 percent of its heat input belonged to the same subcategory. This modified the make-up of the top performing units and the resulting.

 The CO emission data analysis was revi sed to incorporate measurement error resulting from calibration error, system bias, and drift requirements using the reported instrument calibration span as th e basis for estimating the error. This increased CO emission levels in several ca ses in order to better incorporate this error in the final standard.

 Several elements of the MACT floor analysis statistical approach were incorporated from public commenters, wh ich resulted in increased emission limits in some cases. The memorandum “Revised MACT Floor Analysis (2011) for the Industrial, Commercial, a nd Institutional Boilers and Process Heaters National Emission Standards for Hazardous Air Po llutants—Major Source” details the specific statistical modifications that were made in the final analysis.
3.1 Major Sources To estimate the national cost impacts of th e proposed rule for existing sources, EPA developed average baseline emission factors for each fuel type/control device combination based on the emission data obtained and contained in the Boiler MACT emission database. If a unit reported emission data, we assigned its unit-specific emission data as its baseline emissions. For units that did not report emission data, but simila r units at the same fac ility reported data, we assigned the average emission factors from simila r units at the same facility to the baseline emissions of the unit. For all other units that did not report emission data, we assigned the appropriate emission factors to each existing unit in the inventory database, based on the average emission factors for boilers with similar fuel, design, control devices. We then compared each3-3 unit’s baseline emission factors to the proposed MACT floor em ission limit to determine if control devices were needed to meet the emission limits. The control analysis considered fabric filters and activated carbon injection to be th e primary control devices for mercury control, electrostatic precipitators for units meeting mercury limits but requiring additional control to meet the PM limits, wet scrubbers and dry injecti on fabric filter combinations to meet the HCl limits, tune-ups, replacement burners, combustion controls, and CO oxida tion catalyst for CO and organic HAP control. We identified where one control device could achieve reductions in multiple pollutants, for example a fabric filter was expected to achieve both PM and mercury control in order to avoid overestimating the co sts. We also included costs for testing and monitoring requirements contained in the proposed rule. The resul ting total national cost impact of the proposed rule is 5.1 bill ion dollars in capital expenditure s and 1.8 billion dollars per year in total annual costs. Considering estimated fu el savings resulting from work practice standards and combustion controls, the total annualized cost s are reduced to 1.4 billion. The total capital and annual costs include costs for control devi ces, work practices, testing and monitoring. Table 3-1 of this shows the capital and annual cost impacts for each subcategory. Costs include testing and monitoring cost s, but not recordkeepi ng and reporting costs. Table 3-1. Summary of Capital and Annual Costs for New and Existing Major Sources a

Source Subcategory Estimated/ Projected No. of Affected Units Capital Costs (million) Annualized Cost (million per yr) a Annualized Cost (million per yr) b Existing Units Solid units 1,014 $2,182 $873 $846 Liquid units 713 $2,656 $833 $828 Non-Continental Liquid Units 27 $86 $24 $21
Gas 1 units 10,797 $70 $31 $(325) Gas 1 Metallurgical Furnaces 694 $4.5 $2.0 $(6)
Gas (other) units 118 $79 $39 $37 Limited Use 477 $3.1 $1.3 $(25) Energy Audit
New Units ALL 27 $27 Total Existing Costs 13,840 $5,081 $1,803 $1,403 Solid units 0 0 0 0 Liquid units 13 $21 $6.1 $6.1 Gas 1 units 34 $0.2 $0.1 0 Total New Costs 47 $21.2 $6.1 $6.1

a Does not include fuel savings. b Includes Fuel Savings.3-4 Using Department of Energy (DOE) projecti ons on fuel expenditures, the number of additional boilers that could be potentially constr ucted was estimated. A detailed description of how the DOE data was used to project new units e xpected to be constructed is discussed in the memorandum, “ Revised New Unit Analysis Industrial, Commercial, and Institutional Boilers and Process Heaters National Emission Standards for Hazardous Air Pollutants—Major Source (Gibson et al., 2011).” This analysis consisted of two steps, (1) calculatin g the predicted percent change in fuel consumption in the industrial and commercial/institutional sectors between the baseline year (2008) of the major source boiler a nd process heater inventory and the target years of the projection (2013 and 2015); and (2) calculating the expected fuel consumption in future in boilers and heaters and predicting the number of new units required to fire a particular fuel. The resulting total national cost impact of the proposed rule in the 3rd year is 21 million dollars in capital expenditures and 6 million dollars per year in total annual costs, when considering a 1 percent fuel savings.
The fuel savings are based on an estimated sa vings of 1 percent from tuning the boiler to adjust oxygen levels and inspecting the flam e pattern, consistent with manufacturer’s specifications. For units with an automatic air to fuel ratio control, it al so includes inspection and calibration of that system. It is primarily the t une-ups that are expected to obtain these fuel savings, but approximately 440 units are estimated to retrofit thei r units with linkageless boiler management systems in order to reduce CO em issions and improve boi ler efficiency. Fuel savings of one percent was selected for the basis of the fuel savings analysis. The fuel savings was estimated for units firing gaseous, liquid, and coal fuels only. Biomass fuels were not considered in the fuel savings analysis since it is difficult to establish a market value for the various biomass fuel streams
The estimated fuel savings between proposal and promulgation are similar. At proposal, fuel savings from existing units was estimated to be $376 million, this assumed that 12,887 units would install combustion controls or conduct a tune-up in order to meet the CO emission limit or comply with a work practice standard. At the fi nal rule, the fuel savings was estimated to be $428 million, based on 13,279 units installing combusti on controls or conducting a work practice standard. The difference resulted from two main changes:

 First, more units were added to the i nventory, based on comments received from additional facilities that we re left out of the analysis at the proposal. Many of these additional units will conduct a tune -up or install combustion controls in order to meet the work practice or CO emission limit.3-5  Second, the baseline emission and emissi on limits have been modified, which have in turn increased some of the limits for CO. As a result, the assumptions associated with how many units will conduct a tune-up or install combustion controls in order to meet the emission limits have changed. At proposal, 458 units were estimated to install a linkageless boiler management system, one boiler was expected to install a replacement burne r, and 12,428 boilers were estimated to conduct a tune-up. In the final analysis, 440 units were estimated to install a linkageless boiler management system, 1 unit was expected to install a replacement LNB, and 12,838 boilers we re expected to conduct a tune-up.

 Third, the treatment of gas 2 units ha s been modified to use a fuel gas specification. This is expected to increas e the number of gas units eligible for a work practice standard instead of an emi ssion limit. As a result of this change more of these units are expected to conduc t a tune-up to fulfil l the requirements of the work practice standard.
Although the age of the bo iler can affect combustion efficienc y, periodic tune-ups are expected to improve the combustion units of both new and existing units. Even new units are susceptible of being operated at air-to-fuel ratios that are not optimal. EPA adopted the same fuel savings assumptions for both new and existing units. The an alysis estimates that if the annual tune-up provisions are incorporated, a savings of one pe rcent would be achieved on an annual basis when compared to a new boiler installation that does not conduct ro utine tune-ups. The analysis projects the construction of 6,424 new liquid fired boilers at ar ea sources, as opposed to 155 new coal units and 200 biomass units, respectively. A ll of these boilers are expected to conduct a tune-up in order to meet the requirements of the final rule. Liquid-fired units have the greatest energy cost on a per mmBtu basis of any of the subcategories, on which the fuel savings are based. For residual fuels, the energy cost was calculated to be $12.96/mmBtu. Distillate fuels were found to have an energy cost of either $22.66 for commercial units, units or $23.23 for industrial units. The combination of the large number of projected units in the liquid subcategory and the high energy cost result ed in the fuel savings shown.
A discussion of the methodology used to estimat e cost impacts is presented in “Revised Methodology for Estimating Cost and Emission Impacts for Industrial, Commercial, and Industrial Boilers and Proce ss Heaters National Emission Standards for Hazardous Air Pollutants—Major Source (2011)” in an Appendix.
3.2 Area Sources To estimate the national cost impacts of th e proposed rule for existing sources, EPA developed several model boilers and determined th e cost of control for these model boilers. The EPA assigned a model boiler to each existing unit based on the fuel, size, and current controls.3-6 The analysis considered all ai r pollution control equipment curre ntly in operation at existing boilers. Model costs were then assigned to all existing units that could not otherwise meet the proposed standards. The resulting total national cost impact of the final ru le for existing units is $487 million dollars in total annual ized costs after considering fu el savings from efficiency improvements. The total annualized costs for in stalling controls, conducting an annual tune-up and an energy assessment, and implementing testing and monitoring requirements, is $535 million. Table 3-2 of this preamble shows the tota l annualized cost impacts for each subcategory. Table 3-2. Summary of Annual Costs for New and Existing Area Sources

Source Subcategory Estimated/ Projected No. of Affected Units Total Annualized Cost (million per yr) a Existing Units Coal 3,710 $37
Biomass 10,958 $24
Oil 168,003 $374 Facility Energy Audit All $52
New Units b Coal 155 $0.4
Biomass 200 $2.6
Oil 6,424 $45

a TAC does not include fuel savings from improving combustion efficiency. b Impacts for new units assume the number of units online in the first 3 years of this rule (2010 to 2013). EPA also estimated the number of additional boilers that could be potentially constructed. The resulting total national cost impact of the final rule on new sources by the 3rd year, 2013, is $48 million dollars in total annualized costs. When accounting for a 1 percent fuel savings resulting from improvements to combustion effi ciency, the total nationa l cost impact on new sources is ($3.6 million). The size of the fuel savi ngs estimate resulted primarily from the large number of new liquid fired boilers, for which th e cost of energy is the highest on a per mmBtu basis. This large energy cost created to a corr espondingly high cost savings with the increased combustion efficiency.
3.3 Section 3 References Gibson et al., 2011. Revised New Unit Analysis Industrial, Commercial, and Institutional Boilers and Process Heaters National Emission Standards for Hazardous Air Pollutants—Major Source.4-1 SECTION 4 ECONOMIC IMPACT ANALYSIS EPA prepares an RIA to provide decision make rs with a measure of the social costs of using resources to comply with a program (EPA , 2000). The social costs can then be compared with estimated social benefits (as presente d in Section 6). As noted in EPA’s (2010) Guidelines for Preparing Economic Analyses , several tools are available to estimate social costs and range from simple direct compliance cost methods to the development of a more complex market analysis that estimates market changes (e.g., price and consumption) and economic welfare changes (e.g., changes in cons umer and producer surplus).
The Office of Air Quality Planning and Sta ndards (OAQPS) adopted a standard market analysis as described in the Office’s resour ce manual (EPA, 1999). The approach uses a singleperiod multimarket partial equi librium model to compare pre- policy market baselines with expected post-policy market outcomes. The an alysis’ time horizon is the short run; some production factors are fixed and some are variable and is distinguished from the very short run where all factors are fixed and producers cannot adjust inputs or outputs (EPA, 1999, 5-6). The time horizon allows us to capture important trans itory stakeholder outcomes. Key measures in this analysis include industry-level changes in price levels, production and consumption, jobs, international trade, and social costs (c hanges in producer and consumer surplus).
4.1 Partial Equilibrium Analysis (Multiple Markets) The partial equilibrium analysis develops a market model that simulates how stakeholders (consumers and industries) might respond to the additional regulatory program costs. In this section, we provide an overview of the economic model. Appendix A provides additional details on the behavioral assumptions, data , parameters, and model equations.
4.1.1 Overview Although several tools are available to estima te social costs, cu rrent EPA guidelines suggest that multimarket models “...are best us ed when potential impacts on related markets might be considerable” and modeling using a co mputable general equilibrium model is not available or practical (EPA, 2010, p. 9-21). Other guides for e nvironmental economists offer similar advice (Berck and Hoffmann, 2002; Just, Hueth, and Schmitz, 2004). Multimarket models focus on “short-run” time horizons and measure a policy’s near-term or transition costs (EPA, 1999). Note that the multimarket model is not desi gned to directly estimate employment impacts. Job effects are discussed later, in section 4.1.2.4. The multimarket model contains the following features:4-2  Industry sectors and benchmark data set

– 100 industry sectors

– a single benchmark year (2010)

– industry employment data

 Economic behavior

– industries respond to regulatory co sts by changing production rates

– market prices rise and fall to reflect higher energy a nd other non-energy material costs and changes in demand

– customers respond to these price increases and consumption falls

 Model scope

– 100 sectors are linked with each other ba sed on their use of energy and other nonenergy materials. For example, the c onstruction industry is linked with the petroleum, cement, and steel industries a nd is influenced by price changes that occur in each sector. The links allow EP A to account for indirect effects the regulation has on related markets.

– production adjustments influence employment levels

– international trade (imports/exports) responds to domestic price changes

 Model time horizon (“short r un”) for a single period (2014)

– fixed production resources (e.g., capital) l ead to an upward-sloping industry supply function

– firms cannot alter input mixes; there is no substitution among intermediate production inputs

– price of labor (i.e., wage) is fixed

– investment and government expenditures are fixed
4.1.2 Economic Impact Analysis Results
4.1.2.1 Market-Level Results Market-level impacts include price and quant ity adjustments includ ing the changes in international trade (Figure 4-1) . Under the major source NESHAP , the Agency’s economic model suggests the average national pr ice increases for industrial se ctors are less than 0.01%, while average annual domestic production may fall by le ss than 0.01%. Because of higher domestic prices, imports slightly rise. Ma rket-level effects for the area source NESHAP are smaller when compared to the major source rule; average pric e, production, and import changes are less than 0.01%. Industrial sector detail s are provided in Appendix B.4-3 ‐ 0.0200% ‐ 0.0150% ‐ 0.0100% ‐ 0.0050% 0.0000% 0.0050% 0.0100% 0.0150% 0.0200% Selected Option Alternative Option Final MACT/GACT Approach Proposed MACT Approach Selected Option Alternative Option Major Sources Area Sources Major & Area SourcesMarket‐LevelEconomicVaiables:PercentageChangefromBaseline

Prices Production Imports Consumption Exports

Figure 4-1. Market-Level Changes by Source and Option

4.1.2.2 Social Cost Estimates Major Source Rule In the near term, the Agency’s economic mode l suggests that industries are able to pass on $0.5 billion (2008$) of the major source rule’s cost s to U.S. households in the form of higher prices (Table 4-1). Existing U.S. industries’ surplus falls by $1.4 b illion and the net U.S. loss in aggregate is $1.9 billion. As U.S. prices rise, other countries ar e affected through international trade relationships. The price of goods produced in the United States increase slightly and domestic production declines, replaced to a cert ain degree by imports; the model estimates a net gain of less than $0.1 billion to foreign companies. As shown in Figure 4-2, the U.S. surplus losses are concentrated in other services (23 pe rcent), lumber, paper, a nd printing (16 percent), energy industries (15 percent) , and chemicals (13 percent).4-4 Table 4-1. Distribution of Social Co sts Major Sources (billion, 2008$): 2014

Method Selected Opti on Alternative Option Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus

− $0.530

− $0.600 Change in U.S. producer surplus

− $1.360

− $1.730 Change in U.S. surplus

− $1.890

− $2.330 Direct Compliance Costs Me thod (Not Modeled) Total annualized costs, new major sources a

− $0.006

− $0.920 Fuel savings, existing major sources $0.430 $0.400 Fuel savings, new major sources a Less than $0.001 Less than $0.001 Net Change in U.S. Surplus b

− $1.470

− $1.940

Net change in rest of world surplus $0.060 $0.090 Net change in total surplus

− $1.410

− $1.850

a Estimates for the Alternative option are assumed to be the same as the Selected option. b U.S. surplus changes add the partial equilibrium model es timates and the direct compliance estimates not included

in the partial equilibrium model. For example, the selected option’s net change in U.S. surplus is $1.890 + (0.430 – 0.006) = –$1.470 billion.

22.5%

16.2%

15.1%

12.6%

8.7%

5.3%

5.0%

3.0%

2.4%

2.1%

2.0%

1.4%

1.2%

1.0%

0.8%

0.5% 22.0%

28.4%

10.7%

9.2%

8.3%

3.1%

3.3%

5.2%

2.0%

1.8%

0.6%

1.3%

1.9%

0.8%

0.7%

0.6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Other Services Lumber, paper, and printing Energy Industries
Chemicals Food, beverages, and textiles Primary Metals Transportation Equipment Nonmanufacturing
Plastics and Rubber Transportation Services Machinery and Equipment
Wholesale and Retail Trade
Other
Fabricated Metals
Electronic Equipment
Nonmetallic Minerals Selected Option: $1.9 Billion U.S. Surplus Change Alternative Option: $2.3 Billion U.S. Surplus Change

Figure 4-2. Distribution of U.S. Surplus Changes by Sector: Major Sources4-5 The Agency also considered other elements of the engineering cost analysis that could not be modeled within the multimarket model (e.g., fu el savings benefits [existing and new major sources] and total annualized compliance costs [n ew major sources]). The net effect of the adjustments is a U.S. surplus loss estimate of $1.5 billion.
4.1.2.3 Social Cost Estimates Area Source Rule In the near term, the Agency’s economic mode l suggests that industries are able to pass on $0.2 billion (2008$) of the area source rule’s costs to U.S. households in the form of higher prices (Table 4-2). Existing U.S. industries’ surplu s falls by $0.3 billion and the net loss for U.S. stakeholders is $0.5 billion. As U.S. prices rise, other countries are affect ed through international trade relationships. Households that buy U.S. exports pay higher prices and purchase fewer U.S. produced goods. Other countries that that sell goods to the United Stat es benefit; the model estimates a net rest of the world gain of less than $0.01 billion. As shown in Figure 4-3, the U.S. surplus losses are concentrated in the other se rvices (82 percent ). Other services include information, finance and insurance, real estate, professional services, management, administrative services, education, health care, arts, accommodations, and public services.
Table 4-2. Distribution of Social Co sts Area Sources (billion, 2008$): 2014

Method Final MACT/GACT Approach Proposed MACT Approach Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus − $0.240

− $0.300 Change in U.S. producer surplus − $0.250

− $0.330 Change in U.S. surplus − $0.490

− $0.630
Direct Compliance Costs Me thod (Not Modeled) Total annualized costs, un known existing area sources

− $0.003

− $0.008 Total annualized costs, new area sources

− $0.050

− $0.270 Fuel savings, existing and new area sources) $0.050 $0.050
Net Change in U.S. Surplus a − $0.490

− $0.850

Net change in rest of world surplus $0.004 $0.005 Net change in total surplus − $0.480

− $0.850

a U.S. surplus changes add the partial equilibrium model es timates and the direct compliance estimates not included

in the partial equilibrium model. For example, the Final MACT/GACT net change in U.S. surplus is $0.490 + (0.050 – 0.050–0.003) = –$0.490 billion.4-6 81.6%

4.9%

2.6%

4.7%

4.2%

0.5%

0.3%

0.6%

0.1%

0.2%

0.1%

0.1%

0.1%

0.0%

0.0%

0.0% 79.3%

6.7%

3.1%

4.9%

4.0%

0.6%

0.3%

0.5%

0.2%

0.2%

0.1%

0.1%

0.1%

0.0%

0.0%

0.0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Other Services Food, beverages, and textiles

Wholesale and Retail Trade
Nonmanufacturin g Lumber, paper, and printing Transportation Services
Chemicals
Other
Plastics and Rubber

Energy Industries
Nonmetallic Minerals Primary Metals
Fabricated Metals Machinery and Equipment Transportation Equipment
Electronic Equipment Final MACT/GACT Approach: $0.4 Billion U.S. Surplus Change Proposed MACT Approach: $0.6 Billion U.S. Surplus Change

Figure 4-3. Distribution of Total Surplus Changes by Sector: Area Sources

a Other services include information, finance and insu rance, real estate, profe ssional services, management,

administrative services, education, health car e, arts, accommodations, and public services. The Agency also considered other elements of the engineering cost analysis that could not be modeled within the multimarket model (e.g., fu el-savings benefits [existing and new area sources] and total annualized compliance costs [unknown existing and new area sources]). The net effect of the adjustments is a to tal surplus loss estim ate of $0.5 billion.
4.1.2.4 Job Effects 4.1.2.4.1 Background
In addition to estimating this rule’s social costs and benefits, EPA has estimated the employment impacts of the final rule based on Morgenstern, Pizer and Shih (2002). A standalone analysis of jobs is not included in a sta ndard cost-benefit analysis. Executive Order 13563 however, states, “Our regulatory system must protect public hea lth, welfare, safety, and our environment while promoting economic growt h, innovation, competitiveness, and job creation ” (emphasis added). Therefore, we have provided this analysis to inform the discussion of job4-7 impacts. EPA continues to explore the relevant theoretical and empirical literature and to seek public comments in order to ensure that such estimates are as accurate as possible.
From an economic perspective labor is an input into producing goods and services; if regulation requires that more la bor be used to produce a given amount of output, that additional labor is reflected in an increas e in the cost of production. Mo reover, when the economy is near full employment, jobs created in one industry as a result of regulation displace jobs in other industries. On the other hand, in periods of high unemployment, an increase in labor demand due to regulation may have a stimula tive effect that results in a net increase in overall employment. With significant numbers of workers unempl oyed, the opportunity costs associated with displacing jobs in other sectors ar e likely to be much smaller.
For this reason, this RIA looks carefully at a subset of the employment consequences of this final rule. It is important to note that EPA has estimated only a portion of the employment effects -- namely, those associated with the di rect impacts on employment in the regulated industry. A full analysis would include estimates of the direct impacts on other industries (e.g. suppliers of pollution control equipment) as well as the indirect and induced effects on employment throughout the economy as a whole in response to changes in output and factor prices.
We expect that the rule’s direct impact on employment will be small. The Agency’s analysis does not include all the di rect effects of this regulation. For example, EPA is currently exploring ways to quantify the job impacts in the pollution control sector th at result from these and future regulations. Furthermore, we have not quantified the rule’s indirect or induced impacts. What follows is an overview of the various ways that envi ronmental regulation can affect employment, followed by a discussion of the estimated impacts of this rule. An environmental regulation can affect the demand for labor in several ways:

 Direct Effects :

– Increased prices for industry output may reduce the demand for labor: Environmental regulations increase pr oduction costs causing firms to increase prices; higher prices reduce consump tion (and production), thus reducing demand for labor within the regulated industry. Th e extent of this effect will depend on the extent of the price increase and the elasticity of the demand curve.

– Regulated firms demand labor workers to operate and maintain pollution controls within those firms. Once pollution control equipment is installed, regulated firms may hire worker s to operate and maintain it, just as they would hire4-8 workers to produce more output. The extent of this effect will depend in part on whether the operation and ma intenance of pollution cont rols are labor intensive

– Increased demand for pollution control equipment and services: When a regulation requiring emission reductions is promulgated, affected sources must immediately place orders for pollution control equipment and services. Filling these orders will require a scale-up in manufacturing of pollution control equipment, performance of engineering an alyses and significant expenditures for assembly and installation of such equipment. These activities will be job-creating during the period before firm s must comply with the rule, at which point all pollution control equipment must be installed and operating.

. Indirect and Induced Effects:

– Environmental regulations create employment in many basic industries . In addition to the increase in employment in the environmental protection industry (increased orders for pollution cont rol equipment), environmental regulations also create employment in industries that pr ovide intermediate goods to the environmental protection industry. For example, capit al expenditures to redu ce air pollution involve the purchase of abatement equipment. The equipment manufacturer s, in turn, order steel, tanks, vessels, blowers, pumps, and chemicals to manufacture and install the equipment. On the other hand, demand for labor will decrease in sectors that supply inputs for, or demand the outputs of the regulated industry. None of these impacts is accounted for in the current analysis. We also do not estimate employment impacts “induced” by increased output of the environmental protection sector, or decreased ou tput of the regulated sectors.
4.1.2.4.2 Methodology and Results The estimated impacts of the final rule on empl oyment in affected sources are based on an empirically derived relationship reported in Mo rgenstern, Pizer and Shih (2002), a peer-reviewed, published study. Estimates of the employment imp acts of the capital investments and other nonrecurring requirements of the rule are derived from the cost analys is developed for the regulation. 1 Morgenstern, Pizer and Shih (2002): Overview of Conceptual Approach
The fundamental insight of Morgenstern, Pizer and Shih (2002) is that environmental regulations can be understood as requiring regulated firms to add a new output (environmental quality) to their product mixes. Although legally compelled to satisfy this new demand, regulated firms have to finance this addi tional production with the proceeds of sales of their other (market) products. Satisfying this new demand requires add itional inputs, including labor, and may alter the relative proportions of labor and capital used by regulated firm s in their production processes.

1 Richard D. Morgenstern, William A. Pizer, and Jhih-Shyang Shih, Journal of Environmental Economics and Management | May 2002 | Vol. 43, no. 3 | pp. 412-436.4-9 Thus, Morgenstern et al., decompose the overa ll effect of a regulation on employment into the following three subcomponents:

 The “Demand Effect”: higher production costs raise market prices,reducing consumption (and production), thereby reducing demand for labor within the regulated industry 2;

 The “Cost Effect”: As production costs incr ease, plants use mo re of all inputs, including labor, to maintain a given level of output. For example, in order to reduce pollutant emissions while holding out put levels constant, regulated firms may require additional labor;

 The “Factor-Shift Effect”: Regulated firm s’ production technologies may be more or less labor intensive after complying with a regulation (i.e., more/less labor is required per dollar of output).
Decomposing the overall employment impact of environmental regulation into three subcomponents clarifies the con ceptual relationship between environmental regulation and employment in regulated sectors, and permitted Morgenstern, et al. to provide an empirical estimate of the net impact. For pres ent purposes, the net effect is of particular interest, and is the focus of our analysis.
Morgenstern, Pizer and Shi h (2002): Empirical Results Morgenstern et al. empirically estimate a mode l for four highly polluting, regulated industries (pulp and paper, plastics, petrol eum refining and steel) to examin e the effect of higher abatement costs from regulation on employment. They co nclude that increased abatement expenditures generally do not cause a significant change in employment. More specifically, their results show that, on average across the four industries, each additional $1 million spending on pollution abatement results in a (statistically insi gnificant) net increase of 1.55 (+/- 2.24) jobs. 3 “In plastics and petroleum, [Morgenstern et al] find that increased regula tion raises employment by a small but statistically significant amount: 6.9 and 2.2 j obs per million dollars of regulatory expense, respectively. In pulp and paper and steel, the estimates are even smaller and insignificantly different from zero.” 4 By applying these estimates to pollu tion abatement costs, we estimated the

2 The Morgenstern et al. results rely on industry dema nd and supply elasticities to determine cost pass-through and reductions in output. 3 These results are similar to Berman and Bui, who find that while sharply increased air quality regulation in Los Angeles to reduce NOx emissions resulted in large abatement costs they did not result in substantially reduced employment. "Environmental regulation and labor demand: evidence from the South Coast Air Basin." Journal of Public Economics 79(2): 265-295. 4 Morgenstern, Pizer and Shih, p. 413.4-10 net employment effect for major and areas sources to range from -4,100 to +8,500 jobs in the directly affected sector s with a central estimate of +2,200 (Table 4-3). 5, 6

5 Since Morgenstern’s analysis reports environmental expenditures in $1987, we make an inflation adjustment the engineering cost analysis using GDP implicit price deflator (64.76/108.48) = 0.60) 6 Net employment effect = 1.55× $2,400 million × 0.604-11 Table 4-3. Employment Impacts Using Mo rgenstern, Pizer, Shih (2002) (FTE)

Demand

Effect Cost Effect Factor Shift Effect Net Effect Change in Full-Time Jobs per Million Dollars of Environmental Expenditure a − 3.56 2.42 2.68 1.55

Standard Error 2.03 1.35 0.83 2.24
EPA estimate for Major Sources Rule − 3,900

− 8,200 to +500 2,600 +900 to +4,400 2,900 0 to +5,800 1,700

− 3,100 to +6,500
EPA estimate for Area Source Rule − 1,200

− 2,600 to +100 800 +300 to +1,400 900 0 to +1,800 500

− 1,000 to +2,000
EPA estimate for both Rules b − 5,100

− 10,800 to +600 3,500 +1,100 to +5,800 3,900 0 to 7,700 2,200

− 4,100 to +8,500

a Estimates from Morgenstern, Pizer, and Shih (2002) expressed in 2010 dollars using the GDP price deflator (see

footnote 7). b Totals may not add due to independent rounding.4-12 ‐ 3.9

‐ 4.8 ‐ 1.2 ‐ 0.9

‐ 5.1

‐ 5.7 2.6 3.2

0.8 0.6 3.5 3.9 2.9 3.6

0.9 0.7 3.9 4.3

1.7 2.1 0.5 0.4 2.2 2.5

‐ 15 ‐ 14 ‐ 13 ‐ 12 ‐ 11 ‐ 10 ‐ 9 ‐ 8 ‐ 7 ‐ 6 ‐ 5 ‐ 4 ‐ 3 ‐ 2 ‐ 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Selected Option Alternative Option Final MACT/GACT Approach Proposed MACT Approach Selected Option Alternative Option Major Sources Area Sources Major & Area Sources1,000JobsBase=144,100

Demand Effect: Estimate derived from Morgenstern, et al. Cost Effect: Estimate derived from Morgenstern, et al.

Factor Shift Effect: Estimate derived from Morgenstern, et al. Net Effect: Estimate derived from Morgenstern, et al. Figure 4-3. Employment Impacts Using Morg enstern, Pizer, Shih (2002) (1,000 FTEs)

Limitations of the Analysis
Although the Morgenstern et al. paper provides in formation about the potential job effects of environmental protection programs, there are se veral caveats associated with using those estimates to analyze the final rule. First, the Mo rgenstern et al. estimates presented in Table 4-3 and used in EPA’s analysis represent the weighted average parameter estimates for a set of manufacturing industries (pulp and paper, plastics, petroleum, and steel). This set of industries only partially overlaps with the sectors affected by this rule. Second, relyi ng on Morgenstern et al. implicitly assumes that estimates derived from 1979–1991 data are still applicable. Third, the methodology used in Morgenstern et al. assumes that regulations affect pl ants in proportion to their total costs. In other words, each additional dollar of regulatory burden affects a plant by an amount equal to that plant's total costs relative to the aggregate industry co sts. By transferring the4-13 estimates, EPA assumes a similar distribution of regulatory costs by pl ant size and that the regulatory burden does not disproportionately fall on smaller or larger plants. Further, Morgenstern et al. does not include most i ndirect effects and all induced effects.5-1 SECTION 5 SMALL ENTITY ANALYSES The RFA as amended by SBREFA generally requ ires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or an y other statute, unless the agency certifies that the rule will not have a significant economic imp act on a substantial number of small entities (SISNOSE). Small entities include small businesse s, small governmental jurisdic tions, and small not-for-profit enterprises. EPA assessed the poten tial small entity economic impa cts using a screening analysis. After reviewing screening analysis results, EPA has determined it cannot certify that the rules will not have a SISNOSE and presumes that both rules are not eligible for certification under the RFA as amended by SBREFA.
5.1 Small Entity Screening Analysis
5.1.1 Small Businesses The sectors covered by the rule were identified through lists of small entities at major and area sources provided by the engine ering analysis. Table 5-1 provides a list of the sectors affected (3-digit NAICS) and the range of SBA size definitions.
5.1.1.1 Representative Small Business Analysis Us ing Census Statistics of U.S. Businesses For each 3-digit NAICS code, the SUSB provi des national information on the distribution of economic variables by industr y and enterprise size (U.S. Ce nsus Bureau, 2008). The Census Bureau and the Office of Advocacy of the SBA s upported and developed these files for use in a broad range of economic analyses. 1 Statistics include the total number of establishments and receipts for all entities within an industry; however, only a subset of entities will be covered by the rule. SUSB also provides statistics by enterprise employment and receipt size.

The Census Bureau’s definitions used in the SUSB are as follows:

 Establishmen t: An establishment is a single phys ical location wh ere business is conducted or where services or i ndustrial operations are performed.

 Receipts : Receipts (net of taxes) are define d as the revenue for goods produced, distributed, or services provided, in cluding revenue earned from premiums, commissions and fees, rents, interest, divi dends, and royalties. Receipts exclude all revenue collected for local, state, and federal taxes.

1 See http://www.census.gov/csd/susb/ and http://www.sba .gov/advo/research/data.html for additional details.5-2 Table 5-1. Affected Sectors and Size Standards

2007 NAICS Description Size Standard (Effective August 22, 2008) 211 Oil and Gas Extraction 500 employees 212 Mining (except Oil and Gas) 500 employees
221 Utilities a 311 Food Manufacturing 500 to 1,000 employees 312 Beverage and Tobacco Product Ma nufacturing 500 to 1,000 employees 313 Textile Mills 500 to 1,000 employees 321 Wood Product Manufacturing 500 employees 322 Paper Manufacturing 500 to 750 employees 323 Printing and Related Support Activities 500 employees 324 Petroleum and Coal Products Manufacturing Typically 500 to 1,500 employees 325 Chemical Manufacturing 500 to 1,000 employees 326 Plastics and Rubber Products Manufacturing Typically 500 to 1,000 employees 327 Nonmetallic Mineral Product Manufacturing 500 to 1,000 employees 331 Primary Metal Manufacturing 500 to 1,000 employees 332 Fabricated Metal Product Manufacturing 500 to 1,500 employees 335 Electrical Equipment Manufacturing 500 to 1,000 employees 336 Transportation Equipment Manufacturing 500 to 1,000 employees 337 Furniture and Related Product Manufacturing 500 employees 339 Miscellaneous Manufacturing 500 employees 423 Merchant Wholesalers, Durable Goods 100 employees 493 Warehousing and Storage $25.5 million in annual receipts 562 Waste Management and Remediation Services Typically $7 to $14 million in annual receipts 611 Educational Services Typically $7 to $35.5 million in annual receipts

a NAICS codes 221111, 221112, 221113, 221119, 221121, 221122: A firm is small if, including its affiliates, it is

primarily engaged in the generation, transmission, and/or distribution of electric energy for sale and its total electric output for the preceding fiscal year did not exceed 4 million megawatt hours.5-3  Enterprise : An enterprise is a business organi zation consisting of one or more domestic establishments that were speci fied under common ownership or control. The enterprise and the establishment are the same for single-establishment firms. Each multi-establishment company forms one enterprise—the enterprise employment and annual payrol l are summed from the a ssociated establishments. Enterprise size designations are determ ined by the total employment of all associated establishments.
Because the SBA’s business size definitions (SBA, 2008) apply to an establishment’s “ultimate parent company,” we assumed in this an alysis that the “enterpri se” definition above is consistent with the concept of ultimate parent company that is typically used for SBREFA screening analyses, and the terms are used interchangeably.
The analysis generated a set of establishment sales tests (represented as cost-to-receipt ratios) for NAICS codes associated with sector s listed in Table 5-2. Although the appropriate SBA size definition should be applied at the pa rent company (enterpris e) level, we can only compute and compare ratios for a model establishm ent owned by an enterpri se within an SUSB size range (employment or receipts). Using the SUSB size range helps us account for receipt differences between establishments owned by large and small enterprises and also allows us to consider the variation in small business definition s across affected industries. Using establishment receipts is also a conservativ e approach, because an establis hment’s parent company (the “enterprise”) may have other economic resources th at could be used to cover the costs of the regulatory program. Table 5-2. Major Sources: Sales Tests Usin g Small Companies Identified in the Combustion Survey

Sample Statistic Proposal Select ed Option Alternative Option
Mean 4.9% 4.0% 3.8%
Median 0.4% 0.2% 0.4%
Maximum 72.9% 59.8% 31.4%
Minimum <0.01% <0.01% <0.01% Ultimate parent company observations 50 50 50 Ultimate parent companies with sale tests exceeding 3% 14 8 13

For each representative establishment in the SU SB data, we developed a range of facilitylevel cost numerators based on th e engineering cost analysis. Fo r major sources, we used the maximum and minimum small entity facility-lev el costs observed within each 3-digit NAICS5-4 code. For area sources, we were limited to two re presentative small entity facility-level costs (approximately $27,000,000 to $43,000). 2 Using these cost data and the Census estimates of average establishment receipts, a substantial number of SUSB NAI CS/enterprise categories have ratios over 3% (Figure 5-1).

68% 65%

25% 25% 21% 17%

8% 8% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Selected Alternative Final MACT/GACT Approach Proposed MACT Approach

Major Area Upper Bound Lower Bound

Figure 5-1. Share of NAICS/Enterprise Empl oyment Categories (<500 employees) with Sales Tests Exceeding 3%

5.1.1.2 Additional Small Business Analysis Using Sample of Small Businesses Identified in Combustion Facility Survey Next, we performed a more detailed anal ysis that compares the Census SUSB representative small entity results with a firm-sp ecific sample of major small private enterprises. In this approach, we identified a sample of survey facility names listed as small, traced the ultimate parent company name to verify the facility was owned by a small business, and collected

2 Prior to computing the cost-to-receipt ratios, we adjusted the engineering compliance co sts to reflect 2002 dollars using the implicit price deflators for gross domestic product (GDP). The values used are 2002 = 92.118 and 2008 = 108.483 (U.S. BEA, 2010).5-5 the most recent parent company sales and employ ment figures. As Table 5-2 shows, the average cost-to-sales ratios for small major source comp anies are above 3%. The median ratios are below one percent.
5.1.2 Small Governmental Jurisdictions and Not-for-Profit Enterprises In addition to the private sector, this rule al so covers sectors that include entities owned by small and large governments and not-for-profit en terprises. Given the uncertainty and data limitations associated with identifying and appropri ately classifying these entities, we computed a “revenue” test, where the annuali zed compliance cost is a percen tage of annual revenues (U.S. Census, 2005a and b).
Compliance costs were estimated for model f acilities for major and area sources for multiple options. A summary of the compliance costs used for the small entity analysis follows:
Major Sources:

 Selected option : $1.1 million (median cost small public facility)

 Alternative Option : $0.9 million (median cost small public facility)

Area Sources 3 :

 Final MACT/GACT Approach and Proposed MACT Approach :

– Other Public: $45,000

– Hospital: $11,300

– Schools: $4,500

– Churches: $2,200 From the 2002 Census (in 2008 dollars), th e average revenue for small governments (counties and municipalities with populations fewer than 10,000) are $3 million per entity, and the average revenue for local governments with populations fewer than 50,000 is $7 million per entity. Churches are assumed to have an operation budget of $150,000.
Representative small major public entities would have cost-to-re venue ratios above 10 percent. The following types of representative small area source facilities would have cost-torevenue ratios exceeding 1 percent but below 3 percent:

3 Graham Gibson, Susan McClutchey, and Amanda Singleton, ERG. (January, 2011). Methodology for Estimating Impacts from Industrial, Commercial, Institutional Boilers at Area Sources of Hazardous Air Pollutant Emissions.5-6  Final MACT/GACT Approach : other public (ratio > 1.7 percent) and churches (ratio = 1.5 percent)

 Proposed MACT Approach : other public (ratio > 1.7 percent) and churches (ratio = 1.5 percent)
5.2 Final Regulatory Flexibility Analysis (FRFA): Major Sources Pursuant to section 603 of the RFA, EPA prepar ed an initial regulator y flexibility analysis (IRFA) for the proposed rule and convened a Sm all Business Advocacy Review Panel to obtain advice and recommendations of representatives of the regulated small entities. A detailed discussion of the Panel’s advice and recommendati ons is found in the final Panel Report (Docket ID No. EPA-HQ-OAR-2002-0058-0797). A summary of the Panel’s recommendations is also presented in the preamble to the proposed rule at 75 FR 32044- 32045 (June 4, 2010). In the proposed rule, EPA included provisions consistent with four of the Panel’s recommendations. As required by section 604 of the RFA, we also prep ared a final regulatory flexibility analysis (FRFA) the final rule.
5.2.1 Need for Rule and Objectives
The rule is intended to reduce emissions of HAP as required unde r section 112 of the CAA. Section II.A of the final rule’s preamble de scribes the reasons that EPA is finalizing this action.
5.2.2 Summary of the Significant Issues raised by the Public Comments and Agency Assessment Many significant issues were raised duri ng the public comment period, and EPA’s responses to those comments are presented in sect ion V of this preamble or in the response to comments document contained in the docket. Significan t changes to the rule that resulted from the public comments are described in secti on IV of the final rule’s preamble.
The primary comments on the IRFA were provi ded by SBA, with the remainder of the comments generally supporting SBA’s comments. Those comments included the following: EPA should have adopted a health-based compliance alternative (HBCA) whic h provides alternative emission limits for threshold chemicals; EPA should have adopted additional subcategories, including the following: subcateg ories based on fuel type (incl uding coal rank, bagasse, biomass by type, and oil by type), unit design type (e.g., pr ocess heater, fluidized bed, stoker, fuel cell, suspension burner), duty cycle, geographic loca tion, boiler size, burner t ype (with and without low-NO x burners), and hours of use (limited us e); EPA should have minimized facility monitoring and reporting requirements; EPA should not have proposed the energy audit5-7 requirement; EPA’s proposed emissions standard s are too stringent; a nd, EPA should provide more flexibility for emissions averaging.
In response to the comments on the IRFA and other public comments, EPA made the following changes to the final rule. EPA adopted additional subcategories, including a limited-use subcategory for units that operate less than 10 percent of the operati ng hours in a year, a noncontinental liquid unit subcategor y for units with the unique challenges faced by remote island locations, and a combination suspension/grate bo iler subcategory. EPA also consolidated the subcategories for units combusti ng various types of solid fuels, which will simplify compliance and will allow units to combust varying percenta ges of different solid fuels without triggering subcategory changes. EPA also decreased mon itoring and testing costs by eliminating the CO CEMS requirement for units greater than 100 mmBtu/hr and changi ng the dioxin testing requirement to a one-time test. The final rule al so includes work practice standards for additional subcategories, including limited-use units, new small units, and units combusting gaseous fuels that are demonstrated to have similar contaminant levels to natural gas. Finally, EPA is finalizing emission limits that are less stringent than the pr oposed limits for most of the category/pollutant combinations. The emission limit changes are largel y due to the changes in subcategories, data corrections, and incorporation of new data into the floor calculations. Additional details on the changes discussed in this paragraph are included in sections IV and V of th e final rule’s preamble.
5.2.3 Description of and Estimate of the Number of Small entities to Which the Rules Will Apply
Based on the distribution of major source facilities with aff ected boilers or process heaters reported in the 2008 survey entitled “Information Co llection Effort for Faci lities with Combustion Units. (ICR No. 2286.01),” there are 1,639 existing fac ilities with affected boilers or process heaters. Of these, 94 percent are located in th e private sector and the remaining 6 percent are located in the public sector. Table 5-3 summarizes the types of small en tities expected to be affected by the major source rule. The number of sm all entities is based on the facilities which responded to the ICR by confirming small entity stat us. If that question wa s not answered, or if the facility answered that th eir small entity status was unknown, it was assumed that those facilities were not small entities. The summary in Table 5-3 is for major source facilities which have at least one boiler or pr ocess heater expected to be covered by the rule. Table 5-4 summarizes the EPA estimates of the number of ar ea source facilities expect ed to be affected by the major source rule (84,700 tota l). EPA does not have sufficien t information to estimate the number of small entities expected to be covered by the area source rule.5-8 Table 5-3. Information Collection Effort for Facilities with Combustion Units: Major Sources

NAICS NAICS Description Total Number of

Facilities Total SelfReported Facilities

Owned by Small Businesses 111 Crop Production 1 0 113 Forestry and Logging 1 0 115 Support Activities for Agriculture and Forestry 1 0 211 Oil and Gas Extraction 24 3 212 Mining (Except Oil and Gas) 14 1 221 Utilities 183 23 311 Food Manufacturing 110 7 312 Beverage and Tobacco Product Manufacturing 5 0 313 Textile Mills 14 1 314 Textile Product Mills 1 0 316 Leather and Allied Product Manufacturing 3 1 321 Wood Product Manufacturing 183 18 322 Paper Manufacturing 186 14 323 Printing and Related Support Activities 33 5 324 Petroleum and Coal Products Manufacturing 84 8 325 Chemical Manufacturing 220 17 326 Plastics and Rubber Products Manufacturing 89 11 327 Nonmetallic Mineral Product Manufacturing 41 2 331 Primary Metal Manufacturing 57 6 332 Fabricated Metal Product Manufacturing 46 8 333 Machinery Manufacturing 13 0 334 Computer and Electronic Product Manufacturing 2 0 335 Electrical Equipment, Appliance, and Component Manufacturing 12 0 336 Transportation Equipment Manufacturing 100 7 337 Furniture and Related Product Manufacturing 45 8 339 Miscellaneous Manufacturing 15 1 423 Durable Goods Merchant Wholesalers 1 1 424 Nondurable Goods Merchant Wholesalers 1 0 441 Motor Vehicle an d Parts Dealers 1 0 481 Air Transportation 7 0 482 Rail Transportation 1 0 486 Pipeline Transportation 60 0 488 Support Activities for Transportation 3 0 493 Warehousing and Storage 5 1 (continued)5-9 Table 5-3. Information Collection Effort for Facilities with Combustion Units: Major Sources (continued)

NAICS NAICS Description Total Number of

Facilities Total SelfReported Facilities

Owned by Small Businesses 531 Real Estate 1 0 541 Professional, Scientific, and Technical Services 8 0 561 Administrative and Support Services 1 0 562 Waste Management and Remediation Services 7 2 611 Educational Services 29 2 622 Hospitals 4 0 623 Nursing and Residential Care Facilities 1 0 811 Repair and Maintenance 1 0 921 Executive, Legislative, and Other General Government Support 2 0 928 National Security and International Affairs 23 0

As discussed in Section 5.1 of this RIA, using these cost data and the Census estimates of average establishment receipts, a substantial number of SUSB NAI CS/enterprise categories have ratios over 3%. EPA determined 8 of 50 small ultimate parent companies owning major source facilities listed in Table 5-3 will experience an impact of over 3 percent of revenues. Representative small major public entities would have cost-to-revenue rati os above 10 percent.
5.2.4 Description of the Projected Reporting, Record keeping and Other Compliance Requirements of the Rule
The information collection activit ies in this ICR include initia l and annual stack tests, fuel analyses, operating parame ter monitoring, continuous O 2 monitoring for all units greater than 10 mmBtu/hr, continuous emission monitoring for PM at units greater than 250 mmBtu/hr, certified energy audits, annual or biennial tune-ups (depending on the size of the combustion equipment), preparation of a startup, shut down, malfunction plan (SSMP), pr eparation of a site-specific monitoring plan and a site-specific fuel monito ring plan, one-time and periodic reports, and the maintenance of records.
For sources that can demonstrate compliance th rough fuel analysis, the regulation requires an initial fuel analysis and monthly fuel analyses . Sources must conduct additional fuel analyses if they burn a new type of fuel. For sources that are demonstrating that their gaseous fuels other than natural gas and refinery gas meet the specifications for H 2 S and Hg contained in the final rule, they must conduct either an initia l or monthly fuel analysis to re main in the gas 1 subcategory. If5-10 the content of these constituen ts are not going to exceed the specifications, these units may conduct an initial testing and include a statement th at the gas will not exceed the specification in the initial Notification of Compliance Status. If the gaseous fuel constituents will vary, the unit is required to conduct monthly testing and maintain records to demonstrate that the gaseous fuels meet the specifications.
An initial performance test must be comp leted for particulate matter, mercury, hydrogen chloride, carbon monoxide, and dioxin and furans for affected sources with applicable emission limits. During the initial performance test, the owner or operator must establish maximum or minimum values for each operating parameter. Ther eafter, the owner or operator must, in some cases, conduct annual stack tests for particul ate matter, mercury, hydrogen chloride, carbon monoxide, and dioxin and furans and must contin uously monitor the operating parameters. If a source is required to use PM CEMS, performance testing is not required for particulate matter.
Following the initial performance test, the owne r or operator must submit a report that documents the performance test results and the va lues for their required operating parameters.
All existing units will be required to conduct an initial certified energy audit by qualified personnel which includes a visual inspection of the boiler system, establishing operating characteristics, identifying major energy cons uming systems and energy savings potential, reviewing available engineering plans, and lis ting major energy conservation measures. A signed certification that an audit has been completed sh ould be submitted to the Agency for each energy audit.
All new and existing small and limited use units , and all large units firing natural gas, refinery gas, or other gas 1 fuels meeting the fuel spec can demonstrat e compliance by conducting a tune-up of the boiler. Small and limited use units are requested to conduct a tune-up biennially and large natural gas, refinery gas, or other ga s 1 units will conduct a tune-up annually. Any large natural gas, refinery gas, or other gas 1 unit will also submit a notification of alternative fuel use if the unit fires alternative fuels during periods of gas curtailment or ga s supply emergencies.

For all units other than small and limited use boilers and process heaters and units firing natural gas a semiannual report is required that documents the values for the operating parameters; any deviation; the resu lts of any annual stack tests; the results of any fuel analysis and emissions calculations; fuel usag e, and if no deviation occurred, a statement that no deviations occurred.5-11 As specified in the ICI Boiler and Process Heat er NESHAP, owners or operators of boilers and process heaters must keep records of certain parameters and information for a period of five years. Owners or operators must maintain record s of the initial performance test, annual stack tests, fuel analyses, and any s ubsequent stack tests or fuel an alyses. Owners or operators must also maintain records of the monitoring data fo r the operating parameters and daily fuel usage.
Owners or operators must also maintain reco rds for boiler or process heater malfunctions and any deviations from the operating parameters. Records must also be maintained of all monitoring device calibration data.
The Agency expects that persons with know ledge of .pdf software, spreadsheet and relational database programs will be necessary in order to prepare the report or record. Based on experience with previous emissi on stack testing, we expect most facilities to contract out preparation of the reports asso ciated with emission stack tes ting, including creation of the Electronic Reporting Tool submittal, which will minimize the need for in depth knowledge of databases or spreadsheet software at the source. We also expect affected sources will need to work with web-based applicability tools and flowcharts to determ ine the requirements applicable to them, knowledge of the heat input capacity and fu el use of the combustion units at each facility will be necessary in order to develop the reports and determine initial applicability to the rule. Affected facilities will also need skills asso ciated with vendor selec tion in order to identify service providers that can help them complete their compliance requirements, as necessary.
5.2.5 Description of the steps the Agency has Taken to Minimize the Significant Economic Impact on Small Entities While EPA did make significant changes base d on public comment, EPA did not finalize a HBCA or HBELs and is maintaining, but clar ifying, the energy assessment requirement. The discussion of the HBCA decision is included in section V of this preamble. Some changes to the energy assessment requirement that will reduce costs for small entities include a the following provisions: the energy assessment fo r facilities with affected boilers and process heaters using less than 0.3 trillion Btu per year heat input will be one day in length maximum. The boiler system and energy use system accounting for at least 50 per cent of the energy output will be evaluated to identify energy savings opport unities, within the limit of performing a one-day energy assessment; and the energy assessmen t for facilities with affected boilers and process heaters using 0.3 to 1.0 trillion Btu per year will be 3 days in length maximum. The boiler system and any energy use system accounting for at least 33 perc ent of the energy output will be evaluated to identify energy savings opportuni ties, within the lim it of performing a 3-day energy assessment. While EPA did not make major adjustments to th e emissions averaging provisions, the change to5-12 a solid fuel subcategory will enable all solid fuel -fired units at a facility to use the emissions averaging provision for Hg, PM, and HCl.
As required by section 212 of SBREFA, EPA also is preparing a Small Entity Compliance Guide to help small entities comply with this rule . Small entities will be ab le to obtain a copy of the Small Entity Compliance guide at the following web site: http://www.epa.gov/ttn/atw/boiler/boilerpg.html.
5.3 Final Regulatory Flexibility Analysis (FRFA): Area Sources Pursuant to section 603 of the RFA, EPA prepar ed an initial regulator y flexibility analysis (IRFA) for the proposed rule and convened a Sm all Business Advocacy Review Panel to obtain advice and recommendations of representatives of the regulated small entities. A detailed discussion of the Panel’s advice and recommendati ons is found in the final Panel Report (Docket ID No. EPA-HQ-OAR-2002-0058-0797). A summary of the Panel’s recommendations is also presented in the preamble to the proposed rule at 75 FR 32044- 32045 (June 4, 2010). In the proposed rule, EPA included provisions consistent with four of the Panel’s recommendations. As required by section 604 of the RFA, we also prep ared a final regulatory flexibility analysis (FRFA) the final rule.
5.3.1 Need for Rule and Objectives
The rule is intended to reduce emissions of HAP as required unde r section 112 of the CAA. Section II.A of the final rule’s preamble de scribes the reasons that EPA is finalizing this action.
5.3.2 Summary of the Significant Issues raised by the Public Comments and Agency Assessment Many significant issues were raised duri ng the public comment period, and EPA’s responses to those comments are presented in sect ion V of this preamble or in the response to comments document contained in the docket. Significan t changes to the rule that resulted from the public comments are described in secti on IV of the final rule’s preamble.
The primary comments on the IRFA were provi ded by SBA, with the remainder of the comments generally supporting SBA’s comments. Those comments applicable to the proposal regarding area source boilers included the following: EPA should have adopted additional subcategories, including the following: unit desi gn type (e.g., fluidized bed, stoker, fuel cell, suspension burner), duty cycle, geographic loca tion, boiler size, burner t ype (with and without low-NO x burners), and hours of use (limited us e); EPA should have minimized facility5-13 monitoring and reporting requirements; EPA should not have proposed the energy audit requirement; and EPA’s proposed emissi ons standards are too stringent.
In response to the comments on the IRFA and other public comments, EPA made the following changes to the final rule. EPA is promulgating management practice standards requiring the implementation of a boiler tune-up program for area so urce boilers in the biomass and oil subcategories instead of the proposed CO emission limits. This change will significantly reduce the monitoring and testing costs for existing and new biom ass-fired and oil-fired area source boilers. EPA also decreased monitoring and testing costs for coal-fired area source boilers by eliminating the CO CEMS requirement for bo ilers greater than 100 MM Btu/h. The final rule also includes work practice standards or manage ment practice standards, instead of emission limits, for new area source boilers less than 10 MMBtu/h. Finally, EPA is finalizing emission limits that are less stringent th an the proposed limits. The emissi on limit changes are largely due to the changes in data correcti ons and incorporation of new data into the floor calculations. Additional details on the changes di scussed in this paragraph are in cluded in sections IV and V of the final rule’s preamble.
5.3.3 Description of and Estimate of the Number of Small entities to Which the Rules Will Apply
Table 5-4 summarizes the EPA estimates of th e number of area source facilities expected to be affected by the major source rule (84,700 tota l). EPA does not have sufficient information to estimate the number of small entities expect ed to be covered by the area source rule.
As discussed in Section 5.1 of this RIA, using these cost data and the Census estimates of average establishment receipts, a substantial number of SUSB NAI CS/enterprise categories have ratios over 3%. The following types of representa tive small area source public facili ties would have cost-to-revenue ratios exceeding 1 percent but below 3 percent: other public facilities (ratio > 1.7 percent) and church es (ratio = 1.5 percent).5-14 Table 5-4. Estimated Affected Facilities Usin g 13 State Boiler Inspector Inventory: Area Sources

SIC Total Number of Affect ed Facilities in SIC Code
01 0 02 247 07 0 09 0 14 83 16 0 17 247 20 5,733 23 83 24 2,676 26 0 40 329 41 0 42 83 43 0 44 0 45 0 47 0 48 741 50 165 51 247 52 0 53 494 54 0 55 801 56 0 57 0 58 905 59 288 60 329 64 0 (continued)5-15 Table 5-4. Estimated Affected Facilities Usin g 13 State Boiler Inspector Inventory: Area Sources (continued)

SIC Total Number of Affect ed Facilities in SIC Code 65 2,878 70 4,893 72 2,138 73 165 75 1,606 76 0 79 1,151 80 15,293 81 0 82 33,303 83 0 84 165 86 3,330 87 666 91 to 98 5,098 Unknown 576

5.3.4 Description of the Projected Reporting, Record keeping and Other Compliance Requirements of the Rule
The information collection activit ies in this ICR include initia l and annual stack tests, fuel analyses, operating parame ter monitoring, continuous O 2 monitoring for all coal-fired area source boilers greater than 10 MMBtu/h, certified energy assessments for area source facilities having a boiler greater than 10 MMBtu/ h, biennial tune-ups, prepar ation of a startup, shutdown, malfunction plan (SSMP), preparation of a site-sp ecific monitoring plan and a site-specific fuel monitoring plan, one-time and periodic reports, and the maintenance of records. Based on 13 states’ inventories of boilers, there are an estimated 92,000 existing facilities with affected boilers. It is estimated that 53 percent are loca ted in the private sector and the remaining 47 percent are located in the public sector. Of these, only about 0 .3 percent of the area source facilities are subject to emission limits and the testing and monitoring requirements in the final rule. A table included in the FRFA summarizes the types and number of each type of small entities expected to be aff ected by the area source rule.5-16 The Agency expects that persons with know ledge of .pdf software, spreadsheet and relational database programs will be necessary in order to prepare the report or record. Based on experience with previous emissi on stack testing, we expect most facilities to contract out preparation of the reports asso ciated with emission stack tes ting, including creation of the Electronic Reporting Tool submittal which will mi nimize the need for in depth knowledge of databases or spreadsheet software at the source. We also expect affected sources will need to work with web-based applicability tools and flowcharts to determ ine the requirements applicable to them, knowledge of the heat input capacity and fu el use of the combustion units at each facility will be necessary in order to develop the reports and determine initial applicability to the rule. Affected facilities will also need skills asso ciated with vendor selec tion in order to identify service providers that can help them complete their compliance requirements, as necessary.
5.3.5 Description of the steps the Agency has Taken to Minimize the Significant Economic Impact on Small Entities While EPA did make significant changes base d on public comment, EPA is maintaining, but clarifying, the energy assessment requirem ent. Some changes to the energy assessment requirement that will reduce costs for small entitie s include a the following provisions: the energy assessment for facilities with a ffected boilers using less than 0.3 trillion Btu per year heat input will be one day in length maximum. The boiler system and energy use system accounting for at least 50 percent of the energy output will be eval uated to identify energy savings opportunities, within the limit of performing a one-day en ergy assessment; and the energy assessment for facilities with affected boilers using 0.3 to 1.0 trillion Btu per y ear will be 3 days in length maximum. The boiler system and any energy use sy stem accounting for at least 33 percent of the energy output will be evaluated to identify energy savings opportunities, within the limit of performing a 3-day energy assessment. In addition, the final rule allows facilities to use a previously completed energy assessment to satisfy the energy assessment requirement.
As required by section 212 of SBREFA, EPA also is preparing a Small Entity Compliance Guide to help small entities comply with this rule . Small entities will be ab le to obtain a copy of the Small Entity Compliance guide at the following web site: http://www.epa.gov/ttn/atw/boiler/boilerpg.html.
5.4 Section 5 References U.S. Bureau of Economic Analysis. 2010. “Implicit Price Deflators for Gross Domestic Product” (Table 1.1.9). <http://www.b ea.gov/national/nipaweb/Selec tTable.asp?Selected=N>.5-17 U.S. Census Bureau. 2005a. 2002 Census of Governments, Volume 4, Number 3, Finances of County Governments: 2002 GC02(4)-3 (Table 12).Washington, DC: U.S. Government Printing Office.
U.S. Census Bureau. 2005b. 2002 Census of Governments, Volume 4, Number 4, Finances of Municipal and Township Governments: 2002 GC02(4)-4 (Table 13). Washington, DC: U.S. Government Printing Office.
U.S. Census Bureau. 2008. “Firm Size Data from th e Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002.” <http://www.cen sus.gov/csd/susb/download_susb02.htm>.
U.S. Small Business Administration. 2008. “Table of Small Business Size Standards Matched to North American Industry Classification System Codes.” <http://www.sba.gov/services/contractingopportunities/sizestandardstopics/ size/index.html>.6-1 SECTION 6 AIR QUALITY MODELING OF EMISSIONS REDUCTIONS
6.1 Synopsis
This section describes the ai r quality modeling performed by EPA in support of the final boiler MACT rule. A national scale air quality modeling analysis was performed to estimate the impact of the sector emissions chan ges on future year: annual and 24-hour PM 2.5 concentrations, 8-hr maximum ozone, total mercury deposition, as well as visibility impairment. Air quality benefits are estimated with the Comprehens ive Air Quality Model with Extensions (CAM x ) model. CAM x simulates the numerous physical and chemical processes involved in the formation, transport, and destruction of ozone, part iculate matter and air toxi cs. In addition to the CAM x model, the modeling platform includes the emissions, meteorology, and initial and boundary condition data which are inputs to this model.
Emissions and air quality modeling decisions ar e made early in the analytical process. For this reason, it is important to note that the inventories used in the air quality modeling and the benefits modeling are different than the final boiler sector i nventories presented in the RIA. At the time of proposal, we did not have the results of the ICR; those results have been incorporated into this final analysis as e xplained below. The boiler ICR does not have the emissions release point information such as stac k height, exit velocity, ex it temperature, etc. necessary for photochemical modeling so the ICR emissions data was matched with the National Emissions Inventory (NEI). Since States and loca l agencies have different criteria for emissions that get inventoried as a point source with stack information a nd area sources (no specific stack information) the ability to match ICR emissions to NEI varies from State to State depending on the level of information provided in the ICR and NEI.
Photochemical grid models use state of th e science numerical algorithms to estimate pollutant formation, transport, and deposition over a variety of spatial sc ales that range from urban to continental. Emissions of precursor species are injected into the model where they react to form secondary species such as ozone and then transport around the modeling domain before ultimately being removed by deposition or chemical reaction. Photochemical model source apportionment tracks the formation and transport of primarily and secondarily formed pollutants from emissions sources and allows the estimation of contributions at rece ptors. This type of emissions apportionment is useful to understa nd what types of sour ces or regions are contributing to pollutants estimat ed by photochemical grid models.6-2 The 2005-based CAM x modeling platform was used for the air quality modeling for this rule. This platform represents a structured syst em of connected modeling-related tools and data that provide a consistent and tr ansparent basis for assessing the air quality response to projected changes in emissions. The base ye ar of data used to construct this platform includes emissions and meteorology for 2005. The platform is intend ed to support a variety of regulatory and research model applications and analyses. This modeling platform and analysis is described below. Additional information about the photochem ical modeling is availa ble as part of the modeling technical assessment document to suppor t this source sector rule (USEPA, 2010).
6.2 Photochemical Model Background
CAM x version 5.20 is a freely available computer model that simulates the formation and fate of photochemical oxidants, ozone, primary a nd secondary PM concentrations, and air toxics, over regional and urban spatial scales for give n input sets of meteorological conditions and emissions. CAM x includes numerous science modules that simulate the emission, production, decay, deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the atmosphere (Baker and Scheff, 2007; Nobel et al., 2001; Russell, 2008). CAM x is applied with ISORROPIA inorganic chemistry (Nenes et al., 1999), a semi-volatile equilibrium scheme to partition condensable organi c gases between gas and particle phase (Strader et al., 1999), Regional Acid Deposition Model (RADM) aqueous phase chemistry (Chang et al., 1987), and Carbon Bond 05 (CB05) gas-phase chemistry module (ENVIRON, 2008; Gery et al., 1989). Mercury oxidation pathways are represented for both the gas and aqueous phases in addition to aqueous phase reduction r eactions (ENVIRON, 2008).

CAM x contains a variety of ozone source appo rtionment tools, including the original ozone source apportionment tool (OSAT) a nd the anthropogenic pre-cursor culpability assessment (APCA) tool (ENVIRON, 2008). Ozone source apportionment tracers are treated using the standard model algorithms for vertic al advection, vertical diffusion, and horizontal diffusion. Horizontal advective fluxes for each of the regular model species that make up nitrogen oxides (NO x ) and volatile organic compounds (VOC ) are combined and normalized by a concentration based weighted mean. Se parate ozone tracers are used in CAM x to track ozone formation that happens under NO x and VOC limited conditions.
Particulate matter source apportionment technology (PSAT) implemented in CAM x estimates the contribution from speci fic emissions source groups to PM 2.5 and all forms of mercury using reactive tracers (ENVIRON, 2008; Wa gstrom et al., 2008). Th e tracer species are estimated with source apportionment algorithms ra ther than by the host model routines. PSAT6-3 tracks contribution to PM 2.5 sulfate, nitrate, ammonium, sec ondary organic aerosol, and inert primarily emitted species. Non-linear processes lik e gas and aqueous phase chemistry are solved for bulk species and then apportioned to the tagg ed species. Emissions of nitrogen oxides are tracked through all intermediate nitrogen species to particulate nitrate ion. Ammonia emissions are tracked to particulate ammonium ion. This modeling assessment used the PSAT approach to estimate source contribution to PM 2.5 species and mercury and th e APCA method to estimate source contribution to modeled ozone.
6.3 Model Domain and Grid Resolution The modeling analyses were performed for a domain covering the continental United States, as shown in Figure 6-1. Th is domain has a parent horizontal grid of 36 km with two finerscale 12 km grids over portions of the eastern and western U.S. The m odel extends vertically from the surface to 100 millibars (approximately 15 km) using a sigma-pressure coordinate system. Air quality conditions at the outer boundary of the 36 km domain were taken from a global model and vary in time and space. The 36 km grid was only used to establish the incoming air quality concentrations along the boundari es of the 12 km grids. Only the finer grid data were used in determining the impacts of the emission standard program changes. Table 6-1 provides some basic geographic information regarding the photochemical model domains.

Figure 6-1. Map of the photochemical mode ling domains. The black outer box denotes the 36 km national modeling domain; th e red inner box is the 12 km western U.S. grid; and the blue inner box is the 12 km eastern U.S. grid.6-4 Table 6-1. Geographic Elements of Doma ins Used in Photochemical Modeling
Photochemical Modeling Configuration
National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid Map Projection Lambert Conformal Projection
Grid Resolution 36 km 12 km 12 km
Coordinate Center 97 deg W, 40 deg N
True Latitudes 33 deg N and 45 deg N
Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14
Vertical extent 14 Layers: Surface to 100 mil libar level (see Table II-3)

6.4 Emissions Input Data
6.4.1 2005 Baseline Emissions The emissions data used in the 2005 base year are from the EPA’s 2005-based v4.1 modeling platform. This platform is based on the 2005 National Emissions Inventory (NEI), version 2. Emissions were processed to photoche mical model inputs with the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 2000).
This platform includes criteria pollutants and precursors: particulate matter less than 10 microns (PM 10 ), PM 2.5 , nitrogen oxides (NO X ), sulfur dioxide (SO 2 ), carbon monoxide (CO), volatile organic compounds (VOC), ammonia (NH 3 ) and hazardous air pollutants (HAP): hydrogen chloride, chlorine and mercury. Additi onally, for some sectors, HAP emissions of benzene, formaldehyde, acetaldehyde and methanol are used from the inventory for chemical speciation VOC. For this rule, mercury emissions were added to the v4.1 platform to reflect the needs for the rule development will not be used for other rules. The mercury emissions included in this platform are primarily from the 2005 Na tional Air Toxics Assess ment (NATA) inventory, which was updated from the 2005 NEI v2 in order to incorporate updated data for particular source categories such as cement and hazardous waste incineration, and also revised from comments from state and local inventory providers as a resu lt of NATA review.
This inventory was further modified to rem ove sources that were found to have shut down prior to 2005 and to update the gold mine em issions per information collected during the Gold Mine Ore and Production NESHAP. In addition, mercury emissions were revised for the boiler sector to allow for greater consistency w ith the Information Collection Request (ICR) data collected for this rule. In particular, we used the unit-specific ICR mercury emissions for all ICR6-5 facilities that could be mapped to the NATA Inventory. 1 We used the NEI to add important emissions release point information necessary for photochemical grid modeling such as geographic coordinates, stack coor dinates, stack release height, ex it temperature, exit velocity because the ICR lacks this information.
The replacement of the NATA mercury with the ICR mercury is described in more detail in the air quality modeling TSD. ICR emissions were not used di rectly for any other pollutants used in the v4.1 platform; however they info rmed numerous corrections and updates to the inventory including the removal of duplicates and facilities that had shut down prior to 2005, and the inclusion of control information.
The stationary inventory used in the v4.1 platform is separate d into modeling sectors such as EGU point (ptipm), non-EGU point (ptnonipm), and stationary emissions not included in the point source inventories (nonpt). This nonpoint category is genera lly referred to as the “area” source inventory, and this category is not a direct representation of sources classified as area sources for the purposes of National Emissi ons Standards for Hazardous Air Pollutants (NESHAP) and Maximum Available Cont rol Technology (MACT) standards.
6.4.2 Future Year Baseline Emissions
The 2016 baseline emissions are intended to re present the emissions associated with growth and controls in 2016. The pr ojections used for this effort are unique to th is project and are not associated with a pa rticular modeling platform.
The EGU point source (ptipm) emissions estimates for the future year reference were created by the Integrated Planning Model (IPM) ve rsion 3.02 for criteria pollutants, hydrochloric acid, and mercury in 2015. 2 For the non-EGU point (ptnonip m) and nonpoint (nonpt) sectors, both control and growth factors we re applied to a subset of the 2005 v4.1 platform data to create the 2016 reference case. The 2014 projection factor s developed for the Transport Rule proposal (see http://www.epa.gov/ttn/chief/emch/index.html#transport ) were further enhanced and updated for these 2016 baseline projections.
The projected inventory incor porates emissions projections for the proposed Transport Rule, cement kiln NESHAP, RICE NESHAP, gold mine NESHAP, changes to boiler emissions

1 The “NEI_UNIQUE_ID” field was used to map the ICR facilities to the NATA inventory. We used 7032 units’ Hg emissions out of 7,738 total units in the ICR. These 703 2 units from the ICR sum to 4.66 tons. ICR emissions that were not included sum to 0.177 tons. 2 The 2015 IPM run represents the average of 2014 to 2016.6-6 based on the ICR database developed for this ru le, and known consent decrees. A complete list of rules included in the future point source baseline inventory is shown in Table 6-2. Table 6-2. Control Strategies and/or Growth Assumptions Included in the 2016 Projection

Projections Carried Forward from the proposed Transport Rule a,b Description Pollutants MACT rules, national, VOC: national applied by SCC, MACT VOC Consent Decrees and Settlements, including refi nery consent decrees, and settlements for: (1) Alcoa, TX and (2) Premcor (formerly MOTIVA), DE All

Municipal Waste Combustor Reductions—plant level PM
Hazardous Waste Combustion PM Hospital/Medical/Infectious Waste Incinerato r Regulations under Section 129d/11 1d NO X , PM, SO 2 Large Municipal Waste Combustors—gro wth applied to specific plants All MACT rules, plant-level, VOC: Auto Plants VOC MACT rules, plant-level, PM & SO 2 : Lime Manufacturing PM, SO 2 MACT rules, plant-level, PM: Taconite Ore PM Municipal Waste Landfills: project factor of 0.25 applied All Livestock Emissions Growth from year 2002 to 2016 NH 3 , PM Residential Wood Combustion Growth and Changeouts from year 2005 to year 2016 All Gasoline Stage II growth and control from year 2005 to year 2016 VOC Portable Fuel Container MSAT2 inventory growth and control from year 2005 to year 2016 VOC Additional Projections Used In Boiler MACT modeling c Emission Reductions resulting from controls put on specific boiler units (not due to MACT) after 2005, identified through analysis of the co ntrol data gathered from the ICR from the ICI Boiler NESHAP. NO X , SO 2 , HCL

NESHAP: Portland Cement (09/09/10)—plant level based on Industrial Sector Integrated Solutions (ISIS ) policy emissions in 2013. The ISIS results are from the ISIS-Cement model runs for the NESHAP and NSPS analysis of July 28, 2010. HG, NO X , SO 2 , PM, HCL

NESHAP: Gold Mine Ore Processing and Production Area Source Category (based on proposed rule 04-15-10)—finalized 12/2010 HG
(continued)6-7 Table 6-2. Control Strategies and/or Growth Assumptions Included in the 2016 Projection (continued)

Projections Carried Forward from the proposed Transport Rule a,b New York SIP reductions VOC, NO X Additional plant and unit closures All NESHAP: Reciprocating Internal Combustion Engines d NO X , CO, PM

a They were only changed in that the projection year was 2015 or 2016, rather than 2012 / 2014. b We inadvertently did not apply closures that had been applied for the Transport Rule proposal; emissions from

these plants sum to 3300 tons VOC, 178 tons PM 2.5 , 1982 tons SO 2 , 1639 tons NO X , 6 tons NH 3 and 379 tons CO. At the state level, the largest impact is in West Virginia (717 tons NO X , which is 2% of emissions in ptnonipm) and 1604 tons SO 2 , which is 7% of the ptnonipm sector. When considering emissions from other sectors, the percentages will be much smaller. All other errors are under 500 tons (less than 1% of the ptnonipm sector).

c We inadvertently did not apply LaFarge and SaintGobain consent decrees, since one of the LaFarge facilities was

already covered in the cement ISIS projections, the reduc tions missed were lower than estimated by the consent decree were on the order of 20,000 tons SO 2 , 15,000 tons NO X , 400 tons HCL, 200 tons PM 2.5. d Note that SO 2 reductions are expected to occur to due fuel sulfur limits but were excluded from the projection.

They were expected to reduce SO 2 by 27,000 tons, nationwide.

The 2016 onroad emissions reflect control program implementation through 2016 and include the Light-Duty Vehicle Tier 2 Rule, the Onroad Heavy-Duty Rule, and the Mobile Source Air Toxics (MSAT) final rule and the cate gory 3 marine diesel en gines Clean Air Act and International Maritime Organiza tion standards which includes th e establishment of emission control areas for these ships. Emission reducti ons and increases from the Renewable Fuel Standard version 2 (RFS2) are no t included. The future baseline case nonroad mobile emissions reductions for these years include reductions to locomotives, various non road engines including diesel engines and various marine engine types, fuel sulfur co ntent, and evaporative emissions standards.
6.4.3 Future Year Sector Contribution Approach
The 2016 reference scenario for the boilers affected by the rule includes 2005 emissions estimates with some emissions removed from the inventory because of shut downs. The length of time required to conduct emissions and photochemi cal modeling precluded the use of the final facility-specific emissions estimates based on controls implemented for this rule. A 2016 “control” or emissions adjustment scenario was developed by tracking the total contribution from potentially controllable boiler sector emissions from the 2016 ba seline inventory. This total contribution estimate, essentially a “zero-out, ” of the sector creates a policy space where potential control impacts would be maximized at all locations. Since em issions reductions at controllable sources are not 100% (100% ~ total contribution), the boiler sector air quality6-8 contribution estimates from the 2016 source appor tionment model simulation are adjusted based on nation-wide estimates of control percentages by pollutant to create a final 2016 “control” emissions scenario.

The 2016 estimated controllable emissions for the boiler sector are shown in Table 6-3. Boiler sector emissions are contained in several different general classes of emissions used for emissions modeling: non-point or area ( nonpt), point EGU (ptipm), and point non-EGU (ptnonipm). These totals are the sum of emissi ons in the eastern and western U.S. modeling domains so the non-point (area) category contai ns some double-counting of emissions where the model domains overlap in the central United States (see Figure 6-2). Table 6-3. Estimated Future Year (2016) Controllable Boiler Sector Emissions

Sector VOC NO X SO 2 PM 2.5 NH 3 HG 0 HG 2 PM 2.5 HG Total (TPY) Non-point—Boiler (nonpt) 14,107 394,459 1,149,402 108,386 8,489 0 0 0 Total (TPY) EGU Point—Boiler (ptipm) 57 0 54,337 826 0 0 0 0 Total (TPY) Non-EGU Point—Boiler (ptnonipm) 15,451 215,809 492,676 28,838 706 1 2 1

Pct of Sector Non-point—Boiler (nonpt) 0 19 82 9 5 Pct of Sector EGU Point—Boiler (ptipm) 0 0 1 0 0
Pct of Sector Non-EGU Point—Boiler (ptnonipm) 1 9 26 6 0 10 7 12

Figure 6-2 shows the locations of boilers in the NEI point source inventory used in the modeling analysis. Non-point boile rs in the NEI do not have stack location information and are not shown. These boilers are spatially distri buted in the modeling domain using spatial surrogates appropriate for this sector.
Boiler emissions in the non-point/area (nonpt) modeling sector are used as the basis of estimating air quality impacts and health benefits for the area source rule. These emissions are based on SCC codes shown in Table II-5. Boiler emissions in the point non-EGU (ptnonipm) and point EGU (ptipm) are used to estimate impact s of the major source rule. Facilities were identified for the major source rule based on m eeting several criteria: (1) NEI facility ID and process level fuel type matched to a facility in the boiler I CR database, (2) NEI source had a boiler SCC code, and (3) unit design capacity was less than 25 MW.6-9 Figure 6-2. Locations of boilers in the NEI point inventory for the future baseline (2016)

Major source mercury emissions were identif ied as the units added from the boiler ICR database plus units in the NATA Hg inventory that were not in th e ICR but were considered part of the Boiler MACT Universe. A percent emissi ons reduction was estimated based on the rule proposal unit-specific facility-f uel combination in the ICR da tabase. Each f acility/process matching the facility-fuel combinations that had emission reductions larger than 1% based on the proposed rule were tracked for source contribution.
6.5 Model Results As part of the analysis for this rulemaki ng, the modeling system was used to calculate daily and annual PM 2.5 concentrations, 8-hr maximum ozone, annual total mercury deposition levels and visibility impairment. Model predic tions are used to estimate future-year design values of PM 2.5 and ozone. The annual PM 2.5 design value 3 determines whether a monitor location is attaining the annual PM 2.5 NAAQS. Specifically, we compare a 2016 reference scenario, a scenario without the boiler sector cont rols, to a 2016 control s cenario which includes the adjustments to the bo iler sector. This is done by calculating the simulated air quality ratios

3 The full details involved in calculating the annual PM 2.5 design value are provided in appendix N of 40 CFR part 50.6-10 between any particular future year simulation an d the 2005 base. These predicted ratios are then applied to ambient base year design values. The design value projecti on methodology used here followed EPA guidance for such analyses (USE PA, 2007). Additionally, the raw model outputs are also used in a relative sense as inputs to the health and welfare im pact functions of the benefits analysis. Only model pr edictions for mercury deposition were analyzed using absolute model changes, although percent changes between the control case and two future baselines are also estimated.
The 36 km and both 12 km modeling domains we re modeled for the entire year of 2005 and projected year 2016. Data from the entire ye ar were utilized when looking at the estimation of PM 2.5 , total mercury deposition, and visibility imp acts from the regulation. Data from April through October is used to estimate ozone im pacts. All air quality impacts are based on improvements in future year pollution based on emissions changes from this source sector.
6.5.1 Impacts of Sector on Total Mercury Deposition
This section summarizes the results of our modeling of total mercury deposition impacts in the future based on changes to boiler emissi ons. Available data indi cate that the mercury emissions from these sources are a mixture of gaseous elemental mercury (25%), inorganic divalent mercury (reactive gas phase mercury) (50%), and particulat e bound mercury (25%). Model results for the eastern and central United States indicate that estimated total mercury deposition (wet and dry forms) reductions from this sector would be 1,393 μg/m 2 (1.5% of total mercury deposition from all sources); approximately 51% in dry form and 49% in wet form. The chemical composition of this estimated reductio n is approximately 60% reactive gas phase (HG 2 ) and 40% particulate. A reduction of 75 μg/m 2 (0.5% of total mercury deposition from all sources) is estimated for the western United Stat es; approximately 56% in dry form and 44% in wet form. The chemical composition of the reductions in the western model domain is approximately 63% reactive gas phase (HG 2 ) and 37% particulate. These reductions are related to changes in emissions at major boiler source s using the ptnonipm sector as a surrogate.
6.5.2 Impacts of Sector on Future Annual PM 2.5 Levels
This section summarizes the results of our modeling of annual average PM 2.5 air quality impacts in the future due to estimated reductions in emissions from this sector. Specifically, we compare a 2016 reference scenario to a 2016 control scenario. The modeling assessment indicates a decrease up to 0.81 μg/m 3 in annual PM 2.5 design values is possi ble given an area’s proximity to controlled sources and the amount of reduced su lfur dioxide and primary PM 2.5 emissions from major sources under the recomme nded solid fuel category option. The estimated6-11 mean reduction over all mon itor locations is 0.15 μg/m 3 for major source reductions under the recommended solid fuel category. A decrease up to 0.66 μg/m 3 in annual PM 2.5 design values is possible given an area’s proxim ity to controlled sources and the amount of reduced sulfur dioxide and primary PM 2.5 emissions from major sources und er the alternative option. The estimated mean reduction over al l monitor locations is 0.12 μg/m 3 for major source reductions under the alternative option. Area source reductions show a decrease up to 0.01 μg/m 3 in annual PM 2.5 design values is possible given an area’s proximity to controlled area sources and the amount of reduced sulfur dioxide and primary PM 2.5 emissions.
6.5.3 Impacts of Sector on Future 24-hour PM 2.5 Levels
This section summarizes the results of our modeling of 24-hr average PM 2.5 air quality impacts in the future due to reductions in emissi ons from this sector. Specifically, we compare a 2016 reference scenario to a 2016 control scen ario. The modeling assessment indicates an estimated average decrease of 0.50 μg/m 3 in 24-hr average PM 2.5 design values over all monitor locations in the United States is possible given the amount of reduced sulf ur dioxide and primary PM 2.5 emissions from major sources under the re commended solid fuel category option. An estimated average decrease of 0.39 μg/m 3 in 24-hr average PM 2.5 design values over all monitor locations in the United States is possible given the amount of reduced sulf ur dioxide and primary PM 2.5 emissions from major sources under the al ternative option. An estimated decrease up to 0.03 μg/m 3 in 24-hr average PM 2.5 design values at monitor loca tions in the United States is possible given an area’s proxim ity to controlled sources and the amount of reduced sulfur dioxide and primary PM 2.5 emissions from area sources.
6.5.4 Impacts of Sector on Fu ture Visibility Levels Air quality modeling conducted for this final rule was used to project visibility conditions in 138 mandatory Class I federal areas across the U.S. in 2016 (USEPA, 2007). The level of visibility impairment in an area is based on the light-extinction coefficient and a unitless visibility index, called a “deciview ,” which is used in the valua tion of visibility. The deciview metric provides a scale for perceived visual ch anges 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. Higher deciview values ar e indicative of worse visibility. Thus, an improvement in visibility is a decrease in deciview value.
The modeling assessment indicates an estimated average visibility improvement of 0.51 deciviews in annual 20% worst visibility days ove r all Class I area monitors based on controls for major sources under the recommended solid fuel category option. An estimated average6-12 visibility improvement of 0.42 deciviews in annu al 20% worst visibility days over all Class I area monitors based on controls for major sour ces under the alternative option. An improvement in visibility up to 0.03 deciviews at Class I mon itor locations in the United States is possible given an area’s proximity to controlled sources and the amount of reduc ed sulfur dioxide and primary PM 2.5 emissions from area sources.
6.5.5 Impacts of Sector on Future Ozone Levels
This section summarizes the results of our modeling of 8-hr maximum ozone air quality impacts in the future due to reductions in emissi ons from this sector. Specifically, we compare a 2016 reference scenario to a 2016 control scen ario. The modeling assessment indicates a decrease of less than 0.01 ppb in ozone design valu es is possible given an area’s proximity to controlled sources and the amount of reduced VOC emissions under all major and area source options. The full details involved in calculating design value are given in appendix P of 40 CFR part 50. Projected air quality benefits are estimat ed using procedures outlined by United States Environmental Protection Agency modeling guidan ce (USEPA, 2007).
6.6 Section 6 References Baker, K., Scheff, P., 2007. Photoc hemical model performance for PM 2.5 sulfate, nitrate, ammonium, and precursor species SO 2 , HNO 3 , and NH 3 at background monitor locations in the central and eastern United Stat es. Atmospheric Environment 41, 6185-6195. Chang, J.S., Brost, R.A., Isaksen, I.S.A., Ma dronich, S., Middleton, P., Stockwell, W.R., Walcek, C.J. 1987. A 3-dimensional eulerian acid deposition model—physical concepts and formulation. J. Geophys. Res.-Atmos . 92, 14681-14700. ENVIRON, 2008. User’s Guide Comprehensive Ai r Quality Model with Extensions. ENVIRON International Corporation, Novato. Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics mechanism for urban and regi onal scale computer modeling. J. Geophys. Res.-Atmos . 94, 12925-12956. Houyoux, M.R., Vukovich, J.M., Coats, C.J., Whee ler, N.J.M., Kasibhatla, P.S., 2000. Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of Geophys ical Research-Atmospheres 105, 9079-9090. Nenes, A., Pandis, S.N., Pilinis, C., 1999. C ontinued development and testing of a new thermodynamic aerosol module for urban and regional air quality models. Atmospheric Environment 33, 1553-1560. Nobel, C.E., McDonald-Buller, E.C., Kimura , Y., Allen, D.T., 2001. Accounting for spatial variation of ozone productivity in NO x emission trading. Environmental Science & Technology 35, 4397-4407.6-13 Russell, A.G., 2008. EPA Supersites Program-re lated emissions-based particulate matter modeling: Initial applicati ons and advances. J. Air Wa ste Manage. Assoc. 58, 289-302. Strader, R., Lurmann, F., Pandis, S.N., 1999. Evaluation of secondary organic aerosol formation in winter. Atmospheric Environment 33, 4849-4863.USEPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze, RTP. USEPA, 2010. Air Quality Modelin g Technical Support Document: Boiler Source Sector Rules (EPA-454/R-10-006), Research Tria ngle Park, North Carolina. Wagstrom, K.M., Pandis, S.N., Yarwood, G., Wilson, G.M., Morris, R.E., 2008. Development and application of a computat ionally efficient particulate matter apportionment algorithm in a three-dimensional chemical transpor t model. Atmospheric Environment 42, 5650- 5659.7-1 SECTION 7 BENEFITS OF EMISSIONS REDUCTIONS
7.1 Synopsis In this section, we provide an estimate of th e monetized benefits as sociated with reducing exposure to particulate matter (PM) and ozone fo r the final Boiler MACT and Boiler Area Source Rule. The PM 2.5 reductions are the result of emission limits on PM as well as emission limits on other pollutants, including hazardous air pollutants (HAPs). The total PM 2.5 reductions are the consequence of the technologies installed to m eet these multiple limits. The latter are often referred to as “co-benefits.” Ozone reductions are the result of reductions in emissions of VOCs, which are precursors to ozone formation. These be nefit estimates include the number of cases of avoided morbidity and premature mort ality among populations exposed to PM 2.5 and ozone, as well as the monetized value of those avoided ca ses. Because we were unable to monetize the direct benefits associated with reducing HAP s, the monetized benefits estimate is an underestimate of the total benefits. The extent of this underest imate, whether small or large, is unknown. Using a 3% discount rate, we estimate the total combined monetiz ed benefits of the final Boiler MACT and Boiler Area Source Ru le to be $22 billion to $54 billion in the implementation year (2014). Using a 7% discount ra te, we estimate the total combined monetized benefits of the final Boiler MACT and Boiler Area Source Rule to be $20 billion to $49 billion in the implementation year. Higher or lower estim ates of benefits are possible using other assumptions; examples of this are provided in Figure 7-1. All estimates are in 2008$ and include any energy disbenefits associated with the increased emissions from additional energy usage.

These monetized estimates reflect EPA’s current interpretation of the scientific literature (U.S. EPA, 2009c). In addition, these estimates in corporate an array of improvements introduced since the proposal, incl uding boiler sector-specific air quali ty modeling data, lowest measured level (LML) assessment, mercury deposition ma ps, ozone benefits, and energy disbenefits. Methodological and time limitations under the c ourt-ordered schedule prevented EPA from monetizing the benefits from several important bene fit categories, including direct benefits from reducing hazardous air pollutants, ecosystem eff ects, and visibility im pairment. The direct benefits from reducing other air pollutants have not been monetiz ed in this analysis, including reducing a combined 113,000 tons of carbon monoxi de, 30,000 tons of HCl, 830 tons of HF, 2,900 pounds of mercury, 3,000 tons of other metals, and 23 grams of dioxins/furans (TEQ) each year. We assess the direct benefits of these em ission reductions qualitative ly in this analysis.7-2 $0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000

Pope et al. Laden et al.Millions(2008$)

Benefits estimates derived from 2 epidemiology functions and 12 expert functions Benefits 3% Benefits 7%

Figure 7-1. Total Monetized PM 2.5 and Ozone Benefits for the Final Boiler MACT and Boiler Area Source Rule in 2014 a

a This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the

Pope et al. (2002) study and the Laden et al. (2006) st udy, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response function provided in those studies. These estimates do not include the direct benefits from re ducing HAP emissions, but they do include the energy disbenefits associated with the increased emissions from additional energy usage. Due to methodology and time limitations under the court-ordered schedule, we were un able to monetize the benefits associated with several categories of benefits, including direct exposure to HAPs and SO 2 , as well as ecosystem effects, and visibility effects. These benefits reflect existing boilers and new boilers anticipated to come online by 2014.
7.2 Calculating Benefits The benefit categories associat ed with the emission reduction anticipated for these rules can be broadly categorized as those benefits dire ctly attributable to reduced exposure to HAPs, the PM co-benefits associated with reducing HAPs (i.e., PM reduced by introducing technology designed to reduce HAPs), and those attributable to exposure to ot her pollutants. Several of the HAPs that would be reduced by these rules have been classified as known or probable human7-3 carcinogens. 1 As a result, one benefit of the proposed regulation is a reduction in the risk of cancer. Other benefit categories in clude potential reduced inciden ce of neurological effects and irritations of the lungs and skin, reduced mortality and other morbid ity effects associated with PM and SO 2 . In addition to health impacts occurring as a result of reductions in HAPs and other pollutant emissions, there are welfare impacts whic h can also be identified. In general, welfare impacts include effects on vegetation, visibility impairment, and acidification of water bodies. We were unable to monetize the direct benefits asso ciated with reducing HAPs in this analysis. In Section 7.5.5 of this RIA, we provide a full qualit ative discussion of the direct health benefits associated with the reductions in emissions of HAPs anticipated by these rules, including a full discussion of the complexity associated with mo netizing HAP benefits. We also provide maps of reduced mercury deposition in that section. Theref ore, all monetized benefits provided in this analysis only reflect improvements in ambient PM 2.5 and ozone concentrations. Thus, the monetized benefits estimate is an underestimate of the total benefits. The extent of this underestimate, whether small or large, is unknown. In addition to pollutant s we do not directly monetize, these rulemakings are expected to reduce emissions of PM 2.5 , SO 2 , and VOCs. Because SO 2 is also a precursor to PM 2.5, reducing SO 2 emissions will also reduce PM 2.5 formation, human exposure, and therefore redu ce estimated in cidence of PM 2.5 -related health effects. The estimated PM reductions are the result of emission limits on PM as well as emission limits on other pollutants, including hazardous air pollu tants for these rules. The total PM 2.5 reductions are the consequence of the technologies installed to meet these multiple limits. In addition, these rules are expected to result in reductions in VOCs, which will result in changes in ambient ozone concentrations.
In implementing these rules, emission cont rols may lead to reductions in ambient PM 2.5 below the National Ambient Air Quality Standard s (NAAQS) for PM in some areas and assist other areas with attaining th e PM NAAQS. Because the PM NAAQS RIAs also calculate PM benefits, there are important differences worth no ting in the design and anal ytical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a new air quality standard nationwide based on an array of emission control st rategies for different sources. In short, NAAQS RIAs hypothesize, but do not pred ict, the control strate gies that States may choose to enact when implementing a NAAQS. The setting of a NAAQS doe s not directly result in costs or benefits, and as such, the NAAQS RI As are merely illustrative and are not intended to be added to the costs and benefits of other regula tions that result in speci fic costs of control and

1 As described in Section 7.5, form aldehyde, several PAHs, and acetaldehyde are classified as probable human carcinogens. Different nickel compou nds are classified as human carcino gens and probable human carcinogens.7-4 emission reductions. However, some costs and be nefits estimated in this RIA account for the same air quality improvements as estimated in the illustrative PM 2.5 NAAQS RIA. By contrast, the emission reductions for th is rule are from a specific class of wellcharacterized sources. In general, EPA is more confident in the magnitude and location of the emission reductions for these rules. It is importa nt to note that emission reductions anticipated from these rules do not result in emission incr eases elsewhere (other than potential energy disbenefits). Emission reductions achieved unde r these and other promulgated rules will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the incremental costs and benefits associated w ith attaining the NAAQS. EPA remains forward looking towards the next iteration of the 5-year review cycle for the NAAQS, and as a result does not issue updated RIAs for existing NAAQS that retroactively update the baseline for NAAQS implementation. For more information on the relationship between the NAAQS and rules such as analyzed here, please see Section 1.2.4 of the SO 2 NAAQS RIA (U.S. EPA, 2010h).
7.2.1 Methodology Improvements since Proposal This benefits analysis for the final Boiler MACT and Area Source Rule incorporates an array of policy and technical improvements si nce the proposal analysis in 2010 (U.S. EPA, 2010c), including:
1. Boiler sector-specific air quality modeling data . The benefits estimates for this final analysis are derived from air quality data modeled using CAM x that reflect the emissions from the boiler sector a nd their contribution to ambient PM 2.5 and ozone levels. These data provide a different a nd likely superior re presentation of the geographic distribution of emissions a nd associated ambient concentrations specifically from boilers than the generic benefit-per-ton estimates used in the proposal that reflected emissions for the entire non-EGU (non-el ectrical generating unit) category. For more information rega rding the modeling inputs and assumptions, please see Section 6 of this RIA. For more information regarding the derivation of boiler sector-specific benef it-per-ton estimates and how they are superior to the estimates used in the proposal, pl ease see Section 7.2.6 of this RIA.
2. Lowest Measured Level (LML) assessment . Consistent with the rationale outlined in the Cement NESHAP proposal RIA (U.S. EP A, 2009a), EPA has recently changed its approach to characterizing the uncertainty a ssociated with benefits estimated at lower air quality levels. Specifically, EPA no w estimates PM-related mortality without assuming an threshold at 10 μ g/m 3 in the concentration-response function. Consistent with recent scientific advice, we are re placing the previous threshold sensitivity analysis with a new LML assessment. We di scuss this assessment in more detail in Section 7.2.4 and provide the results of this LML assessment in Section 7.3.7-5 3. Mercury deposition . The air quality modeli ng data provide an estimate of the reduction in mercury deposition associated with the mercury emission reductions anticipated as a result of these rules. We provide maps of the reduced mercury deposition in Section 7.5.4.1. EPA did not model mercury methylatio n, bioaccumulation in fish tissue, and human consumption of mercury-contaminated fish that would be needed in order to estimate the human health benefits from reducing me rcury emissions.
4. Ozone benefits . The air quality modeling data provide an estimate of the change in ambient ozone concentrations associat ed with the VOC emission reductions anticipated as a result of the Boiler MACT . We provide a table of the ozone-related health benefits in Section 7.3.
5. Energy disbenefits . For this final analysis, we include an estimate of the increased CO 2 emissions associated with the additional elect ricity required to operate control devices. We provide the results of th is analysis in Section 7.4.
6. Improved characterization of uncertainty . For this final analysis, we characterize uncertainty using four methods (Monte Carlo methods, LML assessment, alternate concentration-response functi ons, and qualitative descrip tions). While the proposal incorporated some of these approach es, the Monte Carlo methods and LML assessment are an improvement for the fina l analysis. Confidence intervals reflecting the standard errors in the underlying ep idemiology and economic s literature are provided for incidence and valuatio n results in Tables 7-4 to 7-8.
7.2.2 Benefits Analysis Approach for PM 2.5 and Ozone We follow a “damage-function” approach in calculating total bene fits of the modeled changes in environmental qualit y. This approach estimates cha nges in individual health and welfare endpoints and assi gns values to those changes assumi ng independence of the individual values. Total benefits are calcu lated simply as the sum of th e values for all non-overlapping health and welfare endpoints. The “damage-function” approach is the standard method for assessing costs and benefits of environmental quality programs and has been used in several recent published analyses (Levy et al., 2009; Hubbell et al., 2009; Tagaris et al., 2009).
To assess economic value in a damage-functi on framework, the changes in environmental quality must be translated into effects on people or on the things that people value. For changes in PM 2.5 or ozone, a health impact analysis (HIA) mu st first be conducted to convert air quality changes into effects that can be assigned dollar valu es. For this RIA, the health impacts analysis is limited to those health effects that are directly linked to ambient levels of air pollution and specifically to those linked to PM 2.5 and ozone. We also provide qualitative discussions of the impact of changes in other environmental and ecol ogical effects, including the benefits associated with decreasing deposition of sulf ur to terrestrial and aquatic eco systems, but we are unable to7-6 place an economic value on these changes due to time limitations under the court-ordered schedule.
We note at the outset that EPA rarely has th e time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses. Thus, similar to Kunzli et al. (2001) and other recent health impact analyses, our estimates are based on the best available methods of benefits tran sfer. Benefits transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under anal ysis. Adjustments are made for the level of environmental quality change, the socio-demographi c and economic characteristics of the affected population, and other factors to improve the accuracy and robu stness of benefits estimates.
7.2.3 Health Impact Analysis (HIA) The HIA quantifies the potential changes in the incidence of adverse health impacts resulting from estimated changes in human expos ure to air pollution. HIAs are a well-established approach for estimating the retrospective or pr ospective change in adverse health impacts resulting from population-level ch anges in exposure to pollutants (Levy et al., 2009). Analysts have applied the HIA approach to estimate hu man health impacts resu lting from hypothetical changes in pollutant levels (Hubbell et al., 2005 ; Davidson et al., 2007; Ta garis et al., 2009). For this analysis, we used the environmental Be nefits Mapping and Analysis Program (BenMAP), which is a PC-based tool that can systematize he alth impact analyses by applying a database of key input parameters, including health im pact functions and population projections. 2
The HIA approach used in this analysis i nvolves three basic steps: (1) utilizing CAM x generated projections of PM 2.5 and ozone air quality and estimating the change in the spatial distribution of the ambient air qua lity; (2) determining the subse quent change in population-level exposure; (3) calculating health impacts by applyi ng concentration-response relationships drawn from the epidemiological literature (Hubbell et al ., 2009) to this change in population exposure.
A typical health impact f unction might look as follows:

where y 0 is the baseline incidence rate for the hea lth endpoint being quantified (for example, a health impact function quantifying changes in mortality would use the baseline, or background,

2 For this analysis, we used BenMAP version 3.0 (Abt Associates, 2008). This model is available for free download on the Internet at <http://www.epa.gov/air/benmap>.7-7 mortality rate for the given population of interest); Pop is the population affected by the change in air quality;  x is the change in air quality; and β is the effect coefficient drawn from the epidemiological study. For this analysis, we systematize the HIA calculation process using BenMAP’s library of existing air quality monitoring data, population data and health impact functions. Figure 7-2 provides a simplified overvie w of this approach, and Figure 7-3 identifies the data inputs and outputs for the BenMAP model for a PM 2.5 analysis.

Figure 7-2. Illustration of BenMAP Approach7-8 Figure 7-3. Data Inputs and Outputs for the BenMAP Model for a PM 2.5 Analysis The benefits estimates in this analysis were derived using modified versions of the health impact functions used in the PM NAAQS Regulat ory Impact Analysis (RIA) (U.S. EPA, 2006) and the Ozone NAAQS RIA (U.S. EPA, 2010a). 3 While many of the functions are identical to those used in the PM NAAQS RIA, we have updated a few of the underlying assumptions over the last few years. For a detailed descripti on of the underlying functions, studies, baseline incidence rates, and population data used in this analysis, please refer to Chapter 5 of the recently proposed Transport Rule (U.S. EPA, 2010e). Table 7-1 identifies which human health and welfare endpoints are included in the monetized benef its and which endpoints remain unquantified. In summary, the estimate of monetized PM benef its includes premature mortality and eleven morbidity endpoints, and the estimate of monetized ozone benefits includes premature mortality and five morbidity endpoints.

3 All of the RIAs cited in this analys is are available on EPA’s website at http://www.epa.gov /ttn/ecas/ria.html

. Census Population Data

Baseline and PostControl PM 2.5 Concentrations

PM 2.5 Health

Functions

Economic Valuation Functions Population Projections

PM 2.5 Incremental Air Quality Change

PM 2.5 Related Health Impacts Woods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized PM 2.5 -

related Benefits Blue identifies a user-selected input within the BenMAP model Green identifies a data input generated outside of the BenMAP model7-9 Table 7-1. Human Health and Welfare E ffects of Air Pollutants Affected

Pollutant/Effect Quantified and Monetized Unquantified

PM: health a Premature mortalit y based on cohort study estimates b Low birth weight Pre-term births Premature mortality based on expert elicitation estimates Pulmonary function Nonfatal cardiovascular outcomes other than myocardial infarctions Chronic bronchitis Hospital admissions: respiratory and cardiovascular Chronic respiratory diseases other than chronic bronchitis
Emergency room visits for asthma Non-asthma respiratory emergency room visits Nonfatal heart attacks (myocardial infarctions) UVb exposure (+/

− ) c
Lower and upper respiratory illness Minor restricted activity days Work loss days Asthma exacerbations (among asthmatic populations Respiratory symptoms (among asthmatic populations) Infant mortality

PM: welfare Visibility in Class I areas Household soiling Visibility in residential and non-class I areas UVb exposure (+/

− ) c Global climate impacts c

Ozone: health Premature mortality based on shortterm study estimates Chronic respiratory damage Premature mortality due to long-term exposures Hospital admissions: respiratory Premature aging of the lungs Emergency room visits for asthma Non-asthma respiratory emergency room visits Minor restricted activity days UVb exposure (+/

− ) c School loss days

Ozone: welfare Decreased outdoor worker productivity Yields for: – Commercial forests – Fruits and vegetables, and – Other commercial and noncommercial crops Damage to urban ornamental plants Recreational demand from damaged forest aesthetics Ecosystem functions UVb exposure (+/

− ) c

SO 2 : health Respiratory hospital admissions Asthma emergency room visits Asthma exacerbation Acute respiratory symptoms Premature mortality Pulmonary function (continued)7-10 Table 7-1. Human Health and Welfare Effect s of Air Pollutants Affected (continued)
Pollutant/Effect Quantified and Monetized Unquantified

SO 2 : welfare Commercial fishing and forestry from acidic deposition Recreation in terrestrial and aquatic ecosystems from acid deposition Increased mercury methylation
Mercury: health Incidence of neurological disorders Incidence of learning disabilities Incidences in developmental delays Potential cardiovascular effects including: – Altered blood pressure regulation – Increased heart rate variability – Incidences of heart attack Potential reproductive effects Mercury: environment Impact on birds and mammals (e.g., reproductive effects, cardiovascular effects) Mercury: welfare Impacts to commercial., subsistence and recreational fishing HAPs Health effects associ ated with HAP exposure

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

with PM health effects including mor phological changes and altered host de fense mechanisms. The public health impact of these biological responses may be partly represented by our quantified endpoints. b Cohort estimates are designed to examine the effects of long-term exposures to ambient pollution, but relative risk

estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion of this issue). While some of the effects of short-term exposure are likely to be capture d by the cohort estimates, there may be additional premature mort ality from short term PM exposure no t captured in the cohort estimates included in the primary analysis.

c May result in benefits or disbenefits.
7.2.4 Estimating PM 2.5 -related Premature Mortality Consistent with all RIAs since the proposal RIA for the Portland Cement NESHAP (U.S. EPA, 2009a), the PM 2.5 benefits estimates utilize the concentration-response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA’s expert elicitation study as a characterization of uncertainty.

 One estimate is based on the concentra tion-response (C-R) function developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002), a study that EPA has previously used to generate its primary benefits estimate. When calculating the estimate, EPA applied the effect coefficient as reported in the study without an adjustment for assumed concentration threshold of 10 μg/m 3 as was done in recent (2006–2009) Office of Air and Radiation RIAs.

 EPA strives to use the best available science to support our benefits analyses, and we recognize that interpretation of the sc ience regarding air pollution and health is dynamic and evolving. Based on our review of the current body of scientific7-11 literature, EPA now estimates PM-related mortality without applying an assumed concentration threshold. EPA’s Integrat ed Science Assessmen t for Particulate Matter (U.S. EPA, 2009c), which was recently reviewed by EPA’s Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded that the scientific literature consistently finds that a no-threshold loglinear model most adequately portrays the PM-mortality concentration-response relationship while recognizi ng potential uncerta inty about the exact shape of the concentration-response func tion. Since then, the Hea lth Effects Subcommittee (U.S. EPA-SAB, 2010) of EPA’s Council concluded, “The HES fully supports EPA’s decision to use a no-threshold mode l to estimate mortality reductions. This decision is supported by the data, which ar e quite consistent in showing effects down to the lowest measured levels. Anal yses of cohorts using data from more recent years, during which time PM concentr ations have fallen, continue to report strong associations with mortality. One estimate is based on the C-R function developed from the extended analysis of th e Harvard Six Cities cohort, as reported by Laden et al. (2006). This study, publis hed after the completion of the Staff Paper for the 2006 PM 2.5 NAAQS, has been used as an alternative estimate in the PM 2.5 NAAQS RIA and PM 2.5 benefits estimates in RIAs completed since the PM 2.5 NAAQS. When calculating the estimate, EPA applied the effect coefficient as reported in the study without an adjust ment for assumed concentration threshold of 10 μg/m 3 as was done in recent (2006–2009) RIAs.

 Twelve estimates are based on the C-R f unctions from EPA’s expert elicitation study (IEc, 2006; Roman et al., 2008) on the PM 2.5 -mortality relationship and interpreted for benefits analysis in EPA’s final RIA for the PM 2.5 NAAQS. For that study, twelve experts (labeled A through L) provided independent estimates of the PM 2.5 -mortality concentration-response function. EPA practice has been to develop independent estimates of PM 2.5 -mortality estimates corresponding to the concentration-response function provided by each of the twelve experts, to better characterize the degree of variab ility in the expert responses.
Therefore, there is no eviden ce to support a trunca tion of the CRF [con centration-response function].” In conjunction with the underlying scie ntific literature, this document provided a basis for reconsidering the applic ation of thresholds in PM 2.5 concentration-respons e functions used in EPA’s RIAs. For a summary of these scientific review statements and the panel members commenting on thresholds since 2002, please co nsult the Technical Support Document (TSD) Summary of Expert Opinions on the Exis tence of a Threshold (U.S. EPA, 2010d).

Consistent with the recent scientific advice summarized above, we are replacing the previous threshold sensitivity analysis with a new “Lowest Measured Level” (LML) assessment. This approach summarizes the distribution of avoided PM mortality impacts according to the baseline PM 2.5 levels experienced by the population receiving the PM 2.5 mortality benefit. In the results section, we identify on the figures the lowe st air quality levels m easured in each of the7-12 primary cohort studies that estimate PM-related mo rtality. This information allows readers to determine the portion of PM-related mortality benef its occurring above or below the LML of each study; in general, our co nfidence in the estimated PM mortal ity decreases as we consider air quality levels further below the LML in th e two epidemiological studies. While an LML assessment provides some insight into the level of uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a th reshold and continues to quantify PM-related mortality impacts using a full range of modeled air quality concentrations. Unlike an assumed threshold, which is a modeling assumption that re duces the magnitude of the estimated health impacts, the LML is a characterization of the frac tion of benefits that ar e more uncertain. It is important to emphasize that just because we have greater confidence in the benefits above the LML, this does not mean that we have no c onfidence that benefits occur below the LML.
Analyses of these cohorts using data from more recent years, during which time PM concentrations have fallen, continue to report st rong associations with mortality. As we model mortality impacts among populations exposed to levels of PM 2.5 that are successively lower than the LML of each study, our confidence in the results diminishes. As air pollution emissions continue to decrease over time, there will be more people in areas where we do not have published epidemiology studies. However, each successive cohort study has shown evidence of effects at successively lower levels of PM 2.5 . As more cohort studies follow large populations over time, we will likely have more studies with lower LML as air quality levels continue to improve. Even in the absence of a definable thres hold, we have more confidence in the benefits estimates above the LML of the large cohort studie s. To account for the uncertainty in each of the studies that we base our mort ality estimates on, we provide the LML for each of the cohort studies. However, the finding of effects at the lowest LML from recent studies indicates that confidence in PM 2.5 -related mortality effects down to at least 7.5 μg/m 3 is high.
In implementing these rules, emission cont rols may lead to reductions in ambient PM 2.5 below the PM NAAQS in some areas. While be nefits occurring below the standard may be somewhat more uncertain than those occurring above the standard, EPA considers them to be legitimate components of the total benefits estimate. Furthermore, given that the epidemiological literature in most cases has not provided estimates based on threshold models, there would be additional uncertainties imposed by assuming th resholds or other non- linear concentrationfunctions for the purposes of benefits analysis.7-13 7.2.5 Economic Valuation of Health Impacts These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premat ure mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by pa rticle type (U.S. EPA, 2009c). Directly emitted PM, SO 2 , and VOC are the dominant PM 2.5 precursors affected by this rule. Even though we assume that all fine particles have equivalent health effects, the benefit-per-ton estimates (described in detail in Section 7.2.6) vary be tween precursors because each ton of precursor reduced has a different propensity to form PM 2.5 and a different pattern of transport, resulting geographic distribution of exposure. When more people are exposed, the benefits per ton are greater. For example, SO 2 tends to have a lower benefit- per-ton estimate than direct PM 2.5 because sulfate particles formed from SO 2 emissions can transport many miles, meaning that higher exposures may occur over ar eas with low populations. On the other hand, to the extent that direct PM 2.5 emissions occur in high density population areas, exposures will tend to be higher there, leading to highe r monetized health be nefits for direct PM 2.5 than for SO 2 emissions. Economic Valuation of Health Impacts
After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. Please refer to Table 5-11 in the recently proposed Transport Rule (U.S. EPA, 2010e) for a detailed description of the underl ying valuation functions and the monetized unit values for each e ndpoint incorporated into this analysis. 4 The monetized mortality benefits dominate the total benefits estimates.
As is the nature of RIAs, the assumptions and methods used to estimate air quality benefits evolve over time to reflect the Agency’s most current interpretation of the scientific and economic literature. For a period of time ( 2004–2006), the Office of Air and Radiation (OAR) valued mortality risk reductions using a value-of -a-statistical-lif e (VSL) estimate derived from a limited analysis of some of the available studi es. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of the wage-risk literature. The

4 To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). These benefits are estimated for a specific analysis year (i.e ., 2014), and most of the PM benefits occur within that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 years of follow-up medical costs and lo st wages. For premature mortality, we assume that there is a “cessation” lag between PM exposures and the total realization of changes in health effects. Although the structure of the lag is uncertain, EPA follows the advice of the SAB-HES to assume a segmented lag structure characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction in PM 2.5 (U.S. EPA-SAB, 2004). Changes in the lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Therefore, discounting only aff ects the AMI costs after the analysis year and the valuation of premature mort alities that occur after the analysis year. As such, the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate.7-14 $1 million value represented the lower end of the in terquartile range from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 mil lion value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta-analysis of 43 studies. The mean estimate of $5.5 million (2000$) 5 was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analy sis. However, the Agency neither changed its official guidance on the use of VSL in rule-mak ings nor subjected the interim estimate to a scientific peer-review process through the Scie nce Advisory Board (SAB ) or other peer-review group. During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a repo rt from meta-analytic experts to evaluate methodological questions raised by EPA and the SAB on combining estimates from the various data sources. In addition, the Ag ency consulted several times w ith the Science Advisory Board Environmental Economics Advisory Committee (SAB-EEAC) on the i ssue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, appropriate meta-analytic techniques to combine estimates from unique data sources and different studies, including those using diffe rent methodologies (i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007).
Until updated guidance is availabl e, the Agency determined that a single, peer-reviewed estimate applied consistently best reflects the SAB-EEAC advice it has re ceived. Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 6 while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation stud ies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 7 The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB-EEAC’s specific recommendations.

5 After adjusting the VSL to account for a different currency year (2008$) and to account for income growth to 2015, the $5.5 million VSL is $7.9 million. 6 In the (draft) update of the Economic Guidelines (U.S . EPA, 2008b), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future. Therefore, this report does not represent final agency policy. 7 In this analysis, we adjust the VSL to account for a different currency year (2008$ ) and to account for income growth to 2015. After applying these adjustments to the $6.3 million value, the VSL is $9.1 million.7-15 7.2.6 Calculating Boiler Sector-specific Benefit-per-ton Estimates Benefit per-ton (BPT) estimates can be used to quantify the health impacts and monetized human health benefits of an incremental change in air pollution precursor emissions. In situations when we are unable to specifically model a re gulatory option because of data or resource constraints, this approach can provide a reliable estimate of the benefits of emission reduction scenarios. EPA has used the BPT technique in previous RIAs, including the recent Ozone NAAQS RIA (U.S. EPA, 2010a) and Federal Transport Rule RIA (U.S. EPA, 2010e). For this analysis, the emissions inventories used in th e air quality modeling and benefits modeling are slightly different from the final boiler sector emi ssion inventories reflected in the rules’ emission

memos. 8 These discrepancies exist because emissi ons and air quality modeling decisions are made early in the analytical process, and th e sheer number of boilers (over 180,000 area boilers and 13,000 major boilers) made it impossible to r econcile the data comp letely. Part of the discrepancy is because the emissions inventory categorizes boilers as “point” and “non-point” instead of “major source” or “area source.” We assume that the boilers in the point source inventory are appropriate surrogates for the majo r source boilers and thatthe boilers in the nonpoint source inventory are appropriate surrogate s for the area source boi lers. Furthermore, the emission inventories used for air quality modeling do not reflect emissions from new sources anticipated to come online by the analysis year . To address concerns about the discrepancies between these inventories, we utilized the air quality modeling to derive boiler sector-specific BPT estimates, which are then multiplied by the tons of emissions reduced for each regulatory option.
To derive the BPT estimates for this analysis, we:
1. Quantified the PM 2.5 -related human health and m onetized benefits of the SO 2 emission reductions for major source boilers . We first quantified the health impacts and monetized benefits of total PM 2.5 mass formed from the SO 2 reductions, allowing us to isolate the PM air quality impacts from SO 2 reductions alone. This procedure allowed us to develop PM 2.5 BPT estimates that quantified the PM 2.5 -related benefits of incremental changes in SO 2 emissions.
2. Divided the health impacts and monetiz ed benefits by the emission reduction. This calculation yields BPT estimates for PM-related SO 2 . The resulting BPT estimates

8 Technical details regarding the emissions inventory used in the air quality modeling are available in the Air Quality

Modeling TSD (U.S. EPA, 2010f). Technical details regardin g the emission reductions estimated for this rule are available in these docket memos: “ Revised Development of Baseline Emis sion Factors for Boilers and Process Heaters at Commercial, Industrial, and Institutional Facilities ” and “ Revised Methodology for Estimating Cost and Emissions Impacts for Industrial, Commercial, Institutional Boilers and Process Heaters National Emission Standards for Hazardous Air Pollutants. ”7-16 were then multiplied by the projected SO 2 emission reductions to produce an estimate of the PM-related health imp acts and monetized benefits.
This process was repeated for directly emitted PM 2.5 from major sources and for PM 2.5 and SO 2 emissions from area source boilers. This process was also repeated using the 2.5 th percentile and 97.5 th percentile benefits to provide confidence intervals for the BPT estimates. 9 To calculate the total benefits, we simply multiply the BPT estimate by the emission reductions of directly emitted PM 2.5 and SO 2 for each regulatory option. Deriving boiler sector-specific BPT estimates provides equivalent benefits result s as scaling the air quality data directly to co rrespond to the inventory differences, but it provides greater flexibil ity to examine alternate regulatory scenarios.
The BPT estimates used in this analysis are different from the BPT estimates used in the proposal analysis. While these boiler sector-specifi c BPT estimates are methodologically consistent with the BPT estimates in Fann, Fulc her, and Hubbell (2009) that were used in the proposal analysis, the BPT estimates applied in th is final analysis provid e a different and likely superior representation of the health impacts and monetized benefits from boilers compared to the proposal. This is because the BPT used in the proposal lumped emissions from major boilers together with other emission sources in the non-EGU category and area bo ilers together with other emission sources in the area category. For example, the non-EGU category includes emissions from many different large stationa ry sources, including boilers, metal production, mineral products, chemical manufacturing, petrol eum industry, pulp and paper production, and oil and gas production, and waste dis posal, which all have different geographic distributions across the country. Because the geographi c distribution of boilers is notlikely to exactly match the average distribution of all of th ese sources, ambient air quality a nd the associated benefits for reducing boiler emissions are goi ng to be different than for the entire non-EGU category. In addition to the location of the emission sources, the geographic differences also affect the benefits to due to geographic differences in local ai r chemistry, meteorology, population density, and baseline health incidence.
In addition, the air quality modeling for these rules reflected higher spatial resolution (12 km by 12 km nationally) than the previous BPT estimates and reflects an updated baseline emissions inventory. 10 We believe that these updates result in benefits estimates that are more

9 These confidence intervals only reflect the standard errors within the epidemiology studies and valuation functions, but they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. 10 For more information on the changes in the emissions inve ntory, please see Table 2-2 of U.S. EPA, (2010g), which provides a list of the differences between the 2002 and 2005 base inventory. In addition to including consent decrees and several recent mobile source rules, the emi ssions platform used for this analysis also removed duplicates and plant closures and an updated version of IPM to project EGU emissions.7-17 accurate than the proposal. For comparison, the BPT for directly emitted PM 2.5 in this analysis is lower for major and area sources than the proposal, whereas the BPT for SO 2 is higher in this analysis than the proposal. In addition, we have updated the analysis year used in this final analysis to be consistent with the implementati on timeline, but this revision would not have a significant impact on the benefits results. In the proposal, the BPT estimates reflected emissions inventories, income growth, and population gr owth for 2015 because we did not have BPT estimates available for the proposal analysis year (2013). For this final analysis, the emissions inventories are for 2016, but income growth and popul ation growth are for th e final analysis year (2014). Because the projected emissions inventory does not assume growth in the boiler sector, the boiler benefits are not likely to change due to a 2016 inventory instead of a 2014 inventory. We provide the boiler-specific BPT estimates in Section 7.3. We also provide maps of the total ambient PM 2.5 impacts from the modeled point and non-poi nt boilers in Figur es 7-4 through 7-7.

Figure 7-4. Change in Ambient PM 2.5 Levels from SO 2 Emissions from Point Source Boilers No change 0.01 - 0.10 0.10 - 0.50 0.50 - 1.00 1.00 - 3.057-18 Figure 7-5. Change in Ambient PM 2.5 Levels from PM Emissions from Point Source Boilers

Figure 7-6. Change in Ambient PM 2.5 Levels from SO 2 Emissions from Non-point Source Boilers No change 0.01 - 0.10 0.10 - 0.50 0.50 - 1.00 1.00 - 3.05

No change 0.01 - 0.10 0.10 - 0.50 0.50 - 1.00 1.00 - 3.057-19 Figure 7-7. Change in Ambient PM 2.5 Levels from PM Emissions from Non-point Source Boilers Although VOCs are also precursors to PM 2.5 , we have not monetized their contribution to the PM 2.5 benefits in this analysis for several reasons. Analysis of organic carbon measurements suggest only a fraction of secondarily formed orga nic carbon aerosols are of anthropogenic origin. The current state of the science of seconda ry organic carbon formation indicates that anthropogenic VOC contribution to secondary organi c carbon aerosols is often lower than the biogenic (natural) contribution. Given that a fraction of sec ondarily formed organic carbon aerosols from anthropogenic VOC emissions and the extremely small amount of VOC emissions from this sector relative to the entire VOC i nventory, it is unlikely this sector has a large contribution to ambient secondary organic carbo n aerosols. Photochemical models typically estimate secondary organic carbon aerosols from anthropogenic VOC emissions to be less than 0.1 μg/m 3 . Given the resource requirements to appl y source apportionment technology in the photochemical model for secondary organic carbon aerosols and that only a very small portion of secondary organic carbon aerosols (i.e., from an thropogenic sources) would be apportioned, this option was not employed to estimate the impact of this sector’s VOC emissions on secondary organic carbon aerosols. Lastl y, the contribution of VOC reduc tions to total monetized PM 2.5 benefits would likely be very sm all for these rules. For example, while not an appropriate source No change 0.01 - 0.10 0.10 - 0.50 0.50 - 1.00 1.00 - 3.057-20 for this application, if the BPT for VOC emissions from industrial point sources and EGUs from Fann, Fulcher, and Hubbell (2009) were applied to the boiler rule, th e VOC-related benefits would be less than $6 million for the Boiler MACT.

The differences between the VOC emissions inventories are comparatively small, and VOCs are only reduced by 20% for both of the Bo iler MACT options. In addition, the complex non-linear chemistry of ozone formation intr oduces uncertainty to the development and application of a BPT estimate. Therefore, we used the scaled air quality modeling results directly to estimate the ozone benefits associated with VOC reductions. As the ozone-related benefits are very small, we do not believe that the differenc es in the VOC emissions inventory contribute much uncertainty to the overall benefits resu lts. Because the VOC emission reductions for the Boiler Area Source Rule are less than 1%, we do not include the ozone-related benefits associated with that rule, as the benefits would be very small.
7.3 Health Benefits Results
The health benefits have increased since the proposal analysis for two reasons: (1) emission reductions of SO 2 have increased, and (2) the BPT for SO 2 has increased. The decreases in directly emitted PM 2.5 emissions and lower BPT for PM 2.5 since the proposal do not offset the increase in benefits from SO 2 emission reductions.
Tables 7-2 and 7-3 provide a summary of the monetized PM 2.5 benefits (including the indirect PM co-benefits) using th e anchor points of Pope et al. (2002) and Laden et al. (2006) at discount rates of 3% and 7% for the fina l Boiler MACT and Boile r Area Source Rule, respectively. Tables 7-4 and 7-5 provide the reductions in health incidences as a result of the reduction in ambient PM 2.5 levels for the final Boiler MACT and Boiler Area Source Rule, respectively. Tables 7-6 and 7- 7 provide the total monetized PM 2.5 benefits derived from Pope et al. (2002) and Laden et al. (2006) as well as the expert elicitation for the final Boiler MACT and Boiler Area Source Rule, respectively. Table 7-8 provides the estimated reductions in health incidences and the monetized benefits associat ed with estimated reductions in ambient ozone concentrations.7-21 Table 7-2. Summary of Monetized Benefits Estimates for Fina l Boiler MACT in 2014 (millions of 2008$) a

Pollutant Emissions Reductions (tons) Benefit per ton (Pope,

3%) Benefit per ton (Laden,

3%) Benefit per ton (Pope,

7%) Benefit per ton (Laden,

7%) Total Monetized Benefits (millions of 2008$ at 3%) Total Monetized Benefits (millions of 2008$ at 7%)SelectedOption Direct PM 2.5 29,007 $72,000 $180,000 $65,000 $160 ,000 $2,100 to $5,100 $1,900 to $4,600
SO 2 439,901 $46,000 $110,000 $42,000 $100,000 $20,000 to $49,000 $18,000 to $45,000 Total $22,000 to $54,000 $20,000 to $49,000AlternativeOption Direct PM 2.5 28,139 $72,000 $180,000 $65,000 $160 ,000 $2,000 to $5,000 $1,800 to $4,500 SO 2 337,514 $46,000 $110,000 $42,000 $100,000 $15,000 to $38,000 $14,000 to $34,000 Total $17,000 to $43,000 $16,000 to $39,000

a All estimates are for the implementati on year (2014), and are rounded to two significant figures so numbers may

not sum across columns. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evid ence is not yet sufficient to allow differentiation of effect estimates by particle type. The benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . The monetized benefits inco rporate the conversion from precursor emissions to ambient fine particles. These estimates do not include benefits from reducing HAP emissions, VOC emissions and ozone exposure, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section. These benefits reflect existing boilers and 47 new boilers anticipated to come online by 2014. Table 7-3. Summary of Monetized Benefits Esti mates for Final Boiler Area Source Rule in 2014 (millions of 2008$) a

Pollutant Emissions Reductions (tons) Benefit per ton (Pope,

3%) Benefit per ton (Laden,

3%) Benefit per ton (Pope,

7%) Benefit per ton (Laden,

7%) Total Monetized

Benefits (millions of 2008$ at 3%) Total Monetized

Benefits (millions of 2008$ at 7%)ProposedMACTApproach Direct PM 2.5 590 $120,000 $290,000 $110,000 $260,000 $69 to $170 $63 to $150 SO 2 3,197 $41,000 $100,000 $37,000 $91,000 $130 to $320 $120 to $290 Total $200 to $490 $180 to $440FinalMACT/GACTApproach Direct PM 2.5 678 $120,000 $290,000 $110,000 $260,000 $79 to $190 $72 to $180 SO 2 3,197 $41,000 $100,000 $37,000 $91,000 $130 to $320 $120 to $290 Total $210 to $520 $190 to $470

a All estimates are for the implementati on year (2014), and are rounded to two significant figures so numbers may

not sum across columns. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evid ence is not yet sufficient to allow differentiation of effect estimates by particle type. The benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . The monetized benefits inco rporate the conversion from precursor emissions to ambient fine particles. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits associated with the in creased emissions from additional energy usage described in the next section.7-22 Table 7-4. Summary of Estimated Reducti ons in Health Incidences from PM 2.5 for the Final Boiler MACT in 2014 (95 th percentile confidence interval) a

Selected Option Alternative Option Avoided Premature Mortality

Pope et al. (2002) 2,500 2,000 (1,600

– 8,100) (1,400

– 7,100) Laden et al. (2006) 6,500 5,100 (6,200

– 19,000) (5,500

– 16,000) Woodruff (Infant mortality) 10 8 (0

– 58) (0

– 51) Avoided Morbidity

Chronic Bronchitis 1,600 1,300 (340

– 6,000) (300

– 5,200) Acute Myocardial Infarction 4,000 3,100 (2,400

– 13,000) (2,100

– 11,000) Hospital Admissions, Respiratory 610 480 (520

– 1,800) (460

– 1,600) Hospital Admissions, Cardiovascular 1,300 1,000 (1,800

– 3,000) (1,600

– 2,700) Emergency Room Visits, Respiratory 2,400 1,900 (2,600

– 6,800) (2,300

– 6,000)

Acute Bronchitis 3,700 2,900 (0

– 15,000) (0

– 13,000) Work Loss Days 310,000 250,000 (520,000

– 690,000) (460,000

– 600,000)

Asthma Exacerbation 41,000 32,000 (5,800

– 250,000) (5,000

– 220,000) Minor Restricted Activity Days 1,900,000 1,500,000 (3,000,000

– 4,200,000) (2,600,000

– 3,700,000) Lower Respiratory Symptoms 44,000 35,000 (37,000

– 130,000) (32,000

– 120,000) Upper Respiratory Symptoms 34,000 26,000 (16,000

– 110,000) (14,000

– 99,000)

a All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not ye t sufficient to allow differe ntiation of effect estimates by particle type. These estimates do not include benefits from reducing HAP emissions, VOC emissions and ozone exposure, nor energy disbenefits associated with the incr eased emissions from additional energy usage described in the next section. These confidence inte rvals only reflect the standard errors within the epidemiology studies, but they do not reflect other sources of uncertainty inherent within the boiler-specific BP T estimates. These benefits reflect existing boilers and 47 new boilers anticipated to come online by 2014.7-23 Table 7-5. Summary of Estimated Reducti ons in Health Incidences from PM 2.5 for the Final Boiler Area Source Rule in 2014 (95 th percentile confidence interval) a

Proposed MACT Approach Final MACT/GACT Approach Avoided Premature Mortality

Pope et al. (2002) 23 24 (8

– 38) (8

– 40) Laden et al. (2006) 58 61 (29

– 87) (31

– 91) Woodruff (Infant mortality) 0 0 (0

– 0) (0

– 0) Avoided Morbidity

Chronic Bronchitis 16 17 (2

– 30) (2

– 31) Acute Myocardial Infarction 38 40 (12

– 64) (12

– 67) Hospital Admissions, Respiratory 6 6 (3

– 9) (3

– 9) Hospital Admissions, Cardiovascular 12 13 (8

– 14) (9

– 15) Emergency Room Visits, Respiratory 20 21 (11

– 28) (11

– 30)

Acute Bronchitis 37 38 (0

– 77) (0

– 81) Work Loss Days 3,100 3,200 (2,600

– 3,500) (2,800

– 3,700)

Asthma Exacerbation 400 420 (29

– 1,200) (31

– 1,300) Minor Restricted Activity Days 18,000 19,000 (15,000

– 21,000) (16,000

– 22,000) Lower Respiratory Symptoms 430 460 (190

– 680) (200

– 710) Upper Respiratory Symptoms 330 350 (83

– 580) (87

– 610)

a All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not ye t sufficient to allow differe ntiation of effect estimates by particle type. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section. These confidence intervals only reflect the standard errors within the epidemiology studies, but they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. These benefits reflect existing boilers and 6,779 new boilers anticipated to come online by 2014.7-24 Table 7-6. Summary of Monetized Benefits Estimates from PM 2.5 for the Final Boiler MACT in 2014 (95 th percentile confidence interval) a

Selected Option Alternative Option 3% 7% 3% 7% Based on Epidemiology Literature

Pope et al. (2002) $22,000 $20,000 $17,000 $16,000 ($1,900

– $68,000) ($1,600

– $62,000) ($1,500

– $53,000) ($1,300

– $49,000) Laden et al. (2006) $54,000 $49,000 $43,000 $39,000 ($4,900

– $160,000) ($4,300

– $140,000) ($3,800

– $120,000) ($3,400

– $110,000) Based on Expert Elicitation Expert A $58,000 $52,000 $45,000 $41,000 ($3,400

– $190,000) ($3,000

– $170,000) ($2,700

– $150,000) ($2,400

– $140,000) Expert B $44,000 $39,000 $34,000 $31,000 ($1,700

– $180,000) ($1,500

– $160,000) ($1,300

– $140,000) ($1,200

– $130,000) Expert C $44,000 $40,000 $35,000 $31,000 ($2,600

– $170,000) ($2,300

– $150,000) ($2,100

– $130,000) ($1,800

– $120,000) Expert D $31,000 $28,000 $24,000 $22,000 ($2,100

– $100,000) ($1,800

– $91,000) ($1,600

– $78,000) ($1,400

– $71,000) Expert E $72,000 $65,000 $56,000 $51,000 ($6,200

– $210,000) ($5,500

– $190,000) ($4,900

– $170,000) ($4,400

– $150,000) Expert F $40,000 $36,000 $31,000 $28,000 ($3,900

– $120,000) ($3,500

– $110,000) ($3,100

– $92,000) ($2,700

– $84,000) Expert G $26,000 $24,000 $21,000 $19,000 ($250

– $95,000) ($160

– $86,000) ($190

– $74,000) ($120

– $68,000) Expert H $33,000 $30,000 $26,000 $24,000 ($300

– $130,000) ($210

– $120,000) ($240

– $100,000) ($160

– $92,000) Expert I $44,000 $39,000 $34,000 $31,000 ($2,500

– $140,000) ($2,200

– $130,000) ($1,900

– $110,000) ($1,700

– $100,000) Expert J $36,000 $32,000 $28,000 $25,000 ($2,700

– $140,000) ($2,400

– $120,000) ($2,100

– $110,000) ($1,900

– $97,000) Expert K $8,400 $7,800 $6,600 $6,100 ($250

– $54,000) ($160

– $50,000) ($190

– $43,000) ($120

– $39,000) Expert L $29,000 $27,000 $23,000 $21,000 ($1,200

– $110,000) ($1,000

– $100,000) ($930

– $89,000) ($790

– $81,000)

a All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not ye t sufficient to allow differe ntiation of effect estimates by particle type. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section. These confidence intervals only reflect the standard errors within the epidemiology studies and valuation functions, but they do not reflect other sources of uncertainty inherent within the boiler-specific BP T estimates. These benefits reflect existing boilers and 47 new boilers anticipated to come online by 2014.7-25 Table 7-7. Summary of Monetized Benefits Estimates from PM 2.5 for the Final Boiler Area Source Rule in 2014 (95 th percentile confidence interval) a

Proposed MACT Approach Final MACT/GACT Approach 3% 7% 3% 7% Based on Epidemiology Literature

Pope et al. (2002) $200 $180 $210 $190 ($17

– $610) ($15

– $560) ($18

– $650) ($16

– $590) Laden et al. (2006) $490 $440 $520 $470 ($44

– $1,400) ($39

– $1,300) ($46

– $1,500) ($41

– $1,400) Based on Expert Elicitation Expert A $520 $470 $550 $490 ($30

– $1,700) ($27

– $1,500) ($32

– $1,800) ($28

– $1,600) Expert B $390 $360 $410 $370 ($15

– $1,600) ($14

– $1,500) ($16

– $1,700) ($14

– $1,500) Expert C $400 $360 $420 $380 ($24

– $1,500) ($21

– $1,400) ($25

– $1,600) ($22

– $1,400) Expert D $280 $250 $300 $270 ($19

– $900) ($17

– $820) ($20

– $950) ($17

– $860) Expert E $640 $580 $680 $610 ($56

– $1,900) ($50

– $1,700) ($59

– $2,000) ($52

– $1,800) Expert F $360 $330 $380 $340 ($35

– $1,100) ($31

– $960) ($37

– $1,100) ($33

– $1,000) Expert G $240 $220 $250 $230 ($2.1

– $850) ($1.6

– $780) ($2.2

– $900) ($1.6

– $820) Expert H $300 $270 $310 $280 ($2.6

– $1,200) ($2.0

– $1,000) ($2.7

– $1,200) ($2.1

– $1,100) Expert I $390 $350 $410 $370 ($22

– $1,300) ($20

– $1,200) ($23

– $1,400) ($21

– $1,200) Expert J $320 $290 $340 $300 ($24

– $1,200) ($22

– $1,100) ($25

– $1,300) ($23

– $1,200) Expert K $77 $71 $81 $75 ($2.1

– $490) ($1.6

– $450) ($2.2

– $520) ($1.6

– $480) Expert L $270 $240 $280 $250 ($11

– $1,000) ($10

– $930) ($12

– $1,100) ($10

– $970)

a All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not ye t sufficient to allow differe ntiation of effect estimates by particle type. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section. These confidence intervals only reflect the standard errors within the epidemiology studies and valuation functions, but they do not reflect other sources of uncertainty inherent within the boiler-specific BP T estimates. These benefits reflect existing boilers and 6,779 new boilers anticipated to come online by 2014.7-26 Table 7-8. Summary of Monetized Benefits Estimates from Ozone for the Final Boiler MACT in 2014 (95 th percentile confidence interval) a

Avoided Premature Mortality Incidence Valuation (millions of 2008$)

NMMAPS Bell et al. (2004) 0 $3.3 (0 – 1) ($0.27 – $9.8) Schwartz (2005) 1 $5.0 (0 – 1) ($0.41 – $15) Huang and Bell (2005) 1 $5.5 (0 – 1) ($0.46 – $16)

Meta-analyses Bell et al. (2005) 1 $11 (1 – 2) ($0.94 – $31) Ito et al. (2005) 2 $15 (1 – 2) ($1.4 – $41) Levy et al. (2005) 2 $15 (1 – 2) ($1.4 – $41) Avoided Morbidity Adult Hospital Admissions, Respiratory 3 $0.07 (0 – 5) ($0.01 – $0.12) Infant Hospital Admissions, Respiratory 2 $0.03 (1 – 4) ($0.01 – $0.04) Emergency Room Visits, Respiratory 2 < $0.01 (0 – 5) ($0.00 – $0.01) School Loss Days 810 $0.08 (330 – 1,200) ($0.03 – $0.11) Worker Productivity N/A $0.02 ($0.02 – $0.02) Minor Restricted Activity Days 2,300 $0.15 (1,100 – 3,600) ($0.06 – $0.28)

a All estimates are for the analysis year (2014) and are rounded to two significan t figures. Health effects associated with ozone exposure are assumed to occu r within the analysis year. Therefore, the monetized benefits are the same for any discount rate. These confidence intervals reflect th e standard errors within the epidemiology studies and valuation functions. These benefits reflect existing boilers and 47 new boilers anticipated to come online by 2014.7-27 Figures 7-8 and 7-9 illustrate the relative br eakdown of the estimated monetized health benefits associated with changes in PM 2.5 and ozone, respectively. Figure 7-10 shows the total combined monetized benefits for the final Boile r MACT and Area Source Rule at discount rates of 3% and 7%. Figures 7-11 and 7-12 provide a breakdown of the es timated monetized PM 2.5 benefits by precursor pollutant for the fina l Boiler MACT and Boiler Area Source Rule, respectively. Figures 7-13 and 7-14 pr ovide a breakdown of the monetized PM 2.5 benefits by subcategory for the final Boiler MACT a nd Boiler Area Source Rule, respectively.
This analysis shows that the majority of th e population is exposed to PM levels at or above the lowest LML of the cohor t studies (Figures 7-15 and 7-16) , increasing our confidence in the PM mortality analysis. Because we used BPT estimates, we are unable to provide an estimate of the mortality impacts that occur at various PM levels. Figure 7-15 shows a bar chart of the percentage of the adult p opulation exposed to each PM 2.5 level in the baseline. Figure 7-16 shows a cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies.
Using the Pope et al. (2002) study, approxima tely 79% of the population is exposed to annual mean PM 2.5 levels at or above the LML of 7.5 μg/m 3 . Using the Laden et al. (2006) study, 34% of the population is exposed to annual mean PM 2.5 levels at or above the LML of 10 μg/m 3

. As we model mortality impacts among populations exposed to levels of PM 2.5 that are successively lower than the LML of the lowest cohort study, our confidence in the results diminishes. However, the analysis above confirms that the great majority of the impacts occur at or above the lowest cohort study’s LML. It is important to emphasi ze that we have high confidence in PM 2.5 -related effects down to the lowest LML of the major cohort studies.7-28 Figure 7-8. Breakdown of Monetized PM 2.5 Health Benefits Estimates using Mortality Function from Pope et al. (2002) a

a This pie chart breakdown is illustrative, using the results based on Pope et al. (2002 ) as an example. Using the

Laden et al. (2006) function for premat ure mortality, the percentage of total monetized benefits due to adult mortality would be 97%. This chart shows the breakdown using a 3% discount rate, and the results would be similar if a 7% discount rate was used.7-29 Figure 7-9. Breakdown of Monetized Ozone He alth Benefits Estimates using Mortality Function from Bell et al. (2004) a

a This pie chart breakdown is illustrative, using the results based on Bell et al. (2004) as an example. Using the Levy

et al. (2005) function for premature mortality, the percentage of total monetized benefits due to mortality would be 98%.7-30 $0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000

Pope et al. Laden et al.Millions(2008$)

Benefits estimates derived from 2 epidemiology functions and 12 expert functions Benefits 3% Benefits 7%

Figure 7-10. Total Monetized PM 2.5 and Ozone Benefits Estimates for the Final Boiler MACT and Boiler Area Source Rule in 2014 a

a This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the

Pope et al. (2002) study and the Laden et al. (2006) st udy, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response function provided in those studies. These estimates do not include benefits from reducing HAP emissions, but they do include the energy disbenefits associated with the increased emissions from additional energy usage. Due to methodology and time limitations under the court-ordered schedule, we were unable to monetize the benefits associated with several categories of benefits, including direct exposure to HAPs and SO 2 , as well as ecosystem effects, and visibility effects. These benefits reflect existing boilers and new boilers anticipated to come online by 2014.7-31 Figure 7-11. Breakdown of Monetized PM 2.5 Benefits Estimates by Precursor for the Final Boiler MACT in 2014 a

Figure 7-12. Breakdown of Monetized PM 2.5 Benefits Estimates by Precursor for the Final Boiler Area Source Rule in 2014 a7-32 Figure 7-13. Breakdown of Monetized PM 2.5 Benefits Estimates by Subcategory for the Final Boiler MACT in 2014 a

Figure 7-14. Breakdown of Monetized PM 2.5 Benefits Estimates by Subcategory for the Final Boiler Area Source Rule in 2014 a7-33 0% 5% 10% 15% 20% 25%

1 2 3 4 5 6 7 7.5 8 9 10 11 12 13 14 15 16 17 18 19 20
Baseline Annual Mean PM 2.5 Level (μg/m 3 ) LML of Laden et al. (2006) study LML of Pope et al. (2002) study

Figure 7-15. Percentage of Population Exposed to Baseline Air Quality Levels for Final Boiler MACT and Boiler Area Source Rule a

a Approximately 79% of the population is exposed to baseline exposure to annual mean PM 2.5 levels at or above 7.5

μg/m 3 , which is the lowest air quality level considered in the ACS cohort study by Pope et al. (2002).7-34 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 2 3 4 5 6 7 7.5 8 9 10 11 12 13 14 15 16 17 18 19 20 Baseline Annual Mean PM 2.5 Level (μg/m 3 ) LML of Pope et al. (2002) study

LML of Laden et al. (2006) study

Figure 7-16. Cumulative Percentage of Populat ion Exposed to Baseline Air Quality Levels for Final Boiler MACT and Boiler Area Source Rule a

a Approximately 79% of the population is exposed baseline exposure to annual mean PM 2.5 levels at or above 7.5

μg/m 3 , which is the lowest air quality level considered in the ACS cohort study by Pope et al. (2002).
7.4 Energy Disbenefits In this section, we provide an estimate of the energy disbenefits associated with the increased emissions from additional energy usage. 11 Electricity usage associated with the operation of control devices is an ticipated to increase emissions of pollutants from electric utility boilers (EGU boilers) that suppl y electricity to the non-EGU bo iler facilities. We estimate emission increases of 910,000 tpy CO 2 for major boilers and 22,000 tpy CO 2 for area boilers.
7.4.1 Social Cost of Carbon an d Greenhouse Gas Disbenefits EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 ) emissions using recent estimates of the “social cost of carbon” (S CC). The SCC is an estimate of the monetized damages associated with an in cremental increase in carbon emi ssions in a given year. It is

11 As we use the term “energy disbenefits” in this analysis, we are not referring to the cost of purchasing additional electricity or fuel.7-35 intended to include (but is not limited to) change s in net agricultural pr oductivity, human health, property damages from increased flood risk, and the valu e of ecosystem services due to climate change. The SCC estimates used in this analysis were developed through an interagency process that included EPA and other executive branch en tities, and concluded in February, 2010. EPA first used these SCC estimates in the benefits an alysis for the final joint EPA/DOT Rulemaking to establish Light-Duty Vehicle Gr eenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; see the rule’s preamble for discussion about application of SCC (75 FR 25324; 5/7/10). The SCC Technical Support Do cument (SCC TSD) provides a complete discussion of the methods used to develop these SCC estimates. 12
The interagency group selected four SCC values for use in regulatory analyses, which we have applied in this analysis: $5, $21, $35, and $65 per metric ton of CO 2 emissions 13 in 2010, in 2007 dollars. The first three valu es are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, a nd 5 percent, respectivel y. SCCs at several discount rates are included because the literatu re shows that the SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context. The fourth value is the 95th percen tile of the SCC from all three models at a 3 percent disc ount rate. It is included to repr esent higher-than-expected impacts from temperature change further out in the ta ils of the SCC distribution. Low probability, high impact events are incorporated in to all of the SCC values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity. Treating climate sens itivity probabilistically results in more high temperature outcomes, which in turn lead to higher projections of damages.

The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic sy stems become more stressed in response to greater climatic change. Note th at the interagency group estimated the growth rate of the SCC directly using the three integr ated assessment models rather than assuming a constant annual

12 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, De partment of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Departme nt of Treasury (February 2010). Also available at http://www.epa.gov/otaq/climate/regulations.htm 13 The interagency group decided that these estimates apply only to CO 2 emissions. Given that warming profiles and impacts other than temperature cha nge (e.g., ocean acidifica tion) vary across GHGs, the group concluded “transforming gases into CO 2 -equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, would not result in accurate estimate s of the social costs of non-CO 2 gases” (SCC TSD, pg 13).7-36 growth rate. This helps to ensure that the estimates are internally consistent with other modeling assumptions. The SCC estimates for the analysis years of 2014, in 2005 dollars are provided in Table 7-9.
Table 7-9. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 ) for 2014 a

Discount Rate and Statistic SCC estimate (2008$) 5% (Average) $5.7 3% (Average) $24.2 2.5% (Average) $39.1 3% (95 th percentile) $73.9

a The SCC values are dollar-year and emissions-year specifi c. SCC values represent on ly a partial accounting of

climate impacts. When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A recent report from the National Academies of Science (NRC, 2009) points out that any assessmen t will suffer from uncertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) th e effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questi ons of science, economics, and et hics and should be viewed as provisional.
The interagency group noted a number of limita tions to the SCC analysis, including the incomplete way in which the integrated a ssessment models capture catastrophic and noncatastrophic impacts, their in complete treatment of adapta tion and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of re search linking climate impacts to economic damages makes the interagency modeling exercise ev en more difficult. The interage ncy group hopes that over time researchers and modelers will work to fill thes e gaps and that the SCC estimates used for regulatory analysis by the Federal government w ill continue to evolve with improvements in modeling. Additional details on these limita tions are discussed in the SCC TSD.
In light of these limitations , the interagency group has co mmitted to updating the current estimates as the science and economic understandi ng of climate change and its impacts on society improves over time. Specifically, the interagenc y group has set a preliminary goal of revisiting7-37 the SCC values within two years or at such time as substantially updated models become available, and to continue to support research in this area.
Applying the global SCC estimates to the estimated increases in CO 2 emissions for the range of policy scenarios, we estimate the dollar va lue of the climate-relate d disbenefits captured by the models for each analysis year. For inte rnal consistency, the annual disbenefits are discounted back to NPV terms using the same discount rate as each SCC estimate (i.e., 5%, 3%, and 2.5%) rather than 3% and 7%. 14 These estimates are provided in Table 7-10.
Table 7-10. Monetized SCC-D erived Disbenefits of CO 2 Emission Increase s in 2014 (millions of 2008$) a

Discount Rate and Statistic Selected Option (Major) Alternative Option (Major) Proposed MACT Approach (Area) Final MACT/GACT Approach (Area) Tons of CO 2 911,048 1,524,572 24,936 22,191 5% (Average) $5.2 $8.7 $0.1 $0.1 3% (Average) $22 $37 $0.6 $0.5 2.5% (Average) $36 $60 $1.0 $0.9 3% (95 th percentile) $67 $113 $1.8 $1.6

a The SCC values are dollar-year and emissions-year specifi c. SCC values represent on ly a partial accounting of

climate impacts.
7.5 Unquantified or Nonmonetized Benefits
The monetized benefits estimated in this RIA only reflect the portion of benefits attributable to the health imp acts associated with exposure to ambient fine particles. Data, resource, and methodological limitations preven ted EPA from quantifyi ng or monetizing the benefits from several important benefit categories, including benefits from reducing toxic emissions, ecosystem effects, and visibility impair ment. The direct health benefits from reducing HAPs have not been monetized in this analysis. In addition to being a PM 2.5 precursor, SO 2 emissions also contribute to adverse effects fr om acidic deposition in a quatic and terrestrial ecosystems, increase mercury methylation, as well as visibility impairment. In addition to health effects, ozone is associated with advers e vegetation effects to forests and crops.
7.5.1 Other SO 2 Benefits In addition to being a precursor to PM 2.5 , SO 2 emissions are also associated with a variety of respiratory health effects. Unfortunately, we were unable to estimate the health benefits

14 It is possible that other benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at rates that differ from those used to develop the SCC estimates.7-38 associated with reduced SO 2 exposure in this analysis because we do not have air quality modeling data available. Without knowing the location of the emission reductions and the resulting ambient concentra tions, we were unable to es timate the exposure to SO 2 for nearby populations. Therefore, this analysis only quantifies and monetizes the PM 2.5 benefits associated with the reductions in SO 2 emissions.
Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment (ISA) for Sulfur Dioxide concluded that there is a causal relationship between respiratory heal th effects and short-term exposure to SO 2 (U.S. EPA, 2008a). According to summary of the ISA in EP A’s risk and exposure assessment (REA) for the SO 2 NAAQS “the immediate effect of SO 2 on the respiratory system in humans is bronchoconstriction” (U.S. EPA, 2009b). In add ition, the REA summarized from the ISA that “asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease.” A clear concentr ation-response relationship has been demonstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of as thmatics adversely affected (U.S. EPA, 2009b). Based on our review of this inform ation, we identified four short-term morbidity endpoints that the SO 2 ISA identified as a “causal relationship”: asthma exacerbation, respiratoryrelated emergency department visits, and resp iratory-related hospita lizations. The differing evidence and associated strength of the evidence fo r these different effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the re lationship between short-term SO 2 exposure and premature mortality was “suggestive of a cau sal relationship” because it is difficult to attribute the mortalit y risk effects to SO 2 alone. Although the SO 2 ISA stated that studies are generally consistent in repor ting a relationship between SO 2 exposure and mortality, there was a lack of robustness of the observed associ ations to adjustment for pollutants.

SO 2 emissions also contribute to adverse welf are effects from acidi c deposition, visibility impairment, and enhanced mercury methylation (U .S. EPA, 2008c). Deposition of sulfur causes acidification, which can cause a loss of biodiversity of fi shes, zooplankton, and macro invertebrates in aquatic ecosystems, as well as a decline in sensitive tree species, such as red spruce ( Picea rubens ) and sugar maple ( Acer saccharum ) in terrestrial ecosystems. In the northeastern United States, the surface waters aff ected by acidification are a source of food for some recreational and subsistence fishermen and for other consumers and support several cultural services, including aesthetic and educational services and recreational fishi ng. Biological effects of acidification in terrestrial ecosystems are ge nerally linked to aluminum toxicity, which can cause reduced root growth, which restricts the ability of the plant to take up water and nutrients.7-39 These direct effects can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial acidification affects several important ecological services, including declines in habitat for threatened and endange red species (cultural), declines in forest aesthetics (cultural), declines in forest productivity (p rovisioning), and increases in fore st soil erosion and reductions in water retention (cultural and regulating)
7.5.2 Carbon Monoxide Benefits Carbon monoxide (CO) exposure is associated with a variety of health effects. Without knowing the location of the emission reductions a nd the resulting ambient concentrations using fine-scale air quality modeling, we were unabl e to estimate the exposure to CO for nearby populations. Due to methodological and time limitati ons under the court-ordered schedule, we were unable to estimate the benefits associated with the reductions in CO emissions that would occur as a result of this rule.

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of carbon-containing fuels and photoc hemical reactions in the atmosphere. The amount of CO emitted from these reactions, re lative to carbon dioxide (CO 2 ), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that are dependent upon concentration and dur ation of exposure.
The Integrated Science Assessment for Ca rbon Monoxide (U.S. EPA, 2010b) concluded that short-term exposure to CO is “likely to have a causal relationship” with cardiovascular morbidity, particularly in individu als with coronary heart disease. Epidemiologic studies associate short-term CO exposure with increased risk of emergency department visits and hospital admissions. Coronary heart disease includes those w ho have angina pectoris (cardiac chest pain), as well as those who have expe rienced a heart attack. Other s ubpopulations potentially at risk include individuals with diseases such as chr onic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as older adults or the developing young. The evidence is suggestive of a causal relationship between short-term exposure to CO and respiratory morbidity and mortality. The evid ence is also suggestive of a causal relationship for birth outcomes and developmental effects foll owing long-term exposure to CO, and for central nervous system effects linked to shor t- and long-term exposure to CO.7-40 7.5.3 Visibility Benefits Reducing SO 2 and PM emissions would improve th e level of visibility throughout the United States (U.S. EPA, 2009c). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). These suspended particles and gases degrade visibil ity by scattering and absorbing light. Higher visibility impairment levels in the East are due to generally higher concentra tions of fine particles, particularly sulfates, and higher av erage relative humidity levels. In fact, particulate sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually and 75% during summer). In the western U.S., particulate sulfate contributes to 20–50% of regional haze (U.S. EPA, 2009c). Visibility has direct significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in recreationa l activities. Due to time limitations under the court-ordered schedule, we were unable to estim ate the monetized benefits associated with visibility improvements. Previous analyses (U.S. EPA, 2006; U.S. EPA, 2010e) show that visibility benefits are a signifi cant welfare benefit category. As shown in Section 6.5.4 of this RIA, an average visibility improvement of 0.51 deciviews in annual 20% worst visibility days over all Class I area monitors is antic ipated as a result of these rules.
7.5.4 Ozone Vegetation Benefits Exposure to ozone has been associated with a wide array of vegetation and ecosystem effects in the published literature (U.S. EPA, 2010a). Sensitivity to ozone is highly variable across species, with over 65 plan species identified as “o zone-sensitive,” many of which occur in state and national parks and forests. These effects include those that damage or impair the intended use of the plant or ecosystem. Such effects are considered adverse to the public welfare and can include reduced growth and/or biomass production in sensitive pl ant species, including forest trees, reduced crop yields, visible foliar injury, reduced plant vi gor (e.g., increased susceptibility to harsh weather, disease, insect pest infestat ion, and competition), species composition shift, and changes in ecosystems and associated ecosystem services.
7.5.5 Direct HAP Benefits Americans are exposed to ambient concentrations of air toxics at levels which have the potential to cause adverse health effects. 15 The levels of air toxics to which people are exposed vary depending on where people live and work and the kinds of activities in which they engage. In order to identify and prioriti ze air toxics, emission source t ypes and locations that are of

15 U.S. EPA. (2009) 2002 National-Scale Air Toxi cs Assessment. http://www.epa.gov/ttn/atw/nata2002/7-41 greatest potential concern, U. S. EPA conducts the National- Scale Air Toxics Assessment (NATA). The most recent NATA was conducted for calendar year 2002, and was released in June 2009. 16 NATA for 2002 includes four steps: 1. Compiling a national emissions inventory of air toxics em issions from outdoor sources 2. Estimating ambient concentrations of air toxics across the United States 3. Estimating population exposures across the United States 4. Characterizing potential public health risk due to inhala tion of air toxics including both cancer and noncancer effects
Noncancer health effects can result from chronic, 17 subchronic, 18 or acute 19 inhalation exposures to air toxics, and incl ude neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the immune and re productive systems. According to the 2002 NATA, nearly the entire U.S. population was exposed to an average concentration of air toxics that has the potential for adverse noncancer respiratory health effects. 20 Figures 7-17 and 7-18 depict estimated county-level carcinogenic risk and non cancer respiratory hazard from the assessment. Results from the 2002 NATA suggest that acrolein is the primary driver for noncancer respiratory risk. 21 Large reductions in HAP emissions may not n ecessarily translate into significant reductions in health risk because toxicity varies by polluta nt and whether or not there are exposures at or above levels of concern is not known. For exampl e, acetaldehyde mass emissions are more than double acrolein emissions on a national basi s, according to EPA’s 2005 National Emissions Inventory (NEI). However, the Integrated Risk Information System (IRIS) reference concentration (RfC) for acrolein is considerably lower than that for acetaldehyde, suggesting that acrolein could be potentially more toxic th an acetaldehyde. Thus, it is important to

16 U.S. EPA. (2009) 2002 National-Scale Air Toxi cs Assessment. http://www.epa.gov/ttn/atw/nata2002/ 17 Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database (http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than approximately 10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal species). 18 Defined in the IRIS database as exposure to a substa nce spanning approximately 10% of the lifetime of an organism. 19 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 20 The NATA modeling framework has a number of limitations which prevent its use as the sole basis for setting regulatory standards. These limitations and uncertainties are discussed on the 2002 NATA website. Even so, this modeling framework is very useful in identifying air toxic pollutants and sources of greatest concern, setting regulatory priorities, and informing the decision maki ng process. U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 21 Details about the overall confidence of certainty ranking of the individual pieces of NATA assessments including both quantitative (e.g., model-to-monitor ratios) and qua litative (e.g., quality of data, review of emission inventories) judgments can be found at h ttp://www.epa.gov/ttn/atw/nata/roy/page16.html.7-42 Figure 7-17. Estimated County Level Carcinoge nic Risk from HAP exposure from outdoor sources (2002 NATA)

Figure 7-18. Estimated County Level Noncancer (Respiratory) Risk from HAP exposure from outdoor sources (2002 NATA)7-43 account for the toxicity and exposure, as well as the mass of the targeted emissions when designing reduction strategies to maximize health benefits.
Due to methodology and time limitations under the court-ordered schedule, we were unable to estimate the benefits associated with the hazardous air pollutants that would be reduced as a result of these rules. In a few previous anal yses of the benefits of reductions in HAPs, EPA has quantified the benefits of poten tial reductions in the incidences of cancer and non-cancer risk (e.g., U.S. EPA, 1995). In those analyses, EPA relied on unit risk f actors (URF) developed through risk assessment procedures. 22 These URFs are designed to be conservative, and as such, are more likely to represent the high end of the distribution of risk ra ther than a best or most likely estimate of risk. As the purpose of a benefit analys is is to describe the benefits most likely to occur from a reduction in pollution, use of high-end, conservative risk estimates would overestimate the benefits of the regulation. While we used high-end risk estimates in past analyses, advice from the EPA’s Science Adviso ry Board (SAB) recommended that we avoid using high-end estimates in benefit analyses (U.S. EPA, 2002). Since this time, EPA has continued to develop better methods for anal yzing the benefits of reductions in HAPs.
In the Second Prospective 812 Analysis, EPA conducted a case study analysis of the health effects associated with reducing exposure to benzene in Houston from implementation of the Clean Air Act (IEc, 2009). While reviewing the draft report, EPA’ s Advisory Council on Clean Air Compliance Analysis concluded that “the challenges for assessing progress in health improvement as a result of reductions in emi ssions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure-response func tions, uncertainties in em issions inventories and background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of tracking health progress for diseases, such as cancer, that have long la tency periods” (U.S. EPASAB, 2008).
In 2009, EPA convened a workshop to address th e inherent complexities, limitations, and uncertainties in current methods to quantify the benefits of reducing HAPs. Recommendations from this workshop included identifying rese arch priorities, focu sing on susceptible and vulnerable populations, and improving dose-respon se relationships (Gwinn et al., 2011).
In summary, monetization of the benefits of reductions in cancer incidences requires several important inputs, includi ng central estimates of cancer risk s, estimates of exposure to

22 The unit risk factor is a quantitative estimate of the ca rcinogenic potency of a pollutant, often expressed as the probability of contracting cancer from a 70-year lifetime continuous exposure to a concentration of one μg/m 3 of a pollutant.7-44 carcinogenic HAPs, and estimates of the value of an avoided case of cance r (fatal and non-fatal). Due to methodology and time limitations under the court-ordered schedule, we do not have sufficient information on emissions from specific s ources and thus are unable to model changes in population exposures to ambient concentrations of HAPs. For this reason, we did not attempt to quantify or monetized the heal th benefits of reductions in HAPs in this analysis. 23 Instead, we provide a qualitative analysis of the health effects associated with the HAPs anticipated to be reduced by these rules. EPA remains committed to improving methods for estimating HAP benefits by continuing to explor e additional concepts of benef its, including changes in the distribution of risk.

Available emissions data show that boilers emit several different HAPs, either contained within the fuel burned or formed during the combustion process. Although numerous HAPs may be emitted from boilers, a few HAPs account fo r the majority of th e total mass of HAPs emissions. See Table 7-11 for the list of the major HAPs for each fuel type. These rules are anticipated to reduce a combined 113,000 tons of carbon monoxide, 30,000 tons of HCl, 830 tons of HF, 2,900 pounds of mercury, 3,000 tons of ot her metals, and 23 grams of dioxins/furans (TEQ) each year. We discuss the health effects associated with these top HAPs as well as the HAPs for which we have em ission reduction estimates. Table 7-11. Top HAPs by Mass from Boilers by Fuel Type

Coal Gas Biomass Oil 68% HCl 44% Formaldehyde 32 % Acetaldehyde 28% Nickel

5% HF 25% PAH 28% HCl 19% Manganese 3% Toluene 25% Formaldehyde

7.5.5.1 Mercury
Mercury is a highly neurotoxic contaminant th at enters the food web as a methylated compound, methylmercury (U.S. EPA, 2008c). The cont aminant is concentrated in higher trophic levels, including fish eaten by humans. Experi mental evidence has es tablished that only inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA,

23 Due to time limitations under the court-ordered schedule for this rule, we were unable to model mercury methylation, bioaccumulation in fish tissue, and human co nsumption of mercury-contamin ated fish that would be needed in order to estimate the human health benefits from reducing mercury emissions. However, it is important to emphasize, that we generally have more data and accept ed methods to estimate mercury benefits than we have for other HAPs.7-45 2008c). Current evidence indicates that in watersheds where mercur y is present, increased sulfate deposition very likely results in methylmerc ury accumulation in fish (Drevnick et al., 2007; Munthe et al., 2007). The NO x SO X Ecological ISA concluded that evidence is sufficient to infer a casual relationship between sulfur deposition and increased mercury methylation in wetlands and aquatic environments (U.S. EPA, 2008c).

In addition to the role of su lfate deposition on methylation, these rules would also reduce mercury emissions. Mercury is emitted to the air from various man-made and natural sources. These emissions transport through the atmosphere a nd eventually deposit to land or water bodies. This deposition can occur locally, regionally, or globally, depending on the form of mercury emitted and other factors such as the weather. The form of mercury emitted varies depending on the source type and other factors. Available data indicate that the mercury emissions from these sources are a mixture of gaseous elemental merc ury (25%), inorganic divalent mercury (reactive gas phase mercury) (50%), and particulate bound mercury (25%) (U.S. EPA, 2010f). Gaseous elemental mercury can be transported very long di stances, even globally, to regions far from the emissions source (becoming part of the global “pool”) before deposit ion occurs. Inorganic divalent and particulate bound me rcury have a shorter atmosphe ric lifetime and can deposit to land or water bodies closer to the emissions so urce. Furthermore, elemental mercury in the atmosphere can undergo transformation into diva lent mercury, providi ng a significant pathway for deposition of emitted elemental mercury.
Potential exposure routes to mercury emi ssions include both direct inhalation and consumption of fish containing methylmercury. Th e primary route of human exposure to mercury emissions from industrial sources is generally indirectly through the consumption of fish containing methylmercury. As described above, mercury that has been emitted to the air eventually settles into water bodies or onto land wh ere it can either move directly or be leached into waterbodies. Once deposited, certain microor ganisms can change it into methylmercury, a highly toxic form that builds up in fish, shellfish and animals that eat fis h. Consumption of fish and shellfish are the main sources of methylme rcury exposure to humans. Methylmercury builds up more in some types of fish and shellfish than in others. The levels of methylmercury in fish and shellfish vary widely depending on what they eat, how long they live, and how high they are in the food chain. Most fish, including ocean sp ecies and local freshwat er fish, contain some methylmercury. For example, in recent studies by EPA and the U.S. Geological Survey (USGS) of fish tissues, every fish sampled from 291 streams across the country contained some methylmercury (Scudder, 2009).7-46 The majority of fish consumed in the U.S. are ocean species. The methylmercury concentrations in ocean fish species are pr imarily influenced by the global mercury pool. However, the methylmercury found in local fish can be due, at least partly, to mercury emissions from local sources. Research shows that most people’s fish consumption does not cause a mercury-related health concern. However, certain people may be at higher risk because of their routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families who rely heavily on fish for a subs tantial part of their diet). It has been demonstrated that high levels of methylmercury in the bloodstream of unborn babies and young children may harm the developing nervous system, making the child less able to think and learn. Moreover, mercury exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people of all ages.
Several studies suggest that the methylme rcury content of fish may reduce cardioprotective effects of fish consum ption. Some of these studies al so suggest that methylmercury may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of the literature concerning methylme rcury health effects took note of two epidemiological studies that found an association between dietary exposure to methylmerc ury and cardiovascular effects. 24 In a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al. (1995) observed a relationship between methylmercury exposure vi a fish consumption and acute myocardial infarction (AMI or heart attacks), death from co ronary heart disease or cardiovascular disease. 25 The NRC also noted a study of 917 seven-year ol d children in the Faroe Islands, whose initial exposure to methylmercury was in utero although postnatal exposures may have occurred as well. At seven years of age, these children exhibited an increase in blood pressu re and a decrease in heart rate variability. 26 Based on these and other studies, NRC concluded in 2000 that, “Although the data base is not as extensive as it is fo r other end points (i.e., ne urologic effects) the cardiovascular system appears to be a target for methylmercury toxicity in humans and

24 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. pp.168-173. 25 Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid peroxidation, and the risk of myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men.” Circulation, 91 (3):645-655. 26 Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. We ihe, and Grandjean, P., 1999. “Prenatal Methylmercury Exposure As A Cardiovascular Risk Factor At Seven Years of Age,” Epidemiology, pp370-375.7-47 animals.” 27 NRC also stated that “additiona l studies are needed to better characterize the effect of methylmercury exposure on blood pressure and cardiov ascular function at various stages of life.”
Since publication of the NRC report there have been some 30 published papers presenting the findings of studies that have examined the possible cardiovascular e ffects of methylmercury exposure. These studies include epidemiological, t oxicological, and toxicokinetic investigations. Over a dozen review papers have also been publ ished. If there were a cau sal relationship between methylmercury exposure and adverse cardiovascul ar effects, then reducing exposure to methylmercury would result in public health be nefits from reduced cardiovascular effects.
In early 2010, EPA sponsored a workshop in which a group of experts were asked to assess the plausibility of a causal relationship between methylmercury exposure and cardiovascular health effects a nd to advise EPA on methodologie s for estimating population level cardiovascular health impacts of reduced me thylmercury exposure. The report from that workshop is in preparation.
Baseline emissions for major and area source boilers in the U.S. are about 11 tons of mercury in the air. 28 Based on the EPA’s National Emission Inventory, about 103 tons of mercury were emitted from all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury we re emitted worldwide by anthropogenic sources in 2005. We believe that total mercury emissions in the U.S. and globally in 2008 were about the same magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 11% of the total anthropogenic mercury emissions in the U.S. and about 0.5% of the global emissions.
Overall, the major and area source rules would reduce mercury emissions by about 1.5 tons (2,900 pounds, or a 13% reduction) per year from current levels, and therefore, contribute to reductions in mercury exposures and health effects. Due to time limitations under the courtordered schedule for this rule, we were unable to model mercury methylation, bioaccumulation in fish tissue, and human consumpti on of mercury-contaminated fish that would be needed in order to estimate the human health benefits from re ducing mercury emissions. However, we were able to model the change in mercury deposition using CAM x for the final Boiler MACT and Boiler Area Source Rule. 29 These modeling results indicate si gnificantly reduced total mercury

27 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. p. 229. 28 See “ Revised Development of Baseline Emission Factor s for Boilers and Process Heaters at Commercial, Industrial, and Institutional Facilities, ” which available in the docket. 29 See Section 6 of this RIA and U.S. EPA (2010f) for more information on the air quality modeling conducted for these rules.7-48 deposition (wet and dry forms) in some areas, including reducing total de position from all sources by an average of 1.4% in the East and 0.5% in the West in the analys is year. This modeling indicates that mercury deposition reductions tend to be greatest nearest the sources. Figure 7-19 shows the percentage reduction in total mercury deposition as a re sult of the final Boiler MACT and Boiler Area Source Rule in the Eastern U.S. , and Figure 7-20 shows the percentage reduction in mercury deposition in the Western U.S. ba sed on the air quality modeling conducted for these rules.

Figure 7-19. Percentage Reduction in Total Mercury Deposition in the Eastern U.S.7-49 Figure 7-20. Percentage Reduction in Total Mercury Deposition in the Western U.S.

7.5.5.2 Hydrogen Chloride (HCl) 30
Hydrogen chloride gas is intensely irritating to the mucous membranes of the nose, throat, and respiratory tract. Brief exposure to 35 ppm ca uses throat irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour. The greatest impact is on the upper respiratory tract; exposure to high concentrations can rapidl y lead to swelling and spasm of the throat and suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures may develop an accumulation of fluid in the lungs. Exposure to hydrogen ch loride can lead to R eactive Airway Dysfunction Syndrome (RADS), a chemically- or irritant-ind uced type of asthma. Children may be more vulnerable to corrosive agents than adults becau se of the relatively smaller diameter of their airways. Children may also be more vulnerabl e to gas exposure because of increased minute ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not been classified for carcinogenic effects.

30 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Availabl e on the Internet at <http://www.atsdr.cdc.gov/Mhmi/mmg173.html>.7-50 7.5.5.3 Chlorine Gas (Cl 2 ) 31 Chlorine gas is irritating and corrosive to the eyes, skin, and respiratory tract. Exposure to chlorine may cause burning of the eyes, nose, and throat; cough as well as constriction and edema of the airway and lungs can occur.
7.5.5.4 Hydrogen Cyanide (HCN) 32 Hydrogen cyanide is highly toxic by all routes of exposure and may ca use abrupt onset of profound central nervous system, cardiovascular, and respiratory effects, l eading to death within minutes. Exposure to lower concentrations of hydrogen cyanide may produce eye irritation, headache, confusion, nausea, and vomiting followed in some cases by coma and death. Hydrogen cyanide acts as a cellular asphyxiant. By binding to mitochondrial cytochro me oxidase, it prevents the utilization of oxygen in cellular metabolism. The central ne rvous system and myocardium are particularly sensitive to th e toxic effects of cyanide.
7.5.5.5 Hydrogen Fluoride (HF) 33 Acute (short-term) inhalation exposure to gaseous hydrogen fluoride can cause severe respiratory damage in humans, including severe irritation and pulmonary edema. Chronic (longterm) exposure to fluoride at low levels has a be neficial effect of dent al cavity prevention and may also be useful for the treatment of osteopor osis. Exposure to higher levels of fluoride may cause dental fluorosis. One study reported menstr ual irregularities in women occupationally exposed to fluoride. The EPA has not classi fied hydrogen fluoride for carcinogenicity.
7.5.5.6 Toluene 34 Toluene is found in evaporative as well as ex haust emissions from motor vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessment , there is inadequate information to assess

31 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Medical Management Guidelines for Chlorine (CAS 7782-50-5; UN 1017). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Availabl e on the Internet at http://www.atsdr.cdc.gov/M HMI/mmg172.html#bookmark02

. 32 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Medical Management Guidelines for Hydrogen Cyanide (HCN) (CAS#: 7782-50-5). Atlanta, GA: U.S. Department of Health and Human Services, Pu blic Health Service. Available on the Internet at http://www.atsdr.cdc.gov/Mhmi/mmg8.html#bookmark02

. 33 All health effects language for this section came from: U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf 34 All health effects language for this section came from: U.S. EPA. 2005. “Full IRIS Summary for Toluene (CASRN 108-88-3)” Environmental Protection Agency, Integrated Risk Information System (IRIS), Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH. Available on the Internet at < http://www.epa.gov/iris/subst/0118.htm >.7-51 the carcinogenic potential of toluene because st udies of humans chronically exposed to toluene are inconclusive, toluene was not carcinogenic in adequate inhalati on cancer bioassays of rats and mice exposed for life, and increased incidences of mammary cancer and leukemia were reported in a lifetime rat oral bioassay.
The central nervous system (CNS) is the pr imary target for toluene toxicity in both humans and animals for acute and chronic exposur es. CNS dysfunction (which is often reversible) and narcosis have been frequently observed in hu mans acutely exposed to low or moderate levels of toluene by inhalation; symptoms include fa tigue, sleepiness, headach es, and nausea. Central nervous system depression has been reported to occu r in chronic abusers expos ed to high levels of toluene. Symptoms include ataxia, tremors, cerebral atrophy, nystagmus (involuntary eye movements), and impaired speech, hearing, and visi on. Chronic inhalation exposure of humans to toluene also causes irritation of the upper respiratory tract, ey e irritation, dizziness, headaches, and difficulty with sleep.

Human studies have also re ported developmental effects, such as CNS dysfunction, attention deficits, and minor cr aniofacial and limb anomalies, in the children of women who abused toluene during pregnancy. A substantial da tabase examining the effects of toluene in subchronic and chronic occupationa lly exposed humans exists. The weight of evidence from these studies indicates neurolog ical effects (i.e., impaired color vision, impaired hearing, decreased performance in neurobehavioral analysis, ch anges in motor and sensory nerve conduction velocity, headache, and dizziness) as the most sensitive endpoint.
7.5.5.7 Formaldehyde Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys. 35 EPA is currently reviewing recently published epidemiological data. For inst ance, research conducted by the National Cancer Institute (NCI) found an increased risk of nasopharyngeal cancer and lymphohematopoietic malignancies such as leukemia among workers exposed to formaldehyde. 36,37 In an analysis of the lymphohematopoietic cancer mortality from an extended follow-up of these workers, NCI

35 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to Formaldehyde, Office of Pesticides and Toxic Substances, April 1987. 36 Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Morta lity from lymphohematopoetic malignancies among workers in formaldehyde industries. Jo urnal of the National Cancer Institute 95 : 1615-1623. 37 Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mort ality from solid cancers among workers in formaldehyde industries. American Journal of Epidemiology 159: 1117-1130.7-52 confirmed an association be tween lymphohematopoietic cancer risk and peak exposures. 38 A recent National Institute of Occupational Safety and Health (NIOSH) st udy of garment workers also found increased risk of death due to le ukemia among workers exposed to formaldehyde. 39 Extended follow-up of a cohort of British chemical workers did not find evidence of an increase in nasopharyngeal or lymphohema topoietic cancers, but a conti nuing statistically significant excess in lung cancers was reported. 40
In the past 15 years there has been substa ntial research on the inhalation dosimetry for formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the Chemical Industry Institute of T oxicology), with a focus on use of rodent data for refinement of the quantitative cancer dose-response assessment. 41 , 42 , 43 CIIT’s risk assessment of formaldehyde incorporated mechanistic and dosimetric info rmation on formaldehyde. However, it should be noted that recent research published by EPA indicat es that when two-stage modeling assumptions are varied, resulting dose-res ponse estimates can vary by several orders of magnitude. 44 , 45 , 46 , 47 These findings are not supportive of interpre ting the CIIT model results as providing a conservative (health protective) estimate of human risk. 48 EPA research also examined the contribution of the two-stage modeling for form aldehyde towards characterizing the relative weights of key events in the mode-of-action of a carcinogen. For example, the model-based inference in the published CIIT st udy that formaldehyde’s direct mu tagenic action is not relevant

38 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Ha yes, R. B.; Hoover, R. N.; Hauptmann, M. 2009. Mortality from lymphohematopoietic malignancies among workers in formaldehyde industries: The National Cancer Institute cohort. J. National Cancer Inst. 101: 751-761. 39 P inkerton, L. E. 2004. Mortality among a cohort of garmen t workers exposed to formaldehyde: an update. Occup. Environ. Med. 61: 193-200. 40 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Exte nded follow-up of a cohort of British chemical workers exposed to formaldehyde. J National Cancer Inst. 95:1608-1615. 41 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologically motivated computational modeling of formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432-447. 42 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, an d FJ Miller. 2004. Human respiratory

tract cancer risks of inhaled formaldehyde: Dose-response predictions derived from biologically-motivated computational modeling of a combined rodent and human dataset. Tox Sci 82: 279-296. 43 Chemical Industry Institute of Toxicology (CIIT). 19 99. Formaldehyde: Hazard char acterization and dose-response assessment for carcinogenicity by the route of inhalation. CIIT, September 28, 1999. Research Triangle Park, NC. 44 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal Growth Models for Formaldehyde with Relevance to Other Biologically-Based Dose Response (BBDR) Models. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-08/103, 2006 45 Subramaniam, R; Chen, C; Crump, K; et al. (2006) Uncertainties in biologi cally-based modeling of formaldehydeinduced cancer risk: identification of key issues. Risk Anal 28(4):907-923. 46 Subramaniam, R; Chen, C; Crump, K; et al. (2007). Uncertainties in the CIIT 2-stage model for formaldehydeinduced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237 47 Crump, K; Chen, C; Fox, J; et al. (2006) Sensitivity anal ysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 48 Crump, K; Chen, C; Fox, J; et al. (2006) Sensitivity anal ysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495.7-53 to the compound’s tumorigenicity was found not to hold under variations of modeling assumptions. 49
Based on the developments of the last d ecade, in 2004, the working group of the IARC concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient evidence in humans and sufficient evidence in ex perimental animals—a higher classification than previous IARC evaluations. After reviewing the currently availabl e epidemiological evidence, the IARC (2006) characterized the human evidence for formaldehyde carcinogenici ty as “sufficient,” based upon the data on nasopharyngeal cancers; the epidemiologic evidence on leukemia was characterized as “strong.” 50 EPA is reviewing the recent work cited above from the NCI and NIOSH, as well as the analysis by the CIIT Centers for Health Research and other studies, as part of a reassessment of the human hazard and dose-response associated with formaldehyde.
Formaldehyde exposure also causes a range of noncancer health effects, including irritation of the eyes (burning and watering of th e eyes), nose and throat. Effects from repeated exposure in humans include resp iratory tract irritation, chronic bronchitis and na sal epithelial lesions such as metaplasia and loss of cilia. An imal studies suggest that formaldehyde may also cause airway inflammation—including eosinophil infi ltration into the airways. There are several studies that suggest that form aldehyde may increase the risk of asthma—particularly in the young. 51, 52

7.5.5.8 Polycyclic Aromatic Hydrocarbons (PAHs) At least seven PAH compounds are classifi ed by EPA as probable human carcinogens based on animal data, including benzo(a)anthracene, 53 benzo(b)fluoranthene, 54

49 Subramaniam, R; Chen, C; Crump, K; et al. (2007). Uncertainties in the CIIT 2-stage model for formaldehydeinduced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237 50 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-tert-Butoxypropan-2- ol. Monographs Volume 88. World Health Organization, Lyon, France. 51 Agency for Toxic Substances and Disease Registry (A TSDR). 1999. Toxicological profile for Formaldehyde. Atlanta, GA: U.S. Department of Health an d Human Services, Pub lic Health Service. http://www.atsdr.cdc.gov/to xprofiles/tp111.html 52 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint sponsorship of the United Nations Environment Programme, the International Labour Organization, and the World Health Organization, and produced within the framework of the Inter-Organization Programme for the Sound Management of Chemicals. Geneva. 53 U.S. EPA (1997). Integrated Risk Information System F ile of benzo(a)anthracene. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea /iris/subst/0454.htm. 54 U.S. EPA (1997). Integrated Risk Information System File of benzo(b)fluoranthene. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea /iris/subst/0453.htm.7-54 benzo(k)fluoranthene, 55 benzo(a)pyrene, 56 chrysene, 57 dibenz(a,h)anthracene, 58 and indeno(1,2,3- cd)pyrene. 59 Recent studies have found that maternal exposures to PAHs in a population of pregnant women were associated with several ad verse birth outcomes, including low birth weight and reduced length at birth, as well as impaired cognitive development at age three. 60 , 61 EPA has not yet evaluated these recent studies.
7.5.5.9 Acetaldehyde Acetaldehyde is classified in EPA’s IRIS da tabase as a probable human carcinogen, based on nasal tumors in rats, and is considered toxi c by the inhalation, oral, and intravenous routes. 62 Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS in the 11 th Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the IARC. 63 , 64 EPA is currently conducting a reassessment of cancer risk from inhalation exposure to acetaldehyde.
The primary noncancer effects of exposure to acetaldehyde vapors incl ude irritation of the eyes, skin, and respiratory tract. 65 In short-term (4 week) rat st udies, degeneration of olfactory

55 U.S. EPA (1997). Integrated Risk Information System File of benzo(k)fluoranthene. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea /iris/subst/0452.htm. 56 U.S. EPA (1998). Integrated Risk Information System File of benzo(a)pyrene. Research and Development, National Center for Environmental Assessment, Washington, DC . This material is available electronically at http://www.epa.gov/ncea /iris/subst/0136.htm. 57 U.S. EPA (1997). Integrated Risk Information System File of chrysene. Research and Development, National Center for Environmental Assessment, Washington, DC . This material is available electronically at http://www.epa.gov/ncea /iris/subst/0455.htm 58 U.S. EPA (1997). Integr ated Risk Information System File of dibe nz(a,h)anthracene. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea /iris/subst/0456.htm. 59 U.S. EPA (1997). Integrated Risk Information System File of indeno(1,2,3-cd)pyrene. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea /iris/subst/0457.htm. 60 Perera, F.P.; Rauh, V.; Tsai, W-Y.; et al. (2002) Effect of transplacental exposure to environmental pollutants on birth outcomes in a multiethnic population. Environ Health Perspect. 111: 201-205. 61 Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D. ; Diaz, D.; Hoepner, L.; Barr, D.; Tu, Y.H.; Camann, D.; Kinney, P. (2006) Effect of prenatal exposure to airborne polycyclic aromatic hydrocarbons on neurodevelopment in the first 3 years of life among inner-city ch ildren. Environ Health Perspect 114: 1287-1292. 62 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and Development, National Center for Environmental Assessment, Washington, DC . This material is available electronically at http://www.epa.gov/iris/subst/0290.htm. 63 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183. 64 International Agency for Research on Cancer (IARC). 1999. Re-evaluation of some organic chemicals, hydrazine, and hydrogen peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of Chemical to Humans, Vol 71. Lyon, France. 65 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material is available electronically at http://www.e pa.gov/iris/subst/0290.htm.7-55 epithelium was observed at various concen tration levels of acetaldehyde exposure. 66 Data from these studies were used by EPA to develop an inhalation reference concentration. Some asthmatics have been shown to be a sensitiv e subpopulation to decrem ents in functional expiratory volume (FEV1 test) and br onchoconstriction upon acetaldehyde inhalation. 67 The agency is currently conducting a reassessment of the health hazards from inhalation exposure to acetaldehyde.
7.5.5.10 Nickel 68
Nickel is an essential element in some anim al species, and it has been suggested it may be essential for human nutrition. Nickel dermatitis, consisting of itching of the fingers, hand and forearms, is the most common effect in human s from chronic (long-term) skin contact with nickel. Respiratory effects have also been reported in humans fr om inhalation exposure to nickel. No information is available regarding the reprod uctive or developmental effects of nickel in humans, but animal studies have reported such effects. Human and animal studies have reported an increased risk of lung and na sal cancers from exposure to nick el refinery dusts and nickel subsulfide. Animal studies of so luble nickel compounds (i.e., nick el carbonyl) have reported lung tumors. The EPA has classified nickel refinery subsulfide as Group A, human carcinogens and nickel carbonyl as a Group B2, probable human carcinogen.
7.5.5.11 Manganese 69
Health effects in humans have been associat ed with both deficiencies and excess intakes of manganese. Chronic (long-term) exposure to low le vels of manganese in th e diet is considered to be nutritionally essential in humans, with a recommended daily allowance of 2 to 5 milligrams per day. Chronic exposure to high levels of mang anese by inhalation in humans results primarily in CNS effects. Visual reaction time, hand steadin ess, and eye-hand coordination were affected in chronically exposed workers. Manganism, charact erized by feelings of weakness and lethargy, tremors, a masklike face, and psychological distur bances, may result from chronic exposure to higher levels. Impotence and loss of libido have been noted in male workers afflicted with

66 Appleman, L.M., R.A. Woutersen, and V. J. Feron. (1982). Inhalation toxicity of acetaldehyde in rats. I. Acute and subacute studies. Toxicology. 23: 293-297. 67 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized acet aldehyde induces histaminemediated bronchoconstriction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1): 940-943. 68 All health effects language for this section came from: U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf 69 All health effects language for this section came from: U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf7-56 manganism attributed to inhalation exposures. Th e EPA has classified ma nganese in Group D, not classifiable as to carcinogenicity in humans.
7.5.5.12 Dioxins (Chlorinated dibenzodioxins (CDDs) 70
A number of effects have been observed in people exposed to 2,3,7,8-TCDD levels that are at least 10 times higher than background leve ls. The most obvious health effect in people exposure to relatively large amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin disease with acne-like lesions th at occur mainly on the face and upper body. Other skin effects noted in people exposed to high doses of 2,3,7,8- TCDD include skin rashes, discoloration, and excessive body hair. Changes in blood and urine th at may indicate liver damage also are seen in people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglobin, lipids, sugar, and protein have been re ported in people exposed to rela tively high concentrations of 2,3,7,8-TCDD. Most of the effects ar e considered mild and were reversible. However, in some people these effects may last for many years. Slight increases in the risk of diabetes and abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-TCDD. We do not have enough information to know if expos ure to 2,3,7,8-TCDD would result in reproductive or developmental effects in people, but animal st udies suggest that this is a potential health

concern. In certain animal species, 2,3,7,8-TCDD is especi ally harmful and can cause death after a single exposure. Exposure to lower le vels can cause a variety of effect s in animals, such as weight loss, liver damage, and disruption of the endoc rine system. In many species of animals, 2,3,7,8- TCDD weakens the immune system and causes a decrea se in the system’s ability to fight bacteria and viruses at relatively lo w levels (approximately 10 times higher than human background body burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused reproductive damage and birth defects. Some animal species exposed to CDDs during pregnancy ha d miscarriages and the offspring of animals exposed to 2,3,7,8-TCDD duri ng pregnancy often had severe birth defects including skeletal deformities, kidney defects, and weakened immune responses. In some studies, effects were observed at body burdens 10 times higher than human background levels.

70 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry (ATSDR). 1999. ToxFAQs for Chlorinated Dibenzo-p- dioxins (CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6). Atlanta, GA: U.S. Department of Health and Human Services , Public Health Service. Available on the Internet at http://www.atsdr.cdc.gov/tfacts104.html

.7-57 7.5.5.13 Furans (Chlorinated dibenzofurans (CDFs)) 71
Most of the information on the adverse health effects comes from studies in people who were accidentally exposed to food contaminated with CDFs. The amounts that these people were exposed to were much higher than are likely from environmental e xposures or from a normal diet. Skin and eye irritations, especially severe acne, darkened skin color, and swollen eyelids with discharge, were the most obvious health e ffects of the CDF poisoning. CDF poisoning also caused vomiting and diarrhea, anemia, more fre quent lung infections, numbness, effects on the nervous system, and mild changes in the live r. Children born to exposed mothers had skin irritation and more difficulty lear ning, but it is unknown if this e ffect was permanent or caused by CDFs alone or CDFs and polychlorin ated biphenyls in combination.
Many of the same effects that occurred in people accidentally exposed also occurred in laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomachs, livers, kidneys, and immune systems were seriously injured. Some animals had birth defects and testicular damage, and in severe cases, some an imals died. These effects in animals were seen when they were fed large amounts of CDFs ove r a short time, or small amounts over several weeks or months. Nothing is known about the pos sible health effects in animals from eating CDFs over a lifetime.

7.5.5.14 Other Air Toxics
In addition to the compounds described a bove, other compounds in gaseous hydrocarbon and PM emissions would be affected by thes e rules, including metal and organic HAPs. Information regarding the health effects of these compounds can be found in EPA’s IRIS database. 72

7.6 Limitations and Uncertainties The National Research Council (NRC) (2002) concluded that EPA’s general methodology for calculating the benefits of reducing air polluti on is reasonable and informative in spite of inherent uncertainties. To addr ess these inherent uncertainties , NRC highlighted the need to conduct rigorous quantitative anal ysis of uncertainty and to present benefits estimates to decisionmakers in ways that foster an appropriate apprec iation of their inhe rent uncertainty. In response to these comments, EPA’s Office of Air and Radiation (OAR) is developing a

71 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry (ATSDR). 1995. ToxFAQsTM for Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc.gov/tfacts32.html>. 72 U.S. EPA Integrated Risk Information System (IR IS) database is available at: www.epa.gov/iris7-58 comprehensive strategy for characterizing the aggr egate impact of uncertainty in key modeling elements on both health incidence and benefits estimates. Components of that strategy include emissions modeling, air quality modeling, health effects incide nce estimation, and valuation.
In this analysis, we use three methods to assess uncertainty quantitatively: Monte Carlo analysis, LML assessment, and alternate concen tration-response functions for PM- and ozonerelated mortality. We also provide a qualitative assessment for thos e aspects that we are unable to address quantitatively in this an alysis. Each of these analyses is described in the following sections.
This analysis includes many data sources as inputs, including emi ssion inventories, air quality data from models (with their associated parameters and inputs), population data, health effect estimates from epidemiology studies, and economic data for monetizing benefits. Each of these inputs may be uncertain and would affect the benefits estimat e. When the uncertainties from each stage of the analysis are compounded, small un certainties can have large effects on the total quantified benefits. In this analys is, we are unable to quantify the cu mulative effect of all of these uncertainties, but we provide the following analyses to characterize many of the largest sources of uncertainty.
7.6.1 Monte Carlo Analysis Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the concentra tion response functions and economic valuation functions. Monte Carlo simulation uses random sa mpling from distributions of parameters to characterize the effects of uncertainty on output variables, such as in cidence of morbidity. Specifically, we used Monte Carlo methods to ge nerate confidence intervals around the estimated health impact and monetized benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates, as shown in Tables 7-4 and 7-5. The confidence intervals around the monetized benef its in Tables 7-6 and 7-7 incorporate the epidemiology standard errors as well as th e distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates, nor do they re flect baseline incidence rate s, populations exposed and transferability of the effect estimate to divers e locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.7-59 7.6.2 LML Assessment for PM 2.5 PM 2.5 mortality benefits are the largest benefit cat egory that we monetized in this analysis. To better characterize the uncertainty associated with mortality impacts that are estimated to occur in areas with low baseline levels of PM 2.5 , we included the LML assessment. We have more confidence in the mortality impacts am ong populations exposed to levels of PM 2.5 above the lowest LML of the large cohort studies, and our c onfidence in the results diminish as we model that are lower than the LML. While an LML assessment provides some insight into the level of uncertainty in the estimated PM mortality bene fits, EPA does not view the LML as a threshold and continues to quantify PM-related mortality imp acts using a full range of modeled air quality concentrations. It is important to emphasize that just because we have gr eater confidence in the benefits above the LML, this does not mean that we have no confidence that benefits occur below the LML. In Section 7.3, we provi de the results of the LML assessm ent in Figures 7-15 and 7-16.
7.6.3 Alternate Concentration-response Functions for PM Mortality
PM 2.5 mortality benefits are the largest benefit cat egory that we monetized in this analysis. To better understand the concentrat ion-response relationship between PM 2.5 exposure and premature mortality, EPA conducted an expert elic itation in 2006 (Roman et al., 2008; IEc, 2006). In general, the results of the expert elicitati on support the conclusion th at the benefits of PM 2.5 control are very likely to be s ubstantial. In previous RIAs, EPA presented benefits estimates using concentration response functions derived from the PM 2.5 Expert Elicitation as a range from the lowest expert value (Expert K) to the highest expert value (Expert E). However, this approach did not indicate the agency’s judgment on what the be st estimate of PM benefits may be, and EPA’s Science Advisory Board described this presentati on as misleading. Therefore, we began to present the cohort-based studies (Pope et al., 2002; and Laden et al., 2006) as our core estimates in the proposal RIA for the Portland Cement NESHAP (U .S. EPA, 2009a). Using a lternate relationships between PM 2.5 and premature mortality supplied by expert s, higher and lower benefits estimates are plausible, but most of the expert-based estimates fall between the two epidemiology-based estimates (Roman et al., 2008).
In this analysis, we present the results derive d from the expert elic itation as indicative of the uncertainty associated with a major component of the health impact f unctions, and we provide the independent estimates derived from each of the twelve experts to better characterize the degree of variability in the expe rt responses. In this section, we provide the results using the concentration-response functions derived from the expert elicitation in both tabular (Tables 7-7 and 7-8) and graphical form (Figur e 7-10). Please note that these re sults are not the direct results7-60 from the studies or expert elicitation; rather, th e estimates are based in part on the concentrationresponse function provide d in those studies.
7.6.4 Alternate Concentration-response Functions for Ozone Mortality While particulate matter is the criteria polluta nt most clearly associated with premature mortality, recent research suggests that short-term repeated ozone exposure also likely contributes to premature death. In 2008 the National Resear ch Council (NRC) of the National Academy of Science (NAS) (NRC, 2008) issued a series of recommendations to the EPA regarding the quantification and valuation of ozone-related short-term morta lity. Chief among these was that “...short-term exposure to ambient ozone is likel y to contribute to premature deaths” and the committee recommended that “ozone-related mortality be included in future estimates of the health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatest emphasis be placed on the multicity and NMMAPS studies without exclusion of the metaanalyses” (NRC, 2008).
In view of the findings of the NAS panel, we include used estimates of short-term ozone mortality from the Bell et al. (2004) NMMAPS analysis, the Schw artz (2005) multi-city study, the Huang and Bell (2005) multi-city study as well as e ffect estimates from the three meta-analyses (Bell et al., 2005; Levy et al ., 2005; Ito et al., 2005).
7.6.5 Qualitative Assessment of Uncertainty and Other Analysis Limitations Although we strive to incorporate as many qua ntitative assessments of uncertainty, there are several aspects for which we are only able to address qualitativ ely. These aspects are important factors to consider when evaluating the relative benefits of the attainment strategies for each of the altern ative standards:
Above we present the estimates of the total monetized benefits, based on our interpretation of the best available scientif ic literature and methods and supported by the SAB-HES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions and uncertainties. For example, the key assumptions underlying the estimates for premature mortality, which typically account for at least 90% of the total monetized benefits, include the following:
1. We assume that all fine particles, regard less of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 released from diesel engines and other industrial sources, but the scientific evidence is not yet sufficient to allow diffe rentiation of effect estimates by particle type.7-61 2. We assume that the health impact functi on for fine particles is linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5, including both regions that are in attainment with fine particle standard and those that do not meet the standard down to th e lowest modeled concentrations.
3. To characterize the uncertainty in the relationship between PM 2.5 and premature mortality (which typically accounts for 85% to 95% of total monetized benefits), we include a set of twelve estimates based on results of the expert elicitation study in addition to our core estimates. Even these multiple characterizations omit the uncertainty in air quality estimates, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis. For more information on the uncertainties associated with PM 2.5 benefits, please consult the PM 2.5 NAAQS RIA (Table 5.5).
It is important to note that the monetized benefit-per-ton estimates used here reflect specific geographic patterns of emissions redu ctions and specific air quality and benefits modeling assumptions. For example, these estimates assume that emissions are reduced equally across all boilers and that the geographic distribution of boiler emissions will remain constant through the analysis year. As we did not model fuel subcategories, we assu me that each fuel type is equally represented in the emissions and geogr aphic distribution. Use of these $/ton values to estimate benefits associated with a specific fu el type (e.g., for reduci ng emissions from liquid fuel) may lead to higher or lower benefit estimates than if benefits were calculated based on direct air quality modeling of that fuel subcategor y. Great care should be ta ken in applying these estimates to emission reductions occurring in any specific location, as these are all based on national emission reduction assumptions and theref ore represent an averag e benefits-per-ton over the entire United States. The benefits- per-ton for variable emission reductions in specific locations may be very different from the estimate s presented here. Despite our inability to fully characterize and quantify these relatively small un certainties, we believe that, on net, the air quality impacts and associated monetized benefits are representative of the magnitude of benefits anticipated from these rules.
As previously described, we st rive to monetize as many of th e benefits anticipated from these rules as possible, but the monetized benefits estimated in this RIA inevitably only reflect the portion of benefits. Specifically, only the benefits attributable to the health impacts associated with exposure to ambient fine particles and oz one have been monetized in this analysis. Methodological and time limitations under the court-ordered schedule for this rule prevented EPA7-62 from quantifying or monetizing the benefits from several important benefit categories, including benefits from reducing toxic emissions, ecosyst em effects, and visibi lity impairment. Data limitations include limited monitoring for HAPs, in complete emissions inventories for HAPs, and limited photochemical air quality modeling for n on-mercury HAPs. Resource limitations include limited staff and extramural funding in conjunction with a heavy regulatory workload, which led EPA to not model these endpoints. Methodolog ical limitations include an absence of concentration-response functions for many HAP health effects, w ith issues such as exposure misclassification, small number of cases, confoundi ng, and extrapolation of toxicological effects down to ambient levels. Despite our inability to monetize all of th e benefit categories, the total combined monetized benefits still exc eed the costs by a substantial margin.
This RIA does not include the type of detail ed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2006). However, the results of the Monte Carlo anal yses of the health and welfare benefits presented in Chapter 5 of the PM RIA can provide some evidence of the uncertainty surrounding the benefits re sults presented in this analysis.
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Available on the Internet at <http://www.epa.gov/ttn/naaqs/standards/ so2/data/Risk%20and%20Exposure%20Assess ment%20to%20Support%20the%20Review%20of %20the%20SO2%20Primary%20Natio nal%20Ambient%20Air%20Q uality%20Standards-%20F inal%20Report.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2009c. Integrated Science Assessment for Particulate Matter (Final Report). EPA-600-R- 08-139F. National Center for Environmental Assessment—RTP Di vision. December. Available at <http://cfpub.epa.gov/ncea/cfm/reco rdisplay.cfm?deid=216546>. U.S. Environmental Protection Agency (U.S. EPA). 2010a. Regulatory Impact Analysis, National Ambient Air Quality Standards for Ozone , Office of Air Quality Planning and Standards, Research Triangle Park, NC. October. Available on the Internet at < http://www.epa.gov/ttn/ecas/regdata/RIAs/ s1-supplemental_analysis_full.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010b. Integrated Science Assessment for Carbon Monoxide . National Center for Environmenta l Assessment, Research Triangle Park, NC. EPA/600/R-09/019F. January. Available at <http://cfpub.epa.gov/ncea/cfm/reco rdisplay.cfm?deid=218686>. U.S. Environmental Protection Agency (U.S. EPA). 2010c. Regulatory Impact Analysis for Regulatory Impact Analysis: National Emissi on Standards for Hazardous Air Pollutants for Industrial, Commercial, and Instit utional Boilers and Process Heaters. Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at <http://www.epa.gov/airquality/combustion/docs/boilerria20100429.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010d. Summary of Expert Opinions on the Existence of a Threshold in the C oncentration-Response Function for PM 2.5 -related Mortality: Technical Support Document . Compiled by Office of Air Quality Planning and Standards, Research Triangle Pa rk, NC. July. Available at < http://www.epa.gov/ttn/ecas/regdata /Benefits/thresholdstsd.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010e. Regulatory Impact Analysis for the Proposed Federal Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. July. Available at <http://www.epa.gov/ttn/ecas/regdata/RIA s/proposaltrria_final.pdf>. U.S. Environmental Protection Agency. (U.S. EPA). 2010f. Air Quality Modeling Technical Support Document: Boiler Source Sector Rules (EPA-454/R-10-006), Research Triangle Park, North Carolina. December. Available at <http://www.epa.gov/scram001/reportsindex.htm>.7-68 U.S. Environmental Protection Agency. (U.S. EPA). 2010g. Technical Support Document: Preparation of Emissions Inventories for the Version 4, 2005-based Platform. Office of Air Quality Planning and Standa rds, Air Quality Assessment Division, Research Triangle Park, North Carolina. July. Available at <ftp://ftp.epa.gov/EmisInventory/2005v4/2005_emissions_tsd_07jul2010.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010h. Regulatory Impact Analysis for the SO 2 NAAQS. Office of Air Quality Planning and St andards, Research Triangle Park, NC. June. Available at < http://www.epa.gov/ttn /ecas/regdata/RIAs/fs o2ria100602full.pdf>. Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World.” Journal of Risk and Uncertainty . 27(1): p. 5-76.8-1 SECTION 8 COMPARISON OF MONETIZED BENEFITS AND COSTS
8.1 Boiler MACT Because we were unable to monetize the bene fits associated with reducing HAPs, all monetized benefits reflect improvements in ambient PM 2.5 and ozone concentra tions. This results in an underestimate of the monetized benefits. Using a 3% discount rate, we estimate the total monetized benefits of the major source rule to be $22 billion to $54 billio n in the implementation year (2014) (Table 8-1). Using a 7% discount rate, we estimate the total monetized benefits to be $20 billion to $49 billion. The annualized costs ar e $1.5 billion at a 7% in terest rate. The net benefits are $21 billion to $53 billion at a 3% di scount rate for the benefits and $19 million to $48 billion at a 7% discount rate. All estimates are in 2008$. The bene fits from reducing other air pollutants have not been monetized in this analysis, including reduc ing 112,000 tons of carbon monoxide, 30,000 tons of HCl, 820 tons of HF , 2,800 pounds of mercury, 2,700 tons of other metals, and 23 grams of dioxi ns/furans (TEQ) each year.
8.2 Boiler Area Source Rule
Because we were unable to monetize the bene fits associated with reducing HAPs, all monetized benefits reflect improvements in ambient PM 2.5 and ozone concentra tions. This results in an underestimate of the monetized benefits. Using a 3% discount rate, we estimate the total monetized benefits of the area source ru le to be $210 million to $520 million in the implementation year (2014) (Table 8-2). Usi ng a 7% discount rate, we estimate the total monetized benefits to be $190 million to $470 mi llion. The annualized costs are $490 million at a 7% interest rate. The net benefits are

− $280 million to $30 million at a 3% discount rate for the benefits and

− $300 million to

− $20 million at a 7% discount rate. All estimates are in 2008$. The benefits from reducing other air pollutants have not been monetiz ed in this analysis, including reducing 1,100 tons of carbon monoxide, 340 tons of HCl, 8 tons of HF, 90 pounds of mercury, 320 tons of other metals, and less than 1 gram of dioxins/furans (TEQ) each year.8-2 Table 8-1. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Boiler MACT in 2014 (millions of 2008$) a

3% Discount Rate 7% Discount Rate Selected Total Monetized Benefits b $22,000 to $54,000 $20,000 to $49,000 Total Social Costs c $1,500 $1,500 Net Benefits $20,500 to $52,500 $18,500 to $47,500 Non-monetized Benefits 112,000 tons of carbon monoxide 30,000 tons of HCl 820 tons of HF 2,800 pounds of mercury 2,700 tons of other metals 23 grams of dioxins/furans (TEQ) Health effects from SO 2 exposure Ecosystem effects Visibility impairment Alternative Total Monetized Benefits b $18,000 to $43,000 $16,000 to $39,000 Total Social Costs b $1,900 $1,900 Net Benefits $16,100 to $41,100 $14,100 to $37,100 Non-monetized Benefits 112,000 tons of carbon monoxide 22,000 tons of HCl 620 tons of HF 2,400 pounds of mercury 2,600 tons of other metals 23 grams of dioxins/furans (TEQ) Health effects from SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementati on year (2014), and are rounded to two significant figures. These results

include units anticipated to come online and the lowest cost disposal assumption. b The total monetized benefits reflect the human health benefits associat ed with reducing exposure to PM 2.5 through

reductions of directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 , as well as reducing exposure to ozone through reductions of VOCs. It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, rega rdless of their chemical com position, are equally potent in causing premature mortality because the scientific evidence is not yet suffici ent to allow differentiation of effect estimates by particle type. These estimates include energy disbenefits associated with the increased emissions from additional energy usage valued at $22 million for the selected option and $37 million for the alternative option. Ozone benefits are valued at $3.6 to $15 million for both options.

c The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both discount rates.8-3 Table 8-2 Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Boiler Area Source Rule in 2014 (millions of 2008$) a

3% Discount Rate 7% Discount Rate Final MACT/GACT Approach Total Monetized Benefits b $210 to $520 $190 to $470 Total Social Costs c $490 $490 Net Benefits

− $280 to $30

− $300 to

− $20 Non-monetized Benefits 1,100 tons of carbon monoxide 340 tons of HCl 8 tons of HF 90 pounds of mercury 320 tons of other metals <1 gram of dioxins/furans (TEQ) Health effects from SO 2 exposure Ecosystem effects Visibility impairment Proposed MACT Approach Total Monetized Benefits b $200 to $490 $180 to $440 Total Social Costs c $850 $850 Net Benefits

− $650 to

− $360

− $670 to

− $410 Non-monetized Benefits 1,100 tons of carbon monoxide 340 tons of HCl 8 tons of HF 90 pounds of mercury 320 tons of other metals <1 gram of dioxins/furans (TEQ) Health effects from SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementati on year (2014), and are rounded to two significant figures. These results

include units anticipated to come online and the lowest cost disposal assumption. b The total monetized benefits reflect the human health benefits associat ed with reducing exposure to PM 2.5 through

reductions of directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (200 6). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evid ence is not yet sufficient to allow differentiation of effect estimates by particle ty pe. These estimates include en ergy disbenefits associated with the increased emissions from additional energy usage valued at less than $1 million.

c The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both discount rates.8-4 Figures 8-1 and 8-2 show the full range of ne t benefits estimates (i.e., annual benefits minus annualized costs) qua ntified in terms of PM 2.5 benefits for the implem entation year (2014).

$0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000

Pope et al. Laden et al.Millions(2008$)

Cost estimate combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions
Figure 8-1. Net Benefits for the Final Major and Area Source Boiler Rules at 3% Discount Rate a

a Net benefits are quantified in terms of PM 2.5 benefits for the implementation year (2014). This graph shows 14

benefits estimates combined with the cost estimate. These m odels assume that all fine particles, regardless of their chemical composition, are equally potent in causing premat ure mortality because the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.8-5 $0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000

Pope et al. Laden et al.Millions(2008$)

Cost estimate combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions
Figure 8-2. Net Benefits for the Final Major and Area Source Boiler Rules at 7% Discount Rate a

a Net benefits are quantified in terms of PM 2.5 benefits for the implementation year (2014). This graph shows 14

benefits estimates combined with the cost estimate. These m odels assume that all fine particles, regardless of their chemical composition, are equally potent in causing premat ure mortality because the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.
8.3 Section 8 References Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air Pollution and Mortality.” American Journal of Respiratory and Critical Care Medicine 173:667-672. Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.APPENDIX A OAQPS MULTIMARKET MODEL TO ASSESS THE ECONOMIC IMPACTS OF ENVIRONMENTAL REGULATIONA-1 A.1 Introduction An economic impact analysis (EIA) provides information about a policy’s effects (i.e., social costs); emphasis is also placed on how th e costs are distributed among stakeholders (EPA, 2000). In addition, large-scale policies require add itional analysis to bett er understand how costs are passed across the economy. A lthough several tools are available to estimate social costs, current EPA guidelines suggest that multimarke t models “...are best used when potential economic impacts and equity effects on related markets might be cons iderable” and modeling using a computable general equilibrium model is not available or practical (EPA, 2000, p. 146). Other guides for environmental economists offe r similar advice (Berck and Hoffmann, 2002; Just, Hueth, and Schmitz, 2004).

Multimarket models focus on “short-run” time horizons and measure a policy’s near term or transition costs (EPA, 1999). R ecent studies suggest short-run analyses can complement full dynamic general equilibrium analysis.
The multimarket model described in this appe ndix is a new addition to the Office of Air Quality Planning and Standards’ (OAQPS’s) economic model tool ki t; it is designed to be used as a transparent tool that can respond quickly to requests about how stak eholders in 100 U.S. industries might respond to new environmental policy. Next, we provide an overview of the model, data, and parameters.
A.2 Multimarket Model The multimarket model contains the following features:

 Industry sectors and benchmark data set

– 100 industry sectors

– a single benchmark year (2010)

– estimates of industry carbon dioxide (CO 2 ) emissions

– estimates of industry employment

 Economic behavior

– industries respond to regulatory co sts by changing production rates

– market prices rise to reflect higher energy and other nonenergy material costs

– customers respond to these price increases and consumption falls

 Model scopeA-2  100 sectors are linked with each other based on their use of energy and other nonenergy materials. For example, the c onstruction industry is linked with the petroleum, cement, and steel industries a nd is influenced by price changes that occur in each sector. The links allow EP A to account for indirect effects the regulation has on related markets.

– Links come from input-output inform ation used from OAQPS’s computable general equilibrium model (EMPAX)

– production adjustments influence employment levels

– international trade (imports/e xports) behavior considered

 Model time horizon (“short-run”)

– fixed production resources (e.g., capital) leads to an upward-sloping industry supply function

– firms cannot alter input mixes; there is no substitution among intermediate production inputs

– price of labor (i.e., wage) is fixed

– investment and government expenditures are fixed A.2.1 Industry Sectors and Benchmark Data Set
The multimarket model includes 100 industries. For the benchmark year, the model uses information from OAQPS’s computable general equilibrium model’s balanced social accounting matrix (SAM) and the following accounting identity holds:
Output + Imports = Consumption + Investment + Government + Exports (A.1)

If we abstract and treat each industry as a national market, the identity represents the prepolicy market-clearing condi tion, or benchmark “equilibrium” ; supply equals demand in each market. In Table A-1, we identify the 100 i ndustries for the multimarket model; Table A-2 provides the 2010 benchmark data set. Since the benchmark data are reported in value terms, we also use the common “Harberger co nvention” and choose units where are all prices are one in the benchmark equilibrium (Shoven and Whalley, 1995).A-3 Table A-1. Industry Sectors In cluded in Multimarket Model

Industry Label Description Representative NAICS a Energy Industries

COL Coal 2121 CRU Crude Oil Extraction 211111 (exc. nat gas) ELE Electric Generation 2211 GAS Natural Gas 211112 2212 4862 OIL Refined Petroleum 324 Nonmanufacturing

AGR Agricultural 11 MIN Mining 21 less others CNS Construction 23 Manufactured Goods Food, beverages, and textiles

ANM Animal Foods 3111 GRN Grain Milling 3112 SGR Sugar 3113 FRU Fruits and Vegetables 3114 MIL Dairy Products 3115 MEA Meat Products 3116 SEA Seafood 3117 BAK Baked Goods 3118 OFD Other Food Products 3119 BEV Beverages and Tobacco 312 TEX Textile Mills 313 TPM Textile Product Mills 314 WAP Wearing Apparel 315 LEA Leather 316 Lumber, paper, and printing SAW Sawmills 3211 PLY Plywood and Veneer 3212 LUM Other Lumber 3219 PAP Pulp and Paper Mills 3221 CPP Converted Paper Products 3222 PRN Printing 323 Chemicals

CHM Chemicals and Gases 3251 RSN Resins 3252 FRT Fertilizer 3253 MED Drugs and Medicine 3254 PAI Paints and Adhesives 3255 SOP Soap 3256 OCM Other Chemicals 3259 (continued)A-4 Table A-1. Industry Sectors Included in Multimarket Mo del (continued)

Industry Label Description Representative NAICS a Plastics and Rubber

PLS Plastic 3261 RUB Rubber 3262 Nonmetallic Minerals

CLY Clay 3271 GLS Glass 3272 CEM Cement 3273 LIM Lime and Gypsum 3274 ONM Other Non-Metallic Minerals 3279 Primary Metals

I_S Iron and Steel 3311 3312 33151 ALU Aluminum 3313 331521 331524 OPM Other Primary Metals 3314 331522 331525 331528 Fabricated Metals

FRG Forging and Stamping 3321 CUT Cutlery 3322 FMP Fabricated Metals 3323 BOI Boilers and Tanks 3324 HRD Hardware 3325 WIR Springs and Wires 3326 MSP Machine Shops 3327 EGV Engraving 3328 OFM Other Fabricated Metals 3329 Machinery and Equipment CEQ Construction and Agricultural Equipment 3331
IEQ Industrial Equipment 3332 SEQ Service Industry Equipment 3333 HVC HVAC Equipment 3334 MEQ Metalworking Equipment 3335 EEQ Engines 3336 GEQ General Equipment 3339 Electronic Equipment

CPU Computers 3341 CMQ Communication Equipment 3342 TVQ TV Equipment 3343 SMI Semiconductor Equipment 3344 INS Instruments 3345 MGT Magnetic Recording Equipment 3346 LGT Lighting 3351 APP Appliances 3352 (continued)A-5 Table A-1. Industry Sectors Included in Multimarket Mo del (continued)

Industry Label Description Representative NAICS a Electronic Equipment (continued)

ELQ Electric Equipment 3353 OEQ Other Electric Equipment 3359 Transportation Equipment

M_V Motor Vehicles 3361 TKB Truck Bodies 3362 MVP Motor Vehicle Parts 3363 ARC Aircraft 3364 R_R Rail Cars 3365 SHP Ships 3366 OTQ Other Transport Equipment 3369 Other FUR Furniture 337 MSC Miscellaneous Manufacturing 339 Services Wholesale and Retail Trade

WHL Wholesale Trade 42 RTL Retail Trade 44

– 45 Transportation Services

ATP Air Transportation 481 RTP Railroad Transportation 482 WTP Water Transportation 483 TTP Freight Truck Transportation 484 PIP Pipeline Transport 486 OTP Other Transportation Services 485 487 488 Other Services

INF Information 51 FIN Finance and Insurance 52 REL Real Estate 53 PFS Professional Services 54 MNG Management 55 ADM Administrative Services 56 EDU Education 61 HLT Health Care 62 ART Arts 71 ACM Accommodations 72 OSV Other Services 81 PUB Public Services 92

a NAICS = North American Industry Classification System. Industry assignments are based on data used in the

EMPAX-modeling system, which relies on the commodity code system used in IMPLAN.A-6 Table A-2. 2010 Benchmark Data Set (billion 2006$)

Industry

Label Industry Description Output Imports Consumption Investment and Government Exports ACM Accommodations $828 $6 $816 $17 $1 ADM Administrative Services $795 $37 $771 $61 Less than $1 AGR Agricultural $314 $53 $333 $5 $29 ALU Aluminum $65 $17 $70 $4 $8 ANM Animal Foods $45 Less than $1 $36 Less than $1 $9 APP Appliances $25 $19 $35 $6 $3 ARC Aircraft $217 $60 $58 $120 $98 ART Arts $252 Less than $1 $246 $3 $3 ATP Air Transportation $154 $28 $91 $32 $59 BAK Baked Goods $61 $3 $61 $2 Less than $1 BEV Beverages and Tobacco $ 133 $54 $186 Less than $1 $1 BOI Boilers and Tanks $27 $2 $19 $9 $2 CEM Cement $52 Less than $1 $47 $3 $2 CEQ Construction and Agricultural Equipment $70 $24 $47 $33 $14

CHM Chemicals and Gases $284 $75 $300 $10 $49 CLY Clay $8 $4 $10 $1 $2 CMQ Communication Equipment $73 $40 $47 $56 $11

CNS Construction $983 $77 $594 $465 Less than $1 COL Coal $44 $2 $42 Less than $1 $4 CPP Converted Paper Products $52 $2 $43 $6 $6
CPU Computers $145 $76 $132 $52 $37 CRU Crude Oil Extraction $67 $189 $255 Less than $1 Less than $1 CUT Cutlery $11 $5 $9 $5 $2 EDU Education $970 Less than $1 $257 $701 $13 EEQ Engines $36 $14 $30 $6 $13 EGV Engraving $21 Less than $1 $9 $5 $7 ELE Electric Generation $317 Less than $1 $287 $31 Less than $1 ELQ Electric Equipment $33 $16 $23 $17 $10 FIN Finance and Insurance $2,015 $106 $1,972 $43 $106

FMP Fabricated Metals $66 $1 $58 $7 $2 FRG Forging and Stamping $20 Less than $1 $17 $1 $2 FRT Fertilizer $42 $5 $33 $4 $10 (continued)A-7 Table A-2. 2010 Benchmark Data Set (billion 2006$) (continued)

Industry

Label Industry Description Output Imports Consumption Investment and Government Exports FRU Fruits and Vegetables $74 $12 $76 $4 $5 FUR Furniture $66 $38 $84 $17 $2 GAS Natural Gas $139 $32 $160 $6 $6 GEQ General Equipment $54 $32 $47 $25 $14 GLS Glass $30 Less than $1 $18 $2 $10 GRN Grain Milling $77 $9 $74 $2 $10 HLT Health Care $1,863 Less than $1 $1,823 $20 $20 HRD Hardware $8 $4 $5 $4 $3 HVC HVAC Equipment $34 $9 $26 $10 $6 I_S Iron and Steel $125 $42 $143 $10 $13 IEQ Industrial Equipment $26 $14 $16 $14 $11 INF Information $1,305 $77 $1,217 $154 $11 INS Instruments $112 $44 $71 $65 $20 LEA Leather $4 $26 $29 Less than $1 $1 LGT Lighting $12 $11 $16 $5 $1 LIM Lime and Gypsum $7 Less than $1 $1 Less than $1 $5 LUM Other Lumber $41 $2 $32 $9 $2 M_V Motor Vehicles $272 $190 $313 $106 $43 MEA Meat Products $174 $9 $169 $5 $10 MED Drugs and Medicine $258 $102 $316 $18 $27 MEQ Metalworking Equipment $24 $11 $16 $14 $4

MGT Magnetic Recording Equipment $15 $2 $13 $2 $2
MIL Dairy Products $87 $3 $84 $4 $2 MIN Mining $53 $2 $30 $15 $11 MNG Management $469 Less than $1 $378 Less than $1 $92 MSC Miscellaneous Manufacturing $178 $129 $189 $73 $46
MSP Machine Shops $38 $2 $32 $5 $2 MVP Motor Vehicle Parts $220 $75 $226 $17 $52 OCM Other Chemicals $45 $2 $23 $9 $15 OEQ Other Electric Equipment $31 $16 $28 $7 $11

OFD Other Food Products $92 $7 $90 $2 $7 OFM Other Fabricated Metals $56 $28 $50 $22 $12
(continued)A-8 Table A-2. 2010 Benchmark Data Set (billion 2006$) (continued)

Industry

Label Industry Description Output Imports Consumption Investment and Government Exports OIL Refined Petroleum $415 $106 $462 $12 $47 ONM Other Non-Metallic Minerals $13 $5 $15 $1 $2
OPM Other Primary Metals $40 $27 $52 $2 $12 OSV Other Services $2,321 Less than $1 $1,479 $598 $244 OTP Other Transportation Services $245 Less than $1 $202 $22 $22
OTQ Other Transport Equip $23 $10 $14 $13 $5 PAI Paints and Adhesives $36 $1 $28 $3 $6 PAP Pulp and Paper Mills $131 $21 $133 $5 $14 PFS Professional Services $2,103 $84 $1,715 $461 $10 PIP Pipeline Transport $37 $83 $20 $98 $1 PLS Plastic $145 $14 $139 $4 $15 PLY Plywood and Veneer $19 $8 $25 $1 $1 PRN Printing $51 $1 $34 $11 $6 PUB Public Services $1,099 $22 $355 $766 Less than $1 R_R Rail Cars $11 $2 $6 $6 $2 REL Real Estate $2,719 $2 $2,559 $131 $31 RSN Resins $107 $23 $98 $6 $26 RTL Retail Trade $1,440 $53 $1,412 $82 Less than $1 RTP Railroad Transportation $70 Less than $1 $42 $18 $11 RUB Rubber $38 $20 $36 $15 $6 SAW Sawmills $29 $9 $36 $1 $1 SEA Seafood $13 $3 $14 $1 $1 SEQ Service Industry Equipment $29 $23 $22 $24 $6
SGR Sugar $34 $6 $36 $2 $3 SHP Ships $36 $6 $13 $29 Less than $1 SMI Semiconductor Equipment $141 $69 $157 $12 $41
SOP Soap $82 $5 $74 $3 $9 TEX Textile Mills $29 $9 $31 $1 $6 TKB Truck Bodies $58 $12 $34 $32 $5 TPM Textile Product Mills $27 $19 $37 $7 $2 TTP Freight Truck Transportation $301 Less than $1 $211 $39 $51
(continued)A-9 Table A-2. 2010 Benchmark Data Set (billion 2006$) (continued)

Industry

Label Industry Description Output Imports Consumption Investment and Government Exports TVQ TV Equipment $19 $37 $50 $3 $3 WAP Wearing Apparel $25 $94 $117 $1 Less than $1 WHL Wholesale Trade $1,309 $22 $1,021 $172 $138 WIR Springs and Wires $5 $2 $1 $3 WTP Water Transportation $45 $14 $12 $19A-10 A.2.1.2 Employment Data The model includes employment forecasts fo r each of the 100 sectors. Employment estimates are based on data from three sources: the AEO 2009 estimates of employment (AEO supplemental Table 126 and indicators of Macroe conomic Activity), and Global Insights, Inc., and the Bureau of Labor Statistics (BLS) 2008 end-of year-employment (Current Employment Statistics—CES [National]). Typically, 3-digit NAICS sectors’ employment estimates are either directly reported in the updated AEO 2009 rele ase or Global Insights For multimarket industries with finer NAICs detail, estimates were calcul ated by selecting a primary NAICS supersector estimate (AEO or Global Insights) and distribu ting total employment from the larger NAICS supersectors across more detailed NAICS sectors within the super-sector. The distributions were determined using observed 2008 BLS employment da ta. In the last step, In order to match aggregate U.S. employment numbers reported in the AEO 2009 release (140.1 million), a single adjustment factor was applied to all sector s that use Global Insight s’ supersector data. 1 Table A-4 reports the baseline employment for each of the 100 sectors.
A.2.2 Economic Behavior A.2.2.1 U.S. Supply In a postpolicy scenario, industry responds to changes in the new market-clearing “net” price for the good or service sold:

% Δ ”net” price = % Δ market price

− % Δ direct costs

− % Δ indirect costs (A.2)

The % Δ direct costs are approximated using the engineering cost anal ysis and baseline value of output. For example, a $1 billion increase in compliance costs for the electricity sector (ELE) would be represented in the model as follows:

% Δ direct costs = $1/$317= 0.03% (A.3)
As shown in Figure A-1, the cost change pr ovides the industry with incentives to alter production rates at current market prices; market prices must rise to maintain the original prepolicy production levels (Q).

1 This step is required because Global Insight’s data used by EPA are an older vintage than the forecasts used in the AEO.A-11 Table A-4. 2010 U.S. Employment Projections

Industry Label Industry Description Projected Employment (1,000) ACM Accommodations 11,239 ADM Administrative Services 9,274 AGR Agricultural 1,607 ALU Aluminum 87 ANM Animal Foods 45 APP Appliances 33 ARC Aircraft 449 ART Arts 1,939 ATP Air Transportation 506 BAK Baked Goods 247 BEV Beverages and Tobacco 92 BOI Boilers and Tanks 67 CEM Cement 164 CEQ Construction and Agricultural Equipment 176 CHM Chemicals and Gases 147 CLY Clay 38 CMQ Communication Equipment 73 CNS Construction 7,446 COL Coal 79 CPP Converted Paper Products 306 CPU Computers 104 CRU Crude Oil Extraction 384 CUT Cutlery 34 EDU Education 2,892 EEQ Engines 75 EGV Engraving 100 ELE Electric Generation 219 ELQ Electric Equipment 72 FIN Finance and Insurance 6,051 FMP Fabricated Metals 285 FRG Forging and Stamping 75 FRT Fertilizer 35 FRU Fruits and Vegetables 153 FUR Furniture 327 (continued)A-12 Table A-4. 2010 U.S. Employmen t Projections (continued)

Industry Label Industry Description Projected Employment (1,000) GAS Natural Gas 98 GEQ General Equipment 198 GLS Glass 71 GRN Grain Milling 55 HLT Health Care 15,190 HRD Hardware 20 HVC HVAC Equipment 109 I_S Iron and Steel 205 IEQ Industrial Equipment 88 INF Information 2,939 INS Instruments 250 LEA Leather 3 LGT Lighting 26 LIM Lime and Gypsum 10 LUM Other Lumber 216 M_V Motor Vehicles 170 MEA Meat Products 450 MED Drugs and Medicine 279 MEQ Metalworking Equipment 139 MGT Magnetic Recording Equipment 20 MIL Dairy Products 113 MIN Mining 599 MNG Management 1,732 MSC Miscellaneous Manufacturing 180 MSP Machine Shops 251 MVP Motor Vehicle Parts 485 OCM Other Chemicals 92 OEQ Other Electric Equipment 63 OFD Other Food Products 144 OFM Other Fabricated Metals 196 OIL Refined Petroleum 70 ONM Other Non-metallic Minerals 61 OPM Other Primary Metals 87 OSV Other Services 5,271 OTP Other Transportation Services 1,064 (continued)A-13 Table A-4. 2010 U.S. Employmen t Projections (continued)

Industry Label Industry Description Projected Employment (1,000) OTQ Other Transport Equipment 36 PAI Paints and Adhesives 60 PAP Pulp and Paper Mills 121 PFS Professional Services 18,989 PIP Pipeline Transport 43 PLS Plastic 473 PLY Plywood and Veneer 74 PRN Printing 248 PUB Public Services 21,787 R_R Rail Cars 25 REL Real Estate 2,158 RSN Resins 102 RTL Retail Trade 15,283 RTP Railroad Transportation 236 RUB Rubber 117 SAW Sawmills 84 SEA Seafood 36 SEQ Service Industry Equipment 77 SGR Sugar 62 SHP Ships 140 SMI Semiconductor Equipment 245 SOP Soap 104 TEX Textile Mills 110 TKB Truck Bodies 126 TPM Textile Product Mills 32 TTP Freight Truck Transportation 1,429 TVQ TV Equipment 15 WAP Wearing Apparel 153 WHL Wholesale Trade 5,869 WIR Springs and Wires 36 WTP Water Transportation 67 Total 144,100A-14 The multimarket model also simultaneously considers how the policy influences other important production costs (via changes in energy a nd other intermediate material prices). As a result, the multimarket model can provide addi tional information about how policy costs are transmitted through the economy. As shown in Figure A-2, the indirect cost change provides the industry with additional incentives to alter production rates at curre nt market prices.

The % Δ indirect effects associated with each i nput are approximated using an input “use” ratio and the price change that occurs in the input market.

% Δ indirect costs = input use ratio x % Δ input price (A.4)
The social accounting matrix provides an intern ally consistent estimate of the use ratio and describes the dollar amount of an input that is required to produce a dollar of output. Higher ratios suggest strong links between industries, wh ile lower ratios suggest weaker links. Given the short time horizon, we assume the input use ratio is fixed and cannot adjust their input mix; this is a standard assumption in public and commerc ial input-output (IO) and SAM multiplier models (Berck and Hoffmann, 2002). Morgenstern and co lleagues (2004) and Ho and colleagues (2008) also use this assumption when examining n ear-term effects of environmental policy.

Figure A-1. Direct Compliance Costs Reduce Pr oduction Rates at Benchmark PricesA-15 Figure A-2. Indirect Costs Further Reduce Pr oduction Rates at Benchmark Prices

Following guidance in the OAQPS economic resource manual (EPA, 1999), we use a general form for the U.S. industry supply function:

g ε n

i i i gi g g ) P P ( t P b Q      

 

     

# 

 1  (A.5)
where

g Q  = with-policy supply quantity (g)

b = calibrated scale parameter for the supply price relationship

g P  = with-policy price for output (g)

t = direct compliance costs per unit of supply

gi  = input use ratio (g using input i)

i P  = with-policy input (i) price

i P = benchmark input (i) price

g  = price elasticity of supply for output (g) The key supply parameter that controls the industry production adjust ments is the price elasticity of supply ( ε g ). To our knowledge, there is no existing empirical work that estimates short-run supply elasticities for all industry group s used in the multimarket model. As a result, we assume the U.S. supply elasticities are a f unction of econometrically estimated rest-of-worldA-16 (ROW) export supply elasticities (see discussi on in the next section) . We report the values currently available in the model in Table A-5. A.2.2.2 International Competition International competition is capture d by a single ROW supply function: 

#   ROW g ε g g 

### P c Q    (A.6)
where

g Q  = with-policy supply quantity (g) c = calibrated scale parameter for the supply and price relationship

g P  = with-policy U.S. price for output (g)

ROW g  = price elasticity of supply of goods from the ROW to the United States (imports) (g) The key supply parameter that controls the ROW supply adjustments is the price elasticity of supply ( ROW g  ). We obtained these estimates for a variety of industry groups from a recently published article by Broda and colleagues (2008b). A.2.2.3 Price Elasticity of Suppl y: Rest of World (ROW) Broda and colleagues (2008a and 2008b) provide an empirical basis for the multimarket model supply elasticiti es. Broda et al. provide over 1,000 inve rse elasticities th at RTI organized to be comparable with the 100-sector model. Th e first step was to match the Harmonized Trade System (HS) elasticities estimated in the article to the appropri ate NAICS codes. Many of the HS codes correspond with a detailed NAICS codes (5 - and 6-digit level), while the multimarket sector industries typically corres pond with more aggregated sect ors (NAICS 2-, 3-, or 4-digit levels). To adapt these labels to our model, we combined the 5- and 6-digit NAICS under their 3- and 4-digit codes and calculated an average inverse elasticity valu e for codes that fell within the multimarket model’s aggregate industrial sector s. This gives a crude way to account for the variety of products detailed in the original data se t. We also restricted the elasticity sample to those that Broda et al. classify as “medium” a nd “low” categories; these categories tend to have lower elasticity values that are consistent with the multimarket model’s modeling horizon (i.e., in the short run importers are likely to have le ss flexibility to respond to price changes and elasticities are low). 1

1 Broda et al.’s intent was to use these categories to describe or proxy for domestic market power.A-17 Table A-5. Supply Elasticities

Industry Label Industry Description Rest of World (ROW) U.S. ACM Accommodations 0.7 0.7 ADM Administrative Services 0.7 0.7 AGR Agricultural 1.0 1.0 ALU Aluminum 0.8 0.5 ANM Animal Foods 1.1 0.8 APP Appliances 0.9 0.8 ARC Aircraft 0.9 0.6 ART Arts 0.7 0.7 ATP Air Transportation 0.7 0.7 BAK Baked Goods 0.8 0.7 BEV Beverages and Tobacco 2.9 2.9 BOI Boilers and Tanks 1.1 0.8 CEM Cement 0.9 0.7 CEQ Construction and Agricultural Equipment 0.8 0.6 CHM Chemicals and Gases 1.1 0.8 CLY Clay 0.8 0.6 CMQ Communication Equipment 2.5 1.0 CNS Construction 0.7 0.7 COL Coal 2.2 2.2 CPP Converted Paper Products 0.9 0.7 CPU Computers 1.0 0.7 CRU Crude Oil Extraction 3.7 3.7 CUT Cutlery 1.4 1.1 EDU Education 0.7 0.7 EEQ Engines 1.2 1.0 EGV Engraving 1.1 0.8 ELE Electric Generation 2.0 2.0 ELQ Electric Equipment 0.8 0.6 FIN Finance and Insurance 0.7 0.7 FMP Fabricated Metals 1.2 1.1 FRG Forging and Stamping 1.6 1.5 (continued)A-18 Table A-5. Supply Elasticities (continued)

Industry Label Industry Description Rest of World (ROW) U.S. FRT Fertilizer 1.0 0.7 FRU Fruits and Vegetables 1.0 0.7 FUR Furniture 1.9 1.9 GAS Natural Gas 12.2 12.2 GEQ General Equipment 1.0 0.7 GLS Glass 0.8 0.6 GRN Grain Milling 1.7 1.5 HLT Health Care 0.7 0.7 HRD Hardware 1.1 0.8 HVC HVAC Equipment 0.9 0.6 I_S Iron and Steel 1.0 0.6 IEQ Industrial Equipment 0.9 0.6 INF Information 0.7 0.7 INS Instruments 0.9 0.6 LEA Leather 0.9 0.7 LGT Lighting 1.1 0.7 LIM Lime and Gypsum 0.9 0.7 LUM Other Lumber 0.9 0.7 M_V Motor Vehicles 1.3 0.7 MEA Meat Products 1.2 3.9 MED Drugs and Medicine 1.3 1.0 MEQ Metalworking Equipment 0.7 0.5 MGT Magnetic Recording Equipment 1.0 0.7 MIL Dairy Products 1.1 0.9 MIN Mining 2.2 2.2 MNG Management 0.7 0.7 MSC Miscellaneous Manufacturing 1.0 0.8 MSP Machine Shops 1.1 0.8 MVP Motor Vehicle Parts 0.9 0.6 OCM Other Chemicals 1.1 0.6 OEQ Other Electric Equipment 1.0 0.7 OFD Other Food Products 1.1 0.7 (continued)A-19 Table A-5. Supply Elasticities (continued)

Industry Label Industry Description Rest of World (ROW) U.S. OFM Other Fabricated Metals 0.9 0.6 OIL Refined Petroleum 1.0 0.7 ONM Other Non-metallic Minerals 1.5 0.7 OPM Other Primary Metals 0.7 0.5 OSV Other Services 0.7 0.7 OTP Other Transportation Services 0.7 0.7 OTQ Other Transport Equipment 1.0 0.7 PAI Paints and Adhesives 1.0 0.7 PAP Pulp and Paper Mills 1.1 0.7 PFS Professional Services 0.7 0.7 PIP Pipeline Transport 2.0 2.0 PLS Plastic 1.0 0.7 PLY Plywood and Veneer 1.3 1.3 PRN Printing 1.0 0.7 PUB Public Services 0.7 0.7 R_R Rail Cars 1.8 0.7 REL Real Estate 0.7 0.7 RSN Resins 1.0 0.7 RTL Retail Trade 0.7 0.7 RTP Railroad Transportation 0.7 0.7 RUB Rubber 1.3 1.1 SAW Sawmills 0.8 0.6 SEA Seafood 1.1 0.8 SEQ Service Industry Equipment 0.8 0.6 SGR Sugar 1.1 0.8 SHP Ships 1.0 0.7 SMI Semiconductor Equipment 1.2 1.0 SOP Soap 0.8 0.6 TEX Textile Mills 1.0 0.7 TKB Truck Bodies 3.2 3.1 TPM Textile Product Mills 0.8 0.6 TTP Freight Truck Transportation 0.7 0.7 TVQ TV Equipment 5.8 5.4 (continued)A-20 Table A-5. Supply Elasticities (continued)

Industry Label Industry Description Rest of World (ROW) U.S. WAP Wearing Apparel 1.2 0.8 WHL Wholesale Trade 0.7 0.7 WIR Springs and Wires 1.9 0.8 WTP Water Transportation 0.7 0.7 Note: RTI mapped Broda et al. data for their industry aggregation to the multimarket model’s 100 industries. Domestic supply elasticities are typically assumed to be within one standard deviation of the sample of supply elasticities used for the ROW. In selected cases where this information is not available, the U.S. supply elasticity is set equal to the ROW. Source: Broda, C., N. Limao, and D. Weinstein. 2008a. “Export Supply Elasticities.” http://faculty.chicagobooth.edu/christian.broda/website/r esearch/unrestricted/Trad eElasticities/TradeElastic ities.html. Accessed September 2009. Our ideal preference was to use an exact 3- or 4-digit match from the medium category if one was available. If the multimarket model had a 4-digit code for which there was no direct match, we aggregated up a level and applied the re levant 3-digit elasticity. If a multimarket code was not covered in the medium set of elasticiti es, we used the low elasticity category. This method was sufficient for mapping the majority of the sectors in the model. After applying our inverse elasticity values to the multimarket sectors, we calculated the inverse of the value to arrive at the actual supply elastic ity. Since Broda et al.’s articl e focused on industrial production goods, some of the multimarket sectors were not covered in the elasticity data. These sectors included mainly service industries, transportation, and energy sources.
In order to fill these gaps, we turned to the source substi tution elasticities from Purdue University’s Global Trade Analysis Project (GTAP). 1 Although the elasti cities in the GTAP model are a different type of international trade elasticity and cannot be directly applied in the multimarket model (e.g., they are based on the Armington structure 2 ), the parameters provide us with some additional information about the relati ve trade elasticity differences between industry sectors. To use the GTAP information to develop assumptions about the multimarket model sectors with missing elastic ities, we chose a base industrial sect or (iron and steel) for which we had parameter value from Broda et al. Next, we developed industry-spec ific ratios for missing industries using the corresponding GTAP sector tr ade elasticities and the GTAP iron and steel

1 See Chapter 14 of the GTAP 7 Database Documentati on for the full description of the parameters at https://www.gtap.agecon.purdue.edu/ resources/download/4184.pdf; see Table 14.2 for elasticities. 2 Detailed documentation of the entire GTAP 7 Database is available at https://www.gtap.agecon.purdue.edu/databases/v7/v7_doco. asp. The GTAP also uses a unique system of categorizing commodities that does not ma tch the NAICS or HS system exactly.A-21 sector. We multiplied the resulting ratio by the Broda et al. iron and steel parameter (1.0). For example, the GTAP trade elasticity for coal (6.10) is approximately 2.2 tim es the trade elasticity for iron and steel (2.95). As a result, the multi market import supply elasticity for coal is computed as 2.2 (2.2 x 1.0). A.2.2.4 Price Elasticity of Supply: United States
We also used Broda et al.’s elasticities to derive a set of domesti c supply elasticities for the model. We have assumed that a product’s dome stic supply would be equal to or less elastic than other countries’ supply of imports. When we aggregated and averaged the original elasticities to the 3- and 4-di git NAICS level for our foreign supply elasticities, we also calculated the standard deviation of each 3- a nd 4-digit NAICS sample. By adding the standard deviation to the corresponding fo reign supply and then taking the inverse, we were able to calculate a domestic supply elasticity for each se ctor that was lower than its foreign counterpart while maintaining the structure of the original elasticities. For sectors in which no standard deviation was available, 1 we used professional judgment to ap ply the closest available substitute from a similar industry. Without a comparable way of scaling our foreign elasticities for the sectors in which we used the GTAP elasticities, we elected to keep the domestic and foreign supply elasticities the same.
A.2.2.5 Demand Uses for industry output are divided into three groups: investment/government use, domestic intermediate uses, and other final us e (domestic and exports). Given the short time horizon, investment/government does not change. In termediate use is determined by the input use ratios and the industry output decisions.

g gi i Q Q     (A.7)

i Q  = with-policy input demand quantity (i)

gi  = input use ratio (g using input i)

g Q  = with-policy output quantity (g) Other final use does respond to market pri ce changes. Following guidance in the OAQPS economic resource manual (EPA, 1999), we use a general form for the U.S. industry demand function:

1 No standard deviations were calculated for the 3- and 4-digit codes that had only one observation (i.e., Broda et al.’s model used the exact 3- or 4-digit code).A-22 

#   g

g g P a Q     (A.8)
where

g Q  = with-policy demand quantity (g)

a = calibrated scale parameter for the demand and price relationship

g P  = with-policy price for output (g)

g  = price elasticity of demand (g) The key parameter that controls consumpti on adjustments is the price elasticity of demand ( η g ). To approximate the response, we use dema nd elasticities that were simulated with a general equilibrium model (Ho, Morgenstern, an d Shih, 2008). Table A-6 reports the values currently available in the model. A.2.2.6 Model Scope
The multimarket model includes 100 sectors covering energy, manufacturing, and service applications. Each sector’s production technol ogy requires the purchase of energy and other intermediate goods made by other sectors include d in the model. Linking the sectors in this manner allows the model to trace direct and indi rect policy effects across different sectors. Therefore, it is best used when potential economic impacts and e quity effects on related markets might be important to stakeholders not directly affected by an environmental policy. However, the model can also be run in single-market pa rtial equilibrium mode to support and provide insights for other types of environmental policies.
A.2.2.7 Model Time Horizon The model is designed to address short-run and transitional effect s associated with environmental policy. Production te chnologies are fixed; the mode l does not assess substitution among production inputs (labor, energy intermedia tes, and other interm ediates) and assumes each investment cannot be changed during the time frame of the analysis. These issues are better addressed using other frameworks such as comp utable general equilibrium modeling. Similarly, government purchases from each sector do not adju st in response to changes in goods/service prices. Although, employment levels (number of jobs) adjust as pr oduction levels change, wages are assumed to be fixed.A-23 Table A-6. U.S. Demand Elasticities

Industry Label Industry Description Demand Elasticity η g ACM Accommodations

− 0.7 ADM Administrative Services

− 0.7 AGR Agricultural

− 0.8 ALU Aluminum

− 1.0 ANM Animal Foods

− 0.6 APP Appliances

− 2.6 ARC Aircraft

− 2.5 ART Arts

− 0.7 ATP Air Transportation

− 0.8 BAK Baked Goods

− 0.6 BEV Beverages and Tobacco

− 0.6 BOI Boilers and Tanks

− 0.5 CEM Cement

− 0.8 CEQ Construction and Agricultural Equipment

− 1.7 CHM Chemicals and Gases

− 1.0 CLY Clay

− 0.8 CMQ Communication Equipment

− 2.6 CNS Construction

− 0.8 COL Coal

− 0.1 CPP Converted Paper Products

− 0.7 CPU Computers

− 2.6 CRU Crude Oil Extraction

− 0.3 CUT Cutlery

− 0.5 EDU Education

− 0.7 EEQ Engines

− 1.7 EGV Engraving

− 0.5 ELE Electric Generation

− 0.2 ELQ Electric Equipment

− 2.6 FIN Finance and Insurance

− 0.7 FMP Fabricated Metals

− 0.5 FRG Forging and Stamping

− 0.5 FRT Fertilizer

− 1.0 FRU Fruits and Vegetables

− 0.6 FUR Furniture

− 0.7 (continued)A-24 Table A-6. U.S. Demand Elasticities (continued)

Industry Label Industry Description Demand Elasticity η g GAS Natural Gas

− 0.3 GEQ General Equipment

− 1.7 GLS Glass

− 0.8 GRN Grain Milling

− 0.6 HLT Health Care

− 0.7 HRD Hardware

− 0.5 HVC HVAC Equipment

− 1.7 I_S Iron and Steel

− 1.0 IEQ Industrial Equipment

− 1.7 INF Information

− 0.7 INS Instruments

− 2.6 LEA Leather

− 1.1 LGT Lighting

− 2.6 LIM Lime and Gypsum

− 0.8 LUM Other Lumber

− 0.7 M_V Motor Vehicles

− 2.5 MEA Meat Products

− 0.6 MED Drugs and Medicine

− 1.0 MEQ Metalworking Equipment

− 1.7 MGT Magnetic Recording Equipment

− 2.6 MIL Dairy Products

− 0.6 MIN Mining

− 0.6 MNG Management

− 0.7 MSC Miscellaneous Manufacturing

− 1.7 MSP Machine Shops

− 0.5 MVP Motor Vehicle Parts

− 2.5 OCM Other Chemicals

− 1.0 OEQ Other Electric Equipment

− 2.6 OFD Other Food Products

− 0.6 OFM Other Fabricated Metals

− 0.5 OIL Refined Petroleum

− 0.1 ONM Other Non-metallic Minerals

− 0.8 OPM Other Primary Metals

− 1.0 OSV Other Services

− 0.7 OTP Other Transportation Services

− 0.8 (continued)A-25 Table A-6. U.S. Demand Elasticities (continued)

Industry Label Industry Description Demand Elasticity η g OTQ Other Transport Equip

− 2.5 PAI Paints and Adhesives

− 1.0 PAP Pulp and Paper Mills

− 0.7 PFS Professional Services

− 0.7 PIP Pipeline Transport

− 0.8 PLS Plastic

− 1.0 PLY Plywood and Veneer

− 0.7 PRN Printing

− 0.7 PUB Public Services

− 0.7 R_R Rail Cars

− 2.5 REL Real Estate

− 0.7 RSN Resins

− 1.0 RTL Retail Trade

− 0.7 RTP Railroad Transportation

− 0.8 RUB Rubber

− 1.0 SAW Sawmills

− 0.7 SEA Seafood

− 0.6 SEQ Service Industry Equipment

− 1.7 SGR Sugar

− 0.6 SHP Ships

− 2.5 SMI Semiconductor Equipment

− 2.6 SOP Soap

− 1.0 TEX Textile Mills

− 1.1 TKB Truck Bodies

− 2.5 TPM Textile Product Mills

− 1.1 TTP Freight Truck Transportation

− 0.8 TVQ TV Equipment

− 2.6 WAP Wearing Apparel

− 2.4 WHL Wholesale Trade

− 0.7 WIR Springs and Wires

− 0.5 WTP Water Transportation

− 0.8 Note: RTI assigned an elasticity using the most similar in dustry from Ho and colleagues’ industry aggregation. Source: Ho, M. S, R. Morgenstern, and J. S. Shih. 2008. “Impact of Carbon Price Policies on US Industry.” RFF Discussion Paper 08-37. Http://Www.Rff.Org /Publications/Pages/Publicationdetails.Aspx?. Publicationid=20680. Accesse d August 2009. Table B.6.A-26 A.3 Appendix A References Berck, P., and S. Hoffmann. 2002. “Assessing the Em ployment Impacts of Environmental and Natural Resource Policy.” Environmental and Resource Economics 22(1):133-156. Broda, C., N. Limao, and D.E. Weinstein. 2008a. “Optimal Tariffs and Market Power: The Evidence.” American Economic Review 98(5):2032-2065. Broda, C., N. Limao, and D. Weinstein. 2008b. “Export Supply Elastici ties.” <http://faculty. chicagobooth.edu/christian.broda/website/res earch/unrestricted/Tr adeElasticities/ TradeElasticities.html.> Ho, M.S., R. Morgenstern, and J.S. Shih. 2008. “Impact of Carbon Price Policies on US Industry” (RFF Discussion Paper 08-37). <http://www.Rff.Org/Publications/P ages/Publicationdetails.Aspx?. Publicationid=20680>. Just, R.E., D.L. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy

. Northampton, MA: Edward Elgar. Morgenstern, R.D., M. Ho, J.S. Shih, and X. Zhang. 2004. “The Near-Term Impacts of Carbon Mitigation Policies on Manufacturing Industries.” Energy Policy 32(16): 1825-1841. Shoven, J.B., and J. Whalley. 1995. Applying General Equilibrium . New York: Cambridge University Press. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. 1999. OAQPS Economic Analysis Resource Document

. http://www.epa.gov/ttn/ecas/analguid.html. U.S. Environmental Protection Agency. 2000. “G uidelines for Preparing Economic Analyses” (EPA 240-R-00-003). <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>.APPENDIX B DETAILED ECONOMIC MOD EL RESULTS BY SECTORB-1 Table B-1. Prices (Percentage Change from Benchmark): Industry Detail

Major Area
Selected Option Alternative Option Selected Option Alternative Option Energy 0.0279% 0.0228%

− 0.0001%

− 0.0001% Nonmanufacturing 0.0007% 0.0054% 0.0013% 0.0016% Manufacturing Food, beverages, and textiles 0.0106% 0.0118% 0.0016% 0.0030% Lumber, paper, and printing 0.1006% 0.2427% 0.0133% 0.0162%
Chemicals 0.0082% 0.0038%

− 0.0003%

− 0.0005%
Plastics and rubber 0.0183% 0.0147%

− 0.0006%

− 0.0008%
Nonmetallic minerals 0.0033% 0.0041%

− 0.0004%

− 0.0005% Primary metals 0.0221% 0.0139%

− 0.0002%

− 0.0002%
Fabricated metals 0.0036% 0.0018%

− 0.0003%

− 0.0004% Machinery and equipment 0.0051% 0.0004%

− 0.0002%

− 0.0003% Electronic equipment

− 0.0004%

− 0.0006%

− 0.0001%

− 0.0001% Transportation equipment 0.0025% 0.0020%

− 0.0001%

− 0.0001%
Other 0.0035% 0.0079% 0.0005% 0.0006%
Wholesale and retail trade

− 0.0018%

− 0.0024% 0.0000% 0.0001% Transportation services

− 0.0010%

− 0.0022%

− 0.0001%

− 0.0002%
Other services 0.0001% 0.0000% 0.0012% 0.0014%B-2 Table B-2. Production (Percentage Change from Benchmark): Industry Detail

Major Area
Selected Option Alternative Option Selected Option Alternative Option Energy

− 0.0087%

− 0.0084%

− 0.0002%

− 0.0002% Nonmanufacturing

− 0.0029%

− 0.0046%

− 0.0007%

− 0.0011% Manufacturing Food, beverages, and textiles

− 0.0121%

− 0.0136%

− 0.0013%

− 0.0022% Lumber, paper, and printing

− 0.0442%

− 0.0999%

− 0.0057%

− 0.0071%
Chemicals

− 0.0168%

− 0.0150%

− 0.0001%

− 0.0002%
Plastics and rubber

− 0.0153%

− 0.0155%

− 0.0003%

− 0.0004%
Nonmetallic minerals

− 0.0038%

− 0.0054%

− 0.0003%

− 0.0004% Primary metals

− 0.0233%

− 0.0167%

− 0.0001%

− 0.0001%
Fabricated metals

− 0.0047%

− 0.0047%

− 0.0002%

− 0.0003% Machinery and equipment

− 0.0071%

− 0.0029%

− 0.0001%

− 0.0001% Electronic equipment

− 0.0026%

− 0.0034%

− 0.0001%

− 0.0001% Transportation equipment

− 0.0059%

− 0.0049% 0.0000% 0.0000%
Other

− 0.0077%

− 0.0162%

− 0.0013%

− 0.0016%
Wholesale and retail trade

− 0.0012%

− 0.0015%

− 0.0002%

− 0.0004% Transportation services

− 0.0030%

− 0.0035%

− 0.0002%

− 0.0003%
Other services

− 0.0011%

− 0.0014%

− 0.0006%

− 0.0008%B-3 Table B-3. Consumption (Percentage Change from Benchmark): Industry Detail

Major Area
Selected Option Alternative Option Selected Option Alternative Option Energy

− 0.0057%

− 0.0059%

− 0.0002%

− 0.0002% Nonmanufacturing

− 0.0026%

− 0.0039%

− 0.0006%

− 0.0009% Manufacturing Food, beverages, and textiles

− 0.0063%

− 0.0074%

− 0.0009%

− 0.0016% Lumber, paper, and printing

− 0.0273%

− 0.0566%

− 0.0018%

− 0.0024%
Chemicals

− 0.0114%

− 0.0109%

− 0.0002%

− 0.0003%
Plastics and rubber

− 0.0094%

− 0.0104%

− 0.0003%

− 0.0005%
Nonmetallic minerals

− 0.0032%

− 0.0045%

− 0.0003%

− 0.0004% Primary metals

− 0.0117%

− 0.0088%

− 0.0001%

− 0.0002%
Fabricated metals

− 0.0036%

− 0.0036%

− 0.0002%

− 0.0003% Machinery and equipment

− 0.0034%

− 0.0018%

− 0.0001%

− 0.0001% Electronic equipment

− 0.0014%

− 0.0016%

− 0.0001%

− 0.0001% Transportation equipment

− 0.0033%

− 0.0027% 0.0000% 0.0000%
Other

− 0.0025%

− 0.0050%

− 0.0004%

− 0.0006%
Wholesale and retail trade

− 0.0012%

− 0.0015%

− 0.0002%

− 0.0003% Transportation services

− 0.0026%

− 0.0031%

− 0.0002%

− 0.0003%
Other Services

− 0.0011%

− 0.0014%

− 0.0006%

− 0.0008%B-4 Table B-4. Imports (Percentage Change from Benchmark): Industry Detail

Major Area
Selected Option Alternative Option Selected Option Alternative Option Energy 0.0509% 0.0413%

− 0.0001%

− 0.0002% Nonmanufacturing 0.0010% 0.0037% 0.0008% 0.0010% Manufacturing Food, beverages, and textiles 0.0134% 0.0155% 0.0018% 0.0032% Lumber, paper, and printing 0.1034% 0.2467% 0.0136% 0.0166%
Chemicals 0.0093% 0.0045%

− 0.0004%

− 0.0006%
Plastics and rubber 0.0197% 0.0158%

− 0.0006%

− 0.0009%
Nonmetallic minerals 0.0012% 0.0022%

− 0.0001%

− 0.0001% Primary metals 0.0208% 0.0132%

− 0.0002%

− 0.0002%
Fabricated metals 0.0042% 0.0023%

− 0.0003%

− 0.0004% Machinery and equipment 0.0045% 0.0004%

− 0.0002%

− 0.0003% Electronic equipment

− 0.0003%

− 0.0003%

− 0.0001%

− 0.0002% Transportation equipment 0.0036% 0.0029% 0.0000%

− 0.0001%
Other 0.0059% 0.0137% 0.0011% 0.0013%
Wholesale and retail trade

− 0.0012%

− 0.0016% 0.0000% 0.0001% Transportation services

− 0.0007%

− 0.0014% 0.0000%

− 0.0001%
Other services

− 0.0002%

− 0.0004% 0.0001% 0.0001%B-5 Table B-5. Exports (Percentage Change from Benchmark): Industry Detail

Major Area
Selected Option Alternative Option Selected Option Alternative Option Energy

− 0.0041%

− 0.0033% 0.0000% 0.0000% Nonmanufacturing

− 0.0004%

− 0.0040%

− 0.0010%

− 0.0013% Manufacturing Food, beverages, and textiles

− 0.0069%

− 0.0076%

− 0.0010%

− 0.0019% Lumber, paper, and printing

− 0.0702%

− 0.1690%

− 0.0093%

− 0.0113%
Chemicals

− 0.0081%

− 0.0038% 0.0003% 0.0005%
Plastics and rubber

− 0.0181%

− 0.0145% 0.0006% 0.0008%
Nonmetallic minerals

− 0.0027%

− 0.0034% 0.0003% 0.0004% Primary metals

− 0.0211%

− 0.0133% 0.0002% 0.0002%
Fabricated metals

− 0.0018%

− 0.0009% 0.0002% 0.0002% Machinery and equipment

− 0.0084%

− 0.0006% 0.0004% 0.0005% Electronic equipment 0.0010% 0.0015% 0.0003% 0.0004% Transportation equipment

− 0.0062%

− 0.0049% 0.0001% 0.0002%
Other

− 0.0034%

− 0.0069%

− 0.0002%

− 0.0002%
Wholesale and retail trade 0.0013% 0.0018% 0.0000%

− 0.0001% Transportation services 0.0008% 0.0018% 0.0001% 0.0002%
Other services

− 0.0001%

− 0.0001%

− 0.0009%

− 0.0011%