### 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 Plann ing 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 In stitutional 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 Employ

ment ................................ ................................ ............... 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 Employ

m ent ................................ ................................ ............. 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 Employ

ment ................................ ................................ ............. 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 Introductio n ................................ ................................ ............................ 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 Employ

ment ................................ ................................ ............. 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 Engi neering Cost Anal ys is ................................ ................................ ............................ 3 - 1 3.1 Major Sources ................................ ................................ ................................ ...... 3 - 2 3.2 Area Sources ................................ ................................ ................................ ........ 3 - 5 3.3 Section 3 References ................................ ................................ ............................ 3 - 6 4 Economic Impact Anal ys is ................................ ................................ ........................... 4 - 1 4.1 Partial Equilibrium Analy sis (Multiple Markets) ................................ ................ 4 - 1 4.1.1 Overview ................................ ................................ ................................ .. 4 - 1 4.1.2 Economic Impact Anal ys is Results ................................ ......................... 4 - 2 4.1.2.1 M arket - 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

Anal ys es ................................ ................................ ................................ ... 5 - 1 5.1 Small Entity

Screening Analy sis ................................ ................................ ......... 5 - 1 5.1.1 S mall Businesses ................................ ................................ ...................... 5 - 1 5.1.1.1 Representative Small Business Analy sis Using Census Statistics of U.S. Businesses ................................ ....................... 5 - 1 5.1.1.2 Additional Small Business Analy sis 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 ys is (FRFA): Major Sources ........................... 5 - 6 5.2.1 Need for Rule and Objectives ................................ ................................ .. 5 - 6 5.2.2 Summary

of the Significant I ssues 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 Reporting, Record keeping and Other Compliance Requirements of the Rule ................................ .................... 5 - 9vi 5.2.5 Description of the steps the Agency has Taken to Minimize the Significant Economic Impact on Small Entities ................................ .... 5 - 11 5.3 Final Regulatory

Flexibility

Anal ys is (FRFA): Area Sources ........................... 5 - 12 5.3.1 Need for Rule and Objectives ................................ ................................ 5 - 12 5.3.2 Summary

of the Significant I ssues 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 Reporting, Record keeping and Other Compliance Requirements of the Rule ................................ .................. 5 - 15 5.3.5 Description of the steps the Agency 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 Sy nopsis ................................ ................................ ................................ ............... 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 Total 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 Future 24 - hour PM2.5 Levels .............................. 6 - 11 6.5.4 Impacts of Sector on Future Visibility L evels ................................ ....... 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 Sy nopsis ................................ ................................ ................................ ............... 7 - 1 7.2 Calculating Benefits ................................ ................................ ............................. 7 - 2vii 7.2.1 Methodology

I mprovements since Proposal ................................ ............ 7 - 4 7.2.2 Benefits Anal ys is Approach for PM2.5 and Ozone ................................ . 7 - 5 7.2.3 Health Impact Anal ys is (HIA) ................................ ................................ . 7 - 6 7.2.4 Estimating PM2.5 - r elated Premature Mortalit y ................................ ..... 7 - 10 7.2.5 Economic Valuation of Health Impact ................................ .................. 7 - 12 7.2.6 Calculating Boiler Sector - specific Benefit - per - ton Estimates ............... 7 - 14 7.3 Health Benefits Results ................................ ................................ ...................... 7 - 2 0 7.4 Energy

Disbenefits ................................ ................................ ............................. 7 - 34 7.4.1 Social Cost of Carbon and Greenhouse G as Disbenefits ....................... 7 - 34 7.5 Unquantified or Nonmonetized 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 Hy drogen Chloride (HCl) ................................ ......................... 7 - 49 7.5.5.3 Chlorine Gas (Cl2) ................................ ................................ ... 7 - 50 7.5.5.4 Hy drogen C yanide (HCN) ................................ ........................ 7 - 50 7.5.5.5 Hy drogen Fluoride (HF) ................................ ........................... 7 - 50 7.5.5.6 Toluene ................................ ................................ ..................... 7 - 50 7.5.5.7 Formaldeh yd e ................................ ................................ ........... 7 - 51 7.5.5.8 Poly cy clic Aromatic Hy drocarbons (PAHs) ............................ 7 - 53 7.5.5.9 Acetaldeh y de ................................ ................................ ............ 7 - 54 7.5.5.10 Nickel ................................ ................................ ....................... 7 - 55 7.5.5.11 Manganese ................................ ................................ ................ 7 - 55 7.5.5.12 Dioxins (Chlorinated dibenzodioxins (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 An al ys is ................................ ................................ ............ 7 - 58 7.6.2 LM L Assessment for PM2.5 ................................ ................................ .. 7 - 59 7.6.3 Alternate Concentration - response Functions for PM Mortality ............ 7 - 59 7.6.4 Alternate Concentration - response Functions for Ozone Mortality ........ 7 - 60viii 7.6.5 Qualitative Assessment of Uncertainty and Other Analy sis 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 Ec onomic Impacts of Environmental Regulation ................................ ................................ .................. A - 1 B Detailed Economic Model Results by Sector ................................ ...................... B - 1ix LIST OF FIGURES Number Page 2 - 1. Distribution of Employ ment within Food Manufacturing (NAICS 311): 2006 ........... 2 - 3 2 - 2. Distribution of Total Value of Shipments 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. Employ

ment 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 Food Manufacturing (NAICS 311) ............................. 2 - 15 2 - 10. Producer Price Trends in Food Manufacturing (NAI CS 311) ................................ .... 2 - 16 2 - 11. Distribution of Value of Shipments within Wood Product Manufacturing (NAICS 322): 20 07 ................................ ................................ ................................ ..... 2 - 19 2 - 12. Distribution of Employ ment within Wood Product Manufacturing (NAICS 322): 2007 ................................ ................................ ................................ ................... 2 - 20 2 - 13. Electrical Power Use Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997

–2005 ................................ ................................ ........................... 2 - 24 2 - 14. Establishment Concentration in the Wood Product Manufacturing Industry (NAICS 321): 2002 ................................ ................................ ................................ ..... 2 - 26 2 - 15. Capacity

Utilization Trends in the Wood Product Manufacturing Industry (NAICS 321) ................................ ................................ ................................ ............... 2 - 27 2 - 16. Employ

ment Concentration in the Wood Product Manufacturing Industry (NAICS 321): 2002 ................................ ................................ ................................ ..... 2 - 28 2 - 17. Capacity

Tre nds 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 the Wood Product Manufacturing Industry (NAICS 321 )] ................................ ................................ ................................ .............. 2 - 32 2 - 20. Producer Price Trends in the Wood Product Manufacturing Industry (NAICS 321) ................................ ................................ ................................ ............................. 2 - 33 2 - 21. Distribution of Value of Shipments within Paper Manufacturing (NAICS 322): 2007 ................................ ................................ ................................ ............................. 2 - 36 2 - 22. Distribution of Employ ment within Paper Manufacturing (NAICS 32 2): 2007 ........ 2 - 36 2 - 23. Electrical Power Use Trends in the Paper Manufacturing Industry : 1997

–2005 ....... 2 - 41 2 - 24. Establishment Concentration in Paper Manufacturing Industry (NAICS 322): 2002 ................................ ................................ ................................ ............................. 2 - 43x

2 - 25. Capacity

Utilization Trends in the Paper Manufacturing Industry (NAICS 322) ...... 2 - 44 2 - 26. Employ

ment Concentration in the Paper Manufacturing Industry (NAICS 322): 2002 ................................ ................................ ................................ ............................. 2 - 45 2 - 27. Capacity

Trends in the Paper Manufacturing Industry (NAICS 322) ........................ 2 - 46 2 - 28. Industrial Production Trends in the Paper Manufacturing Industry (NAICS 322): 1997

–2009 ................................ ................................ ................................ ................... 2 - 49 2 - 29. International Trade Trends in the Paper Manufacturing Industry (NAICS 322) ........ 2 - 50 2 - 30. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222) ............... 2 - 50 2 - 31. Distribution of Employ ment within Chemical Manufacturin g (NAICS 325): 2007 ................................ ................................ ................................ ............................. 2 - 53 2 - 32. Distribution of Total Value of Shipments within Chemical Manufacturing (NAICS 325): 2007 ................................ ................................ ................................ ..... 2 - 53 2 - 33. Electric Power Use Trends in Chemical Manufacturing (NAICS 325): 1997

– 2005 ................................ ................................ ................................ ............................. 2 - 58 2 - 34. Establishment Concentration in Chemica l Manufacturing (NAICS 325): 2002 ........ 2 - 60 2 - 35. Capacity

Utilization Trends in Chemical Manufacturing (NAICS 325) .................... 2 - 61 2 - 36. Employ

ment Concentration in Chemical Manufacturing (NAICS 325): 2002 .......... 2 - 61 2 - 37. Capacity

Trends in Chemical Manufacturing (NAI CS 325) ................................ ...... 2 - 62 2 - 38. Industrial Production Trends in Chemical Manufacturing (NAICS 325) ................... 2 - 66 2 - 39. International Trade Trends in Chemical 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 Changes b y Sector: Major Sources ................................ . 4 - 4 4 - 3. Distribution of Total Surplus Changes b y Sector: Area Sources ................................ .. 4 - 6 4 - 4. Employ

ment Impacts Using Morgenstern, Pizer, Shih (2002) (1,000 FTEs) ............. 4 - 10 5 - 1. Sh are of NAI CS/Enterprise Employ ment Categories (<500 emplo ye es) 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. gr id; and the blue inner box is the 12 km eastern U.S. grid. ................................ ......... 6 - 3 6 - 2. Locations of boilers in the NEI point inventory 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 2 014 ................................ ................................ .................. 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 Analy sis ....................... 7 - 8 7 - 4. Change in Ambient PM 2.5 Lev els from SO 2 Emissions from Point Source Boilers ................................ ................................ ................................ ......................... 7 - 1 7 7 - 5. Change in Ambient PM 2.5 Levels from PM Em issions from Point Source Boilers .... 7 - 1 8xi

7 - 6. Change in Ambient PM 2.5 Lev els from SO 2 Emissions from Non - point Source Boilers ................................ ................................ ................................ ......................... 7 - 1 8 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 Heal th 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 F inal 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 L evels for Final Boiler MACT and Boiler Area Source Rule ................................ ............................... 7 - 33 7 - 16. Cumulative Percentage of Population Exposed to Baseline Air Qualit y Levels for Final Boiler MACT and Boiler Area Source Rule ................................ ................ 7 - 34 7 - 17. Estimated County

L evel Carcinogenic Risk from HAP exposure from outdoor sources (2002 NATA) ................................ ................................ ................................ . 7 - 42 7 - 18. Estimated County

L evel Noncancer (Respiratory) 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 Final Major and Area Source Boiler Rules at 3% Discount Rate ................................ ................................ ................................ ............................... 8 - 4 8 - 2. Net Benefits for the Final Major an d Area Source Boiler Rules at 7% Discount Rate ................................ ................................ ................................ ............................... 8 - 5xii LIST OF TABLES Number Page 1 - 1. Summary

of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2014 (millions of 2008$) ................................ ........ 1 - 4 1 - 2. Summary

of the Monetized Benefit s, Social Costs, and Net Benefits for the Boiler Area Source Rule in 2014 (millions of 2008$) ................................ .................. 1 - 5 2 - 1. Key Statistics: Food Manufacturing (North American Industry Classification Sy stem [NAI CS] 311) ................................ ................................ ................................ ... 2 - 2 2 - 2. Industry

Data: Food Manufacturing (NAICS 311) ................................ ....................... 2 - 2 2 - 3. Costs of Goods and Services Used in Food Manufacturing (NAI CS 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 Manufacturing (NAICS 311) ................................ ..................... 2 - 6 2 - 6. Demand b y Sector: Foo d 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 for Food Manufacturing (NAICS 311) .............. 2 - 12 2 - 9. Small Business Size Standards: Food Manufacturing (NAICS 311) .......................... 2 - 13 2 - 10. Distribution of Economic Data b y Enterprise Size: Food Manufacturing (NAICS 311) ................................ ................................ ................................ ............... 2 - 14 2 - 11. Key Statistics: Wood Product Manufacturing (NAICS 321) ................................ ...... 2 - 17 2 - 12. Industry

Data: Wood Product Manufacturing (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 - 22 2 - 15. Energy

Used in Wood Product Manufacturing (NAI CS 321) ................................ .... 2 - 23 2 - 16. Demand b y Sector: Wood Product Manufacturing (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 b y Enterprise Size: Wood Product Manufacturing (NAICS 321) ................................ ................................ ...................... 2 - 30 2 - 19. Small Business Size Standards: Woo d Product Manufacturing (NAICS 321) ........... 2 - 31 2 - 20. Key Statistics: Paper Manufacturing (NAICS 322) ................................ .................... 2 - 34 2 - 21. Industry

Data: Paper Manufacturing (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 Manufacturing (NAICS 322) ................................ .................. 2 - 40 2 - 25. Estimated Energy

Sources for the U.S. Pulp and Paper Industry ............................... 2 - 41 2 - 26. Demand b y Sector: P aper 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 b y Enterprise Size: Paper Manufacturing (NAICS 322) ................................ ................................ ................................ ............... 2 - 47 2 - 29. Small Business Size Standar ds: Paper Manufacturing (NAICS 322) ......................... 2 - 48 2 - 30. Key Statistics: Chemical Manufacturing (NAICS 325) ................................ .............. 2 - 52 2 - 31. Industry

Data: Chemical Manufacturing (NAICS 325) ................................ .............. 2 - 52 2 - 32. Key Goods and Services Used in Chemical Manufacturing (NAI CS 325) ($2007, millions) ................................ ................................ ................................ ......... 2 - 54 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 b y Sector: Chemical Manufacturing (NAI CS 325) ($2007 millions) .......... 2 - 59 2 - 36. Top Chemical Pr oducers: 2007 ................................ ................................ ................... 2 - 63 2 - 37. 2007 Corporate Income and Profitability (NAICS 325) ................................ ............. 2 - 63 2 - 38. Small Business Size Standards: Chemical Manufacturing (NAICS 325) .................. 2 - 65 2 - 39. Distribution of Economic Data b y Enterprise Size: Chemical Manufacturing (NAICS 325) ................................ ................................ ................................ ............... 2 - 66 3 - 1. Summary

of Capital and Annual Costs for 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 Major Sources (billion, 2008$): 2014 ............................. 4 - 4 4 - 2. Distribution of Social Costs Area Sources (billion, 2008$): 2014 ............................... 4 - 5 4 - 3. Employ

ment Impacts Using Morgenstern, Pizer, Shih (2002) (FTE) .......................... 4 - 9 4 - 4. Cost Approach Jobs Impacts in FTEs ................................ ................................ ......... 4 - 12 4 - 5. Employ

ment Impacts ................................ ................................ ................................ .. 4 - 13 4 - 6. Aggregate Employ

ment in FTEs ................................ ................................ ................ 4 - 13 4 - 7. Aggregate Employ

ment Distributed 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. In formation Collection Effort for Facilities with Combust ion Units: Major Sources ................................ ................................ ................................ .......................... 5 - 8 5 - 4. Estimated Affected Facilities Using 13 State Boiler Inspector Inventory : Area Sources ................................ ................................ ................................ ........................ 5 - 14xiv

6 - 1. Geographic Elements of Domains Used in Photochemical Modeling .......................... 6 - 4 6 - 2. Control Strategies and/or Growth Assumptions Included in the 2016 Projection ........ 6 - 6 6 - 3. Estimated Future Year (2016) Controllable Boiler Sector Emissions .......................... 6 - 8 7 - 1. Human Health and Welfare Effects of Air Pollutants Affected ................................ ... 7 - 9 7 - 2. Summary

of Monetized Benefits Estimates for Final Boiler MACT in 2014 (millions of 2008$) ................................ ................................ ................................ ..... 7 - 21 7 - 3. Summary

of Monetized Benefits Estimates 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 confiden ce 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 MA CT 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 Estimates from Ozone f or 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 Ty pe ................................ ......................... 7 - 44 8 - 1. Summary

of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler MACT in 2014 (millions of 2008$) ................................ ................................ ... 8 - 2 8 - 2 Summary

of the Monetized Benefits, Social Costs, and Net Benefits for the Boile r Area Source Rule in 2014 (millions of 2008$) ................................ .................. 8 - 31 - 1 SECTION 1 INTRODUCTION The U.S. Environmental Protection Agency (EPA) 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 technology (MACT). In the other rule, EPA is promulgating a NESHAP for two area source categories: industrial boilers and institutional and commercial boilers. 2 The emission standards for controlling mercury and pol yc yc lic organic matter (POM) emissions are based on the MACT. The emission standards for contr olling other HAPs are based on EPA ’ s determination as to what constitutes the generall y available control technology (GACT) or management practices. As part of the regulatory process, EPA is required to develop a regulatory

impact anal ysis (RIA). The RIA i ncludes an economic impact anal ys is (EIA) and a small entity

impacts analy sis and documents 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 tot al 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 in dustrial sectors could be small (less than 0.01% higher). Average annual domestic production may also fall by less than 0.01%. Because of higher domestic prices, imports rise by less than 0.01% per ye ar. Market - level effects for the area source NESHAP are smaller when compared to the major source rule; avera ge price, production, and import changes are less than 0.01%.

 Social Cost Analysis: The estimated social cost of the majo r source rule is just under $1.5 billion (2008$). The Agency ’ s economic model sugg ests that industries are able to pass approximately $0.5 billion of the rule ’ s costs to consumers (e.g., higher market prices). Domestic industries ’ surplus falls by $1.4 billion, while other countries on net benefit from higher prices (a net increase in r est - of - the world [ROW] surplus of less than $0.1 billion). Additional 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 Columbia Circuit (DC Circuit) vacated the NESHAP for industrial/commercial/institutiona l boilers and process heaters. This act ion provides EPA ’ s rule in response to the court ’ s vacatur. 2 G as - fired boilers are not part of the area source categories 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 sour ce rule is approximately $0.5 billion (2008$). The Agency ’ s economic model suggests that industries 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 surplus is less than $0.1 billion. Additional costs and fuel savings for unknown, existing, and new area sources not included in the economic model results represent a net benefit of less than $0.1 billion.

 Employment Changes : The estimated empl oy ment ch anges range between

− 3100 to 6,500 empl o ye es, wi th a central estimate of +1,700 employ ees for the major source NESHAP . The estimated emplo ym ent changes range between

− 1,000 to 2, 0 00 employ ees, with a central estimate of + 5 00 employ ees for the area source NESHAP .

 Small En tity Analys e s: EPA performed a scre ening analy sis for impacts on small entities by

comparing compliance costs to sales/revenues (e.g., sales and revenue tests). EPA ’ s analy sis found the tests were ty pically higher than 3% for small entities included in the screening anal ys is. Pursuant to section 603 of the RFA, EPA prepared an initial regulatory flexibility

analy sis (IRFA) for the proposed rule and convened a Small 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 recommendations 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 propo sed rule at 75 FR 32044 - 32045 (Jun e 4, 2010). Inthe p r oposed rule, EPA included provisions consistent with four of the Panel ’ s recommendations. As required by section 604 of the RFA, we also prepared a final regulatory flexibility

analy sis (FRFA) the fina l rule (see Section 5).

 Benefits Analysis:

– The benefits from reducing some air pollutants have not been monetized in this anal ys is, including reducing 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 other metals, and 23 grams of dioxins/furans (TEQ) each y ear. We assess the benefits of these emission reductions qualitatively in this analy sis.

– We have monetized the benefits from reducing PM (as a surrogate for metal HAP), as well as the co - benefits that result from the HAP emissions reductions (e.g., the pollution control equipment for HCl also reduces sulfur dioxide, a pre cursor to PM 2.5 ). Thus all monetized benefits reported reflect improvements in ambient PM 2.5 and ozone concentratio ns. As such, although the monetized benefits likely

underestimate the total benefits, the extent of the underestimate is unclear.

– Using a 3% discount rate, we estimate the total monetized benefits 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 monetized 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 implementation y ear. Using alternate relationships between PM 2.5 and premature mortality supplied 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 benefits of the Boiler Area Source Rule to be $210 million to $520 m illion in the implementation y ear (2014). Using a 7% discount rate, we estimate the total monetized of the Boiler Area Source Rule to be $190 million to $470 million in the implementation y ear.

– Using a 3% discount rate, we estimate the total monetized ben efits of the combined Boiler MACT and Boiler Area Source Rule to be $22 billion to $58 billion in the implementation y ear (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 $50 billion in the implementation y ear. All estimates are in 2008$.

 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 Source 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 results are shown in Tables 1 - 1 and 1 - 2. 1.2 Organization of this Rep ort The remainder of this report supports and details the methodology and the results of the EI A:

 Section 2 presents the affected industry profiles.

 Section 3 describes the engineering cost analy sis.

 Section 4 describes the economic impact anal ys is.

 Sectio n 5 describes the small entity anal ys es.

 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 analy sis .  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 Selec ted 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 2,80 0 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 Socia l 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 implementation year (2014), and are rounded to tw o significant figures. These results include units anticipated to come online and the lowes t 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 , 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, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect est imates 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.1 - 5

Table 1 - 2. Summary of the Monetized Benefits, Soci al 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 Benefit s −$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 o f 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), and are ro unded to tw o 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 reduction s 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 premature mortality because the scientific evidence is not yet sufficient 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 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.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 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 Mortalit y, and L ong - term Exposure to Fine Particulat e 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 high facility population counts within 3 - digit NAICs industries reported in the combustion facility survey . The purpose is to give the reader a general understanding of economic aspects and industry trends to provide additional context for the economic impact analy sis. 2.1 Food Manufa cturing 2.1.1 Introduction Food manufacturing involves the transformation of raw agricultural and livestock products into processed food. Between 1997 and 2002, shipment values stagnated, falling 0.38%, while the number of employ ees and pay roll 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 emplo ye es and pay roll fell 5.94% and 3.28% respectively (Table 2 - 1). Shipments, pay roll, and employ

ment continued to increase betwee n 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, pay roll per employ ee grew 4.91% from 1997 to 2002 and continued to increase, albeit at a slower rate of 2.83%, from 2002 to 2006. Between 2006 and 2007, the payroll per emplo yee declined as the growth in employ ees outpaced the increase in the annual pay roll (Table 2 - 2). The food manufacturing industry consists of nine different industry groups, each distinguished by

the livest ock or agricultural products used as raw materials for the processed food products as follows:

 Animal Food Manufacturing (North American Industry Classification S ys tem [NAI CS] 3111)

 Grain and Oilseed Milling (NAICS 3112)

 Sugar and Confectionery Product Manufacturing (NAI CS 3113)

 Fruit and Vegetable Preserving and Specialt y Food Manufacturing (NAICS 3114)

 Dairy Product Manufacturing (NAICS 3115)

 Animal Slaughtering and Processing (NAICS 3116)

 Seafood Product Preparation and Packaging (3117)

 Bakeries and Tort illa Manufacturing (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) $48,1 18 $51,852 $50,151 $50,467 Em ploy ees 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 Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997. ” <http://factfinder.census.gov>; (July 8, 2008) . U.S. Census Bureau; generated by RTI International; us ing American FactFinder; “ Sector 00: All sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (2007 NAICS Basis): 2002 and 2007. ” <http://factfinder.census.gov>; (January 4, 2010) . Table 2 - 2. Industry Data: Food Manufacturing (NAICS 311)

Industry Data 1997 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 Shipm ents 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 establishm ent 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 Statistics for the United States and the States (1997 NAICS Ba sis): 2002 and 1997. ” <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 Statist ics for the United States and the States (2007 NAICS Basis): 2002 and 2007. ” <http://factfinder.census.gov>; (January 4, 2010) . In 2006, Animal Slaughtering and Processing made up the largest share of both employ ment (33%) and the value of shipments (27%) in food manufacturing (Figures 2 - 1 and 2 - 2).2 - 3 Anim al Food Manufac tu ri ng , 3% Gr ain a nd Oilsee d

Milling, 4% Suga r an d Confe ct ioner y Produc t Man ufa ct uring, 5% Fr uita nd Ve ge tab le

Pres ervi ng and Spe ci al ty F ood Man ufa ct ur ing, 12% Dair y Product Manufac tu ri ng , 9% Anim al Slaught eri ng and Pr oc es sing, 33 % Se af ood Produc t Prep ar atio n an d Pa cka ging, 3 % Bak eri es a nd T ort illa Manufac tu ri ng , 20% Ot he r Food Man ufa ct uring, 11%

Figure 2 - 1. Distribution of Employment 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) . Ani mal Food Man ufa ct ur ing, 6% Gr ai n a nd Oilsee d

Milling, 1 0% Suga r an d

Confe ct ione ry Produc t Man ufa ct uring , 6% Fr uita nd Ve ge tab le Pres erv inga nd Spec ia lt y Food Man ufac tu ri ng , 12% Dair y Product Man ufac tu ri ng , 14% Ani mal Slaught eri ng an d Proces sing, 2 7% Se af ood Produc t Pr epa ra ti on and Pa ck aging, 2 % Bake ri es a nd T or tilla Man ufac tu ri ng , 11% Ot he r Food

Manufac tu ri ng , 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 directl y affect the food manufacturing industry and/or other markets that provide key goods and services to the industry (e.g., energy). RTI ’ s multimarket model is specificall y designed to analyze these ty pes of regulations. The model emphasizes the links among industrial sectors and provides policy makers with new insights about the direct and indirect effects of a regulatory program and the distribution of costs across the U.S. econom y. 2.1.2 Supply and Dema nd Characteristics Next, we provide a broad overview of the supply and demand sides of the food manufacturing industry . We emphasize the economic interactions this industry has with other industries and people, including identifying the key goods and servi ces 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 employ ee compensation acc ounted for 12% of this value, with half of that coming from production workers ’ wages (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 industry groups (food products, animal products, and crop products) accounting for over half of the total intermediate inputs (Table 2 - 4). Electric power generation, transmission, and distribution accounted for 2% of the total intermediate inputs, whereas 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 its 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 19.24% of the conventional boiler use fuel consumption by all manufacturing industries (DOE, Energy Information Administration, 2007a ).2 - 5

Table 2 - 3. Costs of Goods and Services Used in Food Manufacturing (NAICS 311)

($2007) Industry Ratios 2005 Share 2006 Share Total shipments ($millions) $563,797 100% $552,075 100% Total compensation ($mil lions) $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 $45,176 Total production w orkers ’ wa ges ($millions) $33,983 6% $33,670 6% Total production w orkers 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 Manufa ctures: 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 Prod ucts 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 Plasti cs and rubber products 3260 $9,641 Electric pow er 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 and fuel each accounted for 1% of the total value of shipments in food manufacturing (Table 2 - 3). In 2002, total energy consumption totaled 1,116 TBTU, a 7% in crease 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 electricit y, food manufacturing used more electricit y (Figure 2 - 3). From 2005 to 2006, the electricit y consumption increased b y nearl y 9% (Table 2 - 5). Table 2 - 5. Energy Used in Food Manufacturing (NAICS 311)

Fuel Type 1998 2002 2006 Net electricitya (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 s hort 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, mi nus quantities sold and transferred out. It does not include electricity 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 in cludes 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, marketers, and any mar keting 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 proces sing plants, including plants that fractionate raw natural gas liquids (NGLs). e Other includes net steam (the sum of purchases, generation from rene wables, and net transfers), and other energy that respondents indicated w as used to produce heat and power. * Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007 a . “ 2002 Energy Consumption by

Manufacturers

—Data Tables. ” Ta bles 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. ” Ta ble 3.1. Washington, DC: DOE. <http://www.e ia.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-05Index Value (Jan-97=100)

Man ufa ct ur ing Food Figure 2 - 3. Electric Power Use Trends in Food Manufacturing (NAICS 311): 1997 –2005 Source: Federal Reserve Board. 200 9 . “ Industrial Production and Capacity Utilization: Electric Power Use: Manufacturing and Mining. ” <http://www.fede ralreserve.gov/datadow nload/> . 2.1.2.3 Uses and Consumers The majority

of food manufacturing ’ s total commodity 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 Characteris tics This remaining subsection describes geographic, production, and market data. These data provide the basis for further anal ys is, including regulatory flexibility

anal yses, and give a complete picture of the recent historical trends of production and pricing. 2.1.3.1 Location In 2002, California had the most food manufacturing establishments in the United States, followed b y New York and Texas (see Figure 2 - 4). In addition, Pennsylvania, I llinois, Wisconsin, New Jersey , and Florida had over 1,000 establishments in their states.2 - 8

Table 2 - 6. Demand by Sector: Food Manufacturing (NAICS 311) ($2007, millions) Sector BEA Code Food Products Food manufacturing 3110 $91,518 Food services and drinking places 7220 $37,291 Animal production 1120 $15,870 General state an d 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 int ermediate use T001 $217,570 Personal consumption expenditures F010 $301,748 Exports of goods and services F040 $28,151 Im ports 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 Concentr ation 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 Statistics for the States, Metropolitan and Micropolitan Statis tical 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 the food manufacturing industry did not fall off d

uring the recession of 2001 as much as the manufacturing sector as a whole (Figure 2 - 5). Food manufacturing ’ s capacit y utilization has remained higher than manufacturing as a whole and went above 85% in the spring of 2008. The effects of the recent economi c downturn have not affected capacity

utilization as sharpl y in the food industry relative 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-08Percentage Capacity Utilization

Man ufa ct ur ing Food Figure 2 - 5. Capacity Utilization Trends in Food Manufacturing (NAICS 311) Source: Federal Reserve Board. 200 9 . “ I ndustrial Production and Capacity Utilization: Capacity Utilization. ” <http://www.federalreserve.gov/datadow nload/> . 2.1.3.3 Employment The geographic distribution of employ ment in food manufacturing varies substantially from the distribution of establishments. In 2002, Arkansas, ranked thirt y - fi rst in number of establishments and had the eighth most employ ees (53,844) because of its national high of 199 employ ees per establishment. New York, ranked second in number of establishments, had only the tenth most employ ees (50,012). North Carolina and Georgia also had greater than 50,000 employ ees, 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 Amer ican FactFinder; “ Sector 31: Manufacturing: Geographic Area Series: Industry Statistics 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 capacit y in food manufacturing onl y grew 17.94% between 1997 and earl y 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 ye ar 2007, the top eight food manufacturing companies each had greater than $10 billion in sales. These companies, however, are global, man y 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 compan y, Kraft Foods Inc., has 50.27% of its long - lived assets located outside of the United States (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-09Index Value (January 1997 = 100)

Manufac tu ri ng Food Figure 2 - 7. Capacity Trends in Food Manufacturing (NAICS 311) Source: Federal Reserve Board. 200 9 . “ Industrial Production and Capacity Utilization: Industrial Capacity. ” <http://www.federalreserve.gov/datadow nload/> . Table 2 - 7. Top Publicly Held U.S. Food Companies: 2007 Sales ($m illions) % of S ales in North America Kraft Foods Inc. 37,241 57.8% Ty son 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 C o. 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 industry grew between 2004 and 2005. Although the overall number of compan ies continued to grow in 2006, the number of those with a positive net income declined along with profit margins and total receipts (Table 2 - 8). Table 2 - 8. Corporate Income and Profitability for Food Manufacturing (NAICS 311) 2004 2005 2006 Number of cor

porations 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) $477,906,423 $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. 2008 a . “ Corporation Source Book: Data File 2005. ” <http://www.irs. gov/taxstats/article/0,,id=167415,00.html>; (January 14 , 2009). 2.1.3.6 Size Distribution The primary

criterion for categorizing a business as small is number of employ ees, using definitions by

the SBA for regulatory

flexibility

analy ses. 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 employ ees. In 2002, enterprises with fewer than 500 employ ees accounted for 32% of employ ment and 23% of receipts within food m anufacturing (Table 2 - 10). 2.1.3.7 Domestic Production Between 1997 and earl y 2008, overall manufacturing production grew faster (34.88%) than the food manufacturing component (26.18%) (Figure 2 - 8). The food manufacturing industry has been less volatile, particularl y 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 manufacturing it had briefl y 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 Manufacturi ng 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 Soy

bean Processing 500 311223 Other Oilseed Processing 1,000 311225 Fats and Oils Re fining 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 Manufacturing from Cacao Beans 500 311330 Confectio nery 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 31 1422 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 Manufactur ing 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 Doug h Manufacturing from Purchased Flour 500 (continued)2 - 14

Table 2 - 9. Small Business Size Standards: Food 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 May onnaise, Dressing and Other Prepared Sauce Manufacturing 500 31 1942 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 . “ Ta ble of Small Business Size Standards Matched to North American Industry Classification System Codes. ” Effective August 22, 2008. <http://www.sba.gov/services/contractingopportunities/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 Em ployees a 20 to 99

Em ployees 100 to 499

Em ployees 500 to 749

Em ployees 750 to 999

Em ployees 1,000 to 1,499

Em ployees 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 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 Si zes: 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-09Index Value (2002=100)

Manufac tu ri ng Food, Be ve ra ge , and Tob ac co Figure 2 - 8. Industrial Production Trends in Food Manufacturing (NAICS 311) Source: Federal Reserve Board. 2008 . “ Industrial Production and Capacity Utilization: Industrial Production. ” <http://www.federalreserve.gov/datadow nload/>.

0 10 20 30 40 50 60

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

U.S. Dom es ti c Ex port s (FA S V alu e) U.S. Imp ort s for Consum pt ion (CI F Val ue) Figure 2 - 9. International Trade Trends in Food Manufacturing (NAICS 311) Source: U.S. International Trade Commission. 200 8 . “ U.S. Domestic Exports ” & “ U.S. Imports for Consumption. ” <http://dataweb.usitc.gov/scripts/user_set.asp>.2 - 16

2.1.3.9 Market Prices Prices of goods in food manufacturing have moved generally in line wi th prices in overall manufacturing (see Figure 2 - 10). Both indexes increased over 31% since between earl y 2003 and earl y 2008, a CAGR of 5.13%. This rise was followed b y a marked decline in recent ye ars along with the downward trend in prices throughout th e econom y.

0 20 40 60 80 100 120 140 160 180 20019901991199219931994199519961997199819992000200120022003200420052006200720082009Index Values (December 1984=Base)

Man ufac tu ri ng 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 b y Standard & Poor ’ s (2008) , a number of factors are shaping the current economic environment for wood products, including, but not limited to, the housing slump, high input costs, low prices for lumber and other building materials, and a weak dollar. Table 2 - 11 shows that revenues in this industry are not entirel y predictabl e, exhibiting a drop in shipment revenue between 1997 and 2002 but a rise back to within $5 billion 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 Em ploy ees 570,034 543,459 536,094 519,651 Establishments 17,367 17,255 NA 14,862 NA = Not available. Sourc es: 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 Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997. ” <http: //factfinder.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 Stat istics: 2007. ” Accessed on December 27, 2009 . While total pay

roll dropped 3% over from 1997 to 2007, annual pay roll per employ ee rose 6.5% because of the decline in the number of employ ees (Table 2 - 12). Shipments per emplo yee grew 10.6% from 1997 to 2006 and dropped 8.9% from 20 06 to 2007 (Table 2 - 12). Table 2 - 12. Industry Data: Wood Product Manufacturing (NAICS 321)

Industry Data 1997 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 Statistics for the United States and the S tates (1997 NAICS Basis): 2002 and 1997. ” <http://factfinder.census.gov>; (July 8, 2008) . U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “ Sector 00: EC0700A1: All Sector s: Geographic Area Series: Economy - Wide Key Statistics: 2007. ” Accessed on December 27, 2009 .2 - 18

The U.S. Census Bureau categorizes this industry ’ s facilities into three categories: “ sawmills and wood preservation; ” “ veneer, ply wood, and engineered wood product manufacturing; ” and “ other wood product manufacturing. ” These are further divided into the following t ypes of facilities as defined b y the Census Bureau:

 Sawmills and Wood Preservation

– Sawmills and Wood Preservation (NAI CS 32111): This industry comprises establishments primar ily

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 treating them with preservatives; and (c) treating wood sawed, planed, or shaped in other establishments with creosote or other preservatives to prevent decay and to protect against fire and insects. Sawmills may

plane the rough lumber that they make with a plann i ng machine to achieve smoothness and uniformity of size.

 Veneer, Ply

wood, and Engineered Wood Product Manufacturing

– Veneer, Ply

wood, and Engineered Wood Product Manufacturing (NAICS 32121): This industry

comprises establishments primarily engaged in one or more of the following manufacturing activities: (a) veneer and/or ply wood, (b) engineered wood members, and (c) reconstituted wood products. This industry includes manufacturing pl ywood from veneer made in the same establishment or from veneer made in oth er establishments, and manufacturing pl ywood faced with non wood 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 sizes. These establishments gener all y use woodworking machinery, such as jointers, planers, lathes, and routers to shape wood.

– Wood Container and Pallet Manufacturing (NAI CS 32192): This industry comprises establishments primarily engaged in manufacturing wood pallets, wood box shook, woo d boxes, other wood containers, and wood parts for pallets and containers.

– All Other Wood Product Manufacturing (NAICS 32199): This industry comprises establishments primarily

engaged in manufacturing wood products (except establishments operating sawmills and wood preservation facilities; and establishments manufacturing veneer, ply wood, engineered wood products, millwork, wood containers, or pallets).2 - 19

Figure 2 - 11 shows that the industry proportion of the value of shipments for other wood product manufacturing (51%) was greater than the value of shipments for sawmills and wood preservation (27%) and veneer, pl ywood, and engineered wood products (22%). Figure 2 - 12 indicates that the majority of employ ees in this industry fell under other wood products (60%). Veneer, ply

wood, and engineered wood products had the same percentage (20%) of employ ees as sawmills and wood preservation (20%), even though it contributed to a lesser portion of the value of shipments.

Ve ne er, P lyw ood, & En gine ered W ood Produc t Mfg (N AI CS 3212) , 22%

Sa wmi lls & W ood Pres erv ation (N AI CS 3211) , 27% Ot he r Wood Pr oduct M fg (N AI CS 3219) , 51%

Figure 2 - 11. Distribution of Value of Shipments 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 Statistics: 2007. ” < http://factfinder.census.gov >. Accessed on December 27, 2009 . [Source for 2007 numbers]2 - 20 Ot her Wood Product M fg (N AI CS 3219), 60% Sa wmi lls & Wood Pres erva ti on (N AI CS 3211), 2 0%

Ve ne er, P lyw ood, & En gine ered W ood Pro duc t Mfg (N AI CS 3212) , 20% Figure 2 - 12. Distribution of Employment within Wood Product Manufacturing (NAICS

322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “ Sector 00: EC0 700A1: All Sectors: Geographic Area Series: Economy - Wide Key Statistics: 2007 ” Release Date: 12/22/09. <http://factfinder.census.gov>. Accessed 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 industry has with other industries and people and identify the key goods and services used b y the industry and the major uses and consumers wood product s. 2.2.2.1 Goods and Services Used in W ood 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 employ ees represented 22% of the total v alue in 2007. Both the number of total shipments and the number of emplo ye es in this industry

decreased between 2005 and 2007

—the former b y 14% and the latter b y 3%. The top 10 industry

groups supplying inputs to the wood product industry accounted for 80% of the total intermediate inputs according to 2008 Bureau of Economic Analy sis data (Table 2 - 14). The largest comes from the wood product industry itself. This is quite understandable, since the descriptions of the various industries within wood product m anufacturing 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 are rounded out by forestry and logging products, wholesale trade, management of companies and enter prises, 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 52 4,212 Average compensation per employee $43,286 $43,473 $42,947 Total production w orkers ’ wa ges (millions) $13,363 11% $13,132 11% $12,086 12% Total production w orkers 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: Detaile d 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 industry . The 2008 Annual Energy Outlook predi cts that the wood product industry will be one of five (out of eight) non - energy intensive 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 550 0 $2,853 Truck transportation 4840 $2,542 Electric pow er 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 su pport 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 total energy use between 1998 and 2002. Figure 2 - 13 shows that electrical power use 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 mainte nance and repair construction consume 35% of the total commodity

output in this industry . The top 10 consumers of wood products make up 54% of the demand for wood products. Although man y of the top consumers deal with construction, repair, or real estate s ervices, other ty pes of consumers, such as food services and drinking places, rail transportation, plastics and rubber products manufacturing, and other, use these products.2 - 23

Table 2 - 15. Energy Used in Wood Product Manufacturing (NAICS 321)

Fuel Type 1998 2002 2006 Net electricitya (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 electricity 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 fue l 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, marketers, and any marketing subsidiaries of util ities. 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, generation from rene wables, and net transfers), and other energy that respondents indicated w as used to produce heat and power. * Estimate less than 0.5. Q = Withheld because relative standard error is greater than 50%. Sources: U.S. Department of Energy, Energy Information Administration. 2007 a . “ 2002 Energy Consumption by

Manufac turers —Data Tables. ” Ta bles 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. ” Ta bles 3.1. < http://www.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-05Index Value (Jan-97=100)

Man ufac tu ri ng El ect ri c Pow er Use f or N AI CS 321 Fig ure 2 - 13. Electrical Power Use Trends in the Wood Product Manufacturing Industry

(NAICS 321): 1997 –2005 Source: Federal Reserve Board. 2009 . “ Industrial Production and Capacity Utilization: Electric Power Use: Manufactur ing and Mining. ” <http://www.federalreserve.gov/datadow nload/> 2.2.3 Firm and Market Characteristics This section describes geographic, production, and market data. These data provide the basis for further analy sis, including regulatory flexibility

anal ys es, 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 with the largest number of wood product manufacturing establishments are dispersed throughout the country , with a signific ant 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 Product 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 manufa cturing 336A $2,516 Real estate 5310 $2,335 Food services and drinking 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 dw elling 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. Washin gton, DC: BEA. 2.2.3.2 Production Capacity and Utilization Capacity utilization of the wood product manufacturing industry has been experiencing capacity utilization increases and declines with more extreme fluctuations than those of all manufacturing industries c ombined. The decline in wood product manufacturing is similar to total manufacturing between 1997 and 2002. However, capacity utilization in total manufacturing, which peaked in 2006, started increasing at a faster rate than wood product manufacturing, but decreased sharpl y after its peak. Wood product manufacturing experienced its own rapid decrease in capacity 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 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 Statistics for the States, Metropolitan and Micropo litan Statistical Areas, Counties, and Places: 2002. ” <http://factfinder.census.gov>; (July 23, 2008) . 2.2.3.3 Employment California has the largest number of emplo ye es 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 employ ees do not directly correlate with the states with the highest number of establishments. States such as Indiana, Ge orgia, Arkansas, and Oregon had fewer than 600 establishments, as shown in Figure 2 - 14, but had more than 20,000 employ ees, whereas states such as Ohio and New York had fewer than 20,000 employ ees but more than 600 establishments. 2.2.3.4 Plants and Capacity While the capacit y of the manufacturing sector has been growing consistently since 1997, the wood product manufacturing industry has experienced inconsistent growth. After a small amount of growth in capacity between 1997 and 2001, the wood product manufacturin g 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-09Percentage Capacity Utilization

Man ufac tu ri ng Wood Produc ts Figure 2 - 15. Capacity Utilization Trends in the Wood Product Manufacturing Industry

(NAICS 32 1) Source: Federal Reserve Board. 2009 . “ Industrial Production and Capacity Utilization: Capacity Utilization. ” <http://www.federalreserve.gov/datadow nload/> 2.2.3.5 Firm Characteristics In 2006, the top 10 paper and forest prod uct companies produced over $75 billion in sales, with the top two companies

— International Paper and Wey erhaeuser

— generating nearl y $22 billion each (Table 2 - 17). The top two companies ’ revenue consists of 58% of the revenue of the top 10 companies in Stan dard & Poor ’ s (S&P ’ s) list (Benwart, 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 catego rizing a business as small is the number of employ ees, using definitions by

the SBA for regulatory

flexibility

anal ys es. According to SUSB reports for 2002, small companies were the recipients of the majority of receipts in 2002; 53% of receipts were gener ated b y companies with fewer than 500 employees (Table 2 - 18). The number of employ ees in the small business cutoff is 500 employ ees for all subindustries 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 th e 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 Statistics for the States, Metropolitan and Micropoli tan 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 m anufacturing have decreased since 2006 compared to the steady increase in production for the manufacturing sector since 1997 (Figure 2 - 18). Similar to capacit y 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 industry has contributed to an increasing trade deficit (Figure 2 - 16). The value of imports has fluctuated greatl y 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-09Index Value (Jan-97=100)

Man ufac tu ri ng Wood Produc ts Figure 2 - 17. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321) Source: Federal Reserve Board. 2009. “ Industrial Production and Capacity Utilization: Industrial Capacity. ” <http://www. federalreserve.gov/datadow nload/>. 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 Tem ple - Inland 5,558 Bow ater 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: Benw art, S.J. 2006 . “ Paper & Forest Produc ts. ” Standard and Poor ’ s Industry Surveys . 176(28).2-30 Table 2 - 18. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 321) Enterprises with Variable Total 1 to 20 Employees a 20 to 99

Employees 100 to 499

Employees 500 to 7 49

Employees 750 to 999

Employees 1,000 to 1,499

Employees Firms 15,198 9,740 3,280 791 63 27 30 Establishments 17,052 9,758 3,482 1,271 166 91 133 Em ployment 534,011 65,423 132,612 118,910 19,784 11,944 18,533 Receipts ($thousands ) $88,649 $8,204 $18 ,276 $19,717 $3,192 $1,902 $3,118 Receipts/firm ($thousands) $5,833 $842 $5,572 $24,927 $50,673 $70,453 $103,927 Receipts/establishment ($thousands) $5,199 $841 $5,249 $15,513 $19,231 $20,904 $23,442 Receipts/employment ($) $166,006 $125,393 $137,818 $1 65,814 $161,363 $159,262 $168,231

a Excludes Statistics 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 Siz e 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 Hardw ood Veneer and Plyw ood 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 3 21991 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 . “ Ta ble of Small Business Size Standards Matched to North American Industry Classification System Codes. ” Effective August 22, 2008. <http://www.sba.gov/services/contractingopportunities/sizestandardstopics/size/index.html>. 2.2.3.9 Mar

ket Prices Prices of goods in the wood product manufacturing industry have remained roughl y the same since 2005. The prices for the entire manufacturing sector increased between 2003 and 2008 but have decreased since August 2008. Producer price indices (PP Is ) show that producer prices for wood products increased b y 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)

Manuf ac tu ri ng Wo od Pr odu cts Figure 2 - 18. Industrial Production Trends in the Wood Product Manufacturing Industry

(NAICS 321): 1997 –2009 Source: Federal Reserve Board. 2009. “ Industrial Production and Capacity Utilization: Industrial Production. ” <http://www.federalreserve.gov/datadow nload/>.

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)

Wo od Pro duct E xports Wo od Product I mp or ts 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/scripts/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 Pro duct s PPI Al l Manufac tu ring PPI Figure 2 - 20. Producer Price Trends in the Wood Product Manufacturing Industry

(NAICS 321) Sour ce: U.S. Bureau of Labor Statistics (BLS). 2009 a . “ 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 Introd

uction The paper manufacturing subsector is an essential component of all business operations worldwide. Broadl y speaking, paper and paperboard are manufactured b y converting timber or other recy cled material into products such as printing and writing pape rs, newsprint, tissue, and containerboard (Benwart, 2006 ). The subsector has been experiencing a decline in shipments as of late. From 1997 to 2007, shipments in the industry declined 7%, and employ ment declined by 27% (Table 2 - 21). Whi le total pay roll dropped 26% over this time, annual pay roll per employ ee rose 2% from 1997 to 2007 because of the decline in the number of emplo yees (Table 2 - 20). Shipments per employ ee grew 28% from 1997 to 2007, with much of that growth taking place betw een 2002 and 2006 (Table 2 - 21).2 - 34

Table 2 - 20. Key Statistics: Paper 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 Em ploy ees 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 Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997. ” <http://factfinder.census.gov>; (July 8, 2008) . U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactF inder; “ Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy - Wide Key Statistics: 2007. ” Accessed on December 28, 2009 . [Source for 2007 numbers] Table 2 - 21. Industry Data: Paper Manufacturing (NAICS 322)

Industry Data 1997 2002 2006 2007 Total shipments ($2007, mi llions) $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 Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997. ” <http://factfinder.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 f or 2007 numbers]2 - 35

The U.S. Census Bureau categorizes this industry ’ s facilities into two categories: pulp, paper, and paperboard manufacturing and converted paper product manufacturing. These are further divided into the following ty pes of facilities as de fined by the Census Bureau (2001) :

 Pulp, Paper, and Paperboard:

– Pulp Mills (NAI CS 32211): This industry comprises establishments primarily engaged in manufacturing pulp without manufacturing paper or paperboard. The pulp is mad e by

separating the cellulose fibers from the other impurities in wood or other materials, such as used or recy cled rags, linters, scrap paper, and straw.

– Paper Mills (NAICS 32212): This industry comprises establishments primarily engaged in manufacturing paper from pulp. These establishments may manufacture or purchase pulp. In addition, the establishments may convert the paper they

make. The activity

of making paper classifies an establishment into this industry

regardless of the output.

– Paperboard Mills (NA IC S 32213): This industry comprises establishments primarily engaged in manufacturing paperboard from pulp. These establishments may manufacture or purchase pulp. In addition, the establishments may also convert the paperboard they make.

 Converted Paper Products:

– Paperboard Containers Manufacturing (NAICS 32221): This industry comprises establishments primarily

engaged in converting paperboard into containers without manufacturing paperboard. These establishments use corrugating, cutting, and shaping mac hinery

to form paperboard into containers. Products made by these establishments include boxes; corrugated sheets, pads, and pallets; paper dishes; and fiber drums and reels.

– Paper Bag and Coated and Treated Paper Manufacturing (NAICS 32222): This industry comprises establishments primarily engaged in one or more of the following manufacturing activities: cutting 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 combinations of paper and foil with plastics film; laminated aluminum and other converted metal foils from purchased foils; and surface coating paper or paperboard .

– Stationary

Product Manufacturing (NAICS 32223): This industry comprises establishments primarily

engaged in converting paper or paperboard into products used for writing, filing, art work, and similar applications.

– Other Converted Paper Products (NAICS 3 2229): 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 paperboard, and stationery products), or2 - 36  converting pulp into pulp products, such as disposable diapers, or molded pulp egg cartons, food tray s, and dishes. Figure 2 - 21 shows that the value of shipments for converted paper products was 54% of the value of all paper products in 2007, while the val ue of shipments for pulp, paper, and paperboard products was 46%. Figure 2 - 22 indicates that 70% of industry employ ees worked in the converted paper product category of the industry

due to the labor intensive aspects of those facilities.

PPP (3221) 46% CP P (3222) 54%

Figure 2 - 21. Dis tribution of Value of Shipments 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 Indu stry for the United States: 2007. ” Accessed on December 28, 2009 .

PPP (3221) 30% CP P ( 3222) 70%

Figure 2 - 22. Distribution of Employment 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. ” < http://factfinder.census.gov >. Accessed on December 28, 2009 .2 - 37

2.3.2 Supply and Dem and Characteristics Next, we provide a broad overview of the supply and demand sides of the paper manufacturing industry . We emphasize the economic interactions this industry has with other industries and people and identify the key goods and services used b y 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). Total compensation of employees represented 15% of the total value in 2007, down from 17% in 2005. The total number of employ ees dropped b y 2%, between 2005 and 2007, while shipments increased b y 3% in the same period. The top 10 industry

groups su

pplying inputs to the paper manufacturing subsector accounted for 70% of the total intermediate inputs according to 2008 Bureau of Economic Analy sis (BEA) data (Table 2 - 23). Inputs for pulp, paper, and paperboard products are notably different from inputs for converted paper products because the NAICS 3221 group represents the initial step in the paper manufacturing process; thus, its inputs include more raw resources such as wood products, forestry and logging products, natural gas, and electricity. This b ecomes evident when observing inputs for converted paper 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 - intensiv e subsector. The 2008 Annual Energy Outlook predicts that the paper - producing subsector will be one of four subsectors experiencing positive average growth of delivered energy consumption between 2006 and 2030 (DOE, 2008 ). Energy generati on from the recovery boiler is often insufficient for total plant needs, so facilities augment recovery boilers with fossil fuel

– fired and wood waste

– fired boilers (hogged fuel) to generate steam and often electricity . Industry 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 energy use decreased between 1998 and 2006 by 14%. Figure 2 - 24 indicates that total electrical power use changed sporadically between 2002 and 2004 but decreased consistently and rapidl y after 2004.2 - 38

Table 2 - 22. Costs of Goods and Services Used 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% Tota l employees 426,748 414,049 417,367 Average compensation per employee $67,596 $67,121 $65,051 Total production w orkers wages ($2007, millions) $14,965 9% $14,689 8% $14,190 8% Total production w orkers 331,228 321,684 321,937 Total production h ours (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 electri city $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: EC073 1I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007. ” <http://factfinder.census.gov>. Accessed on December 28, 2009 . [Source for 2007 numbers]2 - 39

Table 2 - 23. Key Goods and Services Us ed 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 $3,91 6 $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 pow er 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 Sourc e: 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 electricitya (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 (milli on 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 electricity 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 produ cers, gas brokers, marketers, 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, generation from rene wables, and net transfers), and other energy that respondents indic ated w as used to produce heat and power. * Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007 a . “ 2002 Energy Consumption by

Manufacturers —Data Tables. ” Ta bles 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. ” Ta ble 3.1. < http://www.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html >. Accessed on December 27, 2009. [S ource for 2006 numbers] Over the last 25 ye ars, the pulp and paper subsector has changed its energy generation methods from fossil fuels to a greater use of processes such as increases in the use of wood wastes in place of fuel (Table 2 - 25). During the 197 2

– 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-05Index Value (Jan-97=100)

Man ufa ct uring Pa per Figure 2 - 23. Electrical Power Use Trends in the Paper Manufacturing Industry: 1997–

2005 Source: Federal Reserve Board. 2009. “ Industrial Production and Capacity Utilization: Electric Power Use: Manu facturing and Mining. ” <http://www.federalreserve.gov/datadow nload/> . Table 2 - 25. Estimated Energy Sources for 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 ener gy — — — 6.7% Waste w ood and w ood 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 pow er 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/resources/publications/assistance/sectors/notebooks/ index.html>.2 - 42

2.3.2.2 Uses and Consumers Products manufactured in the NAICS groups 322 1 and 3222 have different, but complementary , consumer profiles. NAICS 3221 supplies a significant portion of NAI CS 3222 demand (37% of total commodity output). Both industries specialize in products with intermediate uses, with an average of 92% of sales between the two going toward this purpose. NAI CS 3222 has a very

diverse assortment of subsector groups from which it receives demand. Food manufacturing makes up 21% of the demand, making members of this industry the largest consumer of converted paper pr oducts (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 Manufacturing 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 activities 3230 $13,320 $3,874 $17,194 Gene ral 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 good s and services F040 $7,724 $5,799 $13,523 Im ports 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 I nput - 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 analy sis, including regulatory flexibility

anal ys es, 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, f ollowed b y Il linois and some bordering northeastern states. The location of establishments in the paper manufacturing industry varies a great deal by subsector. Wisconsin and New York have the most pulp, paper, and paperboard establishments, while Californ ia dominates with over 500 converted paper product establishments. Overall, the United States has 561 pulp, paper, and paperboard establishments 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 Manufactu ring Industry (NAICS

322): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for the States, Metropolitan and Micropolitan Statistical Areas, C ounties, 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 manufacturing 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 consistentl y 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-09Percentage Capacity Utilization

Man ufa ct uring Pa per (N AI CS 322) Figure 2 - 25. Capacity Utilization T rends in the Paper Manufacturing Industry

(NAICS 322) Source: Federal Reserve Board. 2009 . “ Industrial Production and Capacity Utilization: Capacity Utilization. ” <http://www.federalreserve.gov/datadow nload/>. 2.3.3.3 Employment Wisconsin has the largest number of emplo ye es in the paper manufacturing subsector with over 38,008 reported in the 2002 census followed b y 29,379 in California (Figure 2 - 26). The converted paper products group has more employ ees per establishment, 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: G eographic Area Series: Industry Statistics 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 Capa city While the manufacturing sector has been growing consistently since 1997, the paper manufacturing sector has not experienced the same amount of success in the same period. Despite a small amount of growth in capacit y between 1997 and 2001, the paper manufacturing subsector ’ s capacit y has declined to as much as 7% below 1997 capacit y l evels (Figure 2 - 27). 2.3.3.5 Firm Characteristics In 2006, the top 10 paper and forest product companies produced over $75 billion in sales, with the top two companies

— Internationa l Paper and Wey erhaeuser

— generating nearl y $22 billion each (Table 2 - 27). The top two companies ’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor ’ s (S&P ’ s) list (Benwart, 2006 ). Although these numbers do not exclusively reflect paper products, they do convey

the market environment in which firms in this sector compete.2 - 46 90 100 110 120 130 140 1501997-011997-071998-011998-071999-011999-072000-012000-072001-012001-072002-012002-072003-012003-072004-012004-072005-012005-072006-012006-072007-012007-072008-012008-072009-012009-07Index Value (Jan-97=100)

Manufa ct uring Pa pe r (NA ICS 322) Figure 2 - 27. Capacity Trends in the Paper Manufacturing Industry (NAICS 322) Source: Federal Reserve Board. 2009 . “ Industrial Production and Capacity Utilization: Industrial Capacity. ” <http://www.federalreserve.gov/datadow nload/>. Table 2 - 27. Largest U.S. Paper and Forest Products Companies: 2006 Company Revenues ($millions) a International Paper 21,99 5 Weyerhaeuser 21,896 Smurfit - Stone 7,157 MeadWestvaco 6,530 Tem ple - Inland 5,558 Bow ater 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 product s in certain cases. Sources: Benw art, 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 categor izing a business as small is the number of employ ees, using definitions by

the SBA for regulatory

flexibility

anal ys es. According to SUSB reports for 2002, large companies dominated revenue - generating transactions in the paper manufacturing subsector; 80% of receipts were generated b y companies with 500 employees or more (Table 2 - 28). This was especiall y true in the pulp, paper, and paperboard group, in which large companies generated 92% of receipts. The number of emplo ye es in the small business cutoff var ies according to six - digit NAICS codes (Table 2 - 29). The cutoff for all subsectors in the pulp, paper, and paperboard group is 750 employ ees, while the cutoff for most converted paper product groups is 500 employ ees. Table 2 - 28. Distribution of Economic Da ta 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 E stablishments 5,546 1,488 1,271 755 83 69 138 Em ployment 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 $179,57 7 $206,001 Receipts/establishment ($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 ent ities 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 Em ployment 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 New sprint) 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 Manufac turing 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 Multiw all 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 Rel ated 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 . “ Ta b le of Small Business Size Standards Matched to North American Industry Classification System Codes. ” Effective August 22, 2008. <http://www.sba.gov/services/contractingopportunities/sizestandardstopics/size/index.html>. 2.3.3.7 Domestic Production Similar to indus try

capacity rates, subsector production rates for paper manufacturing have witnessed a decreasing rate of production compared to the stead y 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 production trend since then.2 - 49

Figure 2 - 28. Industrial Production Trends in the Paper Manufacturing Industry (NAICS

322): 1997–2009 So urce: Federal Reserve Board. 2009 . “ Industrial Production and Capacity Utilization: Industrial Production. ” <http://www.federalreserve.gov/datadow nload/>. 2.3.3.8 International Trade Since 1997, paper manufacturing products, both 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 similar rates since 1999. 2.3.3.9 Market Prices Prices of goods in paper manufacturing have been increasing at a rate consistent with all manufacturing products (Figure 2 - 30). Producer price indices (PPIs) show that producer prices for paper in 2007 increased by 20% since 1997, while producer prices for all manufacturing goods in creased 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)

Yea r Pa pe r Ex por ts Pa pe r Imp or ts Figure 2 - 29. International Trade Trends in the Paper Manufacturing Industry (NAICS

322) Source: U.S. International Trade Commission. 2008 . “ U.S. Dom estic Exports ” & “ U.S. Imports fo r Consumption. ” <http://dataweb.usitc.gov/scripts/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-09Index Value (Jan-97=100)

Manu fa ct ur ing Pa pe r (N AI CS 322) Figure 2 - 30. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222) Source: U.S. Bureau of Labor Statistics (BLS). 2009 b . “ 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 produces over 70,000 chemical substances, man y of which are ubiquitous in American life. Broadl y speaking, chemical manufacturing operates b y converting feedstocks into chemical products that 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% (Table 2 - 30). While total pay roll dropped 0.6% over this time, annual pay roll per employ ee rose 7.8% from 1997 to 2007 because of the decline in the number of emplo yees (Table 2 - 31). Shipments per employ ee 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 energ y - intensive industry groups as aggregated b y the Department of Energy (DOE). Basic Chemical Manufacturing (NAICS 3251); Resin, Rubber, and Artificial Fibers Manufacturing (NAI CS 3252); and Agricultural Chemical Manufacturing (NAICS 3253).

 Pharmaceutical an d Medicine Manufacturing (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 approximately one - third of the total employ ment 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: Chemical Manufacturing (NAICS 325) 1997 2002 2006 2007 Shipme nts ($2007, millions) $521,251 $531,173 $675,223 $738,303 Payroll ($2007, millions) $49,961 $51,317 $46,981 $49,648 Em ploy ees 882,645 853,224 747,134 814,024 Establishments 13474 13,475 NA 12,937 NA = Not available. Sources: U.S. Census Bureau; generat ed 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 Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997. ” <http://factfinder.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://factfind er.census.gov>. Accessed on December 27, 2009 . [Source for 2007 numbers] Table 2 - 31. Industry Data: Chemical Manufacturing (NAICS 325)

Industry Data 1997 2002 2006 2007 Total shipments ($2007, millions) $521,251 $531,173 $675,223 $738,303 Shipm ents per establishment ($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 Em ploy ees 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 Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997. ” <http://factfinder.census.gov>; (July 8, 2008) . U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “ Sect or 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]2 - 53 Bulk Che mic al s, 35%

Pha rm ac eu tic al s, 31% Ot her Chem ic als, 34%

Figure 2 - 31. Distribution of Employment within Chemical Manufac turing (NAICS 325):

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 U.S.: 2007. ” Release date: October 30, 2009. Accessed on December 27, 2009 .

Bulk Chem ic als, 50%

Pha rm ac eu ti ca ls, 26% Ot he r Che mi ca ls, 24%

Figure 2 - 32. Distribution of Total Value of Shipments within Chemical Manufacturing

(NAICS 325): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFin der; “ Sector 31:EC0731I1: Manufacturing Industry Series: Detailed Statistics by Industry for U.S.: 2007. ” <http://factfinder.census.gov>. Accessed 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 industry has with other industries and people, including identifying the key goods and services used by the industry

and the major uses and consum ers of chemical manufacturing products. The top 10 industry

groups supplying inputs to the chemical manufacturing industry in 2002 accounted for 71% of the total intermediate inputs (Table 2 - 32). Bulk chemicals ’ production was the most energy intensive, us ing 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 Manufacturing (NAICS 325)

($2007, mil lions ) Good or Service BEA Code Bulk Chemicals Pharm aceuticals 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 Pharm aceuticals 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 pow er generation, transmission, and distribution 2211 $6,025 $716 $807 $7,548 Natural gas distribution 2212 $6,390 $154 $390 $6,934 Total intermediate use T00 5 $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. Washing ton, 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 employees 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 industry . Pharmaceuticals and other chemical manufacturing are categorized as non - energy - intensive industries, grouped together with oth er industry groups under the “ Balance of Manufacturing ” category (DOE, 2008 ). Fuel used in chemical production can either facilitate chemical processes 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 ’ Reill y, 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 ov er the last 10 y ears. According to DOE, natural gas use b y the chemical manufacturing industry dropped 30% from 1998 to 2006, and electricity

use fell 10% (Table 2 - 34). From 1997 to 2005, when data ceased to be available, chemical manufacturing used less e lectricity relative to the manufacturing 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 inte rmediate use, representing 93% of its total commodity output and 56% of the total intermediate use of chemical manufacturing products. Pharmaceuticals has both a high level of demand from personal consumption, accounting for 67% of the total personal consumption of chemical manufacturing products, and a large 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 em ploy ee $82,887 $82,559 $79,333 Total production w orkers ’ wa ges (millions) $22,643 4% $22,231 3% $23,157 3% Total production w orkers 431,502 430,880 463,802 Total production hours (thousands) 899,499 885,993 948,244 Average production wages pe r 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 electricitya (million kWh) 169,233 153,104 151,646 Residual fuel oi l (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 1,1 58 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 electricity inputs fr om 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 in cludes natural gas obtained from utilities, local distribution companies, and any other supplier(s), such as independent gas producers, gas brokers, marketers, 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 Othe r includes net steam (the sum of purchases, generation from rene wables, and net transfers), and other energy that respondents indicated w as used to produce heat and power. * Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Adm inistration. 2007b . “ 2006 Energy Consumption by

Manufacturers

—Data Tables. ” Ta ble 3.1. Washington, DC: DOE. <http://www.eia.doe.gov/emeu/mecs/ mecs2006/2006tables.html>. [Source fo r 2006 numbers] U.S. Department of Energy, Energy Information Administration. 2007 a . “ 2002 Energy Consumption by Manufacturers

—Data Tables. ” Ta bles 3.2 and N3.2. Washington, DC: DO E. <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-05Index Value (Jan-97=100)

Man ufa ct uring Che mi ca l M an ufa ct ur ing Figure 2 - 33. Electric Power Use Trends in Chemical Manufacturing (NAICS 325):

1997–2005 Source: Federal Reserve Board. 2009 . “ Industrial Product ion and Capacity Utilization: Electric Power Use: Manufacturing and Mining. ” <http://www.federalreserve.gov/datadow nload/> . 2.4.3 Firm and Market Characteristics This remaining subsection describes geographic, production, and market data. These data provide the basis for further anal ys is, including regulatory flexibility

anal yses, and give a complete picture of the recent historical trends of production and pricing. 2.4.3.1 Location In 2002, California had the most chemical manufacturing establishments in the United Stat es, followed by

Texas and New Jersey

(Figure 2 - 34). The composition of establishments in these states differs among the different industry groups. Despite the fact that each group employ ed an approximately equal share of people in 2002, 54% of the total es tablishments were other chemicals establishments, and only 13% were pharmaceutical establishments.2 - 59

Table 2 - 35. Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007 millions)

Sector BEA Code Bulk Chemicals Pharm aceuticals Other Chemicals Total Plast ics and rubber products manufacturing 3260 $39,353 $0 $3,057 $42,410 Basic chemical manufacturing 3251 $33,972 $0 $1,675 $35,647 Pharm aceutical and medicine manufacturing 3254 $4,778 $25,125 $462 $30,365 Resin, rubber, and artificial fibers manufacturin g 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 consum

ption expenditures F010 $4,449 $123,746 $55,882 $184,077 Exports of goods and services F040 $47,121 $15,683 $13,136 $75,940 Im ports 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 Es tablishments Fewer than 100 100 - 249 250 - 474 475 - 750 More than 750 Figure 2 - 34. Establishment Concentration in Chemical Manufacturing (NAICS 325):

2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 31: Manufacturing: Geograph ic Area Series: Industry Statistics 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 an d Utilization Capacity utilization of the chemical manufacturing industry has been broadly

in line with the manufacturing sector (Figure 2 - 35). In the second half of 2005, the chemical manufacturing industry ’ s capacity

utilization fell dramaticall y because of the multiple hurricanes affecting the Gulf Coast states. The impact of the economic downturn in 2001 can be seen in the capacity utilization of both manufacturing and chemical manufacturing. 2.4.3.3 Employment The geographic distribution of employ ment in chemi cal manufacturing differs largel y among the different groups. In California, 52% of the chemical manufacturing employ ment comes from the pharmaceutical industry , while 60% of the chemical manufacturing employ ment in the Gulf Coast states comes from bulk ch emicals manufacturing (Figure 2 - 36). 2.4.3.4 Plants and Capacity Production capacit y in chemical manufacturing has grown 33% since 1997. This growth, however, is 9% less than the growth rate for the manufacturing 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-09Percentage Capacity Utilization

Man ufac tu ri ng Che mi ca l Manufac tu ri ng Figure 2 - 35. C apacity Utilization Trends in Chemical Manufacturing (NAICS 325) Source: Federal Reserve Board. 2009 . “ Industrial Production and Capacity Utilization: Capacity Utilization. ” <http://www.federalreserve.gov/datadow nload/>.

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 Chemical Manufacturing (NAICS 325): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for the State s, 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-09Index Value (Jan-97=100)

Man ufac tu ri ng Che mi ca l Manufac tu ri ng Figure 2 - 37. Capacity Trends in Chemical Manufacturing (NAICS 325) So urce: Federal Reserve Board. 2009 . “ Industrial Production and Capacity Utilization: Industrial Capacity. ” <http://www.federalreserve.gov/datadow nload/>. 2.4.3.5 Firm Characteristics In 2007, the top six companies by chemical sal es had greater than $10 billion in sales. Together, their sales are greater than the next 44 highest chemical companies combined. These, however, are global companies, with a large portion of both sales and production coming from operations outside of the United States (Table 2 - 36). The largest chemical manufacturing compan y, Dow Chemicals, has 108 out of 150 manufacturing sites located outside of the United States (Dow Chemical Company , 2008 ). In 2007, 58% of U.S. chemica l manufacturing corporations generated net income. In cluding those with and without net income, chemical manufacturers had an average before - tax profit margin of 10.24%. Profitability is highest for pharmaceutical and medicine corporations (Table 2 - 37).2 - 63

Ta ble 2 - 36. Top Chemical Producers: 2007 Chemical Sales ($m illions) % of Total Sales % of Sales in United States Dow Chemical 53,513 100% 35% ExxonMobil 36,826 9% 38% DuPont 29,218 100% 38% Lyo ndell 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 calculated from total sales, not chemical sales. b Percentage of sa les 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)

Ind ustry Number of

Corporations Number of

Corporations with Net Income Total

Receipts ($thousands) Business

Receipts ($thousands) Before Tax Profit Margin After - Tax

Profit Margin Basic chemical 1,244 757 $195,022,700 $178,019,490 5.07% 4.10% Resin, synth etic rubber, and artificial synthetic fibers and filaments 1,067 648 $44,692,366 $40,078,009 8.06% 6.33% Pharm aceutical 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 $808,897,81 0 10.24% 7.89% Source: Internal Revenue Service, U.S. Department of Treasury. 2008 b . “ Corporation Source Book: Data File 2007. ” <http://www.irs.gov/taxstats/article/0,,id=167415,00.html>; (January, 15, 2010).2 - 64

2.4.3.6 Size Distribution The primary

criterion for categorizing a business as small is number of employ ees, using definitions by

the SBA for regulatory

flexibility

analy ses. The data describing size standards are provided in Table 2 - 38 and T able 2 - 39. In 2002, enterprises with fewer than 500 employ ees accounted for 27% of employ ment and 15% of receipts within the chemical manufacturing industry). 2.4.3.7 Domestic Production In the late 1990s, overall manufacturing production was growing much faster t han the chemical manufacturing component (Figure 2 - 38). Following the recession of 2001, however, the components have moved broadl y in line with one another, except for the drop in chemical manufacturing production caused b y the hurricane season of 2005. 2.4.3.8 I nternational Trade In the ye ar 2000, the United States moved from having a trade surplus to a trade deficit in chemical manufacturing products (Figure 2 - 39). This change occurred because the trade deficit in pharmaceutical manufacturing, currentl y at $35 b illion, overwhelmed the trade surplus of bulk chemicals and other chemical manufacturing combined, currentl y at $22 billion. 2.4.3.9 Market Prices Prices of goods in chemical manufacturing have accelerated rapidly in the last 2 y ears, having outpaced overall manuf acturing since 2002 (Figure 2 - 40). Much of this recent acceleration seen in the industry PPI is due to the bulk chemicals segment, largel y reflecting the rapid increase in fertilizer prices.2 - 65

Table 2 - 38. Small Business Size Standards: Chemical Manufacturing (NAICS 325) NAICS Description Em ployees 325110 Petrochemical Manufacturing 1,000 325120 Industrial Gas Manufacturing 1,000 325131 Inorganic Dye and Pigment Manufacturing 1,000 325132 Synthetic Organic Dye and Pigment Manufacturing 750 325181 Al kalies and Chlorine Manufacturing 1,000 325182 Carbon Black Manufacturing 500 325188 All Other Basic Inorganic Chemical Manufacturing 1,000 325191 Gu m and Wood Chemical Manufacturing 500 325192 Cy clic Crude and Intermediate Manufacturing 750 325193 Ethyl Al cohol Manufacturing 1,000 325199 All Other Basic Organic Chemical Manufacturing 1,000 325211 Plastics Material and Resin Manufacturing 750 325212 Synthetic Rubber Manufacturing 1,000 325221 Cellulosic Organic Fiber Manufacturing 1,000 325222 Noncellul osic 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 Agricultural 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 Manuf acturing 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 32591 0 Printing Ink Manufacturing 500 325920 Explosives Manufacturing 750 325991 Custom Compounding of Purchased Resins 500 325992 Photographic Film, Paper, Plate and Chemical Manufacturing 500 325998 All Other Miscellaneous Chemical Product and Preparation

Manufacturing 500 Source: U. S. Small Business Administration (SBA). 2008 . “ Table of Small Business Size Standards Matched to North American Industry Classification System Codes. ” Effective August 2 2, 2008. <http://www.sba.gov/services/contractingopportunities/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 Em ployees a 20 to 99

Em ployees 100 to 499

Em ployees 500 to 749

Em ployees 750 to 999

Em ployees 1,000 to 1,499

Em ployees 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 28,33 8 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. Businesses: 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-09Index Value (Jan-97=100)

Man ufac tu ri ng Che mi ca l Manufac tu ri ng Figure 2 - 38. Industrial Production Trends in Chemical Manufacturing (NAICS 325) Source: Federa l Reserve Board. 2009 . “ Industrial Production and Capacity Utilization: Industrial Production. ” <http://www.federalreserve.gov/datadow nload/>.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)

Imp ort s Ex port s Figure 2 - 39. International Trade Trends in Chemical Manufacturing (NAICS 32 5) Source: U.S. International Trade Commission. 2008 . “ U.S. Domestic Exports ” & “ U.S. Imports for Consumption. ” <http://dataweb.usitc.gov/scripts/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)

Man ufac tu ri ng Che mi ca l Manufac tu ri ng Figure 2 - 40. Producer Price Trends in Chem ical Manufacturing (NAICS 325) Source: U.S. Bureau of Labor Statistics (BLS). 2009 c . 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 Compan y. 2008. “ Transforming: The Dow Chemical Company 2007 10 - K and Stockholder Summary . ” <http://www.dow.com/financial/2007ann/>. Federal Reserve Board . 2009. “ Industrial Prod uction and Capacit y 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/taxstats/article/0,,id=167415,00.html>. Internal Revenue Service, U.S. Department of Treasury . 2008b . “ Corporation Source Book: Data File 2007. ” <http://www.irs.gov/taxstats/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 netAdvantage [electronic resource]. New York: Standard & Poors. U.S. Bureau of Economic Anal ys is (BEA). 2008. “ 2002 Benchmark Input - Output Accounts: 2002 Standard Make and Use Tables at the Summary L evel. ” 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. “ Producer Price Index. ” Series ID: PCU322

– 322

– & PCUOMFG

– OMFG

– . <http://www.bls.go v/ppi/home.htm>. U.S. Bureau of Labor Statistics. 2009c. “ Producer 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 /E 322.HTM#def>. U.S. Census Bureau. 2008. “ Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employ ment Sizes: 2002. ” < http://www.census.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 NAI CS Basis): 2002 and 1997 ” (generated b y RTI International using American FactFinder). <http://factfinder.census.g ov>. 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 NAI CS Basis): 2002 and 2007 ” (generated b y RTI International using American FactFind er). <http://factfinder.census.gov>. Accessed January 4, 2010. U.S. Census Bureau. “ Sector 00: EC0700A1: All Sectors: Geographic Area Series: Econom y Wide Key

Statistics: 2007 ” (generated b y Kapur Energy and Environment using American FactFinder). <http:// 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 and 2005 ” (generated b y RTI International using American FactFinder). <http://factfinder.census.gov>. Accessed July 8, 2008. U.S. Census Bureau. “ Sector 31: EC0731I 1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007 ” (generated by Kapur Energy

and Environment using American FactFin der). <http://factfinder.census.gov>. Accessed December 27, 2009. U.S. Census Bureau. “ Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002 ” (gene rated by

RTI International using American FactFinder). <http://factfinder.census.gov>. Accessed July 23, 2008. 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 >. 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 >. U.S. Department of Energy , Energy Information 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 & P CUOMFG

– OMFG

– Total Manufacturing>. U.S. Environmental Protection Agency . 2002. “ Profile of the Pulp and Paper Industry . Sector Notebook Project. ” <http://www.epa.gov/Compliance/resources/publications/assistance/ sectors/notebooks/index.html>. U.S. Internati onal Trade Commission. 2008 . “ U.S. Domestic Exports ” and “ U.S. Imports for Consumption. ” <http://dataweb.usitc.gov/scripts/user_set.asp>.2 - 70

U.S. Small Business Administration. 2008. “ Table of Small Business Size Standards Matched to North American Industry C lassification Sy stem Codes. ” <http://www.sba.gov/services/ contractingopportunities/sizestandardstopics/size/index.html>.3 - 1 SECTION 3 ENGINEERING COST ANA LYSIS We provide an overview of the engineering cost analy sis used to estimate the additional private expenditur es industry may make in order to comply with the rule. A detailed discussion of the methodology

used to estimate cost impacts is presented in Appendices C and D. Several provisions in the rule and the data used to generate regulatory impacts anal ys is betwe en proposal and the final rule. As a result, the costs of the RIA anal ys is decreased from the proposal to final anal ys is. The major regulatory

provisions resulting in a reduction in the cost estimates were:

 Consolidating the biomass and coal subcategories into a single solid fuel subcategory

for fuel - based HAP and HAP surrogates (Hg, HCl, PM). This provision has reduced the number of biomass units expected to require HCl control in order to meet the emission limits.

 Creating a h ybrid grate/suspension categ ory for combustion based HAP and HAP surrogates to address boilers that express unique design features to handle the combustion of fuels with a very high moisture content. This increased the CO emission level for this subcategory which resulting in less un its needing CO catal ys t or advanced combustion controls in order to meet the limit.

 Creating a limited use subcategory . This subcategory removes the control, testing and monitoring costs for 134 large boilers firing 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 pract ice standard instead of meeting emission limits. The number 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 an al ys is.

 Removing the CO CEMS monitoring requirement 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 monito ring 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/furan emissions. The final rule requi res a one - time initial t est instead of annual testing. The major data anal ys is elements resulting in a reduction in cost were:

 As discussed in detail in the “ Handling and Processing of Corrections and New Data in the EPA ICR Databases ” memorandum, several new data submissions and corrections to existing entries were received from the public comment process. These new data incorporated into the revised MACT floor, baseline emission, and emission reduction anal ysis.

 The criteria for consi dering data in the MACT floor anal ys is 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 existing sources and 100 percent of its heat input belonging to the same subcategory for new sources. At proposal, data were considered relevant for consideration in the MACT floor anal ys is 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 analy sis was revised to incorporate measurement error resulting from calibration error, s ys tem bias, and drift requirements using the reported instr

ument calibration span as the basis for estimating the error. This increased CO emission levels in several cases in order to better incorporate this error in the final standard.

 Several elements of the MACT floor anal ys is statistical approach were incorpor ated from public commenters, which resulted in increased emission limits in some cases. The memorandum “ Revised MACT Floor Anal ys is (2011) for the Industrial, Commercial, and Institutional Boilers and Process Heaters National Emission Standards for Hazardo us Air Pollutants

—Major Source ” details the specific statistical modifications that were made in the final anal ys is. 3.1 Major Sources To estimate the national cost impacts of the proposed rule for existing sources, EPA developed average baseline emission fact ors for each fuel t ype/ 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 similar units at the same facilit y reported data, we assigned the average emission factors from similar units at the same facility to the baseline emissions of the unit. For all other units that did not report emission d ata, 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 p roposed MACT floor emission limit to determine if control devices were needed to meet the emission limits. The control analy sis considered fabric filters and activated carbon injection to be the primary control devices for mercury control, electrostatic pr ecipitators for units meeting mercury limits but requiring additional control to meet the PM limits, wet scrubbers and dry injection fabric filter combinations to meet the HCl limits, tune - ups, replacement burners, combustion controls, and CO oxidation cat aly st 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 costs. We als o included costs for testing and monitoring requirements contained in the proposed rule. The resulting total national cost impact of the proposed rule is 5.1 billion dollars in capital expenditures and 1.8 billion dollars per y ear in total annual costs. Co nsidering estimated fuel savings resulting from work practice standards and combustion controls, the total annualized costs are reduced to 1.4 billion. The total capital and annual costs include costs for control devices, work practices, testing and monito ring. Table 3 - 1 of this shows the capital and annual cost impacts for each subcategory . Costs include testing and monitoring costs, but not recordkeeping 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 (m illion) Annualized

Cost (m illion per yr) a Annualized Cost (m illion per yr) b Existing Units Solid units 1,014 $2,182 $873 $846

Liquid units 713 $2,656 $833 $828 Non - Contine ntal 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) projections on fuel ex penditures, the number of additional boilers that could be potentially constructed was estimated. A detailed description of how the DOE data was used to project new units expected 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 analy sis consisted of two steps, ( 1) calculating the predi cted percent change in fuel consumption in the industrial and commercial/institutional sectors between the baseline year (2008) of the major source boiler and process heater inventory and the target ye ars of the projection (2013 and 2015); and ( 2) calculat ing 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 ye ar is 21 million dollars in capital exp enditures and 6 million dollars per y ear in total annual costs, when considering a 1 percent fuel savings. The fuel savings are based on an estimated savings of 1 percent from tuning the boiler to adjust oxygen levels and inspecting the flame pattern, cons istent with manufacturer ’ s specifications. For units with an automatic air to fuel ratio control, it also includes inspection and calibration of that s ys tem. It is primaril y the tune - ups that are expected to obtain these fuel savings, but approximately 440 units are estimated to retrofit their units with linkageless boiler management s ys tems in order to reduce CO emissions and improve boiler efficiency . Fuel savings of one percent was selected for the basis of the fuel savings anal ys is. The fuel savings was estimated for units firing gaseous, liquid, and coal fuels onl y. Biomass fuels were not considered in the fuel savings anal ys is since it is difficult to establish a market value for the various biomass fuel streams The estimated fuel savings between propo sal 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 final rule, the fuel savings was estimated to be $428 million, based on 13,279 units installing combustion controls or conducting a work practice standard. The difference resulted from two main changes:

 First, more units wer e added to the inventory , based on comments received from additional facilities that were left out of the analysis at the proposal. Man y of these additional units will conduct a tune - up or install combustion controls in order to meet the work practice or C O emission limit.3 - 5  Second, the baseline emission and emission 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 control s in order to meet the emission limits have changed. At proposal, 458 units were estimated to install a linkageless boiler management sy stem, one boiler was expected to install a replacement burner, and 12,428 boilers were estimated to conduct a tune - up. In the final anal ys is, 440 units were estimated to install a linkageless boiler management sy stem, 1 unit was expected to install a replacement LNB, and 12,838 boilers were expected to conduct a tune - up.

 Third, the treatment of gas 2 units has been modified to use a fuel gas specification. This is expected to increase the number of gas units eligible for a work practice standard instead of an emission limit. As a result of this change more of these units are expected to conduct a tune - up to fulfill the requi rements of the work practice standard. Although the age of the boiler can affect combustion efficiency , 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 - t o - fuel ratios that are not optimal. EPA adopted the same fuel savings assumptions for both new and existing units. The anal ys is estimates that if the annual tune - up provisions are incorporated, a savings of one percent would be achieved on an annual basis when compared to a new boiler installation that does not conduct routine tune - ups. The anal ys is projects the construction of 6,424 new liquid fired boilers at area sources, as opposed to 155 new coal units and 200 biomass units, respectively. All 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 e nergy 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 resulted in the fuel savings shown. A discussion of the methodology used to estimate cost impacts is presented in “ Revised Methodology

for Estimating Cost and Emission Impacts for Industrial, Commercial, and Industrial Boilers and P rocess 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 the 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 anal ys is considered all air pollution control equipment currentl y in operation at existing boilers. Model co sts were then assigned to all existing units that could not otherwise meet the proposed standards. The resulting total national cost impact of the final rule for existing units is $487 million dollars in total annualized costs after considering fuel saving s from efficiency improvements. The total annualized costs for installing 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 total 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

(m illion per yr) a Existing Units Coal 3,710 $37 Biomass 1 0,958 $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 TA C does not include fuel savings from improving combustion efficiency. b Im pacts for ne w 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 b y the 3rd year, 2013, is $48 million dol lars in total annualized costs. When accounting for a 1 percent fuel savings resulting from improvements to combustion efficiency , the total national cost impact on new sources is ($3.6 million). The size of the fuel savings estimate resulted primarily fro m the large number of new liquid fired boilers, for which the cost of energy is the highest on a per mmBtu basis. This large energy

cost created to a correspondingl y high cost savings with the increased combustion efficiency . 3.3 Section 3 References Gibson e t 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 ANAL YSIS EPA prepares an RIA to provide decision makers wi th 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 presented 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 anal ys is that estimates market changes (e.g., price and co nsumption) and economic welfare changes (e.g., changes in consumer and producer surplus). The Office of Air Quality Planning and Standards (OAQPS) adopted a standard market anal ys is as described in the Office ’ s resource manual (EPA, 1999 ). The approach uses a single period multimarket partial equilibrium model to compare pre - policy market baselines with expected post - policy

market outcomes. The anal ys is ’ time horizon is the short run; some production factors are fixed and some are var iable 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 transitory stakeholder outcomes. Key measures in this anal ys is include industry - level changes in price levels, production and consumption, jobs, international trade, and social costs (changes in producer and consumer surplus). 4.1 Partial Equilibrium Analysis (Multiple Markets) The partial equilibrium analy sis 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 estimate social costs, current EPA guidelines suggest that multimarket models “ ...are best used when potential impacts on related markets might be considerable ” and modeling using a computable general equilibrium model is not available or practical (EPA, 2010 , p. 9 - 21). Other guides for environmental 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 multimar ket model is not designed to directl y 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 y ear (2010)

– industry

employ ment data

 Economic behavior

– industries respond to regulatory costs by changing production rates

– market prices rise and fall to reflect higher energy and other non - energy

material costs and changes in demand

– customers respond to th ese price increases and consumption falls

 Model scope

– 100 sectors are linked with each other based on their use of energy and other non energy materials. For example, the construction industry is linked with the petroleum, cement, and steel industries and is influenced b y price changes that occur in each sector. The links allow EPA to account for indirect effects the regulation has on related markets.

– production adjustments influence employ ment levels

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

 Model time horizon ( “ short run ” ) for a single period (201 4 )

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

– firms cannot alter input mixes; there is no substitution among intermediate productio n 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 quantity adjustments including the changes in international trade (F igure 4 - 1). Under the major source NESHAP, the Agency ’ s economic model suggests the average national price increases for industrial sectors are less than 0.01%, while average annual domestic production may fall by less than 0.01%. Because of higher domesti c prices, imports slightly

rise. Market - level effects for the area source NESHAP are smaller when compared to the major source rule; average price, production, and import changes are less than 0.01%. Industrial sector details 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% Se lected Option Alternative Opti on Fin al MACT /GAC T Approach Propos ed MA CT

Approach Se lected Option Alternative Opti on Major Sources Area Sources Major & Area Source sMarket-Level Economic Vaiables: Percentage Change from Baseline

Prices Production Imp orts Cons umption Ex ports 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 model suggests that industries are able to pass on $0.5 billion (2008$) of the major source rule ’ s costs to U.S. househol ds in the form of higher prices (Table 4 - 1). Existing U.S. industries ’ surplus falls by $1.4 billion and the net U.S. loss in aggregate is $1.9 billion. As U.S. prices rise, other countries are affected through international trade relationships. The price of goods produced in the United States increase slightly and domestic production declines, replaced to a certain degree b y 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 loss es are concentrated in other services (23 percent), lumber, paper, and printing (16 percent), energy industries (15 percent), and chemicals (13 percent).4 - 4

Table 4 - 1. Distribution of Social Costs Major Sources (billion, 2008$): 2014

Me thod Selected Option 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 Method (Not Modeled) Total annualized costs, new major sources a −$0.006 −$0.920 Fuel savings, existing major sources $0.430 $0.400 Fuel savings, ne w 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 estimates and the direct complian ce 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 , a nd printi ng Energy In dus tries Chemicals Fo od, b ev erages, and textiles

Primary Metals Tr ansportatio n Equ ipmen t Nonmanufactu ring Plastics and Rubber Tr ansportatio n Se rvic es Machinery and Equipmen t Wholes ale and Retail Tr ade

Other Fab ricated Metals Electronic Equip ment Nonmetallic Minerals Selected Option: $1.9 Billion U.S. S urplusC hang e Alternative Opti on: $2.3 Billio n U .S . S urplus C hang e

Figure 4 - 2. Distribution of U.S. Surplus Changes by Sector: Major Sources4 - 5

The Agency also considered other elements of the engineering cost anal ys is that could not be modeled within the multimarket model (e.g., fuel savings benefits [existing and new major sources] and total annualized compliance costs [new 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 model 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 ’ surplus 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 affected through international trade relations hips. 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 States benefit; the model estimates a net rest of the world gain of less than $0.01 billion. As shown in Figure 4 - 3, the U.S. surplus losses are concentrated in the other services (82 percent ). Other services include information, finance and insurance, real estate, professional services, management, administrative services, education, health care, arts, accommo dations , and public services. Table 4 - 2. Distribution of Social Costs Area Sources (billion, 2008$): 2014 Me thod 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 Method (Not Modeled) Total annualized costs, unknown existing area sources −$0.003 −$0.008 Total annualized costs, new area sour ces −$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 estimates 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% Othe r Se rvices Fo od, b everages, an d te xt iles Wholes ale an d Retail Tr ade Nonm anufactu ring Lumber , pa per , and printi ng Tr ans portatio n Services

Che micals Other Pl asti csa nd Rubber Ene rgy In dustrie s Nonm etallic Minerals Pri mary Metals Fa bricated Metals Machinery and Equipme nt Tr ans portatio n Equ ipmen t Electronic Equip ment Fi na l MACT /GAC T Approach : $0.4 Billion U.S . SurplusC hang e Propos ed MACT Appr

oach : $0.6 Billion U.S . SurplusC hang e

Figure 4 - 3. Distri bution of Total Surplus Changes by Sector: Area Sources a Other services include information, finance and insurance, real estate, professional services, management, administrative services, education, health care, arts, accommodations , and public services. The Agency

also considered other elements of the engineering cost anal ys is that could not be modeled within the multimarket model (e.g., fuel - 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 total surplus loss estimate 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 employ ment impacts of the final rule based on Morgenstern, Pizer and Shih (2002). A stand alone anal ys is of jobs is not included in a standard cost - benefit anal ys is. Executive Order 13563 however, states, “ Our regulatory s ys tem must protect public health, welfare, safet y, and our environm ent while promoting economic growth, innovation, competitiveness, and job creation ” (emphasis added). Therefore, we have provided this analy sis to inform the discussion of job4 - 7

impacts. EPA continues to explore the relevant theoretical and empirical lite rature 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 labor be used to produce a given amo unt of output, that additional labor is reflected in an increase in the cost of production. Moreover, when the econom y is near full employ

ment, jobs created in one industry as a result of regulation displace jobs in other industries. On the other hand, in periods of high unemploy ment, an increase in labor demand due to regulation may

have a stimulative effect that results in a net increase in overall employ ment. With significant numbers of workers unemploy ed, the opportunity costs associated with displacin g jobs in other sectors are likel y to be much smaller. For this reason, this RIA looks carefull y 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 employ

ment effects -- name ly , those associated with the direct impacts on employ ment in the regulated industry . A full analy sis would include estimates of the direct impacts on other industries (e.g. suppliers of pollution control equipment) as well as the indirect and induced effe cts on employ ment throughout the econom y as a whole in response to changes in output and factor prices. We expect that the rule’s direct impact on emplo yment will be small. The Agency ’s anal ys is does not include all the direct effects of this regulation. For example, EPA is currentl y exploring way s to quantify the job impacts in the pollution control sector that 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 way s that environmental regulation can affect emplo ym ent, followed b y a discussion of the estimated impacts of this rule. An environmental regulation can affect the demand for labor in several way s:

 Direct Effects :

– Increased prices for ind ustry output may reduce the demand for labor: Environmental regulations increase production costs causing firms to increase prices; higher prices reduce consumption (and production), thus reducing demand for labor within the regulated industry . The extent of this effect will depend on the extent of the price increase and the elasticit y of the demand curve.

– Regulated firms demand labor workers to operate and maintain pollution controls within those firms. Once pollution control equipment is installed, regu lated firms may

hire workers 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 maintenance of pollution controls are labor intensive

– Increased dema nd 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 anal ys es and significant expenditures for assembly and installation of such equipment. These activities will be job - creating during the period before firms 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 employ ment in the environmental protectio n industry (increased orders for pollution cont rol equipment), environmental regulations also create employ ment in industries that provide intermediate goods to the environmental protection industry. For example, capit a l expenditures to reduce air polluti on involve the purchase of abatement equipment. The equipment manufacturers, 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 su pply inputs for, or demand the outputs of the regulated industry . None of these impacts is accounted for in the current anal ys is. We also do not estimate employ ment impacts “induced” b y increased output of the environmental protection sector, or decreased output of the regulated sectors. 4.1.2.4.2 Methodology

and Results The estimated impacts of the final rule on emplo yment in affected sources are based on an empirically

derived relationship reported in Morgenstern, Pizer and Shih (2002) , a peer - reviewed, published study . Estimates of the emplo ym ent impacts of the capital investments and other non recurri ng requirements of the rule are derived from the cost anal ys is developed for the regul at ion. 1 Morgenstern, Pizer and Shih (2002): Overview of Conceptual A pproach 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 legall y compelled to satisf y this new demand, regulated firms have to finance this additional production with the proceeds of sales of their other (market) products. Satisfying this new demand requires additional inputs, including labor, and may alter the relative proportions of lab or and capital used by regulated firms in their production processes.

1 Richard D. M orgenstern, 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 overall effect of a regulation on employ ment into the following three sub components:

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

 The “Cost Effect”: As production costs increase, plants use more of all inputs, including labor, to maintain a given level of output. For exampl e, in order to reduce pollutant emissions while holding output levels constant, regulated firms may require additional labor;

 The “Factor - Shift Effect”: Regulated firms’ production technologies may be more or less labor intensive after compl yi ng with a re gulation (i.e., more/less labor is required per dollar of output). Decomposing the overall employ ment impact of environmental regulation into three subcomponents clarifies the conceptual relationship between environmental regulation and employ ment in regul ated sectors, and permitted Morgenstern, et al. to provide an empirical estimate of the net impact. For present purposes, the net effect is of particular interest, and is the focus of our anal ys is. Morgenstern, Pizer and Shih (2002): Empirical Results Morg enstern et al. empirically estimate a model for four highl y polluting, regulated industries (pulp and paper, plastics, petroleum refining and steel) to examine the effect of higher abatement costs from regulation on employ ment. They

conclude that increase d abatement expenditures generall y do not cause a significant change in employ ment. More specifically , their results show that, on average across the four industries, each additional $1 million spending on pollution abatement results in a (statistically in significant) net increase of 1.5 5 (+/ - 2.24) jobs. 3 “In plastics and petroleum, [Morgenstern et al] find that increased regulation raises employ ment by a small but statistically

significant amount: 6.9 and 2.2 jobs per million dollars of regulatory expens e, respectivel y. In pulp and paper and steel, the estimates are even smaller and insignificantl y different from zero.” 4 By applying these estimates to pollution abatement costs , w e estimated the

2 The Morgenstern et al. results rely on industry demand 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 em p loyment. "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 employ

ment effect for major and areas sources to range from - 4,100 to +8,500 jobs in the directly affected sectors with a central estimate of +2,200 (Table 4 - 3). 5 , 6

5 Since Morgenstern’s analysis reports environmental expenditures in $1987, w e ma ke an inflation adjustment the engineering cost analysis using GDP implicit price deflator (64.76/108.48) = 0.60) 6 Net employment effect = 1 .5 5 × $ 2,4 00 million × 0.604 - 11

Table 4 - 3 . Employment Impacts Using Morgenstern, Pizer, Shih (2002) (FTE) Demand Effect Cost Effect Factor Shift Effect Net

Effect Change in Full - Tim e 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 b oth 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 E stimates from Morgenstern, Pizer, and Shih (2002) e xpressed 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 Se lected Option Alte rnative Op ti on Fin al MAC T/ GA CT Approach Propos ed M ACT

Approach Se lected Option Alt er native Op ti on Major Sources Area Source s Majo r & A rea Sources1,000 JobsBase =144,100

De mand Effe ct: E stima te d er iv ed f rom Morge nste rn, eta l. Cost Effe ct: Estimate d erive d fr om Morgenste rn, etal . Factor Shi ft Effec t: Es ti mate d er ived fr om Morgenstern, etal . NetE ffect: Esti mate de ri ve d from Morge nste rn, eta l. Figure 4 - 3. Em ployment Impacts Using Morgenstern, Pizer, Shih (2002) ( 1,000 FTE s ) Limitations of the Analysis Although the Morgenstern et al. paper provides information a bout the potential job effects of environmental protection programs, there are several caveats associated with using those estimates to anal yz e the final rule . First, the Morgenstern et al. estimates presented in Table 4 - 3 and used in EPA ’s anal ys is repres ent the weighted average parameter estimates for a set of manufacturing industries (pulp and paper, plastics, petroleum, and steel) . This set of industries only partiall y overlaps with the sectors affected by this rule . Second , rel yi ng on Morgenstern et a l. implicitly assumes that estimates derived from 1979

– 1991 data are still applicable. Third, the methodology used in Morgenstern et al. assumes that regulations affect plants in proportion to their total costs. In other words, each additional dollar of r egulatory burden affects a plant b y an amount equal to that plant's total costs relative to the aggregate industry costs. By transferring the4 - 13

estimates, EPA assumes a similar distribution of regulatory costs by plant size and that the regulatory

burden doe s not disproportionately fall on smaller or larger plants . Further, Morgenstern et al. does not include most indirect effects and all induced effects.5 - 1 SECTION 5 SMALL ENTITY ANALYSE S The RFA as amended b y SBREFA generall y requires an agency to prepare a regulato ry flexibility anal ys is of an y 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 impact on a substantial number of small entities (SI SNOSE). Small entities include small businesses, small governmental jurisdictions, and small not - for - profit enterprises. EPA assessed the potential small entity economic impacts using a screening anal ys is. After reviewing screenin g anal ys is 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 b y S BREFA. 5.1 Small Entity Screening Analysis 5.1.1 Small Businesses The sector s covered by

the rule were identified through lists of small entities at major and area sources provided b y the engineering anal ys is. Table 5 - 1 provides a list of the sectors affected (3 - digit NAI CS) and the range of SBA size definitions. 5.1.1.1 Representative Sm all Business Analysis Using Census Statistics of U.S. Businesses For each 3 - digit NAI CS code, the SUSB provides national information on the distribution of economic variables b y industry and enterprise size (U.S. Census Bureau, 2008 ). The Census Bureau and the Office of Advocacy of the SBA supported and developed these files for use in a broad range of economic anal ys es. 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 employ

ment and receipt size. The Census Bureau ’ s definitions used in the SUSB are as follows:

 Establishmen t: An establishment is a single ph ys ical l ocation where business is conducted or where services or industrial operations are performed.

 Receipts : Receipts (net of taxes) are defined as the revenue for goods produced, distributed, or services provided, including revenue earned from premiums, commis sions and fees, rents, interest, dividends, and royalties. Receipts exclude all revenue collected for local, state, and federal taxes.

1 Se e http://www.census.gov/csd/susb/ and http://www.sba.gov/advo/research/data.html for ad ditional 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 Manufacturing 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 Ty pically 500 to 1,500 employees 325 Chemical Manufactur ing 500 to 1,000 employees 326 Plastics and Rubber Products Manufacturing Ty pically 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 Ty pically $7 to $14 million in annual receipts 611 Educational Services Ty pically $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 organization consisting of one or more domestic establishments that were specified under common ownership or control. The enterprise and the establishment are the same for single - establishment firms. Each multi - establishment compan y forms one enterprise

—the enterprise employ ment and annual pay

roll are summed from the associated establishments. Enterpri se size designations are determined by the total employ

ment of all associated establishments. Because the SBA ’ s business size definitions (SBA, 2008 ) appl y to an establishment ’ s “ ultimate parent company , ” we assumed in this analy sis that the “ enterprise ” definition above is consistent with the concept of ultimate parent company that is ty picall y used for SBREFA screening anal ys es, and the terms are used interchangeabl y. The anal ys is generated a set of establishment sales tests (represented as cost - to - receipt ratios) for NAICS codes associated with sectors listed in Table 5 - 2. Although the appropriate SBA size definition should be applied at the parent company (enterprise) level, we can only compute and compare ratios for a model establishme nt owned b y an enterprise within an SUSB size range (emplo ym ent 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 sma ll business definitions across affected industries. Using establishment receipts is also a conservative approach, because an establishment ’ s parent compan y (the “ enterprise ” ) may

have other economic resources that could be used to cover the costs of the re gulatory

program. Table 5 - 2. Major Sources: Sales Tests Using Small Companies Identified in the

Combustion Survey Sample Statistic Proposal Selected 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 SUSB data, we developed a range of facility level cost numerators based on the engineering cost analy sis. For major sources, we used the maximum and minimum small entity facility - level costs observed within each 3 - digit NAICS5 - 4

code. For area sources, we were limited to two representative small entit y facility - level costs (approximate ly

$27,000,000 to $43,000). 2 Using these cost data and the Census estimates of average establishment receipts, a substantial number of SUSB NAICS/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 Fin al MACT /GA CT Approach Propos ed MAC T Approach Major Area Up per Bound Lower Bound

Figure 5 - 1. Share of NAICS/Enterprise Employment Cat egories (<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 ys is that compares the Census SUSB representative sm all entity results with a firm - specific sample of major small private enterprises. In this approach, we identified a sample of survey facility names listed as small, traced the ultimate parent compan y name to verify the facility

was owned by

a small busine ss, and collected

2 Prior to computing the cost - to - receipt ratios, w e adjusted the engineering compliance costs 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 employment figures. As Table 5 - 2 shows, the average cost - to - sales ratios for small major source companies 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 also covers sectors that include entities owned by small and large governments and not - for - profit enterprises. Given the uncertainty and data limitations associated with identifying and appropriately classifying these entities, we computed a “ revenue ” test, where the annualized compliance cost is a percentage of annual revenues (U.S. Census, 2005a and b ). Compliance costs were estimated for model facil ities for major and area sources for multiple options. A summary of the compliance costs used for the small entity anal ys is follows: Major Sources:

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

 Alternative Option : $0.9 million (median c ost 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), the average revenue for small governments (count ies 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. R epresentative small major public entities would have cost - to - revenue ratios above 10 percent. The following types of representative small area source facilities would have cost - to revenue ratios exceeding 1 percent but below 3 percent:

3 Graham Gibson, Susan McClutchey, and Amanda Singleton, ERG . (January, 2011). Methodology for Estimat ing Im pacts 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 prepared an initial regulatory flexibility

anal ys is (I RFA) for the proposed rule and convened a Small Business Advocacy Review Panel to obtain advice and recommendations of representatives of the regulated small entities. A detailed discussio n of the Panel ’ s advice and recommendations 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 b y section 604 of the RFA, we also prepared a final regulatory flexibility anal ys is (FRFA) the final rule. 5.2.1 Need for Rule and Objectives The rule is intended to reduce emissions of HAP as required under section 112 of the CAA. Section II.A of the final rule ’ s preamble describes the reasons that EPA is finalizing this action. 5.2.2 Summary of the Significant Issues raised by the Public Comments and Agenc y Assessment Many significant issues were raised during the public comment period, and EPA ’ s responses to those comments are presented in section V of this preamble or in the response to comments document contained in the docket. Significant changes to the rule that resulted from the public comments are described in section IV of the final rule ’ s preamble. The primary

comments on the I RFA were provided by SBA, with the remainder of the comments generally

supporting SBA ’ s comments. Those comments included th e following: EPA should have adopted a health - based compliance alternative (HBCA) which provides alternative emission limits for threshold chemicals; EPA should have adopted additional subcategories, including the following: subcategories based on fuel ty p e (including coal rank, bagasse, biomass by t ype, and oil by t ype), unit design type (e.g. , process heater, fluidized bed, stoker, fuel cell, suspension burner), dut y cy cle, geographic location, boiler size, burner t ype (with and without low - NO x burners), and hours of use (limited use); EPA should have minimized facility monitoring and reporting requirements; EPA should not have proposed the energy audit5 - 7

requirement; EPA ’ s proposed emissions standards are too stringent; and, EPA should provide more flexibil ity

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 operating hours in a y ear, a non continental liquid unit subcategory for units with the unique challenges faced by remote island locations, and a combination suspension/grate boiler subcategory . EPA also consolidated the subcategories for un its combusting various ty pes of solid fuels, which will simplify compliance and will allow units to combust varying percentages of different solid fuels without triggering subcategory

changes. EPA also decreased monitoring and testing costs by eliminating the CO CEMS requirement for units greater than 100 mmBtu/hr and changing the dioxin testing requirement to a one - time test. The final rule also 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 proposed limits for most of the category /pollutant combinations. The emissi on limit changes are largely 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 the 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 affected boilers or process heaters reported in the 2008 survey entitled “ Information Collectio n Effort for Facilities with Combustion Units. (I CR No. 2286.01), ” there are 1,639 existing facilities with affected boilers or process heaters. Of these, 94 percent are located in the private sector and the remaining 6 percent are located in the public se ctor. Table 5 - 3 summarizes the ty pes of small entities expected to be affected b y the major source rule. The number of small entities is based on the facilities which responded to the ICR b y confirming small entit y s tatus. I f that question was not answered , or if the facility

answered that their 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 process heater expected to be covered b y the rule. Table 5 - 4 summarizes the EPA estimates of the number of area source facilities expected to be affected by the major source rule (84,700 total). EPA does not have sufficient 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 Fac ilities with Combustion Units: Major

Sources

NAICS NAICS Description Total Number of Facilities Total SelfReported Facilities Owned by Sm all Businesses 111 Crop Produc tion 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 Man ufacturing 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 Manufactur ing 46 8 333 Machinery Manufacturing 13 0 334 Com

puter 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 and Parts Dealers 1 0 481 Air Transportation 7 0 482 Rail Transportation 1 0 486 Pipelin e 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 Numbe r of Facilities Total SelfReported Facilities Owned by Sm all Businesses 531 Real Estate 1 0 541 Professional, Scientific, and Technical Services 8 0 561 Ad ministrative and Support Services 1 0 562 Waste Management and Remediation Services 7 2 611 Edu cational 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 disc ussed in Section 5.1 of this RIA, using these cost data and the Census estimates of average establishment receipts, a substantial number of SUSB NAICS/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 ratios above 10 percent. 5.2.4 Description of the Projected Reporting, Record keeping and Other Compliance Requirements of the Rule The information collection activities in this I CR include initial and annual stack tests, fuel anal ys es, operating parameter monitoring, continuous O 2 monitoring for all units greater than 10 mmBtu/hr, continuous e mission 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, shutdown, malfunction plan (SSMP), preparation of a site - speci fic monitoring plan and a site - specific fuel monitoring plan, one - time and periodic reports, and the maintenance of records. For sources that can demonstrate compliance through fuel anal ys is, the regulation requires an initial fuel anal ys is and monthly fue l analy ses. Sources must conduct additional fuel anal ys es if they burn a new ty

pe 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 rul e, they must conduct either an initial or monthly fuel anal ys is to remain in the gas 1 subcategory . If5 - 10

the content of these constituents are not going to exceed the specifications, these units may conduct an initial testing and include a statement that the gas will not exceed the specification in the initial Notification of Compliance Status. I f 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 specifi cations. An initial performance test must be completed for particulate matter, mercury , hy drogen 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. Thereafter, the owner or operator must, in some cases, conduct annual stack tests for particulate matter, mercury , hy drogen chloride, carbon monoxide, and dioxin and furans and must continuously 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 owner or operator must submit a report that documents the perfor

mance test results and the values 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 s ys tem, establishing operati ng characteristics, identifying major energy consuming s ys tems and energy savings potential, reviewing available engineering plans, and listing major energy conservation measures. A signed certification that an audit has been completed should 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 demonstrate compliance by conducting a tune - up of the boiler. Small and limited use units are requested to conduct a tune - up bienniall y and large natural gas, refinery gas, or other gas 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 alter native fuel use if the unit fires alternative fuels during periods of gas curtailment or gas 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 d ocuments the values for the operating parameters; an y deviation; the results of an y annual stack tests; the results of an y fuel anal ys is and emissions calculations; fuel usage, and if no deviation occurred, a statement that no deviations occurred.5 - 11

As speci fied in the ICI Boiler and Process Heater NESHAP, owners or operators of boilers and process heaters must keep records of certain parameters and information for a period of five ye ars. Owners or operators must maintain records of the initial performance te st, annual stack tests, fuel anal ys es, and any subsequent stack tests or fuel analy ses. Owners or operators must also maintain records of the monitoring data for the operating parameters and daily fuel usage. Owners or operators must also maintain records for boiler or process heater malfunctions and an y deviations from the operating parameters. Records must also be maintained of all monitoring device calibration data. The Agency

expects that persons with knowledge of .pdf software, spreadsheet and relation al database programs will be necessary in order to prepare the report or record. Based on experience with previous emission stack testing, we expect most facilities to contract out preparation of the reports associated with emission stack testing, includin g 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 determine the requirements applicable to them, knowledge of the heat input capacit y and fuel 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 faci lities will also need skills associated with vendor selection 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 based on public comment, EPA did not finalize a HBCA or HBELs and is maintaining, but clarifying, 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 for 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 s ys tem and energy

use sy stem accounting for at least 50 percent of the energy output will be evaluated to identify energy savings opportunities, within the limit of performing a one - day energy assessment; and the energy assessment for facilities with affected boilers and process heaters using 0.3 to 1.0 trillion Btu per year will be 3 day s in length maximum. The boiler sy stem and any energy use s ys tem accounting for at least 33 percent o f the energy output will be evaluated to identify energy savings opportunities, within the limit of performing a 3 - day energy assessment. While EPA did not make major adjustments to the 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 b y s ection 212 of SBREFA, EPA also is preparing a Small Entity Compliance Guide to help small entities comply with this rule. Sm all entities will be able to obtain a cop y 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 p repared an initial regulatory flexibility

anal ys is (I RFA) for the proposed rule and convened a Small 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 recommendations 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 b y section 604 of the RFA, we also prepared a final regulatory flexibility anal ys is (FRFA) the final rule. 5.3.1 Need for Rule and Objectives The rule is intended to reduce emissions of HAP as required under section 112 of the CAA. Section II.A of the final rule ’ s preamble describes the reasons that EPA is finalizing this action. 5.3.2 Summary of the Significant Issues raised by the Public Comments and Agency Assessment Man y significant issues were raised during the public comment period, and EPA ’ s responses to those comments are presented in section V of this preamble or in the response to comments document contained in the docket. Significant changes to the rule that resul ted from the public comments are described in section IV of the final rule ’ s preamble. The primary

comments on the I RFA were provided 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 design ty pe (e.g. , fluidized bed, stoker, fuel cell, suspension burner), dut y cy cle, geographic location, boiler size, bur ner t ype (with and without low - NO x burners), and hours of use (limited use); EPA should have minimized facility5 - 13

monitoring and reporting requirements; EPA should not have proposed the energy audit requirement; and EPA ’ s proposed emissions 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 source boil ers in the biomass and oil subcategories instead of the proposed CO emission limits. This change will significantl y reduce the monitoring and testing costs for existing and new biomass - 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 boilers greater than 100 MMBtu/h. The final rule also includes work practice standards or management practice standards, instead of emission limits, for new are a source boilers less than 10 MMBtu/h. Finall y, EPA is finalizing emission limits that are less stringent than the proposed limits. The emission limit changes are largely due to the changes in data corrections and incorporation of new data into the floor c alculations. Additional details on the changes discussed in this paragraph are included 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 EP A estimates of the number of area source facilities expected to be affected b y the major source rule (84,700 total). EPA does not have sufficient information to estimate the number of small entities expected to be covered b y the area source rule. As discus sed in Section 5.1 of this RIA, using these cost data and the Census estimates of average establishment receipts, a substantial number of SUSB NAICS/enterprise categories have ratios over 3%. The following t ypes of representative small area source public f acilities would have cost - to - revenue ratios exceeding 1 percent but below 3 percent: other public facilities (ratio > 1.7 percent) and churches (ratio = 1.5 percent).5 - 14

Table 5 - 4. Estimated Affected Facilities Using 13 State Boiler Inspector Inventory: Area

Sources SIC Total Number of Affected 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 90 5 59 288 60 329 64 0 (continued)5 - 15

Table 5 - 4. Estimated Affected Facilities Using 13 State Boiler Inspector Inventory: Area

Sources (continued) SIC Total Number of Affected 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 activities in this I CR include initial and annual stack tests, fuel anal ys es, operating parameter 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, preparation of a startup, shutdown, malfunction plan (SSMP), preparation of a site - specific monitoring plan and a site - specific fuel monitoring plan, one - time and periodic reports, and the maintenance of records. Based on 13 st ates ’ inventories of boilers, there are an estimated 92,000 existing facilities with affected boilers. Itis estimated that 53 percent are located 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 ty pes and number of each ty pe of small entities expected to be affected b y the area source rule.5 - 16

The Agency

expects that persons with knowledge of .pdf software, spreadsheet and relational database programs will be necessary in order to prepare the report or record. Based on experience with previous emission stack testing, we expect most facilities to contract out preparation of the reports associated with emission stack testing, 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 determine the requirements applicable to them, knowledge of the heat input capacit y and fuel use of the combustion units at each facility will b e necessary

in order to develop the reports and determine initial applicability to the rule. Affected facilities will also need skills associated with vendor selection in order to identify service providers that can help them complete their compliance requ irements, 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 based on public comment, EPA is maintaining, but clarifying, the energy assessment re quirement. Some changes to the energy assessment requirement that will reduce costs for small entities include a the following provisions: the energy assessment for facilities with affected boilers using less than 0.3 trillion Btu per year heat input will be one day

in length maximum. The boiler sy stem and energy use sy stem accounting for at least 50 percent of the energy output will be evaluated to identify energy savings opportunities, within the limit of performing a one - day energy assessment; and the en ergy assessment for facilities with affected boilers using 0.3 to 1.0 trillion Btu per ye ar will be 3 day s in length maximum. The boiler sy stem 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 b y s ection 212 o f SBREFA, EPA also is preparing a Small Entity Compliance Guide to help small entities comply with this rule. Small entities will be able to obtain a cop y of the Small Entity

Compliance guide at the following web site: http://www.epa.gov/ttn/atw/boiler/boi lerpg.html. 5.4 Section 5 References U.S. Bureau of Economic Anal ys is. 2010. “ Implicit Price Deflators for Gross Domestic Product ” (Table 1.1.9). <http://www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N>.5 - 17

U.S. Census Bureau. 2005a. 2002 Census of Govern ments, 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 the Statistics of U.S. Businesses: U.S. Detail Employ ment Sizes: 2002. ” <http://www.census.gov/csd/susb/download_susb02.htm>. 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/contractingopportunities/sizestandardstopics/ size/index.html>.6 - 1 SECTION 6 AIR QUALITY MODE LING OF EMISSIONS RE DUCTIONS 6.1 Synopsis This section describes the air qualit y modeling performed b y EPA in support of the final boiler MACT rule. A national scale air quality modeling anal ys is was performed to estimate the impact of the sector emissions changes 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 Comprehensive Air Quality Model with Extensions (CAM x ) model. CAM x simulates the numerous ph ys ical and chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and air toxics. Inaddition 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 are made early in the anal yt ical 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 inventories presented in the RIA. At the time of proposal, we did not have the results of the I CR; those results have been incorporated into this final anal ys is as explained below. The boiler ICR does not have the emissions release point information such as stack height, exit velocity, exit temperature, etc. necessary for photochemical modeling so the I CR emissions data was matched with the National Emissions Inventory

(NEI). Since States and local agencie s have different criteria for emissions that get inventoried as a point source with stack information and area sources (no specific stack information) the ability

to match I CR emissions to NEI varies from State to State depending on the level of informatio n provided in the I CR and NEI . Photochemical grid models use state of the science numerical algorithms to estimate pollutant formation, transport, and deposition over a variet y of spatial scales 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 receptors. This ty pe of emissions apportionment is useful to understand what ty pes of sources or regions are contributing to pollutants estimated 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 s ys tem of connected modeling - related tools and data that provid e a consistent and transparent basis for assessing the air qualit y 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 intended to support a variet y of regulatory and research model applications and anal ys es. This modeling platform and analy sis is described below. Additional information about the photochemical modeling is available as part of the modeling technical assessment document to support this s ource sector rule (USEPA, 2010 ). 6.2 Photochemical Model Background CAM x version 5.20 is a freel y available computer model that simulates the formation and fate of photochemical oxidants, ozone, primary and secondary

PM concentrations, and air toxics, over regional and urban spatial scales for given 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 I SORROPIA inorganic chemistry (Nenes et al., 1999 ), a semi - volatile equilibrium scheme to partition condensable organic gases between gas and particle phase (Strader et al., 1999 ), Regional Acid Deposition Model (RADM) aqueous phase che mistry (Chang et al., 1987 ), and Carbon Bond 05 (CB05) gas - phase chemistry module (ENVIRON, 2008 ; Gery et al., 1989 ). Mercury oxidation pathway s are represented for both the gas and aqueous phases in addition to aqueous phase reduction reactions (ENVIRON, 2008). CAM x contains a variet y of ozone source apportionment tools, including the original ozone source apportionment tool (OSAT) and the anthropogenic pre - cursor culpability assess ment (APCA) tool (ENVIRON, 2008 ). Ozone source apportionment tracers are treated using the standard model algorithms for vertical 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. Separate ozone tracers are used in CAM x to track ozone formation that happens under NO x and VOC l imited conditions. Particulate matter source apportionment technology (PSAT) implemented in CAM x estimates the contribution from specific emissions source groups to PM 2.5 and all forms of mercury using reactive tracers (ENVI RON, 2008 ; Wagstrom et al., 2008 ). The tracer species are estimated with source apportionment algorithms rather than b y the host model routines. PSAT6 - 3

tracks contribution to PM 2.5 sulfate, nitrate, ammonium, secondary organic aerosol, a nd inert primarily emitted species. Non - linear processes like gas and aqueous phase chemistry are solved for bulk species and then apportioned to the tagged species. Emissions of nitrogen oxides are tracked through all intermediate nitrogen species to part iculate 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 the APCA method to estimate source contribution to modeled ozon e. 6.3 Model Domain and Grid Resolution The modeling anal ys es were performed for a domain covering the continental United States, as shown in Figure 6 - 1. This domain has a parent horizontal grid of 36 km with two finer scale 12 km grids over portions of the e astern and western U.S. The model extends vertically from the surface to 100 millibars (approximately 15 km) using a sigma - pressure coordinate sy stem. 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 boundaries 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 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 the 12 km eastern U.S. grid.6 - 4

Table 6 - 1. Geographic Elements of Domains Used in Photochemical Modeling Photochemical Modeling Configuration National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid Map Projection Lambert C onformal 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 millibar 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 (NE I), version 2. Emissions were proce ssed to photochemical model inputs with the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (Hou youx et al., 2000 ). This platform includes criteria pollutants and precursors: particulate matter less than 10 mic rons (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): hy drogen chloride, chlorine and mercury . Additionally , for some sectors, HAP emission s of benzene, formaldeh yde, acetaldeh yde 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 National Air Toxics Assessment (NATA) inventory , which was updated from the 2005 NEI v2 in order to incorporate updated data for particular source categories such as cement and haz ardous waste incineration, and also revised from comments from state and local inventory providers as a result of NATA review. This inventory

was further modified to remove sources that were found to have shut down prior to 2005 and to update the gold mine emissions per information collected during the Gold Mine Ore and Production NESHAP. Inaddition, mercury emissions were revised for the boiler sector to allow for greater consistency with the Information Collection Request (ICR) data collected for this ru le. In particular, we used the unit - specific ICR mercury emissions for all I CR6 - 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 ge ographic coordinates, stack coordinates, stack release height, exit 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. IC R emissions were not used directl y for any other pollutants used in the v4.1 platform; however they informed numerous corrections and updates to the inventory including the removal of duplicates and facilities that had shut down prior to 2005, and the incl usion of control information. The stationary

inventory

used in the v4.1 platform is separated 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 generally

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 Emissions Standards for Hazardous Air Pollutants (NESHAP) and Maximum Available Control Technology (MACT) standards. 6.4.2 Future Year Baseline Emissions The 2016 baseline emissions are intended to represent the emissions associated with growth and controls in 2016. The projections used for this effort are unique to this project and are not associated with a particular modeling platform. The EGU point source (ptipm) emissions estimates for the future year reference were created b y the Integrated Planning Model (IPM) version 3.02 for criteria pollutants, hydrochloric acid, and mer cury

in 2015 . 2 For the non - EGU point (ptnonipm) and nonpoint (nonpt) sectors, both control and growth factors were applied to a subset of the 2005 v4.1 platform data to create the 2016 reference case. The 2014 projection factors 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

incorporate s emissions projections for the proposed Transport Rule, cement kiln NESHAP, RI CE NESHAP, gold mine NESHAP, changes to boiler emissions

1 The “NEI_UNIQUE_ID” field w as 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 7032 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 rule, and known consent decrees. A complete list of rules included in the f

uture point source baseline inventory is shown in Table 6 - 2. Table 6 - 2. Control S trategies and/or Growth Assumptions Included in the 2016

P rojection Projections Carried Forward fromthe proposed Transport Rule a , b

Description Pollutants MACT rules, nation al, VOC: national applied by SCC, MACT VOC Consent Decrees and Settlements , including refinery consent decrees, and settlements for: ( 1) Alcoa, TX and ( 2) Premcor (formerly MOTIVA), DE All Municipal Waste Combustor Reductions

— plant level PM Hazardous W aste Combustion PM Hospital/Medical/Infectious Waste Incinerator Regulations under Section 129d/11 1d NO X , PM, SO 2 Large Municipal Waste Combustors

— growth applied to specific plants All MACT rules, plant - level, VOC: Auto Plants VOC MACT rules, plant - le vel, 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 Grow th and Change outs 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 m odeling c E mission Reductions resulting from controls put on specific boiler units (not due to MACT) after 2005, identified through analysis of the control 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 fromthe proposed Transport Rul e 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 inadvertent ly

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 V irginia (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 th e 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 reductions missed were low er than estimated by the consent decree w ere 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 L ight - Dut y Vehicle Tier 2 Rule, the Onroad Heav y - Dut y Rule, and the Mobile Source Air Toxics (MSAT) final rule and the category 3 marine diesel engines Clean Air Act and International Maritime Organization standards which includes the establishment of emission control areas for these ships. Emission reductions and increases from the Renewable Fuel Standard version 2 (RFS2) are not included. The future baseline case nonroad mobile emissions reductions for these years include reductions to locomotives, various nonroad engines including diesel engines and various marine engine t ypes, fuel sulfur content, and evaporative emissions standards. 6.4.3 Future Year Sector Contribu tion 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 photochemical modeli ng 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 sec tor emissions from the 2016 baseline 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 emissions reductions at controllable sources are not 100% (100% ~ total contribution), the boiler sector air quality6 - 8

contribution estimates from the 2016 source apportionment model simulation are adjusted based on nation - wide estimates of control percentages by pollutant to create a final 20 16 “ 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 emissions in the eastern and western U.S. modeling domains so the non - point (area) category contains 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 To tal (TPY) EGU Point

—Boiler (ptipm) 57 0 54,337 826 0 0 0 0 Total (TPY) Non - EGU Poi nt

—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 NEIpoint source inventory used in the modeling analy sis. Non - point boilers in the NEIdo not have stack location information and are not shown . These boilers are spatiall y distributed 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 benefit s 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 impacts of the major source rule. Facilities were identified for the major s ource rule based on meeting several criteria: (1) NEIfacilit y ID and process level fuel t ype matched to a facility in the boiler I CR database, (2) NEI source had a boiler SCC code, and (3) unit design capacit y 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 identified as the units added from the boiler ICR database plus units in the NATA Hg inventory that were not in the ICR but were considered part of the Boiler MACT Universe. A percent emissions reduction was estimated based on the rule proposal unit - specific facility- fuel combination in the I CR database. Each facility /process matching the facilit y - 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 anal ys is for this rulemaking, the modeling s ys tem was used to calculate daily and annual PM 2.5 concentrations, 8 - hr maximum ozone, annual total mercury dep osition levels and visibility

impairment. Model predictions are used to estimate future - ye ar 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. Specificall y, we compar e a 2016 reference scenario, a scenario without the boiler sector controls, to a 2016 control scenario which includes the adjustments to the boiler sector. This is done by calculating the simulated air qualit y 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 an y particular future year simu lation and the 2005 base. These predicted ratios are then applied to ambient base year design values. The design value projection methodology used here followed EPA guidance for such anal ys es (USEPA, 2007 ). Additionally , the raw mode l outputs are also used in a relative sense as inputs to the health and welfare impact functions of the benefits anal ys is. Onl y m odel predictions for mercury deposition were analyzed using absolute model changes, although percent changes between the contro l case and two future baselines are also estimated. The 36 km and both 12 km modeling domains were modeled for the entire year of 2005 and projected ye ar 2016. Data from the entire y ear were utilized when looking at the estimation of PM 2.5 , total mercury

d eposition, and visibility impacts from the regulation. Data from April through October is used to estimate ozone impacts. 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 emissions. Available data indicate that the mercury emissions from these sources are a m ixture of gaseous elemental mercury (25%), inorganic divalent mercury

(reactive gas phase mercury) (50%), and particulate 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 reduction is approximately 60% reactive gas phas e (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 States; approximately 56% in dry

form and 44% in wet form. The chemical composition of the reductions in the wester n model domain is approximately

63% reactive gas phase (HG 2 ) and 37% particulate. These reductions are related to changes in emissions at major boiler sources using the ptnonipm sector as a surrogate. 6.5.2 Impacts of Sector on Future Annual PM 2.5 Levels This s ection 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. Specificall y, we compare a 2016 reference scenario to a 2016 control scenario. The modeling a ssessment indicates a decrease up to 0.81 μg/m 3 in annual PM 2.5 design values is possible given an area ’ s proximity to controlled sources and the amount of reduced sulfur dioxide and primary PM 2.5 emissions from major sources under the recommended solid fu el category option. The estimated6 - 11

mean reduction over all monitor 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 proximity to controlled sources and the amount of reduced sulfur dioxide and primary

PM 2.5 emissions from major sources under the alternative option. The estimated mean reduction over all 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 F uture 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 emissions from this sector. Specifically , we compare a 2016 reference scenario to a 2016 control sc enario. 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 sulfur dioxide and primary PM 2.5 emissions fr om major sources under the recommended 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 sulfur dioxide and p rimary PM 2.5 emissions from major sources under the alternative option. An estimated decrease up to 0.03 μg/m 3 in 24 - hr average PM 2.5 design values at monitor locations in the United States is possible given an area ’ s proximity to controlled sources and th e amount of reduced sulfur dioxide and primary

PM 2.5 emissions from area sources. 6.5.4 Impacts of Sector on Future Visibility Levels Air quality

modeling conducted for this final rule was used to project visibility conditions in 138 mandatory

Class I federal ar eas 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 valuation of visibility . The deciview metric pr ovides a scale for perceived visual changes over the entire range of conditions, from clear to haz y. Under many scenic conditions, the average person can generally perceive a change of one deciview. Higher deciview values are 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 day s over all Class Iarea 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 annual 20% worst visibility days over all Class I area monitors based on controls for major sources under the alternative optio n. An improvement in visibility

up to 0.03 deciviews at Class I monitor locations in the United States is possible given an area ’ s proximity to controlled sources and the amount of reduced sulfur dioxide and primary PM 2.5 emissions from area sources. 6.5.5 Impac ts of Sector on Future Ozone Levels This section summarizes the results of our modeling of 8 - hr maximum ozone air qualit y impacts in the future due to reductions in emissions from this sector. Specifically , we compare a 2016 reference scenario to a 2016 co ntrol scenario. The modeling assessment indicates a decrease of less than 0.01 ppb in ozone design values 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 estimated using procedures outlined by United States Environmental Protection Agency modeling guidance (USEPA, 2007). 6.6 Section 6 Refer ences Baker, K., Scheff, P., 2007. Photochemical 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 States. Atmospheric Environment 41, 6185 - 619 5. Chang, J.S., Brost, R.A., Isaksen, I.S.A., Madronich, S., Middleton, P. , Stockwell, W.R., Walcek, C.J. 1987. A 3 - dimensional eulerian acid deposition model

— phy sical concepts and formulation . J. Geophys. Res. - Atmos . 92, 14681 - 14700. ENVIRON, 2008. User ’ s Guide Comprehensive Air Quality Model with Extensions. ENVI RON International Corporation, Novato. Gery , M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics mechanism for urban and regional scale computer modeling . J. Geophys. Res. - Atmos . 94, 12925 - 12956. Houy oux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S., 2000. Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of Geoph ys ical Research - A tmospheres 105, 9079 - 9090. Nenes, A., Pandis, S.N., Pilinis, C., 1999. Continued development and testing of a new thermody namic 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 - related emissions - based particulate matter mod eling: Initial applications and advances. J. Air Waste 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 Analy ses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze, RTP. USEPA, 2010. Air Quality Modeling Technical Support Document: Boiler Source Sector Rules (EPA - 454/R - 10 - 006), Research Triangle Par k, North Carolina. Wagstrom, K.M., Pandis, S.N., Yarwood, G., Wilson, G.M., Morris, R.E., 2008. Development and application of a computationally efficient particulate matter apportionment algorithm in a three - dimensional chemical transport model. Atmospher ic Environment 42, 5650 - 5659.7 - 1 SECTION 7 BENEFITS OF EMISSION S REDUCTIONS 7.1 Synopsis In this section, we provide an estimate of the monetized benefits associated with reducing exposure to particulate matter (PM) and ozone for the final Boiler MACT and Boiler Area So urce 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 meet these mu ltiple 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 benefit estimates include the number of cases of avoided morbidity

and prem ature mortalit y among populations exposed to PM 2.5 and ozone, as well as the monetized value of those avoided cases. Because we were unable to monetize the direct benefits associated with reducing HAPs, the monetized benefits estimate is an underestimate o f the total benefits. The extent of this underestimate, whether small or large, is unknown . Using a 3% discount rate, we estimate the total combined monetized benefits of the final Boiler MACT and Boiler Area Source Rule to be $22 billion to $54 billion in the implementation y ear (2014). Using a 7% discount rate, we estimate the total combined monetized benefits of the final Boiler MACT and Boiler Area Source Rule to be $20 billion to $49 billion in the implementation y ear. Higher or lower estimates of bene fits are possible using other assumptions; examples of this are provided in Figure 7 - 1. All estimates are in 2008$ and include any energy disbenefits associated with the increased emissions from additional energy usage. These monetized estimates reflect E PA ’ s current interpretation of the scientific literature (U.S. EPA, 2009c ). In addition, these estimates incorporate an array of improvements introduced since the proposal, including boiler sector - specific air quality modeling data, lowest measured level (LML) assessment, mercury deposition maps, ozone benefits, and energy disbenefits. M ethodological and time limitations under the court - ordered schedule prevented EPA from monetizing the benefits from several important benefit categor ies, including direct benefits from reducing hazardous air pollutants, ecosy stem effects, and visibility impairment. The direct benefits from reducing other air pollutants have not been monetized in this anal ys is, including reducing 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 other metals, and 23 grams of dioxins/furans (TEQ) each ye ar. We assess the direct benefits of these emission reductions qualitatively in this anal ys is.7 - 2 $0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000

Pope etal. La den eta l.Millions (2008$)

Benefits estimates der iv ed from 2 epidemiology functions and 12 e xper t f unctions

Benefits 3 % Benefits 7%

Figur e 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) study, as w ell 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 elicitati on; 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 reducing HAP emissions, but they do include the energy disbenefits associated with the inc reased 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 w ell 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 associated with the emission reduction anticipated for these rules can be broadl y categorized as those benefits directly attributable to reduced exposure to HAPs , the PM co - benefits associated with reducing HAPs (i.e., PM reduced b y introducing technology designed to reduce HAPs), and those attributable to exposure to other pollutants. Several of the HAPs that would be reduced b y these rules have been classified as known or probabl e human7 - 3

carcinogens. 1 As a result, one benefit of the proposed regulation is a reduction in the risk of cancer. Other benefit categories include po tential reduced incidence of neurological effects and irritations of the lungs and skin, reduced mortalit y and other morbidity 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 which can also be identified. Ingeneral, welfare impacts include effects on vegetation, visibility impairment, and acidification of water bodies. We were unable to monetize the direct benefits associated with reducing HAPs in this analy sis. In Section 7.5.5 of this RIA, we provide a full qualitative 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 assoc iated with monetizing HAP benefits. We also provide maps of reduced mercury

deposition in that section. Therefore, all monetized benefits provided in this anal ys is onl y reflect improvements in ambient PM 2.5 and ozone concentrations. Thus, the monetized ben efits estimate is an underestimate of the total benefits. The extent of this underestimate, whether small or large, is unknown. In addition to pollutants we do not directl y monetize, these rulemakings are expected to reduce emissions of PM 2.5 , SO 2 , and VOC s. 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 the refore reduce estimated incidence 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 pollutants 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 res ult in reductions in VOCs, which will result in changes in ambient ozone concentrations. In implementing these rules, emission controls may lead to reductions in ambient PM 2.5 below the National Ambient Air Quality Standards (NAAQS) for PM in some areas an d assist other areas with attaining the PM NAAQS. Because the PM NAAQS RIAs also calculate PM benefits, there are important differences worth noting in the design and analy tical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and bene fits of attaining a new air quality standard nationwide based on an array of emission control strategies for different sources. In short, NAAQS RIAs hypothesize, but do not predict, the control strategies that States may choose to enact when implementing a NAAQS. The setting of a NAAQS does not directl y result in costs or benefits, and as such, the NAAQS RIAs are merel y illustrative and are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and

1 As described in Section 7.5, formaldehyde, several PAHs, and acetaldehyde are classified as probable human carcinogens. Different nickel compounds are classified as human carcinogens and probable human carcino gens.7 - 4

e mission reductions. However, some costs and benefits estimated in this RIA account for the same air qualit y improvements as estimated in the illustrative PM 2.5 NAAQS RIA. By contrast, the emission reductions for this rule are from a specific class of well characterized sources. In general, EPA is more confident in the magnitude and location of the emission reductions for these rules. It is important to note that emission reductions anticipated from these rules do not result in emission increases elsewhere (other than potential energy disbenefits). Emission reductions achieved under these and other promulgated rules will ultimately

be reflected in the baseline of future NAAQS anal ys es, which would reduce the incremental costs and benefits associated with att aining the NAAQS. EPA remains forward looking towards the next iteration of the 5 - ye ar review cy cle 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 info rmation on the relationship between the NAAQS and rules such as anal yz ed 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 anal ys is for the final Boiler MACT and Area Source Rule incorporates an array of policy and technical improvements since the proposal anal ys is in 2010 (U.S. EPA, 2010c ), including: 1. Boiler sector- specific air quality modeling data . The benefits estimates for this fin al anal ys is are derived from air quality data modeled using CAM x that reflect the emissions from the boiler sector and their contribution to ambient PM 2.5 and ozone levels. These data provide a different and likel y superior representation of the geographic distribution of emissions and associated ambient concentrations specificall y from boilers than the generic benefit - per - ton estimates used in the proposal that reflected emissions for the entire non - EGU (non - electrical generating unit) category . For more i nformation regarding the modeling inputs and assumptions, please see Section 6 of this RIA. For more information regarding the derivation of boiler sector - specific benefit - per - ton estimates and how they are superior to the estimates used in the proposal, p lease 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. EPA, 2009a) , EPA has recentl y changed its approach to characterizing the uncertaint y associated with benefits estimated at lower air quality

levels. Specifically , EPA now 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 replacing the previous threshold sensitivity anal ys is with a new LML assessment . We discuss this assessment in more detail in Section 7.2.4 and provide the results of this L MLassessment in Section 7.3.7 - 5

3. Mercury deposition . The air quality modeling 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 methylation, 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 mercury emissions. 4. Ozone benefits . The air quality modeling data provide an estimate of the change in ambient ozone concentrations associated 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 anal ys is, we include an estimate of the incre ased CO 2 emissions associated with the additional electricity required to operate control devices. We provide the results of this analy sis in Section 7.4. 6. Improved characterization of uncertainty . For this final anal ys is, we characterize uncertaint y using four methods (Monte Carlo methods, L MLassessment, alternate concentration - response functions, and qualitative descriptions). While the proposal incorporated some of these approaches, the Monte Carlo methods and L ML assessment are an improvement for the f inal analy sis. Confidence intervals reflecting the standard errors in the underlying epidemiology and economics literature are provided for incidence and valuation 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 benefits of the modeled changes in environmental quality . This approach estimates changes in individual health and welfare endpoints and assigns values to those changes assuming independence of the individual va lues. Total benefits are calculated simpl y as the sum of the 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 anal ys es (Lev y et al., 2009 ; Hubbell et al., 2009 ; Tagaris et al., 2009 ). To assess economic value in a damage - function framework, the chan ges 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 anal ys is (HIA) must first be conducted to convert air qualit y changes into effects that can be assigne d dollar values. For this RIA, the health impacts anal ys is is limited to those health effects that are directly linked to ambient levels of air pollution and specificall y to those linked to PM 2.5 and ozone. We also provide qualitative discussions of the impact of changes in other environmental and ecological effects, including the benefits associated with decreasing deposition of sulfur to terrestrial and aquatic ecosy stems, but we are unable to7 - 6

place an economic value on these changes due to time limitatio ns under the court - ordered schedule . We note at the outset that EPA rarel y has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory anal ys es. Thus, similar to Kunzli et al. (2 001) and other recent health impact anal ys es, our estimates are based on the best available methods of benefits transfer. Benefits transfer is the science and art of adapting primary

research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under anal ys is. Adjustments are made for the level of environmental quality

change, the socio - demographic and economic characteristics of the affected population, and other factors to improve the accuracy and robustness 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 exposure to air pollution. HIAs are a well - established approach for estimating the retrospective or prospective change in adverse health impacts resulting from population - level changes in exposure to pollutants (L ev y et al., 2009 ). Anal ys ts have applied the HIA approach t o estimate human health impacts resulting from hy pothetical changes in pollutant levels (Hubbell et al., 2005 ; Davidson et al., 2007 ; Tagaris et al., 2009 ). For this an aly sis, we used the environmental Benefits Mapping and Anal ys is Program (BenMAP), which is a PC - based tool that can s ys tematize health impact anal ys es b y applying a database of key input parameters, including health impact functions and population projecti ons. 2 The HIA approach used in this analy sis involves 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 quality ; (2) determining the subsequent change in population - level exposure; (3) calculating health impacts by appl yi ng concentration - response relationships drawn from the epidemiological literature (Hubbell et al., 2009 ) to this change in population exposure. A typical health impact function might look as follows:

where y 0 is the baseline incidence rate for the health 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 dow nload 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 b y 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 analy sis, we sy stematize the HIA calculation process using BenMAP ’ s library

of existing air qualit y monitoring data, population data and health imp act functions. Figure 7 - 2 provides a simplified overview of this approach, and Figure 7 - 3 identifies the data inputs and outputs for the BenMAP model for a PM 2.5 analy sis.

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 analy sis were derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analy sis (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 ye ars. For a detailed de scription of the underl yi ng functions, studies, baseline incidence rates, and population data used in this analy sis, please refer to Chapter 5 of the recently proposed Transport Rule (U.S. EPA, 2010e ). Table 7 - 1 identifies which hum an health and welfare endpoints are included in the monetized benefits and which endpoints remain unquantified. In summary , the estimate of monetized PM benefits includes premature mortality and eleven morbidity endpoints, and the estimate of monetized ozo ne benefits includes premature mortality and five morbidity

endpoints.

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

Census Population Data Baseline and Post Control PM 2.5 Concent rations

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 Effects of Air Pollutants Affected Pollutant/Effect Quantified and Monetized Unquantified

PM: health a Prem ature mortality based on cohort study estimates b Lo w birth weight Pre - term births Prem ature mortality based on expert elicitation estimates Pulmonary function Nonfatal cardiovascular outcomes other than myocardial infarctions Chronic bronchitis Hospital admissions: respiratory and cardiov ascular Chronic respiratory diseases other than chronic bronchitis Em ergency room visits for asthma Non - asthm a respiratory emergency room visits Nonfatal heart attacks (myocardial infarctions) UVb exposure (+/

− ) c Lo wer 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 Prem ature mortality based on short term study estimates Chronic respiratory damage Prem ature mortality due to long - t erm exposures Hospital admissions: respiratory Prem ature aging of the lungs Em ergency room visits for asthma Non - asthm a respiratory emergency room visits Minor restricted activity days UVb exposure (+/

− ) c School loss days

Ozone: welfare Decrease d outdoor w orker productivity Yields for:

– Commercial forests

– Fruits and vegetables, and

– Other commercial and noncommercial crops Dam age 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 Ac ute respiratory symptoms Prem ature mortality Pulmonary function (continued)7 - 10

Table 7 - 1. Human Health and Welfare Effects of Air Pollutants A ffected (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 Mer cury: 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

– Incidence s of heart attack Potential reproductive effects Mercury: environment Im pact on birds and mammals (e.g., reproductive effects, cardiovascular effects) Mercury: welfare Im pacts to commercial., subsistence and recreational fishing HAPs Health effects associated with HAP exposure

a In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health effects including morphological changes and altered host defense mechanisms. The public health im pact 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 captured by the cohort estimates, there may be additional premature mortality from short term PM exposure not captured in the cohort estimates included in the primary analysis. 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 characteriz ation of uncertainty .

 One estimate is based on the concentration - response (C - R) function developed from the extended anal ys is of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002) , a study that EPA has previou sly

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 anal ys es, and we recognize that interpretation of the science regarding air pollution and health is dy namic and evolving. Based on our review of the current body of scientific7 - 11 literature, EPA now estimates PM - related mortality without appl yi ng an assumed concentration threshold. EPA ’ s Integrated Science Assessment 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 log linear model most adequately p ortray s the PM - mortality concentration - response relationship while recognizing potential uncertainty about the exact shape of the concentration - response function. Since then, the Health Effects Subcommittee (U.S. EPA - SAB, 2010 ) o f EPA ’ s Council concluded, “ The HES fully supports EPA ’ s decision to use a no - threshold model to estimate mortality reductions. This decision is supported by

the data, which are quite consistent in showing effects down to the lowest measured levels. Analy s es of cohorts using data from more recent ye ars, during which time PM concentrations have fallen, continue to report strong associations with mortality . One estimate is based on the C - R function developed from the extended anal ys is of the Harvard Six Citie s cohort, as reported by L aden et al. (2006) . This study , published 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 i n RIAs completed since the PM 2.5 NAAQS. When calculating the estimate, EPA applied the effect coefficient as reported in the stud y without an adjustment for assumed concentration threshold of 10 μg/m 3 as was done in recent (2006

–2009) RIAs.

 Twelve estimat es are based on the C - R functions from EPA ’ s expert elicitation study (IEc, 2006 ; Roman et al., 2008 ) on the PM 2.5 - mortality relationship and interpreted for benefits analy sis 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 conce ntration - response function provided by each of the twelve experts, to better characterize the degree of variability in the expert responses. Therefore, there is no evidence to support a truncation of the CRF [concentration - response function]. ” Inconjuncti on with the underlying scientific literature, this document provided a basis for reconsidering the application of thresholds in PM 2.5 concentration - response functions used in EPA ’ s RIAs. For a summary of these scientific review statements and the panel members commenting on thresholds since 2002, please consult the Technical Support Document (TSD) Summary of Expert Opinions on the Existence of a Threshold (U.S. EPA, 2010d ). Consistent with the recent scientific advice summarized abov e, we are replacing the previous threshold sensitivity anal ys is with a new “ Lo west Measured Level ” (LML) assessment . This approach summarizes the distribution of avoided PM mortality impacts according to the baseline PM 2.5 levels experienced b y the populat ion receiving the PM 2.5 mortality benefit. In the results section, we identify on the figures the lowest air quality levels measured in each of the7 - 12

primary cohort studies that estimate PM - related mortality . This information allows readers to determine the portion of PM - related mortalit y benefits occurring above or below the LML of each study ; in general, our confidence in the estimated PM mortality decreases as we consider air quality levels further below the LMLin the two epidemiological studies. While an LM L assessment provides some insight into the level of uncertaint y in the estimated PM mortality benefits, EPA does not view the LML as a threshold and continues to quantify PM - related mortality impacts using a full range of modeled air quality concentrat ions. Unlike an assumed threshold, which is a modeling assumption that reduces the magnitude of the estimated health impacts, the L MLis a characterization of the fraction of benefits that are more uncertain. It is important to emphasize that just because we have greater confidence in the benefits above the LM L, this does not mean that we have no confidence that benefits occur below the L ML . Analy ses of these cohorts using data from more recent y ears, during which time PM concentrations have fallen, continue to report strong associations with mortality . As we model mortality impacts among populations exposed to levels of PM 2.5 that are successivel y lower than the L MLof each study , our confidence in the results diminishes. As air pollution emissions continu e 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 successivel y 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 qualit y levels continue to improve. Even in the absence of a definable threshold, we have more confidence in the benefits estimates above the LML of the large cohort studies. To account for the uncertaint y in each of the studies that we base our mortality estimates on, we provide the LML for each of the cohort studies. However, the finding of effects at the lowest LMLfrom 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 controls may lead to reductions in ambient PM 2.5 below the PM NAAQS in some areas. While benefits 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 add itional uncertainties imposed by assuming thresholds or other non - linear concentration functions for the purposes of benefits analy sis.7 - 13

7.2.5 Economic Valuation of Health Impacts These models assume that all fine particles, regardless of their chemical compositi on, are equally potent in causing premature mortality because the scientific evidence is not y et sufficient to allow differentiation of effect estimates b y particle type (U.S. EPA, 2009c) . Directly emitted PM, SO 2 , and VOC are the dominant PM 2.5 precursors affected b y 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 between precursors because each ton of precursor reduced has a different propens ity 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 man y miles, meaning that higher exposures may

occur over areas with low populations. On the other hand, to the extent that direct PM 2.5 emissions occur in high density population areas, exposures w ill tend to be higher there, leading to higher monetized health benefits 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 recentl y proposed Transport Rule (U.S. EPA, 2010e ) for a detailed description of the underlying valuation functions and the monetized unit values for each endpoint incorpor ated into this analy sis. 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 interpretat ion 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 - life (VSL) estimate derived from a limited analy sis of some of the availa ble studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta - anal ys es 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 tw o exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 years of follow - up medical costs and lost w ages. 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 affects the AMI costs after the analysis year and the valuation of premature mortalities 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 interquartile range from the Mrozek and Taylor (2002) meta - anal ys is of 33 studies. The $10 million value represented the upper end of the interquartile range from the Viscusi and Ald y (2003) meta - anal ys is of 43 studies. The mean estimate of $5.5 million (2000$) 5 was also consistent with the mean VSLof $5.4 million estimated in the Kochi et al. (2006) meta - anal ys is. However, the Agency neither changed its official guidance on the use of VSL in rule - makings nor subjecte d the interim estimate to a scientific peer - review process through the Science Advisory Board (SAB) or other peer - review group. During this time, the Agency continued work to update its guidance on valuing mortalit y risk reductions, including commissionin g a report from meta - anal yt ic experts to evaluate methodological questions raised b y EPA and the SAB on combining estimates from the various data sources. In addition, the Agency consulted several times with the Science Advisory Board Environmental Economi cs Advisory Committee (SAB - EEAC) on the issue. With input from the meta - anal yt ic experts, the SAB - EEAC advised the Agency to update its guidance using specific, appropriate meta - anal yt ic techniques to combine estimates from unique data sources and different studies, including those using different methodologies (i.e., wage - risk and stated preference) (U.S. EPA - SAB, 2007 ). Until updated guidance is available, the Agency determined that a single, peer - reviewed estimate applied cons istently best reflects the SAB - EEAC advice it has received. Therefore, the Agency has decided to apply

the VSL that was vetted and endorsed b y the SAB in the Guidelines for Preparing Economic Analy ses (U.S. EPA, 2000 ) 6 while the Agen cy 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 studies 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 grow th to 2015, the $5.5 million VSL is $7. 9 million. 6 In the (draft) update of the Economic Guidelines (U.S. EPA, 200 8b ), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in th e near future. Therefore, this report does not represent final agency policy. 7 In this analysis, we adjust the VSL to account for a different currency year (2008$) and to account for income grow th to 2015. After applying these adjustments to the $6.3 milli on 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 hea l th benefits of an incremental change in air pollution precursor emissions. In situations when we are unable to spe cificall y model a regulatory 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 RI A (U.S. EPA, 2010a ) and Federal Transport Rule RIA (U.S. EPA, 2010e ). For this anal ys is, the emissions inventories used in the air quality modeling and benefits modeling are slightly different from the final boiler sector emission inventories reflected in the rules ’ emission memos. 8 These discrepancies exist because emissions and air qualit y modeling decisions are made early

in the anal yt ical process, and the sheer number of boilers (over 180,000 area boilers and 13,000 major boilers) made it impossible to reconcile the data completely . 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 major source boilers and that the boilers in the non point source inventory

are appropriate surrogates for the area source boilers. Furthermore, the emission inventories used for air qualit y modeling do not reflect emissions from new sources anticipated to come online by the anal ys is y ear. 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 anal ys is, we: 1. Quantified the PM 2.5 - related human health and monetized benefits of the SO2 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 estimate s that quantified the PM 2.5 - related benefits of incremental changes in SO 2 emissions. 2. Divided the health impacts and monetized benefits by the emission reduction. This calculation yi elds 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, 201 0f ). Technical details regarding the emission reduc tions estimated for this rule are available in these docket memos: “ Revised Development of Baseline Emission Factors for Boilers and Process Heaters at Commercial, Industrial, and Institutional Facilities ” and “ Revised Methodology for Estimating Cost and E missions Impacts for Industrial, Commercial, Institutional Boilers and Process Heaters National Emission Standards for Hazardous Air Pollutants . ”7 - 16 wer e then multiplied by

the projected SO 2 emission reductions to produce an estimate of the PM - related health impacts and monetized benefits. This process was repeated for directl y emitted PM 2.5 from major sources and for PM 2.5 and SO 2 emissions from area so urce 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 o f directl y emitted PM 2.5 and SO 2 for each regulatory option. Deriving boiler sector - specific BPT estimates provides equivalent benefits results as scaling the air quality data directly

to correspond to the inventory differences, but it provides greater fle xibility to examine alternate regulatory scenarios. The BPT estimates used in this analy sis are different from the BPT estimates used in the proposal anal ys is. While these boiler sector - specific BPT estimates are methodologicall y consistent with the BPT e stimates in Fann, Fulcher, and Hubbell (2009) that were used in the proposal anal ys is, the BPT estimates applied in this final anal ys is provide a different and likely superior representation of the health impacts a nd 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 boilers together with other emission sources in the area category . For example, the non - EGU category includes emissions from many

different large stationary sources, including boilers, metal production, mineral products, chemical manufacturing, petroleum industry , pulp and paper production, and oil and gas production, and waste disposal, which all have different geographic distributions across the country . Because the geographic distribution of boilers is not likely to exactly

match the average distribution of all of these sources, ambient air qualit y and the associated benefits for reducing boiler emissions are going 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 differ ences in local air 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 nationall y) than the previous BPT estimates and reflect s 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 fun ctions, 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 inventory, please see Table 2 - 2 of U.S. EPA, (2010g) , which pro vides a list of the differences betw een the 2002 and 2005 base inventory. In addition to including consent decrees and several recent mobile source rules, the emissions platform used for this analysis also removed duplicates and plant closures and an updat ed version of IPM to project EGU emissions.7 - 17

accurate than the proposal. For comparison, the BPT for directl y emitted PM 2.5 in this analy sis is lower for major and area sources than th e proposal, whereas the BPT for SO 2 is higher in this anal ys is than the proposal. Inaddition, we have updated the analy sis y ear used in this final anal ys is to be consistent with the implementation timeline, but this revision would not have a significant i mpact on the benefits results. In the proposal, the BPT estimates reflected emissions inventories, income growth, and population growth for 2015 because we did not have BPT estimates available for the proposal analy sis y ear (2013). For this final analy sis, the emissions inventories are for 2016, but income growth and population growth are for the final anal ys is y ear (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 - point boilers in Figures 7 - 4 through 7 - 7.

Figure 7 - 4. Change in Ambient PM 2.5 Levels from SO 2 Emissions from Point Source Boilers QUARTERL

YMEN 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

Bo ilers QUARTERL

YMEN No change 0.01 - 0.10 0.10 - 0.50 0.50 - 1.00 1.00 - 3.05

QUARTERL

YMEN 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 anal ys is for several reasons. Analy sis of organic carbon measurements suggest onl y a fraction of secondaril y formed organic carbon aerosols are of anthropogenic origin. The current state of the science of secondary organic carbon formation indicates that anthropogenic VOC contribution to second ary organic carbon aerosols is often lower than the biogenic (natural) contribution. Given that a fraction of secondarily formed organic carbon aerosols from anthropogenic VOC emissions and the extremely small amount of VOC emissions from this sector relat ive to the entire VOC inventory , it is unlikely this sector has a large contribution to ambient secondary organic carbon aerosols. Photochemical models ty picall y estimate secondary

organic carbon aerosols from anthropogenic VOC emissions to be less than 0. 1 μg/m 3 . Given the resource requirements to apply source apportionment technology in the photochemical model for secondary organic carbon aerosols and that onl y a very small portion of secondary organic carbon aerosols (i.e., from anthropogenic sources) wo uld be apportioned, this option was not employ ed to estimate the impact of this sector’s VOC emissions on secondary organic carbon aerosols. Lastl y, the contribution of VOC reductions to total monetized PM 2.5 benefits would likely

be very small for these r

ules. For example, while not an appropriate source QUARTERL

YMEN 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, the 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 onl y reduced by 20% for both of the Boiler MACT options. Inaddition, the complex non - lin ear chemistry

of ozone formation introduces uncertaint y to the development and application of a BPT estimate. Therefore, we used the scaled air quality modeling results directl y to estimate the ozone benefits associated with VOC reductions. As the ozone - re lated benefits are very small, we do not believe that the differences in the VOC emissions inventory contribute much uncertaint y to the overall benefits results. Because the VOC emission reductions for the Boiler Area Source Rule are less than 1%, we do no t 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 anal ys is 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 the anchor points of Pope et al. (2002) and Laden et al. (2006) at discount rates of 3% and 7% for the final Boiler MACT and Boiler Area Source Rule, respectivel y. 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, respectivel y. 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, respectivel y. Table 7 - 8 provides the estimate d reductions in health incidences and the monetized benefits associated with estimated reductions in ambient ozone concentrations.7 - 21

Table 7 - 2. Summary of Monetized Benefits Estimates for Final 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 (m illions of 2008$ at 3%) Total Monetized

Benefits (m illions of 2008$ at 7%)SelectedOption Direct P M 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 implemen tation year (2014), and are rounded to tw o 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 evidence 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 incorporate 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 usag e 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 Estimates for Final Boiler Area Source Rule in

2014 (millions of 2008$) a

Pollutant E mi ssions

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 (m illions of 2008$ at 3%) Total

Monetized Benefits (m illions 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 implementation year (2014), and are rounded to tw o 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 evidence is not yet sufficient to allow differe ntiation 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 incorporate the conversion from precursor emissions to ambient fine p articles. 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.7 - 22

Table 7 - 4. Summary of Estimated Reductions in Health I ncidences from PM 2.5 for the

Final Boiler MACT in 2014 (95 th percentile confidence interval) a Selected Option Alternative Option Avoided Prem ature 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) Ac ute Myocardial Infarction 4,000 3,100 (2,400 – 13,000) (2,100 – 11,00 0) 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) Em ergency Room Visits, Respiratory 2,400 1,900 (2,600 – 6,800) (2,300 – 6,000) Ac ute Bronchiti s 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) Lo wer 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 mo dels assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow d ifferentiation 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 increased emissions from additional energy usage describe d 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 boil ers and 47 new boilers anticipated to come online by 2014.7 - 23

Table 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) a Proposed MACT Approach Final MACT/GA CT Approach Avoided Prem ature 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 – 3 1) Ac ute 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) Em ergency Room Visits, Respiratory 20 21 (11 – 28) (11 – 30) Ac ute Bronch itis 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) Lo wer Respiratory Sym

ptoms 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 mo dels assume that all fine particles , regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation 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 Epide miology 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 – $190,00 0) ($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) ($2,3 00 – $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 – $190,0 00) ($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 $2 7,000 $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 mo dels assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type . These estimates do not include benefits from re ducing 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 funct ions, but they do not reflect other sources of uncertainty inherent within the boiler - specific BPT 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 Epide miology 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 mo dels assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation 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 BPT 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 Prem ature Mortality Incidence Valuation (m illions 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)

Me ta - 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) Em ergency 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 (1,1 00

– 3,600) ($0.06

– $0.28) a All estimates are for the analysis year (2014) and are rounded to tw o significant figures. Health effects associated with ozone exposure are assumed to occur within the analysis year. Therefore, the monetized benefits are the same for any discount rate. These confidence intervals reflect the 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 breakdown of the estimated monetized health benefits associated with changes in PM 2.5 and ozone, respectivel y. Figure 7 - 10 shows the total combined monetized benefits for the final Boiler MACT and Area Source Rule at discount rates of 3% and 7%. Figures 7 - 11 and 7 - 12 provide a breakdown of the estimated monetized PM 2.5 benefits b y precursor pollutant for the final Boiler MACT and Boiler Area Source Rule, respectivel y. Figures 7 - 13 and 7 - 14 provide a breakdown of the monetized PM 2.5 b enefits by subcategory

for the final Boiler MACT and Boiler Area Source Rule, respectively . This analy sis shows that the majority of the population is exposed to PM levels at or above the lowest LML of the cohort studies (Figures 7 - 15 and 7 - 16), increasin g our confidence in the PM mortality

anal ys is. 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 p ercentage of the adult population 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 L MLfor each of the major cohort studies. Using the Pope et al. (2002) study , approximately 79% o f the population is exposed to annual mean PM 2.5 levels at or above the LM L 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 L MLof 10 μg/m 3

. As w e m odel mortality

impacts among populations exposed to levels of PM 2.5 that are successivel y lower than the LMLof the lowest cohort study , our confidence in the results diminishes. However, the anal ys is above confirms that the great majorit y of the impacts o ccur at or above the lowest cohort study ’s LML. It is important to emphasize that we have high confidence in PM 2.5 - related effects down to the lowest L MLof the major cohort studies.7 - 28

Figure 7 - 8 . Breakdown of Monetized PM 2.5 Health Benefits Estimates us ing Mortality

Function from Pope et al. (2002) a a This pie chart breakdow n is illustrative, using the results based on Pope et al. (2002) as an example. Using the Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult mo rtality wo uld 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 Health Benefits Estimates using Mortality

Function from Bell et al. (2004) a a This pie chart breakdow n 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 e t al. La den etal.Millions (2008$)

Benef its estimates de ri ve d from 2 e pidemiology functions and 12 exper t f unctions

Ben efits 3% Ben efits 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 Th is 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) study, as w ell as 12 effect coeffici ents 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 we re 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

Fi gure 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 Bas eline Annual Me an PM 2.5 Le ve l (μ g/ m 3 ) LML of Laden et al . (2006) study LML of Po pe et al . (2002) study

Figure 7 - 15 . Perce ntage of Population Exposed to Baseline Air Quality Level s 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 Base line Annual M ea n PM 2.5 Le ve l (μ g/ m 3 ) LM L of Pope e t al. ( 2002) st udy

LM L of L ade n et al. (2 006) st udy

Figure 7 - 16 . Cumulative Percentage of Population Exposed to Baseline Air Quality Level s

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 sect ion, 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 anticipated to increase emissions of pollutants from electric utility boilers (EGU boilers) that supply electricity

to the non - EGU boiler 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 and 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 ” (SCC). The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given y ear . 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) changes in net agricultural productivity , human health, propert y damages from increased flood risk, and the value of ecos ys tem services due to climate change. The SCC estimates used in this analy sis were developed through an interagency process that included EPA and other executive branch entities, and concluded in February , 2010. EPA first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking to establish L ight - Dut y Vehi cle Greenhouse 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 Document (SCC TSD) provides a complete discussion of the me thods used to develop these SCC estimates. 12 The interagency

group selected four SCC values for use in regulatory anal yses, which we have applied in this anal ysis: $5, $21, $35, and $65 per metric ton of CO 2 emissions 13 in 2010, in 2007 dollars. The first three values are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent, respectively . SCCs at several discount rates are included because the literature shows that the SCC is quite sensitive to assumpti ons 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 percentile of the SCC from all three models at a 3 percent discount rate. It is included to represent higher - than - expected impacts from temperature change further out in the tails of the SCC distribution. L ow probability , high impact events are incorporated into all of the SCC values through explicit consideration of their effects in two of the three model s as well as the use of a probability density function for equilibrium climate sensitivity . Treating climate sensitivity probabilistically

results in more high temperature outcomes, which in turn lead to higher projections of damages. The SCC increases ove r time because future emissions are expected to produce larger incremental damages as phy sical and economic sy stems become more stressed in response to greater climatic change. Note that the interagency group estimated the growth rate of the SCC directly u sing the three integrated assessment models rather than assuming a constant annual

12 Docket ID EPA - HQ - OAR - 2009 - 0472 - 114577, Technical Support Document: Socia l 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, Department 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 Department of Treas ury (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 w arming profiles and im pacts other than temperature change ( e.g. , ocean acidification) vary across GHGs, the group concluded “ transforming gases into CO 2 - equivalents using GWP, and then multiplying the carbon - equivalents by the SCC, wo uld not result in accurate estimates 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 anal ys is years of 2014, in 2005 dol lars 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 specific. SCC values represent only a partial accounting of climate impacts. When attempting to assess the incremental economic impacts of carbon dioxide emissions, the anal ys t faces a number of serious ch allenges. A recent report from the National Academies of Science (NRC, 2009 ) points out that any assessment will suffer from uncertainty , speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate sy stem, (3) the impact of changes in climate on the phy sical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, an y effort to quantify and mone tize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional. The interagency

group noted a number of limitations to the SCC analy sis, including the incomplete way

in whic h the integrated assessment models capture catastrophic and non catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertaint y in the extrapolation of damages to high temperatures, and assumptions regarding risk aversi on. The limited amount of research linking climate impacts to economic damages makes the interagency

modeling exercise even more difficult. The interagency group hopes that over time researchers and modelers will work to fill these gaps and that the SCC es timates used for regulatory

anal ys is by

the Federal government will continue to evolve with improvements in modeling. Additional details on these limitations are discussed in the SCC TSD. In light of these limitations, the interagency group has committed t o updating the current estimates as the science and economic understanding of climate change and its impacts on society improves over time. Specifically , the interagency group has set a preliminary goal of revisiting7 - 37

the SCC values within two y ears or at s uch 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 value of the climate - related disbenefits captured by the models for each analy sis y ear. For internal 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 - Derived Disbenefits of CO 2 Emission Increases in 2014 (millions

of 2008$) a Discount Rate and Statistic Selected Option

(M ajor) Alternative Option

(M ajor) 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 specific. SCC values represent only a partial accounting of climate impacts. 7.5 Unquantified or Nonmonetized Benefits The monetized benefits estimated in this RIA onl y reflect the portion of benefits attributable to the health impacts associat ed with exposure to ambient fine particles. Data, resource, and methodological limitations prevented EPA from quantifying or monetizing the benefits from several important benefit categories, including benefits from reducing toxic emissions, ecosy stem effe cts, and visibility impairment. The direct health benefits from reducing HAPs have not been monetized in this analy sis. In addition to being a PM 2.5 precursor, SO 2 emissions also contribute to adverse effects from acidic deposition in aquatic and terrestri al ecos ys tems, increase mercury methylation, as well as visibility impairment. In addition to health effects, ozone is associated with adverse vegetation effects to forests and crops. 7.5.1 Other SO2 Benefits In addition to being a precursor to PM 2.5 , SO 2 emissi ons are also associated with a variet y of respiratory

health effects. Unfortunatel y, 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 kn owing the location of the emission reductions and the resulting ambient concentrations, we were unable to estimate the exposure to SO 2 for nearb y populations. Therefore, this analy sis onl y quantifies and monetizes the PM 2.5 benefits associated with the red

uctions 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 health ef fects and short - term exposure to SO 2 (U.S. EPA, 2008a ). According to summary of the ISA in EPA ’ s risk and exposure assessment (REA) for the SO 2 NAAQS “ the immediate effect of SO 2 on the respiratory s ys tem in humans is bronchoconstri ction ” (U.S. EPA, 2009b ). In addition, 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 concentration - res ponse 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 asthmatics adversely affected (U.S. EPA, 2009b ). Based on our review of this information, we identified four short - term morbidity endpoints that the SO 2 IS A identified as a “ causal relationship ” : asthma exacerbation, respiratory related emergency

department visits, and respiratory - related h ospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in the SO 2 IS A. The SO 2 IS A also concluded that the relationship between short - term SO 2 exposure and premature mortality

was “ suggestive of a causal relationship ” because it is difficult to attribute the mortality

risk effects to SO 2 alone. Although the SO 2 IS A stated that studies are generall y consistent in reporting a relationship between SO 2 exposure and mortality , there was a lack of robustness of the observed associations to adjustment for pollutants. SO 2 emissions also contribute to adverse welfare effects from acidic deposition, visibility impairment, and enhanced mercury methylation (U.S. EPA, 2008c ) . Deposition of sulfur causes acidification, which can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic ecosy stems, as well as a decline in sensitive tree species, such as red spruce ( Picea rubens ) and sugar maple ( Acer saccharum ) in terrestrial ecosy stems . In the northeastern United States, the surface waters affected b y acidification are a source of food for some recreational and subsistence fishermen and for other consumers and support several cultural services, i ncluding aesthetic and educational services and recreational fishing. B iological effects of acidification in terrestrial ecos ys tems are generally 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 canop y trees. Terrestrial acidification a ffects several important ecological services, including declines in habitat for threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil erosion and reductions in water retention (cultural and regulating) 7.5.2 Carbon Monoxide Benefits Carbon monoxide (CO) exposure is associated with a variet y of health effects. Without knowing the location of the emission reductions and the resulting ambient concentration s using fine - scale air quality

modeling, we were unable to estimate the exposure to CO for nearby populations. Due to methodological and time limitations under the court - ordered schedule , we were unable to estimate the benefits associated with the reductio ns 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 photochemical reactions in the atmosphere. The amount of CO emitted from these rea ctions, relative to carbon dioxide (CO 2 ), is sensitive to conditions in the combustion zone, such as fuel ox ygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory s ystem to the blood, which can cause h ypoxia (r educed ox ygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ s ys tems that are dependent upon concentration and duration of exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010b ) concluded that short - term exposure to CO is “ likely to have a causal relationship ” with cardiovascular morbidity , particularly

in individuals with coronary heart disease. Epidemiologic studies associate short - term CO exposure with i ncreased risk of emergency department visits and hospital admissions. Coronary

heart disease includes those who have angina pectoris (cardiac chest pain), as well as those who have experienced a heart attack. Other subpopulations potentially at risk includ e individuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as older adults or the developing yo ung. The evidence is suggestive of a causal relationship betw een short - term exposure to CO and respiratory

morbidity and mortality . The evidence is also suggestive of a causal relationship for birth outcomes and developmental effects following long - term exposure to CO, and for central nervous s ys tem effects linked t o short - and long - term exposure to CO.7 - 40

7.5.3 Visibility Benefits Reducing SO 2 and PM emissions would improve the level of visibility throughout the United States (U.S. EPA, 2009c ). Fine particles with significant light - extinction efficie ncies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996 ). These suspended particles and gases degrade visibility by scattering and absorbing light. Higher visibility impairment levels in the East are due to generall y higher concentrations of fine particles, particularl y sulfates, and higher average 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% d uring 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 enjoy ment of daily

activities and their overall sense of wellbeing. Goo d visibility increases the qualit y of life where individuals live and work, and where they engage in recreational activities. Due to time limitations under the court - ordered schedule , we were unable to estimate the monetized benefits associated with visibi lity improvements. Previous anal ys es (U.S. EPA, 2006 ; U.S. EPA, 2010e ) show that visibility benefits are a significant welfare benefit category . As shown in Section 6.5.4 of this RIA, an average visibility imp rovement of 0.51 deciviews in annual 20% worst visibility day s over all Class I area monitors is anticipated 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 ecosy stem effects in the published literature (U.S. EPA, 2010a ). Sensitivity to ozone is highl y variable across species, with over 65 plan species identified as “ ozone - sensitive, ” man y of which occur in state and national parks and forests. These eff ects include those that damage or impair the intended use of the plant or ecosy stem. Such effects are considered adverse to the public welfare and can include reduced growth and/or biomass production in sensitive plant species, including forest trees, redu ced crop yi elds, visible foliar injury , reduced plant vigor (e.g., increased susceptibility to harsh weather, disease, insect pest infestation, and competition), species composition shift, and changes in ecos ys tems and associated ecos ys tem services. 7.5.5 Direc t 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 kind s of activities in which they engage. In order to identify

and prioritize air toxics, emission source t ypes and locations that are of

15 U.S. EPA. (2009 ) 2002 National - Scale Air Toxics 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 co nducted for calendar y ear 2002, and was released in June 2009. 16 NATA for 2002 includes four steps: 1 . Compiling a national emissions inventory of air toxics emissions 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 inhalation of air toxics including both cancer and noncancer effects Noncancer health effects can result from chronic, 17 subchronic, 18 o r acute 19 inhalation exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the immune and reproductive sy stems. According to the 2002 NATA, nearl y the entire U.S. population was expos ed 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 noncancer 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 necessarily

translate into significant reductions in health risk because toxicity varies by pollutant and whether or not th ere are exposures at or above levels of concern is not known. For example, acetaldeh yde mass emissions are more than double acrolein emissions on a national basis, according to EPA ’ s 2005 National Emissions Inventory (NE I). However, the Integrated Risk Inf ormation Sy stem (IRIS) reference concentration (RfC) for acrolein is considerably lower than that for acetaldehy de, suggesting that acrolein could be potentially more toxic than acetaldehy de. Thus, it is important to

16 U.S. EPA. (2009 ) 2002 National - Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 17 Chronic exposure is defined in the glossary of the Integrated Risk Informati on (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 substance 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 w ebsite. Even so, this mo deling framework is very useful in identifying air toxic pollutants and sources of greatest concern, setting regulatory priorities, and informing the decision making 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 - mo nitor ratios) and qualitative (e.g., quality of data, review of emission inventories) judgments can be found at http://www.epa.gov/ttn/atw/nata/roy/page16.html .7 - 42

Figure 7 - 17 . Estimated County Level Carcinogenic 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 h azardous air pollutants that would be reduced as a result of these rules. In a few previous anal ys es of the benefits of reductions in HAPs, EPA has quantified the benefits of potential reductions in the incidences of cancer and non - cancer risk (e.g., U.S. EPA, 1995 ). In those anal ys es, EPA relied on unit risk factors (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 distributio n of risk rather than a best or most likely estimate of risk. As the purpose of a benefit anal ysis 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 anal ys es, advice from the EPA ’ s Science Advisory Board (SAB) recommended that we avoid using high - end estimates in benefit anal ys es (U.S. EPA, 2002 ). Since this time, E PA has continued to develop better methods for anal yz ing the benefits of reductions in HAPs. In the Second Prospective 812 Anal ys is, EPA conducted a case study analysis of the health effects associated with reducing exposure to benzene in Houston from impl ementation of the Clean Air Act (IEc, 2009 ). While reviewing the draft report, EPA ’ s Advisory Council on Clean Air Compliance Analy sis concluded that “ the challenges for assessing progress in health improvement as a result of reductions i n emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure - response functions, uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of trac king health progress for diseases, such as cancer, that have long latency periods ” (U.S. EPA SAB, 2008 ). In 2009, EPA convened a workshop to address the inherent complexities, limitations, and uncertainties in current methods to quantify the benefits of reducing HAPs. Recommendations from this workshop included identifying research priorities, focusing on susceptible and vulnerable populations, and improving dose - response relationships (Gwinn et al . , 2011 ). In summary , monetization of the benefits of reductions in cancer incidences requires several important inputs, including central estimates of cancer risks, estimates of exposure to

22 The unit risk factor is a quantitative estimate of the carcinogenic 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 ca ncer (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 sources 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 health benefits of reductions in HAPs in this analy sis. 23 Instead, we provide a qualitative analy sis of the health effects associated with the HAPs anticipated to be reduced b y these rules. EPA remains committed to improving methods for estimating HAP benefits b y continuing to explore additional concepts of benefits, 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 for the majority of the total mass of HAPs emissions. See Table 7 - 11 for the list of the major HAP s for each fuel t ype. 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 other metals, and 23 grams of dioxins/furans (TEQ) each ye ar . We discuss the health effects associated with these top HAPs as well as the HAPs for which we have emission reduction estimates. Table 7 - 11. Top HAPs by Mass from Boilers by Fuel Type Coal Gas Biom ass Oil 68% HCl 44% Formaldehyde 32% Acetaldehyde 28% Nickel 5% HF 25% P AH 28% HCl 19% Manganese 3% Toluene 25% Formaldehyde 7.5.5.1 Mercury Mercury is a highl y neurotoxic contaminant that enters the food web as a methylated compound, methylmercury (U.S. EPA, 2008c ). The contaminant is concentrated in high er trophic levels, including fish eaten by humans. Experimental evidence has established 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 , w e were unable to model mercury methylation, 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 mercury emissions. How ever, it is important to emphasize, that we generally have more data and accepted methods to estimate mercury benefits than we have for other HAPs.7 - 45

2008c ). Current evidence indicates that in watersheds where mercury is present, increased sulfate deposition very

likel y results in methylmercury accumulation in fish (Drevnick et al., 2007 ; Munthe et al . , 2007 ). The NO x SO X Ecological I SA 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 sulfate depositio n 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 and eventuall y deposit to land or water bodies. This deposition can occur locally , regionall y, 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 t ype and other factors. Available data indicate that the mercury emissions f rom these sources are a mixture of gaseous elemental mercury (25%), inorganic divalent mercury (reactive gas phase mercury) (50%), and particulate bound mercury (25%) (U.S. EPA, 2010f ). Gaseous elemental mercury

can be transported v ery long distances, even globally , to regions far from the emissions source (becoming part of the global “ pool ” ) before deposition occurs. Inorganic divalent and particulate bound mercury have a shorter atmospheric lifetime and can deposit to land or water bodies closer to the emissions source. Furthermore, elemental mercury in the atmosphere can undergo transformation into divalent mercury, providing 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 meth yl mercury . The primary route of human exposure to mercury emissions from industrial sources is generall y indirectl y through the consumption of fish containing meth yl mercury . As de scribed above, mercury that has been emitted to the air eventuall y settles into water bodies or onto land where it can either move directl y or be leached into waterbodies. Once deposited, certain microorganisms can change it into methylmercury , a highl y to xic form that builds up in fish, shellfish and animals that eat fish. Consumption of fish and shellfish are the main sources of meth yl mercury exposure to humans. Methy lmercury builds up more in some ty

pes of fish and shellfish than in others. The levels of methylmercury in fish and shellfish vary

widel y depending on what they eat, how long they live, and how high they are in the food chain. Most fish, including ocean species and local freshwater fish, contain some methylmercury . For example, in recent studi es 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 primaril y influenced b y the global mercury pool. However, the meth yl mercury found in local fish can be due, at least partl y, 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 rel y heavil y on fish for a su bstantial part of their diet). It has been demonstrated that high levels of meth yl mercury

in the bloodstream of unborn babies and y oung children may harm the developing nervous s ys tem, making the child less able to think and learn. Moreover, mercury exposure at high levels can harm the brain, heart, kidneys, lungs, and immune sy stem of people of all ages. Several studies suggest that the methylmercury content of fish may

reduce cardio protective effects of fish consumption. Some of these studies also sugges t that methylmercury may cause adverse effects to the cardiovascular s ystem. For example, the NRC (2000) review of the literature concerning methylmercury health effects took note of two epidemiological studies that found an association between dietary exposure to methylmercury and cardiovascular effects. 24 In a study of 1,833 males in Finland aged 42 to 60 y ears, Solonen et al. (1995) observed a relationship between methylmercury exposure via fish consumption and acute my ocardial infarction (AM I or heart attacks), death from coronary heart disease or cardiovascular disease . 25 The NRC also noted a study of 917 seven - ye ar old children in the Faroe Is lands, whose initial exposure to methylmercury was in utero alth ough postnatal exposures may have occurred as well. At seven years of age, these children exhibited an increase in blood pressure and a decrease in heart rate variability . 26 Based on these and other studies, NRC concluded in 2000 that, “ Although the data ba se is not as extensive as it is for other end points (i.e., neurologic effects) the cardiovascular s ys tem appears to be a target for methylmercury toxicity in humans and

24National 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., S eppanen, 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. ” Circulat ion, 91 (3):645 - 655. 26 Sorensen, N, K. Murata, E. Budtz - Jorgensen, P. Weihe, and Grandjean, P., 1999 . “ Prenatal Methylmercury Exposure As A Cardiovascular Risk Factor At S even Years of Age ,” Epidemiology, pp370 - 375.7 - 47

animals . ” 27 NRC also stated that “ additional studies are needed to better characterize the effect of methylmercury

exposure on blood pressure and cardiovascular 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 c ardiovascular effects of methylmercury exposure. These studies include epidemiological, toxicological, and toxicokinetic investigations. Over a dozen review papers have also been published. If there were a causal relationship between methylmercury

exposure and adverse cardiovascular effects, then reducing exposure to methylmercury

would result in public health benefits from reduced cardiovascular effects. In earl y 2010, EPA sponsored a workshop in which a group of experts were asked to assess the plausibili ty

of a causal relationship between methylmercury exposure and cardiovascular health effects and to advise EPA on methodologies for estimating population level cardiovascular health impacts of reduced meth yl mercury exposure. The report from that workshop i s 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. i n 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury were 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. The refore, 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 ye ar from current levels, and therefore, contribute to reductions in mercury

exposures and health effects. Due to time limitations under the court ordered schedule for this rule , we were unable to model mercury methylat ion, 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 mercury emissions. However, we were able to model the change in mercury deposition usi ng CAM x for the final Boiler MACT and Boiler Area Source Rule. 29 These modeling results indicate significantly reduced total mercury 27National Research Council (NRC). 2000 . Toxicological Effects of Methylmercury. Committee on the Toxicological Effects of Methylmercury, Board on Environmental Studi es and Toxicology. National Academies Press. Washington, DC. p. 229. 28 See “ Revised Development of Baseline Emission Factors for Boilers and Process Heaters at Commercial, Industrial, and Institutional Facilities, ” which available in the docket. 29 See Secti on 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 deposition from all sources by an average of 1.4% in the East and 0.5% in the West in the analy sis y ear. 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 result of the final Boiler M ACT 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. based 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 Hy drogen chloride gas is intensely irritating to the mucous membranes of the nose, throat, and respiratory

tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 100 ppm are barel y tolerable for 1 hour. The greatest impact is on the upper respiratory tract; exposure to high concentrations can rapidly 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 hy drogen c hloride can lead to Reactive Airway Dy sfunction Sy ndrome (RADS), a chemically - or irritant - induced t ype of asthma. Children may be more vulnerable to corrosive agents than adults because of the relativel y smaller diameter of their airway s. Children may

als o be more vulnerable to gas exposure because of increased minute ventilation per kg and failure to evacuate an area promptly when exposed. Hy drogen chloride has not been classified for carcinogenic effects. 30 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR). M edical Management Guidelines for Hydrogen Chloride (HCl) . CAS# : 7647 - 01 - 0 . Atlanta , GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at < http://www.ats dr.cdc.gov/Mhmi/mmg173.html >.7 - 50

7.5.5.3 Chlorine Gas (Cl 2 ) 31 Chlorine gas is irritating and corrosive to the ey es, skin, and respiratory tract. Exposure to chlorine may

cause burning of the ey es, 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 Hy drogen cy anide is highly to xic by all routes of exposure and may cause abrupt onset of profound central nervous sy stem, cardiovascular, and respiratory effects, leading to death within minutes. Exposure to lower concentrations of h ydrogen cyanide may produce ey e irritation, headache , confusion, nausea, and vomiting followed in some cases b y coma and death. Hy drogen cy anide acts as a cellular asphy xiant. By binding to mitochondrial cy tochrome oxidase, it prevents the utilization of oxygen in cellular metabolism. The central nervous s y stem and my ocardium are particularl y sensitive to the toxic effects of cyanide. 7.5.5.5 Hydrogen Fluoride (HF) 33 Acute (short - term) inhalation exposure to gaseous hy drogen fluoride can cause severe respiratory

damage in humans, including severe irritation and pulm onary edema. Chronic (long term) exposure to fluoride at low levels has a beneficial effect of dental cavity prevention and may also be useful for the treatment of osteoporosis. Exposure to higher levels of fluoride may cause dental fluorosis. One study re ported menstrual irregularities in women occupationally exposed to fluoride. The EPA has not classified hy drogen fluoride for carcinogenicit y. 7.5.5.6 Toluene 34 Toluene is found in evaporative as well as exhaust emissions from motor vehicles. Under the 2005 Guideli nes for Carcinogen Risk Assessment, there is inadequate information to assess

31 All health effects language for this section came from: 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. Available on the Internet at http://www.atsdr.cdc.gov/MHMI/mmg172.html#bookmark02 . 32 All health effects language for this section came from: 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 Hum an Services, Public 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. EP A, “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.g ov/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), Off ice 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 studies of humans chronicall y exposed to toluene are inconclusive, toluene was not carcinogenic in adequate inhalation cancer bioass ay s 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 s ys tem (CNS) is the primary target for toluene toxicity in both humans and animals for acute and chronic exposures. CNS dy sfunction (which is often reversible) and narcosis have been frequently observed in humans acutel y exposed to low or moderate levels of toluene b y inhalation; symptoms include fatigue, sleepiness, headaches, and nausea. Central nervous s ys tem depression has been reported to occur in chronic abusers exposed to high levels of toluene. Sy

mptoms include ataxia, tremors, cerebral atroph y, n ys tagmus (involuntary ey e movements), and impaired speech, hearing, and vision. Chronic inhalati on exposure of humans to toluene also causes irritation of the upper respiratory tract, ey e irritation, dizziness, headaches, and difficult y with sleep. Human studies have also reported developmental effects, such as CNS dy sfunction, attention deficits, an d minor craniofacial and limb anomalies, in the children of women who abused toluene during pregnancy . A substantial database examining the effects of toluene in subchronic and chronic occupationall y exposed humans exists. The weight of evidence from these studies indicates neurological effects (i.e., impaired color vision, impaired hearing, decreased performance in neurobehavioral anal ys is, changes in motor and sensory nerve conduction velocity , headache, and dizziness) as the most sensitive endpoint. 7.5.5.7 Form aldehyde Since 1987, EPA has classified formaldeh yde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkey s. 35 EPA is currently reviewing recentl y published epidemiological data. For instance, research conducted by the National Cancer Institute (NCI) found an increased risk of nasopharyngeal cancer and l ymphohematopoietic malignancies such as leukemia among workers exposed to formaldeh yde. 36 , 37 In an anal ys is of the ly mphohematopoietic cancer mortalit y from an exten ded 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 Form aldehyde, Office of Pesticides and Toxic Substances, April 1987. 36 Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003 . Mortality from lymphohematopoetic malignancies among workers in formaldehyde industries. Journal of the National Cancer Institute 95: 1615 - 1623. 37 Hauptmann, M.; Lubin, J. H.; St ew art, P. A.; Hayes, R. B.; Blair, A. 2004 . Mortality from solid cancers among workers in formaldehyde industries. American Journal of Epidemiology 159: 1117 - 1130.7 - 52

confirmed an association between l ymphohematopoietic cancer risk and peak exposures. 38 A recent National Institute of Occupational Safet y and Health (NIOSH) study of garment workers also found increased risk of death due to leukemia among workers exposed to formaldehy de. 39 Extended follow - up of a cohort of British chemical workers did not find evidence of an increase in nasophary

ngeal or ly

mphohematopoietic cancers, but a continuing statistically significant excess in lung cancers was reported. 40 In the past 15 years there has been substantial research on the inhalation dosimetry for formaldeh yde in rodents and primates b y the CIIT Centers for Health Research (formerl y the Chemical Industry

Institute of Toxicology), with a fo cus on use of rodent data for refinement of the quantitative cancer dose - response assessment. 41 , 42 , 43 CIIT ’ s risk assessment of formaldeh yd e incorporated mechanistic and dosimetric information on formaldeh yde. However, it should be noted that recent research published by EPA indicates that when two - stage modeling assumptions are varied, resulting dose - response estimates can vary by several orders of magnitude. 44 , 45 , 46 , 47 These findings are not supportive of interpreting the CIIT model results as providing a conser vative (health protective) estimate of human risk. 48 EPA research also examined the contribution of the two - stage modeling for formaldehy de towards characterizing the relative weights of key

events in the mode - of

- action of a carcinogen. For example, the mod el - based inference in the published CIIT stud y that formaldehy de ’ s direct mutagenic action is not relevant

38 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009 . Mortality from lymphohem atopoietic 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 garment workers exposed to form aldehyde: an update. Occup. Environ. Med. 61: 193 - 200. 40 Coggon, D, EC Harris, J Poole, KT Palmer. 2003 . Extended follow - up of a cohort of British chemical w orkers exposed to formaldehyde. J National Ca ncer Inst. 95:1608 - 1615. 41 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003 . Biologically mo tivated computational mo deling 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, and FJ Miller. 2004 . Human respiratory tract cancer risks of inhaled formaldehyde: Dose - response predictions derived from biologically - mo tivated computational modeling of a combined rodent and human dataset. Tox Sci 82: 279 - 296. 43 Chemical Industry Institute of Toxicology (CIIT). 1999 . Formaldehyde: Hazard characterization 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 Subr am aniam, R; Chen, C; Crump, K; et al. (2006 ) Uncertainties in biologically - based m odeling of formaldehyd e induced cancer risk: identification of key issues. Risk Anal 28(4):907 - 923. 46 Subr am aniam, R; Chen, C; Crump, K; et al. (2007 ). Uncertainties in the CIIT 2 - stage model for formaldehyde induced nas al 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 analysis of biologically motivated model for formaldehyde induced respir atory cancer in humans. Ann Occup Hyg 52:481 - 495. 48 Crump, K; Chen, C; Fox, J; et al. (2006 ) Sensitivity analysis of biologically motivated model for formaldehyde induced respiratory cancer in humans. Ann O ccup Hyg 52:481 - 495.7 - 53

to the compound ’ s tumorigenicit y was found not to hold under variations of modeling assumptions. 49 Based on the developments of the last decade, i n 20 04, the working group of the IARC concluded that formaldehy de is carcinogenic to humans (Group 1), on the basis of sufficient evidence in humans and sufficient evidence in experimental animals

— a higher classification than previous IARC evaluations. After r eviewing the currentl y available epidemiological evidence, the IARC (2006) characterized the human evidence for formaldeh yd e carcinogenicity as “ sufficient, ” based upon the data on nasophary ngeal cancers; the epidemiologic evidence on l eukemia was characterized as “ strong. ” 50 EPA is reviewing the recent work cited above from the NCI and NI OSH, as well as the analy sis by the CIIT Centers for Health Research and other studies, as part of a reassessment of the human hazard and dose - response associated with formaldehy de. Formaldeh yd e exposure also causes a range of noncancer health effects, including irritation of the ey es (burning and watering of the ey es), nose and throat. Effects from repeated exposure in humans include respiratory tract irritation, chronic bronchitis and nasal epithelial lesions such as metaplasia and loss of cilia. Animal studies suggest that formaldehy de may also cause airway

inflammation

— including eosinophil infiltration into the airways. There are several studies that s uggest that formaldeh yde ma y increase the risk of asthma

— particularl y in the yo ung. 51 , 52 7.5.5.8 Polycyclic Aromatic Hydrocarbons (PAHs) At least seven PAH compounds are classified b y EPA as probable human carcinogens based on animal data, including benzo(a)anthrac ene , 53 benzo(b)fluoranthene , 54

49 Subrama niam, R; Chen, C; Crump, K; et al. (2007 ). Uncertainties in the CIIT 2 - stage model for formaldehyde induced 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 (ATSDR). 1999 . T oxicological profile for Formaldehyde. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. http://www.atsdr.cdc.gov/toxprofiles/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 File of benzo (a)anthracene . Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/nc ea/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)py rene , 56 chry sene , 57 dibenz(a,h)anthracene , 58 and indeno(1,2,3 - cd)p yrene . 59 Recent studies have found that maternal exposures to PAHs in a population of pregnant women were associated with several adverse 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 y et evaluated these recent studies. 7.5.5.9 Acetaldehyde Acetaldeh yd e is classified in EPA ’ s I RI S database as a pr obable human carcinogen, based on nasal tumors in rats, and is considered toxic by the inhalation, oral, and intravenous routes. 62 Acetaldeh yd e is reasonably anticipated to be a human carcinogen by the U.S. DHHS in the 11 th Report on Carcinogens and is clas sified as possibly carcinogenic to humans (Group 2B) by the IARC. 63 , 64 EPA is currentl y conducting a reassessment of cancer risk from inhalation exposure to acetaldeh yde. The primary

noncancer effects of exposure to acetaldehy de vapors include irritation of the ey es, skin, and respiratory tract. 65 Inshort - term (4 week) rat studies, 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). I ntegrated Risk Information System File of chrysene . Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronical ly at http://www.epa.gov/ncea/iris/subst/0455.htm 58 U.S. EPA (1997). Integrated Risk Information System File of dibenz(a,h)anthracene . Research and Development, Nation al 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 children. Environ Health Perspect 114: 1287 - 1292. 62 U.S. EPA (1988) . Integ rated 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 electroni cally at http://www.epa.gov/iris/subst/0290.htm.7 - 55

epithelium was observed at various concentration levels of acetaldeh yd e exposure. 66 Data from these studies were used by EPA to develop an inhalation refe rence concentration. Some asthmatics have been shown to be a sensitive subpopulation to decrements in functional expiratory

volume (FEV1 test) and bronchoconstriction upon acetaldehyde inhalation. 67 The agency is currentl y conducting a reassessment of the h ealth hazards from inhalation exposure to acetaldeh yde. 7.5.5.10 Nickel 68 Nickel is an essential element in some animal species, and it has been suggested it may be essential for human nutrition. Nickel dermatitis, consisting of itching of the fingers, hand and fore arms, is the most common effect in humans from chronic (long - term) skin contact with nickel. Respiratory

effects have also been reported in humans from inhalation exposure to nickel. No information is available regarding the reproductive or developmental e ffects of nickel in humans, but animal studies have reported such effects. Human and animal studies have reported an increased risk of lung and nasal cancers from exposure to nickel refinery dusts and nickel subsulfide. Animal studies of soluble nickel com pounds (i.e., nickel carbonyl) have reported lung tumors. The EPA has classified nickel refinery subsulfide as Group A, human carcinogens and nickel carbon yl as a Group B2, probable human carcinogen. 7.5.5.11 Manganese 69 Health effects in humans have been associated with both deficiencies and excess intakes of manganese. Chronic (long - term) exposure to low levels of manganese in the diet is considered to be nutritionally

essential in humans, with a recommended daily allowance of 2 to 5 milligrams per day . Chronic exp osure to high levels of manganese b y inhalation in humans results primarily in CNS effects. Visual reaction time, hand steadiness, and ey e - hand coordination were affected in chronicall y exposed workers. Manganism, characterized by feelings of weakness and lethargy , tremors, a masklike face, and psy chological disturbances, 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 My ou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993 ) Aerosolized acetaldehyde induces histamine mediated bronchoconstriction in asthmatics. Am. Rev. R espir.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 Reg ister 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/b oiler/fr13ja03.pdf7 - 56

manganism attributed to inhalation exposures. The EPA has cla ssified manganese 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 l evels. 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 that occur mainl y on the face and upper bod y. Other skin effects noted in people ex posed to high doses of 2,3,7,8 - TCDD include skin rashes, discoloration, and excessive body

hair. Changes in blood and urine that may indicate liver damage also are seen in people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglo bin, lipids, sugar, and protein have been reported in people exposed to relativel y high concentrations of 2,3,7,8 - TCDD. Most of the effects are considered mild and were reversible. However, in some people these effects may

last for many

y ears. Slight incre ases 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 exposure to 2,3,7,8 - TCDD would result in reproductive or developmental effects in people, but animal studies suggest that this is a potential health concern. In certain animal species, 2,3,7,8 - TCDD is especially harmful and can cause death after a single exposure. Exposure to lower levels can cause a variety of effects in animals, suc h as weight loss, liver damage, and disruption of the endocrine sy stem. In man y species of animals, 2,3,7,8 - TCDD weakens the immune sy stem and causes a decrease in the s ys tem ’ s ability to fight bacteria and viruses at relativel y low levels (approximately 1 0 times higher than human background bod y burdens). In other animal studies, exposure to 2,3,7,8 - TCDD has caused reproductive damage and birth d efects. Some animal species exposed to CDDs during pregnancy had miscarriages and the offspri ng of animals expos ed to 2,3,7,8 - TCDD during 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 from: Agency for Toxic Substances and Disease Registry (ATSDR). 1999 . ToxFAQs for Chlorinated Dibenzo - p - dioxins (C DDs) (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 lik el y from environmental exposures or from a normal diet. Skin and ey e irritations, especiall y severe acne, darkened skin color, and swollen eyelids with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the nervous s ys tem, and mild changes in the liver. Children born to exposed mothers had skin irritation and more difficulty learning, but it is unknown if this effect was permanent or caused b y CDFs alone or CDFs and poly chlorinated biphenyls in combination. Many of the same effects that occurred in people accidentall y exposed also occurred in laboratory

animals that ate CDFs. Animals also had severe weight loss, and their stomachs, livers, kidney s, and immune sy stems were seriously injured. Some animals had birth defects and testicular damage, and in severe cases, some animals died. These effects in animals were seen when they

were fed large amounts of CDFs over a short time, or smal l amounts over several weeks or months. Nothing is known about the possible health effects in animals from eating CDFs over a lifetime. 7.5.5.14 Other Air Toxics In addition to the compounds described above, other compounds in gaseous hy drocarbon and PM emissions w ould be affected b y these rules, including metal and organic HAPs. In formation regarding the health effects of these compounds can be found in EPA ’ s I RI S 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 pollution is reasonable and informative in spite of inherent uncertainties. To address these inherent uncertainties , NRC highlighted the need to conduct rigorous quantit ative anal ys is of uncertainty and to present benefits estimates to decisionmakers in way s that foster an appropriate appreciation of their inherent uncertainty . In response to these comments, EPA ’ s Office of Air and Radiation (OAR) is developing a

71 All health e ffects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR). 1995 . ToxFAQsTM for Chlorodibenzofurans (CDFs) . Atlanta, GA: U.S. Department of Heal th 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 (IRIS) database is available at: www.epa.gov/iris7 - 58

comprehe nsive strategy

for characterizing the aggregate 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 incidence estimatio n, and valuation. In this analy sis, we use three methods to assess uncertaint y quantitativel y: Monte Carlo anal ys is, LML assessment, and alternate concentration - response functions for PM - and ozone related mortalit y. We also provide a qualitative assessme nt for those aspects that we are unable to address quantitativel y in this analy sis. Each of these anal ys es is described in the following sections. This analy sis includes many data sources as inputs, including emission inventories, air quality data from mo dels (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 estimate. When the uncert ainties from each stage of the anal ys is are compounded, small uncertainties can have large effects on the total quantified benefits. In this analy sis, we are unable to quantify the cumulative effect of all of these uncertainties, but we provide the followi ng anal ys es to characterize man y of the largest sources of uncertaint y. 7.6.1 Monte Carlo Analysis Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the concentration response functions and econom ic valuation functions. Monte Carlo simulation uses random sampling from distributions of parameters to characterize the effects of uncertainty on output variables, such as incidence of morbidity . Specificall y, we used Monte Carlo methods to generate confi dence 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 ar ound the monetized benefits in Tables 7 - 6 and 7 - 7 incorporate the epidemiology

standard errors as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertaint y inherent within the boiler - specific BPT estimates, nor do they reflect 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 ov erall uncertaint y in the benefits estimates.7 - 59

7.6.2 LML Assessment for PM 2.5 PM 2.5 mortality

benefits are the largest benefit category that we monetized in this analy sis. To better characterize the uncertaint y 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 among populations exposed to levels of PM 2.5 above the lowest L MLof the large cohort studies, and our confidence in the r esults diminish as we model that are lower than the LML . While an LML assessment provides some insight into the level of uncertaint y in the estimated PM mortality benefits, EPA does not view the LML as a threshold and continues to quantify PM - related morta lity impacts using a full range of modeled air quality concentrations. 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 confidence that benefits occur below the L ML . In Section 7.3, we provide the results of the L MLassessment 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 category that we monetized in this analy sis. To better unde rstand the concentration - response relationship between PM 2.5 ex posure and premature mortality , EPA conducted an expert elicitation in 2006 (Roman et al., 2008 ; IEc, 2006 ). In general, the results of the expert e licitation support the conclusion that the benefits of PM 2.5 control are very

likel y to be substantial. In previous RIAs, EPA presented benefits estimates using concentration response functions derived from the PM 2.5 Ex pert 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 best estimate of PM benefits may be, and EPA ’ s Science Advisory

Board described this presentation 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 alternate relationships between PM 2.5 and premature mortality supplied by experts, 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 analy sis, we present the results derived from the expert elicitation as indicative of the uncertaint y associated with a major component of the health impact functions, and we provide the independent estimates derived from each of the twelve experts to better characterize the degree of variability

in the expert 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 (Figure 7 - 10 ). Please note that these results are not the direct results7 - 60

from the studies or expert elicitation; rather, the estimates are based in part on the concentration response function provided in those studies. 7.6.4 Altern ate Concentration - response Functions for Ozone Mortality While particulate matter is the criteria pollutant most clearl y associated with premature mortality , recent research suggests that short - term repeated ozone exposure also likely contributes to premature death. In 2008 the National Research 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 mortality . Ch ief among these was that “ ...short - term exposure to ambient ozone is likely 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... ” Th e NAS also recommended that “ ...the greatest emphasis be placed on the multicity and NMMAPS studies without exclusion of the meta anal ys es ” (NRC, 2008 ). In view of the findings of the NAS panel, we include used estimates of short - term ozon e mortality from the Bell et al. (2004) NMMAPS analy sis, the Schwartz (2005) multi - city study , the Huang and Bell (2005) multi - city study as well as effect estimates from the three meta - anal ys es (Bell et al., 2005 ; Lev y 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 quantitative assessments of uncertainty , there are several aspects for which we are onl y able to address qualitativel y. These aspects are important factors to consider when evaluating the relative benefits of the attainment strategies for each o f the alternative standards: Above we present the estimates of the total monetized benefits, based on our interpretation of the best available scientific literature and methods and supported b y the SAB - HES and the NAS (NRC, 2002 ). The be nefits estimates are subject to a number of assumptions and uncertainties. For example, the key assumptions underlying the estimates for premature mortality , which ty pically account for at least 90% of the total monetized benefits, include the following: 1. We assume that all fine particles, regardless 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

fr om direct PM 2.5 released from diesel engines and other industrial sources, but the scientific evidence is not y et sufficient to allow differentiation of effect estimates by particle ty pe .7 - 61

2. We assume that the health impact function for fine particles is lin ear down to the lowest air quality

levels modeled in this analy sis. 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 stan dard and those that do not meet the standard down to the lowest modeled concentrations. 3. To characterize the uncertainty in the relationship between PM 2.5 and premature mortality (which t ypically accounts for 85% to 95% of total monetized benefits), we inc lude 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 uncertaint y in air quality estimates, baseline incidence rates, populations exposed and transf erability 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 uncertaint y in the PM 2.5 estimates. This information should be interpreted within the context of the larger uncertaint y surrounding the entire anal ys is. 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 estimate s used here reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions. For example, these estimates assume that emissions are reduced equall y across all boilers and that the geographic distribut ion of boiler emissions will remain constant through the anal ys is y ear. As we did not model fuel subcategories, we assume that each fuel t ype is equally represented in the emissions and geographic distribution. Use of these $/ton values to estimate benefit s associated with a specific fuel t ype (e.g., for reducing 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 subcategory . Great care should be taken in applying these estimates to emission reductions occurring in an y specific location, as these are all based on national emission reduction assumptions and therefore represent an average benefits - per - ton over the entire United States. The benefits - per - to n for variable emission reductions in specific locations may

be very different from the estimates presented here. Despite our inability to fully characterize and quantify these relativel y small uncertainties, we believe that, on net, the air quality impact s and associated monetized benefits are representative of the magnitude of benefits anticipated from these rules. As previously

described, we strive to monetize as many of the benefits anticipated from these rules as possible, but the monetized benefits e stimated in this RIA inevitably onl y reflect the portion of benefits. Specifically , onl y the benefits attributable to the health impacts associated with exposure to ambient fine particles and ozone have been monetized in this analy sis. M ethodological and t ime 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, ecosy stem effects, and visibility impairme nt. Data limitations include limited monitoring for HAPs, incomplete emissions inventories for HAPs, and limited photochemical air quality modeling for non - mercury HAPs. Resource limitations include limited staff and extramural funding in conjunction with a heav y regulatory workload , which led EPA to not model these endpoints . Methodological limitations include an absence of concentration - response functions for many HAP health effects, with issues such as exposure misclassification, small number of cases, c onfounding, and extrapolation of toxicological effects down to ambient levels . Despite our inability to monetize all of the benefit categories, the total combined monetized benefits still exceed the costs by a substantial margin. This RIA does not include the ty pe of detailed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2006 ). However, the results of the Monte Carlo analy ses of the health and welfare benefits presented in Chapter 5 of the PM RIA can p rovide some evidence of the uncertaint y surrounding the benefits results presented in this analysis. 7.7 Section 7 References Abt Associates, Inc. 2008. Environmental Benefits and Mapping Program (Version 3.0) . Bethesda, MD. Prepared for U.S. Environmental Pro tection Agency

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– 110. Hubbell , B . J . , A . Hallberg , D . McCubbin , and E. Post. 2005. “ Health - Related Benefits of Attaining the 8 - Hr Ozone Standard. ” Environ Health Perspect . 113: p. 73 - 82. In dustrial Economics, Inc (IEc). 2006. Expanded Expert Judgment Assessment of the Concentration -Response Relationship Between PM 2.5 Exposure and Mortality . Prepared for the U.S. EPA, Office of Air Qualit y Planning and Standards, September. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty /pm_ee_report.pdf>. Industrial Economics, Inc (IEc). 2009. Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act: Air Toxics Case Study

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— Science Advisory

Board (U.S. EPA - SAB). 2002. Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants: Developing Best Estimates of Dose -Response Functions An SAB W orkshop Report of an EPA/SAB Workshop (Final Report). EPA - SAB - EC - WKSHP - 02 - 001. January . Available on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/34355712EC011A358525719A00 5BF6F6/$File/ecwkshp02001%2Bappa - g.pdf>. U.S. Environmental Protection Agency

— Science Advisory

Board (U.S. EPA - SAB). 2004. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA ’ s Second Prospective Analysis

—

Benefits and Costs of the Clean Air Act, 1990

–2020 . Advisory

by the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analy sis. EPA - SAB - COUNCIL - ADV - 04 - 002. March. Availa ble on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E540 0433D5D/$File/council_adv_04002.pdf>. U.S. Environmental Protection Agency

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Board (U.S. EPA - SAB). 2007. SAB Advisory on EPA ’

s Issues in Valuing Mo rtality Risk Reductio n. EPA - SAB - 08 - 001. October. Available at < http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$Fi l e/sab - 08 - 001.pdf >. U.S. Environmental Protection Agency

— Science Advisory

Board (U.S. EPA - SAB). 2008. Benefits of Reducing Benzene Emissions in Houston, 1990

–2020 . EPA - COUNCIL - 08 - 001. July . Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9D AEDA8A548525748600728A83 /$File/EPA - COUNCIL - 08 - 001 - unsigned.pdf>. U.S. Environmental Protection Agency

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s Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPA - COUNCIL - 09 - 008. May . Available at < http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c0 9954fcb85256ead006be86e /73ACCA834AB44A10852575BD0064346B/$File/EPA - CASAC - 09 - 008 - unsigned.pdf >.7 - 66

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Board (U.S. EPA - SAB). 2009b. Consultation on EPA ’

s Particulate Matter National Ambient Air Quality Standar ds: Scope and Methods Plan for Health Risk and Exposure Assessment . EPA - COUNCIL - 09 - 009. May . Available on the Internet at < http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e /723FE644C5D758DF852575BD00763A32/$File/EPA - CASAC - 09 - 009 - unsigned.pdf >. U.S. Environmental Protection Agency

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Board (U.S. EPA - SAB). 2010. R eview of EPA ’

s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act . EPA - COUNCIL - 10 - 001. June. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB28525774500738776/ $File/EPA - COUNCIL - 1 0 - 001 - unsigned.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 1995. Regulatory Impact Analysis for the Petroleum Refinery NESHAP. Revised Draft for Promulgation . Office of Air Quality Planning and Standards, Research Triangle Park, N.C. Available o n the Internet at <http://www.reg - markets.org/admin/authorpdfs/page.php?id=705>. U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses . EPA 240 - R - 00 - 003. National Center for Environmental Economics, Office of Pol icy Economics and In novation. Washington, DC. September. Available at < http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf >. U.S. E nvironmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate Matter , Chapter 5 . Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205 -- Benefits.pdf >. U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for Sulfur Oxides

—

Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. September. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=198843>. U.S. Environmental Protection Agency (U.S. EPA). 2008b. Guidelines for Preparing Economic Analyses: External Review Draft. National Center for Environm ental Economics, Office of Policy Economics and Innovation. Washington, DC. September. Available at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE - 0516 - 01.pdf/$file/EE - 0516 - 01.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Scien ce Assessment for Oxides of Nitrogen and Sulfur

– Ecological Criteria National (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R - 08/139. December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm? deid=201485>. U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry . Office of Air Quality Planning and Standards, Resear ch Triangle Park, NC. April. Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4 - 20 - 09.pdf >.7 - 67

U.S. Environmental Protection Agency (U.S. EPA). 20 09b. Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air Quality Standards: Final Report. Office of Air Quality Planning and Standards, Research Triangle Park, NC. August. 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%20Quality %20Standards - %20Final%20Report.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2009c. I

ntegrated Science Assessment for Particulate Matter (Final Report). EPA - 600 - R - 08 - 139F. National Center for Environmental Assessment

—RTP Division. December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=216546>. U.S. Environmental Protec tion Agency (U.S. EPA). 2010a. Regulatory Impact Analysis, National Ambient Air Quality Standards for Ozone , Office of Air Qualit y Planning and Standards, Research Triangle Park, NC. October. Available on the Internet at < http://www.epa.gov/ttn/ecas/regdata/RIAs/s1 - supplemental_anal ys is_full.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010b. Integrated Science Assessment for Carbon Monoxide . National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R - 09/019F. January . Ava ilable at < http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=218686 >. U.S. Environmental Protection Agency (U.S. EPA). 2010c. Regulatory Impact Analysis for Regulatory Impac t Analysis: National Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters . Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at <http://www.epa.g ov/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 Concentration -Response Function for PM 2.5 - related Mortality: Technical Support Document . Compiled by Office of Air Quality

Planning and Standards, Research Triangle Park, 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/RIAs/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 Agenc y. (U.S. EPA). 2010g. Technical Support Document: Preparation of Emissions Inventories for the Version 4, 2005 - based Platform. Office of Air Quality

Planning and Standards, Air Qualit y Assessment Division, Research Triangle Park, North Carolina. July . Avai lable 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 Standards, Research Triangle Park, NC. June. Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf>. Viscusi, V.K., and J.E. Aldy . 2003. “ The Value of a Statistical L ife: 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 MONETI ZED BENEFITS AND COS TS 8.1 Boiler MACT Because we were unable to monetize the benefits associated with reducing HAPs, all monetized benefits reflect improvements in ambient PM 2.5 and ozone concentrations. 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 billion in the implementation ye ar (2014) (Table 8 - 1). Using a 7% discount rate, we estimate th e total monetized benefits to be $20 billion to $49 billion. The annualized costs are $1.5 billion at a 7% interest rate. The net benefits are $21 billion to $53 billion at a 3% discount rate for the benefits and $19 million to $48 billion at a 7% discount rate. All estimates are in 2008$. The benefits from reducing other air pollutants have not been monetized in this analy sis, including reducing 112,000 tons of carbon monoxide, 30,000 tons of HCl, 820 tons of HF, 2,800 pounds of mercury , 2,700 tons of othe r metals, and 23 grams of dioxins/furans (TEQ) each y ear. 8.2 Boiler Area Source Rule Because we were unable to monetize the benefits associated with reducing HAPs, all monetized benefits reflect improvements in ambient PM 2.5 and ozone concentrations. This res ults in an underestimate of the monetized benefits. Using a 3% discount rate, we estimate the total monetized benefits of the area source rule to be $210 million to $520 million in the implementation y ear (2014) (Table 8 - 2). Using a 7% discount rate, we es timate the total monetized benefits to be $190 million to $470 million. 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 monetized in this anal ys is, 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 y ear.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 Monetiz ed 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 eff

ects from SO 2 exposure Ecosystem effects Visibility impairment a All estimates are for the implementation year (2014), and are rounded to tw o significant figures. These results include units anticipated to come online and the lowest cost disposal a ssumption. 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 , 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, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particl e 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 milli on 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 ton s 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), and are rounded to tw o 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 premature mortality because the scientific evidence is not yet sufficient to allow differ entiation of effect estimates by particle type . These estimates include energy 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 net benefits estimates (i.e., annual benefits minus annualized costs) quantified in terms of PM 2.5 benefits for the implementation y ear (2014).

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

Pope etal. La den etal.Millions (2008$)

Cost esti mate c ombi ned wi th total mone tize d benefi ts esti mate s der iv ed f rom 2 epi demiol ogy func tions and 12 e xper t func ti ons 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 est imates combined with the cost estimate. 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 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. La den et al.Millions (2008$)

Cost estimate combi ned with total mone tize d benefi ts esti mate s der iv ed f rom 2 epi demi ology functions and 12 e xpert functi ons 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 models assume that all fine particles, regardless of their chemical composition, are equally potent in c ausing premature mortality because the scientific 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. Cal le, 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 MO DEL TO ASSESS T HE ECONOMIC

IMPACTS OF ENVIRONME NTAL

REGULATIONA - 1

A.1 Introduction An economic impact analy sis (EIA) provides information about a policy ’ s effects (i.e., social costs); emphasis is also placed on how the costs are distributed among stakeholders (EPA, 200 0 ). In addition, large - scale policies require additional analy sis to better understand how costs are passed across the economy . Although several tools are available to estimate social costs, current EPA guidelines suggest that multimar ket models “ ...are best used when potential economic impacts and equity effects on related markets might be considerable ” and modeling using a computable general equilibrium model is not available or practical (EPA, 2000 , p. 146). Other guides for environmental economists offer similar advice (Berck and Hoffmann, 2002 ; Just, Hueth, and Schmitz, 2004 ). Multimarket models focus on “ short - run ” time horizons and measure a pol icy ’ s near term or transition costs (EPA, 1999 ). Recent studies suggest short - run analy ses can complement full dy namic general equilibrium analy sis. The multimarket model described in this appendix is a new addition to the Office of Air Quality Planning and Standards ’ (OAQPS ’ s) economic model tool kit; it is designed to be used as a transparent tool that can respond quickly to requests about how stakeholders in 100 U.S. industries might respond to new environmental policy . Next, we pr ovide 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 diox ide (CO 2 ) emissions

– estimates of industry

employ ment

 Economic behavior

– industries respond to regulatory costs by changing production rates

– market prices rise to reflect higher energy and other nonenergy

material costs

– customers respond to these price incre ases 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 construction industry is linked with the petroleum, cement, and steel industries and is influenced b y price changes that occur in each sector. The links allow EPA to account for indirect effects the regulation has on related markets.

– Li nks come from input - output information used from OAQPS ’ s computable general equilibrium model (EMPAX)

– production adjustm ents influence employ ment levels

– international trade (imports/exports) 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 y ear, 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 abstrac t and treat each industry as a national market, the identity represents the prepolicy market - clearing condition, or benchmark “ equilibrium ” ; supply equals demand in each market. In Table A - 1, we identify the 100 industries 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 convention ” and choose units where are all prices are one in the benchmark equilibrium (Shoven and Whalley , 1995 ).A - 3

Table A - 1. Industry Sectors Included 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 G as 211112 2212 4862 OIL Refined Petroleum 324 Nonm anufacturing 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 FR U 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 31 5 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 Res ins 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 Model (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 Hardw are 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 Me talw orking Equipment 3335 EEQ Engines 3336 GEQ General Equipment 3339 Electronic Equipment CPU Com puters 3341 CMQ Communication Equipment 3342 TVQ TV Equipment 3343 SMI Semiconductor Equipment 3344 INS Instruments 3345 MGT Magnetic Recording Equ ipm ent 3346 LGT Lighting 3351 APP Appliances 3352 (continued)A - 5

Table A - 1. Industry Sectors Included in Multimarket Model (continued)

Industry Label Description Representative NAICS a Electronic Equipment (continued) ELQ Electric Equipment 3353 OEQ Othe r 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 M iscellaneous Manufacturing 339 Services Wholesale and Retail Trade WHL Wholesale Trade 42 RTL Retail Trade 44 –45 Transportation Services AT P 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 Ad ministrative Serv ices 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 sys tem , 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 Ad ministrative 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 AT P 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 Com

puters $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 Engi nes $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 Gov ernment 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 Hardw are $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 Instr

uments $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 $1 74 $9 $169 $5 $10 MED Drugs and Medicine $258 $102 $316 $18 $27 MEQ Metalw orking

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 $1 1 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 Governmen t 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 tha n $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 $2 4 $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 $1 9 $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 T V 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 employ ment forecasts for each of the 100 sectors. Employ ment estimates are based on data from three sources: the AEO 2009 estimates of employ ment (AEO supplemental Table 126 and indicators of Macroeconomic Activity), and Global Insights, Inc., and the Bureau of Labor Statistics (BL S) 2008 end - of year - employ ment (Current Employ

ment Statistics

— CES [National]). Ty picall y, 3 - digit NAICS sectors ’ employ ment estimates are either directly reported in the updated AEO 2009 release or Global Insights For multimarket in dustries with finer NAICs detail, estimates were calculated by selecting a primary

NAI CS supersector estimate (AEO or Global Insights) and distributing total employ ment from the larger NAICS supersectors across more detailed NAICS sectors within the super - sector. The distributions were determined using observed 2008 BLS emplo ym ent data. In the last step, In order to match aggregate U.S. employ

ment numbers reported in the AEO 2009 release (140.1 million), a single adjustment factor was applied to all sectors that use Global Insights ’ supersector data. 1 Table A - 4 reports the baseline employ ment 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 f or 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 analy sis and baseline value of output. For example, a $1 billion increase in compliance costs for the electricity s ector (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 provides the industry with incentives to alter production rates at current market prices; market prices must rise to m aintain 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 Employm ent (1,000) ACM Accommodations 11,239 ADM Ad ministrative Services 9,274 AGR Agricultural 1,607 ALU A luminum 87 ANM Animal Foods 45 APP Appliances 33 ARC Aircraft 449 ART Arts 1,939 AT P 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 Com puters 104 CRU Crude Oil Extraction 384 CUT Cutlery 34 EDU Education 2,892 EEQ Engines 75 EGV Engra ving 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. Employment Projections (continued) Industry Label Industry Description Projected Employm ent (1,000) GAS Natural Gas 98 GEQ General Equipment 198 GLS Glass 71 GRN Grain Milling 55 HLT Health Care 15,190 HRD Hardw are 20 HVC HVAC Equipment 109 I_S Ir on 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 Metalw or king 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 Equip me nt 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. Employment Projections (continued) Industry Label Industry Description Projected Employm ent (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 3 6 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 We aring 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 and other intermediate material prices). As a result, the multimarket model can provide additional 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 i ncentives to alter production rates at current market prices. The %Δ indirect effects associated with each input 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, while lower ratios suggest weaker links. Given the short time horiz on, we assume the input use ratio is fixed and cannot adjust their input mix; this is a standard assumption in public and commercial input - output (I O) and SAM multiplier models (Berck and Hoffmann, 2002 ). Morgenstern and coll eagues (2004 ) and Ho and colleagues (2008) also use this assumption when examining near - term effects of environmental policy .

Figure A - 1. Direct Compliance Costs Reduce Production Ra tes at Benchmark PricesA - 15

Figure A - 2. Indirect Costs Further Reduce Production Rates at Benchmark Prices Following guidance in the OAQPS economic resource manual (EPA, 1999 ), we use a general form for the U.S. industry suppl y function:

g ε n i i

i gi g g ) P P ( t P b Q      





     

# 

 1  (A.5) where g Q  = with - policy

suppl y quantity

(g) b = calibrated scale parameter for the suppl y 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 e lasticit y of suppl y for output (g) The key suppl y parameter that controls the industry production adjustments is the price elasticity of suppl y ( ε g ). To our knowledge, there is no existing empirical work that estimates short - run supply

elasticities for all industry groups used in the multimarket model. As a result, we assume the U.S. supply elasticities are a function of econometricall y estimated rest - of - worldA - 16

(ROW) export supply

elasticities (see discussion 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 captured b y a single ROW supply function: 

#   ROW

g ε g g 

### P c Q    (A.6) where g Q  = with - policy

suppl y quantity

(g) c = calibrated sc ale parameter for the suppl y and price relationship g P  = with - policy

U.S. price for output (g) ROW g  = price elasticit y of suppl y of goods from the ROW to the United States (imports) (g) The key suppl y parameter that co ntrols the ROW supply adjustments is the price elasticity of suppl y ( ROW

g  ). We obtained these estimates for a variet y of industry groups from a recentl y published article by Broda and colleagues (2008b) . A.2.2.3 Price Elasticity of Supply: Rest of World (ROW) Broda and colleagues (2008a and 2008b) provide an empirical basis for the multimarket model supply

elasticities. Broda et al. provide over 1,000 inver se elasticities that RTIorganized to be comparable with the 100 - sector model. The first step was to match the Harmonized Trade Sy stem (HS) elasticities estimated in the article to the appropriate NAICS codes. Many of the HS codes correspond with a detaile d NAICS codes (5 - and 6 - digit level), while the multimarket sector industries ty

pically correspond with more aggregated sectors (NAICS 2 - , 3 - , or 4 - digit levels). To adapt these labels to our m odel, we combined the 5 - and 6 - digit NAI CS under their 3 - and 4 - digit codes and calculated an average inverse elasticity value for codes that fell within the multimarket model ’ s aggregate industrial sectors. This gives a crude way to account for the variet y of products detailed in the original data set. We also restri cted the elasticity sample to those that Broda et al. classify as “ medium ” and “ low ” categories; these categories tend to have lower elasticit y values that are consistent with the multimarket model ’ s modeling horizon (i.e., in the short run importers are l ikely to have less 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 dom estic market pow er.A - 17

Table A - 5. Supply Elasticities Industry Label Industry Description Rest of World (ROW) U.S. ACM Accommodations 0.7 0.7 ADM Ad ministrative Services 0.7 0.7 AGR Agricu ltural 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 AT P 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 C em ent 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 Com puters 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 Hardw are 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 Metalw orking Equipment 0.7 0.5 MGT Magnetic Recordi ng 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 Transportati on 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 W HL 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. Dom estic 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.br oda/w ebsite/research/unrestricted/TradeElasticities/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 fo r which there was no direct match, we aggregated up a level and applied the relevant 3 - digit elasticity . If a multimarket code was not covered in the medium set of elasticities, we used the low elasticity category . This method was sufficient for mapping th e majority of the sectors in the model. After appl yi ng our inverse elasticit y values to the multimarket sectors, we calculated the inverse of the value to arrive at the actual supply elasticity . Since Broda et al. ’ s article 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 substitution elasticities from Purdu e University ’ s Global Trade Anal ys is Project (GTAP) . 1 Although the elasticities in the GTAP model are a different t yp e of international trade elasticity and cannot be directly

applied in the multimarket model (e.g., they are based on the Armington structur e 2 ), the parameters provide us with some additional information about the relative trade elasticity differences between industry sectors. To use the GTAP information to develop assumptions about the multimarket model sectors with missing elasticities, we c hose a base industrial sector (iron and steel) for which we had parameter value from Broda et al. Next, we developed industry - specific ratios for missing industries using the corresponding GTAP sector trade elasticities and the GTAP iron and steel

1 See Chapter 14 of the GTAP 7 Database Documentation for the full description of the parameters at https://www.gtap.agecon.purdue.edu/resources/download/418 4.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 match 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 times the trade elasticity for iron and steel (2.95). As a result, the multimarket import supp ly 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 domestic supply elasticities for the model. We have assumed that a product ’ s domestic suppl y would be equal to or less elastic than other countries ’ supply of imports. When we aggregated and averaged the origina l elasticities to the 3 - and 4 - digit NAICS level for our foreign suppl y elasticities, we also calculated the standard deviation of each 3 - and 4 - digit NAICS sample. By adding the standard deviation to the corresponding foreign supply and then taking the inverse, we were able to calculate a domestic supply elasticit y for each sector that was lower than its foreign counterpart while maintain ing the structure of the original elasticities. For sectors in which no standard deviation was available, 1 we used professional judgment to appl y the closest available substitute from a similar industry . Without a comparable way of scaling our foreign elas ticities 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 use (domestic and exports). Given the short time horizon, investment/government does not change. Intermediate 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 price changes. Following guidance in the OAQPS econo mic 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 m odel 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 sca le parameter for the demand and price relationship g P  = with - policy

price for output (g) g  = price elasticit y of demand (g) The key parameter that controls consumption adjustments is the price elasticity of demand ( η g ). To approximate the response, we use demand elasticities that were simulated with a general equilibrium model (Ho, Morgenstern, and Shih, 2008 ). Table A - 6 reports the values currentl y 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 technology requires the purchase of energy and other intermediate goods made by other sectors included in the model. Li nking the sectors in this manner allows the model to trace direct and indirect policy effects across different sectors. Therefore, it is best used when potential economic impacts and equity effects on related markets might be important to stakeholders n ot directl y affected b y an environmental policy. However, the model can also be run in single - market partial equilibrium mode to support and provide insights for other t ypes of environmental policies. A.2.2.7 Model Time Horizon The model is designed to address short - run and transitional effects associated with environmental policy . Production technologies are fixed; the model does not assess substitution among production inputs (labor, energy intermediates, and other intermediates) and assumes each investme nt cannot be changed during the time frame of the anal ys is. These issues are better addressed using other frameworks such as computable general equilibrium modeling. Similarly , government purchases from each sector do not adjust in response to changes in g oods/service prices. Although, emplo yment levels (number of jobs) adjust as production levels change, wages are assumed to be fixed.A - 23

Table A - 6. U.S. Demand Elasticities Industry Label Industry Description Dem and Elasticity η g ACM Accommodations −0.7 ADM Ad ministrative 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 AT P 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 Com puters −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 Meta ls −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 Dem and 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 Hardw are −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 Lig hting −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 Metalw orking 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 Dem and 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 Ra il 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 Equip me nt −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 industry from Ho and colleagues ’ industry aggregation. Source: Ho, M. S, R. Morgenstern, and J. S. Shih. 2008 . “ Im pact of C arbon Price Policies on US Industry. ” RFF Discussion Paper 08 - 37. Http://Www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?. Publicationid=20680. Accessed August 2009. Table B.6.A - 26

A.3 Appendix A References Berck, P., and S. Hoffmann. 2002. “ Assessing the Employ ment 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 - 2 065. Broda, C., N. Limao, and D. Weinstein. 2008b. “ Export Supply Elasticities. ” <http://faculty . chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/ 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/Pages/Publicationdetails.Aspx?. Publicationid=20 680 >. 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 Indu stries. ” 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 Anal

ysis Resource Document . http://www.epa.gov/ttn/ecas/analguid.html . U.S. Environmental Protection Agency . 2000. “ Guidelines for Preparing Economic Anal ys es ” (EPA 240 - R - 00 - 003). < http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf >.APPENDIX B DETAILED ECONOMIC MO DEL RESULTS BY SECTO RB - 1

Table B - 1. Prices (Percentage Change from B enchmark): 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

Tab le 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% El ectronic 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 −0.003 6% −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% Prim ary 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% M anufacturing 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%