### April 2010 

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

Industrial, Commercial, and Institutional Boilers and Process Heaters

Draft Report

Prepared for Tom Walton U.S. Environmental Protection Agency Office of Air Quality Planning and Standards (OAQPS) Air Benefit and Cost Group (MD-C439-02) Research Triangle Park, NC 27711
Prepared by

RTI International 3040 Cornwallis Road Research Triangle Park, NC 27709 and Kajal B. Kapur, Energy, Environment, and Economics Consulting 1 Steubin Lane Charlottesville, VA 22911
RTI Project Number 0209897.004.074_________________________________ RTI International is a trade name of Research Triangle Institute. RTI Project Number 0209897.004.074 

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

Industrial, Commercial, and Institutional Boilers and Process Heaters

Draft Report

April 2010
Prepared for

Tom Walton U.S. Environmental Protection Agency Office of Air Quality Planning and Standards (OAQPS) Air Benefit and Cost Group (MD-C439-02) Research Triangle Park, NC 27711
Prepared by

RTI International 3040 Cornwallis Road Research Triangle Park, NC 27709 and Kajal B. Kapur, Energy, Environment, and Economics Consulting 1 Steubin Lane Charlottesville, VA 22911iii CONTENTS

Section Page

1 Introduction ................................................................................................................ ... 1-1

1.1 Executive Summary ............................................................................................. 1-1

1.2 Organization of this Report .................................................................................. 1-4

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 2.1.3 Firm and Market Characteristics .............................................................. 2-7

2.2 Wood Product Manufacturing............................................................................ 2-16 2.2.1 Introduction ............................................................................................ 2-16 2.2.2 Supply and Demand Characteristics ...................................................... 2-19 2.2.3 Firm and Market Characteristics ............................................................ 2-24

2.3 Paper Manufacturing .......................................................................................... 2-33 2.3.1 Introduction ............................................................................................ 2-33 2.3.2 Supply and Demand Characteristics ...................................................... 2-37 2.3.3 Firm and Market Characteristics ............................................................ 2-43

2.4 Chemical Manufacturing ................................................................................... 2-51 2.4.1 Introduction ............................................................................................ 2-51 2.4.2 Supply and Demand Characteristics ...................................................... 2-54 2.4.3 Firm and Market Characteristics ............................................................ 2-58

3 Engineering Cost Analysis ............................................................................................ 3-1

3.1 Major Sources ...................................................................................................... 3-1

3.2 Area Sources ........................................................................................................ 3-2iv 4 Economic Impact Analysis ........................................................................................... 4-1

4.1 Partial Equilibrium Analysis (Multiple Markets) ................................................ 4-1 4.1.1 Overview .................................................................................................. 4-1 4.1.2 Economic Impact Analysis Results ......................................................... 4-2

5 Small Entity Analyses ................................................................................................... 5-1

5.1 Small Entity Screeni ng Analysis ......................................................................... 5-1 5.1.1 Small Businesses ...................................................................................... 5-1 5.1.2 Small Governmental Jurisdictions and Not-for--Profit Enterprises ......... 5-5

5.2 Initial Regulatory Flexibili ty Analysis (IFRA) .................................................... 5-6 5.2.1 Reasons Why Action Is Being Considered .............................................. 5-6 5.2.2 Statement of Objectives and Lega l Basis of Proposed Rules .................. 5-6 5.2.3 Description and Estimate of the Nu mber of Small Entities ..................... 5-7 5.2.4 Description and Comp liance Costs .......................................................... 5-7 5.2.5 Description of Federal Rules that May Overlap or Conflict with Proposed Rules......................................................................................... 5-7 5.2.6 Description of Regulatory Altern atives that Minimize Significant Economic Impacts on Small Entities ....................................................... 5-8

6 Human Health Benefits of Emissions Reductions ........................................................ 6-1

6.1 Synopsis ............................................................................................................... 6-1

6.2 Calculation of PM 2.5 Human Health Benefits ...................................................... 6-1

6.3 Unquantified Benefits ........................................................................................ 6-12 6.3.1 Carbon Monoxide Benefits .................................................................... 6-12 6.3.2 Other SO 2 Benefits ................................................................................. 6-13 6.3.3 HAP Benefits ......................................................................................... 6-15

6.4 Characterization of Uncertainty in the Monetized PM 2.5 Benefits ..................... 6-27

6.5 Comparison of Benefits and Costs ..................................................................... 6-29

7 Supplemental Economic Analyses for Non-Hazardous Solid Waste Definition .......... 7-1

7.1 Economic Impact Analysis Results ..................................................................... 7-1 7.1.1 Market-Level Results ............................................................................... 7-1v 7.1.2 Social Cost Estimates ............................................................................... 7-1 7.1.3 Job Effects ................................................................................................ 7-1

7.2 Benefits Analysis Results .................................................................................... 7-4

References .................................................................................................................... ..........R-1 Appendixes A: OAQPS Multimarket Model to Assess the Economic Impacts of Environmental Regulation ................................................................................. A-1 B: Detailed Economic Model Results by Sector ......................................................B-1 C: Major Sources Cost a nd Emissions Memorandum ..............................................C-1

D: Area Sources Cost and Emissions Memorandum ............................................... D-1vi LIST OF FIGURES

Number Page

2-1. Distribution of Employment within Food Manufacturi ng (NAICS 311): 2006 ........ 2-3 2-2. Distribution of Total Value of Sh ipments within Food Manufacturing (NAICS 311): 2006 .................................................................................................... 2-3 2-3. Electric Power Use Trends in Food Manufacturing (NAICS 311): 1997–2005 ........ 2-7 2-4. Establishment Concentration in Food Manufacturing (NAICS 311): 2002 .............. 2-8 2-5. Capacity Utilization Trends in Food Manufacturing (NAICS 311) .......................... 2-9 2-6. Employment Concentration in Food Manufacturing (NAICS 311): 2002 .............. 2-10 2-7. Capacity Trends in Food Manufacturing (NAICS 311) .......................................... 2-11 2-8. Industrial Production Trends in Food Manufacturing (NAICS 311) ....................... 2-15 2-9. International Trade Trends in F ood Manufacturing (NAICS 311) .......................... 2-15 2-10. Producer Price Trends in Food Manufacturing (NAICS 311) ................................. 2-16 2-11. Distribution of Value of Shipment s within Wood Product Manufacturing (NAICS 322): 2007 .................................................................................................. 2-19 2-12. Distribution of Employme nt within Wood Product Manufacturing (NAICS 322): 2007 ................................................................................................................ 2-2 0 2-13. Electrical Power Use Trends in th e Wood Product Manuf acturing Industry (NAICS 321): 1997–2005 ........................................................................................ 2-24 2-14. Establishment Concentration in th e Wood Product Manuf acturing Industry (NAICS 321): 2002 .................................................................................................. 2-26 2-15. Capacity Utilization Trends in the Wood Product Manufacturing Industry (NAICS 321) ............................................................................................................ 2-27 2-16. Employment Concentration in th e Wood Product Manufacturing Industry (NAICS 321): 2002 .................................................................................................. 2-28 2-17. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321) ....... 2-29 2-18. Industrial Production Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997–2009 ........................................................................................ 2-32 2-19. International Trade Trends in th e Wood Product Manufacturing Industry (NAICS 321)]........................................................................................................... 2-32 2-20. Producer Price Trends in the Wood Pr oduct Manufacturing Industry (NAICS 321) .......................................................................................................................... 2-33 2-21. Distribution of Value of Shipments within Paper Manufacturing (NAICS 322): 2007 ................................................................................................................ 2-3 6 2-22. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007 ..... 2-36 2-23. Electrical Power Use Trends in the Pa per Manufacturing In dustry: 1997–2005 .... 2-41 2-24. Establishment Concentration in Pape r Manufacturing Industry (NAICS 322): 2002.......................................................................................................................... 2-43vii 2-25. Capacity Utilization Trends in the Paper Manufacturing Industry (NAICS 322) ............................................................................................................ 2-44 2-26. Employment Concentration in the Paper Manufacturing Industry (NAICS 322): 2002 ................................................................................................................ 2-4 5 2-27. Capacity Trends in the Paper Manu facturing Industry (NAICS 322) ..................... 2-46 2-28. Industrial Production Trends in the Paper Manufact uring Industry (NAICS 322): 1997–2009 ...................................................................................................... 2-49 2-29. International Trade Trends in the Pape r Manufacturing Industry (NAICS 322) ..... 2-50 2-30. Producer Price Trends in the Paper Ma nufacturing Industry (NAICS 222) ............ 2-50 2-31. Distribution of Employme nt within Chemical Ma nufacturing (NAICS 325): 2007.......................................................................................................................... 2-53 2-32. Distribution of Total Value of Ship ments within Chemical Manufacturing (NAICS 325): 2007 .................................................................................................. 2-53 2-33. Electric Power Use Trends in Chem ical Manufacturing (NAICS 325): 1997– 2005.......................................................................................................................... 2-58 2-34. Establishment Concentration in Chemi cal Manufacturing (NAICS 325): 2002 ..... 2-60 2-35. Capacity Utilization Trends in Chem ical Manufacturing (NAICS 325) ................. 2-61 2-36. Employment Concentration in Chemical Manufacturing (NAICS 325): 2002 ....... 2-61 2-37. Capacity Trends in Chemical Manufacturing (NAICS 325) ................................... 2-62 2-38. Industrial Production Trends in Chemi cal Manufacturing (NAICS 325) ................ 2-66 2-39. International Trade Trends in Chem ical Manufacturing (NAICS 325) ................... 2-67 2-40. Producer Price Trends in Chemical Manufacturing (NAICS 325) .......................... 2-67 4-1. Market-Level Changes by Source and Option ........................................................... 4-3 4-2. Distribution of Total Surplus Cha nges by Sector: Major Sources ............................. 4-4 4-3. Distribution of Total Surplus Cha nges by Sector: Area Sources ............................... 4-6 4-4. Job Losses/Gains Associated with the Proposed Rules: 2013 ................................... 4-8 5-1. Share of NAICS/Enterprise Employme nt Categories (<500 employees) with Sales Tests Exceeding 3% ......................................................................................... 5-4 6-1. Breakdown of Monetized PM 2.5 Health Benefits using Mortality Function from Pope et al. (2002) .............................................................................................. 6-6 6-2. Total Monetized PM 2.5 Benefits for the Proposed Major and Area Source Boiler Rules in 2013 ................................................................................................ 6-10 6-3. Breakdown of Monetized Benefits for the Proposed Boiler Rules by PM 2.5 Precursor Pollutant and Source ................................................................................ 6-11 6-4. Breakdown of Monetized Benefits fo r the Proposed Boiler MACT (Major Sources) by Fuel Type ............................................................................................. 6-11 6-5. Breakdown of Monetized Benefits for the Proposed Boiler Area Source Rule by Fuel Type ............................................................................................................ 6-12 6-6. Net Benefits for the Proposed Major and Area Source Boiler Rules at 3% Discount Rate ........................................................................................................... 6-33 6-7. Net Benefits for the Proposed Major and Area Source Boiler Rules at 7% Discount Rate ........................................................................................................... 6-34viii LIST OF TABLES

Number Page

1-1. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2013 (millions of 2008$) ..................................... 1-3 1-2. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the Boiler Area Source Rule in 2013 (millions of 2008$) ............................................... 1-4 2-1. Key Statistics: Food Manuf acturing (North American Industry Classification System [NAICS] 311) ................................................................................................ 2-1 2-2. Industry Data: Food Manufact uring (NAICS 311) .................................................... 2-2 2-3. Costs of Goods and Services Used in Food Manufacturing (NAICS 311) ($2007) ....................................................................................................................... 2-5 2-4. Key Goods and Services Used in Food Manufacturing (NAICS 311) ($2007, millions) ........................................................................................................ 2-5 2-5. Energy Used in Food Manufact uring (NAICS 311) .................................................. 2-6 2-6. Demand by Sector: Food Manufacturing (NAICS 311) ($2007, millions) ............... 2-7 2-7. Top Publicly Held U.S. Food Companies: 2007 ...................................................... 2-11 2-8. Corporate Income and Profitability fo r Food Manufacturing (NAICS 311) ........... 2-12 2-9. Small Business Size Standards: Food Manufacturing (NAICS 311) ....................... 2-13 2-10. Distribution of Economic Data by En terprise Size: Food Manufacturing (NAICS 311) ............................................................................................................ 2-14 2-11. Key Statistics: Wood Product Manu facturing (NAICS 321) ................................... 2-17 2-12. Industry Data: Wood Product Ma nufacturing (NAICS 321) ................................... 2-17 2-13. Costs of Goods and Services in Wood Product Manufacturing (NAICS 321) ($2007) ..................................................................................................................... 2 -21 2-14. Key Goods and Services Used in Wood Product Manufacturing (NAICS 321) ($2007, millions) ...................................................................................................... 2-22 2-15. Energy Used in Wood Product Manu facturing (NAICS 321) ................................. 2-23 2-16. Demand by Sector: Wood Product Ma nufacturing (NAICS 321) ($2007, millions) ................................................................................................................... 2 -25 2-17. Largest U.S. Paper and Forest Products Companies: 2006 ..................................... 2-29 2-18. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 321) ................................................................................... 2-30 2-19. Small Business Size Standards: Wood Pr oduct Manufacturing (NAICS 321) ........ 2-31 2-20. Key Statistics: Paper Manufact uring (NAICS 322) ................................................. 2-34 2-21. Industry Data: Paper Manufact uring (NAICS 322) ................................................. 2-34 2-22. Costs of Goods and Services Used in the Paper Manufacturing Industry (NAICS 322) ............................................................................................................ 2-37ix 2-23. Key Goods and Services Used in the Paper Manufacturing Industry (NAICS 322) ($millions, $2007) ............................................................................. 2-39 2-24. Energy Used in Paper Manuf acturing (NAICS 322) ............................................... 2-40 2-25. Estimated Energy Sources for the U.S. Pulp and Paper Industry ............................ 2-41 2-26. Demand by Sector: Paper Manufacturing Industry (NAICS 322) ($millions, $2007) ...................................................................................................................... 2 -42 2-27. Largest U.S. Paper and Forest Products Companies: 2006 ..................................... 2-46 2-28. Distribution of Economic Data by En terprise Size: Paper Manufacturing (NAICS 322) ............................................................................................................ 2-47 2-29. Small Business Size Standards: Pape r Manufacturing (NAICS 322) ...................... 2-48 2-30. Key Statistics: Chemical Ma nufacturing (NAICS 325)........................................... 2-51 2-31. Industry Data: Chemical Manuf acturing (NAICS 325) ........................................... 2-52 2-32. Key Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007, millions) ...................................................................................................... 2-54 2-33. Costs of Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007) ..................................................................................................................... 2 -55 2-34. Energy Used in Chemical Manufacturing (NAICS 325) ......................................... 2-57 2-35. Demand by Sector: Chemical Manufactur ing (NAICS 325) ($2007 millions) ....... 2-58 2-36. Top Chemical Producers: 2007 ................................................................................ 2-63 2-37. 2007 Corporate Income and Profitability (NAICS 325) .......................................... 2-63 2-38. Small Business Size Standards: Chemi cal Manufacturing (NAICS 325) ............... 2-65 2-39. Distribution of Economic Data by Ente rprise Size: Chemical Manufacturing (NAICS 325) ............................................................................................................ 2-66 3-1. Summary of Capital and Annual Costs fo r New and Existing Major Sources .......... 3-2 3-2. Summary of Annual Costs for New and Existing Area Sources ............................... 3-3 4-1. Distribution of Social Costs Majo r Sources (billio n, 2008$): 2013 .......................... 4-4 4-2. Distribution of Social Costs Area Sources (billion, 2008$): 2013 ............................ 4-5 5-1. Affected Sectors and Size Standards ......................................................................... 5-2 5-2. Major Sources: Sales Tests Using Small Companies Identified in the Combustion Survey .................................................................................................... 5-5 6-1. Human Health and Welfare Effects of PM 2.5 ............................................................. 6-2 6-2. Summary of Monetized Benefits Esti mates for Boiler MACT (for Major Sources) in 2013 (2008$) ........................................................................................... 6-7 6-3. Summary of Monetized Benefits Estima tes for Boiler Area Source Rule in 2013 (2008$) .............................................................................................................. 6-7 6-4. Summary of Reductions in H ealth Incidences from PM 2.5 Benefits for the Proposed Major and Area Source Boiler Rules in 2013 ............................................ 6-8 6-5. All PM 2.5 Benefits Estimates for the Propos ed Major and Area Source Boiler Rules at discount rates of 3% and 7% in 2013 (in millions of 2008$) ...................... 6-9 6-6. Top HAPs by Mass from Boilers by Fuel Type ...................................................... 6-15 6-7. Sensitivity Analyses for Monetized PM 2.5 Health Benefits (in millions of 2008$) ...................................................................................................................... 6 -27x 6-8. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2013 (millions of 2008$) ................................... 6-31 6-9. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the Boiler Area Source Rule in 2013 (millions of 2008$) ............................................. 6-32 7-1. Market-Level Price and Quantity Cha nges: 2013 (Alternate Definition) .................. 7-2 7-2. Distribution of Social Co sts (million, 2008$): 2013 .................................................. 7-3 7-3. Employment Changes: 2013 ...................................................................................... 7-3 7-4. Summary of Monetized Benefits fo r the Boiler MACT for Existing Major Sources in 2013 (2008$) (A lternate Approach) ......................................................... 7-4 7-5. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2013 (millions of 2008$) ..................................... 7-51-1 SECTION 1 INTRODUCTION
The U.S. Environmental Protection Agency (EPA) is proposing national emission standards for hazardous air po llutants (NESHAP) for new and existing industrial, commercial, and institutional boilers and process heaters. 1 The proposed rule would require all major sources to meet hazardous air pollutant (HAP) emissions standards reflecting th e application of the maximum achievable control technology (MACT). Under a se parate action, EPA is also proposing a NESHAP for two area source categories: industrial boilers an d institutional and commercial boilers. 2 The proposed emission standards for controlling mercury and polycyclic organic matter (POM) emissions are based on the MACT. The proposed emission standards for controlling other HAPs are based on EPA’s proposed determination as to what constitutes the generally available control technology (GACT) or management practic es. As part of the regulatory process, EPA is requ ired to develop a regulatory imp act analysis (RIA). The RIA includes an economic impact anal ysis (EIA) and a small entity impacts analysis 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 proposed major source NESHAP’s total annualized costs will be $2.9 billion (2008$). For the area source NESHAP, EPA estimates the total annualized costs will be $0.5 billion.

 Market Analysis: Under the proposed major source NESHAP, the Agency’s economic model suggests the average national prices for industrial sectors could be 0.01% higher with the NESHAP , while average annual dom estic production may fall by about 0.01%. Because of higher domestic prices, imports rise by 0.01% per year. Market-level effects for the proposed area source NESHAP are smaller when compared to the proposed major source ru le; average price, production, and import changes are less than 0.01%.

 Social Cost Analysis: The estimated social cost of the proposed major source rule is just under $2.9 billion (2008$). In the n ear term, the Agency’s economic model suggests that industries are able to pass a pproximately $0.8 billion of the rule’s costs to consumers (e.g., higher market prices). Domestic industries’ surplus falls by $2.5 billion, while other countries on net benefit from higher pric es (a net increase in restof-the world [ROW] surplus of $0.1 billion) . Additional costs and fuel savings for

1 On June 19, 2007, the U.S. Court of Appeals for the District of Co lumbia Circuit (DC Ci rcuit) vacated the NESHAP for industrial/commercial/institutional boilers and process heaters. This action provides EPA’s proposed rule in response to the court’s vacatur. 2 Gas-fired boilers are not part of the area source categorie s of industrial boilers and institutional/commercial boilers.1-2 new and existing major sources that are not included in the economic model represent a net benefit of $0.4 billion. The estimated so cial cost of the proposed area source rule is approximately $0.5 billion (2008$). In the near term, the Agency’s economic model suggests that industries are able to pass approximately $0.3 b illion 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 b illion. Additional costs and fuel savings for unknown, existing, and new area sources not included in the economic model results represent a net benefit of $0.1 billion.

 Employment Changes: Near-term employment changes associated with the proposed major source rule are estimated to be less than 8,000 job losses; over a longer time period, net employment effect s range between 6,000 job losses to 12,000 job gains. For the area source rule, near-term employment changes associated with the proposed major source rule are estimated to be less than 1,000 job losses; over a longer time period, net employment effect s also range between 1,000 job losses to 3,000 job gains.

 Small Entity Analyses: EPA performed a screening analysis for impacts on small entities by comparing compliance costs to sales/revenues (e.g., sales and revenue tests). EPA’s analysis found the tests were t ypically higher than 3% for small entities included in the screening analysis. EPA has prepared an Initial Regulatory Flexibility Analysis (IRFA) that discusses alternative regulatory or policy op tions that minimize the rule’s small entity impacts. It includes key information about key results from the Small Business Advocacy Review (SBAR) panel.

 Benefits Analysis: In the year of full implemen tation (2013), EPA estimates the PM 2.5 co-benefits of the proposed major source rule are $17 billion to $41 billion and $15 billion to $37 billion, at 3% and 7% disc ount rates respectively. In the year of full implementation (2013), EPA estimates the PM 2.5 co-benefits of this proposed area source rule are $1.0 billion to $2.4 billion a nd $910 million to $2.2 billion, at 3% and 7% discount rates respectiv ely. All estimates are in 2008 dollars. Using alternate relationships between PM 2.5 and premature mortality suppl ied by experts, higher and lower co-benefits estimates are plausible, but most of the expert-based estimates fall between these estimates. The benefits fr om reducing other air pollutants have not been monetized in this analysis, incl uding reducing 370,000 tons of carbon monoxide, 37,000 tons of HCl, and 1,000 tons of HF, 8.3 tons of mercury, 3,400 tons of other metals, and 1,200 grams of dioxins/furans each year from major and area sources. In addition, ecosystem benefits and visibility be nefits have not been monetized in this analysis.

 Net Benefits: The net benefits for the proposed major source rule only are $14 billion to $38 billion and $12 billion to $34 bill ion, at 3% and 7% discount rates, respectively in 2013. The net benefits for th e area source rule only are $500 million to $1.9 billion and $410 million to $1.7 billion in 2013, at 3% and 7% discount rates, respectively. All estimates are in 2008 dollars.1-3 Table 1-1. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2013 (millions of 2008$) 1

Proposed Option 3% Discount Rate 7% Discount Rate
Total Monetized Benefits 2 $17,000 to $41,000 $15,000 to $37,000
Total Social Costs 3 $2,900 $2,900 Net Benefits $14,000 to $38,000 $12,000 to $34,000 Option 1N and 1E 3% Discount Rate 7% Discount Rate
Total Monetized Benefits 4 $17,000 to $41,000 $15,000 to $37,000
Total Social Costs 3 $12,000 $12,000 Net Benefits $5,000 to $30,000 $3,400 to $26,000 Proposed Option with Alternate Solid Waste Definition 3% Discount Rate 7% Discount Rate
Total Monetized Benefits 5 $3,100 to $7,700 $2,800 to $6,900
Total Social Costs 3 $2,200 $2,200 Net Benefits $930 to $5,500 $640 to $4,700

1 All estimates are for the implementation year (20 13), and are rounded to two significant figures.
2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 29,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 1,700 tons of VOC and 340,000 tons of SO 2 . The benefits from reducing 340,000 tons of carbon monoxide, 37,000 tons of HCl, 1,000 tons of HF, and 7.5 tons of mercury, 3,200 tons of other metals, and 720 grams of dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included.
3 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.
4 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 29,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 6,700 tons of VOC and 350,000 tons of SO 2 . The benefits from reducing 390,000 tons of carbon monoxide, 42,000 tons of HCl, 8,600 tons of HF, and 8.1 tons of mercury, 3,200 tons of other metals, and 760 grams of dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included.
5 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 8,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 4,700 tons of VOC and 44,000 tons of SO 2 . The benefits from reducing 280,000 tons of carbon monoxide, 5,100 tons of HCl, 1,100 tons of HF, and 7.1 tons of mercury, 1,600 tons of other metals, and 290 grams of dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included.1-4 Table 1-2. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Boiler Area Source Rule in 2013 (millions of 2008$) 1

Proposed Option 3% Discount Rate 7% Discount Rate
Total Monetized Benefits 2 $1,000 to $2,400 $910 to $2,200
Total Social Costs 3 $500 $500 Net Benefits $500 to $1,900 $410 to $1,700 Option 1N and 1E 3% Discount Rate 7% Discount Rate
Total Monetized Benefits 4 $8,300 to $20,000 $7,500 to $18,000
Total Social Costs 3 $35,000 $35,000 Net Benefits $-27,000 to $-15,000 $-28,000 to $-17,000

1 All estimates are for the implementation year (20 13), and are rounded to two significant figures.
2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 2,700 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 1,200 tons of VOC and 1,500 tons of SO 2 . The benefits from reducing 39,000 tons of carbon monoxide, 130 tons of HCl, 5 tons of HF, and 0.75 tons of mercury, 250 tons of other metals, and 470 grams of dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecos ystem effects and visibility impairment are not included.
3 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.
4 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 23,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 2,100 tons of VOC and 1,700 tons of SO 2 . The benefits from reducing 58,000 tons of carbon monoxide, 140 tons of HCl, 6.4 tons of HF, and 1.5 tons of mercury, 6,200 tons of other metals, and 530 grams of dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included. 1.2 Organization of this Report The remainder of this report supports and details the methodology a nd the results of the EIA:

 Section 2 presents the af fected industry profiles.

 Section 3 describes the e ngineering cost analysis.

 Section 4 describes the economic impact analysis.

 Section 5 describes the small entity analyses.

 Section 6 presents the benefits estimates.

 Section 7 presents supplemental economic analyses for an alternative non-hazardous solid waste definition

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

 Appendix B provides additional economic model result tables by sector.2-1 SECTION 2 INDUSTRY PROFILES
In this section, we provide an introduction selected industries that are affected by the proposed rules. The industries were selected based on high facility popul ation counts within 3- digit NAICs industries reported in the combustion facility survey. The purpose is to give the reader a general understanding of economic aspect s and industry trends to provide additional context for the economic impact analysis. 2.1 Food Manufacturing

2.1.1 Introduction Food manufacturing involves the transforma tion of raw agricultural and livestock products into processed food. Between 1997 and 2002, shipment values stagnated, falling 0.38%, while the number of employees and payr oll increased 2.71% and 7.76%, respectively (Table 2-1). This trend reversed between 2002 and 2006, as shipment values rose 4.77 % and number of employees and payroll fell 5.94% a nd 3.28% respectively (Tab le 2-1). Shipments, payroll, and employment conti nued to increase between 2006 and 2007, but there was a notable drop in the number of establishments between 2002 and 2007 (Table 2-1). As Table 2-2 shows, payroll per employee grew 4.91% from 1997 to 2002 and continued to increase, albeit at a slower rate of 2.83%, from 2002 to 2006. Be tween 2006 and 2007, the payroll per employee declined as the growth in employees outpaced th e increase in the annual payroll (Table 2-2).
The food manufacturing industry consists of nine different industry groups, each distinguished by the livestock or agricultural products used as raw materials for the processed food products as follows:

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

 Grain and Oilseed Milling (NAICS 3112)

 Sugar and Confectionery Produc t Manufacturing (NAICS 3113)

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

 Dairy Product Manufacturing (NAICS 3115)

 Animal Slaughtering and Processing (NAICS 3116)

 Seafood Product Preparation and Packaging (3117)

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

1997 2002 2006 2007 Shipments ($2007, millions) $528,928 $526,939 $552,075 $589,550 Payroll ($2007, millions) $48,118 $51,852 $50,151 $50,467 Employees 1,466,956 1,506,781 1,417,274 1,466,683 Establishments 26,302 27,899 NA 22,055

NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (2007 NAICS Basis): 2002 and 2007.” <http://factfind er.census.gov>; (January 4, 2010).
Table 2-2. Industry Data: Food Manufacturing (NAICS 311)

Industry Data 1 997 2002 2006 2007 Total shipments ($2007, millions) $528,928 $526,939 $552,075 589,550 Shipments per establishment ($2007, thousands) $20,110 $18,887 NA $26,731 Average Shipments per employee ($2007) $360,561 $349,712 $389,533 $401,961 Average Shipments per $ of payroll ($2007) $10.99 $10.16 $11.01 $11.68 Average Annual payroll per employee ($2007) $32,800.97 $34,412.12 $35,385.46 $34,409.00 Average Employees per establishment 56 54 NA 67

NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (2007 NAICS Basis): 2002 and 2007.” <http://factfind er.census.gov>; (January 4, 2010).
In 2006, Animal Slaughtering and Processing made up the largest share of both employment (33%) and the value of shipments (27%) in food manufacturing (Fi gures 2-1 and 2-2).2-3 Figure 2-1. Distribution of Employment with in Food Manufacturing (NAICS 311): 2006 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008).

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). Animal Food Manufacturing, 3% Grain and Oilseed Milling,

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

Packaging, 3% Bakeries and Tortilla

Manufacturing, 20% Other Food Manufacturing, 11%

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

Fruit and Vegetable

Preserving and Specialty Food Manufacturing, 12%

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

Packaging, 2% Bakeries and Tortilla

Manufacturing, 11% Other Food Manufacturing, 12%2-4 Many major environmental regulations direc tly affect the food manufacturing industry and/or other markets that provide key goods a nd services to the indus try (e.g., energy). RTI’s multimarket model is specifically designed to an alyze these types of regulations. The model emphasizes the links among industrial sectors an d provides policy makers with new insights about the direct and indirect e ffects of a regulatory program and the distribution of costs across the U.S. economy. 2.1.2 Supply and Demand Characteristics Next, we provide a broad overview of th e supply and demand sides of the food manufacturing industry. We emphasize the economic interactions this indu stry has with other industries and people, including identifying the key goods and serv ices used by the industry and the major uses and consumers of food manufacturing products. 2.1.2.1 Goods and Services Used in Food Manufacturing In 2006, the cost of materials made up 57% of the value of shipments in food production. Total employee compensation accounted for 12% of th is value, with half of that coming from production workers’ wa ges (Table 2-3).
The top 10 industry groups supplying inputs to food production accounted for 84% of the total intermediate inputs to the industry, with the top three indust ry groups (food products, animal products, and crop products) accounting for over half of the tota l intermediate inputs (Table 2-4). Electric power generation, transm ission, and distribution accounted for 2% of the total intermediate inputs, wher eas boilers, tanks, and shipping containers accounted for 1%. 2.1.2.2 Energy The Department of Energy (DOE) classifies the entire food products industry as an energy-intensive industry to model within its Industrial Demand Module (DOE, 2008a). In 2002, food manufacturing accounted for 6.86% of the total fuel consumption by all manufacturing industries (NAICS 311–339) and 1 9.24% of the conventional boiler use fuel consumption by all manufacturing industries (DOE, Ener gy Information Administration, 2007).2-5 Table 2-3. Costs of Goods and Services Us ed in Food Manufacturing (NAICS 311) ($2007)

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

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

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

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

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

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

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

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

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

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

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

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

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

that respondents indicated was us ed to produce heat and power.
* Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007. “2002 Energy Consumption by Manufacturers—Data Tables.” Tables 3.2 and N3.2. Washington, DC: DOE <http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>. U.S. Department of Energy, Energy Information Administration. 2009a. “2006 Energy Consumption by Manufacturers—Data Tables.” Table 3.1. Washington, DC: DOE. <http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html>.2-7 Figure 2-3. Electric Power Use Trends in Food Manufacturing (NAICS 311): 1997–2005
Source: Federal Reserve Board. 2008. “Industrial Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G311.S. <http://www.federalreserve.gov/datadownload/>.

2.1.2.3 Uses and Consumers The majority of food manufacturing’s total co mmodity output (58%) is sold for personal consumption. Of the sales for intermediate use, 42% are sold back into the food manufacturing industry (Table 2-6).

2.1.3 Firm and Market Characteristics This remaining subsection describes geographi c, production, and market data. These data provide the basis for further an alysis, including regul atory flexibility analyses, and give a complete picture of the recent histori cal trends of production and pricing.

2.1.3.1 Location In 2002, California had the most food manufact uring establishments in the United States, followed by New York and Texas (see Figure 2-4). In addition, Pennsylvania, Illinois, Wisconsin, New Jersey, and Florida had ove r 1,000 establishments in their states. 90 95 100 105 110 115 120Jan‐97Jun‐97Nov‐97Apr‐98Sep‐98Feb‐99Jul‐99Dec‐99May‐00Oct‐00Mar‐01Aug‐01Jan‐02Jun‐02Nov‐02Apr‐03Sep‐03Feb‐04Jul‐04Dec‐04May‐05Oct‐05IndexValue(Jan‐97=100)

Manufacturing Food2-8 Table 2-6. Demand by Sector: Food Manuf acturing (NAICS 311) ($2007, millions)

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

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

Figure 2-4. Establishment Concentration in Food Manufacturing (NAICS 311): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).

Establishments
Fewer than 250
250 - 499
500 - 999 1,000 - 2,000 More than 2,0002-9 2.1.3.2 Production Capacity and Utilization Capacity utilization of th e food manufacturing industry did not fall off during the recession of 2001 as much as the manufacturi ng sector as a whole (Figure 2-5). Food manufacturing’s capacity utiliz ation has remained higher than manufacturing as a whole and went above 85% in the spring of 2008. The effect s of the recent economic downturn have not affected capacity utilization as sharply in the food industry rela tive to the overall manufacturing sector (Figure 2-5).

Figure 2-5. Capacity Utilization Trends in Food Manufacturing (NAICS 311)
Source: Federal Reserve Board. 2008. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” Series ID: G17/CAPUTL/CAPUTL.GM F.S & G17/CAPUTL/CAPUTL.G311.S. <http://www.federalreserve.gov/datadownload/>. 2.1.3.3 Employment The geographic distribution of employment in food manufact uring varies substantially from the distribution of establishments. In 2002, Arkansas, ranked thirty-first in number of establishments and had the eighth most employ ees (53,844) because of its national high of 199 employees per establishment. New York, ranked second in number of establishments, had only the tenth most employees (50,012). North Caro lina and Georgia also had greater than 50,000 employees, despite having fewer than 600 establishments (Figure 2-6). 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08PercentageCapacityUtilization

Manufacturing Food2-10 Figure 2-6. Employment Concentration in Food Manufacturing (NAICS 311): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008). 2.1.3.4 Plants and Capacity Production capacity in food manufacturing only grew 17.94% between 1997 and early 2008, a compound annual growth rate (CAGR) of 1.45% . This is substantially less than the 42.50% growth for the manufacturing industry as a whole (Figure 2-7).

2.1.3.5 Firm Characteristics In fiscal year 2007, the top eight food manuf acturing companies each had greater than $10 billion in sales. These companies, however, are global, many with a large portion of both sales and production coming from operations outside of the United States (Table 2-7). The largest U.S. food manufacturing company, Kraft Foods Inc., has 50.27% of its long-lived assets located outside of the United St ates (Kraft Foods Inc., 2008). Employment Fewer than 10,000 10,000 to 19,999 20,000 to 39,999 40,000 to 80,000 More than 80,0002-11 Figure 2-7. Capacity Trends in Food Manufacturing (NAICS 311)
Source: Federal Reserve Board. 2008. “Industrial Production and Capacity Utilization: Industrial Capacity.” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G311.S. <http://www.federalreserve.gov/datadownload/>. Table 2-7. Top Publicly Held U.S. Food Companies: 2007

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

a Percentage of sales in the United States is actually percentage of sales in North America. Source: Graves, T. 2008. “Food and Nonalcoholic Beverages.” Standard and Poor’s Industry Surveys . 176(25). 0 20 40 60 80 100 120 140 160Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(January1997=100)

Manufacturing Food2-12 For the industry as a whole, the number of corporations as well as the number of corporations with net income in the food manufacturing indu stry grew between 2004 and 2005. Although the overall number of co mpanies continued to grow in 2006, the number of those with a positive net income declined along with prof it margins and total receipts (Table 2-8). Table 2-8. Corporate Income and Profitabili ty for Food Manufacturing (NAICS 311)

2004 2005 2006 Number of corporations 14,408 14,956 16,146 Number of corporations with net income 6,541 7,503 7,333 Total receipts (thousands) $502,149 ,944 $504,944,378 $484,193,319 Business receipts (thousands) $477,9 06,423 $465 ,369,666 $ 459,884,663 Before-tax profit margin 5.27% 10.09% 7.43% After-tax profit margin 3.74% 7.62% 5.11% Source: Internal Revenue Service, U.S. Department of Treasury. 2008. “Corporation Source Book: Data File 2005.” <http://www.irs.gov/taxs tats/article/0,,id=167415,00.html>; (January, 14 2009).
2.1.3.6 Size Distribution The primary criterion for categorizing a busine ss as small is number of employees, using definitions by the SBA for regulatory flexibility analyses. The data describing size standards are provided in Table 2-9. Over 80% of the NAICS industries within the food manufacturing industry use a cutoff of 500 employees. In 2002, enterprises with fewer than 500 employees accounted for 32% of employment and 23% of r eceipts within food manuf acturing (Table 2-10).

2.1.3.7 Domestic Production Between 1997 and early 2008, overall manufactur ing production grew faster (34.88%) than the food manufacturing component (26.18%) (Figure 2-8). The food manufacturing industry has been less volatile, particularly during the recession of 2001 and the current economic downturn.

2.1.3.8 International Trade In 2006, the United States regained a trade surplus in food manufact uring it had briefly lost during 2004 to 2005 (see Figure 2-9). The trade surplus in 2007 was over $4 billion. Both exports and imports have declined since their 2008 peak as a result of the global economic recession.2-13 Table 2-9. Small Business Size Standards: Food Manufacturing (NAICS 311)

NAICS Description Employees 311111 Dog and Cat Food Manufacturing 500 311119 Other Animal Food Manufacturing 500 311211 Flour Milling 500 311212 Rice Milling 500 311213 Malt Manufacturing 500 311221 Wet Corn Milling 750 311222 Soybean Processing 500 311223 Other Oilseed Processing 1,000 311225 Fats and Oils Refining and Blending 1,000 311230 Breakfast Cereal Manufacturing 1,000 311311 Sugarcane Mills 500 311312 Cane Sugar Refining 750 311313 Beet Sugar Manufacturing 750 311320 Chocolate and Confectionery Ma nufacturing from Cacao Beans 500 311330 Confectionery Manufacturing from Purchased Chocolate 500 311340 Non-Chocolate Confectionery Manufacturing 500 311411 Frozen Fruit, Juice and Vegetable Manufacturing 500 311412 Frozen Specialty Food Manufacturing 500 311421 Fruit and Vegetable Canning3 3,500 311422 Specialty Canning 1,000 311423 Dried and Dehydrated Food Manufacturing 500 311511 Fluid Milk Manufacturing 500 311512 Creamery Butter Manufacturing 500 311513 Cheese Manufacturing 500 311514 Dry, Condensed, and Evaporated Dairy Product Manufacturing 500 311520 Ice Cream and Frozen Dessert Manufacturing 500 311611 Animal (except Poultry) Slaughtering 500 311612 Meat Processed from Carcasses 500 311613 Rendering and Meat By-product Processing 500 311615 Poultry Processing 500 311711 Seafood Canning 500 311712 Fresh and Frozen Seafood Processing 500 311811 Retail Bakeries 500 311812 Commercial Bakeries 500 311813 Frozen Cakes, Pies, and Other Pastries Manufacturing 500 311821 Cookie and Cracker Manufacturing 750 311822 Flour Mixes and Dough Manufacturing from Purchased Flour 500 (continued)2-14 Table 2-9. Small Business Size Standards: F ood Manufacturing (NAICS 311) (continued)

NAICS Description Employees 311823 Dry Pasta Manufacturing 500 311830 Tortilla Manufacturing 500 311911 Roasted Nuts and Peanut Butter Manufacturing 500 311919 Other Snack Food Manufacturing 500 311920 Coffee and Tea Manufacturing 500 311930 Flavoring Syrup and Concentrate Manufacturing 500 311941 Mayonnaise, Dressing and Other Prepared Sauce Manufacturing 500 311942 Spice and Extract Manufacturing 500 311991 Perishable Prepared Food Manufacturing 500 311999 All Other Miscellaneous Food Manufacturing 500 Source: U. S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/sizestandardstopics/size/index.html>.

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

Enterprises with:

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

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

fraction of the year.
Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Busi nesses: U.S. Detail Employment Sizes: 2002.” <http://www.census.gov/csd/ susb/download_susb02.htm>.2-15 Figure 2-8. Industrial Production Trends in Food Manufacturing (NAICS 311)
Source: Federal Reserve Board. 2008. “Industrial Produc tion and Capacity Utilization: Industrial Production.” Series ID: G17/IP_MAJOR_INDUSTRY_GROUP S/IP.GMF.N & G17/IP_MAJOR_INDUSTRY_ GROUPS/IP.G311A2.N. <h ttp://www.federalreserve.gov/datadownload/>.

Figure 2-9. International Trade Trends in Food Manufacturing (NAICS 311) Source: U.S. International Trade Commission. 2009a. “U.S. Domestic Exports” & “U.S. Imports for Consumption.” <http://www.federalreserve.gov/datadownload/>. 60 70 80 90 100 110 120Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(2002=100)

Manufacturing Food, Beverage, and Tobacco

0 10 20 30 40 50 60

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

U.S. Domestic Exports (FAS Value) U.S. Imports for Consumption (CIF Value)2-16 2.1.3.9 Market Prices Prices of goods in food manufacturing have mo ved generally in line wi th prices in overall manufacturing (see Figure 2-10) . Both indexes increased over 31% since between early 2003 and early 2008, a CAGR of 5.13%. This rise was followed by a marked decline in recent years along with the downward trend in prices throughout the economy.

Figure 2-10. Producer Price Trends in Food Manufacturing (NAICS 311) Source: U.S. Department of Labor; Bureau of Labor Statistics. 2010. “Producer Price Indexes.” <http://www.bls.gov/pPI/Series Id: PCU311—311—Food Manufacturing & PCUOMFG–OMFG–Total Manufacturing>. 2.2 Wood Product Manufacturing

2.2.1 Introduction According to a report by Sta ndard & Poor’s (2008), a numbe r of factors are shaping the current economic environment for wood products, including, but not li mited to, the housing slump, high input costs, low prices for lumber a nd other building material s, and a weak dollar. Table 2-11 shows that revenues in this industry ar e not entirely predictable, exhibiting a drop in shipment revenue between 1997 and 2002 but a rise back to within $5 bil lion of the 1997 value in 2006 and a decline to within $14 billion of the 2006 value in 2007. 0 20 40 60 80 100 120 140 160 180 20019901991199219931994199519961997199819992000200120022003200420052006200720082009IndexValues(December1984=Base)

Manufacturing Food2-17 Table 2-11. Key Statistics: Wood Product Manufacturing (NAICS 321)

1997 2002 2006 2007 Shipments ($2007, millions) $110,956 $102,721 $115,390 $101,879

Payroll ($2007, millions) $17,959 $18,528 $18,623 $17,439 Employees 570,034 543,459 536,094 519,651 Establishments 17,367 17,255 NA 14,862

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

Industry Data 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 Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” Accessed on December 27, 2009.2-18 The U.S. Census Bureau categorizes this i ndustry’s facilities into three categories: “sawmills and wood preservation;” “veneer, plywood, and engineered wood product manufacturing;” and “other wood product manufact uring.” These are further divided into the following types of facilities as defined by the Census Bureau:

 Sawmills and Wood Preservation

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

 Veneer, Plywood, and Engineered Wood Product Manufacturing

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

 Other Wood Product Manufacturing

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

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

– All Other Wood Product Manuf acturing (NAICS 32199): This industry comprises establishments primarily engaged in manufacturing wood products (except establishments operating sawmills a nd wood preservation facilities; and establishments manufacturing veneer , plywood, engineered wood products, millwork, wood containers, or pallets).2-19 Figure 2-11 shows that the i ndustry proportion of the value of shipments for other wood product manufacturing (51%) was greater than th e value of shipments for sawmills and wood preservation (27%) and veneer , plywood, and engineered wood products (22%). Figure 2-12 indicates that the majority of employees in th is industry fell under ot her wood products (60%). Veneer, plywood, and engineered wood products ha d the same percentage (20%) of employees as sawmills and wood preservation (20%), even though it contributed to a lesser portion of the value of shipments. 2.2.2 Supply and Demand Characteristics Next, we provide a broad overview of the supply and demand sides of the wood product manufacturing industry. We emphasize the economic interactions this indu stry has with other industries and people and identify the key goods a nd services used by the industry and the major uses and consumers wood products.

Figure 2-11. Distribution of Value of Shipm ents within Wood Product Manufacturing (NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statis tics: 2007.” <http://factfinde r.census.gov>. Accessed on December 27, 2009. [S ource for 2007 numbers] Veneer, Plywood, & Engineered Wood Product Mfg (NAICS 3212), 22%

Sawmills & Wood Preservation (NAICS 3211),

27% Other Wood Product Mfg (NAICS 3219),

51%2-20 Figure 2-12. Distribution of Employment wi thin Wood Product Manufacturing (NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key St atistics: 2007” Release Date: 12/22/09. <http://factfinder.census.gov>. Accessed on December 27, 2009 . [Source for 2007 numbers]
2.2.2.1 Goods and Services Used in Wood Product Manufacturing In 2007, the cost of materials made up 59% of the total shipment value of goods in the wood product manufacturing industry (Table 2-13). Total compensation of employees represented 22% of the total va lue in 2007. Both the number of total shipments and the number of employees in this industry decreased between 2005 and 2007—the former by 14% and the latter by 3%.
The top 10 industry groups supplying input s to the wood product industry accounted for 80% of the total intermediate inputs accordi ng to 2008 Bureau of Economic Analysis data (Table 2-14). The largest comes from the wood product industry itself. This is quite understandable, since the desc riptions of the various i ndustries within wood product manufacturing imply that they supply each ot her 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 en terprises, and truck transportation, which together make up 70% of the total cost of input. Other Wood Product Mfg (NAICS 3219),

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

Veneer, Plywood, &

Engineered Wood Product Mfg (NAICS 3212),

20%2-21 Table 2-13. Costs of Goods and Services in Wood Product Manufacturing (NAICS 321) ($2007)

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

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

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

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

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

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

Table 2-15 shows that total energy use between 1998 and 2002 Figure 2-13 shows that electrical power us e decreased, since 2000.

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

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

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

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

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

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

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

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

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

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

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

— Data Tables.” Tables 3.1. <http://w ww.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html>. [Source for 2006 numbers]2-24 Figure 2-13. Electrical Power Use Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997–2005
Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G321.S. <http://www.federalrese rve.gov/datadownload/>. Acce ssed on December 15, 2009.
2.2.3 Firm and Market Characteristics This section describes geographic, production, and market data. These data provide the basis for further analysis, includ ing regulatory flexibility analyses , as well as a complete picture of the recent historical trends of production and pricing.

2.2.3.1 Location As Figure 2-14 illustrates, the states w ith the largest number of wood product manufacturing establishments are disperse d throughout the country, with a significant concentration of establishments in the northeastern states. Other states with many establishments include California, Texas, and North Carolina. 85 90 95 100 105 110Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(Jan‐97=100)

Manufacturing Electric Power Use for NAICS 3212-25 Table 2-16. Demand by Sector: Wood Pro duct Manufacturing (NAICS 321) ($2007, millions)

Sector BEA Code 3210 Wood Products New residential construction 2302 $19,997 New nonresidential construction 2301 $11,854 Furniture and related product manufacturing 3370 $8,197 Maintenance and repair construction 2303 $4,048 Motor vehicle body, trailer and parts manufacturing 336A $2,516 Real estate 5310 $2,335
Food services and drin king places 7220 $2,307 Other miscellaneous manufacturing 3399 $1,311 Wholesale trade 4200 $1,284 Rail transportation 4820 $1,138 Retail trade 4A00 $1,047 Plastics and rubber products manufacturing 3260 $877 General state and local government use S007 $3,116 Owner occupied dwelling S008 $11,209 Private fixed investment F020 $7,933 Exports of goods and services F040 $3,978 Total final uses (gross domestic product [GDP]) T004 $3,719 Total commodity output T007 $101,753
Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA. 2.2.3.2 Production Capacity and Utilization Capacity utilization of the wood product ma nufacturing industry has been experiencing capacity utilization increases and declines with more extreme fluctuations than those of all manufacturing industries combined. The declin e in wood product manufacturing is similar to total manufacturing between 1997 and 2002. Howe ver, capacity utilization in total manufacturing, which peaked in 2006, started in creasing at a faster rate than wood product manufacturing, but decreased sharply after it s peak. Wood product manufacturing experienced its own rapid decrease in cap acity utilization between 2007 and 2009, though not at the same rate as total manufacturing (Figure 2-15).2-26 Figure 2-14. Establishment Co ncentration in the Wood Product Manufacturing Industry (NAICS 321): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008). 2.2.3.3 Employment California has the largest number of em ployees in the wood product manufacturing industry with over 39,000 reported in the 2002 census followed by over 32,000 in Oregon (Figure 2-17). The states with the highest number of employees do not direc tly correlate with the states with the highest number of establishments. States such as Indiana, Georgia, Arkansas, and Oregon had fewer than 600 establishments, as shown in Figure 2-14, but had more than 20,000 employees, whereas states such as Ohio and New York had fewer than 20,000 employees but more than 600 establishments. 2.2.3.4 Plants and Capacity While the capacity of the manufacturing sector has been growing c onsistently since 1997, the wood product manufacturing industry has expe rienced inconsistent growth. After a small amount of growth in capacity between 1997 and 2001, the wood product manufacturing industry’s capacity dipped between 2002 and 2005 but has been growing at a slow rate since then though it started to dip again in 2008 and 2009 (Figure 2-17). Establishments by State

Less than 100

100 - 199

200 - 399

400 - 600

More than 6002-27 Figure 2-15. Capacity Utilization Trends in the Wood Product Manufacturing Industry (NAICS 321)
Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” Series ID: G17/CAPUTL/CAPUTL.GM F.S & G17/CAPUTL/CAPUTL.G321.S. <http://www.federalrese rve.gov/datadownload/>. Acce ssed on December 15, 2009.
2.2.3.5 Firm Characteristics In 2006, the top 10 paper and forest produc t companies produced over $75 billion in sales, with the top two companies—Internati onal Paper and Weyerhaeuser—generating nearly $22 billion each (Table 2-17. The top two companies’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor’s (S&P’s) list (Benwa rt, 2006). Although these numbers do not exclusively reflect wood products , they do convey the market environment in which firms in this sector compete.

2.2.3.6 Size Distribution The primary criterion for categorizing a busin ess as small is the number of employees, using definitions by the SBA for regulatory flexibility analyses. According to SUSB reports for 2002, small companies were the recipients of the majority of receipts in 2002; 53% of receipts were generated by companies with fewer than 500 employees (Table 2-18). The number of employees in the small business cutoff is 500 employees for all subi ndustries in the wood product manufacturing industry (Table 2-19). 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PercentageCapacityUtilization

Manufacturing Wood Products2-28 Figure 2-16. Employment Concentration in the Wood Product Manufacturing Industry (NAICS 321): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).
2.2.3.7 Domestic Production Similar to industry capacity rates, industry production rates for wood product manufacturing have decreased si nce 2006 compared to the steady increase in production for the manufacturing sector since 1997 (Figure 2-18). Similar to capacity utilization trends (Figure 2-16), the index shows a faster rate of decline for wood products than the entire manufacturing sector.

2.2.3.8 International Trade Since 1997, the wood product manufacturing indust ry has contributed to an increasing trade deficit (Figure 2-16). The value of impor ts has fluctuated greatly since 1997; however, exports have remained fairly constant , with seasonal changes, since 1997. Employemnt by State

Less than 2,000
2,000 - 4,999
5,000 - 9,999
10,000 - 20,000
More than 20,0002-29 Figure 2-17. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321)
Source: Federal Reserve Board. “Industrial Production and Capacity Utilization: Industrial Capacity.” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G321.S. <http://www.federalreserve.gov/datadownload/>. Accessed on December 15, 2009. Table 2-17. Largest U.S. Paper and Forest Products Companies: 2006

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

a Includes revenues from operations other than paper and forest products in certain cases. Source: Benwart, S.J. 2006. “Paper & Forest Products.” Standard and Poor’s Industry Surveys . 176(28). 100 105 110 115 120 125 130 135 140 145 150Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Wood Products2-30Table 2-18. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 321)Enterprises withVariable Total1 to 20Employeesa20 to 99Employees100 to 499Employees500 to 749Employees750 to 999Employees1,000 to 1,499EmployeesFirms15,1989,7403,280791632730Establishments17,0529,7583,4821,27116691133Employment534,01165,423132,612118,91019,78411,94418,533Receipts ($thousands)$88,649$8,204$18,276$19,717$3,192$1,902$3,118Receipts/firm ($thousands)$5,833$842$5,572$24,927$50,673$70,453$103,927Receipts/establishment($thousands)$5,199$841$5,249$15,513$19,231$20,904$23,442Receipts/employment ($)$166,006$125,393$137,818$165,814$161,363$159,262$168,231aExcludesStatistics of U.S. Businesses(SUSB) employment category for zero employees. These entities only operated for a fraction of the year.Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002.”<http://www.census.gov/csd/susb/download_susb02.htm>.2-31 Table 2-19. Small Business Size Standards: Wood Product Manufacturing (NAICS 321)

NAICS NAICS Description Employees
321113 Sawmills 500
321114 Wood Preservation 500 321211 Hardwood Veneer and Plywood Manufacturing 500 321212 Softwood Veneer and Plywood Manufacturing 500 321213 Engineered Wood Member (except Truss) Manufacturing 500 32121 Truss Manufacturing 500 321219 Reconstituted Wood Product Manufacturing 500 321911 Wood Window and Door Manufacturing 500 321912 Cut Stock, Resawing Lumber, and Planing 500 321918 Other Millwork (including Flooring) 500 321920 Wood Container and Pallet Manufacturing 500 321991 Manufactured Home (Mobile Home) Manufacturing 500 321992 Prefabricated Wood Building Manufacturing 500 321999 All Other Miscellaneous Wood Product Manufacturing 500
Source: U.S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/sizestandardstopics/size/index.html>.
2.2.3.9 Market Prices Prices of goods in the wood product manuf acturing industry have remained roughly the same since 2005. The prices for the entire manuf acturing sector increased between 2003 and 2008 but have decreased since August 2008. Producer price indices (PPIs) show that producer prices for wood products increased by 6% from 2004 to 2007 (Figure 2-20).2-32 Figure 2-18. Industrial Production Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997–2009
Source: Federal Reserve Board. “Industrial Production and Capacity Utilization: Industrial Production.” Series ID: G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.S & G17/IP_MAJOR_INDUSTRY_GROUPS/ IP.G321.S. <http://www.federalre serve.gov/datadownload/>. A ccessed on December 15, 2009.

Figure 2-19. International Trade Trends in the Wood Product Manufacturing Industry (NAICS 321)] Source: U.S. International Trade Commission. 2008a. “U.S. Domestic Exports” & “U.S. Imports for Consumption.” <http://dataweb.usitc.gov/s cripts/user_set.asp>; (July 17, 2008).

60 70 80 90 100 110 120 130 140Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index Value (Jan-97=100)

Manufacturing Wood Products

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

Wood Product Exports Wood Product Imports2-33 Figure 2-20. Producer Price Trends in the Wood Product Manufacturing Industry (NAICS 321) Source: U.S. Bureau of Labor Statistics (BLS). 2009. “Producer Price Index.” Series ID: PCU321—321—& PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home. htm>. Accessed on January 8, 2010. 2.3 Paper Manufacturing

2.3.1 Introduction The paper manufacturing subsector is an esse ntial component of a ll business operations worldwide. Broadly speaking, pa per and paperboard are manufactured by converting timber or other recycled material into pr oducts such as printing and writing papers, newsprint, tissue, and containerboard (Benwart, 2006). Th e subsector has been experienci ng a decline in shipments as of late. From 1997 to 2007, shipments in the industr y declined 7%, and employment declined by 27% (Table 2-21). While total payroll dropped 26 % over this time, annual payroll per employee rose 2% from 1997 to 2007 because of the decline in the number of employees (Table 2-20). Shipments per employee grew 28% from 1997 to 2007, with much of that growth taking place between 2002 and 2006 (Table 2-21). 0 20 40 60 80 100 120 140Dec‐03Jun‐04Dec‐04Jun‐05Dec‐05Jun‐06Dec‐06Jun‐07Dec‐07Jun‐08PPI(Dec‐03=100)

Wood Products PPI All Manufacturing PPI2-34 Table 2-20. Key Statistics: Pap er Manufacturing (NAICS 322)

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

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

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

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

 Pulp, Paper, and Paperboard:

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

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

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

 Converted Paper Products:

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

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

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

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

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

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

Figure 2-22. Distribution of Employment with in Paper Manufacturing (NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” <http://factfinder.census.gov>. A ccessed on December 28, 2009. PPP (3221) 46% CPP (3222) 54%

PPP (3221) 30%

CPP (3222) 70%2-37 2.3.2 Supply and Demand Characteristics Next, we provide a broad overview of th e supply and demand sides of the paper manufacturing industry. We emphasize the economic interactions this indu stry has with other industries and people and identify the key goods a nd services used by the industry and the major uses and consumers of paper manufacturing products. 2.3.2.1 Goods and Services Used in Paper Manufacturing In 2007, the cost of materials made up 53% of the total shipment value of goods in the paper manufacturing industry (Table 2-22). Tota l compensation of employees represented 15% of the total value in 2007, down from 17% in 2005. The total number of employees dropped by 2%, between 2005 and 2007, while shipments increased by 3% in the same period.
The top 10 industry groups supplying input s to the paper manufacturing subsector accounted for 70% of the total intermediate inputs according to 2008 Bureau of Economic Analysis (BEA) data (Table 2-23). Inputs for pulp, paper, and paperboard products are notably different from inputs for converted paper produ cts because the NAICS 3221 group represents the initial step in the paper manufact uring process; thus, its inputs include more raw resources such as wood products, forestry and logging products, natural gas, and electricity. This becomes evident when observing inputs for converted pape r products: 49% of the cost of inputs comes from pulp, paper, and paperboard products.
2.3.2.1.1 Energy

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

that respondents indicated was us ed to produce heat and power.
* Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007. “2002 Energy Consumption by Manufacturers—Data Tables.” Tables 3.2 and N3.2. <http://www.eia.doe.gov/emeu/mecs/mecs2002/ data02/shelltables.html>. Washington, DC: DOE. U.S. Department of Energy, Energy Information Administration. 2007b. “2006 Energy Consumption by Manufacturers—Data Tables.” Table 3.1. <http://www.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html>. Accessed on December 27, 2009. [Source fo r 2006 numbers]
Over the last 25 years, the pulp and paper subsector has changed its energy generation methods from fossil fuels to a greater use of pr ocesses such as increases in the use of wood wastes in place of fuel (Table 2-25). During the 1972–1999 period, the proportion of total industry power generated from the combination of woodroom wastes, spent liquor solids, and other self-generated methods increased from about 41% to about 56%, while coal, fuel oil, and natural gas use decreased from a bout 54% to about 36% (EPA, 2002).2-41 Figure 2-23. Electrical Power Use Trends in the Paper Manufacturing Industry: 1997– 2005
Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G322.S. <http://www.federalreserve.gov/datadownload/>. Table 2-25. Estimated Energy Sources fo r the U.S. Pulp and Paper Industry

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

and bark 6.6% 9.2% 15.4% 13.5%

Spent liquor solids 33.7% 37.3% 39.4% 40.3% Other self-generated power 0.6% 0.8% 1.2% 1.6%
Source: U.S. Environmental Protection Agency. 2002. “Profile of the Pulp and Paper Industry.” Sector Notebook Project. <http://www.epa.gov/Compliance/resour ces/publications/assistance/sectors/notebooks/ index.html>. 90 92 94 96 98 100 102 104 106 108Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(Jan‐97=100)

Manufacturing Paper2-42 2.3.2.2 Uses and Consumers
Products manufactured in the NAICS groups 3221 and 3222 have different, but complementary, consumer profiles. NAICS 322 1 supplies a significant portion of NAICS 3222 demand (37% of total commodity output). Bo th industries speciali ze in products with intermediate uses, with an average of 92% of sales between the two goi ng toward this purpose. NAICS 3222 has a very diverse a ssortment of subsector groups from which it receives demand. Food manufacturing makes up 21% of the demand, ma king members of this industry th e largest consumer of converted paper products (Table 2-26). Pulp, paper, and paperboard products have a large trade deficit, while converted paper products have a very small trade surplus. Table 2-26. Demand by Sector: Paper Manufactu ring Industry (NAICS 322) ($millions, $2007)

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

services S007 $6,065 $7,792 $13,857

Pulp, paper, and paperboard mills 3221 $4,155 $1,415 $5,569 Newspaper, periodical, book, and

directory publishers 5111 $4,851 $168 $5,018

Plastics and rubber products

manufacturing 3260 $1,249 $3,403 $4,651

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

− $15,284

− $5,720

− $21,005 Total final uses (GDP) T004 $4,996 $9,607 $14,604 Total commodity output T007 $81,725 $90,469 $172,195 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.2-43 2.3.3 Firm and Market Characteristics This section describes geographic, production, and market data. These data provide the basis for further analysis, includi ng regulatory flexibility analyses, and give a complete picture of the recent historical trends of production and pricing.

2.3.3.1 Location As Figure 2-24 illustrates, California is home to the most paper manufacturing establishments in the United States, followed by Illinois and some borderi ng northeastern states. The location of establishments in the paper manufacturing industry va ries a great deal by subsector. Wisconsin and New York have the most pulp, paper, and paperboard establishments, while California dominates with over 500 converted paper product establis hments. Overall, the United States has 561 pulp, pape r, and paperboard establishm ents and 4,956 converted paper product establishments.

Figure 2-24. Establishment Concentration in Paper Manufacturing Industry (NAICS 322): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).

Establishments
Fewer than 50
50 - 99
100 - 199
200 - 300
More than 3002-44 2.3.3.2 Production Capacity and Utilization Capacity utilization of the paper manufactur ing subsector has been experiencing a steady decline, similar to the decline of the total manufacturing sector. However, paper manufacturing has managed to use its capacity at a consistently higher rate than the average for manufacturing industries (Figure 2-25).

Figure 2-25. Capacity Utilization Trends in the Paper Manufacturing Industry (NAICS 322)
Source: Source: Federal Reserve Board. 2009. “Indus trial Production and Capacity Utilization: Capacity Utilization.” Series ID: G17/CAPUTL/CA PUTL.GMF.S & G17/CA PUTL/CAPUTL.G322.S. <http://www.federalreserve.gov/datadownload/>. 2.3.3.3 Employment Wisconsin has the largest number of employ ees in the paper manufacturing subsector with over 38,008 reported in the 2002 census fo llowed by 29,379 in California (Figure 2-26). The converted paper products group has more em ployees per establishm ent, 283, than the pulp, paper, and paperboard group, 67. 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PercentageCapacityUtilization

Manufacturing Paper (NAICS 322)2-45 Figure 2-26. Employment Concentration in the Paper Manufacturing Industry (NAICS 322): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).

2.3.3.4 Plants and Capacity While the manufacturing sector has been growing consistently since 1997, the paper manufacturing sector has not experienced th e same amount of success in the same period. Despite a small amount of growth in capacity between 1997 and 2001, the paper manufacturing subsector’s capacity has declined to as much as 7% below 1997 capacity levels (Figure 2-27).

2.3.3.5 Firm Characteristics In 2006, the top 10 paper and forest produc t companies produced over $75 billion in sales, with the top two companies—Internati onal Paper and Weyerhaeuser—generating nearly $22 billion each (Table 2-27). The top two companie s’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor’s (S&P’s) list (Benwa rt, 2006). Although these numbers do not exclusively reflect paper product s, they do convey the market environment in which firms in this sector compete. Employment Fewer than 3,000 3,000 - 5,999 6,000 - 11,999 12,000 - 24,000 More than 24,0002-46 Figure 2-27. Capacity Trends in the Pa per Manufacturing Industry (NAICS 322) Source: Federal Reserve Board. 2009. “Industrial Productio n and Capacity Utilization: Industrial Capacity.” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G322.S. <http://www.federalreserve.gov/datadownload/>. Table 2-27. Largest U.S. Paper and Forest Products Companies: 2006

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

a Includes revenues from operations other than paper and forest products in certain cases. Sources: Benwart, S.J. 2006. “Paper & Forest Products. Standard and Poor’s Industry Surveys.” 176(28). U.S. and international sales data from company reports. 90 100 110 120 130 140 1501997‐011997‐071998‐011998‐071999‐011999‐072000‐012000‐072001‐012001‐072002‐012002‐072003‐012003‐072004‐012004‐072005‐012005‐072006‐012006‐072007‐012007‐072008‐012008‐072009‐012009‐07IndexValue(Jan‐97=100)

Manufacturing Paper (NAICS 322)2-47 2.3.3.6 Size Distribution The primary criterion for categorizing a busin ess as small is the number of employees, using definitions by the SBA for regulatory flexibility analyses. According to SUSB reports for 2002, large companies dominated revenue-generati ng transactions in the paper manufacturing subsector; 80% of receipts were generated by companies with 500 employees or more (Table 2-28). This was especially true in the pulp, paper, and paperboa rd group, in which large companies generated 92% of receipts. The numbe r of employees in the small business cutoff varies according to six-digit NAICS codes (Table 2-29). The cutoff for all subsectors in the pulp, paper, and paperboard group is 750 employees, while the cutoff for most converted paper product groups is 500 employees.

Table 2-28. Distribution of Economic Data by Enterprise Size: Paper Manufacturing (NAICS 322)

Enterprises with

Variable Total 1 to 20 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees
Firms 3,538 1,482 1,200 476 43 22 33
Establishments 5,546 1,488 1,271 755 83 69 138 Employment 495,990 11,325 52,334 78,402 13,293 12,496 23,283 Receipts ($millions ) $154,746 $2,218 $9,483 $17,620 $3,034 $3,951 $6,798 Receipts/firm ($thousands) $43,738 $1,497 $7,903 $37,017 $70,561 $179,577 $206,001 Receipts/establishment ($thousands) $27,902 $1,491 $7,461 $23,338 $36,556 $57,256 $49,261 Receipts/employment ($) $311,994 $195,850 $181,203 $224,742 $228,250 $316,157 $291,974

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

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

322226 Surface-Coated Paperboard Manufacturing 500 322231 Die-Cut Paper and Paperboard Office Supplies, Manufacturing 500 322232 Envelope Manufacturing 500 322233 Stationery, Tablet, and Related Product Manufacturing 500 322291 Sanitary Paper Product Manufacturing 500 322299 All Other Converted Paper Product Manufacturing 500
Source: U.S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/sizestandardstopics/size/index.html>.

2.3.3.7 Domestic Production Similar to industry capacity rates, subsect or production rates for paper manufacturing have witnessed a decreasing rate of production comp ared to the steady increase in production for the manufacturing sector since 1997 (Figure 2-28). It seems that the paper manufacturing sector was not able to return to its former levels of growth following the 2001 recession; it has experienced a downward pr oduction trend since then.2-49 Figure 2-28. Industrial Production Trends in the Paper Manufacturing Industry (NAICS 322): 1997–2009
Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Production.” Series ID: G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.S & G17/IP_MAJOR_INDUSTRY_ GROUPS/IP.G322.S. <http ://www.federalreserve. gov/datadownload/>.
2.3.3.8 International Trade Since 1997, paper manufacturing products, bot h pulp, paper, and paperboard products and converted paper products, have contributed to an increasing trade surplus in this sector (Figure 2-29). Imports and exports have been changing at sim ilar rates since 1999.

2.3.3.9 Market Prices Prices of goods in paper manufacturing have be en increasing at a rate consistent with all manufacturing products (Figure 2- 30). Producer price indices (PPI s) show that producer prices for paper in 2007 increased by 20% since 1997, wh ile producer prices for all manufacturing goods increased by roughly 27%. 75 85 95 105 115 125 135Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Paper (NAICS 322)2-50 Figure 2-29. International Trade Trends in the Paper Manufacturing Industry (NAICS 322) Source: U.S. International Trade Commission. 2008b. “U.S. Total Exports” & “U.S. Imports for Consumption.” <http://dataweb.usitc.gov/s cripts/user_set.asp>.

Figure 2-30. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222) Source: U.S. Bureau of Labor Statistics (BLS). 2009. “Producer Price Index.” Series ID: PCU322–322– & PCUOMFG–OMFG–. <http://www.bls.gov/ppi/home.htm>. 0 5,000 10,000 15,000 20,000 25,000 30,000

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

Year
Paper Exports Paper Imports

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

Manufacturing Paper (NAICS 322)2-51 2.4 Chemical Manufacturing

2.4.1 Introduction The chemical manufacturing industry produ ces over 70,000 chemical substances, many of which are ubiquitous in American life. Br oadly speaking, chemical manufacturing operates by converting feedstocks into chemical products th at can serve as intermediate goods or final products such as medicine, soap, and printe r ink. From 1997 to2007, shipments in the industry grew 42%, while employment declined by 8% (T able 2-30). While tota l payroll dropped 0.6% over this time, annual payroll per employee rose 7.8% from 1997 to 2007 because of the decline in the number of employees (Table 2-31). Sh ipments per employee grew 54% from 1997 to 2007, with much of that growth taking place between 2002 and 2006 (Table 2-31).
Chemical manufacturing (NAICS 325) covers a diverse set of industry groups, which we have aggregated into the following three groups:

 Bulk Chemicals—Includes the most energy- intensive industry groups as aggregated by the Department of Energy (DOE) (DOE/EIA-0554, 2008): Basic Chemical Manufacturing (NAICS 3251); Resin, Rubber, and Artificial Fibers Manufacturing (NAICS 3252); and Agricultural Ch emical Manufacturing (NAICS 3253).

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

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

1997 2002 2006 2007 Shipments ($2007, millions) $521,251 $531,173 $675,223 $738,303 Payroll ($2007, millions) $49,961 $51,317 $46,981 $49,648 Employees 882,645 853,224 747,134 814,024 Establishments 13474 13,475 NA 12,937

NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” <http://factfinder.census.gov>. Accessed on December 27, 2009 . [Source for 2007 numbers] Table 2-31. Industry Data: Chemical Manufacturing (NAICS 325)

Industry Data 1 997 2002 2006 2007 Total shipments ($2007, millions) $521,251 $531,173 $675,223 $738,303 Shipments per establishment ($thousands) $38,686 $39,419 NA $57,069 Shipments per employee ($2007) $590,556 $622,548 $903,750 $906,979 Shipments per $ of payroll ($2007) $10.43 $10.35 $14.37 $14.87 Annual payroll per employee ($2007) $56,603 $60,145 $62,882 $60,991 Employees per establishment 66 63 NA 63

NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” <http://factfinder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” <http://factfinder.census.gov>. A ccessed on December, 27, 2009 . [Source for 2007 numbers]2-53 Figure 2-31. Distribution of Employment with in Chemical Manufacturing (NAICS 325): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detaile d Statistics by Industry for the U.S.: 2007.” Release date: October 30, 2009. Acce ssed on December 27, 2009.

Figure 2-32. Distribution of To tal Value of Shipments with in Chemical Manufacturing (NAICS 325): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31:EC0731I1: Manufacturing Industry Series: De tailed Statistics by Industry for U.S.: 2007.” <http://factfinder.census.gov>. A ccessed on December 27, 2009. Bulk Chemicals, 35%

Pharmaceuticals, 31% Other Chemicals, 34%

Bulk Chemicals, 50%

Pharmaceuticals, 26% Other Chemicals, 24%2-54 2.4.2 Supply and Demand Characteristics Next, we provide a broad overview of the supply and demand side of the chemical manufacturing industry. We emphasize the economic interactions this indu stry has with other industries and people, including identifying the key goods and serv ices used by the industry and the major uses and consumers of chemical manufacturing products.
The top 10 industry groups supplying inputs to the chemical manuf acturing industry in 2002 accounted for 71% of the total intermedia te inputs (Table 2-32). Bulk chemicals’ production was the most energy intensive, using 79% of the chemical manufacturing inputs from petroleum and coal products, electric power generation, transmission and distribution, and natural gas distribution. Table 2-32. Key Goods and Services Used in Chemical Manufact uring (NAICS 325) ($2007, millions)

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

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

Natural gas distribution 2212 $6,390 $154 $390 $6,934 Total intermediate use T005 $167,699 $82,403 $91,833 $341,935
Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.2-55 2.4.2.1 Goods and Services Used in Chemical Manufacturing In2007, the cost of materials made up 49% of chemical manufacturing’s total shipment value (Table 2-32). Total compensation to employ ees represented 9% of total shipment value, down from 10% in 2005.
2.4.2.1.1 Energy. The Department of Energy (DOE) classifies bulk chemical manufacturing as an energy-intensive i ndustry. Pharmaceuticals and other chemical manufacturing are categorized as non-energy-intensive industrie s, grouped together with other industry groups under the “Balance of Manufacturing” category (DOE, 2008).
Fuel used in chemical production can either f acilitate chemical proc esses or provide the feedstock to derive value-added chemicals. In 2007, 70% of chemical manufacturing’s energy bill was spent on fuel used as feedstocks (O’R eilly, 2008). These fuel costs represented 2% of chemical manufacturing’s total value of shipments (Table 2-33).
As a whole, chemical manufacturing use less energy over the last 10 years. According to DOE, natural gas use by the chemical manufact uring industry dropped 30% from 1998 to 2006, and electricity use fell 10% (Table 2-34). From 1 997 to 2005, when data ceased to be available, chemical manufacturing used less electricity rela tive to the manufacturi ng sector as a whole (Figure 2-33).

2.4.2.2 Uses and Consumers Products manufactured in the groups bulk chemicals, pharmaceuticals, and other chemicals have very different consumer profiles. Bulk chemicals is dominated by intermediate use, representing 93% of its total commodity out put and 56% of the total intermediate use of chemical manufacturing products. Pharmaceutic als has both a high level of demand from personal consumption, accounting for 67% of the total personal consumption of chemical manufacturing products, and a larg e trade deficit (Table 2-35).2-56 Table 2-33. Costs of Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007)

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

employee $82,887 $82,559 $79,333

Total production workers’ wages (millions) $22,643 4% $22,231 3% $23,157 3%

Total production workers 431,502 430,880 463,802 Total production hours (thousands) 899,499 885,993 948,244

Average production wages per

hour $25 $25 $24

Total cost of materials ($thousands) $299,859 46% $318,945 47% $357,055 49%

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

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

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

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

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

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

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

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

that respondents indicated was us ed to produce heat and power.
* Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007b. “2006 Energy Consumption by Manufacturers—Data Tables.” Table 3.1. Washin gton, DC: DOE. <http://www.eia.doe.gov/emeu/mecs/ mecs2006/2006tables.html>. [Source for 2006 numbers] U.S. Department of Energy, Energy Information Administration. 2007. “2002 Energy Consumption by Manufacturers—Data Tables.” Tables 3.2 and N3.2. Washington, DC: DOE. <http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>.2-58 Figure 2-33. Electric Power Use Trends in Chemical Manufacturing (NAICS 325): 1997– 2005
Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G325.S. <http://www.federalreserve.gov/datadownload/>; (November 17, 2009).

2.4.3 Firm and Market Characteristics This remaining subsection describes geographi c, production, and market data. These data provide the basis for further an alysis, including regul atory flexibility analyses, and give a complete picture of the recent histori cal trends of production and pricing.

2.4.3.1 Location In 2002, California had the most chemical manufacturing establishments in the United States, followed by Texas and Ne w Jersey (Figure 2-34). The composition of establishments in these states differs among th e different industry groups. Despite the fact that each group employed an approximately equal share of people in 2002, 54% of the total establishments were other chemicals establishmen ts, and only 13% were pharm aceutical establishments. 80 85 90 95 100 105 110Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(Jan‐97=100)

Manufacturing Chemical Manufacturing2-59 Table 2-35. Demand by Sector: Chemical Ma nufacturing (NAICS 325) ($2007 millions)

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

Resin, rubber, and artificial fibers manufacturing 3252 $28,249 $0 $1,076 $29,325

Ambulatory health care services 6210 $2,716 $22,900 $934 $26,550 General state and local government services S007 $7,150 $10,586 $8,807 $26,543

Hospitals 6220 $2,936 $15,390 $394 $18,720 Other chemical product and preparation manufacturing 3259 $8,021 $0 $2,680 $10,701

Textile mills 3130 $9,568 $0 $930 $10,498 Soap, cleaning compound, and toiletry manufacturing 3256 $3,886 $0 $6,289 $10,176

Total intermediate use T001 $212,996 $83,279 $82,107 $378,382 Personal consumption expenditures F010 $4,449 $123,746 $55,882 $184,077 Exports of goods and services F040 $47,121 $15,683 $13,136 $75,940 Imports of goods and services F050

− $38,732

− $67,950

− $10,906

− $117,588 Total final uses (GDP) T004 $15,733 $73,485 $58,023 $147,241 Total commodity output T007 $228,729 $156,765 $140,129 $525,623
Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.2-60 Figure 2-34. Establishment Co ncentration in Chemical Ma nufacturing (NAICS 325): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008). 2.4.3.2 Production Capacity and Utilization Capacity utilization of the ch emical manufacturing industry ha s been broadly in line with the manufacturing sector (Figure 2-35). In th e second half of 2005, the chemical manufacturing industry’s capacity utilization fell dramatically because of the multiple hurricanes affecting the Gulf Coast states. The impact of the economic dow nturn in 2001 can be s een in the capacity utilization of both manufacturi ng and chemical manufacturing. 2.4.3.3 Employment The geographic distribution of employment in chemical manufacturing differs largely among the different groups. In California, 52% of the chemical manufacturing employment comes from the pharmaceutical industry, while 60% of the chemical manufacturing employment in the Gulf Coast states comes from bulk chemicals manufacturing (Figure 2-36). 2.4.3.4 Plants and Capacity Production capacity in chemical manufacturi ng has grown 33% since 1997. This growth, however, is 9% less than the growth rate for the manuf acturing industry as a whole (Figure 2-37). Establishments
Fewer than 100
100 - 249
250 - 474
475 - 750
More than 7502-61 Figure 2-35. Capacity Utilization Trends in Chemical Manufacturing (NAICS 325)
Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” Series ID: G17/CAPUTL/CAPUTL.GM F.S & G17/CAPUTL/CAPUTL.G325.S. <http://www.federalreserve.gov/datadownload/>.

Figure 2-36. Employment Concentration in Ch emical Manufacturing (NAICS 325): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008). 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PercentageCapacityUtilization

Manufacturing Chemical Manufacturing

Employment Fewer than 5,000 5,000 - 13,999 14,000 - 29,999 30,000 - 40,000 More than 40,0002-62 Figure 2-37. Capacity Trends in Ch emical Manufacturing (NAICS 325)
Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial Capacity.” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G325.S. <http://www.federalreserve.gov/datadownload/>.
2.4.3.5 Firm Characteristics In 2007, the top six companies by chemical sale s had greater than $10 billion in sales. Together, their sales are greater than the next 44 highest chemi cal companies combined. These, however, are global companies, with a large portion of both sa les and production coming from operations outside of the United States (Tab le 2-36). The largest chemical manufacturing company, Dow Chemicals, has 108 out of 150 manuf acturing sites located outside of the United States (Dow Chemical Company, 2008).
In 2007, 58%of U.S. chemical manufacturi ng corporations generated net income. Including those with and without net income, chemical manufacture rs had an average before-tax profit margin of 10.24%. Profitability is highest for pharmaceutical and medicine corporations (Table 2-37). 100 110 120 130 140 150Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Chemical Manufacturing2-63 Table 2-36. Top Chemical Producers: 2007

Chemical Sales

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

a Percentage of sales in the United States calcu lated from total sales, not chemical sales.
b Percentage of sales in the United States is actually percentage of sales in North America. Sources: O’Reilly, R. 2008. “Chemicals.” Standard and Poor’s Industry Surveys . 176(28).

Table 2-37. 2007 Corporate Income and Profitability (NAICS 325)

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

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

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

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

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

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

Chemical manufacturing 9,564 5,512 $912,353,710 $808,897,810 10.24% 7.89%
Source: Internal Revenue Service, U.S. Department of Treasury. 2008. “Corporation Source Book: Data File 2007.” <http://www.irs.gov/taxs tats/article/0,,id=167415,00.html>; (January, 15, 2010).2-64 2.4.3.6 Size Distribution The primary criterion for categorizing a busine ss as small is number of employees, using definitions by the SBA for regulatory flexibility analyses. The data describing size standards are provided in Table 2-38. In 2002, en terprises with fewer than 500 employees accounted for 27% of employment and 15% of receipts with in the chemical manufacturing industry).

2.4.3.7 Domestic Production In the late 1990s, overall manufacturing pr oduction was growing much faster than the chemical manufacturing component (Figure 2-38 ). Following the recession of 2001, however, the components have moved broadly in line with one another, except for the drop in chemical manufacturing production cause d by the hurricane season of 2005.

2.4.3.8 International Trade In the year 2000, the United States moved from having a trade surplus to a trade deficit in chemical manufacturing products (F igure 2-39). This change occurr ed because the trade deficit in pharmaceutical manufacturing, currently at $35 billion, overwhelmed the trade surplus of bulk chemicals and other chemical manufacturi ng combined, currently at $22 billion.

2.4.3.9 Market Prices Prices of goods in chemical manufacturing ha ve accelerated rapidly in the last 2 years, having outpaced overall manufacturing since 200 2 (Figure 2-40). Much of this recent acceleration seen in the industry PPI is due to the bulk chemicals segment, largely reflecting the rapid increase in fertilizer prices.2-65 Table 2-38. Small Business Size Standards: Chemical Manufactu ring (NAICS 325)
NAICS Description Employees 325110 Petrochemical Manufacturing 1,000 325120 Industrial Gas Manufacturing 1,000 325131 Inorganic Dye and Pigment Manufacturing 1,000 325132 Synthetic Organic Dye a nd Pigment Manufacturing 750
325181 Alkalies and Chlorine Manufacturing 1,000 325182 Carbon Black Manufacturing 500 325188 All Other Basic Inorganic Chemical Manufacturing 1,000 325191 Gum and Wood Chemical Manufacturing 500 325192 Cyclic Crude and Intermediate Manufacturing 750 325193 Ethyl Alcohol Manufacturing 1,000 325199 All Other Basic Organic Ch emical Manufacturing 1,000 325211 Plastics Material and Resin Manufacturing 750 325212 Synthetic Rubber Manufacturing 1,000 325221 Cellulosic Organic Fiber Manufacturing 1,000 325222 Noncellulosic Organic Fiber Manufacturing 1,000 325311 Nitrogenous Fertilizer Manufacturing 1,000 325312 Phosphatic Fertilizer Manufacturing 500 325314 Fertilizer (Mixing Only) Manufacturing 500 325320 Pesticide and Other Agricultu ral Chemical Manufacturing 500 325411 Medicinal and Botanical Manufacturing 750 325412 Pharmaceutical Preparation Manufacturing 750 325413 In-Vitro Diagnostic Substance Manufacturing 500 325414 Biological Product (except Diagnostic) Manufacturing 500 325510 Paint and Coating Manufacturing 500 325520 Adhesive Manufacturing 500 325611 Soap and Other Detergent Manufacturing 750 325612 Polish and Other Sanitation Good Manufacturing 500 325613 Surface Active Agent Manufacturing 500 325620 Toilet Preparation Manufacturing 500 325910 Printing Ink Manufacturing 500 325920 Explosives Manufacturing 750 325991 Custom Compounding of Purchased Resins 500 325992 Photographic Film, Paper, Plat e and Chemical Manufacturing 500 325998 All Other Miscellaneous Chem ical Product and Preparation Manufacturing 500

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

Enterprises with

Variable Total 1 to 20 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees Firms 9,341 5,413 1,974 790 95 56 71 Establishments 13,096 5,433 2,208 1,352 250 185 276 Employment 827,430 34,838 78,090 113,326 28,025 18,119 28,338 Receipts ($millions) $468,211 $9,631 $21,394 $39,111 $12,217 $7,324 $14,762 Receipts/firm ($thousands) $50,124 $1, 779 $10,838 $49,507 $128,603 $130,779 $207,913 Receipts/establishment ($thousands) $35,752 $1,773 $9,689 $28,928 $48,869 $39,587 $53,485 Receipts/employment ($) $565,862 $276,464 $273,971 $345,117 $435,942 $404,195 $520,920

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

Figure 2-38. Industrial Production Trends in Chemical Manufacturing (NAICS 325)
Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Production.” Series ID: G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.S & G17/IP_MAJOR_INDUSTRY_ GROUPS/IP.G325.S. <http ://www.federalreserve. gov/datadownload/>. 100 110 120 130 140Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Chemical Manufacturing2-67 Figure 2-39. International Trade Trends in Chemical Manufacturing (NAICS 325) Source: U.S. International Trade Commission. 2008a. “U.S. Domestic Exports” & “U.S. Imports for Consumption.” <http://dataweb.us itc.gov/scripts/user_set.asp>.

Figure 2-40. Producer Price Trends in Chemical Manufacturing (NAICS 325) Source: U.S. Bureau of Labor Statistics (BLS). 200 9. Producer Price Index. Series ID: PCU325—325— &PCUOMFG—OMFG—. <http://www .bls.gov/ppi/home.htm>. 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000

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

Imports Exports

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

Manufacturing Chemical Manufacturing3-1 SECTION 3 ENGINEERING COST ANALYSIS
We provide an overview of the engineering cost analysis used to estimate the additional private expenditures industry may make in order to comply with the rule. A detailed discussion of the methodology used to estimate cost im pacts is presented in Appendices C and D. 3.1 Major Sources To estimate the national cost impacts of th e proposed rule for existing sources, EPA developed average baseline emission factors for each fuel type/control device combination based on the emission data obtained and contained in the Boiler MACT emission database. If a unit reported emission data, we assigned its unit-specific emission data as its baseline emissions. For units that did not report emission data, we assi gned 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 co mpared each unit’s baseline emission factors to the proposed MACT floor emission limit to determine if control devices were needed to meet the emission limits. The control analysis consider ed fabric filters, car bon bed adsorbers, and activated carbon injection to be the primary cont rol devices for mercury control, electrostatic precipitators for units meeting mercury limits but requiring additional control to meet the PM limits, wet scrubbers to meet the HCl limits, tune-ups, replacement burners, and combustion controls for CO and organic HAP control, and carbon injection for dioxi n/furan control. We identified where one control device could achieve reductions in multiple pollutants, for example a fabric filter was expected to achieve bot h PM and mercury control in order to avoid overestimating the costs. We also included co sts for testing and monitoring requirements contained in the proposed rule. The resulting tota l national cost impact of the proposed rule is 10.0 billion dollars in capital expend itures and 3.2 billion dollars per year in total annual costs. Considering estimated fuel savings resulting from work practice standards and combustion controls, the total annualized costs are reduced to 2.9 billion. The total capital and annual costs include costs for control devices, work practices, testing and monitoring. Table 3-1 of this shows the capital and annual cost impacts for each subc ategory. Costs include testing and monitoring costs, but not recordkeep ing and reporting costs.3-2 Table 3-1. Summary of Capital and Annual Costs for New and Existing Major Sources

Source Subcategory Estimated/ Projected No. of Affected Units Capital Costs (million) Annualized Cost (million per yr) Existing Units Coal units 578 $4,500 $1,600 Biomass units 420 $2,000 $600 Liquid units 826 $1,400 $500 Gas (NG/RG) units 11,532 $60 $30 Gas (other) units 199 $1,600 $500 Energy Audit ALL 26
New Units Coal units 0 0 0
Biomass units 0 0 0 Liquid units 11 12 6.1 Gas (NG/RG) units 33 0.2 0.01 Gas (other) units 2 5.5 1.7

Using Department of Energy pr ojections on fuel expenditures, the number of additional boilers that could be potentially constructed was estimated. The resulting total national cost impact of the proposed rule in the 3rd year is 17 million dollars in capital expenditures and 6.2 million dollars per year in total annual costs, when considering a 1 percent fuel savings.
A discussion of the methodology used to estimate cost impacts is presented in “Methodology and Results of Estimating the Co st of Complying with the Industrial, Commercial, and Institutional Bo iler and Process Heater NE SHAP (2010)” in the Docket.

3.2 Area Sources To estimate the national cost impacts of th e proposed rule for existing sources, EPA developed several model boilers and determined th e cost of control for these model boilers. The EPA assigned a model boiler to each existing unit based on the fuel, size, and current controls. The analysis considered all ai r pollution control equipment curre ntly in operation at existing boilers. Model costs were then assigned to all existing units that could not otherwise meet the proposed standards. The resulting total national cost impact of the proposed rule for existing units is $279 million dollars in total annualized cost s after considering fuel savings from efficiency improvements. The total annualized co sts for installing controls, conducting an annual3-3 tune-up and an energy assessment, and implemen ting testing and monitoring requirements, is $696 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 (million per yr) a Existing Units Coal 3,710 $160
Biomass 10,958 $47
Oil 168,003 $436 Facility Energy Audit All $52
New Units b Coal 155 $54
Biomass 200 $13
Oil 6,424 $244

a TAC does not include fuel savings from improving combustion efficiency.
b Impacts for new units assume the number of units online in the first 3 years of this rule (2010 to 2013).
EPA also estimated the number of additional boilers that could be potentially constructed. The resulting total national cost impact of the proposed rule on new sources by the 3rd year, 2013, is $311 million dollars in to tal annualized costs. When accounting for a 1 percent fuel savings resulting from improvements to combusti on efficiency, the total national cost impact on new sources is $260 million.4-1 SECTION 4 ECONOMIC IMPACT ANALYSIS
EPA prepares an RIA to provide decision make rs with a measure of the social costs of using resources to comply with a program (EPA , 2000). The social costs can then be compared with estimated social benefits (as presente d in Section 6). As noted in EPA’s (2000) Guidelines for Preparing Economic Analyses , several tools are available to estimate social costs and range from simple direct compliance cost methods to the development of a more complex market analysis that estimates market changes (e.g., price and consumption) and economic welfare changes (e.g., changes in cons umer and producer surplus).
The Office of Air Quality Planning and Sta ndards (OAQPS) adopted a standard market analysis as described in the Office’s resour ce manual (EPA, 1999). The approach uses a singleperiod multimarket partial equi librium model to compare pre- policy market baselines with expected post-policy market outc omes. The analysis’ time horizon is the intermediate run; some production factors are fixed and some are variable and is distinguished from the very short run where all factors are fixed and producers cannot adjust inputs or outputs (EPA, 1999, 5-6). The intermediate time horizon allows us to capture important transitory stakeholder outcomes. Key measures in this analysis include industry -level changes in price levels, production and consumption, jobs, international trade, and soci al costs (changes in producer and consumer surplus). 4.1 Partial Equilibrium Anal ysis (Multiple Markets) The partial equilibrium analysis develops a market model that simulates how stakeholders (consumers and industries) might respond to the additional regulatory program costs. In this section, we provide an overview of the economic model. Appendix A provides additional details on the behavioral assumptions, data , parameters, and model equations.

4.1.1 Overview Although several tools are available to estima te social costs, cu rrent EPA guidelines suggest that multimarket models “...are best used when potential economic impacts and equity effects on related markets might be considerab le” and modeling using a computable general equilibrium model is not available or pr actical (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 policy’s near-term or transition costs (EPA, 1999). Th e multimarket model contains the following features:4-2  Industry sectors and benchmark data set

– 100 industry sectors

– a single benchmark year (2010)

– estimates of industry employment

 Economic behavior

– industries respond to regulatory co sts by changing production rates

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

– customers respond to these price increases and consumption falls

 Model scope

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

– production adjustments influence employment levels

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

 Model time horizon (“short r un”) for a single period (2013)

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

– firms cannot alter input mixes; there is no substitution among production inputs (capital, labor, energy intermediates, a nd other intermediate goods and services)

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

– investment and government expenditures are fixed 4.1.2 Economic Impact Analysis Results

4.1.2.1 Market-Level Results Market-level impacts include price and quant ity adjustments includ ing the changes in international trade (Figure 4- 1). Under the proposed major source NESHAP, the Agency’s economic model suggests the averag e national prices for industrial sectors could be 0.01% higher with the NESHAP, while average annual domes tic production may fall by about 0.01%. Because of higher domestic prices, imports rise by 0.01% pe r year. Market-level effects for the proposed area source NESHAP are smaller when compared to the proposed major source rule; average4-3 Figure 4-1. Market-Level Changes by Source and Option

price, production, and import chan ges are less than 0.01%. Industrial sector details are provided in Appendix B. 4.1.2.2 Social Cost Estimates Major Source Rule In the near term, the Agency’s economic mode l suggests that industries are able to pass on $0.8 billion (2008$) of the proposed area source rule’s costs to U.S. households in the form of higher prices (Table 4-1). Existi ng U.S. industries’ surplus falls by $2.5 billion and the net loss in aggregate is $3.3 billion. As U.S. prices rise, other countries ar e affected through international trade relationships. The price of goods produced in the United States increase slightly and domestic production declines, replaced to a cert ain degree by imports; the model estimates a net gain of $0.1 billion to foreign companies. After accounting for internatio nal trade effects, the Agency’s economic model projects the net surplus loss associated with the proposed rule is $3.2 billion. As shown in Figure 4-2, the surplus losses are concentrated in other services (20 percent); lumber, paper, and printing (19 perc ent); and chemicals (16 percent) ‐ 0.10% ‐ 0.09% ‐ 0.08% ‐ 0.07% ‐ 0.06% ‐ 0.05% ‐ 0.04% ‐ 0.03% ‐ 0.02% ‐ 0.01% 0.00% 0.01% 0.02% 0.03% 0.04% 0.05% 0.06% 0.07% 0.08% 0.09% 0.10% Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N Major Sources Area Sources Major & Area SourcesPercentageChangefromBaseline

Prices Production Imports Consumption Exports4-4 Table 4-1. Distribution of Social Co sts Major Sources (billion, 2008$): 2013

Approach Primary Option Option 1E and Option 1N Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus

− $0.8

− $3.8 Change in U.S. producer surplus

− $2.5

− $8.9 Change in U.S. surplus

− $3.3

− $12.7 Net change in rest of world surplus $0.1 $0.5 Net change in total surplus

− $3.2

− $12.2 Direct Compliance Costs Method

Total annualized costs, new major sources (not modeled) Less than

− $0.1 Less than

− $0.1 Fuel savings, existing major sources (not modeled) $0.4 $0.3 Fuel savings, new major sources (not m odeled) Less than $0.1 Less than $0.1 Change in Total Surplus

− $2.9

− $11.9

Figure 4-2. Distribution of Total Surpl us Changes by Sector: Major Sources 20.4%

19.1%

16.0%

11.8%

9.7%

5.6%

4.1%

3.3%

2.3%

1.4%

1.5%

1.3%

1.2%

1.1%

0.7%

0.5% 24.8%

9.9%

13.4%

17.8%

6.0%

4.5%

3.0%

7.2%

1.5%

1.8%

1.4%

4.7%

0.8%

1.6%

0.9%

0.6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Other Services
Lumber, paper, and printing

Chemicals
Energy Industries
Food, beverages, and textiles
Primary Metals Nonmanufacturing
Transportation Equipment Machinery and Equipment

Plastics and Rubber

Other
Transportation Services

Wholesale and Retail Trade

Fabricated Metals

Electronic Equipment

Nonmetallic Minerals Primary Option: $3.2 Billion Surplus Change Option 1E and Option 1N: $12.2 billion Surplus Change4-5 The Agency also considered other elements of the engineering cost analysis that could not be modeled within the multimarket model (e.g., fu el savings benefits [existing and new major sources] and total annualized compliance costs [n ew major sources]). The net effect of the adjustments is a total surplus loss estimate of $2.9 billion.

4.1.2.3 Social Cost Estimates Area Source Rule In the near term, the Agency’s economic mode l suggests that industries are able to pass on $0.3 billion (2008$) of the proposed area source rule’s costs to U.S. households in the form of higher prices (Table 4-2). Existing U.S. industr ies’ surplus falls by $0.3 billion and the net loss for U.S. stakeholders is $0.6 billion. As U.S. prices rise, other countri es are affected through international trade relationships. Households that buy U.S. exports pay higher prices and purchase fewer U.S. produced goods. Other countries that that sell goods to the United States benefit; the model estimates a net rest of the world gain of less than $0.1 billion. After accounting for international trade effects, the Agency’s economic model projects the net surplus (consumer and producer) loss associated with the proposed rule is $0.6 billion. As shown in Figure 4-3, the surplus losses are concentrated in the other services (86 percent ).

Table 4-2. Distribution of Social Co sts Area Sources (billion, 2008$): 2013

Approach Primary Option Option 1E and Option 1N Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus

− $0.3

− $16.5 Change in U.S. producer surplus

− $0.3

− $16.5 Change in U.S. surplus

− $0.6

− $33.1 Net change in rest of world surplus Less than $0.1

− $0.1 Net change in total surplus

− $0.6

− $33.2 Direct Compliance Costs Method

Total annualized costs, unknown existing area sources (not modeled) Less than $0.1

− $0.3 Total annualized costs, new area sources (not modeled)

− $0.3

− $2.3 Fuel savings, existing area sources (not modeled) $0.4 $0.4 Fuel savings, new area sources (not m odeled) Less than $0.1 Less than $0.1 Change in Total Surplus

− $0.5

− $35.34-6 Figure 4-3. Distribution of Total Surplus Changes by Sector: Area Sources

The Agency also considered other elements of the engineering cost analysis that could not be modeled within the multimarket model (e.g., fu el-savings benefits [existing and new area sources] and total annualized compliance costs [unknown existing and new area sources]). The net effect of the adjustments is a to tal surplus loss estim ate of $0.5 billion. 4.1.2.4 Job Effects Precise job effect estimates cannot be estimated with certainty. Morg enstern et al. (2002) identify three economic mechanisms by which pollution abatement activities can indirectly influence jobs:

 higher production costs raise market prices , higher prices reduce consumption, and employment within an indus try falls (“demand effect”);

 pollution abatement activities require additi onal labor services to produce the same level of output (“cost effect”); and 86.2%

5.2%

2.8%

1.9%

2.6%

0.2%

0.4%

0.2%

0.1%

0.1%

0.1%

0.1%

0.1%

0.0%

0.0%

0.0% 85.0%

6.1%

2.9%

1.7%

2.8%

0.2%

0.5%

0.2%

0.1%

0.1%

0.1%

0.1%

0.1%

0.0%

0.0%

0.0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Other Services
Food, beverages, and textiles Nonmanufacturing
Lumber, paper, and printing

Wholesale and Retail Trade

Other
Transportation Services

Chemicals
Energy Industries

Primary Metals

Plastics and Rubber

Nonmetallic Minerals

Fabricated Metals

Electronic Equipmen t Machinery and Equipment
Transportation Equipment Primary option: $0.6 billion Surplus Change Option 1E and Option 1N: $33.2 Billion Surplus Change4-7  post regulation production technologies may be more or less labor intensive (i.e., more/less labor is required per dollar of output) (“factor -shift effect”).
Several empirical studies, including Morg enstern et al. (2002), suggest the net employment decline is zero or economically small (e.g., Cole and Elliot, 2007; Berman and Bui, 2001). However, others show the question has not been resolved in the literature (Henderson, 1996; Greenstone, 2002). Morgenstern et al. use a six-year panel (U.S. Census data for plantlevel prices, inputs (including labo r), outputs, and environmental ex penditures) to econometrically estimate the production technologies and industry-level demand elas ticities. Their identification strategy leverages repeat plant-le vel observations over time and us es plant-level and year fixed effects (e.g., plant and time du mmy variables). After estimating their model, Morgenstern show and compute the change in employment associ ated with an additional $1 million ($1987) in environmental spending. Their estimates cover f our manufacturing industr ies (pulp and paper, plastics, petroleum, and steel) and Morgenstern, et al . present results separate ly for the cost, factor shift, and demand effects, as well as the net eff ect. They also estimate and report an industry-wide average parameter that combines the four indus try-wide estimates and weighting them by each industry’s share of envi ronmental expenditures.
EPA has most often estimated employment cha nges associated with plant closures due to environmental regulation or changes in output for the regulated i ndustry (EPA, 1999a; EPA, 2000). This analysis goes beyond what EPA has typi cally done in two ways. First, because the multimarket model provides estimates for changes in output for sectors not directly regulated, we were able to estimate a more co mprehensive “demand effect.” Sec ondly, parameters estimated in the Morgenstern paper were used to estimate a ll three effects (“demand,” “cost,” and “factor shift”). This transfer of results from the Morg enstern study is uncertain but avoids ignoring the “cost effect” and the “factor-shift effect.”
We calculated “demand effect” employment ch anges by assuming that the number of jobs declines proportionally with multi-market model’ s simulated output changes. These results were calculated for all sectors in the EPA m odel that show a change in output.

We also calculated a similar “demand effect” estimate that used the Morgenstern paper. EPA selected this paper because the parameter es timates (expressed in jobs per million ($1987) of environmental compliance expenditures) provide a transparent and tractable way to transfer estimates for an employment effects analysis. Similar estimates were not available from other studies. To do this, we multiplied the point estimate for the total demand effect (

− 3.56 jobs per million ($1987) of environmental compliance expenditure) by the total environmental compliance4-8 expenditures used in the partial equilibrium model. For example, the jobs effect estimate for the Major Source Rule is estimated to be 7,000 jobs (

− 3.56× $3.2 billion × 0.60). 1 Demand effect results are provided in Figure 4-4.

Figure 4-4. Job Losses/Gains Associat ed with the Proposed Rules: 2013

We also present the results of using the Mo rgenstern paper to estimate employment “cost” and “factor-shift” effects. Although using the Morg enstern parameters to estimate these “cost” and “factor-shift” employment ch anges is uncertain, it is helpful to compare the potential job gains from these effects to the job losses associ ated with the “demand” effect. Figure 4-4 shows that using the Morgenstern point es timates of parameters to estimat e the “cost” and “factor shift” employment gains may be greater than the employ ment losses using either of the two ways of estimating “demand” employment losses. The 95% c onfidence intervals are shown for all of the

1 Since Morgenstern’s analysis reports environmental expenditures in $1987, we make an inflation adjustment the engineering cost analysis using GDP implicit price deflator (64.76/108.48) = 0.60) ‐ 5

‐ 19 ‐ 1

‐ 62 ‐ 7

‐ 81 ‐ 7

‐ 26 ‐ 1

‐ 71 ‐ 8

‐ 96 5 18 1 48

6 66

5 19 1 53

6 73

3 11 1 31
4 42

‐ 250 ‐ 225 ‐ 200 ‐ 175 ‐ 150 ‐ 125 ‐ 100 ‐ 75 ‐ 50 ‐ 25

0 25 50 75 100 125 150 175 200 225 250 Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N Major Sources Area Sources Major & Area Sources1,000JobsBase=144,100

Demand Effect: Partial Equilbrium (multiple markets) Demand Effect: Estimate derived from Morgenstern, et al. Cost Effect: Estimate derived from Morgenstern, et al. Factor Shift Effect: Estimate derived from Morgenstern, et al. Net Effect: Estimate derived from Morgenstern, et al.4-9 estimates based on the Morgenstern parameters. As shown, at the 95% confidence level, we cannot be certain if net employment changes are positive or negative.
Although the Morgenstern paper provides add itional information about the potential job effects of environmental protection programs, ther e are several qualificatio ns EPA considered as part of the analysis. First, EPA has used the weighted average parameter estimates for a narrow set of manufacturing industries (p ulp and paper, plastics, petroleum, and steel). Absent other dataand estimates, this approach seems reasonable and the estimates come from a respected peerreviewed source. However, EPA acknowledges th e proposed rule covers a broader set of industries not considered in original empirical study. By transferring th e estimates to other industrial sectors, we make the assumption that estimates are similar in size. In addition, EPA assumes also that Morgenstern et al.’s estima tes derived from the 1979- 1991 still applicable for policy taking place in 2013, almost 20 years late r. Second, the multi-market model only considers near term employment effects in a U.S. ec onomy where production technol ogies are fixed. As a result, the modeling system places more emphasis on the short term “demand effect” whereas the Morgenstern paper emphasizes other important long term responses. For example, positive job gains associated with “factor sh ift effects” are more plausible when production choices become more flexible over time and industries can substi tute labor for other prod uction inputs. Third, the Morgenstern paper estimates rely on sector demand elasticities that are different from the demand elasticity parameters used in the multi-market model. As a result, the demand effects are not directly comparable with the demand effects estimated by the multi-market model. Fourth, Morgenstern identifies the industry average as ec onomically and statistically insignificant effect (i.e., the point estimates are small, measured im precisely, and not distingu ishable from zero.) EPA acknowledges this fact and has reported the 95 pe rcent confidence interval s in Figure 4-4. Fifth, Morgenstern’s methodology assumes large plants bear most of the regulatory costs. By transferring the estimates, EPA assumes a similar distribution of regulatory costs by plant size and that the regulatory burden does not disp roportionately fall on smaller plants.5-1 SECTION 5 SMALL ENTITY ANALYSES
The RFA as amended by SBREFA generally requ ires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or an y other statute, unless the agency certifies that the rule will not have a significant economic imp act on a substantial number of small entities (SISNOSE). Small entities include small businesse s, small governmental jurisdic tions, and small not-for-profit enterprises. EPA assessed the poten tial small entity economic impa cts using a screening analysis. After reviewing screening analysis results, EPA has determined it cannot certify that the proposed rules will not have a SISNOSE and presumes th at both proposed rules are not eligible for certification under the RFA as amended by SBREFA. As a result, EPA has prepared and included an IRFA that discusses alternat ive regulatory or policy options that minimize the rules’ small entity impacts. It includes information about key results from the SBAR panel. 5.1 Small Entity Screening Analysis

5.1.1 Small Businesses The sectors covered by the rule were identified through lists of small entities at major and area sources provided by the engine ering analysis. Table 5-1 provides a list of the sectors affected (3-digit NAICS) and the range of SBA size definitions. 5.1.1.1 Representative Small Business Analysis Us ing Census Statistics of U.S. Businesses For each 3-digit NAICS code, the SUSB provi des national information on the distribution of economic variables by industr y and enterprise size (U.S. Ce nsus, 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 analyses. 1 Statistics include the total number of establishments and receipts for all entities within an industry; however, only a su bset of entities will be covered by the proposed rule. SUSB also provides statistics by enterprise employment and receipt size.

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

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

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

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

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

primarily engaged in the generation, transmission, and/or distribution of electric energy for sale and its total electric output for the preceding fiscal year did not exceed 4 million megawatt hours.5-3  Receipts : Receipts (net of taxes) are define d as the revenue for goods produced, distributed, or services provided, in cluding revenue earned from premiums, commissions and fees, rents, interest, divi dends, and royalties. Receipts exclude all revenue collected for local, state, and federal taxes.

 Enterprise : An enterprise is a business organi zation consisting of one or more domestic establishments that were specifi ed under common ownership or control. The enterprise and the establishment are the same for single-establishment firms. Each multi-establishment company forms one enterprise—the enterprise employment and annual payroll are summed from the asso ciated establishmen ts. Enterprise size designations are determined by the total empl oyment of all associated establishments.
Because the SBA’s business size definitions (SBA, 2008) apply to an establishment’s “ultimate parent company,” we assumed in this an alysis that the “enterpri se” definition above is consistent with the concept of ultimate parent company that is typically used for SBREFA screening analyses, and the terms are used interchangeably.
The analysis generated a set of establishment sales tests (represented as cost-to-receipt ratios) for NAICS codes associated with sector s listed in Table 5-2. Although the appropriate SBA size definition should be applied at the pa rent company (enterpris e) level, we can only compute and compare ratios for a model establishm ent owned by an enterpri se within an SUSB size range (employment or receipts). Using the SUSB size range helps us account for receipt differences between establishments owned by large and small enterprises and also allows us to consider the variation in small business definition s across affected industries. Using establishment receipts is also a conservativ e approach, because an establis hment’s parent company (the “enterprise”) may have other economic resources th at could be used to cover the costs of the regulatory program.
For each representative establishment in the SU SB data, we developed a range of facilitylevel cost numerators based on the engineering cost analysis. For major sources, we used the maximum and minimum small entity facility-lev el costs observed within each 3-digit NAICS code. For area sources, we were limited to two re presentative small entity facility-level costs (approximately $26,000,000 to $1.4 million) 1 . Using these cost data and the Census estimates of average establishment receipts, a substantial number of SUSB NAI CS/enterprise categories have ratios over 3% (Figure 5-1).

1 Prior to computing the cost-to-receipt ratios, we adjusted the engineering compliance co sts to reflect 2002 dollars using the implicit price deflators for gross domestic product (GDP). The values used are 2002 = 92.118 and 2008 = 108.483 (U.S. BEA, 2010).5-4 Figure 5-1. Share of NAICS/Enterprise Empl oyment Categories (<500 employees) with Sales Tests Exceeding 3%

5.1.1.2 Additional Small Business Analysis Using Sample of Small Businesses Identified in Combustion Facility Survey Next, we performed a more detailed anal ysis that compares the Census SUSB representative small entity results with a firm-sp ecific sample of major small private enterprises. In this approach, we identified a sample of survey facility names listed as small, traced the ultimate parent company name to verify the facility was owned by a small business, and collected the most recent parent company sales and employ ment figures. As Table 5-2 shows, the average cost-to-sales ratios for small major source comp anies are above 5%. The median ratios are below one percent for the primary option and above 5 percent for Option 1E. 63% 98%

25% 100%

17% 78%

8% 65%

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

Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N

Major Area Upper Bound
Lower Bound5-5 Table 5-2. Major Sources: Sales Tests Usin g Small Companies Identified in the Combustion Survey

Sample Statistic Primary Option Option 1E
Mean 4.9% 15.6%
Median 0.4% 5.3%
Maximum 72.9% 100.0%
Minimum <0.1% <0.1% Ultimate parent company observations 50 50 Ultimate parent companies with sale tests exceeding 3% 14 30

5.1.2 Small Governmental Jurisdictions and Not-for--Profit Enterprises In addition to the private sector, this rule al so covers sectors that include entities owned by small and large governments and not-for-profit en terprises. Given the uncertainty and data limitations associated with identifying and appropri ately classifying these entities, we computed a “revenue” test, where the annuali zed compliance cost is a percen tage of annual revenues (U.S. Census, 2005a and b).
Compliance costs were estimated for model facilities for major and area sources for multiple options. A summary of the compliance costs used for the small entity analysis follows:
Major Sources:

 Primary option : $3.0 million (median cost small public facility)

 Option 1E and 1N : $4.5 million (median cost small public facility)

Area Sources:

 Primary option:

– Other public: $3.0 million

– Hospital: $11,300

– Schools: $4,500

– Churches: $2,2005-6  Option 1E and Option 1N:

– Other public: $2.9 million

– Hospital: $141,000

– Schools: $346,000

– Churches: $45,000 From the 2002 Census (in 2008 dollars), th e average revenue for small governments (counties and municipalities with populations fewer than 10,000) are $3 million per entity, and the average revenue for local governments with populations fewer than 50,000 is $7 million per entity. Churches are assumed to have an operation budget of $150,000.
The analysis shows that small major source pub lic facilities would have cost-to-revenue ratios that exceed 10% under the both regulatory options. The following small area source facilities would have cost-to-reve nue ratios exceeding 1 percent:

 Primary option: other public (ratio > 10 percent) and chur ches (ratio = 1.5 percent)

 Option 1E and Option 1N: othe r public (ratio > 10 percent), hospitals (ratio > 3 percent), schools (ratio > 10 percent), and church es (ratio >10 percent). 5.2 Initial Regulatory Flexibility Analysis (IFRA) An IRFA illustrates how EPA considered the pr oposed rule’s small entity effects before a rule is finalized and provides information abou t how the objectives of the rule were achieved while minimizing significant economic impacts on small entities. We provide a summary of IRFA elements; the preambles for each rule provide additional details. 5.2.1 Reasons Why Action Is Being Considered In 2004, EPA promulgated national emi ssion NESHAP for new and existing industrial/commercial/institutional boilers and process heaters. However, in 2007, the U.S. Court of Appeals for the District of Columbia Ci rcuit (DC Circuit) vacated the NESHAP for industrial/commercial/institutional boilers and process heaters. The proposed action provides EPA’s proposed rule in response to the court’s vacatur. Under authority of section 112 of the Clean Air Act (CAA), EPA is also proposing a NESHAP for two area source categories: industrial boilers and institutional/co mmercial boilers. 5.2.2 Statement of Objectives and Legal Basis of Proposed Rules The proposed rules would prot ect air quality and promote public health by reducing emissions of the HAPs. Section 112(d) of the CAA requires EPA to establish NESHAPs for both5-7 major and area sources of HAPs that are listed for regulation under CAA section 112(c). A major source emits or has the potential to emit 10 tons per year (tpy) or more of any single HAP or 25 tpy or more of any combination of HAP. An area s ource is a stationary source that is not a major

source. 5.2.3 Description and Estimate of the Number of Small Entities The sectors covered by the rule were identified through lists of small entities at major and area sources included in the survey. A listing of the sectors affected (3-digit NAICS) and the range of SBA size definitions are provided in Table 5-1. EPA believes a substantial number of small entities will be af fected by the proposed rules, but data limitations preclude us from providing precise estimates of the nu mber of small entities affected. 5.2.4 Description and Compliance Costs 5.2.4.1 Major Sources A discussion of the methodology used to estimate cost impacts is presented in “Methodology and Results of Estima ting the Cost of Complying with the Industrial, Commercial, and Institutional Boiler and Proce ss Heater NESHAP” in the Docket and Section 4 of the RIA.

5.2.4.2 Area Sources A detailed discussion of the methodology used to estimate cost impacts is presented in the memorandum “Estimation of Impacts for Industria l, Commercial, and Institutional Boilers Area Source NESHAP” in the Docket and Section 4 of the RIA. 5.2.5 Description of Federal Rules that May Overlap or Conflict with Proposed Rules The proposed major source rule regulates so urce categories covering industrial boilers, institutional boilers, commercial boilers, and pr ocess heaters that may include combustion units that are already regulated by other MACT standa rds. Therefore, EPA proposes to exclude any boiler or process heater that is already or w ill be subject to regulation under another MACT standard. Boilers located at area source facilities may be regulat ed pursuant to CAA section 129. Section 129(h) states that no unit subject to standards under section 129 shall be subject to standards under section 112(d) of the CAA. As a result, EPA proposes to exclude any boiler that is subject to regulation under section 129. EPA has codified ne w source performance standards (NSPS) for industrial boilers ( 40 CFR 60, subparts Db and Dc), but the NSPS does not regulate sources of HAPs. As a result, sources subject to the NSPS will still be subject to the proposed rules. However, EPA has minimized the monitoring requirements, testing requirements, and recordkeeping requirements to a void duplicating NSPS requirements.5-8 5.2.6 Description of Regulatory Alternatives th at Minimize Significant Economic Impacts on Small Entities As required by section 609(b) of the RFA, as amended by SBREFA, EPA has conducted outreach to small entities and convened a SBAR Panel to obtain advice and recommendation of representatives of the small entities that potentia lly would be subject to the requirements of this rule. On January 22, 2009, EPA’s Small Business Advocacy Chairperson convened a Panel under section 609(b) of the RFA. In addition to the Ch air, the Panel consisted of the Director of the Sector Policies and Programs Division within EPA’s Office of Air and Radiation, the Chief Counsel for Advocacy of the SBA, and the Admi nistrator of the Office of Information and Regulatory Affairs within the O ffice of Management and Budget.
As part of the SBAR Panel process, we conducted outreach with representatives from 14 various small entities that would be affected by this rule. The small entity representatives (SERs) included associations representi ng schools, churches, hotels/motels, wood product facilities, and manufacturers of home furnishing s. We met with these SERs to discuss the potential rulemaking approaches and potential opti ons to decrease the impact of the rulemaking on their industries/sectors. We distributed outreach mate rials to the SERs; these materials included background on the rulemaking, possibl e regulatory approaches, preliminary cost and economic impacts, and possible rulemaking alternatives. The Panel met with SERs from the industries that will be directly affected by this rule on February 10, 2009, to disc uss the outreach materials and receive feedback on the approaches and alternativ es detailed in the outrea ch packet. (EPA also met with SERs on November 13, 2008, for an in itial outreach meeting.) The Panel received written comments from the SERs following the mee ting in response to discussions at the meeting and the questions posed to the SERs by the Agenc y. The SERs were specifically asked to provide comment on regulatory alternatives that coul d help minimize the rules’ impact on small businesses. 5.2.6.1 Panel Recommendations for Sm all Business Flexibilities
The Panel recommended that EPA consider and seek comment on a wide range of regulatory alternatives to mitigate the impacts of the rulemaking on small businesses, including those flexibility options described below. Th e following section summarizes the SBAR Panel recommendations. EPA has proposed provisions consistent with four of the Panel’s recommendations.
Consistent with the RFA/SBREFA requireme nts, the Panel evaluated the assembled materials and small-entity comments on issues rela ted to elements of the IRFA. A copy of the Final Panel Report (including all comments received from SERs in response to the Panel’s5-9 outreach meeting), as well as summaries of both outr each meetings that were held with the SERs, is included in the docket for the proposed rule s. A summary of the Pa nel recommendations is detailed below. The proposals include proposed provisions for all but one of the Panel recommendations.

5.2.6.1.1 Work Practice Standards. The panel recommended that EPA consider requiring annual tune-ups, including standardized criteri a outlining proper tune-up methods targeted at smaller boiler operators. The panel further reco mmended that EPA take co mment on the efficacy of energy assessments/audits at improving combusti on efficiency and the cost of performing the audits, especially to smaller boiler operators.
A work practice standard, instead of MACT em ission limits, may be proposed if it can be justified under section 112(h) of the CAA, that is, it is impracticable to enforce the emission standards due to technical or ec onomic limitations. Work practice st andards could reduce fuel use and improve combustion efficiency, whic h would result in reduced emissions.
In general, SERs commented that a re gulatory approach to improve combustion efficiency, such as work practice standards, w ould have positive impacts with respect to the environment and energy use and save on compliance costs. The SERs were concerned with work practice standards that would require energy audi ts and implementation of audit findings. The basis of these concerns rested on the uncertainty th at there is no guarantee that there are available funds to implement a particular audit’s findings.
5.2.6.1.2 Subcategorization. The Panel recommended that EPA allow subcategorizations suggested by the SERs, unless EPA finds that a s ubcategorization is incons istent with the CAA.
SERs commented that subcategorization is a key concept that could ensure that like boilers are compared with similar boilers so th at MACT floors are more reasonable and could be achieved by all units within a subcategory using appropriate emission redu ction strategies. SERs commented that EPA should subcategorize base d on fuel type, boiler type, duty cycle, and location.
5.2.6.1.3 Health Based Compliance Alternatives (HBCA). The Panel recommended that EPA adopt the HBCA as a regulatory flexibility option for the boiler MACT rulemaking. The panel recognized, however, that EPA has concerns about its legal authority to provide an HBCA under the CAA, and EPA may ultimately determine that this flexibility is inconsistent with the CAA.5-10 SERs commented that adopting an HBCA woul d perhaps be the most important step EPA could take to mitigate the serious financial ha rm the boiler MACT would otherwise inflict on small entities using solid fuels nationwide; theref ore, HBCA should be a critical component of any future rule to lessen the impact on small entities.
5.2.6.1.4 Emissions Averaging. The Panel recommended that EPA consider a provision for emission averaging and long averagi ng times for the proposed emission limits.

SERs commented that a measur e EPA should consider to less en the regulatory burden of complying with the boiler MACT is to allow emissions averaging at sources with multiple regulated units. SERs commented that another appr oach that can aide small entity compliance is to set longer averaging times (i.e., 30 days or mo re) rather than looking at a mere 3-run (hour) average for performance. Given the inherent vari ability in boiler perfor mance, an annual or quarterly averaging period for all HAP would prev ent a single spike in emissions from throwing a unit into noncompliance.
5.2.6.1.5 Compliance Costs. The Panel recommended that EPA carefully weigh the potential burden of compliance requirements and consider for small entities options, such as emission averaging within the facility, reduced m onitoring/testing requirements, or allowing more time for compliance.
SERs noted that recordkeeping activities, as written in the vacat ed boiler MACT, would be especially challenging for small entities that do not have a dedicate d environmental affairs department.6-1 SECTION 6 HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS 6.1 Synopsis In this section, we provide an estimate of th e monetized benefits as sociated with reducing particulate matter (PM) for the proposed Boiler MACT Rule a nd Boiler Area Source Rule. For these rules, the PM reductions are the result of emission limits on PM (as a surrogate for metals) as well as emission limits on other HAPs. The total PM 2.5 reductions are the consequence of the technologies installed to meet these multiple lim its. These estimates reflect the monetized human health benefits of reducing cases of morb idity and premature mortality among populations exposed to the PM 2.5 precursors reduced by this rulema king. Using a 3% di scount rate, we estimate the total monetized bene fits of the proposed Major and Area Source Boiler Rules to be $18 billion to $43 billion in the implementation year (2013). Using a 7% discount rate, we estimate the total monetized bene fits of the proposed Major and Area Source Boiler Rules to be $16 billion to $39 billion in the implem entation year. All estimates are in 2008$.

These estimates reflect EPA’s most current in terpretation of the scientific literature. Higher or lower estimates of benefits are possibl e using other assumptions; examples of this are provided in Figure 6-2. Data, resource, and methodological limitations prevented EPA from monetizing the benefits from several important benefit categories, in cluding benefits from reducing hazardous air pollutants, ecosystem effects, and visibility impairme nt. The benefits from reducing hazardous air pollutants have not been m onetized in this analysis, including reducing 370,000 tons of carbon monoxide, 37,000 tons of HCl, 1,000 tons of HF, 8.3 tons of mercury, 3,400 tons of other metals, and 1,200 grams of dioxins/furans each year.

6.2 Calculation of PM 2.5 Human Health Benefits This rulemaking would reduce emissions of PM 2.5 , SO 2, and VOCs. Because SOx and VOCs are also precursors to PM 2.5, reducing these emissions would also reduce PM 2.5 formation, human exposure, and the incidence of PM 2.5 -related health effects. For these rules, the PM reductions are the result of emission limits on PM (a s a surrogate for metals) as well as emission limits on other HAPs. The total PM 2.5 reductions are the consequence of the technologies installed to meet these multiple limits. Due to analytic al limitations, it was not possible to provide a comprehensive estimate of PM 2.5 -related benefits. Instead, we used the “benefit-per-ton” approach to estimate these benefits based on th e methodology described in Fann et al. (2009). The key assumptions are described in detail below. These PM 2.5 benefit-per-ton estimates provide the total monetized human health benefits (the sum of premature mortality and premature morbidity)6-2 of reducing one ton of PM 2.5 from a specified source. EPA has us ed the benefit per-ton technique in several previous RIAs, including the recent NO 2 NAAQS RIA (U.S. EPA, 2010b). Table 6-1 shows the quantified and unquantified benefits captured in those be nefit-per-ton estimates.

Table 6-1. Human Health and Welfare Effects of PM 2.5

Pollutant /

Effect Quantified and Monetized in Primary Estimates Unquantified Effects Changes in:

PM 2.5 Adult premature mortality Bronchitis: chronic and acute Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lower and upper respiratory illness Minor restricted-activity days Work loss days Asthma exacerbations (a sthmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits Visibility Household soiling

Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the PM 2.5 benefits estimates utilize the concentratio n-response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA’s expe rt elicitation study as a sensitivity analysis.

 One estimate is based on the concentrati on-response (C-R) function developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002), a study that EPA has previously used to ge nerate its primary PM benefits estimate.

 One estimate is based on the C-R function de veloped from the extended analysis of the Harvard Six Cities cohort, as reported by Laden 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 in RIAs completed since the PM 2.5 NAAQS.

 Twelve estimates are based on the C-R func tions from EPA’s e xpert elicitation study (Roman et al., 2008) on the PM 2.5 -mortality relationship and interpreted for PM benefits analysis in EPA’s final RIA for the PM 2.5 NAAQS. For that study, twelve experts (labeled A through L) provi ded independent estimates of the PM 2.5 -mortality concentration-response function. EPA pract ice has been to develop independent estimates of PM 2.5 -mortality estimates corresponding to the concentration-response6-3 function provided by each of the twelve expe rts, to better characterize the degree of variability in the expert responses.

The effect coefficients are drawn from epidemiology studies examining two large population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six Cities cohort (Laden et al., 2006). 1 These are logical choices for an chor points in our presentation because, while both studies are well designed and peer reviewed, there are strengths and weaknesses inherent in each, whic h we believe argues for using both studies to generate benefits estimates. Previously, EPA had calculated benefi ts based on these two empirical studies, but derived the range of benefits, including the mi nimum and maximum results, from an expert elicitation of the relationsh ip between exposure to PM 2.5 and premature mortality (Roman et al., 2008). 2 Within this assessment, we include the bene fits estimates derived from the concentrationresponse function provided by each of the twelve e xperts to better character ize the uncertainty in the concentration-response function for mortality and the degree of variability in the expert responses. Because the experts used these cohort studies to inform their concentration-response functions, benefits estimates us ing these functions generally fall between results using these epidemiology studies (see Figure 6-2). In genera l, the expert elicitation results support the conclusion that the benefits of PM 2.5 control are very likely to be substantial.

Readers interested in review ing the methodology for creating th e benefit-per-ton estimates used in this analysis should consult Fann et al. (2009). As desc ribed in the documentation for the benefit per-ton estimates cited above, national per-ton estimates are developed for selected pollutant/source category combinations. The per-t on values calculated therefore apply only to tons reduced from those specific po llutant/source combinations (e.g., SO 2 emitted from electric generating units; NO 2 emitted from mobile sources). Our estimate of PM 2.5 control benefits is therefore based on the total PM 2.5 emissions controlled by sector and multiplied by this per-ton value.
The benefit-per-ton coefficients in this analys is were derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically, this analysis uses the benefit-per-ton method first applied in the Portland Cement NESHAP RIA (U.S. EPA, 2009a), which incorporated three up dates: a new population dataset, an expanded geographic scope of the benef it-per-ton calculation, and the f unctions directly from the

1 These two studies specify multi-pollutant models that control for SO 2 , among other pollutants. 2 Please see the Section 5.2 of the Portland Cement RIA in Appendix 5A for more information regarding the change in the presentation of benefits estimates.6-4 epidemiology studies without an adjustment for an assumed threshold. 3 Removing the threshold assumption is a key difference between the method us ed in this analysis of PM benefits and the methods used in RIAs prior to Portland Cement, and we now calculate in cremental benefits down to the lowest modeled PM 2.5 air quality levels.

EPA strives to use the best available science to support our benefits analyses, and we recognize that interpreta tion of the science rega rding air pollution and h ealth is dynamic and evolving. Based on our review of the body of scien tific literature, EPA a pplied the no-threshold model in this analysis. EPA’s Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by EPA’s Cl ean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded th at the scientific lit erature consistently finds that a no-threshold log-linear model most adequately portrays the PM-mortality concentration-response relationshi p while recognizing po tential uncertainty a bout the exact shape of the concentrati on-response function. 4 In conjunction with the underl ying scientific literature, this document provided a basis for reconsider ing the application of thresholds in PM 2.5 concentration-response functi ons used in EPA’s RIAs. 5
As is the nature of Regulatory Impact Analys es (RIAs), the assumptions and methods used to estimate air quality benefits evolve over time to reflect the Agency’s most current interpretation of the scientif ic and economic literature. For a period of time (2004-2008), the Office of Air and Radiation (OAR) valued mortality ri sk reductions using a va lue of statistical life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of the wage-risk literature. The $1 million value represen ted the lower end of the interquartile range from the Mrozek and Taylor (2002) meta-a nalysis of 33 studies. The $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$) 6 was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the

3 The benefit-per-ton estimates have also been updated since the Cement RIA to incorporate a revised VSL, as discussed on the next page. 4 It is important to note that uncertainty regarding the shape of the concentration-response function is conceptually distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is a discontinuity, which is a specific example of non-linearity. 5 In the Portland Cement RIA (U.S. EPA, 2009a), EPA solicited comment on the use of the no-threshold model for benefits analysis within the preamble of that proposed rule. The comment period for the Portland Cement proposed NESHAP closed on September 4, 2009 (Docket ID No. EPA–HQ–OAR–2002–0051 available at http://www.regulations.gov ). EPA is currently reviewing those comments. 6 In this analysis, we adjust the VSL to account for a different currency year (2008$) and to account for income growth to 2015. After applying these adjustments to the $5.5 million value, the VSL is $7.9m.6-5 Agency neither changed its official guidance on th e use of VSL in rule-makings nor subjected the interim estimate to a scientific peer-review pr ocess through the Science Advisory Board (SAB) or other peer-review group.
During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a repo rt from meta-analytic experts to evaluate methodological questions raised by EPA and the SAB on combining estimates from the various data sources. In addition, the Ag ency consulted several times w ith the Science Advisory Board Environmental Economics Advisory Committee (SAB-EEAC) on the i ssue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, appropriate meta-analytic techniques to combine estimates from unique data sources and different studies, including those using diffe rent methodologies (i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007).
Until updated guidance is availabl e, the Agency determined that a single, peer-reviewed estimate applied consistently best reflects the SAB-EEAC advice it has re ceived. Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 7 while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation stud ies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 8 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. The Agency anticipates presenting results from this effort to the SAB-EEAC in Spring 2010 and that draft guidance will be available shortly thereafter.
Figure 6-1 illustrates the relativ e breakdown of the monetized PM 2.5 health benefits.

7 In the (draft) update of the Economic Guidelines (U.S . EPA, 2008), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future. Therefore, this report doe s not represent final agency policy. 8 In this analysis, we adjust the VSL to account for a different currency year (2008$) and to account for income growth to 2015. After applying these adjustments to the $6.3 million value, the VSL is $9.1m.6-6 Figure 6-1. Breakdown of Monetized PM 2.5 Health Benefits using Mortality Function from Pope et al. (2002) a

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

Laden et al. (2006) function for premat ure mortality, the percentage of total monetized benefits due to adult mortality would be 97%. This chart shows the breakdown using a 3% discount rate, and the results would be similar if a 7% discount rate was used.
Tables 6-2 and 6-3 provide a general summary of the results by pollutant, including the emission reductions and monetized benefits-p er-ton at discount rates of 3% and 7%. 9 Table 6-4 provides a summary of the reductions in health in cidences as a result of the pollution reductions. In Table 6-5, we provide the bene fits using our anchor points of P ope et al. and Laden et al. as well as the results from the expert elicitation on PM mortality. Figures 6-2 and 6-3 provide a visual representation of the ra nge of benefits estimates and the pollutant breakdown of the monetized benefits of the proposed option.

9 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 ., 2013), and most of the PM benefits occur within that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 years of follow-up medical costs and lo st wages. For premature mortality, we assume that there is a “cessation” lag between PM exposures and the total realization of changes in health effects. Although the structure of the lag is uncertain, EPA follows the advice of the SAB-HES to assume a segmented lag structure characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction in PM 2.5 (U.S. EPA-SAB, 2004). Changes in the lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Therefore, discounting only aff ects the AMI costs after the analysis year and the valuation of premature mort alities that occur after the analysis year. As such, the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate.

Adult Mortality ‐ Pope et al. 93% Chronic Bronchitis 4%
AMI 2% Acute Respiratory Symptoms 0.5%

Infant Mortality 0.4%

Work Loss Days 0.2%

Hospital Admissions, Cardio 0.2% Hospital Admissions, Resp 0.04% Asthma Exacerbation 0.01% Acute Bronchitis 0.01% Upper Resp Symp 0.00% Lower Resp Symp 0.00% ER Visits, Resp 0.00% Other 1%6-7 Table 6-2. Summary of Monetized Benefits Estimates for Boiler MACT (for Major Sources) in 2013 (2008$) a

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

3%) Benefit per ton (Laden,

3%) Benefit per ton (Pope,

7%) Benefit per ton (Laden,

7%) Total Monetized Benefits (millions

2008$ at 3%) Total Monetized Benefits (millions

2008$ at 7%)Option 1E and 1N Direct PM 2.5 Major 29,336 $230,000 $560,000 $210,000 $500,000 $6,700 to $16,000 $6,100 to $15,000
PM 2.5 Precursors SO 2 Major 347,114 $29,000 $72,000 $27,000 $65,000 $10,000 to $25,000 $9,300 to $23,000 VOC 6,679 $1,200 $3,000 $1,100 $2,700 $8.1 to $20.0 $7.4 to $18.0 Total $17,000 to $41,000 $15,000 to $37,000Primary Options Direct PM 2.5 Major 29,020 $230,000 $560,000 $210,000 $500,000 $6,600 to $16,000 $6,000 to $15,000
PM 2.5 Precursors SO 2 Major 339,996 $29,000 $72,000 $27,000 $65,000 $10,000 to $25,000 $9,100 to $22,000 VOC 1,786 $1,200 $3,000 $1,100 $2,700 $2.2 to $5.3 $2.0 to $4.8 Total $17,000 to $41,000 $15,000 to $37,000

a All estimates are for the implementati on year (2013), and are rounded to two significant figures so numbers may

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor re duced has a different propensity to become PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. These estimates include 46 new major sources expected to be affected by 2013. Table 6-3. Summary of Monetized Benefits Es timates for Boiler Area Source Rule in 2013 (2008$) a

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

3%) Benefit per ton (Laden,

3%) Benefit per ton (Pope,

7%) Benefit per ton (Laden,

7%) Total Monetized Benefits (millions

2008$ at 3%) Total Monetized Benefits (millions

2008$ at 7%)Option 1E and 1N Direct PM 2.5 Area 22,920 $360,000 $880,000 $330,000 $790,000 $8,200 to $20,000 $7,500 to $18,000
PM 2.5 Precursors SO 2 Area 1,745 $20,000 $49,000 $18,000 $44,000 $35 to $86 $32 to $77 VOC 2,119 $1,200 $3,000 $1,100 $2,700 $2.6 to $6.3 $2.3 to $5.7 Total $8,300 to $20,000 $7,500 to $18,000Primary Options Direct PM 2.5 Area 2,682 $360,000 $880,000 $330,000 $790,000 $960 to $2,400 $880 to $2,100
PM 2.5 Precursors SO 2 Area 1,539 $20,000 $49,000 $18,000 $44,000 $31 to $76 $28 to $68 VOC 1,179 $1,200 $3,000 $1,100 $2,700 $1.4 to $3.5 $1.3 to $3.2 Total $1,000 to $2,400 $910 to $2,200

a All estimates are for the implementati on year (2013), and are rounded to two significant figures so numbers may

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor re duced has a different propensity to become PM 2.5 . The monetized benefits incorporate the conversion from precursor emissi ons to ambient fine particles. These estimates include 6,779 new area sources expected to be affected by 2013.6-8 Table 6-4. Summary of Reductions in Health Incidences from PM 2.5 Benefits for the Proposed Major and Area Source Boiler Rules in 2013 a

Boiler MACT (Major Sources) Boiler Area Source Rule Options 1E and 1N Primary Options Options 1E and 1N Primary Options Avoided Premature Mortality Pope et al. 1,900 1,900 930 110 Laden et al. 4,900 4,800 2,400 300 Avoided Morbidity Chronic Bronchitis 1,300 1,300 670 81 Acute Myocardial Infarction 3,000 3,000 1,500 190 Hospital Admissions, Resp 460 450 220 27 Hospital Admissions, Cardio 980 960 460 57 Emergency Room Visits, Resp 1,800 1,800 690 85 Acute Bronchitis 3,000 3,000 1,600 190 Work Loss Days 250,000 250,000 130,000 16,000 Asthma Exacerbation 33, 000 33,000 17,000 2,100 Acute Respiratory Symptoms 1,500,000 1,500,000 780,000 95,000 Lower Respiratory Symptoms 36,000 36,000 19,000 2,300 Upper Respiratory Symptoms 27,000 27,000 14,000 1,700

a All estimates are for the analysis year (2013) and are rounded to whole numbers with two significant figures. All fine particles are assumed to have eq uivalent health effects, but each PM 2.5 precursor pollutant has a different propensity to form PM 2.5 . Confidence intervals are unavailable for th is analysis because of the benefit-per-ton methodology. These estimates include 46 new major source s and 6,779 new area sources expected to be affected by 2013.6-9 Table 6-5. All PM 2.5 Benefits Estimates for the Propos ed Major and Area Source Boiler Rules at discount rates of 3% and 7% in 2013 (in millions of 2008$) a

Options 1E and 1N Proposal Options
3% 7% 3% 7% Benefit-per-ton Coefficients Deri ved from Epidemiology Literature

Pope et al. $25,000 $23,000 $18,000 $16,000 Laden et al. $62,000 $56,000 $43,000 $39,000 Benefit-per-ton Coefficients D erived from Expert Elicitation Expert A $8,100 $7,300 $46,000 $41,000 Expert B $6,200 $5,600 $35,000 $32,000 Expert C $6,200 $5,600 $35,000 $31,000 Expert D $4,400 $4,000 $25,000 $22,000 Expert E $10,000 $9,100 $57,000 $51,000 Expert F $5,700 $5,100 $32,000 $29,000 Expert G $3,700 $3,400 $21,000 $19,000 Expert H $4,700 $4,200 $26,000 $24,000 Expert I $6,100 $5,500 $34,000 $31,000 Expert J $5,000 $4,500 $28,000 $25,000 Expert K $1,200 $1,100 $6,900 $6,300 Expert L $4,500 $4,100 $25,000 $23,000

a All estimates are rounded to two significant figures. Es timates do not include confidence intervals because they

were derived through the benefit-per-ton technique described above. The benefits estimates from the Expert Elicitation are provided as a reasonable characterization of the uncertainty in the mort ality estimates associated with the concentration-response function. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. These estimates include 45 new major sources and 6,779 new area sources expected to be affected by 2013.6-10 Figure 6-2. Total Monetized PM 2.5 Benefits for the Proposed Major and Area Source Boiler Rules in 2013

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

Pope et al. study and the Laden et al. study, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response function provided in those studies. $0 $10 $20 $30 $40 $50 $60

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

Benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% DR 7% DR6-11 Figure 6-3. Breakdown of Monetized Benefi ts for the Proposed Boiler Rules by PM 2.5 Precursor Pollutant and Source

Figure 6-4. Breakdown of Monetized Benefi ts for the Proposed Boiler MACT (Major Sources) by Fuel Type PM 2.5 Major 38% PM 2.5 Area 5% SO 2 Major 57%

SO 2 Area 0.2% VOC 0.02%

Biomass

27%

Coal 65% Liquid

8%6-12 Figure 6-5. Breakdown of Monetized Benefits for the Proposed Boiler Area Source Rule by Fuel Type

6.3 Unquantified Benefits
The monetized benefits estimated in this RIA only reflect the portion of benefits attributable to the health effect reductions associated with ambient fine particles. Data, resource, and methodological limitations prevented EPA fro m quantifying or monetizing the benefits from several important benefit categorie s, including benefits from re ducing toxic emissions, ecosystem effects, and visibility impairment. The health benefits from reducing thousands of tons of hazardous air pollutants (HAPs) a nd millions of tons of carbon monoxi de each year have not been monetized in this analysis. In addition to being a PM 2.5 precursor, SO 2 emissions also contribute to adverse effects from acidic deposition in aqua tic and terrestrial ecosystems, increase mercury methylation, as well as visibility impairment. 6.3.1 Carbon Monoxide Benefits Carbon monoxide (CO) exposure is associated with a variety of health effects. Without knowing the location of the emission reductions a nd the resulting ambient concentrations using fine-scale air quality modeling, we were unabl e to estimate the exposure to CO for nearby populations. Due to data, resour ce, and methodological limitations , we were unable to estimate Biomass

22%

Coal 66%

Gas 1% Liquid

11%6-13 the benefits associated with the 370,000 tons redu ctions in CO emissions that would occur as a result of this rule.

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of carbon-containing fuels and photoc hemical reactions in the atmosphere. The amount of CO emitted from these reactions, re lative to carbon dioxide (CO 2 ), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that are dependent upon concentration and duration of exposure.
The Integrated Science Assessment for Ca rbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposure to CO is “likely to have a causal relationship” with cardiovascular morbidity, particularly in individu als with coronary heart disease. Epidemiologic studies associate short-term CO exposure with increased risk of emergency department visits and hospital admissions. Coronary heart disease includes those w ho have angina pectoris (cardiac chest pain), as well as those who have expe rienced a heart attack. Other s ubpopulations potentially at risk include individuals with diseases such as chr onic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as older adults or the developing young. The evidence is suggestive of a causal relationship between short-term exposure to CO and respiratory morbidity and mortality. The evid ence is also suggestive of a causal relationship for birth outcomes and developmental effects foll owing long-term exposure to CO, and for central nervous system effects linked to s hort- and long-term exposure to CO.

6.3.2 Other SO 2 Benefits In addition to being a precursor to PM 2.5 , SO 2 emissions are also associated with a variety of respiratory health effects. Unfortunately, we were unable to estimate the health benefits associated with reduced SO 2 exposure in this analysis because we do not have air quality modeling data available. Without knowing the location of the emission reductions and the resulting ambient concentra tions, we were unable to es timate the exposure to SO 2 for nearby populations. Therefore, this analysis only quantifies and monetizes the PM 2.5 benefits associated with the reductions in SO 2 emissions.
Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment (ISA) for Sulfur Dioxide conc luded that there is a causal relationship between respiratory heal th effects and short-term exposure to SO 2 (U.S. EPA, 2008). The immediate effect of SO 2 on the respiratory system in humans is bronchoconstriction.6-14 Asthmatics are more sensitive to the effects of SO 2 likely resulting from pr eexisting 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 per centage of asthmatics adve rsely affected. Based on our review of this informati on, we identified four short-term morbidity endpoints that the SO 2 ISA identified as a “causal relationship”: asth ma exacerbation, respiratory-related emergency department visits, and respirat ory-related hospitalizat ions. The differing evidence and associated strength of the evidence for these differen t effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the rela tionship 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 ISA stated that studies ar e generally consistent in reporting a relationship between SO 2 exposure and mortality, there wa s a lack of robustness of the observed associations to adjustment for pollutants.

SO 2 emissions also contribute to adverse welf are effects from acidi c deposition, visibility impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which can cause a loss of biodiversity of fishes, z ooplankton, and macro invertebrates in aquatic ecosystems, as well as a decline in sensitive tree species, such as red spruce ( Picea rubens ) and sugar maple ( Acer saccharum ) in terrestrial ecosystems. In the northeastern United States, the surface waters affected by acidification are a sour ce of food for some recreational and subsistence fishermen and for other consumers and support seve ral cultural services, including aesthetic and educational services and recreat ional fishing. Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum to xicity, which can cause reduced root growth, which restricts the ability of the plant to take up water and nutrient s. These direct effects can, in turn, increase the sensitivity of th ese plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortalit y of canopy trees. Terrestria l acidification affects several important ecological servic es, including declines in habita t for threatened and endangered species (cultural), declines in forest aesthetics (cultural), d eclines in forest productivity (provisioning), and increases in forest soil erosion and reductions in water retention (cultural and regulating). (U.S. EPA, 2008d)
Reducing SO 2 emissions and the secondary formation of PM 2.5 would improve the level of visibility throughout the United Stat es. Fine particles with signifi cant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). These suspended particles and gases degrade visibil ity by scattering and absorbing light. Higher visibility impairment levels in the East are due to generally higher concentra tions of fine particles,6-15 particularly sulfates, and higher av erage relative humidity levels. In fact, particulate sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually and 75% during summer). In the western U.S., particulate sulfate contributes to 20 -50% of regional haze (U.S. EPA, 2009c). Visibility has direct significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in recreational activities. 6.3.3 HAP Benefits Due to data, resource, and methodology limita tions, we were unable to estimate the benefits associated with the thousands tons of hazardous air pollutants that would be reduced as a result of this rule. Available emissions data show that severa l different HAPs are emitted from boilers, 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 6-6 for the list of the major HAPs for each fuel type. This rule is anticipated to reduce 370,000 tons of carbon monoxide, 37,000 tons of HCl, 1,000 tons of HF, 8.3 tons of mercury, and 3,400 tons of other metals, 1,200 grams of dioxins/furans each year from major and area sour ces. We discuss the health effects associated with these top HAPs as well as the HAPs for which we have emission reduction estimates. Table 6-6. Top HAPs by Mass from Boilers by Fuel Type

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

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

6.3.3.1 Mercury Mercury is a highly neurotoxic contaminant th at enters the food web as a methylated compound, methylmercury (U.S. EPA, 2008d). The cont aminant is concentrated in higher trophic levels, including fish eaten by humans. Experi mental evidence has es tablished that only inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA, 2008d). Current evidence indicates that in watershe ds where mercury is present, increased sulfate deposition very likely results in methylmerc ury accumulation in fish (Drevnick et al., 2007; Munthe et al, 2007). The SO 2 ISA concluded that evidence is sufficient to infer a casual6-16 relationship between sulfur depos ition and increased mercury met hylation in wetlands and aquatic environments (U.S. EPA, 2008d).

In addition to the role of sulfate deposition on methylati on, these proposed rules would also reduce mercury emissions. Mercury is emitted to the air from various man-made and natural sources. These emissions transport through the a tmosphere and eventually deposit to land or water bodies. This deposition can occur locally, re gionally, or globally, de pending on the form of mercury emitted and other factors such as the weather. The form of mercury emitted varies depending on the source type a nd other factors. Available data indicate that the mercury emissions from these sources are a mixture of gaseous elemental mercury, inorganic ionic mercury, and particulate bound mercur y. Gaseous elemental mercury can be transported very long distances, even globally, to regions far from th e emissions source (becoming part of the global “pool”) before deposition occurs. Inorganic i onic 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 atmo sphere can undergo transformation into ionic mercury, providing a significant pathway for deposition of emitted elemental mercury.
Major and area source boilers emitted about 16 t ons of mercury in the air in 2008 in the U.S. Based on the EPA’s National Emission Inve ntory, about 103 tons of mercury were emitted from all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury 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. Therefore, we estimate that in 2008, these sources emitted about 16% of the total anthropogenic mercury emissions in the U.S. and about 0.8% of the global emissions. Overall, this rule would reduce mercury emissions by about 8.3 tons per year from current levels, and therefore, contribute to reductions in mercur y exposures and health effects. Due to time and resource limitations, we were unable to model mercury di spersion, deposition, methylation, bioaccumulation in fish tissue, and human consump tion of mercury-contaminated fish that would be needed in order to estimate the human hea lth benefits from reducing mercury emissions.
Potential exposure routes to mercury emi ssions include both direct inhalation and consumption of fish containing methylmercury. Th e primary route of human exposure to mercury emissions from industrial sources is generally indirectly through the consumption of fish containing methylmercury. As described above, mercury that has been emitted to the air eventually settles into water bodies or onto land wh ere it can either move directly or be leached into waterbodies. Once deposited, certain microor ganisms can change it into methylmercury, a highly toxic form that builds up in fish, shellfish and animals that eat fis h. Consumption of fish6-17 and shellfish are the main sources of methylme rcury exposure to humans. Methylmercury builds up more in some types of fish and shellfish than in others. The levels of methylmercury in fish and shellfish vary widely depending on what they eat, how long they live, and how high they are in the food chain. Most fish, including ocean sp ecies and local freshwat er fish, contain some methylmercury. For example, in recent studies by EPA and the U.S. Geological Survey (USGS) of fish tissues, every fish samp led contained some methylmercury.
The majority of fish consumed in the U.S. are ocean species. The methylmercury concentrations in ocean fish species are pr imarily influenced by the global mercury pool. However, the methylmercury found in local fish can be due, at least partly, to mercury emissions from local sources. Research shows that most people’s fish consumption does not cause a mercury-related health concern. However, certain people may be at higher risk because of their routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families who rely heavily on fish for a subs tantial part of their diet). It has been demonstrated that high levels of methylmercury in the bloodstream of unborn babies and young children may harm the developing nervous system, making the child less able to think and learn. Moreover, mercury exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people of all ages.
Several studies suggest that the methylmercury content of fish may reduce these cardioprotective effects of fish consumption. Some of these studies also suggest that methylmercury may cause adverse effects to the cardiovascular sy stem. For example, the NRC (2000) review of the literature concerning methylme rcury health effects took note of two epidemiological studies that found an association between dietary exposure to methylmerc ury and adverse cardiovascular effects. 10 Moreover, in a study of 1,833 males in Finl and aged 42 to 60 years, Solonen et al. (1995) observed a relationship between methylmerc ury exposure via fish consumption and acute myocardial infarction (AMI or he art attacks), coronary heart disease, cardiovascular disease, and all-cause mortality. 11 The NRC also noted a study of 917 se ven year old children in the Faroe Islands, whose initial exposure to methylmercury was in utero although post natal exposures may have occurred as well. At seven years of age, these children exhibited an increase in blood

10 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the Toxicological

Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. pp.168-173. 11 Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid peroxidation, and the risk of

myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men.” Circulation, 91 (3):645- 655.6-18 pressure and a decrease in heart rate variability. 12 Based on these and other studies, NRC concluded in 2000 that, while “the da ta base is not as extensive fo r cardiovascular effects as it is for other end points (i.e. neurologic effects) the cardiovascular syst em appears to be a target for methylmercury toxicity.” 13
Since publication of the NRC report there have been some 30 published papers presenting the findings of studies that have examined the possible cardiovascular e ffects of methylmercury exposure. These studies include epidemiological, toxicological, a nd toxicokinetic investigations. Over a dozen review papers have also been publis hed. If there is a causal relationship between methylmercury exposure and adverse cardiovascul ar effects, then reducing exposure to methylmercury would result in public health be nefits from reduced cardiovascular effects.
In early 2010, EPA sponsored a workshop in which a group of experts were asked to assess the plausibility of a causal relationship between methylmercury exposure and cardiovascular health effects a nd to advise EPA on methodologie s for estimating population level cardiovascular health impacts of reduced me thylmercury exposure. The report from that workshop is in preparation. 6.3.3.2 Hydrogen Chloride (HCl) 14
Hydrogen chloride gas is intensely irritating to the mucous membranes of the nose, throat, and respiratory tract. Brief exposure to 35 ppm ca uses throat irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour. The greatest impact is on the upper respiratory tract; exposure to high concentrations can rapidl y lead to swelling and spasm of the throat and suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures may develop an accumulation of fluid in the lungs. Exposure to hydrogen ch loride can lead to Re active Airway Dysfunction Syndrome (RADS), a chemically- or irritant-ind uced type of asthma. Children may be more vulnerable to corrosive agents than adults becau se of the relatively smaller diameter of their airways. Children may also be more vulnerabl e to gas exposure because of increased minute

12 Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weih e, and Grandjean, P., 1999. “Prenatal Methylmercury

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370-375. 13 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the Toxicological

Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. p. 229. 14 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry

(ATSDR). Medical Management Guidelines for Hydrogen Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Availabl e on the Internet at <http://www.atsdr.cdc.gov/Mhmi/mmg173.html>.6-19 ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not been classified for carcinogenic effects. 6.3.3.3 Chlorine gas (Cl 2 ) 15 Chlorine gas is irritating and corrosive to th e eyes, skin, and respir atory tract. Exposure to chlorine may cause burning of the eyes, nose, and throat; cough as well as constriction and edema of the airway and lungs can occur. 6.3.3.4 Hydrogen cyanide (HCN) 16 Hydrogen cyanide is highly toxic by all routes of exposure and may ca use abrupt onset of profound central nervous system, cardiovascular, and respiratory effects, l eading to death within minutes. Exposure to lower concentrations of hydrogen cyanide may produce eye irritation, headache, confusion, nausea, and vomiting followed in some cases by coma and death. Hydrogen cyanide acts as a cellular asphyxiant. By binding to mitochondrial cytochro me oxidase, it prevents the utilization of oxygen in cellular metabolism. The central ne rvous system and myocardium are particularly sensitive to th e toxic effects of cyanide. 6.3.3.5 Hydrogen Fluoride (HF) 17 Acute (short-term) inhalation exposure to gaseous hydrogen fluoride can cause severe respiratory damage in humans, including severe irritation and pulmonary edema. Chronic (longterm) exposure to fluoride at low levels has a be neficial effect of dent al cavity prevention and may also be useful for the treatment of osteopor osis. Exposure to higher levels of fluoride may cause dental fluorosis. One study reported menstr ual irregularities in women occupationally exposed to fluoride. The EPA has not classi fied hydrogen fluoride for carcinogenicity.

15 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry

(ATSDR). 2007. Medical Management Guidelines for Chlorine (CAS 7782-50-5; UN 1017). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Availabl e on the Internet at http://www.atsdr.cdc.gov/M HMI/mmg172.html#bookmark02

. 16 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry

(ATSDR). 2007. Medical Management Guidelines for Hydrogen Cyanide (HCN) (CAS#: 7782-50-5). Atlanta, GA: U.S. Department of Health and Human Services, Pub lic Health Service. Available on the Internet at http://www.atsdr.cdc.gov/Mhmi/mmg8.html#bookmark02

. 17 All health effects language for this section came from: U.S. EPA, “National Emission Standards for Hazardous Air

Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf6-20 6.3.3.6 Toluene 18 Toluene is found in evaporative as well as ex haust emissions from motor vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessment , there is inadequate information to assess the carcinogenic potential of toluene because st udies of humans chronically exposed to toluene are inconclusive, toluene was not carcinogenic in adequate inhalati on cancer bioassays of rats and mice exposed for life, and increased incidences of mammary cancer and leukemia were reported in a lifetime rat oral bioassay.
The central nervous system (CNS) is the pr imary target for toluene toxicity in both humans and animals for acute and chronic exposur es. CNS dysfunction (which is often reversible) and narcosis have been frequently observed in hu mans acutely exposed to low or moderate levels of toluene by inhalation; symptoms include fa tigue, sleepiness, headach es, and nausea. Central nervous system depression has been reported to occu r in chronic abusers expos ed to high levels of toluene. Symptoms include ataxia, tremors, cerebral atrophy, nystagmus (involuntary eye movements), and impaired speech, hearing, and visi on. Chronic inhalation exposure of humans to toluene also causes irritation of the upper respiratory tract, ey e irritation, dizziness, headaches, and difficulty with sleep.

Human studies have also re ported developmental effects, such as CNS dysfunction, attention deficits, and minor cr aniofacial and limb anomalies, in the children of women who abused toluene during pregnancy. A substantial da tabase examining the effects of toluene in subchronic and chronic occupationa lly exposed humans exists. The weight of evidence from these studies indicates neurolog ical effects (i.e., impaired color vision, impaired hearing, decreased performance in neurobehavioral analysis, ch anges in motor and sensory nerve conduction velocity, headache, dizziness) as the most sensitive endpoint. 6.3.3.7 Formaldehyde Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys. 19 EPA is currently reviewing recently published epidemiological data. For inst ance, research conducted by the National Cancer Institute (NCI) found an increased risk of nasopharyngeal cancer and lymphohematopoietic

18 All health effects language for this section came from: U.S. EPA. 2005. “Full IRIS Summary for Toluene (CASRN

108-88-3)” Environmental Protection Agency, Integrated Risk Information System (IRIS), Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH. Available on the Internet at < http://www.epa.gov/iris/subst/0118.htm >. 19 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to

Formaldehyde, Office of Pesticides and Toxic Substances, April 1987.6-21 malignancies such as leukemia among workers exposed to formaldehyde. 20,21 In an analysis of the lymphohematopoietic cancer mortality from an extended follow-up of these workers, NCI confirmed an association be tween lymphohematopoietic cancer risk and peak exposures. 22 A recent National Institute of Occupational Safety and Health (NIOSH) st udy of garment workers also found increased risk of death due to le ukemia among workers exposed to formaldehyde. 23 Extended follow-up of a cohort of British chemical workers did not find evidence of an increase in nasopharyngeal or lymphohema topoietic cancers, but a conti nuing statistically significant excess in lung cancers was reported. 24
In the past 15 years there has been substa ntial research on the inhalation dosimetry for formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the Chemical Industry Institute of T oxicology), with a focus on use of rodent data for refinement of the quantitative cancer dose-response assessment. 25 , 26 , 27 CIIT’s risk assessment of formaldehyde incorporated mechanistic and dosimetric info rmation on formaldehyde. However, it should be noted that recent research published by EPA indicat es that when two-stage modeling assumptions are varied, resulting dose-res ponse estimates can vary by several orders of magnitude. 28 , 29 , 30 , 31 These findings are not supportive of interpre ting the CIIT model results as providing a

20 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Mort ality from lymphohematopoetic

malignancies among workers in formaldehyde industries. Jo urnal of the National Cancer Institute 95 : 1615-1623. 21 Hauptmann, M..; Lubin, J. H.; Stewar t, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from solid cancers among

workers in formaldehyde industries. American Journal of Epidemiology 159: 1117-1130. 22 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009.

Mortality from lymphohematopoietic malignancies among workers in formaldehyde industries: The National Cancer Institute cohort. J. National Cancer Inst. 101: 751-761. 23 Pinkerton, L. E. 2004. Mortality among a cohort of garmen t workers exposed to formaldehyde: an update. Occup.

Environ. Med. 61: 193-200. 24 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Exte nded follow-up of a cohort of British chemical workers

exposed to formaldehyde. J National Cancer Inst. 95:1608-1615. 25 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologically

motivated computational modeling of formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432-447. 26 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, an d FJ Miller. 2004. Human respiratory

tract cancer risks of inhaled formaldehyde: Dose-response predictions derived from biologically-motivated computational modeling of a combined rodent and human dataset. Tox Sci 82: 279-296. 27 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. 28 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, 2008 29 Subramaniam, R; Chen, C; Crump, K; .et .al. (2008) Uncertainties in biologically-based modeling of

formaldehyde-induced can cer risk: identification of key issues. Risk Anal 28(4):907-923. 30 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model for formaldehydeinduced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237 31 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495.6-22 conservative (health protective) estimate of human risk. 32 EPA research also examined the contribution of the two-stage modeling for form aldehyde towards characterizing the relative weights of key events in the mode-of-action of a carcinogen. For example, the model-based inference in the published CIIT st udy that formaldehyde’s direct mu tagenic action is not relevant to the compound’s tumorigenicity was found not to hold under variations of modeling assumptions. 33
Based on the developments of the last d ecade, in 2004, the working group of the IARC concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient evidence in humans and sufficient evidence in expe rimental animals - a higher classification than previous IARC evaluations. After reviewing the currently availabl e epidemiological evidence, the IARC (2006) characterized the human evidence for formaldehyde carcinogenici ty as “sufficient,” based upon the data on nasopharyngeal cancers; the epidemiologic evidence on leukemia was characterized as “strong.” 34 EPA is reviewing the recent work cited above from the NCI and NIOSH, as well as the analysis by the CIIT Centers for Health Research and other studies, as part of a reassessment of the human hazard and dose-response associated with formaldehyde.
Formaldehyde exposure also causes a range of noncancer health effects, including irritation of the eyes (burning and watering of th e eyes), nose and throat. Effects from repeated exposure in humans include resp iratory tract irritation, chronic bronchitis and na sal epithelial lesions such as metaplasia and loss of cilia. An imal studies suggest that formaldehyde may also cause airway inflammation – including eosinophil infi ltration into the airways. There are several studies that suggest that formal dehyde may increase the risk of asthma – particularly in the
young. 35,36

32 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 33 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model for formaldehydeinduced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237 34 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-tert-Butoxypropan-2-

ol. Monographs Volume 88. World Health Organization, Lyon, France. 35 Agency for Toxic Substances and Disease Registry (A TSDR). 1999. Toxicological profile for Formaldehyde.

Atlanta, GA: U.S. Department of Health an d Human Services, Pub lic Health Service. http://www.atsdr.cdc.gov/to xprofiles/tp111.html 36 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.6-23 6.3.3.8 Polycyclic Aromatic Hydrocarbons (PAHs) At least seven PAH compounds are classifi ed by EPA as probable human carcinogens based on animal data, including benzo(a)anthracene 37 , benzo(b)fluoranthene 38 , benzo(k)fluoranthene 39 , benzo(a)pyrene 40 , chrysene 41 , dibenz(a,h)anthracene 42 , and indeno(1,2,3- cd)pyrene 43 . Recent studies have found that maternal exposures to PAHs in a population of pregnant women were associated with several ad verse birth outcomes, including low birth weight and reduced length at birth, as well as impaired cognitive development at age three. 44 , 45 EPA has not yet evaluated these recent studies. 6.3.3.9 Acetaldehyde Acetaldehyde is classified in EPA’s IRIS da tabase as a probable human carcinogen, based on nasal tumors in rats, and is considered toxi c by the inhalation, oral, and intravenous routes. 46 Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS in the 11 th Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the

37 U.S. EPA (1997). Integrated Risk Information System F ile of benzo(a)anthracene. Research and Development,

National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea /iris/subst/0454.htm. 38 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. 39 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. 40 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. 41 U.S. EPA (1997). Integrated Risk Information System File of chrysene. Research and Development, National

Center for Environmental Assessment, Washington, DC . This material is available electronically at http://www.epa.gov/ncea /iris/subst/0455.htm 42 U.S. EPA (1997). Integr ated Risk Information System File of dibe nz(a,h)anthracene. Research and Development,

National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea /iris/subst/0456.htm. 43 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. 44 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. 45 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. 46 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and Development, National

Center for Environmental Assessment, Washington, DC . This material is available electronically at http://www.epa.gov/iris/subst/0290.htm.6-24 IARC. 47 , 48 EPA is currently conducting a reassessment of cancer risk from inhalation exposure to acetaldehyde.
The primary noncancer effects of exposure to acetaldehyde vapors incl ude irritation of the eyes, skin, and respiratory tract. 49 In short-term (4 week) rat st udies, degeneration of olfactory epithelium was observed at various concen tration levels of acetaldehyde exposure. 50 Data from these studies were used by EPA to develop an inhalation reference concentration. Some asthmatics have been shown to be a sensitiv e subpopulation to decrem ents in functional expiratory volume (FEV1 test) and br onchoconstriction upon acetaldehyde inhalation. 51 The agency is currently conducting a reassessment of the health hazards from inhalation exposure to acetaldehyde.

6.3.3.10 Nickel 52
Nickel is an essential element in some anim al species, and it has been suggested it may be essential for human nutrition. Nickel dermatitis, consisting of itching of the fingers, hand and forearms, is the most common effect in human s from chronic (long-term) skin contact with nickel. Respiratory effects have also been reported in humans fr om inhalation exposure to nickel. No information is available regarding the reprod uctive or developmental effects of nickel in humans, but animal studies have reported such effects. Human and animal studies have reported an increased risk of lung and na sal cancers from exposure to nick el refinery dusts and nickel subsulfide. Animal studies of so luble nickel compounds (i.e., nick el carbonyl) have reported lung tumors. The EPA has classified nickel refinery subsulfide as Group A, human carcinogens and nickel carbonyl as a Group B2, probable human carcinogen.

47 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens

available at: http://ntp.niehs.nih.gov/go/16183. 48 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. 49 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material is available electronically at http://www.e pa.gov/iris/subst/0290.htm. 50 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. 51 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized acet aldehyde induces histaminemediated bronchoconstriction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1): 940-943. 52 All health effects language for this section came from: U.S. EPA, “National Emission Standards for Hazardous Air

Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf6-25 6.3.3.11 Manganese 53
Health effects in humans have been associat ed with both deficiencies and excess intakes of manganese. Chronic (long-term) exposure to low le vels of manganese in th e diet is considered to be nutritionally essential in humans, with a recommended daily allowance of 2 to 5 milligrams per day. Chronic exposure to high levels of mang anese by inhalation in humans results primarily in CNS effects. Visual reaction time, hand steadin ess, and eye-hand coordination were affected in chronically-exposed workers. Manganism, charact erized by feelings of weakness and lethargy, tremors, a masklike face, and psychological distur bances, may result from chronic exposure to higher levels. Impotence and loss of libido have been noted in male workers afflicted with manganism attributed to inhalation exposures. Th e EPA has classified ma nganese in Group D, not classifiable as to carcinogenicity in humans. 6.3.3.12 Dioxins (Chlorinated dibenzodioxins (CDDs) 54
A number of effects have been observed in people exposed to 2,3,7,8-TCDD levels that are at least 10 times higher than background leve ls. The most obvious health effect in people exposure to relatively large amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin disease with acne-like lesions th at occur mainly on the face and upper body. Other skin effects noted in people exposed to high doses of 2,3,7,8- TCDD include skin rashes, discoloration, and excessive body hair. Changes in blood and urine th at may indicate liver damage also are seen in people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglobin, lipids, sugar, and protein have been re ported in people exposed to rela tively high concentrations of 2,3,7,8-TCDD. Most of the effects ar e considered mild and were reversible. However, in some people these effects may last for many years. Slight increases in the risk of diabetes and abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-TCDD. We do not have enough information to know if expos ure to 2,3,7,8-TCDD would result in reproductive or developmental effects in people, but animal st udies suggest that this is a potential health

concern.
In certain animal species, 2,3,7,8-TCDD is especi ally harmful and can cause death after a single exposure. Exposure to lower le vels can cause a variety of effect s in animals, such as weight

53 All health effects language for this section came from: U.S. EPA, “National Emission Standards for Hazardous Air

Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf 54 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry

(ATSDR). 1999. ToxFAQs for Chlorinated Dibenzo-p-dioxin s (CDDs) (CAS#: 2,3,7,8-TC DD 1746-01-6). Atlanta, GA: U.S. Department of Health and Human Services, Pu blic Health Service. Available on the Internet at http://www.atsdr.cdc.gov/tfacts104.html

.6-26 loss, liver damage, and disruption of the endoc rine system. In many species of animals, 2,3,7,8- TCDD weakens the immune system and causes a decrea se in the system's abil ity to fight bacteria and viruses at relatively lo w levels (approximately 10 times higher than human background body burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused reproductive damage and birth defects. Some animal species exposed to CDDs during pregnancy ha d miscarriages and the offspring of animals exposed to 2,3,7,8-TCDD duri ng pregnancy often had severe birth defects including skeletal deformities, kidney defects, and weakened immune responses. In some studies, effects were observed at body burdens 10 times higher than human background levels. 6.3.3.13 Furans (Chlorinated dibenzofurans (CDFs)) 55
Most of the information on the adverse health effects comes from studies in people who were accidentally exposed to food contaminated with CDFs. The amounts that these people were exposed to were much higher than are likely from environmental e xposures or from a normal diet. Skin and eye irritations, especially severe acne, darkened skin color, and swollen eyelids with discharge, were the most obvious health e ffects of the CDF poisoning. CDF poisoning also caused vomiting and diarrhea, anemia, more fre quent lung infections, numbness, effects on the nervous system, and mild changes in the live r. Children born to exposed mothers had skin irritation and more difficulty lear ning, but it is unknown if this e ffect was permanent or caused by CDFs alone or CDFs and polychlorin ated biphenyls in combination.
Many of the same effects that occurred in people accidentally exposed also occurred in laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomachs, livers, kidneys, and immune systems were seriously injured. Some animals had birth defects and testicular damage, and in severe cases, some an imals died. These effects in animals were seen when they were fed large amounts of CDFs ove r a short time, or small amounts over several weeks or months. Nothing is known about the pos sible health effects in animals from eating CDFs over a lifetime.

6.3.3.14 Other Air Toxics In addition to the compounds described a bove, other compounds in gaseous hydrocarbon and PM emissions from boilers would be affected by this rule. Information regarding the health effects of these compounds can be found in EPA’s IRIS database. 56

55 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry

(ATSDR). 1995. ToxFAQsTM for Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc.gov/tfacts32.html>. 56 U.S. EPA Integrated Risk Information System (IR IS) database is available at: www.epa.gov/iris6-27 6.4 Characterization of Uncertainty in the Monetized PM 2.5 Benefits In any complex analysis, there are likely to be many sources of uncertainty. Many inputs are used to derive the final estimate of econo mic benefits, including emission inventories, air quality models (with their associated paramete rs and inputs), epidemio logical estimates of concentration-response (C-R) f unctions, estimates of values , population estimates, income estimates, and estimates of the future state of the world (i.e., regula tions, technology, and human behavior). For some parameters or inputs it may be possible to provide a st atistical representation of the underlying uncertainty distribution. For other parameters or inputs, the necessary information is not available.
The annual benefit estimates presen ted in this analysis are also inherently variable due to the processes that govern pollutant emissions and am bient air quality in a gi ven year. Factors such as hours of equipment use and weather are consta ntly variable, regardle ss of our ability to measure them accurately. As discussed in the PM 2.5 NAAQS RIA (Table 5.5) (U.S. EPA, 2006), there are a variety of uncertainties associated with these PM benef its. Therefore, the estimates of annual benefits should be viewed as representative of the magnitude of benefits expected, rather than the actual benefits that would occur every year.
We performed a couple of sensitivity analyses on the benefits results to assess the sensitivity of the primary results to various da ta inputs and assumptions. We then changed each default input one at a time and recalculated the total monetized benefits to assess the percent change from the default. We present the results of this sensitivity analysis in Table 6-7. We indicated each input parameter, th e value used as the default, and the values for the sensitivity analyses, and then we provide the total monetarybenefits for each input and the percent change from the default value for the proposed option.
Above we present the estimates of the total monetized benefits, based on our interpretation of the best available scientif ic literature and methods and supported by the SAB-HES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions and uncertainties. For example, for key assumptions underlying the estimates for premature mortality, which typically account for at least 90% of the to tal monetized benefits, we were able to quantify include the following:6-28 Table 6-7. Sensitivity Analyses for Monetized PM 2.5 Health Benefits (in millions of 2008$)

Total PM 2.5

Benefits % Change from Default

Threshold Assumption (with Epidemiology Study) No Threshold (Pope) $18,000 N/A No Threshold (Laden) $43,000 N/A

Threshold (Pope) $13,810 -23% Threshold (Laden) $31,000 -28%

Discount Rate (with Epidemiology Study) 3% (Pope) $18,000 N/A 3% (Laden) $43,000 N/A 7% (Pope) $16,000 -11% 7% (Laden) $39,000 -9%

1. PM 2.5 benefits were derived through benefit per-ton estimates, which do not reflect local variability in popula tion density, meteorology, exposure, baseline health incidence rates, or other local factors thatmight lead to an over-estimate or underestimate of the actual benefits of controlling directly emitted fine particulates. 2. We assume that all fine particles, regard less of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 released from diesel engines and ot her industrial sour ces, but no clear scientific grounds exist for supporting differe ntial effects estimates by particle type. 3. We assume that the health impact functi on for fine particles is linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5, including both regions that are in attainment with fine particle standard and those that do not meet the standard down to th e lowest modeled concentrations. 4. To characterize the uncertainty in the relationship between PM 2.5 and premature mortality (which typically accounts for 85% to 95% of total monetized benefits), we include a set of twelve estimates based on results of the expert elicitation study in addition to our core estimates. Even these multiple characterizations omit the uncertainty in air quality estimates, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis. For more information on the uncertainties associated with PM 2.5 benefits, please consult the PM 2.5 NAAQS RIA (Table 5-5).6-29 This RIA does not include the type of detail ed uncertainty assessment found in the PM NAAQS RIA because we lack the necessary air quality input and monitoring data to run the benefits model. Moreover, it was not possible to develop benefit-per-ton metrics and associated estimates of uncertainty using the benefits estim ates from the PM RIA because of the significant differences between the sources affected in that rule and those regulated here. However, the results of the Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM NAAQS RIA can provide some evidence of the uncertainty surr ounding the benefits results presented in this analysis.
It is important to note that the monetized benefit-per-ton estimates used here reflect specific geographic patterns of emissions redu ctions and specific air quality and benefits modeling assumptions. For example, these estimates do not reflect local va riability in population density, meteorology, exposure, baseline health inci dence rates, or other local factors. Use of these $/ton values to estimate benefits associat ed with different emissi on control programs (e.g., for reducing emissions from large stationary s ources like EGUs) may lead to higher or lower benefit estimates than if benefits were calculated based on direct air quality modeling. Great care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are all based on national or broad regional emission reduction programs and therefore represent average bene fits-per-ton over the entire Unite d States. The benefits- per-ton for emission reductions in specific locations may be very different than the estimates presented here. 6.5 Comparison of Benefits and Costs Using a 3% discount rate, we estimate the to tal combined monetized benefits of the proposed Boiler MACT and Area Source Rule to be $18 billion to $45 billion in the implementation year (2013). Using a 7% discount ra te, we estimate the total monetized benefits of the proposed Boiler MACT a nd Area Source Rule to be $ 17 billion to $40 billion. The combined annualized social co sts of the proposed Boiler MACT and Area Source Rule are $3.4 billion at a 7% interest rate. 57 Thus, the combined net benefits are $15 billion to $41 billion at a 3% discount rate and $13 billion to $37 billion at a 7% discount rate. All estimates are in 2008$ for the year 2013.
The annualized social costs of the proposed Boiler MACT only are $2.9 billion, and the annualized social costs of the proposed Area S ource Rule are $500 million at a 7% discount rate. The net benefits for the proposed Boiler MACT for major sources only are $14 billion to $38

57 For more information on the an nualized social costs, please re fer to Section 4 of this RIA.6-30 billion and $12 billion to $34 bill ion, at 3% and 7% discount rates, respectively. The net benefits for the Boiler Area Source rule only are $500 million to $1.9 billion and $410 million to $1.7 billion, at 3% and 7% discount rates, respectively. All estimate s are in 2008$ for the year 2013.
Tables 6-8 and 6-9 show a summary of the monetized benefits, social costs, and net benefits for the proposed options and the altern ative options for the Boiler MACT for Major Sources and for the Boiler Area Source Rule, re spectively. Figures 6-4 and 6-5 show the full range of net benefits estimate s (i.e., annual benefits minus annualized costs) utilizing the 14 different PM 2.5 mortality functions at discount rates of 3% and 7%. In addition, the benefits from reducing 370,000 tons of carbon monoxide, 37,000 tons of HCl, 1,000 tons of HF, 8.3 tons of mercury, 3,400 tons of other metals, and 1,200 grams of dioxins/furans each year from major and area sources have not been included in these estimat es. EPA believes that the benefits are likely to exceed the costs under this rulemaking even when taking into account uncertainties in the cost and benefit estimates.6-31 Table 6-8. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2013 (millions of 2008$) 1

Proposed Option 3% Discount Rate 7% Discount Rate
Total Monetized Benefits 2 $17,000 to $41,000 $15,000 to $37,000

Total Social Costs 3 $2,900 $2,900
Net Benefits $14,000 to $38,000 $12,000 to $34,000 Option 1N and 1E 3% Discount Rate 7% Discount Rate
Total Monetized Benefits 4 $17,000 to $41,000 $15,000 to $37,000

Total Social Costs 3 $12,000 $12,000
Net Benefits $5,000 to $30,000 $3,500 to $26,000

1 All estimates are for the implementation year (2013) , and are rounded to two significant figures.
2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 29,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 1,700 tons of VOC and 340,000 tons of SO 2 . The benefits from reducing 340,000 tons of carbon monoxide, 37,000 tons of HCl, 1,000 tons of HF, and 7.5 tons of mercury, 3,200 tons of other metals, and 720 grams of dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included.
3 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.
4 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 29,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 6,700 tons of VOC and 350,000 tons of SO 2 . The benefits from reducing 390,000 tons of carbon monoxide, 42,000 tons of HCl, 8,600 tons of HF, and 8.1 tons of mercury, 3,200 tons of other metals, and 760 grams of dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included.6-32 Table 6-9. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Boiler Area Source Rule in 2013 (millions of 2008$) 1

Proposed Option 3% Discount Rate 7% Discount Rate
Total Monetized Benefits 2 $1,000 to $2,400 $910 to $2,200

Total Social Costs 3 $500 $500
Net Benefits $500 to $1,900 $410 to $1,700 Option 1N and 1E 3% Discount Rate 7% Discount Rate
Total Monetized Benefits 4 $8,300 to $20,000 $7,500 to $18,000

Total Social Costs 3 $35,000 $35,000
Net Benefits $-27,000 to $-15,000 $-28,000 to $-17,000

1 All estimates are for the implementation year (2013) , and are rounded to two significant figures.
2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 2,700 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 1,200 tons of VOC and 1,500 tons of SO 2 . The benefits from reducing 40,000 tons of carbon monoxide, 130 tons of HCl, 5 tons of HF, and 0.75 tons of mercury, 250 tons of other metals, and 470 grams of dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecos ystem effects and visibility impairment are not included.
3 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.
4 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 23,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 2,100 tons of VOC and 1,700 tons of SO 2 . The benefits from reducing 58,000 tons of carbon monoxide, 140 tons of HCl, 6.4 tons of HF, and 1.5 tons of mercury, 6,200 tons of other metals, and 530 grams of dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecos ystem effects and visibility impairment are not included.6-33 Figure 6-6. Net Benefits for the Proposed Major and Area Source Boiler Rules at 3% Discount Rate a

a Net Benefits are quantified in terms of PM 2.5 benefits for implementation year (2013). This graph shows 14 benefits

estimates combined with the cost estimate. All combinatio ns are treated as independent and equally probable. All fine particles are assumed to have equivalent health eff ects, but 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. $0 $10 $20 $30 $40 $50

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

Cost estimates combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions6-34 Figure 6-7. Net Benefits for the Proposed Major and Area Source Boiler Rules at 7% Discount Rate a

a Net Benefits are quantified in terms of PM 2.5 benefits for implementation year (2013). This graph shows 14 benefits

estimates combined with the cost estimate. All combinatio ns are treated as independent and equally probable. All fine particles are assumed to have equivalent health eff ects, but 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. $0 $10 $20 $30 $40 $50

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

Cost estimates combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions7-1 SECTION 7 SUPPLEMENTAL ECONOMIC ANALYSES FOR NON-HAZARDOUS SOLID WASTE DEFINITION
EPA also considered an economic analysis for an alternate approach for defining nonhazardous solid waste. Under the alternative approach, the unive rse of sources in the energy recovery and waste burning cement kiln subcategor ies would change while the number of sources in the remaining three subcategories (i.e., in cinerators, burn-off ovens, and small, remote incinerators) does not change. This sec tion provides an overview of the results. 7.1 Economic Impact Analysis Results

7.1.1 Market-Level Results Market-level impacts include price and quant ity adjustments includ ing the changes in international trade (Table 7-1). The Agency’s economic model suggest s the average national market-level variables (prices, production-levels , consumption, and international trade) will not significantly change (e.g. , are less than 0.5%).

7.1.2 Social Cost Estimates In the near term, the Agency’s economic mode l suggests that industries are able to pass on $0.7 billion (2008$) of the costs to U.S. households in the form of higher prices (Table 7-2). Existing U.S. industries’ surplus falls by $2.0 bil lion, and the net loss for U.S. stakeholders is $2.7 billion. Households that buy U.S. exports pay higher prices and purchase fewer U.S. produced goods. Other countries that that sell goods to the United Stat es benefit; the model estimates a net rest of the world gain of $0.1 billion. After accounting fo r international trade effects, the Agency’s economic model projects th e net surplus loss associated with the proposed rule is $2.6 billion. The Agency al so considered other elements of the engineering cost analysis that could not be modeled within the multimark et model (e.g., 0.4 billion in fuel savings) which reduces the social cost estimate to 2.2 billion. 7.1.3 Job Effects Near-term employment changes associated with the proposed rule are estimated to be less than 5,000 job losses; over a longer time peri od, net employment effects could range between 4,000 job losses to 9,000 job gains. Additional detail s and caveats associated with these estimates are present in section 4.7-2 Table 7-1. Market-Level Price and Quanti ty Changes: 2013 (Alternate Definition)

Industry Sector Prices Production Imports Consumption Exports Energy 0.04% -0.01% 0.07% less than 0.01% less than 0.01% Nonmanufacturing less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Manufacturing

Food, beverages, and textiles 0.02% -0.02% 0.02% -0.01% -0.01% Lumber, paper, and printing 0.14% -0.06% 0.14% -0.03% -0.09% Chemicals 0.02% -0.03% 0.02% -0.02% -0.02% Plastics and Rubber less than 0.01% -0.01% less than 0.01% less than 0.01% less than 0.01% Nonmetallic Minerals less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Primary Metals 0.04% -0.04% 0.04% -0.02% -0.04% Fabricated Metals less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Machinery and Equipment 0.01% -0.01% less than 0.01% less than 0.01% -0.02% Electronic Equipment less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Transportation Equipment less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Other less than 0.01% -0.01% less than 0.01% less than 0.01% less than 0.01% Wholesale and Retail Trade less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Transportation Services less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Other Services less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01%7-3 Table 7-2. Distribution of So cial Costs (million, 2008$): 2013

Method Alternate Definition Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus − $0.7
Change in U.S. producer surplus − $2.0
Change in U.S. surplus − $2.7
Net change in rest of world surplus $0.1
Net change in total surplus − $2.6
Direct Compliance Costs Method

Fuel savings (not modeled) $0.4
Change in Total Surplus − $2.2

Table 7-3. Employment Changes: 2013

Method 1,000 Jobs Partial equilibrium model (multiple markets) (demand effect only)

− 4.2
Estimate Derived from Morgenstern, et al. (net effect [A + B +C below]) +2.4 (

− 4.4 to +9.2)

A. Estimate Derived from Morg enstern, et al: Demand effect − 5.5 (

− 11.6 to 0.6)

B. Estimate Derived from Morgen stern, et al: Cost effect +3.7 (+1.2 to +6.2)

C. Estimate Derived from Morgenstern, et al: Factor shift effect +4.1 (+0.1 to +8.2) Note: Totals may not add due to independent rounding. 95 percent confidence intervals are shown in parenthesis.7-4 7.2 Benefits Analysis Results Table 7-4 provides a general summary of th e alternate approach results by pollutant, including the emission reductions an d monetized benefits-per-ton at discount rates of 3% and 7%. These estimates reflect EPA’s most current interp retation of the scientific literature. Higher or lower estimates of benefits are possible usi ng other assumptions, but most expert-derived estimates fall within these estimates. Data, resource, and methodological limitations prevented EPA from monetizing the benefits from several important bene fit categories, including benefits from reducing hazardous air pollutants, ecosystem ef fects, and visibility im pairment. The benefits from reducing hazardous air pollutants have not been monetized in this analysis, including reducing 280,000 tons of carbon monoxide, 5,100 tons of HCl, 1,100 tons of HF, and 7.1 tons of mercury, and 290 grams of dioxins/furans from major sources each year. Tables 7-5 shows a summary of the monetized benefits, social costs, and net benefits for the alternative definition for the Boiler MACT for existing major sources.
Table 7-4. Summary of Monetized Benefits for the Boiler MACT for Existing Major Sources in 2013 (2008$) (Alternate Approach)*

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

3%) Benefit per ton (Laden,

3%) Benefit per ton (Pope,

7%) Benefit per ton (Laden,

7%) Total Monetized Benefits (millions

2008$ at 3%) Total Monetized Benefits (millions 2008$ at 7%)
Direct PM 2.5 Major 8,040 $230,000 $560,000 $210,000 $500,000 $1,800 to $4,500 $1,700 to $4,100
PM 2.5 Precursors
SO 2 Major 44,092 $29,000 $72,000 $27,000 $65,000 $1,300 to $3,200 $1,200 to $2,900 VOC 4,703 $1,200 $3,000 $1,100 $2,700 $5.7 to $14.0 $5.2 to $13.0 Total $3,100 to $7,700 $2,800 to $6,900 *All estimates are for the implementation year (2013), and are rounded to two significant figures so numbers may not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to form PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.7-5 Table 7-5. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2013 (millions of 2008$) 1

Proposed Option with Alternate Solid Waste Definition
3% Discount Rate 7% Discount Rate
Total Monetized Benefits 2 $3,100 to $7,700 $2,800 to $6,900
Total Social Costs 3 $2,200 $2,200 Net Benefits $930 to $5,500 $640 to $4,700

1 All estimates are for the implementation year (2013) , and are rounded to two significant figures. 2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 8,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 4,700 tons of VOC and 44,000 tons of SO 2 . The benefits from reducing 280,000 tons of carbon monoxide, 5,100 tons of HCl, 1,100 tons of HF, and 7.1 tons of mercury, 1,600 tons of other metals, and 290 grams of dioxins/furans each year from major sources are not included in these estimates. In addition, the benefits fr om reducing ecosystem effects and visibility impairment are not included.R-1 REFERENCES Benwart, S.J. 2006. “Paper & Forest Products. St andard and Poor’s Industry Surveys.” 176 (28).
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Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Util ization: Capacity Utilization.” Series ID: G 17/CAPUTL/CAPUTL.GMF.S & G17/CAPUTL/CAPUTL.G321.S. <http://www. federalreserve.gov/datadownload/>. (Wood Products).

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Federal Reserve Board. 2009. “Indust rial Production and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G325.S. <http://www.federalrese rve.gov/datadownload/>. (Chemicals).

Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Util ization: Industrial Capacity.” Series ID: G17/CAP/ CAP.GMF.S & G17/CAP/CAP.G325.S. <http://www.federalreserve.gov/datadownload/>. (Chemicals).R-4 Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Util ization: Industrial Production.” Series ID: G17/IP_MAJ OR_INDUSTRY_GROUPS/IP.GMF.S & G17/IP_MAJOR_INDUSTRY_GROUPS/IP.G325.S. <http://www.federalreserve.gov/datadownload/>. (Chemicals).

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U.S. International Trade Commission. 2009. “U.S . Domestic Exports” & “U.S. Imports for Consumption.” <http://www.federa lreserve.gov/datadownload/>. U.S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Classi fication System Codes.” Effective August 22, 2008. <http://www.sba.gov/serv ices/contractingopportuni ties/sizestanda rdstopics/ size/index.html>. Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76.APPENDIX A OAQPS MULTIMARKET MODEL TO ASSESS THE ECONOMIC IMPACTS OF ENVIRONMENTAL REGULATIONA-1 A.1 Introduction An economic impact analysis (EIA) provides information about a policy’s effects (i.e., social costs); emphasis is also placed on how th e costs are distributed among stakeholders (EPA, 2000). In addition, large-scale policies require add itional analysis to bett er understand how costs are passed across the economy. A lthough several tools are available to estimate social costs, current EPA guidelines suggest that multimarke t models “...are best used when potential economic impacts and equity effects on related markets might be cons iderable” and modeling using a computable general equilibrium model is not available or practical (EPA, 2000, p. 146). Other guides for environmental economists offe r similar advice (Berck and Hoffmann, 2002; Just, Hueth, and Schmitz, 2004).

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

A.2 Multimarket Model The multimarket model contains the following features:

 Industry sectors and benchmark data set

– 100 industry sectors

– a single benchmark year (2010)

– estimates of industry carbon dioxide (CO 2 ) emissions

– estimates of industry employment

 Economic behavior

– industries respond to regulatory co sts by changing production rates

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

– customers respond to these price increases and consumption falls

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

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

– production adjustments influence employment levels

– international trade (imports/e xports) behavior considered

 Model time horizon (“short-run”)

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

– firms cannot alter input mixes; there is no substitution among production inputs (capital, labor, energy intermediates, and other intermediates)

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

– investment and government expenditures are fixed A.2.1 Industry Sectors and Benchmark Data Set
The multimarket model includes 100 industries. For the benchmark year, the model uses information from OAQPS’s computable general equilibrium model’s balanced social accounting matrix (SAM) and the following accounting identity holds (EPA, 2008):

Output + Imports = Consumption + Investment + Government + Exports (A.1)

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

Industry Label Description Representative NAICS a Energy Industries

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

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

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

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

Industry Label Description Representative NAICS a
Plastics and Rubber

PLS Plastic 3261 RUB Rubber 3262 Nonmetallic Minerals

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

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

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

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

Industry Label Description Representative NAICS a Electronic Equipment (continued)

ELQ Electric Equipment 3353 OEQ Other Electric Equipment 3359 Transportation Equipment

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

WHL Wholesale Trade 42 RTL Retail Trade 44-45 Transportation Services

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

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

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

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

Industry

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

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

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

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

Industry

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

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

OFD Other Food Products $92 $7 $90 $2 $7 OFM Other Fabricated Metals $56 $28 $50 $22 $12

(continued)A-8 Table A-2. 2010 Benchmark Data Set (billion 2006$) (continued)

Industry

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

(continued)A-9 Table A-2. 2010 Benchmark Data Set (billion 2006$) (continued)

Industry

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

A.2.2 Economic Behavior A.2.2.1 U.S. Supply In a postpolicy scenario, industry responds to changes in the new market-clearing “net” price for the good or service sold:

% Δ ”net” price = % Δ market price

− % Δ direct costs

− % Δ indirect costs (A.2)

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

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

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

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

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

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

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

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

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

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

g ε n

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

 

     

# 

 1  (A.5)

where

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

b = calibrated scale parameter for the supply price relationship

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

t = direct compliance costs per unit of supply

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

i P  = with-policy input (i) price

i P = benchmark input (i) price

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

#   ROW g ε g g 

### P c Q    (A.6)

where

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

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

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

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

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

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

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

Industry Label Industry Description Rest of World (ROW) U.S. WAP Wearing Apparel 1.2 0.8 WHL Wholesale Trade 0.7 0.7 WIR Springs and Wires 1.9 0.8 WTP Water Transportation 0.7 0.7 Note: RTI mapped Broda et al. data for their industry aggregation to the multimarket model’s 100 industries.

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

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

A.2.2.5 Demand Uses for industry output are divided into three groups: investment/government use, domestic intermediate uses, and other final us e (domestic and exports). Given the short time horizon, investment/government does not change. In termediate use is determined by the input use ratios and the industry output decisions.

g gi i Q Q     (A.7)

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

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

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

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

#   g

g g P a Q     (A.8)

where

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

a = calibrated scale parameter for the demand and price relationship

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

g  = price elasticity of demand (g)
The key parameter that controls consumpti on adjustments is the price elasticity of demand ( η g ). To approximate the response, we use dema nd elasticities that were simulated with a general equilibrium model (Ho, Morgenstern, an d Shih, 2008). Table A-6 reports the values currently available in the model. A.2.2.6 Model Scope
The multimarket model includes 100 sectors covering energy, manufacturing, and service applications. Each sector’s production technol ogy requires the purchase of energy and other intermediate goods made by other sectors include d in the model. Linking the sectors in this manner allows the model to trace direct and indi rect policy effects across different sectors. Therefore, it is best used when potential economic impacts and e quity effects on related markets might be important to stakeholders not directly affected by an environmental policy. However, the model can also be run in single-market pa rtial equilibrium mode to support and provide insights for other types of environmental policies.

A.2.2.7 Model Time Horizon The model is designed to address short-run and transitional effect s associated with environmental policy. Production te chnologies are fixed; the mode l does not assess substitution among production inputs (labor, energy intermedia tes, and other interm ediates) and assumes each investment cannot be changed during the time frame of the analysis. These issues are better addressed using other frameworks such as comp utable general equilibrium modeling. Similarly, government purchases from each sector do not adju st in response to changes in goods/service prices. Although, employment levels (number of jobs) adjust as pr oduction levels change, wages are assumed to be fixed.A-23 Table A-6. U.S. Demand Elasticities

Industry Label Industry Description Demand Elasticity η g ACM Accommodations

− 0.7 ADM Administrative Services

− 0.7 AGR Agricultural

− 0.8 ALU Aluminum

− 1.0 ANM Animal Foods

− 0.6 APP Appliances

− 2.6 ARC Aircraft

− 2.5 ART Arts

− 0.7 ATP Air Transportation

− 0.8 BAK Baked Goods

− 0.6 BEV Beverages and Tobacco

− 0.6 BOI Boilers and Tanks

− 0.5 CEM Cement

− 0.8 CEQ Construction and Agricultural Equipment

− 1.7 CHM Chemicals and Gases

− 1.0 CLY Clay

− 0.8 CMQ Communication Equipment

− 2.6 CNS Construction

− 0.8 COL Coal

− 0.1 CPP Converted Paper Products

− 0.7 CPU Computers

− 2.6 CRU Crude Oil Extraction

− 0.3 CUT Cutlery

− 0.5 EDU Education

− 0.7 EEQ Engines

− 1.7 EGV Engraving

− 0.5 ELE Electric Generation

− 0.2 ELQ Electric Equipment

− 2.6 FIN Finance and Insurance

− 0.7 FMP Fabricated Metals

− 0.5 FRG Forging and Stamping

− 0.5 FRT Fertilizer

− 1.0 FRU Fruits and Vegetables

− 0.6 FUR Furniture

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

Industry Label Industry Description Demand Elasticity η g GAS Natural Gas

− 0.3 GEQ General Equipment

− 1.7 GLS Glass

− 0.8 GRN Grain Milling

− 0.6 HLT Health Care

− 0.7 HRD Hardware

− 0.5 HVC HVAC Equipment

− 1.7 I_S Iron and Steel

− 1.0 IEQ Industrial Equipment

− 1.7 INF Information

− 0.7 INS Instruments

− 2.6 LEA Leather

− 1.1 LGT Lighting

− 2.6 LIM Lime and Gypsum

− 0.8 LUM Other Lumber

− 0.7 M_V Motor Vehicles

− 2.5 MEA Meat Products

− 0.6 MED Drugs and Medicine

− 1.0 MEQ Metalworking Equipment

− 1.7 MGT Magnetic Recording Equipment

− 2.6 MIL Dairy Products

− 0.6 MIN Mining

− 0.6 MNG Management

− 0.7 MSC Miscellaneous Manufacturing

− 1.7 MSP Machine Shops

− 0.5 MVP Motor Vehicle Parts

− 2.5 OCM Other Chemicals

− 1.0 OEQ Other Electric Equipment

− 2.6 OFD Other Food Products

− 0.6 OFM Other Fabricated Metals

− 0.5 OIL Refined Petroleum

− 0.1 ONM Other Non-metallic Minerals

− 0.8 OPM Other Primary Metals

− 1.0 OSV Other Services

− 0.7 OTP Other Transportation Services

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

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

− 2.5 PAI Paints and Adhesives

− 1.0 PAP Pulp and Paper Mills

− 0.7 PFS Professional Services

− 0.7 PIP Pipeline Transport

− 0.8 PLS Plastic

− 1.0 PLY Plywood and Veneer

− 0.7 PRN Printing

− 0.7 PUB Public Services

− 0.7 R_R Rail Cars

− 2.5 REL Real Estate

− 0.7 RSN Resins

− 1.0 RTL Retail Trade

− 0.7 RTP Railroad Transportation

− 0.8 RUB Rubber

− 1.0 SAW Sawmills

− 0.7 SEA Seafood

− 0.6 SEQ Service Industry Equipment

− 1.7 SGR Sugar

− 0.6 SHP Ships

− 2.5 SMI Semiconductor Equipment

− 2.6 SOP Soap

− 1.0 TEX Textile Mills

− 1.1 TKB Truck Bodies

− 2.5 TPM Textile Product Mills

− 1.1 TTP Freight Truck Transportation

− 0.8 TVQ TV Equipment

− 2.6 WAP Wearing Apparel

− 2.4 WHL Wholesale Trade

− 0.7 WIR Springs and Wires

− 0.5 WTP Water Transportation

− 0.8 Note: RTI assigned an elasticity using the most similar in dustry from Ho and colleagues’ industry aggregation. Source: Ho, M. S, R. Morgenstern, and J. S. Shih. 2008. “Impact of Carbon Price Policies on US Industry.” RFF Discussion Paper 08-37. Http://Www.Rff.Org /Publications/Pages/Publicationdetails.Aspx?. Publicationid=20680. Accesse d August 2009. Table B.6.APPENDIX B DETAILED ECONOMIC MOD EL RESULTS BY SECTORB-1 Table B-1. Prices (Percentage Change from Benchmark): Industry Detail

Major Area Primary Option Option 1E Primary Option Option 1N Energy 0.04% 0.18% 0.00% —0.01% Nonmanufacturing 0.00% 0.01% 0.00% 0.02% Manufacturing Food, beverages, and textiles 0.02% 0.05% 0.00% 0.14% Lumber, paper, and printing 0.22% 0.42% 0.01% 0.31% Chemicals 0.02% 0.07% 0.00% —0.02% Plastics and rubber 0.01% 0.08% 0.00% —0.04% Nonmetallic minerals 0.01% 0.06% 0.00% —0.02% Primary metals 0.04% 0.10% 0.00% —0.01% Fabricated metals 0.01% 0.07% 0.00% —0.02% Machinery and equipment 0.01% 0.02% 0.00% —0.01% Electronic equipment 0.00% 0.00% 0.00% —0.01% Transportation equipment 0.00% 0.02% 0.00% 0.00% Other 0.01% 0.03% 0.00% 0.01% Wholesale and retail trade 0.00%

— 0.01% 0.00% 0.01% Transportation services 0.00% 0.02% 0.00% 0.00% Other services 0.00% 0.00% 0.00% 0.08%B-2 Table B-2. Production (Percentage Change from Benchmark): Industry Detail

Major Area Primary Option Option 1E Primary Option Option 1N Energy —0.01% —0.12% 0.00% —0.01% Nonmanufacturing —0.01% —0.02% 0.00% —0.04% Manufacturing Food, beverages, and textiles —0.02% —0.05% 0.00% —0.11% Lumber, paper, and printing —0.10% —0.18% 0.00% —0.16% Chemicals —0.04% —0.12% 0.00% —0.01% Plastics and rubber —0.02% —0.08% 0.00% —0.02% Nonmetallic minerals —0.01% —0.03% 0.00% —0.01% Primary metals —0.04% —0.13% 0.00% 0.00% Fabricated metals —0.01% —0.04% 0.00% —0.01% Machinery and equipment —0.01% —0.04% 0.00% 0.00% Electronic equipment 0.00% —0.02% 0.00% 0.00% Transportation equipment —0.01% —0.06% 0.00% 0.00% Other —0.02% —0.06% 0.00% —0.04% Wholesale and retail trade 0.00% —0.01% 0.00% —0.02% Transportation services 0.00% —0.07% 0.00% —0.01% Other services 0.00% —0.01% 0.00% —0.04%B-3 Table B-3. Consumption (Percentage Change from Benchmark): Industry Detail

Major Area Primary Option Option 1E Primary Option Option 1N Energy —0.01% —0.05% 0.00% —0.01% Nonmanufacturing —0.01% —0.01% 0.00% —0.04% Manufacturing Food, beverages, and textiles —0.01% —0.03% 0.00% —0.08% Lumber, paper, and printing —0.06% —0.11% 0.00% —0.06% Chemicals —0.02% —0.08% 0.00% —0.01% Plastics and rubber —0.01% —0.05% 0.00% —0.02% Nonmetallic minerals —0.01% —0.02% 0.00% —0.02% Primary metals —0.02% —0.07% 0.00% —0.01% Fabricated metals —0.01% —0.03% 0.00% —0.01% Machinery and equipment —0.01% —0.02% 0.00% —0.01% Electronic equipment 0.00% —0.01% 0.00% 0.00% Transportation equipment 0.00% —0.03% 0.00% 0.00% Other —0.01% —0.02% 0.00% —0.02% Wholesale and retail trade 0.00% —0.01% 0.00% —0.02% Transportation services 0.00% —0.02% 0.00% —0.01% Other Services 0.00% —0.01% 0.00% —0.04%B-4 Table B-4. Imports (Percentage Change from Benchmark): Industry Detail

Major Area Primary Option Option 1E Primary Option Option 1N Energy 0.08% 0.28% 0.00% —0.01% Nonmanufacturing 0.00% 0.01% 0.00% 0.01% Manufacturing Food, beverages, and textiles 0.03% 0.06% 0.00% 0.16% Lumber, paper, and printing 0.23% 0.42% 0.01% 0.32% Chemicals 0.02% 0.08% 0.00% —0.02% Plastics and rubber 0.01% 0.09% 0.00% —0.04% Nonmetallic minerals 0.00% 0.02% 0.00% —0.01% Primary metals 0.04% 0.10% 0.00% —0.01% Fabricated metals 0.01% 0.07% 0.00% —0.02% Machinery and equipment 0.01% 0.02% 0.00% —0.01% Electronic equipment 0.00% 0.00% 0.00% —0.01% Transportation equipment 0.00% 0.03% 0.00% 0.00% Other 0.01% 0.05% 0.00% 0.02% Wholesale and retail trade 0.00% —0.01% 0.00% 0.00% Transportation services 0.00% 0.02% 0.00% 0.00% Other services 0.00% 0.00% 0.00% 0.01%B-5 Table B-5. Exports (Percentage Change from Benchmark): Industry Detail

Major Area Primary Option Option 1E Primary Option Option 1N Energy —0.01% —0.02% 0.00% 0.00% Nonmanufacturing 0.00% —0.01% 0.00% —0.01% Manufacturing Food, beverages, and textiles —0.01% —0.03% 0.00% —0.09% Lumber, paper, and printing —0.16% —0.29% 0.00% —0.22% Chemicals —0.02% —0.07% 0.00% 0.02% Plastics and rubber —0.01% —0.08% 0.00% 0.04% Nonmetallic minerals 0.00% —0.05% 0.00% 0.02% Primary metals —0.04% —0.10% 0.00% 0.01% Fabricated metals 0.00% —0.03% 0.00% 0.01% Machinery and equipment —0.02% —0.03% 0.00% 0.02% Electronic equipment 0.00% 0.01% 0.00% 0.02% Transportation equipment —0.01% —0.06% 0.00% 0.01% Other —0.01% —0.03% 0.00% 0.01% Wholesale and retail trade 0.00% 0.01% 0.00% 0.00% Transportation services 0.00% —0.02% 0.00% 0.00% Other services 0.00% 0.00% 0.00% —0.06%APPENDIX C MAJOR SOURCES COST AND EMISSIONS MEMORANDUMC-1 MEMORANDUM TO: Jim Eddinger, U.S. Environmental Protection Agency, OAQPS/SPPD
FROM: Susan McClutchey, Amanda Singleton, and Graham Gibson, ERG
DATE: 15 April 2010
SUBJECT: Methodology for Estimating Cost and Emissions Impacts for Industrial, Commercial, Institutional Boilers an d Process Heaters National Emission Standards for Hazardous Air Pollutants – Major Source



## 1.0 INTRODUCTION
The purpose of this memorandum is to disc uss the methodology used to estimate the costs, emission reductions, and secondary impacts from industrial, commercial, and institutional boilers at major sources of hazardous air polluta nts (HAP). These impacts were calculated for existing units and new units projected to be ope rational by the year 2013, three years after the rule is expected to be promulgated. The results of the impacts analysis are presented for both the most stringent regulatory option evaluated and the regulatory option contained in the proposed rule. The development of the maximum achieva ble control technology (M ACT) floor level of control, projection of new units, and a detailed descripti on of the cost equations used to estimate costs for various control technologie s is presented in other memoranda. 1,2,3 This memorandum is organized as follows:
1.0 Introduction
2.0 Overview of Regulatory Options
3.0 Estimating Cost Impacts
4.0 Methodology for Estimating Emission Reductions
5.0 Methodology for Estimating Secondary Impacts
6.0 ReferencesC-2 

## 2.0 OVERVIEW OF REGULATORY OPTIONS
Five control options were considered for ex isting boilers and pro cess heaters at major sources of HAP. A description of the si x options is include d in this section.


### 2.1 Existing Units

 Option 1E represents the option where all boilers, rega rdless of fuel type or size, must meet PM, HCl, mercury, CO, a nd D/F numerical emission limits.

 Option 2E uses the same control device and testing, and monitoring cost estimation logic outlined above for option 1E, except that it doe s not estimate ACI for units exceeding the MACT floor emission limit for dioxin/furan. Inst ead, it is estimated that most units, when testing for dioxin/furan will be below detecti on levels without installing any additional control devices.

 Option 3E uses the same control device and testing, and monitoring cost estimation logic outlined above for option 4E below, but it does not include the cost of an annual energy audit.

 Option 4E represents the primary proposed opt ion discussed in the preamble. In this option all boilers and process e qual to or greater than or e qual to 10 mmBtu/hr must meet PM, HCl, mercury, CO, and D/F numerical emi ssion limits, except for units in the natural gas/refinery gas and Metallurgic al Process Furnace subcategor ies. All boilers and process heaters less than 10 mmBtu/hr must meet a wo rk practice standard of a biennial boiler tune-up. All large units greater than or equal to 10 mmBtu/hr in the natural gas/refinery gas and Metallurgical Process Furnace subcateg ories must meet a work practice standard of an annual boiler tune-up. All major source facilities with a boile r or process heater must conduct an energy audit.

 Option 5E uses the same control device and testing, and monitoring cot estimation logic outlined above for option 4E above for all units except for refinery gas units. Units firing refinery gas will have a separate numerical emission limit for mercury.

 A revised definition of solid waste is be ing proposed parallel w ith this proposed rulemaking effort. The solid waste definitional rulemaking proposal includes both a primary and alternative definition of solid waste for consideration during the public comment period. Options 1E through 5E analy ze the regulatory impa cts with respect to the primary proposed definition of solid waste. However, to compare the impacts of both definitions of solid waste, the cost and emission impacts of regulatory option 4E discussed above were applied to the inventory of units that were not classified as waste burning units under the alternat ive solid waste definition.C-3 

### 2.2 New Units Three control options were considered for ex isting boilers and process heaters at major sources of HAP. A description of the thr ee options is included in this section.

 Option 1N represents the opti on where all new boile rs, regardless of fuel type or size, must meet PM, HCl, mercury, CO, and D/F numerical emission limits.

 Option 2N uses the same control device and testing, and monitoring cost estimation logic outlined above for option 1N, except that it doe s not estimate ACI for units exceeding the MACT floor emission limit for dioxin/furan. Inst ead, it is estimated that most units, when testing for dioxin/furan will be below detecti on levels without installing any additional control devices.

 Option 4N represents the primary proposed opt ion discussed in the preamble. In this option all boilers and process, regardless of size, meet PM , HCl, mercury, CO, and D/F numerical emission limits, except for units in the natural ga s/refinery gas and Metallurgical Process Furnace subcategories. Al l units in the natural gas/refinery gas and Metallurgical Process Furnace subcategories must meet a work practice standard of an annual boiler tune-up.


## 3.0 ESTIMATING COST IMPACTS
For each option, the cost impacts analysis compares the baseline emissions for each unit to the corresponding MACT floor em ission limit for the unit’s subc ategory. A control device was applied to the unit if its baseline emissions exce eded their applicable MACT floor emission limit. A comparison of the overall capita l and annualized costs of the proposed options 4E and Option 4N are presented in Table 1. The detailed equa tions used to estimate the control, testing, monitoring, and work practice costs ar e discussed in another memorandum. 2 The following logic was used to apply control, te sting, and monitoring costs to each boiler or process heater:


### 3.1 Option 1E/1N Control Cost Impacts
Mercury Control

 A new fabric filter installation was expected to achieve the Hg emission limits in the proposed rule. Where baseline Hg emissions were found to be grea ter than the MACT floor, the cost of a fabric filter was estimated for an individual boiler or process heater, unless the unit already had a fabric filter installe d. A new fabric filter was estimated to be installed at 12,138 existi ng boilers and process heaters. In the case of a unit with a fabricC-4 filter emitting Hg above the MACT floor emi ssion limit, the incremental Hg removal efficiency required to meet the MACT floor was calculated, and then the costs to install activated carbon injection (A CI) technology on the boiler were estimated. If the unit had a packed bed scrubber and did not meet th e MACT floor for Hg, a fabric filter was installed if no other control was in place. Incremental ACI equipment was installed for 155 existing boilers a nd process heaters.

 Wet scrubbers—the technology selected for th e cost analysis to reduce emissions of hydrogen chloride (HCl)—are also capable of achieving modest reductions in Hg. Literature suggests that these scrubbers can achieve a 10-percent reduction in Hg emissions. If a scrubber was be ing installed for HCl, and baseline Hg emissions were within 10 percent of the MACT floor, the wet scrubber was expected to achieve this level of emission reduction without installing a fabric filter.

Particulate Matter Control

 When baseline particulate (PM) emissions ex ceeded the MACT floor, the cost of an ESP was estimated, unless a fabric filter had alrea dy been included in the cost analysis for Hg reduction. ESP technology was estimated to be installed at 397 existing boilers and process heaters.

 Wet scrubbers are also capable of achievi ng a modest reduction in PM. Literature suggests that these scrubbers can achieve an 85-percent reduction in PM emissions. If a scrubber was being installed for HCl, and base line PM emissions were within 85 percent of the MACT floor for PM, the wet scrubber was expected to achieve this level of emission reduction without installing an ESP.

Hydrogen Chloride Control

 When HCl baseline emissions were greater than the MACT floor, the cost of adding a packed bed scrubber was estimated. If the boi ler already reported to have a scrubber installed and the baseline emissions still exceed ed the floor, the incremental required HCl removal efficiency was calculated and the then the cost to increase the sorbent injection rate in the scrubber was estimated in the cost analysis. Wet scrubbers were identified to be necessary to control HCl emissions at 13,269 existing boilers and process heatersC-5 Dioxin/Furan Control

 Where dioxin/furans exceeded the MACT fl oor, the required removal efficiencies of dioxin/furans was calculated, and the costs to install ACI were included in the cost analysis. If ACI had was previously included in the cost analysis for incremental Hg control, the ACI costs based on the highest required removal e fficiency were used in the cost analysis. For example, if a boiler requi red 90 percent reduction in Hg and 98 percent reduction in dioxin/furan, the higher ACI cost s associated with the 98 percent reduction of dioxin/furan were used in the cost anal ysis. ACI technology was estimated to be required at 2,538 existing boilers and proce ss heaters for either Hg or dioxin/furan control.

Carbon Monoxide and Organic HAP Control

 Organic HAP and carbon monoxide can be controlled by either improving the combustion efficiency of the unit, or instal ling an oxidation catalyst on the exhaust of a combustion unit. The control strategy necessa ry to meet the MACT floor emission limit will vary depending on the magnitude between the baseline emissions and the CO MACT floor. A step function was used to delineat e what type of control strategy should be analyzed in the cost impacts analysis:

o Most boilers (other than Dutch ovens a nd PC-coal boilers discussed below) are designed to operate with CO emissions at or near 400 parts per million (ppm). A boiler tune-up, was estimated in the cost impacts analysis if the unit’s baseline emissions exceeded the floor for carbon monoxi de (CO), but were less than or equal to 400 ppm @ 3% O 2 .

o If the baseline CO emissions we re between 400 and 1000 ppm @ 3% O 2 for boilers and process heaters designed to burn liqui d and gaseous fuels, the cost of a replacement low-NOx burner was estimated to achieve the MACT floor emission limits.

o Since stokers, fuel cells, or fluidized bed unit do not have replaceable burners, a linkageless boiler management system (L BMS) was the technology estimated to achieve the MACT floor when baseline CO emissions exceeded the floor and were between 400 and 1000 ppm @ 3% O 2 .C-6 o For boilers and process heaters designed to burn gas and liquids, as well as stokers, fuel cells, and fluidized bed boilers and pro cess heaters design to burn solid fuels, the cost impacts analysis estimated that a CO Oxidation catalyst would be required to meet MACT floor limits if a unit’s base line CO emissions were above 1,000 ppm @
3% O 2 and exceeded the MACT floor.

o Similar to stokers, Dutch oven and suspen sion-fired boilers and process heaters do not have replaceable burners. The desi gn of the Dutch oven/suspension burner combustion system and the high moisture content of the fuels used in Dutch ovens typically cause elevated CO levels compar ed to other combustor designs. A tune-up was estimated in the cost analysis fo r any Dutch oven/susp ension burner with a baseline of less than 5,000 ppm @ 3% O 2 . LBMS costs were estimated for any Dutch oven boiler with ba seline CO between 5,000 – 12,500 ppm @ 3% O 2. If the baseline CO emissions at Dutch oven/suspensi on-fired units exceeded 12,500 ppm @ 3% O 2 , the cost of adding catalytic oxidation was estimated for the cost impacts analysis.

o For PC-boilers, a tune-up was estimated in the cost analysis for any unit with a baseline of less than 1200 ppm @ 3% O 2 . The cost of a replacement LNB was estimated if CO emissions were between 1200 – 3000 ppm @ 3% O 2 and catalytic oxidation was estimated if CO baseline was greater than 3000 ppm @ 3% O 2.
Option 1E of the cost impacts analysis estimated 11,013 existing boilers and process heaters would meet the CO MACT floor with a tune-up, an additional 1,073 existing units would install a LBMS, 1 existing boiler would replace their existing burner with a LNB, and 482 existing units would install a CO oxidation catalyst.


### Testing and Monitoring Cost Impacts Testing and monitoring requirements varied depending on the equipment installed on the unit to control emissions, the de sign capacity of the unit, and th e fuel category the unit was assigned to.

Testing Costs All boilers and process heaters designed to bur n solid and gaseous fuels were expected to conduct an annual compliance test for PM, HCl, Hg, D/F, and CO. The cost to conduct stackC-7 tests for these five pollutant s was estimated to be $44,000 per year. Combustion units greater than 100 mmBtu/hr were expected to install CO CEMS in lieu of conducting a CO stack test and the cost to conduct tests on PM, HCl, Hg, a nd D/F was estimated to be $37,000 per year. Boilers and process heaters de signed to burn liquid fuels were expected to conduct an annual compliance test for PM, D/F, and CO. In lieu of a stack test bo ilers designed to burn liquid fuels were expected to conduct fuel analysis , or report fuel analyses received from a fuel supplier for chlorine and Hg. Conducting stack test s for PM, D/F, and CO and fuel analysis for chlorine and Hg was estimated to be $16,000 per year. Combustion units greater than 100 mmBtu/hr were expected to install CO CEMS in lieu of conducting a CO st ack test and the cost to conduct tests on PM and D/F a nd fuel analysis for chlorine and Hg was estimated to be $10,000 per year. Although other fuels are eligible to comply with the proposed rule through fuel analysis in lieu of stack testing, this cost estimate conservatively assumed that only units designed to fire liquid fuels woul d use this compliance alternative. The methods and data sources used to estimate testing and monitoring costs are discussed in other memoranda. 2
Small boilers often exhaust to small diameter st acks that do not have a ny test ports or test platforms installed. For these small units, we es timated the additional costs to these costs to construct or rent scaffolding and install test ports. The costs incl ude installation of 4 test ports, 90 degrees opposed to each other, and five weeks rental of temporary scaffolding. EPA estimates that these small sources would incur an additi onal $185 million to install test ports and rent temporary scaffolding. Many establishments in each industry, commercial, or institutional sector are associated with multiple (as many as a 700) small units. A summary of the costs by fuel category is shown in Table 1 below.C-8 Table 1. Cost Estimate for Renting Scaffold ing and Constructing Test Ports at Small Boilers and Process Heaters

Fuel Category Number of Small Boilers and Process Heaters (less than 10 mmBtu/hr) Port Costs ($2008) Renting Temporary Scaffolding ($2008) Total Costs ($2008) Coal 3 32,944 42,000 74,944 Biomass 26 285,517 364,000 649,517
Natural Gas/ Refinery Gas 7138 78,385,476 99,932,000 178,317,476
Other Process Gases 2 21,963 28,000 49,963 Liquid 238 2,613,581 3,332,000 5,945,581 Total 7,407 81,339,482 103,698,000 185,037,482

Monitoring Costs Various monitor configurations were inst alled based on the size of the unit and the pollution control devices expected to be installe d to achieve the MACT floor emission limits. For units expected to install pack ed bed wet scrubbers, an annualized cost of $5,600 for a scrubber parametric monitor was included in the cost analysis. For units expected to install a fabric filter, an annualized cost of $9,700 for a bag leak detect ion monitor was included in the cost analysis. If a unit was expected to install ACI, the cost to monitor the carbon injection rate was included in the analysis, based on the unit’s hours of opera tion. For units greater than 100 mmBtu/hr, an annualized cost of $53,300 for a CO CEMS was in cluded in the cost analysis, and for units greater than 250 mmBtu/hr an annua lized cost of $56,100 was used in the cost analysis. For units that did not install a PM CEMS and did not inst all a scrubber to meet HCl limits, an annualized cost of $14,660 for an opacity monitor was includ ed in the cost analysis. No PM CEMS or opacity monitors were assumed for boilers a nd process heaters designed to gaseous fuels.


### Fuel Savings Impacts This cost analysis includes an estimate of en ergy savings for every unit that is expected to install controls to improve combustion, or c onduct an annual tune-up or energy audit. The Department of Energy has conducted energy assessm ents at selected manuf acturing facilities and reports that facilities can redu ce fuel/energy use by 10 to 15 per cent by using best practices to increase their energy efficiency. Many best prac tices are considered pollution prevention because they reduce the amount of fuel combusted wh ich results in a corresponding reduction inC-9 emissions from the fuel combustion. Further boil er tune-ups have been shown to improve the efficiency of a boiler between 1 and 5 percent, depending on the age of the unit and the time lapse since the previous tune-up. Other combus tion controls such as upgrading burners and installation of an LBMS are also expected to improve the effici ency of the unit, thus reducing fuel consumption. This cost analysis assumes an annual fuel savings of 1 percent. The energy savings is estimated using the Equation 1: Annual Fuel Savings (mmBtu/yr) = DC * CF * Op hours * EG (Equation 1) Where: DC = unit design capacity (mmBtu/hr) CF = capacity factor, 90% of design capacity Op hours = annual operating hours reported in 2008 survey (hours/year) EG = Efficiency gain, estimated to be 1% After the fuel savings for each boiler and process heater was calculated, the both industrial and commercial prices fo r coal, #2 distillate fuel oil, #6 residual fuel oil, and natural gas were obtain from the EIA. 3 The EIA data reported fuel prices as $/ton for coal, $/thousand cubic feet for natural gas, and cents per gallon for fuel oil. The higher heating values were obtained from Table C-1 of the EPA Mandatory Reporting Rule (40 CFR part 98 subpart C) and the higher heating values were used to convert the fuel prices to a standard unit of measure, $ per mmBtu. Using the NAICS code reported by each fac ility and the fuel category assigned to each combustion unit, the appropriate fuel price was multiplied by the calculated fuel savings. This cost analysis only estimates the fuel savings fr om units in the coal, liquid and natural gas and other gaseous fuel categories. A fuel savings wa s not estimated for units in the biomass fuel category since the price of biomass fuels is vari able, and often biomass is an on-site industrial byproduct instead of a purchased fuel.


### 3.2 Option 2E/2N Option 2E uses the same control device and testing, and monitoring cost estimation logic outlined above for option 1E, except that it does not estimate ACI for units exceeding the MACT floor emission limit for dioxin/fura n. Instead, it is estimated that most units, when testing for dioxin/furan will be below detection levels with out installing any additional control devices.C-10 

### 3.3 Option 3E Option 3E follows the same logic for estimating control costs as option 2E outlined above, with the exception of sm all units (less than 10 mmBtu/ hr) and units in the natural gas/refinery gas and natural gas-fired metallurgi cal process furnace subcategories. In option 3E, the only cost estimated for small units is the cost of an annual tune-up fo r each boiler or process heater. For all units firing natural gas or refinery gas, regardless of the si ze of the unit, the only cost estimated is the cost of an annual tune-up for each boiler or process heater. Unlike option 2E, there are no testing and m onitoring costs are associated with small units or units of any size desi gned to burn natural gas, refine ry gas, or natural gas-fired metallurgical process furnaces.


### 3.4 Option 4E Option 4E includes control device and testing/ monitoring cost estimation logic identical to Option 3E outlined above, except for the additi on of the estimated cost of one facility-wide energy audit for each facility, annualized over 5 years. As discussed in the memorandum for Estimating Control Costs from Major Source Boilers and Process Heaters, the cost of an energy audit ranges from $75,000 for industrial-scale energy audits to between $2,000 and $5,000 per energy audit for institutional and commercial-scale audits. 2 The cost of each type of audit was annualized over 5 years at 7 percent to obtain an annualized cost estimate. For the cost impacts analysis, 1,608 facilities are expected to conduct an audit, 197 facilities are commercial or institutional and 1,411 faci lities are industrial.


### 3.5 Option 5E For refinery gas units, option 5E includes a co st estimate to install a carbon adsorption system to filter the refinery ga s prior to combustion. Carbon adsorption is expected to remove trace contaminants in the gas, such as Hg. When this option was initially considered, the baseline emission factor for mercury from refinery gas units was estimated to be 1.07E-07 lb per mmBtu. Since that analysis was completed, the baseline em issions from refinery gas units were reviewed and an outlier from a 1990 test, on the order of 10 -5 was identified to be biasing the baseline emission averages toward a higher average. This test occurred at F acility ID: CAExxonMobilTorrance (unit ID 75-F-1) and only one test run wa s available. Several additional mercury tests at other units firing refinery gas at the same facility in the same year were reported two be twoC-11 orders of magnitude lower than the test re ported for 75-F-1. Afte r removing the outlier emissions, the baseline emissions of mercury from refinery gas were comparable to those of natural gas, and a combined average mercur y baseline emission factor of 7.28E-07 lb/mmBtu was used for both refinery gas and natural gas. For analyzing the cost impacts of option 5E EPA estimated one carbon adsorption system for each facility with refinery gas boilers and pr ocess heaters. EPA expected that a centralized adsorption system would provide economies of s cale and would treat all the gas prior to the refinery gas being used in the combustion equipment. The cost impacts assumption estimates that 73 different facilities would inst all a carbon adsorption sy stem to treat refinery gas. Since the revised estimate of mercury baseline emissions es timated from refinery gas units had decreased substantially as a resu lt of adjusting the baseline emissi on factor, the cost to install carbon adsorption was no longer cost effective and th e beyond the floor mercury control option from refinery gas units was not incl uded in the proposal. However, th e estimated costs of installing carbon adsorbers at refineries are in cluded in Appendix A for reference.


### 3.6 Option 4N Option 4N follows the same logic for estimating control costs as option 2E/2N outlined above, with the exception of un its in the natural gas/refine ry gas and natural gas-fired metallurgical process furnace subcategories. In op tion 4N, the only cost estimated for units firing natural gas or refinery gas, regard less of the size of the unit, the onl y cost estimated is the cost of an annual tune-up for each boiler or process heater. Unlike option 2E/2N, there are no testing and m onitoring costs are associated units of any size designed to burn natural gas, refinery gas, or natural gas-fired metallurgical process furnaces. 

### 3.7 Impacts Considering the Alternative Solid Waste Definition The impacts of the alternative solid waste definition follows the identical logic for estimating control costs as option 4E discussed in Section 3.4 above. Unde r this option, there are13,275 boilers identified as not burning waste materials, compar ed to a total of 13,555 boilers and process heaters under the primar y definition of solid waste. The remainders of these units are analyzed under the parallel rulemaking effort fo r Commercial Industrial So lid Waste Incinerators if the units are located in the private sector. Should the alternative definition of solid wasteC-12 become the final promulgated definition, the addi tional waste burning units located in the public sector will be analyzed under the forthcom ing Other Solid Waste Incinerator rulemaking. 

### 3.8 Summary of Cost Impacts Option 4E is the proposed option for existing bo ilers and process heaters and option 4N is the proposed option for new boilers and process h eaters. Tables 1 and 2 summarize the costs of the proposed option for new and existing units . Appendix A of this memorandum provides a detailed summary of the costs according to boiler size, boiler fuel category, and individual control device costs. Appendix A also includes a summary of the costs on existing units under the alternative definiti on of solid waste.

Table 1: Summary of Costs of Proposed Options Costs shown in $10 6 (2008) with capital reco very estimated at 7%

Type of

Unit Option Number of Units TAC TAC considering fuel savings Testing & Monitoring TAC Control TAC Control TCI New 4N 46 $7.9 $6.2 $0.6 $7.2 $17.3 Existing 4E 13,555 $3,248 $2,871 $136 $3,086 $9,489

Table 2: Summary of Costs by Control Ty pe for Existing Units under Option 4E Costs shown in $10 6 (2008) with capital reco very estimated at 7%

Number of

Boilers Fabric Filter ESP Wet Scrubber Increased Caustic Rate Combustion Controls and

Oxidation Catalysts Activated Carbon Injection Energy Audit

TCI TAC TCI TAC TCI TAC TAC TCI TAC TCI TAC TAC
13,555 5,154 1,198 953 161 3,281 1,630 2.4 13.9 2.9 9.5 56.9 26.0C-13 

## 4.0 METHODOLOGY FOR ESTIMATING EMISSION REDUCTIONS
This section discusses the methodology used to estimate emission reductions from boilers and process heaters at both exis ting and new facilities and it pr esents a summary of the results for the regulatory options 1E/1N and 4E/4N.


### 4.1 Emission Reductions from Existing Boilers and Process Heaters The emission reductions analysis for existi ng combustion units was done for each boiler and process heater in the major source inventory. There are a to tal of 13,555 boilers and process heaters at major sources that reported data in the 2008 questionnair e (ICR No. 2286.01). Each combustion unit was assigned a unit-specific or av erage baseline emission factor, depending on the availability of emission data reported for the unit. A detailed discussion of the procedures and results of the baseline emissions analys is is presented in another memorandum. 4

Emission Reductions for Option 1E Emission reductions for PM, HCl, Hg, CO, a nd dioxins/furans were calculated on a ton per year basis by subtracting the baseline emi ssions assigned to each unit from the MACT floor emission limits corresponding to each unit’s subcategory. A detailed discussion of the procedures and results of the MACT floor an alysis is presented in another memorandum. 1 For each combustion unit, a percent reduction in CO was calculated. It was assumed that each combustion unit would achieve an identical percent reduction fr om baseline emissions for THC and VOC as was achieved for CO. Similarly, a per cent reduction was also calculated for HCl. It was assumed that each combustion unit would achieve an identical percent reduction from baseline emissions for HF as was achieved for HCl. A combustion unit is required to install a scrubber for HCl control if it is not currently meeting the HCl floor limit, and if it doesn’t already have a scrubber installed. For units required to install a scrubber, it was assumed that the scrubber will achieve a 95 percent reduction from baseline for SO2. To calculate emission reductions for SO2, baseline emissions were mu ltiplied by a factor of 0.95. A percent reduction in PM was calculated in order to estimate to tal metals reductions. It was assumed that each combustion unit would achieve an identical percent reduction from baseline emissions for each non-Hg metallic HAP as was achieved for PM. PM 2.5 emissions were assumed to be a fractionC-14 of total filterable PM emissions based on fuel and control device confi guration installed on the unit. The methods used to derive the contribution of PM2.5 to overall filterable PM are presented in other memoranda. 4 To calculate emission reductions for PM2.5, the emission reductions for PM were multiplied by the applicable PM2.5 fracti on. Emission reductions for all pollutants for which there was no floor value were cal culated on a ton per year basis. To convert emission reductions from an emissi on rate on a heat input basis to an annual emission rate, Equation 2 was used: Annual Emission Rate (tpy) = ER HI * 0.0005 * Op hours (Equation 2) Where: ER HI = emission rate (lb/mmBtu) 0.0005 = conversion factor, lbs per ton Op hours = annual operating hours reported in 2008 survey (hours/year)

To convert emission reductions from a concen tration basis to an annual emission rate, Equations 3 and 4 were used: Annual Emission Rate (tpy) = ER C * 0.000001 * ρ air * Q S * 60 * Op hours * 0.0005 * (20.946 – O 2 ) / (20.946 – Std O 2 ) (Equation 3) Where: ER C = emission concentration (ppm @ 3% O 2 ) 0.000001 = conversion factor, ppm to parts ρ air = density of air, 0.0749 lb/dscf Q S = exhaust flowrate (dscfm) 60 = conversion factor, minutes to hours Op hours = annual operating hours reported in 2008 survey (hours/year) 0.0005 = conversion factor, lb per ton 20.946 = percentage of oxygen in ambient air O 2 = percentage of oxygen assumed in exhaust gas Std. O 2 = 3 percent oxygen in standardized em ission concentration for proposed rule.C-15 Annual Emission Rate (tpy) = ER C * 0.0283 * Q S * 60 * Op hours * 0.000000001 * 0.0022 * 0.0005 * (20.946 – O 2 ) / (20.946 – Std O 2 ) (Equation 4) Where: ER C = emission concentration (ng/dscm @ 7% O 2 ) 0.0283 = conversion factor, dry standard cubic meter per dry std. cubic foot Q S = exhaust flowrate (dscfm) 60 = conversion factor, minutes per hour Op hours = annual operating hours reported in 2008 survey (hours/year) 0.000000001 = conversion factor, ng to g 0.0022 = conversion factor, g per lb 0.0005 = conversion factor, lb per ton 20.946 = percentage of oxygen in ambient air O 2 = percentage of oxygen assumed in exhaust gas Std O 2 = 7 percent oxygen in standardized em ission concentration for proposed rule.

Converting concentrations to an annual em ission rate required an oxygen concentration and exhaust flowrate estimated for each specific fuel type. The development of these assumptions and estimates is presented in other memoranda. 2 All conversions required the annual operating hours for each combustion unit reported in the 2008 survey. If no operating hours were reported, the unit was assumed to ope rate for 8,400 hours per year (two weeks of downtime).

Emission Reductions for Option 4E The same calculations discussed for estima ting emission reductions for option 1E were applied to all units except small units (less than 10 mmBtu/hr) a nd units of any size firing natural gas or refinery gas. For small units firing any type of fuel and units in the natural gas/refinery gas and natural gas metallurgical process furnace sub categories, the emission reductions were based on a one percent gain in efficiency expected from the annual tune-up work practice standard. Efficiency gains reduce fuel use, and in tur n, emissions of hazardous air pollutants. A one percent reduction in all types of emissions wa s estimated by multiplying the baseline emissions for each unit by a factor of 0.01.

Emission Reductions under the Alternative Solid Waste Definition The same calculations discussed for estima ting emission reductions for option 4E were applied to all units that were identified as not burning waste under the alternative solid waste definition. In this case, the base line emissions were compared to the MACT floor emission limits calculated for units not burning so lid waste under the alternative definition. A discussion of theC-16 methodology used to calculate the MACT floor em ission limits under the a lternative solid waste definition is discussed in another memorandum. 1

Incremental Emission Reductions if Ener gy Audit Findings are Implemented We evaluated the potential additional reductions and fuel savings that could occur as a result of implementing certain cost-effective energy efficiency improvements identified during the audit. Using the Department of Energy refe rences of a 10 to 15 percent improvement in efficiency, a range of incremental emission re ductions was estimated considering efficiency gains between 5 and 10 percent. We applied the control efficiency range s of 5 and 10 percent reduction to the emissions remaining after MACT le vel of controls were installed on the unit to identify the potential for additi onal emission reductions if the fi ndings of an energy audit were implemented. Table 3 summarizes the potential additional emission reducti ons that could occur under a beyond the floor option of implementing a ny findings in the audit with a short term payback. The fuel savings were estimated using Equation 1 described in 3.1 of this memo was used to estimate fuel savings. In this case the e fficiency gain term was modified to be between 5 and 10 percent. A summary of the estimated emission reducti ons at existing units for options 1E, the proposed option 4E, as well as the reductions fr om units considering the alternative proposed solid waste definition are located in Appendix B

.C-17 Table 3. Emission Reductions and Energy Savi ngs Resulting from Implementing Cost Effective Findings of an Energy Audit
Assuming a Five Percent Efficiency Ga in From Implementing Audit Findings

Emission Reductions and Energy Savings Per Facility HCl Emission Reductions (tpy) Filterable PM Emission Reductions (tpy) Non-Hg Metals Emission Reductions (tpy) Hg Emission Reductions (tpy) VOC Emission Reductions (tpy) Energy Saved (mmBtu/yr) Annual Fuel Savings ($/yr)
Min 1.23E-07 1.23E-06 1.49E-07 2.55E-10 2.13E-10 2.05E-01 $ - Max 6.85E+00 2.02E+01 4.98E+00 2.95E-03 2.80E+01 6.31E+06 $ 58,771,608 Average 1.76E-01 8.55E-01 5.07E-02 8.53E-05 2.84E-01 1.69E+05 $ 1,290,803 Median 1.57E-02 1.95E-01 2.97E-03 1.68E-05 5.17E-02 4.50E+04 $ 255,301

TOTAL (all 1,608 facilities) 282 1,375 81 0 457 271,172,354 2,075,611,566 Assuming a 10 Percent Efficiency Gain From Implementing Audit Findings

Emission Reductions and Energy Savings Per Facility HCl Emission Reductions (tpy) Filterable PM Emission Reductions (tpy) Non-Hg Metals Emission Reductions (tpy) Hg Emission Reductions (tpy) VOC Emission Reductions (tpy) Energy Saved (mmBtu/yr) Annual Fuel Savings ($/yr)

Min 2.46E-07 2.46E-06 2.99E-07 5.09E-10 4.26E-10 4.10E-01 $ -

Max 1.37E+01 4.04E+01 9.97E+00 5.89E-03 5.60E+01 1.26E+07 $ 117,543,216 Average 3.51E-01 1.71E+00 1.01E-01 1.71E-04 5.69E-01 3.37E+05 $ 2,581,606

Median 3.14E-02 3.91E-01 5.93E-03 3.36E-05 1.03E-01 8.99E+04 $ 510,601

TOTAL (all 1,608 facilities) 565 2,749 163 0 914 542,344,709 4,151,223,131 * Although the energy savings in mmBtu/yr are calculated for all fuels, additional fuel savings in $/yr consider only units in the coal, liquid and gas 1 fuel types. Biomass and process gases are industrial byproducts and were not assigned a purchase price.



### 4.2 Emission Reductions from New Boilers and Process Heaters
Based on industrial and commercial fuel consum ption projections from the EIA, there are 46 new boilers and process heater s expected to come on-line by 2013. 5 a discussion of the methodology used to project new boilers and process heaters is di scussed in another memorandum. 3
The New Source Performance Standards for Industrial, Commercial and Institutional Boilers (40 CFR part 60, subparts Db, Dc) (NSPS) , was reviewed to identify the expected baseline level of control for projected new units. It was determined that new boilers and process heaters larger than 30 mmBtu/hr and combusting biomass would install an ESP. This technologyC-18 selection is based on the analysis used to es tablish the PM NSPS limit for biomass boilers. New coal units larger than 75 mmBtu/h r would have a fabric filter and wet scrubber installed, while coal new units between 30 and 75 mmBtu/hr would only have a fabric filt er installed and would meet the SO2 limits in the NSPS by using coals with a low sulfur content. New units larger than 30 mmBtu/hr and combusting liquid fuel would have a fabric filter installed. All new units less than 30 mmBtu/hr would have no add-on controls and liquid fuels were expected to meet the NSPS So2 limits using low sulfur fuel oils. Gas-fi red units of all sizes were not expected to install controls to meet any of the NSPS limits. Fo r this impacts analysis, it was assumed that all new solid fuel units would be stokers, since st oker boilers are the most common type of solid fuel boilers and all new units would have NOx cont rol installed as a baseline control, regardless of fuel.

Emission Reductions for Option 1N After an appropriate baseline level of control was determined for each model unit, an average baseline emission factor calculated for ex isting units within the same fuel category and having the same level of control was assigned to each model boiler. The NSPS specifies PM and SO2 limits for new solid- and liquid-fired combus tion units based on heat input. It was assumed that all new solid and liquid units would be constructed to meet thes e limits, so they were used as baseline emission values where applicable. The ba seline emissions for each unit were subtracted from the new source MACT floor emission limit corresponding to each unit’s subcategory. The same calculations discussed in Section 3.1 of this memo were used to estimate the reductions for new units.

Emission Reductions for Option 4N Similar to the methods discussed in Section 4.1 of this memorandum, the emission reductions for new units under regulatory option 4N were calculated by subt racting the baseline emissions assigned to each unit from the MA CT floor emission limits corresponding to each unit’s subcategory, except for units firing natural gas or refinery gas. For any size natural gas or refinery gas-fired units, the emission reductions were estimated base on a 1 percent reduction in emissions, as a result of implementing a tune- up work practice. A summary of the estimated emission reductions at existing units for both option 1 and option 4 are located in Appendix B-1

.C-19 

## 5.0 SECONDARY IMPACTS
Secondary impacts include the solid waste, wate r, wastewater, and electricity required to operate air pollution control devices, as well as the additional energy sa vings resulting from improved combustion controls or work prac tices required by the NESHAP. This section documents the inputs and equations used to esti mate these secondary impacts, and it summarizes the impacts at existing units under proposed re gulatory option 4 and new units under proposed regulatory option 1. Table 4-1 su mmarizes the cost, emission, a nd secondary impacts of this proposed NESHAP. Appendices C-1 and C-2 present a detailed breakdown of the secondary waste, water, and energy impacts from each subc ategory of new and existing boilers and process heaters, respectively.

Table 4-1. Summary of Secondary Impacts
Impact New Units (proposed regulatory Option 4N) Existing Units (proposed regulatory Option 4E)
Water (gal/yr) 197,200 2.4 billion
Wastewater (gal/y r) 142,300 730 million
Solid Waste (tons/yr) 149,800 81,400
Purchased Electricity (k W-hr/yr) 11.2 2.9 billion
Energy Savings* (trillion Btu/yr) 0.1 41.7 * Energy savings is calculated for units in the coal, liquid and gas subcategories.
The secondary impacts were calculated using algorithms and assumptions described in another memorandum. 2 These algorithms and assumptions were applied to the existing boiler and process heaters, where the baseline emissions for each unit exceeded the proposed MACT floor emission limit except for small units (< 10 mmBtu/hr) and units firing natural gas or refinery gas. A one percent energy savings was calculated for all units , including the small and gas-fired units since these units are expected to conduct a tune -up. For new units, the algorithms and assumptions were applied to model units repr esenting units expected to come online between 2010 and 2013, when the baseline emissions for each model exceeded the proposed MACT floor emission limit for new units except for units firing natural gas and refinery gas. Natural gas and refinery gas are not required to meet a numerical emission limit, and ther efore not expected toC-20 incur any secondary waste, water, or electricity impacts from th ese controls. A 1 percent energy savings from natural gas and refinery gas units are included in the energy savings estimate in Table 4-1 since these units are expected to c onduct a tune-up. The methodology used to assign baseline emission factors to new and existing units are discussed in another memorandum. 4


### 5.1 Wastewater and Water Impacts The water required to create a slurry in the packed scrubber and the wastewater generated by the effluent of a packed bed scrubber was ca lculated for every unit expected to install a scrubber to meet the HCl limits in the proposed ru le. Both the water and wa stewater calculations required the use of several constants and variable s. The constants including the density of gas, moles of salt needed per mole of hydrogen chloride in the exhaust gas, the molecular weight of the salt used, the fraction of the waste stream treated, operating hours per year and the molecular weight of the gas. The data sources for thes e constants are provided in another memorandum. 2 The variables used to estimate the quantity of water required and wast ewater generated were calculated based on characteristics reported for each existing unit in the 2008 survey and for the characteristics assigned to each new model unit. The variables included: exhaust flow rate from the combustion unit to the control device in actua l cubic feet per minute, the inlet loading of hydrogen chloride to the control device (mole fraction), and the efficiency of the control device in removing hydrogen chloride from the exhaust gas (percent reduction). The calculations used to estimate each variable are provided in another memorandum. 2 The total national water and wastewater amounts in Table 4.1 were determined by adding the per unit water and wastewater estimates for all new and existing units, respectively.


### 5.2 Solid Waste Impacts Solid waste is generated from collecting dust an d fly ash in fabric filters or ESP control devices, spent carbon associated with ACI or the installation of a carbon be d adsorber, or spent caustic from increasing the caustic injection rate. Solid waste impacts were estimated for every unit expected to install a fabric filter, ACI or carbon bed adsorber to meet mercury emission limits, or install an ESP to meet PM emission lim its. The total national solid waste amounts in Table 4.1 were determined by adding the per unit solid waste estimates for all new and existing units, respectively. To estimate the solid waste co ntribution from each of these control devices, the variables were calculated based on characte ristics reported for each existing unit in the 2008C-21 survey and for the characteristics assigned to ea ch new model unit. The calculations used to estimate each variable and the quantity of solid waste generated are provided in another memorandum. 2 The solid waste (dust, fly ash) generated by the use of an electrostatic precipitator was calculated when an electrostatic precipitator wa s determined to be necessary to meet the NESHAP emission limits for PM. Estimates of the solid waste collected in an ESP was based on several variables including: exha ust flow rate from the combus tion unit to the control device (acfm); the inlet loading of pa rticulate matter to the contro l device (gr/acfm); operating hours (hr/year) and the efficiency of the control device required to meet the PM emission limits in the proposed NESHAP. The solid waste generated from the collection of dust and fly ash in a fabric filter was calculated when a fabric filter was determined to be necessary to meet the proposed NESHAP emission limits for particulate matter and/or me rcury. The calculation required the use of three variables, including: exhaust flow rate from the combustion unit to the control device (dscfm); operating hours (hr/year) and the inlet loading of particulate matter to the control device (gr/acfm). For this analysis, the spent carbon collected from units w ith ACI is assumed to be disposed of instead of being re-generated. The amount of spent carbon created from ACI was calculated when ACI was expected to be neces sary to meet the proposed NESHAP emission limits for mercury or dioxin/furan. The calculation re quired the use of six variables, including: exhaust flow rate from the combustion unit to the control device (dscfm); operating hours (hr/year), required removal efficiency for merc ury and dioxin/furan, and an adjustment factor based required removal efficiency of mercury or dioxin /furan. The solid waste generated by the use of incr eased caustic was calculated for those units where additional caustic was expected to achie ve the proposed NESHAP emission limits for HCl. The calculation required the use of three va riables, including: exhaust flow rate from the combustion unit to the control device (dscfm); operating hours (hr/year), and the required removal efficiency for HCl.C-22 

### 5.3 Electricity Impacts The amount of electricity required to operate a control device was calculated for a packed scrubber, electrostatic precipitato r, and fabric filter, CO oxidation catalyst and the fans for the ductwork associated with this equipment. These impacts were assessed for every unit that was estimated to require hydrogen chloride and/or partic ulate matter control. Electricity requirements are one output of the cost algor ithms used in the analyses, so no additional calculations were necessary. For some units, an electrical demand from multiple control devices was estimated. The total national electricity demand in Table 4.1 was determined by adding the per unit solid waste estimates for all new and existing units, respectively. To estimate the electricity demand from each of these control devices, a set of va riables were calculated based on characteristics reported for each existing unit in the 2008 survey a nd for the characteristics assigned to each new model unit. The constants, variables, and calcu lations used to estimate each variable and the electricity demand to operate the control de vices are provided in another memorandum. 2


### 5.4 Energy Savings Impacts The energy savings from combustion controls such as low NOx burners or linkageless boiler management systems, and work practice standards, including a tune-up, and implementing the energy audit findings with a short-term pa yback can improvements in efficiency, thereby reducing fuel consumption. Although these combustion improvements have been documented to achieve efficiency gains between 5 and 10 percen t from the baseline operating conditions, this secondary impacts analysis estimates a 1 percen t efficiency gain, to be conservative and consistent with the assumptions made in Sect ion 3.1 of this memorandum. Quantifying the exact gains in efficiency from each of these work pr actice standards is difficult, and may depend on the baseline operating efficiency of each unit. Section 3.1 discusses the fuel sa vings impacts in terms of annu alized cost savings to each boiler or process heater, and th e national energy savings presente d in Table 4.1 of this section follows the same methodology as was discussed in Section 3.1 and reflect the savings from boilers in the coal, gas, a nd liquid fuel categories only. 

### 5.5 Estimating Secondary Impacts for Regulatory Options 4E/4N Regulatory Options 4E for existing and 4N fo r new units are both described in detail in Section 3 of this memorandum. For the secondary impacts analysis at ex isting units under optionC-23 4E, the water, wastewater, solid waste, and elec tricity impacts were only assessed for large units, (those greater than or equal to 10 mmBtu/hr) that are in the coal, biomass, liquids, or other process gas subcategories. Seconda ry impacts of water, wastewater , solid waste, and electricity were not assessed for natural gas or refinery gas units or units in the natural gas metallurgical process furnace subcategory. Energy savings were estimated for all units firing anything other than biomass since all units were ex pected to conduct an energy audit. For new units under option 4N, the water, wa stewater, solid waste, and electricity impacts were assessed for any size unit firing coal, biomass, liquid, or other process gases. Secondary impacts of water, wastewater, solid wa ste, and electricity were not assessed for natural gas or refinery gas units or units in the natural gas metallurgical process furnace subcategory. A one percent energy savings was es timated for all units firing coal, liquids or gases that were estimated to require a tune -up or the installation of combustion control equipment to meet the CO limits from new bo ilers. Both tune-ups and combustion controls improve the efficiency of the unit, thereby reducing energy consumption.



## 6.0 REFERENCES

1. Singleton, Amanda. ERG. MACT Floor Met hodology for Major Source Boilers and Process Heaters. April 2010.

2. Singleton, Amanda and S. McClutchey. ERG. Methodology for Estimating Control Costs for the Major Source Industrial, Commercial and Institutional Boilers and Process Heaters National Emission Standards fo r Hazardous Air Pollutants. April 2010.

3. Gibson, Graham. ERG. New Unit Analysis for Industrial, Commercial, and Institutional Boilers and Process Heaters at Major Sources. April 2010.

4. U.S. Department of Energy, EIA. 2008 Deliv ered Fuel Prices to Industrial and Commercial Clients. http://www.eia.doe.gov/

5. Gibson, G. ERG. Development of Baseline Emission Factors for Boilers and Process Heaters at Commercial, Industrial, an d Institutional Faci lities. April 2010.

6. U.S. Department of Energy, EIA. A nnual Energy Outlook 2009. Table 2. Energy Consumption by Sector and Source. http://www.eia.doe.gov/APPENDIX D AREA SOURCES COST AND EMISSIONS MEMORANDUMD-1 MEMORANDUM TO: Jim Eddinger, U.S. Environmen tal Protection Agency, OAQPS/SPPD
FROM: Graham Gibson, Susan McClut chey, and Amanda Singleton, ERG
DATE: April 2010
SUBJECT: DRAFT Methodology for Estimating Impacts from Industrial, Commercial, Institutional Boilers at Area Sources of Hazardous Air Pollutant Emissions



## 1.0 INTRODUCTION
The purpose of this memorandum is to disc uss the methodology used to estimate the costs, emission reductions, and secondary impacts from industrial, commercial, and institutional boilers at area sources of hazardous air polluta nts (HAP). These impacts were calculated for existing units and new units projected to be ope rational by the year 2013, three years after the rule is expected to be promulgated. The results of the impacts analysis are presented for both the most stringent regulatory option evaluated and the regulatory option contained in the proposed rule. The development of the maximum achie vable control technol ogy (MACT) floor and Generally Achievable Control Technology (GACT) le vel of control, projection of new units, and a detailed description of the cost equations used to estimate cost s for various control technologies is presented in other memoranda. 1,2,3 This memorandum is organized as follows:
1.0 Introduction
2.0 Overview of Regulatory Options
3.0 Estimating Cost Impacts
4.0 Methodology for Estimating Emission Reductions
5.0 Methodology for Estimating Secondary Impacts
6.0 ReferencesD-2 

## 2.0 OVERVIEW OF REGULATORY OPTIONS
Three control options were considered for existing boilers at area sources of HAP. A description of the three op tions is described below.


### 2.1 Existing Units

 Option 1E represents the option where all boilers, rega rdless of fuel type or size, must meet mercury and CO numerical emission limits based on MACT and PM numerical emission limits based on GACT. PM GACT was identified to be a multiclone for existing units.

 Option 2E represents the same emission limits as discussed in 1E above for large units (equal to or greater than 10 mmBtu/hr). Small units are exempt from numerical limits and instead are required to meet a work practice stan dard of a biennial t une-up. All facilities are required to conduct an energy audit.

 Option 3E represents the primary proposed opt ion discussed in the preamble. In this option, all coal boilers equal to or greater than 10 mmBtu/hr must meet mercury and CO numeric emission limits based on MACT. All biomass and liquid boilers equal to or greater than 10 mmBtu/hr must meet a CO numerical emission limit, based on MACT. All facilities with a large boiler are required to conduct an energy audit. Small boilers are exempt from numeric emission limits for all pol lutants, but are required to meet a work practice standard of a bienni al tune-up. There are no numeri cal emission limits for PM under this option for any size or type of unit.


### 2.2 New Units Three control options were considered for ne w boilers at area sour ces of HAP. A detailed description of the three op tions is described below.

 Option 1N represents the option where all boilers, rega rdless of fuel type or size, must meet mercury and CO limits based on MACT and PM numerical emission limits based on GACT. GACT for new units is based on PM limits in the New Source Performance Standards (NSPS) for Industrial, Commercial , and Institutional Boile rs (40 CFR part 60 subparts Db, Dc).

 Option 2N represents the same emission limits as discussed in 1N above for large units (equal to or greater than 10 mmBtu/hr). Small units are exempt from numerical emission limits and instead are required to meet a work practice standard of a biennial tune-up.

 Option 3N represents the primary proposed opt ion discussed in the preamble. In this option, all coal boilers, regardless of size, must meet mercury and CO limits based on MACT and PM numerical emission limits based on GACT. All biomass and liquidD-3 boilers, regardless of size, must meet CO limits based on MACT and PM numerical emission limits based on GACT.


## 3.0 ESTIMATING COST IMPACTS
For each option, a percentage of units in each model unit were assumed to require control devices in order to meet the limit when the ba seline emissions for the model unit exceeded the MACT floor emission limit applicable to each m odel. A detailed descripti on of the three options is described below. A summary table compari ng the overall capital a nd annualized costs of option 3E for existing units and option 3N for new units is pres ented in Table 1. The equations used to estimate the control, testing, monitoring, and work practi ce costs are discussed in another memorandum. 3 The following logic was used to apply c ontrol, testing, and m onitoring costs to each boiler:


### 3.1 Option 1E Control Cost Impacts
Mercury Control A new fabric filter installation was expected to achieve the mercury emission limits in the proposed rule. Where baseline mercury emissions we re found to be greater than the MACT floor, the cost of a fabric filter was estimated for a portion of the boilers repr esented by the model unit. Based on the data used in the MACT floor anal ysis, a fraction of units in each subcategory meeting the MACT floor for Hg was estimated. Fo r boilers designed to burn biomass one of the boilers was meeting the floor and the other unit was not meeting the floor. In the cost impacts analysis, 50 percent of the biomass units were estima ted to install a fabric filter to meet the floor. For boilers designed to burn coal, 44 percent of units were achieving the proposed MACT floor emission limit for coal, so a new fabric filter wa s estimated to be installed at 56 percent of the existing coal-fired boilers. For liquid fuel units , there were no area source boilers with emission test data available for mercury. In the absence of other information about the distribution of units that would require a fabric filt er to be installed, this cost impacts analysis assumes that 50 percent of the liquid fuel units would install a fabric filter to meet the mercury limit.D-4 CO/Organic HAP Control Organic HAP and carbon monoxide can be controlled by either improving the combustion efficiency of the unit, or instal ling an oxidation catalyst on the exhaust of a combustion unit. The control strategy necessary to meet the MACT floor emission limit will vary depending on the magnitude between the baseline emissions and the CO MACT floor. Most boilers (other than Dutch ovens and PC -coal boilers) are desi gned to operate with CO emissions at or near 400 parts per million (ppm ). A boiler tune-up was estimated in the cost impacts analysis if the unit’s baseline emissions exceeded the floor for carbon monoxide (CO), but were less than or equal to 400 ppm @ 3% O 2 . The combustor design of the boilers in the area source inventory is not known and this impacts an alysis assumes that all areas source boilers firing solid fuels have a stoker combustor design since this is the predominant combustor design in the major source boiler inventor y. The baseline emissions for ar ea source boilers in all fuel categories are less than 400 ppm, and so it is a ssumed that combustion co ntrols, either a basic tune-up or a more advanced burner replacemen t or installation of a linkageless boiler management system, can achieve the proposed MACT floor emission limits. No oxidation catalysts are estimated to be required to meet the MACT floor emission limits. Based on the emission test data used to calcu late the MACT floor for CO, 29 percent of units burning coal are meeting the MACT floor a nd these units would be expected to install a linkageless boiler management system to comply with the CO limits. For units burning biomass, 72 percent of the units are exceeding the MACT fl oor emission limits, and so 72 percent of the units are estimated to install a linkageless boile r management system. For units burning liquids, 86 percent of the units are exceeding the floor and this cost impacts analysis assumes these units will install a new low NOx replacement burner in order to meet the CO limits. The units not expected to install these advanced controls are expected to conduct an annual tune-up to maintain in compliance with the pr oposed CO limit overtime.

Particulate Matter Control For all units that were not expected to install a fabric filter for merc ury control, the cost impacts analysis for this option assumes that th e unit would install a multiclone to achieve the GACT emission limits for PM. Based on the current MACT floor analysis, 44 percent of coal units and 50 percent of liquid units would in stall a multiclone. Existing biomass units notD-5 expected to install a fabric filter would also in stall a multiclone. Base on the current MACT floor analysis, 50 percent of existing bioma ss units would install a multiclone.


### Testing and Monitoring Cost Impacts Testing and monitoring requirements varied depending on the equipment installed on the unit to control emissions, the de sign capacity of the model unit, and the fuel category of the model unit.

Testing Costs All boilers designed to burn solid fuels were expected to conduct an annual compliance test for PM, Hg, and CO. The cost to conduct stac k tests for these three pollutants was estimated to be $15,000 per year. Boilers greater than 100 mmB tu/hr were expected to install CO CEMS in lieu of conducting a CO stack test and the cost to conduct tests on PM and Hg was estimated to be $12,000 per year. Boilers designed to burn liquid fuels were e xpected to conduct an annual compliance test for PM and CO. In lieu of a stack test boilers designed to burn liquid fuels were expected to conduct fuel analysis, or report fu el analyses received from a fu el supplier for chlorine and Hg. Conducting stack tests for PM and CO was estim ated to be $13,000 per year and the cost to conduct fuel analysis for Hg was estimated to be $600 per year. Combustion units greater than 100 mmBtu/hr were expected to install CO CEMS in lieu of conducting a CO stack test and the cost to conduct tests on PM and fuel analys is for Hg was estimated to be $8,600 per year. Although solid fuels are eligible to comply with the proposed rule thr ough fuel analysis in lieu of stack testing, when the mercury content of the fu el is below the MACT floor emission limit, this cost estimate conservatively assumed that only un its designed to fire liquid fuels would use this compliance alternative. The methods and data s ources used to estimate testing and monitoring costs are discussed in other memoranda. 3

Monitoring Costs Various monitor configurations were inst alled based on the size of the unit and the pollution control devices expected to be installe d to achieve the MACT floor emission limits. For units expected to install a fabric filter, an annualized cost of $9,700 fo r a bag leak detection monitor was included in the cost analysis. For units greater than 100 mm Btu/hr, an annualizedD-6 cost of $53,300 for a CO CEMS was included in the cost analysis. For units that did not install a bag leak detector, an annualized cost of $14,660 for an opacity monitor was included in the cost analysis.


### Fuel Savings Impacts This cost analysis includes an estimate of en ergy savings for every unit that is expected to install controls to improve combustion, or c onduct an annual tune-up or energy audit. The Department of Energy has conducted energy assessm ents at selected manuf acturing facilities and reports that facilities can redu ce fuel/energy use by 10 to 15 per cent by using best practices to increase their energy efficiency. Many best prac tices are considered pollution prevention because they reduce the amount of fuel combusted wh ich results in a corresponding reduction in emissions from the fuel combustion. Further boil er tune-ups have been shown to improve the efficiency of a boiler between 1 and 5 percent, depending on the age of the unit and the time lapse since the previous tune-up. Other combus tion controls such as upgrading burners and installation of an LBMS are also expected to improve the effici ency of the unit, thus reducing fuel consumption. This cost analysis assumes an annual fuel savings of 1 percent. The energy savings is estimated using Equation 1: Annual Fuel Savings (mmBtu/yr) = DC * CF * Op hours * EG (Equation 1) Where: DC = unit design capacity (mmBtu/hr) Op hours = annual operating hours, assumed 8400 (hours/year) EG = Efficiency gain, estimated to be 1% CF = annual average capacity factor, 0.5 for liquid and 0.65 for coal and biomass After the fuel savings for each boiler was calculated, the both industrial and commercial prices for coal, #2 distillate fu el oil, and #6 residual fuel oil were obtained from the EIA. 3 The EIA data reported fuel prices as $/ton for co al, and cents per gallon for fuel oil. The higher heating values were obtained from Table C-1 of the EPA Mandatory Reporting Rule (40 CFR part 98 subpart C) and the higher heating values were used to convert the fuel prices to a standard unit of measure, $ per mmBtu. Using the distribution of SIC codes reported in the 13- state boiler inspector inventory, th e model units were distributed to an industrial or commercial sector, and then the appropriate fuel price wa s multiplied by the calculated fuel savings. This cost analysis only estimates the fuel savings from units in the coal and liquid fuel categories. AD-7 fuel savings was not estimated fo r units in the biomass fuel cate gory since the price of biomass fuels is variable, and often biomass is an on-site industrial byproduct instead of a purchased fuel.


### 3.2 Option 2E Option 2E follows the same logic for estimating control costs as option 1E outlined above, with the exception of sma ll units (less than 10 mmBtu/hr). In option 2E, the only cost estimated for small units is the cost of an annual tune-up for each boiler. No testing and monitoring costs were included in option 2E for sm all units. Option 2E also includes the cost of an energy audit at every area source facility, ap proximately 91,339 facilities . As discussed in the memorandum for Estimating Control Costs from Ma jor Source Boilers and Process heaters, the cost of an energy audit ranges from $75,000 for industrial-scale energy audits to between $2,000 and $5,000 per energy audit for institu tional and commercial-scale audits. 4 The facility’s classification of either an industr ial or commercial facility was a ssigned using the distribution of SIC codes in the 13-state boiler inspector inve ntory. The cost of each type of audit was annualized over 5 years at 7 percent to obtain an annualized cost estimate.


### 3.3 Option 3E The proposed option 3E includes control devi ce and testing/monitoring cost estimation for mercury and CO from large coal units. As me ntioned in option 1E, 56 percent of large coal units, or 321 boilers are expected to install a fabr ic filter in order to meet the mercury limit. In addition, 29 percent of large coal units are expected to install a dvanced combustion controls in order to meet the CO limit. This analysis uses the cost of a linkageless boiler management system to estimate the costs of advanced combus tion control. The remaining 71 percent of large coal units are expected to meet the CO limit with a tune-up. The testing and monitoring costs for large coal units include a test for CO and mercur y, as well as a bag leak detection system for the 321 boilers that are expected to install a fabric filter. Ther e are no proposed numerical PM emission limits under this option, and so no additio nal testing costs for PM or opacity monitoring costs were assessed in the cost impacts analysis for this option. Under option 3E liquid and biomass boilers ar e not subject to nume rical emission limits for mercury and there are no costs included in the impacts analysis to install fabric filters or conduct mercury fuel analysis or stack testing. Large liquid and biomass boilers are subject to numerical emission limits for CO. Th is cost impacts analysis estimates that all the biomass andD-8 liquid fuel units can meet the CO emission lim its by conducting an annual tune-up. These large units must also conduct testing and monitoring acti vities for CO to demonstrate compliance with the numerical emission limits. Option 3E exempts small boilers from numerical emission limits. Instead these units must conduct a work practice standard of a biennial tune-up. The co st impacts analysis does not include any additional testing and monito ring requirements for these small boilers. Finally, option 3E proposes that all facilities with large boilers c onduct an energy audit. For this cost impacts analysis one large boiler per facility was assumed, or 13,268 facilities estimated to conduct an audit. Similar to the discussion under option 2E, the cost of the audit ranged from $75,000 for industrial-scale energy audits to between $2,000 and $5,000 per energy
audit for institutional and commercial-scale audits. 2


### 3.4 Option 1N
New area source boilers are s ubject to an NSPS (40 CFR pa rt 60 subparts Db, Dc) to regulate emissions of PM, NOx and SO2. The cost impacts analysis considered controls that would likely be installed to comply with the N SPS and includes an estimate of any additional control, testing and monitoring costs that would not be already conducted to meet the requirements of the NSPS. Based on a review of the NSPS, this analysis assumes all biomass boilers greater than 30 mmBtu/hr will have an ES P control installed as the baseline to meet the NSPS PM limits; all coal boilers greater than 75 will have an FF and wet scrubber installed to meet PM and SO2 limits; all coal boilers between 30 and 75 will have a Fabric Filter and use low sulfur coal to meet PM and SO2 limits, and a ll liquid boilers greater than 30 will have an FF installed to meet PM limits. The NSPS does not re gulate PM for units less than 30 mmBtu/hr.

Mercury Control A new fabric filter installation was expected to achieve the mercury emission limits in the proposed rule. Where baseline mercury emissions we re found to be greater than the MACT floor, the cost of a fabric filter was estimated for a portion of the boilers repr esented by the model unit. All new boilers, regardless of size or fuel, were exp ected to install a fabric filter in order to meet the mercury limits under this option. Comparing these mercury control requirement to the expected controls under the NSPS, all biomass boilers are expected to install a fabric filter toD-9 meet the mercury limit and all liquid and coal boilers less than or e qual to 30 mmBtu/hr are expected to install a fabric filter to meet the mercury limit.

CO/Organic HAP Control New boilers are expected to be equipped with new and efficient burners, and it was assumed that an annual tune-up could achieve th e CO numeric emission limit for all sizes and types of boilers. Other advanced combustion controls were not cons idered as a control alternative for new boilers. As mentioned under 1E, the contro l strategy necessary to meet the MACT floor emission limit will vary depending on the magnitude between the baseline emissions and the CO MACT floor.

Particulate Matter Control Under this option all units are expected to inst all a fabric filter for mercury control, which has a co-benefit of reducing PM emissions, as well as other non-mercury metallic HAP. No additional control costs were estima ted for PM control at new boilers.


### Testing and Monitoring Cost Impacts Testing and monitoring requirements varied depending on the equipment installed on the unit to control emissions, the de sign capacity of the model unit, and the fuel category of the model unit.

Testing Costs All boilers designed to burn solid fuels were expected to conduct an annual compliance test for PM, Hg, and CO. The cost to conduct stac k tests for these three pollutants was estimated to be $15,000 per year. Combustion units greater than 100 mmBtu/hr were expected to install CO CEMS in lieu of conducting a CO stack test and the cost to conduct tests on PM and Hg was estimated to be $12,000 per year. Boilers designed to burn liquid fuels were e xpected to conduct an annual compliance test for PM and CO. In lieu of a stack test boilers designed to burn liquid fuels were expected to conduct fuel analysis, or report fu el analyses received from a fu el supplier for chlorine and Hg. Conducting stack tests for PM and CO was estim ated to be $13,000 per year and the cost to conduct fuel analysis for Hg was estimated to be $600 per year. Combustion units greater than 100 mmBtu/hr were expected to install CO CEMS in lieu of conducting a CO stack test and theD-10 cost to conduct tests on PM and fuel analys is for Hg was estimated to be $8,600 per year. Although solid fuels are eligible to comply with the proposed rule thr ough fuel analysis in lieu of stack testing, when the mercury content of the fu el is below the MACT floor emission limit, this cost estimate conservatively assumed that only un its designed to fire liquid fuels would use this compliance alternative. The methods and data s ources used to estimate testing and monitoring costs are discussed in other memoranda. 3

Monitoring Costs Various monitor configurations were inst alled based on the size of the unit and the pollution control devices expected to be installe d to achieve the MACT floor emission limits. For units expected to install a fabric filter, an annualized cost of $9,700 fo r a bag leak detection monitor was included in the cost analysis. For units greater than 100 mm Btu/hr, an annualized cost of $53,300 for a CO CEMS was included in the cost analysis. For units that did not install a bag leak detector, an annualized cost of $14,660 for an opacity monitor was included in the cost analysis.


### 3.5 Option 2N Option 2N follows the same logic for estimating control costs as option 1N outlined above, with the exception of sma ll units (less than 10 mmBtu/hr). In option 2N, the only cost estimated for small units is the cost of an annual tune-up for each boiler. No testing and monitoring costs were included in option 2N for small units


### 3.6 Option 3N The proposed option 3N includes identical re quirement for coal units as outlined under option 1N. Under option 3N liquid and biomass bo ilers are not subject to numerical emission limits for mercury and there are no co sts included in the impacts analysis to install fabric filters for mercury control or conduct mercury fuel analys is or stack testing. Liquid and biomass boilers are subject to numerical emission limits for CO. This cost impacts analysis estimates that all the biomass and liquid fuel units can meet the CO emission limits by conducting an annual tune-up. These units must also conduct testing and m onitoring activities for CO to demonstrate compliance with the numerical emission limits.D-11 Option 3N also includes a numerical PM em ission limit for coal, biomass, and liquid boilers, based on the NSPS limits app licable to each of these categor ies. Since all coal units are subject to mercury emission limits, they ar e expected to meet a PM GACT limit of 0.051 lb/mmBtu without any additional control requi rements. The NSPS PM limit for biomass is 0.1 lb/mmBtu, which is based on the performance of an electrostatic precipitator (ESP). Since biomass units greater than 30 mmBtu/hr are alre ady subject to this limit under the NSPS, this cost analysis applies the costs for an ESP to un its less than or equal to 30 mmBtu/hr. The NSPS PM limit for liquids is also 0.051 lb/mmBtu. Base d on the calculated average baseline emission factors analysis distillate liquids are expected to meet that lim it without any additional control. 5 However residual liquid units are exp ected to install a fabric filter to meet the PM emission limit. Under option 3N, all boilers le ss than or equal to 30 mmBtu /hr are estimated to incur costs to test for PM and CO, at an estimate cost of $14,000 per year. Boilers greater than 30
mmBtu/hr will incur PM stack test ing costs under the NSPS. Coal boi lers are estimated to incur additional costs to test for mercury and the cost to conduct tests for PM, CO, and Hg is estimated to be $19,000 per year.


### 3.7 Summary of Cost Impacts Option 3E is the proposed option for existi ng boilers and option 3N is the proposed option for new boilers. Since new boilers and thei r exhaust stacks can be designed to allow for stack testing, the tune-up work practice standa rd does not apply to new small units. Table 1 summarizes the costs of the proposed option for new and existing boile rs at area sources of HAP. Appendix A of this memorandum provides a detaile d summary of the costs for each model unit.

Table 1: Summary of Costs of Proposed Options Costs shown in $10 6 (2008) with capital reco very estimated at 7%

Type of

Unit Option Number of Boilers TAC TAC considering fuel savings Testing & Monitoring TAC Control TAC Control TCI New 3N 6,779 $311 $260 $193 $117 $343 Existing 3E 182,671 $696 $279 $94 $550 $1,792D-12 

## 4.0 METHODOLOGY FOR ESTIMATING EMISSION REDUCTIONS
This section discusses the methodology used to estimate emission reductions from boilers at both existing and new facilitie s and it presents a summary of the results for the regulatory options 1E/1N and 3E/3N.


### 4.1 Emission Reductions from Existing Boilers Each model area source boiler was assigned ba seline emissions based on the calculated baseline averages for existing major source co mbustion units in the same size and fuel subcategory. The development of area source mode l units and the procedures and results of the baseline emissions analysis is presented in other memoranda. 5-6

Emission Reductions for Option 1E Emission reductions for all pollutants were ca lculated on a ton per year basis. Emission reductions of Hg and CO were calculated by subtra cting the baseline emissi ons assigned to each model unit from the MACT floor (or GACT) emi ssion limits corresponding to the subcategory for each model boiler. A detailed discussion of the procedures and results of the MACT floor analysis is presented in another memorandum. 1 For all units expected to install a fabric f ilter to meet the mercury MACT floor emission limits, this fabric filter achieves a co-benef it of reducing emissions of PM and non-mercury metallic HAP. To calculate the PM emission reductions from units expected to install a fabric filter, the baseline emissions assigned to each m odel boiler were subtracted from the calculated average baseline emission factor co rresponding to a fabric fi lter level of control in the same fuel category. For example, the PM baseline emission factor for uncontrolled or multiclone-equipped biomass boilers is 0.26 lb/mmBtu, and the calc ulated baseline emission factors for biomass boilers equipped with a fabric filter is 0.01 lb/mmBtu. The emission reductions were estimated using the difference of these two factors, or 0.25 lb/mmBtu. The methodology used to calculate average baseline emission factors for different fuel and control configurations is discussed in another memorandum. 5 It was assumed that the remaining bo ilers that did not install a fabric filter will install a cyclone or multiclone to redu ce PM emissions. Multiclones were identified as a GACT level of control for PM. Emission reductions for units exp ected to install a multicloneD-13 were estimated by multiplying the baseline emissions of each model unit by the expected PM control efficiency of a multicl one, a 75 percent reduction. These c ontrol efficiencies for various control devices are detailed in another memorandum. 5 Model units with a design capacity greater than 10 mmBtu/hr were expected to already have a multiclone installed as a baseline level of control, so no additional PM em ission reductions were estimated from these units, unless the unit installed a fabric filter for mercury control. To estimate the reductions in other non-mercury metallic HAP, the percent reduction in filterable PM was calculated for each model boiler expected to install a fabric filter for mercurycontrol. This percent reduction was multiplied by the baseline emissions for each of the nonmercury metallic HAP. Since fabric filters capture fine particulate, this analysis assumes that each model boiler would achieve an identical pe rcent reduction from baseline emissions for each non-mercury metallic HAP as was achieved for PM . For model boilers that were expected to install a multiclone to meet the PM GACT lim it, a 10 percent reduction was estimated for nonmercury metallic HAP. PM2.5 emissions comprise a fraction of tota l filterable PM emissions depending on the fuel combusted and control device configuration installed on the unit. The methods used to derive the contribution of PM2.5 to overall filterable PM are presented in other memoranda. 5 To calculate emission reductions for PM2.5 for each m odel boiler, the emission reductions for PM were multiplied by the applicable PM2.5 fraction. For any boiler conducting a tune- up or installing advanced com bustion controls such as a replacement burner or linkageless boiler manageme nt system, a one percent gain in combustion efficiency was estimated, resulting in an es timated one percent emissions reduction of all pollutants. Efficiency gains reduce fuel use, a nd in turn, emissions of hazardous air pollutants. 8 A one percent reduction in emissions for these pollu tants was estimated by multiplying the baseline emissions for each unit by a factor of 0.01. To convert emission reductions from an emissi on rate on a heat input basis to an annual emission rate, Equation 2 was used:D-14 Annual Emission Rate (tpy) = ER HI * 0.0005 * Op hours * CF (Equation 2) Where: ER HI = emission rate (lb/mmBtu) 0.0005 = conversion factor, lbs per ton Op hours = annual operating hours, assumed 8760 (adjusted using capacity factor) CF = annual average capacity factor, 0.5 for liquid and 0.65 for coal and biomass

To convert emission reductions from a concen tration basis to an annual emission rate, Equations 3 and 4 were used:

Annual Emission Rate (tpy) = ER C * 0.000001 * ρ air * Q S * 60 * Op hours * 0.0005 * (20.946 – O 2 ) / (20.946 – Std O 2 ) * CF (Equation 3) Where: ER C = emission concentration (ppm @ 3% O 2 ) 0.000001 = conversion factor, ppm to parts ρ air = density of air, 0.0749 lb/dscf Q S = exhaust flowrate (dscfm) 60 = conversion factor, minutes to hours Op hours = annual operating hours reported in 2008 survey (hours/year) 0.0005 = conversion factor, lb per ton 20.946 = percentage of oxygen in ambient air O 2 = percentage of oxygen assumed in exhaust gas Std. O 2 = 3 percent oxygen in standardized em ission concentration for proposed rule. CF = annual average capacity factor, 0.5 for liquid and 0.65 for coal and biomass Annual Emission Rate (tpy) = ER C * 0.0283 * Q S * 60 * Op hours * 0.000000001 * 0.0022 * 0.0005 * (20.946 – O 2 ) / (20.946 – Std O 2 ) * CF (Equation 4) Where: ER C = emission concentration (ng/dscm @ 7% O 2 ) 0.0283 = conversion factor, dscm to dscf Q S = exhaust flowrate (dscfm) 60 = conversion factor, minutes to hours Op hours = annual operating hours reported in 2008 survey (hours/year) 0.000000001 = conversion factor, ng to g 0.0022 = conversion factor, g per lb 0.0005 = conversion factor, lb per ton 20.946 = percentage of oxygen in ambient air O 2 = percentage of oxygen assumed in exhaust gas Std O 2 = 7 percent oxygen in standardized em ission concentration for proposed rule. CF = annual average capacity factor, 0.5 for liquid and 0.65 for coal and biomass Converting concentrations to an annual em ission rate required an oxygen concentration and exhaust flowrate estimated for each specific fuel type. The development of these assumptions and estimates is presented in another memorandum. 4D-15 Emission Reductions for Option 3E The same calculations discussed for estima ting emission reductions for option 1E were applied to all large coal boilers . For small model boilers, the emi ssion reductions were based on a one percent gain in efficiency expected from the biennial tune-up work practice standard. For large biomass and liquid units no add-on controls for PM or mercury are expected since these units are not subject to numeri cal emission limits for PM or H g. Instead, a similar one percent gain in efficiency is expected to occur as a result of conducting an a nnual tune-up or installing advanced combustion controls necessary to meet the CO numerical limit in each category. Efficiency gains reduce fuel use, and in tur n, emissions of hazardous air pollutants. A one percent reduction in all types of emissions wa s estimated by multiplying the baseline emissions for each unit by a factor of 0.01. A summary of the estimated emission reductions at existing units for both option 1 and option 2a are located in Appendix B-1

.


### 4.2 Emission Reductions from New Boilers
Based on industrial and commercial fuel cons umption projections from the EIA and a history of boiler installation dates in the boiler inspector inventory, there are 6,779 new area source boilers expected to come on-line by 2013. 7 These new projected boilers are expected to fire biomass, coal, and liquid fuels. An aver age (mean) design capacity of area source boilers firing similar fuel type, in the same size cate gory, and in the same sector (industrial or commercial) was estimated to develop new mode l units representative of the existing boiler inventory. New model units were assigned baseline emissions in the same manner as existing area source model units. The projection of new m odel area source boilers and the procedures and
results of the baseline emissions anal ysis is presented in other memoranda. 2
As discussed in Section 3.4, the NSPS for Industrial, Commercial and Institutional Boilers (40 CFR part 60, subparts Db, Dc) (NSPS) were reviewed to identify the expected baseline level of control for projected new un its. Then, the average baseline emission factor corresponding to the expected level of control and fuel category was assigned to each new model boiler. New biomass boilers larger than 30 mmBtu/h r were expected to install an ESP; new coal boilers larger than 75 mmBtu/hr were expected to install a fabric filter and wet scrubber; new coal boilers between 30 and 75 mm Btu/hr would only have a fabric filter installed and were expected to meet the SO2 limits in the NSPS by using coals with a low sulfur content; newD-16 boilers larger than 30 mmBtu/hr and combusting li quid fuels were expected to install a fabric filter. All new boilers less than 30 mmBtu/hr w ould have no add-on controls. For this impacts analysis, it was assumed that all new solid fuel units would be stokers, since stoker boilers are the most common type of solid fuel boilers and all new units would have NOx control installed as a baseline control, regardless of fuel. Based on the EIA fuel projections, all new coal boilers are projected to be less than 10 mmBtu/hr a nd the only 49 model boilers firing biomass are expected to exceed 30 mmBtu/hr.

Emission Reductions for Option 1N After an appropriate baseline level of cont rol was determined for each model unit, an average baseline emission factor was calculated fo r existing units within the same fuel category and having the same level of control was assigne d to each model boiler. The NSPS specifies PM and SO2 limits for new solid- and liquid-fired co mbustion units based on heat input. It was assumed that all new solid and liquid units would be constructed to meet these limits and those limits were used as baseline emission values, wh ere applicable. For units less than 30 mmBtu/hr, the baseline emissions for PM were estimated as suming the unit was uncontrolled and the target PM emission limit from the NSPS was used as the GACT level of control. The baseline emissions for each unit were subtracted from the new source MACT floor for Hg and CO and GACT emission limit for PM corresponding to each unit’s subcategory. The same calculations discussed in Section 4.1 of this memo were us ed to estimate the reductions for new units.

Emission Reductions for Option 3N For new coal boilers, the emission reductions were calculated usi ng the same methods discussed for Option 1N above. For new biom ass boilers less than 30 mmBtu/hr, emission reductions for PM were calculated by subtrac ting the PM NSPS emission limits from a baseline emission factor representing uncontro lled units. Since an ESP is not expected to be very effective at capturing mercury emissions, mercury emissi ons reductions from all biomass units were estimated based on a one percent efficiency im provement, resulting from annual tune-ups or other combustion controls expected to occur in order to demonstrate compliance with CO emission limits. For new biomass boilers greater than or equal to 30 mmBtu/hr, there were no estimated additional PM or non-Hg metallic HAP emission reductions since these larger biomass boilers are already expected to be in comp liance with a PM NSPS limit using an ESP.D-17 New residual liquid boilers less than 30 mmBtu/hr were expected to install a fabric filter to meet the PM emission limit. Since a fabric filt er is effective at captu ring fine particulate, additional emission reductions for mercury were calculated by subtracting the average baseline emission factor for heavy liquid boilers equipped with a fabric filter from the average baseline emission factor corresponding to an uncontrolled heavy liquid unit. The average baseline emission factor for PM at uncontrolled distillate liquid units is less than the NSPS emission limit for liquid units. As a result, no additional PM, Hg, or non-Hg metallic HAP emission reductions were estimated from installing additional PM controls. Instead, these reductions were estimated base d on a one percent efficiency improvement, resulting from annual tune-ups or other combus tion controls expected to occur in order to demonstrate compliance with CO emission limits. Under this proposed option, new small units do not qualify for the same tune-up work practice standards that apply to existing units since it is expected that new units can be designed to allow for stack test diameter s that would be compatible with EPA test methods. As a result, new A summary of the estimated emission reductions at existing units for both option 1N and the proposed option 3N are located in Appendix B-2

. Incremental Emission Reductions if Ener gy Audit Findings are Implemented We evaluated the potential additional reductions and fuel savings that could occur as a result of implementing certain cost-effective energy efficiency improvements identified during the audit. Using the Department of Energy refe rences of a 10 to 15 percent improvement in efficiency, a range of incremental emission re ductions was estimated considering efficiency gains between 5 and 10 percent. We applied the control efficiency range s of 5 and 10 percent reduction to the emissions remaining after MACT le vel of controls were installed on the unit to identify the potential for additi onal emission reductions if the fi ndings of an energy audit were implemented. Table 3 summarizes the potential additional emission reducti ons that could occur under a beyond the floor option of implementing a ny findings in the audit with a short term payback. The fuel savings were estimated using E quation 1 described in 3.1 of this memo that was used to estimate fuel savings. In this case the efficiency gain term was modified to be between 5 and 10 percent. A summary of the estim ated incremental emission reduction and fuel savings is shown in Table 3.D-18 A summary of the estimated emission reduc tions at existing area source boilers for options 1E, 2E, and the proposed option 3E are located in Appendix B-1 . A summary of the estimated emission reductions at new area source boilers for op tions 1N, 2N, and the proposed option 3N are located in Appendix B-2

. Table 3. Emission Reductions and Energy Savi ngs Resulting from Implementing Cost Effective Findings of an Energy A udit at Facilities with Large ( ≥ 10 mmBtu/hr) Boilers
Assuming a Five Percent Efficiency Ga in From Implementing Audit Findings

Emission Reductions and Energy Savings Per Facility HCl Emission Reductions (tpy) Filterable PM Emission Reductions (tpy) Non-Hg Metals Emission Reductions (tpy) Hg Emission Reductions (tpy) VOC Emission Reductions (tpy) Energy Saved (mmBtu/yr) Annual Fuel Savings ($/yr)
Min 3.92E-03 3.01E-02 1.12E-03 9.21E-07 3.59E-04 3.85E+03 $0

Max 2.40E-01 3.11E+00 1.64E-01 3.28E-05 2.93E-02 3.80E+04 $860,704

Average 1.73E-02 1.74E-01 1.98E-02 4.64E-06 1.86E-03 6.15E+03 $95,279

Median 3.92E-03 3.01E-02 2.39E-02 3.45E-06 4.20E-04 5.54E+03 $125,613
TOTAL (13,268 facilities with a large boiler) 2.29E+02 2.31E+03 2.63E+02 6.16E-02 2.47E+01 8.16E+07 $1,264,161,683
Assuming a 10 Percent Efficiency Gain From Implementing Audit Findings

Emission Reductions and Energy Savings Per Facility HCl Emission Reductions (tpy) Filterable PM Emission Reductions (tpy) Non-Hg Metals Emission Reductions (tpy) Hg Emission Reductions (tpy) VOC Emission Reductions (tpy) Energy Saved (mmBtu/yr) Annual Fuel Savings ($/yr)
Min 7.84E-03 6.02E-02 2.25E-03 1.84E-06 7.18E-04 7.69E+03 $0

Max 4.80E-01 6.23E+00 3.28E-01 6.56E-05 5.87E-02 7.60E+04 $1,721,408

Average 3.45E-02 3.48E-01 3.96E-02 9.28E-06 3.73E-03 1.23E+04 $190,558

Median 7.84E-03 6.02E-02 4.79E-02 6.89E-06 8.40E-04 1.11E+04 $251,226
TOTAL (13,268 facilities with a large boiler) 4.58E+02 4.62E+03 5.26E+02 1.23E-01 4.94E+01 1.63E+08 $2,528,323,367 * Although the energy savings in mmBtu/yr are calculated for all fuels, additional fuel savings in $/yr consider only units in the coal and liquid categories. Biomass is often an industrial byproducts and was not assigned a purchase price.D-19 

## 5.0 METHODOLOGY FOR ESTIMATING SECONDARY IMPACTS
Secondary impacts include the solid waste and el ectricity required to operate air pollution control devices, as well as the additional en ergy savings resulting from improved combustion controls or work practices required by the NESHAP. This s ection documents the inputs and equations used to estimate these secondary impacts, and it summarizes the impacts at existing units under proposed regulatory option 3E and ne w units under proposed regulatory option 3N. Table 5-1 summarizes the secondary impacts ofthis proposed NESHAP. Appendices C-1 and C2 present a detailed breakdown of the sec ondary waste and energy impacts from each subcategory of existing and new boilers, respectively.

Table 5-1: Summary of Secondary Impacts

Impact New Units (proposed regulatory Option 4N) Existing Units (proposed regulatory Option 4E)
Solid Waste (tons/yr) 1,800 14,300
Purchased Electricity (kW-hr/yr) 22 million 206 million
Energy Savings* (tBtu/yr) 2.34 19.6 * Energy savings is calculated for units in the coal and liquid subcategories.
The secondary impacts were calculated using algorithms and assumptions described in another memorandum. 3 These algorithms and assumptions we re applied to the existing boilers, where the baseline emissions for each unit exceed ed the proposed MACT floor emission limit. For new units, the algorithms and assumptions were applied to model units representing units expected to come online between 2010 and 2013, when the baseline emissions for each model exceeded the proposed MACT floor or GACT emission limit for new units. The methodology used to assign baseline emission factors to new and existing units are discussed in another memorandum. 5


### 5.1 Solid Waste Impacts Solid waste is generated from collecting dust an d fly ash in fabric filters or ESP control devices. Solid waste impacts were estimated for ev ery unit expected to inst all a fabric filter to meet mercury emission limits, or install an ESP to meet PM emission limits. The total nationalD-20 solid waste amounts in Table 5-1 were determined by adding the per unit solid waste estimates for all new and existing units, respectively. To es timate the solid waste contribution from each of these control devices, the variables were calcula ted based on characteristics reported for each model unit. The calculations used to estimate each variable and the qua ntity of solid waste generated are provided in another memorandum. 3 The solid waste (dust, fly ash) generated by the use of an electrostatic precipitator was calculated when an electrostatic precipitator was determined to be necessary to meet the GACT emission limits for PM. Estimates of the solid waste collected in an ESP was based on several variables including: exhaust flow rate from the combustion unit to the control device (acfm); the inlet loading of particulate matter to the cont rol device (gr/acfm); opera ting hours (hr/year) and the efficiency of the control device required to meet the PM emission limits in the proposed
NESHAP. The solid waste generated from the collection of dust and fly ash in a fabric filter was calculated when a fabric filter was determined to be necessary to meet the proposed NESHAP emission limits for particulate matter and/or me rcury. The calculation required the use of three variables, including: exhaust flow rate from the combustion unit to the control device (dscfm); operating hours (hr/year) and the inlet loading of particulate matter to the control device (gr/acfm).


### 5.2 Electricity Impacts The amount of electricity required to opera te a control device was calculated for an electrostatic precipitator and fabric filter. Th ese impacts were assessed for every unit that was estimated to require particulate matter control. El ectricity requirements are one output of the cost algorithms used in the analyses, so no additional calculations were necessary. For some units, an electrical demand from multiple control device s was estimated. The total national electricity demand in Table 5-1 was determined by adding the per unit solid waste estimates for all new and existing units, respectively. To estimate the electricity demand from each of these control devices, a set of variables were calculated based on characteristics assigned to each model unit. The constants, variables, and calculations used to estimate each variab le and the electricity demand to operate the control devices are provided in another memorandum. 3D-21 

### 5.3 Energy Savings Impacts The energy savings from combustion controls such as low NOx burners or linkageless boiler management systems, and work practice standards, including a tune-up, and implementing the energy audit findings with a short-term pa yback can improvements in efficiency, thereby reducing fuel consumption. Although these combustion improvements have been documented to achieve efficiency gains between 5 and 10 percen t from the baseline operating conditions, this secondary impacts analysis estimates a 1 percen t efficiency gain, to be conservative and consistent with the assumptions made in Sect ion 3.1 of this memorandum. Quantifying the exact gains in efficiency from each of these work pr actice standards is difficult, and may depend on the baseline operating efficiency of each unit. Section 3.1 discusses the fuel sa vings impacts in terms of annu alized cost savings to each boiler, and the national energy savings presented in Table 5-1 of this section follows the same methodology as was discussed in Section 3.1 and refl ect the savings from bo ilers in the coal and liquid fuel categories only. 

### 5.4 Estimating Secondary Impacts for Regulatory Options 3E/3N Regulatory Options 3E for existing and 3N fo r new units are both described in detail in Section 2 of this memorandum. For the secondary impacts analysis at ex isting units under option 3E, the waste and electricity impacts were only as sessed for large units (those greater than or equal to 10 mmBtu/hr) that are in the coal subc ategory. Secondary impacts of solid waste and electricity were not assessed fo r the liquid and biomass subcategor ies because these boilers were not subject to PM or Hg nume rical emission limits and were not expected to install add-on controls. Energy savings were estimated for all un its firing anything other than biomass since all units were expected to conduct a tune -up or install combustion controls. For new units under option 3N, the solid waste and electricity impacts were assessed for any size unit firing coal, liquid, or biomass. A one percent energy savings was estimated for all units firing coal or liquids that were estimated to require a tune-up to meet the CO limits from new boilers. Both tune-ups and combustion controls improve the efficiency of the unit, thereby reducing energy consumption.D-22 

## 6.0 REFERENCES

1. Singleton, A., ERG. MACT Floor Analysis for the Industrial, Commercial, Institutional Boilers National Emission Standards for Hazard ous Air Pollutants – Area Source. April, 2010.

2. Singleton, A., ERG. New Unit Analysis Industr ial, Commercial, and Institutional Boilers National Emission Standards for Hazardous Air Pollutants – Area Source. April, 2010.

3. Singleton, A., ERG. Methodology for Estimating Control Costs for Industrial, Commercial, Institutional Boilers Nationa l Emission Standards for Hazardous Air Pollutants – Area Source. April, 2010.

4. Singleton, A., ERG. Methodology for Estimating Control Costs for Industrial, Commercial, Institutional Boilers and Pro cess Heaters National Emission Standards for Hazardous Air Pollutants – Major Source. April, 2010.

5. Gibson, G. and A. Singleton, ERG. Devel opment of Baseline Emission Factors for Boilers and Process Heaters at Commercial, In dustrial and Institutional Facilities. April, 2010.

6. Singleton, A., ERG. Methodology for Devel oping Model Units for Industrial, Commercial, and Institutional Boilers Nati onal Emission Standards for Hazardous Air Pollutants – Area Source. April, 2010.

7. U.S. Department of Energy. Energy Inform ation Administration. Annual Energy Outlook 2006 (Early Release) with Projections to 2030. Figure 3. Energy Consumption by Fuel, 1980-2030 (quadrillion Btu).

8. Delta Institute. Sector -Based Based Pollution Prevention: Toxic Reductions through Energy Efficiency and Conserva tion Among Industrial Boilers. July 2002. Located online at http://www.delta-institute.org/pub lications/boilers/SectorBasedP2.pdf