## April 2010 

# Regulatory Impact Analysis: Standards of Performance for New Stationary Sources and Emission Guidelines for Existing Sources: Commercial and Industrial Solid Waste Incineration Units

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: Standards of Performance for New Stationary Sources and Emission Guidelines for Existing Sources: Commercial and Industrial Solid Waste Incineration Units

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 22911

### iii CONTENTS

Section Page

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

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

1.2 Organization of this Report .................................................................................. 1-2

2 Industry Profiles ........................................................................................................... . 2-1

2.1 Wood Product Manufacturing.............................................................................. 2-1 2.1.1 Introduction .............................................................................................. 2-1 2.1.2 Supply and Demand Characteristics ........................................................ 2-5 2.1.3 Firm and Market Characteristics .............................................................. 2-8

2.2 Paper Manufacturing .......................................................................................... 2-17 2.2.1 Introduction ............................................................................................ 2-17 2.2.2 Supply and Demand Characteristics ...................................................... 2-21 2.2.3 Firm and Market Characteristics ............................................................ 2-26

2.3 Chemical Manufacturing ................................................................................... 2-33 2.3.1 Introduction ............................................................................................ 2-33 2.3.2 Supply and Demand Characteristics ...................................................... 2-36 2.3.3 Firm and Market Characteristics ............................................................ 2-40

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

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 ......................................................... 4-3

### iv 5 Small Entity Analyses ................................................................................................... 5-1

5.1 Small Entity Screeni ng Analysis ......................................................................... 5-1 5.1.1 Small Businesses ...................................................................................... 5-1

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 .......................................................................................... 6-9 6.3.1 Carbon Monoxide Benefits .................................................................... 6-11 6.3.2 Other SO 2 

### Benefits ................................................................................. 6-11 6.3.3 HAP Benefits ......................................................................................... 6-13

6.4 Characterization of Uncertainty in the Monetized PM 2.5 

### Benefits ..................... 6-20

6.5 Comparison of Benefits and Costs ..................................................................... 6-22

7 Supplemental Economic Analyses for Alternate Approach ......................................... 7-1

7.1 Economic Impact Analysis Results ..................................................................... 7-1 7.1.1 Market-Level Results ............................................................................... 7-1 7.1.2 Social Cost Estimates ............................................................................... 7-1 7.1.3 Job Effects ................................................................................................ 7-3

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 Complian ce Cost Analyses for CISWI Units.............................................B-1

### v LIST OF FIGURES

Number Page

2-1. Distribution of Value of Shipment s within Wood Product Manufacturing (NAICS 322): 2007 .................................................................................................... 2-4 2-2. Distribution of Employme nt within Wood Product Manufacturing (NAICS 322): 2007 .................................................................................................................. 2 -4 2-3. Electrical Power Use Trends in th e Wood Product Manuf acturing Industry (NAICS 321): 1997–2005 .......................................................................................... 2-8 2-4. Establishment Concentration in th e Wood Product Manuf acturing Industry (NAICS 321): 2002 .................................................................................................. 2-10 2-5. Capacity Utilization Trends in the Wood Product Manufacturing Industry (NAICS 321) ............................................................................................................ 2-10 2-6. Employment Concentration in th e Wood Product Manufacturing Industry (NAICS 321): 2002 .................................................................................................. 2-11 2-7. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321) ....... 2-12 2-8. Industrial Production Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997–2009 ........................................................................................ 2-16 2-9. International Trade Trends in th e Wood Product Manufacturing Industry (NAICS 321)]........................................................................................................... 2-16 2-10. Producer Price Trends in the Wood Pr oduct Manufacturing Industry (NAICS 321) .......................................................................................................................... 2-17 2-11. Distribution of Value of Shipments within Paper Manufacturing (NAICS 322): 2007 ................................................................................................................ 2-2 0 2-12. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007 ..... 2-20 2-13. Electrical Power Use Trends in the Pa per Manufacturing In dustry: 1997–2005 .... 2-24 2-14. Establishment Concentration in Pape r Manufacturing Industry (NAICS 322): 2002.......................................................................................................................... 2-26 2-15. Capacity Utilization Trends in the Paper Manufacturing Industry (NAICS 322) ............................................................................................................ 2-27 2-16. Employment Concentration in the Paper Manufacturing Industry (NAICS 322): 2002 ................................................................................................................ 2-2 8 2-17. Capacity Trends in the Paper Manu facturing Industry (NAICS 322) ..................... 2-29 2-18. Industrial Production Trends in the Paper Manufact uring Industry (NAICS 322): 1997–2009 ...................................................................................................... 2-32 2-19. International Trade Trends in the Pape r Manufacturing Industry (NAICS 322) ..... 2-32 2-20. Producer Price Trends in the Paper Ma nufacturing Industry (NAICS 222) ............ 2-33 2-21. Distribution of Employme nt within Chemical Ma nufacturing (NAICS 325): 2007.......................................................................................................................... 2-35

### vi 2-22. Distribution of Total Value of Shipme nts within Chemical Manufacturing (NAICS 325): 2007 .................................................................................................. 2-36 2-23. Electric Power Use Trends in Chem ical Manufacturing (NAICS 325): 1997– 2005.......................................................................................................................... 2-40 2-24. Establishment Concentration in Chemi cal Manufacturing (NAICS 325): 2002 ..... 2-42 2-25. Capacity Utilization Trends in Chem ical Manufacturing (NAICS 325) ................. 2-42 2-26. Employment Concentration in Chemical Manufacturing (NAICS 325): 2002 ....... 2-43 2-27. Capacity Trends in Chemical Manufacturing (NAICS 325) ................................... 2-43 2-28. Industrial Production Trends in Chemi cal Manufacturing (NAICS 325) ................ 2-47 2-29. International Trade Trends in Chem ical Manufacturing (NAICS 325) ................... 2-48 2-30. Producer Price Trends in Chemical Manufacturing (NAICS 325) .......................... 2-48 4-1. Distribution of Total Surplus Changes by Industry (Total Surplus Change = $224 million, 2008$) .................................................................................................. 4-5 4-2. Job Losses/Gains Associated with the Proposed Rule: 2015 ..................................... 4-7 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 CISWI NSPS and Emissions Guidelines in 2015 ..................................................................................................... 6-9 6-3. Breakdown of Monetized Benefits for the Proposed CISWI NSPS and Emissions Guidelines by PM 2.5 

### Precursor Pollutant and Source ............................. 6-10 6-4. Breakdown of Monetized Benefits for the Proposed CISWI NSPS and Emissions Guidelines by Subcategory ..................................................................... 6-10 6-5. Net Benefits for the Proposed CISWI NSPS and Emissions Guidelines at 3% Discount Rate ........................................................................................................... 6-23 6-6. Net Benefits for the Proposed CISWI NSPS and Emissions Guidelines at 7% Discount Rate ........................................................................................................... 6-24

### vii LIST OF TABLES

Number Page

1-1. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the CISWI NSPS and Emissions Guidelines in 2015 (millions of 2008$) ...................... 1-2 2-1. Key Statistics: Wood Product Manu facturing (NAICS 321) ..................................... 2-2 2-2. Industry Ratios: Wood Product Manu facturing (NAICS 321) .................................. 2-2 2-3. Costs of Goods and Services in Wood Product Manufacturing (NAICS 321) .......... 2-5 2-4. Key Goods and Services Used in Wood Product Manufacturing (NAICS 321) ($2007, 10 6 

### )................................................................................................................ 2-6 2-5. Energy Used in Wood Product Manu facturing (NAICS 321) ................................... 2-7 2-6. Demand by Sector: Wood Product Ma nufacturing (NAICS 321) ($2007, 10 6 

### ) ........ 2-9 2-7. Largest U.S. Paper and Forest Products Companies: 2006 ..................................... 2-13 2-8. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 321) ................................................................................... 2-14 2-9. Small Business Size Standards: Wood Pr oduct Manufacturing (NAICS 321) ........ 2-15 2-10. Key Statistics: Paper Manufact uring (NAICS 322) ................................................. 2-18 2-11. Industry Ratios: Paper Manufact uring (NAICS 322) .............................................. 2-18 2-12. Costs of Goods and Services Used in the Paper Manufacturing Industry (NAICS 322) ............................................................................................................ 2-21 2-13. Key Goods and Services Used in the Paper Manufacturing Industry (NAICS 322) ($10 6 

### , $2007) ..................................................................................... 2-22 2-14. Energy Used in Paper Manuf acturing (NAICS 322) ............................................... 2-23 2-15. Estimated Energy Sources for the U.S. Pulp and Paper Industry ............................ 2-25 2-16. Demand by Sector: Paper Manufactur ing Industry (NAICS 322) ($10 6 

### , $2007) .... 2-25 2-17. Largest U.S. Paper and Forest Products Companies: 2006 ..................................... 2-29 2-18. Distribution of Economic Data by En terprise Size: Paper Manufacturing (NAICS 322) ............................................................................................................ 2-30 2-19. Small Business Size Standards: Pape r Manufacturing (NAICS 322) ...................... 2-31 2-20. Key Statistics: Chemical Ma nufacturing (NAICS 325)........................................... 2-34 2-21. Industry Ratios: Chemical Ma nufacturing (NAICS 325) ........................................ 2-34 2-22. Key Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007, 10 6 

### ).............................................................................................................. 2-37 2-23. Costs of Goods and Services Used in Chemical Manufacturing (NAICS 325) ...... 2-38 2-24. Energy Used in Chemical Manufacturing (NAICS 325) ......................................... 2-39 2-25. Demand by Sector: Chemical Ma nufacturing (NAICS 325) ($2007 10 6 

### ) ............... 2-41 2-26. Top Chemical Producers: 2007 ................................................................................ 2-44 2-27. 2007 Corporate Income and Profitability (NAICS 325) .......................................... 2-45

### viii 2-28. Small Business Size Standards: Chemi cal Manufacturing (NAICS 325) ............... 2-46 2-29. Distribution of Economic Data by Ente rprise Size: Chemical Manufacturing (NAICS 325) ............................................................................................................ 2-47 3-1. Summary of Capital and Annual Cost s for Existing CISWI Sources ........................ 3-2 4-1. Market-Level Price and Quantity Cha nges: 2015 (Selected Approach) .................... 4-3 4-2. Distribution of Social Co sts (million, 2008$): 2015 .................................................. 4-4 5-1. Affected Sectors and Size Standards ......................................................................... 5-1 5-2. Sales Tests Using Small Companies Identi fied in the Combustion Survey .............. 5-3 6-1. Human Health and Welfare Effects of PM 2.5 

### ............................................................. 6-2 6-2. Summary of Monetized Benefits Es timates for Proposed CISWI NSPS and Emissions Guidelines in 2015 (2008$) ...................................................................... 6-7 6-3. Summary of Reductions in H ealth Incidences from PM 2.5 

### Benefits for the Proposed CISWI NSPS and Emissi ons Guidelines in 2015 ...................................... 6-7 6-4. All PM 2.5 

### Benefits Estimates for the Propos ed CISWI NSPS and Emissions Guidelines at Discount Rates of 3% and 7% in 2015 (in millions of 2008$) ............ 6-8 6-5. Sensitivity Analyses for Monetized PM 2.5 

### Health Benefits (in millions of 2008$) ...................................................................................................................... 6 -21 6-6. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the CISWI NSPS and Emissions Guidelines in 2015 (millions of 2008$) .................... 6-23 7-1. Market-Level Price and Quantity Cha nges: 2015 (Alternative Approach) ............... 7-2 7-2. Distribution of Social Co sts (million, 2008$): 2015 .................................................. 7-3 7-3. Employment Changes (1,000 Jobs): 2015 ................................................................. 7-3 7-4. Summary of Monetized Benefits for CI SWI NSPS and Emission Guidelines in 2015 (2008$) (Alternate Approach)* ......................................................................... 7-4 7-5. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the CISWI NSPS and Emissions Guidelines in 2015 (millions of 2008$) (Alternate Approach) ................................................................................................. 7-5

### 1-1 SECTION 1 INTRODUCTION
The U.S. Environmental Protection Agency (EPA) is proposing new standards of performance and emission guidelines based on a review of the standards and guidelines as part of the Clean Air Act Section 129(a)(5) requireme nt to review the new source performance standards and emission guidelines every 5 years. Additionally, when revising the standards of performance and emission guidelines we consider ed the District of Columbia Circuit Court rulings on maximum achievable control technol ogy standards that were issued after promulgation of the new source performance sta ndards and emission guidelines for commercial and industrial solid waste incineration units in 2000 and a concurrently proposed definition of nonhazardous secondary materials as solid wast e under the Resource Conservation and Recovery Act. EPA also proposes other amendments that EP A believes are necessary to adequately address air emissions from commercial a nd industrial solid wast e incineration units a nd to clarify certain portions of the rules. As part of the regulatory process, EPA is required to develop a regulatory impact analysis (RIA). The RIA includes an econ omic impact analysis (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 rule ’s total annualized costs will be $216 million (2008$). However, the es timates in this RIA reflect a previous annualized engineering cost estimate of $224 million (2008$), which results in a slight overestimate of the economic impacts.

 

### Market Analysis: Under the proposed rule, the Ag ency’s economic model suggests the average national market-level variable s (prices, production-levels, consumption, international trade) will not change significantly (e.g., are less than 0.1%).

 

### Social Cost Analysis: The estimated social cost is just under $224 million (2008$). In the near term, the Agency’s economic model suggests that industries are able to pass approximately $62 million of the rule’s costs to consumers (e.g., marginally higher market prices). Domestic industries’ surplus falls by $166 million, while other countries on net benefit from higher prices (a net increase in rest-of-the world [ROW] surplus of $4 million). Additional costs a nd savings that are not included in the economic model represent a net benefit of less than $1 million.

 

### Employment Changes: Near-term employment changes associated with the proposed rule are estimated to be less than 500 job losses; over a longer time period, net employment effects range between 400 job losses to 800 job gains.

### 1-2  

### 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 below 1% for small en tities included in the screening analysis.

 

### Benefits Analysis: In the year of full implemen tation (2015), EPA estimates the monetized PM 2.5 

### benefits of the proposed NSPS and Emission Guidelines are $240 million to $580 million and $210 million to $520 million, at 3% and 7% discount rates respectively. Al l estimates are in 2008$ for th e year 2015. Using alternate relationships between PM 2.5 

### and premature mortality suppl ied by experts, higher and lower 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 24,000 tons of carbon monoxide, 560 tons of HCl, 6.0 tons of lead, 5.4 t ons of cadmium, 280 pounds of mercury, and 230 grams of total dioxins/furans each y ear. In addition, ecosystem benefits and visibility benefits have not b een monetized in this analysis

 

### Net Benefits: The net benefits for the NSPS and Emission Guidelines are $19 million to $360 million and $-2.4 million to $310 m illion, at 3% and 7% discount rates respectively (Table 1-1). All estima tes are in 2008$ for the year 2015.
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 the alternate approach

 

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

### 1-3 Table 1-1. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the CISWI NSPS and Emissions Guide lines in 2015 (mi llions of 2008$) 1

Proposed Option
3% Discount Rate 7% Discount Rate
Total Monetized Benefits 2 $240 to $580 $210 to $520
Total Social Costs 3 $220 $220 Net Benefits $19 to $360 $-2.4 to $310 Proposed Option with Alternate Approach
3% Discount Rate 7% Discount Rate
Total Monetized Benefits 4 $2,700 to $6,700 $2,500 to $6,000
Total Social Costs 3 $480 $480 Net Benefits $2,300 to $6,200 $2,000 to $5,600

1 All estimates are for the implementation year (2015) , 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 660 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 1,400 tons of NO x and 2,700 tons of SO 2 . The benefits from reducing 24,000 tons of carbon monoxide, 560 tons of hydrochloric acid, 5.4 tons of cadmium, 6.0 tons of lead, and 280 pounds of mercury, and 230 grams of total dioxins/furans each year are not included in these estimates. In addition, the benefits fr om 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 12,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 520 tons of NO x and 205 tons of SO 2 . The benefits from reducing 128,000 tons of carbon monoxide, 430 tons of hydrochloric acid, 4.3 tons of cadmium, 3.4 tons of lead, 1.2 tons of mercury, and 85 grams of total dioxins/furans each year are not included in these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included.

### 2-1 SECTION 2 INDUSTRY PROFILES
In this section, we provide an introduction selected industries that are affected by the proposed rule. The industries were selected based on high facility population counts within 3- digit NAICs industries reported in the combustion facility survey. The purpose is to give the reader a general understanding of economic aspect s and industry trends to provide additional context for the economic impact analysis.
2.1 Wood Product Manufacturing

2.1.1 Introduction The wood product industry does not have the pote ntial for high returns in the near future. According to a report by Standard & Poor’s (2008), a number of factors are shaping the current economic environment for wood products, includi ng, but not limited to, the housing slump, high input costs, low prices for lumber and other bu ilding materials, and a weak dollar. Table 2-1 shows that revenues in this indus try are not entirely predictable, exhibiting a drop in shipment revenue between 1997 and 2002 but a rise back to within $5 billion of the 1997 value in 2006 and a decline to within $14 billion of the 2006 value in 2007. Upon closer review, one also notices a rise in the cost of labor (shipm ent revenue per dollar of payroll—$5.54 in 2002, $6.20 in 2006, and $5.84 in 2007) and material inputs dur ing this same time period, making high profit margins difficult to predict.
While total payroll dropped 3% over this time, annual payroll per employee rose 6.5% from 1997 to 2007 because of the decline in the number of employees (Table 2-2). Shipments per employee grew 10.6% from 1997 to 2006 and dropped 8.9% from 2006 to 2007 (Table 2-2).
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

### 2-2 Table 2-1. Key Statistics: Wood Product Manufacturing (NAICS 321)

1997 2002 2006 2007 Shipments ($2007, 10 6 ) $110,956 $102,721 $115,390 $101,879 Payroll ($2007, 10 6 ) $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. 

### Table 2-2. Industry Ratios: Wood Product Manufacturing (NAICS 321)

Industry Ratios 1997 2002 2006 2007 Total shipments ($2007, 10 6 ) $110,956 $102,721 $115,390 $101,879 Shipments per establishment ($10 3 ) $25,613 $5,953 NA $6,855 Shipments per employee ($2007) $194,648 $189,014 $215,243 $196,053 Shipments per $ of payroll ($2007) $6.18 $5.54 $6.20 $5.84 Annual payroll per employee ($2007) $31,504 $34,093 $34,738 $33,558 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.

### 2-3 (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). Figure 2-1 shows that the industry 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 2-2 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.

### 2-4 Figure 2-1. 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]



### Figure 2-2. 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] Veneer, Plywood, & Engineered Wood Product Mfg (NAICS 3212), 22%

Sawmills & Wood Preservation (NAICS 3211), 27% Other Wood Product Mfg (NAICS 3219), 51%

Other Wood Product Mfg (NAICS 3219), 60% Sawmills & Wood Preservation (NAICS 3211), 20%

Veneer, Plywood, & Engineered Wood Product Mfg (NAICS 3212), 20%

### 2-5 2.1.2 Supply and Demand Characteristics Next, we provide a broad overview of the supply and demand sides of the wood product manufacturing industry. We emphasize the economic interactions this indu stry has with other industries and people and identify the key goods a nd services used by the industry and the major uses and consumers wood products.
2.1.2.1 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 2-3). Total compensation of employees represented 22% of the total value in 2007. Both the number of total shipments and th e number of employees in this industry decreased between 2005 a nd 2007—the former by 14% and the latter by 3%.

Table 2-3. Costs of Goods and Services in Wood Product Manufacturing (NAICS 321)

Industry Ratios 2005 Share 2006 Share 2007 Share Total shipments ($2007, 10 6 ) $118,705 100% $115,390 100% $102,002 100% Total compensation ($2007, 10 6 ) $23,327 20% $23,306 20% $22,513 22% Annual payroll ($2007) $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 ($2007) $43,286 $43,473 $42,947

Total production workers’ wages ($2007, 10 6 ) $13,363 11% $13,132 11% $12,086 12%

Total production workers 431,569 432,315 417,471 Total production hours (10 3 ) 911,332 887,613 837,074

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

Total cost of materials ($2007, 10 3 ) $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 ($2007) $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]

### 2-6 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 2-4). 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.
Table 2-4. Key Goods and Services Used in Wood Product Manufacturing (NAICS 321) ($2007, 10 6 

### )

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.


### 2.1.2.1.1 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).

### 2-7 Table 2-5 shows that total energy use decreased between 1998 and 2002 by 26 and then between 2002 and 2006, total energy use rose by 19%. However, Figur e 2-3 shows that electrical power use decreased rapidly during this period, in cluding a steady decline in the latter part of 2000. Following a rapid decrease at the end of 2002, electric power use has been increasing steadily since then.
Table 2-5. Energy Used in Wood Pro duct Manufacturing (NAICS 321)

Fuel Type 1998 2002 2006 Total (trillion BTU) 504 375 445 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

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 fu el 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, market ers, 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]

### 2-8 Figure 2-3. 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.



### 2.1.2.2 Uses and Consumers

Table 2-6 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.

2.1.3 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. 85 90 95 100 105 110Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(Jan‐97=100)

Manufacturing Electric Power Use for NAICS 321

### 2-9 Table 2-6. Demand by Sector: Wood Pro duct Manufacturing (NAICS 321) ($2007, 10 6 

### )

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.



### 2.1.3.1 Location As Figure 2-4 illustrates, the states with the largest number of wood product manufacturing establishments are disperse d throughout the country, with a significant concentration of establishments in the northeastern states. Other states with many establishments include California, Texas, and North Carolina.
2.1.3.2 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 2-5).

### 2-10 Figure 2-4. 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).



### Figure 2-5. 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. Establishments by State

Less than 100

100 - 199

200 - 399

400 - 600

More than 600

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

Manufacturing Wood Products

### 2-11 2.1.3.3 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 2-6). 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 2-6, 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.

Figure 2-6. 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). 

### 2.1.3.4 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 2-7). Employemnt by State

Less than 2,000
2,000 - 4,999
5,000 - 9,999
10,000 - 20,000
More than 20,000

### 2-12 Figure 2-7. 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.



### 2.1.3.5 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 2-7). The top two companie s’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor’s (S&P’s) list (Benwa rt, 2006). Although these numbers do not exclusively reflect wood products , they do convey the market environment in which firms in this sector compete.

2.1.3.6 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 2-8). The number of employees in the small business cutoff is 500 em ployees for all sub-industries in the wood product manufacturing industry (Table 2-9). 0 20 40 60 80 100 120 140 160Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(January1997=100)

Manufacturing Food

### 2-13 Table 2-7. Largest U.S. Paper and Forest Products Companies: 2006

Company Revenues ($10 6 ) a International Paper 21,995 Weyerhaeuser 21,896 Smurfit-Stone 7,157 MeadWestvaco 6,530 Temple-Inland 5,558 Bowater 3,530 Grief Inc. 2,628 Louisiana-Pacific 2,235 Packaging Corp. 2,187 Plum Creek 1,627

a Includes revenues from operations other than paper and forest products in certain cases. Source: Benwart, S.J. 2006. “Paper & Forest Products.” Standard and Poor’s Industry Surveys . 176(28).



### 2.1.3.7 Domestic Production Similar to industry capacity rates, industry production rates for wood product manufacturing have decreased since 2006 compared to the stea dy increase in production for the manufacturing sector since 1997 (Fig ure 2-8). Similar to capacity ut ilization trends (Figure 2-8), the index shows a faster rate of decline for wood products than th e entire manufacturing sector.

2.1.3.8 International Trade Since 1997, the wood product manufacturing indust ry has contributed to an increasing trade deficit (Figure 2-9). The value of impor ts has fluctuated greatly since 1997; however, exports have remained fairly constant , with seasonal changes, since 1997.

2.1.3.9 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, while producer prices for all manufacturing goods increased by roughly 34% at its peak and was at 23% in November 2008 and 24% in November 2009 (Figure 2-10).

### 2-14Table 2-8. 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 ($103)$88,649$8,204$18,276$19,717$3,192$1,902$3,118Receipts/firm ($103)$5,833$842$5,572$24,927$50,673$70,453$103,927Receipts/establishment ($103)$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>.

### 2-15 Table 2-9. 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 321214 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>.

### 2-16 Figure 2-8. 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 2-9. 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 Imports

### 2-17 Figure 2-10. 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. 

### 2.2 Paper Manufacturing

2.2.1 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 2-10). 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). Shipments per employee grew 28% from 1997 to 2007, with much of that growth taking place between 2002 and 2006 (Table 2-11). 0 20 40 60 80 100 120 140Dec‐03Jun‐04Dec‐04Jun‐05Dec‐05Jun‐06Dec‐06Jun‐07Dec‐07Jun‐08PPI(Dec‐03=100)

Wood Products PPI All Manufacturing PPI

### 2-18 Table 2-10. Key Statistics: Pap er Manufacturing (NAICS 322)

1997 2002 2006 2007 Shipments ($2007, 10 6 ) $188,496 $175,983 $174,887 $175,806 Payroll ($2007, 10 6 ) $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 2-11. Industry Ratios: Paper Manufacturing (NAICS 322)

Industry Ratios 1997 2002 2006 2007 Total shipments ($2007, 10 6 ) $188,496 $175,983 $174,887 $175,806 Shipments per establishment ($2007, 10 3 ) $32,123 $32,026 NA $36,603 Shipments per employee ($2007) $328,233 $359,614 $422,381 $421,712 Shipments per $ of payroll ($2007) $6.74 $7.17 $8.25 $8.45 Annual payroll per employee ($2007) $48,727 $50,189 $51,174 $49,904 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://factfind er.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]

### 2-19 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), or

### 2-20  converting pulp into pulp products, such as disposable diapers, or molded pulp egg cartons, food trays, and dishes. Figure 2-11 shows that the value of shipment s for converted paper products was slightly higher in 2007, 54%, than the value of shipments for pulp, paper, a nd paperboard products in that year, 46%. However, Figure 2-12 indicates that significantly more employees worked in the converted paper product category of the industr y, 70%, thus making converted paper products more labor intensive.

Figure 2-11. 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 2-12. 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%

### 2-21 2.2.2 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.
2.2.2.1 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 2-12). 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 in creased by 3% in the same period.

Table 2-12. Costs of Goods and Services Us ed in the Paper Manufacturing Industry (NAICS 322)

Variable 2005 Share 2006 Share 2007 Share Total shipments ($2007, 10 6 ) $171,477 100% $174,887 100% $176,018 100% Total compensation ($2007, 10 6 ) $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, 10 6 ) $14,965 9% $14,689 8% $14,190 8% Total production workers 331,228 321,684 321,937 Total production hours (10 3 ) 716,963 691,134 680,732

Average production wages per hour $21 $21 $21 Total cost of materials ($2007, 10 3 ) $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]

### 2-22 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 2-13). 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.
Table 2-13. Key Goods and Services Used in the Paper Manufacturing Industry (NAICS 322) ($10 6 

### , $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.


### 2.2.2.1.1 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

### 2-23 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 2-14 shows that total en ergy use decreased between 1998 and 2006 by 14%. Figure 2-13 indicates that total electrical power use ch anged sporadically between 2002 and 2004 but decreased consiste ntly and rapidly after 2004.
Table 2-14. Energy Used in Paper Manufacturing (NAICS 322)

Fuel Type 1998 2002 2006 Total (trillion BTU) 2,744 2,361 2,354 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

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 fu el 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, market ers, 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]

### 2-24 Figure 2-13. 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/>.


### 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 2-15). 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).

2.2.2.2 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 2-16). Pulp, paper, and paperboard products have a large trade deficit, while converted paper products have a very small trade surplus. 90 92 94 96 98 100 102 104 106 108Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(Jan‐97=100)

Manufacturing Paper

### 2-25 Table 2-15. 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>. 

### Table 2-16. Demand by Sector: Paper Manuf acturing Industry (NAICS 322) ($10 6 

### , $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.

### 2-26 2.2.3 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.

2.2.3.1 Location As Figure 2-14 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 2-14. 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 300

### 2-27 2.2.3.2 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 2-15).

Figure 2-15. Capacity Utilization Trends in the Paper Manufacturing Industry (NAICS 322)
Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” Series ID: G17/CAPUTL/CAPUTL.GM F.S & G17/CAPUTL/CAPUTL.G322.S. <http://www.federalreserve.gov/datadownload/>. 

### 2.2.3.3 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 2-16). 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)

### 2-28 Figure 2-16. 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). 

### 2.2.3.4 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 2-17).

2.2.3.5 Firm Characteristics In 2006, the top 10 paper and forest product companies produced over $1.6 billion in sales, with the top two companies—Internati onal Paper and Weyerhaeuser—generating nearly $22 billion each (Table 2-17). The top two companie s’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor’s (S&P’s) list (Benwa rt, 2006). Although these numbers do not exclusively reflect 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 24,000

### 2-29 Figure 2-17. Capacity Trends in the Pa per Manufacturing Industry (NAICS 322) Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial Capacity.” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G322.S. <http://www.federalreserve.gov/datadownload/>. 

### Table 2-17. Largest U.S. Paper and Forest Products Companies: 2006

Company Revenues ($10 6 ) 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)

### 2-30 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, 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 2-18). 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 2-19). 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.

2.2.3.7 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 2-18). 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.

Table 2-18. 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 ($10 6 ) $154,746 $2,218 $9,483 $17,620 $3,034 $3,951 $6,798 Receipts/firm ($10 3 ) $43,738 $1,497 $7,903 $37,017 $70,561 $179,577 $206,001 Receipts/establishment ($10 3 ) $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>.

### 2-31 Table 2-19. 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>.



### 2.2.3.8 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 2-19). Imports and exports have been changing at sim ilar rates since 1999.

### 2-32 Figure 2-18. 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/>.



### Figure 2-19. 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>. 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)

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

### 2-33 2.2.3.9 Market Prices Prices of goods in paper manufacturing have be en increasing at a rate consistent with all manufacturing products (Figure 2- 20). 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%.

Figure 2-20. 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>. 

### 2.3 Chemical Manufacturing

2.3.1 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 2-20). 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 2-21). Sh ipments per employee grew 54% from 1997 to 2007, with much of that growth taking place between 2002 and 2006 (Table 2-21). 90 95 100 105 110 115 120 125 130 135 140Dec‐03Jun‐04Dec‐04Jun‐05Dec‐05Jun‐06Dec‐06Jun‐07Dec‐07Jun‐08Dec‐08Jun‐09PPI(Dec‐03=100)

Manufacturing PPI Paper PPI

### 2-34 Table 2-20. Key Statistics: Chemic al Manufacturing (NAICS 325)

1997 2002 2006 2007 Shipments ($2007, 10 6 ) $521,251 $531,173 $675,223 $738,303 Payroll ($2007, 10 6 ) $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 2-21. Industry Ratios: Chemical Manufacturing (NAICS 325)

Industry Ratios 1997 2002 2006 2007 Total shipments ($2007, 10 6 ) $521,251 $531,173 $675,223 $738,303 Shipments per establishment ($10 3 ) $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]

### 2-35 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 Chem ical 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 2-21). The bulk chemicals group accounted for the biggest share of chemical manufacturing’s total value of shipments (Figure 2-22).

Figure 2-21. Distribution of Employment with in Chemical Manufacturing (NAICS 325): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detaile d Statistics by Industry for the U.S.: 2007.” Release date: October 30, 2009. Acce ssed on December 27, 2009. Bulk Chemicals, 35%

Pharmaceuticals, 31% Other Chemicals, 34%

### 2-36 Figure 2-22. Distribution of To tal Value of Shipments with in Chemical Manufacturing (NAICS 325): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31:EC0731I1: Manufacturing Industry Series: De tailed Statistics by Industry for U.S.: 2007.” <http://factfinder.census.gov>. A ccessed on December 27, 2009. 

### 2.3.2 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 2-22). 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.
2.3.2.1 Goods and Services Used in Chemical Manufacturing In2007, the cost of materials made up 49% of chemical manufacturing’s total shipment value (Table 2-22). Total compensation to employ ees represented 9% of total shipment value, down from 10% in 2005.

2.3.2.1.1 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). Bulk Chemicals, 50%

Pharmaceuticals, 26% Other Chemicals, 24%

### 2-37 Table 2-22. Key Goods and Services Used in Chemical Manufact uring (NAICS 325) ($2007, 10 6 

### )

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.


### Fuel used in chemical production can either f acilitate chemical processes or provide the feedstock to derive value-added chemicals. In 2007, 70% of chemical manufacturing’s energy bill was spent on fuel used as feedstocks (O’R eilly, 2008). These fuel costs represented 2% of chemical manufacturing’s total valu e of shipments (Table 2-23).
As a whole, chemical manufacturing has b ecome less energy intensive over the last 10 years. According to DOE, natural gas use by th e chemical manufacturi ng industry dropped 30% from 1998 to 2006, and electricity use fell 10% (Table 2-24). From 1997 to 2005, when data ceased to be available, chemical manufacturing b ecame less electricity intensive faster than the manufacturing sector as a whole (Figure 2-23).

### 2-38 Table 2-23. Costs of Goods and Services Used in Chemical Manufacturing (NAICS 325)

Variable 2005 Share 2006 Share 2007 Share Total shipments ($2007) $646,895 100% $675,223 100% $722,494 100% Total compensation ($2007, 10 6 ) $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 ($2007) $82,887 $82,559 $79,333

Total production workers’ wages ($2007, 10 6 ) $22,643 4% $22,231 3% $23,157 3%

Total production workers 431,502 430,880 463,802 Total production hours (10 3 ) 899,499 885,993 948,244 Average production wages per

hour $25 $25 $24

Total cost of materials ($10 3 ) $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.



### 2.3.2.2 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 2-25).

### 2-39 Table 2-24. 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

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 fu el 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, market ers, 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>.

### 2-40 Figure 2-23. 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).


### 2.3.3 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.

2.3.3.1 Location In 2002, California had the most chemical manufacturing establishments in the United States, followed by Texas and Ne w Jersey (Figure 2-24). The composition of establishments in these states differs among th e different industry groups. Despite the fact that each group employed an approximately equal share of people in 2002, 54% of the total establishments were other chemicals establishmen ts, and only 13% were pharm aceutical establishments.
2.3.3.2 Production Capacity and Utilization Capacity utilization of the ch emical manufacturing industry ha s been broadly in line with the manufacturing sector (Figure 2-25). 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 manufacturing and chemical manufacturing. 80 85 90 95 100 105 110Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(Jan‐97=100)

Manufacturing Chemical Manufacturing

### 2-41 Table 2-25. Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007 10 6 

### )

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. 

### 2.3.3.3 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 2-26).
2.3.3.4 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 2-27).

### 2-42 Figure 2-24. 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).



### Figure 2-25. 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/>.

Establishments
Fewer than 100
100 - 249
250 - 474
475 - 750
More than 750

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

### 2-43 Figure 2-26. 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).



### Figure 2-27. 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/>. Employment Fewer than 5,000 5,000 - 13,999 14,000 - 29,999 30,000 - 40,000 More than 40,000

100 110 120 130 140 150Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Chemical Manufacturing

### 2-44 2.3.3.5 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 2-26). 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).
Table 2-26. Top Chemical Producers: 2007

Chemical Sales ($10 6 ) % 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).


### In 2007, 58%of U.S. chemical manufacturi ng corporations generated net income. Including those with and without net income, chemical manufactur ers had an average before-tax profit margin of 10.24%. Profitability is highest for pharmaceutical and medicine corporations (Table 2-27).

2.3.3.6 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 2-28. In 2002, en terprises with fewer than 500 employees accounted for 27% of employment and 15% of recei pts within the chemical manuf acturing industry (Table 2-29).

### 2-45 Table 2-27. 2007 Corporate Income and Profitability (NAICS 325)

Industry Number of Corporations Number of Corporations with Net Income Total Receipts

($10 3 ) Business Receipts

($10 3 ) BeforeTax Profit

Margin After-Tax Profit Margin Chemical manufacturing 9,564 5,512 $912,353,710 $808,897,810 10.24% 7.89% 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%

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).



### 2.3.3.7 Domestic Production In the late 1990s, overall manufacturing pr oduction was growing much faster than the chemical manufacturing component (Figure 2-28 ). 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.

2.3.3.8 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 2-29). 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.

2.3.3.9 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 2-30). 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.

### 2-46 Table 2-28. 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>.

### 2-47 Table 2-29. 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 ($10 6 ) $468,211 $9,631 $21,394 $39,111 $12,217 $7,324 $14,762 Receipts/firm ($10 3 ) $50,124 $1,779 $10,838 $49, 507 $128,603 $130,779 $207,913 Receipts/establishment ($10 3 ) $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 2-28. 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/>. 100 110 120 130 140 150Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(Jan‐97=100)

Manufacturing Chemical Manufacturing

### 2-48 Figure 2-29. 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 2-30. 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 Manufacturing

### 3-1 SECTION 3 ENGINEERING COST ANALYSIS
This section provides an overview of the engi neering 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 co st impacts is presented in “Compliance Cost Analysis for Existing CISWI Units” in Appendix B.
To estimate the national cost impacts of th e proposed rule for existing sources, EPA developed average baseline emission factors for each pollutant and each subcategory of CISWI unit based on the emissions data obtained and contained in the CISWI emissions and inventory database. If a unit reported emissions data, we assigned its unit-specific emissions data as its baseline emissions. If identical uni ts were operated at the facility but no data were available, the emissions data from the unit with data were app lied to the identical units. For units that did not report emissions data, we assigned the appropria te emission factor for the units in that subcategory. We then compared each unit’s baseli ne emission factors for each pollutant to the proposed MACT floor emission limit to determine if control devices were needed to meet the emission limits. The control analysis considered fabr ic filters to be the primary control device for particulate matter, cadmium, and lead control; packed bed scrubbers for hydrogen chloride and sulfur dioxide control; activated carbon injec tion for mercury and dioxins/furans control; selective non-catalytic reduction for oxides of nitrogen; and af terburner retrofits, tune-ups, advanced combustion controls, and oxidation catalysts for car bon monoxide controls. We also considered whether existing control devices c ould be improved to achieve the proposed limits, such as adding more lime to duct sorbent injection syst ems to meet the proposed sulfur dioxide limits. We included costs for testing and monitori ng requirements contained in the proposed rule. Finally, we analyzed the costs of alternative disposal options, such as diverting waste to a landfill or abrasive blasting to remove paint to see if less expensiv e options to incineration were available. In certain cases, such as small remote incinerators, our data suggest that sending waste to a landfill may likely be less costly than ope rating and maintaining an incineration unit. The resulting total national cost impact of the pr oposed rule is 574 million dollars in capital expenditures and 216 million dollars per year in total annual costs. The total capital and annual costs include costs for control devices, work practices, testing and monitoring. Costs include testing and monitoring costs, but not recordkeeping a nd reporting costs. Based on the cost to comply with the proposed rule, availability of al ternatives to incinerati on, and historic negative growth in this source category, we do not anticipate any new or modified units.

### 3-2 Table 3-1. Summary of Capital and Annual Costs for Existing CISWI Sources

Subcategory Number of Affected Units Capital Costs (millions of 2008$) Annualized Costs (millions of 2008$)
Burn-off Ovens 36 5.284 3.199
Cement Kilns 53 113.5 46.70 Energy Recovery Units 40 455.4 165.1
Incinerators 28 0 1.875
Small remote incinerators 19 0.058

− 0.803



### Based on this analysis, EPA anticipates an overall total capital investment of $572 million with an associated total annual cost of $216 million. However, the estimates in this RIA reflect previous estimates of engineering costs with an overall total capital investment of $574 million and an annualized estimate of $224 m illion (2008$), which results in a slight overestimate of the economic impacts.
The requirements result in industry recordk eeping and reporting burden associated with reviewing the amendments for all CISWI and inspections of scrubbers, fabric filters, and other air pollution control devices th at may be used to meet the emission limits for all CISWI. Ongoing parametric monitoring requirements fo r ESPs, SNCR, and activ ated carbon injection are also required of all CISW I units. Stack testing and develo pment of new parameter limits would be necessary for CISWI that need to make performance improvements to meet the proposed emission limits and for CISWI that, pr ior to this proposed action, have not been required to demonstrate compliance with certain pollutants. Visual emissions tests would be required for all subcategories ex cept kilns on an annual basis. En ergy recovery units would be required to continuously monitor opacity, and units larger than 250 MMBtu/hr would be required to monitor PM emissions using a PM CEMS. K ilns would be required to continuously monitor Hg emissions using an Hg CEMS. Any new CI SWI would also be required to continuously monitor CO emissions. Annualized capital/startup costs and ope ration and maintenance (O&M) costs are associated with the EG monitoring requirements, EPA Method 22 of Appendix A-7 testing, initial stack testing, storage of data and re ports, and photocopying and postage.

### 4-1 SECTION 4 ECONOMIC IMPACT ANALYSIS
EPA prepares an EIA 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 Analys is (Multiple Markets) The partial equilibrium analysis develops a market model that simulates how stakeholders (consumers and industries) might respond to the additi onal regulatory program costs. In this section, we provide an overvi ew of the economic model. Appendix A provides additional details on the behavioral assumpti ons, 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). Recen t studies suggest short-run analyses can complement full dynamic general equilibrium analysis. For example, Morgenstern and

### 4-2 colleagues examine carbon price policies with sh ort- and long-term time horizons (Morgenstern and colleagues, 2004; Ho, Morgenstern, and Shi h, 2008). Aldy and Pizer (2 009) assess near-term competitiveness effects of a domestic cap-and-trade program to address stakeholder concerns about shifts in economic activity and jobs to ot her countries. The multimarket model contains the following features:

 

### Industry sectors and benchmark data set

– 100 industry sectors

– a single benchmark year (2010) 1



### – 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 non-en ergy material costs

– 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/e xports) behavior considered

 

### Model time horizon (“short run”)

– fixed production resources (e.g., capital) lead 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

1 For this analysis, we use the model to approximate baseline conditions for 2015.

### 4-3 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 (Table 4-1). The Agency’s economic model suggest s the average national market-level variables (prices, production-levels, consumption, international trade) will not significantly change (e.g. , are less than 0.1%).
Table 4-1. Market-Level Price and Quan tity Changes: 2015 (Selected Approach)

Industry Sector Prices Production Imports Consumption Exports Energy Less than 0.01% Less than 0.01% Less than 0.01% 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 Less than 0.01% Less than 0.01% less than 0.01% Less than 0.01% Less than 0.01% Lumber, paper, and printing 0.02% Less than 0.01% 0.02% Less than 0.01% − 0.02%

Chemicals Less than 0.01% Less than 0.01% Less than 0.01% Less than 0.01% Less than 0.01% Plastics and Rubber Less than 0.01% Less than 0.01% Less than 0.01% Less than 0.01% Less than 0.01% Nonmetallic Minerals 0.03% Less than 0.01% 0.01% Less than 0.01% − 0.03%

Primary Metals Less than 0.01% Less than 0.01% Less than 0.01% Less than 0.01% Less than 0.01% 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 Less than 0.01% Less than 0.01% Less than 0.01% Less than 0.01% Less than 0.01% 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% Less than 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%

### 4-4 4.1.2.2 Social Cost Estimates In the near term, the Agency’s economic m odel suggests that industries are able to pass on $62 million (2008$) the costs to U.S. households in the form of higher prices (Table 4-2). Existing U.S. industries’ surplus falls by $166 mill ion, and the net loss for U.S. stakeholders is $228 million. As U.S. prices rise, other countri es are affected through international trade relationships. Households that buy goods from the United States experience losses, while industries that sell goods to the United States benefit; the model estimates a net gain of $4 million. After accounting for interna tional trade effects, the Agen cy’s economic model projects the net surplus loss associated with the propos ed rule is $224 million. As shown in Figure 4-1, the surplus losses are concentrat ed in lumber, paper, and prin ting (27.1%) and other services (21.1%). The Agency also considered other elements of the engineering cost analysis that could not be modeled within the multimarket model (e .g., fuel savings benefits and total annualized compliance costs [unknown sources]). The net effect of the adjustments is less than $1 million.

Table 4-2. Distribution of So cial Costs (million, 2008$): 2015

Method Selected Approach Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus

− $62 Change in U.S. producer surplus

− $166 Change in U.S. surplus

− $228 Net change in rest of world surplus $4 Net change in total surplus

− $224 Direct Compliance Costs Method

Total annualized costs, unknown sources (not modeled) Less than $1 million Fuel savings (not modeled) $1 Change in Total Surplus

− $224

### 4-5 Figure 4-1. Distribution of Total Surplus Ch anges by Industry (Total Surplus Change = $224 million, 2008$)

4.1.2.3 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

 

### post-regulation production tec hnologies 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 si x-year panel (U.S. Census data for plant27.6%
21.1%
11.2%
8.9%
8.3%
7.8%
4.4%
2.1%
1.6%
1.5%
1.3%
1.1%
1.1%
0.8%
0.7%
0.6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Lumber, paper, and printing
Other Services Energy Industries
Nonmetallic Minerals
Chemicals Nonmanufacturing Food, beverages, and textiles Primary Metals
Other
Wholesale and Retail Trade
Plastics and Rubber Transportation Equipment
Electronic Equipment Machinery and Equipment Transportation Services
Fabricated Metals

### 4-6 level prices, inputs (includi ng labor), outputs, and envir onmental expenditures) to econometrically estimate the production technologie s and industry-level demand elasticities. Their identification strategy leve rages repeat plant-level observa tions over time and uses plantlevel and year fixed effects (e.g., plant and time dummy variables). After estimating their model, Morgenstern show and compute the change in employment associated with an additional $1 million ($1987) in environmental spending. Their estimates covers four manufacturing industries (pulp and paper, plastics, petroleu m, and steel) and Morgenstern, et al present results separately for the cost, factor shift, and demand effects, as well as the net effect. They also estimate and report an industry-wide average parameter that combines the f our industry-wide estimates and weighting them by each industry’s share of environmental 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, 1999; 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 (add caveats) but avoids ignoring the “cost effect” and the “factor-shift effect.”
We calculated “demand effect” employment changes by assuming that the number of jobs declines proportionally with multi-market model’s simulated output changes. These results were calculated for all sector s in the EPA model that s how a change in output.

We also calculated a similar “demand effect” estimate that used the Morgenstern paper. EPA selected this paper because the parameter estimates (expressed in jobs per million ($1987) of environmental compliance expe nditures) 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 compliance expenditures used in the partial equi librium model. For example, the jobs effect estimate for the Major Source Rule is estimated to be 500 jobs (

− 3.56× $224 million × 0.60). 1 

### Demand effect results are provided in Figure 4-2.

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)

### 4-7 We also present the results of using the Mo rgenstern paper to estimate U.S. employment “cost” and “factor-shift” effects. Although using the Morgenstern pa rameters to estimate these “cost” and “factor-shift” employment changes is uncertain, it is he lpful to compare the potential job gains from these effects to the job losses a ssociated with the “demand” effect. Figure 4-2 shows that using the Morgenstern point estimate s of parameters to estimate the “cost” and “factor shift” employment gains may be greater than the employment losses using either of the two ways of estimating “demand” employment lo sses. The 95% confidence intervals are shown for all of the estimates based on the Morgenster n parameters. As shown, at the 95% confidence level, we cannot be certain if net empl oyment changes are positive or negative.

Figure 4-2. Job Losses/Gains Associ ated with the Proposed Rule: 2015

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 data and estimates, this approach seems reasonable and the estimates come from a respected peer- ‐ 0.4

‐ 0.5 0.3 0.4 0.2

‐ 3.0 ‐ 2.5 ‐ 2.0 ‐ 1.5 ‐ 1.0 ‐ 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.01,000JobsBase=144,100

Demand Effect: Partial Equilibrium (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.

### 4-8 reviewed 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 later. Second, the multi-market model only considers near term employment effects in a U.S. economy where product ion technologies are fixed. As a result, the modeling system places mo re 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 shif t effects” are more plausible when production choices become more flexible over time and industries can substitute labor for other production inputs. Third, the Morgenstern paper estimates rely on sector demand elasticities that are different from the demand elasticity parameters us ed in the multi-market model. As a result, the demand effects are not directly comparable w ith the demand effects estimated by the multimarket model. Fourth, Morgenstern identifi es the industry average as economically and statistically insignificant effect (i.e., the point estimates are sma ll, measured imprecisely, and not distinguishable from zero.) EPA acknowledges this fact and has reported the 95 percent confidence intervals in Figure 4-2. Fifth, Morgen stern’s methodology assumes large plants bear most of the regulatory costs. By transferring th e estimates, EPA assumes a similar distribution of regulatory costs by plant size and that the regulatory burden does not disproportionately 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 any other statute, unless the agen cy certifies that the rule will not have a significant economic impact on a su bstantial number of small entities (SISNOSE). Small entities include small bus inesses, small governmental jurisdictions, and small not-forprofit enterprises. EPA assessed the potential sm all entity economic impacts using a screening analysis. After reviewing screeni ng analysis results, EPA has de termined the proposed rules will not have a SISNOSE and presumes that both pro posed rules are eligible for certification under the RFA as amended by SBREFA. The remaini ng sections provide the factual basis for certification.
5.1 Small Entity Screening Analysis

5.1.1 Small Businesses The sectors covered by the rule were identifi ed through lists of sma ll entities provided by the engineering analysis. Table 5- 1 provides a list of the sectors affected (3-digit NAICS) and the range of SBA size definitions.

Table 5-1. Affected Sectors and Size Standards

2007 NAICS Description Size Standard (Effective August 22, 2008)
221 Utilities a 311 Food Manufacturing 500 to 1,000 employees 321 Wood Product Manufacturing 500 employees 322 Paper Manufacturing 500 to 750 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 337 Furniture and Related Product Manufacturing 500 employees 562 Waste Management and Remediation Services Typically $7 to $14 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.

### 5-2 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 industr y; however, only a subset 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.

 

### 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 th e 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 de finitions across affect ed industries. Using establishment receipts is also a conservative approach, because an establishment’s parent company (the “enterprise”) may have other economi c resources that could be used to cover the costs of the regulatory program.

1 See http://www.census.gov/csd/susb/ and http://www.sba .gov/advo/research/data.html for additional details.

### 5-3 For each representative establishment in the SU SB data, we developed a range of facilitylevel cost numerators based on the engineering cost analysis. We used the maximum and minimum small entity facility-level costs observed within each 3-digit NAICS code. 1 

### Using these cost data and the Census estimates of average establishment receipts, several paper and utility SUSB NAICS/enterprise categories had ratios that exceeded 3%. As a result, EPA performed a more detailed analysis with a firm-specifi c sample of small pr ivate enterprises.
5.1.1.2 Additional Small Entity Analysis Using Sample of Small Businesses In this approach, we identified a sample of survey facility names listed as small, traced the ultimate parent company name to verify th e facility was owned by a small business, and collected the most recent parent company sales and employment fi gures. As Table 5-2 shows, the average cost-to-sales ratios for small companies are below 1%. The median ratios are less than 0.1%. Only one entity has a sales test that exceeds 3% and provides wood-residue, natural gasfired cogeneration (NAICS 221).
Table 5-2. Sales Tests Using Small Companie s Identified in the Combustion Survey

Sample Statistic Selected Option

Mean

− 0.8%
Median Less than 0.1%

Maximum 4.0%

Minimum

− 8.0% Ultimate parent company observations 13 Ultimate parent company observations with sales data 5 Ultimate parent companies with Sale Tests Exceeding 3% 1

1 Prior to computing the cost-t o-receipt ratios, we adjusted the engineerin g compliance costs to reflect 2002 dollars using the implicit price deflators for gross domestic product (GDP). The values used are 2002 = 92.118 and 2008 = 108.483 (U.S. BEA, 2010).

### 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 CISWI NSPS and Emissions Guidelines. For this rule, the PM reductions are th e result of emission limits on PM, emission limits on PM 2.5 

### precursors such as NO x 

### and SO 2 

### , as well as emission limits on other pollutants. The total PM 2.5 

### reductions are the consequence of the technologies installed or waste diversion to meet these multiple limits. These estimates reflect the monetized hu man health benefits of reducing cases of morbidity and premature mortality among populations exposed to the PM 2.5 

### precursors reduced by this rulemaking. Using a 3% discount rate, we estimate the total monetiz ed benefits of the proposed CISWI NSPS and Emissions Guidelin es to be $240 million to $580 million in the implementation year (2015). Using a 7% discount ra te, we estimate the total monetized benefits of the proposed CISWI NSPS and Emissions Guidelines to be $210 million to $520 million in the implementation year. All estimates are in 2008$.

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 effect s, and visibility impairment. The benefits from reducing other air pollutants have not been monetized in this analysis, including reducing 24,000 tons of carbon monoxide, 560 tons of HCl, 6.0 tons of lead, 5.4 tons of cadmium, 280 pounds of mercury, and 230 grams of to tal 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 NO 2 

### . Because SOx and NO 2 

### 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 th is rule, the PM reductions are the result of emission limits on PM, emission limits on PM 2.5 

### precursors such as NO x 

### and SO 2 

### , as well as emission limits on other pollutants. The total PM 2.5 

### reductions are the consequence of the technologies installed or waste diversion to meet these multiple limits. Due to analytical limitations, it was not possible to pr ovide a comprehensive estimate of PM 2.5 

### -related benefits. Instead, we used the “benefit- per-ton” approach to estimate these benefits based on the methodology described in Fann et al. (2009). The ke y assumptions are described in detail below.

### 6-2 These PM 2.5 

### benefit-per-ton estimates pr ovide the total monetized hum an health benefits (the sum of premature mortality and prematur e morbidity) of reducing one ton of PM 2.5 

### from a specified source. EPA has used the benefit per-t on technique in several previous RIAs, including the recent NO 2 

### NAAQS RIA (U.S. EPA, 2010b). Tabl e 6-1 shows the quantified and unquantified benefits captured in t hose benefit-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 EP A has previously used to generate its primary PM benefits estimate.

 

### One estimate is based on the C-R function developed from the extended analysis of the Harvard Six Cities cohort, as report ed by Laden et al. (2006). This study, published after the completion of the Staff Paper for the 2006 P M2.5 

### NAAQS, has been used as an alternative estimate in the P M2.5 

### NAAQS RIA and P M2.5 

### benefits estimates in RIAs completed since the P M2.5 

### NAAQS.

 

### Twelve estimates are based on the C-R func tions from EPA’s e xpert elicitation study (Roman et al., 2008) on the P M2.5 

### -mortality relationship and interpreted for PM benefits analysis in EPA’s final RIA for the P M2.5 

### NAAQS. For that study, twelve experts (labeled A through L) provi ded independent estimates of the P M2.5 

### -mortality concentration-response function. EPA pract ice has been to develop independent estimates of P M2.5 

### -mortality estimates correspondi ng to the concentration-response

### 6-3 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 revi ewing the methodology for crea ting the benefit-per-ton estimates used in this analys is should consult Fann et al. (2009). As described 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-ton values calculated therefore apply only to tons re duced from those specific polluta nt/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 theref ore 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 incremental 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 relationship while recogn izing potential uncerta inty about the exact shape of the concentr ation-response function. 4 

### In conjunction with th e underlying scientific literature, this document provided a basis for reco nsidering the applicatio n of thresholds in PM 2.5 

### concentration-response functi ons used in EPA’s RIAs. 5


### As is the nature of Regulatory Impact An alyses (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 risk reductions using a value of statistical life (VSL) estimate derived from a limited analysis of some of the availabl e 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 represented 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 After adjusting the VSL to account for a different currency year (2008$) and to account for income growth to 2015,

the $5.5 million VSL is $7.9m.

### 6-5 Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected the interim estimate to a scientific peer-review process through the Science Advisory Board (SAB) or other pe er-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 tech niques to combine estimates from unique data sources and different studies, including those using differe nt 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 st udies 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 progr ess in responding to the SAB-EEA C’s specific recommendations. The Agency anticipates presenting results from this effort to the SAB-EEAC in Spring 2010 and that draft guidance will be av ailable 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 does 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.


### Table 6-2 provides a general summary of th e primary approach results by pollutant, including the emission reductions and monetized benefits -per-ton at discount rates of 3% and 7%. 9 

### Table 6-3 provides a summary of the reductions in health incidences as a result of the pollution reductions. In Table 6-4, we provide the benefits using our anchor points of Pope et al. and Laden et al. as well as the results from th e expert elicitation on PM mortality. Figures 6-2 through 6-4 provide a visual repres entation of the range of benef its estimates and the pollutant breakdown of the monetized benefits.

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 speci fic analysis year (i.e., 2015), and mo st of the PM benefits occur within that year with two excep tions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 years of follow-up medical co sts and lost wages. For premature mort ality, we assume that there is a “cessation” lag between PM exposures and the total reali zation 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 y ear, 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 la g assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis year and the valuation of prematur e mortalities that occur after the analysis year. As such, the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate. 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 Proposed CISWI NSPS and Emissions Guidelines in 2015 (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%)

Direct PM 2.5 660 $230,000 $560,000 $210,000 $500,000 $150 to $370 $140 to $330

PM 2.5 Precursors

SO 2 2,659 $29,000 $72,000 $27,000 $65,000 $78.0 to $190 $71.0 to $170

NO 2 1,447 $4,900 $12,000 $4,400 $11,000 $7.0 to $17.0 $6.4 to $16.0

Total $240 to $580 $210 to $520

a All estimates are for the implementati on year (2015), 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. 

### Table 6-3. Summary of Reductions in Health Incidences from PM 2.5 

### Benefits for the Proposed CISWI NSPS and Emissions Guidelines in 2015 a

Avoided Premature Mortality Pope et al. 26
Laden et al. 66 Avoided Morbidity
Chronic Bronchitis 18 Acute Myocardial Infarction 41 Hospital Admissions, Respiratory 6 Hospital Admissions, Cardiovascular 13 Emergency Room Visits, Respiratory 25
Acute Bronchitis 42 Work Loss Days 3,400
Asthma Exacerbation 450 Acute Respiratory Symptoms 20,000 Lower Respiratory Symptoms 490 Upper Respiratory Symptoms 370

a All estimates are for the analysis year (2015) 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.

### 6-8 Table 6-4. All PM 2.5 

### Benefits Estimates for the Proposed CISWI NSPS and Emissions Guidelines at Discount Rates of 3% and 7% in 2015 (in millions of 2008$) a

3% 7% Benefit-per-ton Coefficients Deri ved from Epidemiology Literature

Pope et al. $240 $210 Laden et al. $580 $520 Benefit-per-ton Coefficients D erived from Expert Elicitation Expert A $610 $550 Expert B $470 $420 Expert C $470 $420 Expert D $330 $300 Expert E $760 $680 Expert F $420 $380 Expert G $280 $250 Expert H $350 $320 Expert I $460 $420 Expert J $380 $340 Expert K $92 $84 Expert L $340 $310

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.

### 6-9 Figure 6-2. Total Monetized PM 2.5 

### Benefits for the Proposed CISWI NSPS and Emissions Guidelines in 2015

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.



### 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 categories, includ ing benefits from reducing toxic emissions, ecosystem effects, and visibility impairment. Th e health benefits from reducing hazardous air pollutants (HAPs) and carbon monoxide have not been monetized in this an alysis. In addition to being a PM 2.5 

### precursor, SO 2 

### emissions also contribute to adve rse effects from acidic deposition in aquatic and terrestrial ecosystems, increa sed mercury methylation, as well as visibility impairment. $0 $100 $200 $300 $400 $500 $600 $700

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

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

### 6-10 Figure 6-3. Breakdown of Monetized Benefits for the Proposed CISWI NSPS and Emissions Guidelines by PM 2.5 

### Precursor Pollutant and Source

Figure 6-4. Breakdown of Monetized Benefits for the Proposed CISWI NSPS and Emissions Guidelines by Subcategory Burn

‐ off oven 1% Energy recovery unit 95% Incinerator 4%

Small, remote unit 0.1%

### 6-11 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 the benefits associated with the 24,000 tons reduc tions in CO emissions that would occur as a result of this rule.

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of carbon-containing fuels and photoc hemical reactions in the atmosphere. The amount of CO emitted from these reactions, re lative to carbon dioxide (CO 2 

### ), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that are dependent upon concentration and dur ation of exposure.
The Integrated Science Assessment for Ca rbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposure to CO is “likely to have a causal relationship” with cardiovascular morbidity, particularly in individuals with co ronary heart disease. Epidemiologic studies associate short-term CO exposure with increased risk of emergency de partment visits and hospital admissions. Coronary hear t disease includes those who ha ve angina pectoris (cardiac chest pain), as well as those who have e xperienced a heart att ack. Other subpopulations potentially at risk include i ndividuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabet es, and individuals in very early or late life stages, such as older adults or the developing young. The evid ence is suggestive of a causal relationship between short-term exposure to CO and respirator y morbidity and mortality. The evidence is also suggestive of a causal relationship for birth ou tcomes and developmental effects following longterm exposure to CO, and for central nervous sy stem effects linked to short- 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

### 6-12 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 U.S. EPA has concluded that there is a causal relationship betw een respiratory health 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 bronchoconstricti on. Asthmatics are more sensitive to the effects of SO 2 

### likely resulting from preexisting inflammati on associated with this disease. A clear concentration-response relationship has been de monstrated in laborat ory studies following exposures to SO 2 

### at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adve rsely affected. Based on our review of this information, we identified four short- term morbidity endpoints that the SO 2 

### ISA identified as a “causal relationship”: asthma exacer bation, respiratory-related emer gency department visits, and respiratory-related hospitalizations. The differi ng evidence and associated strength of the evidence for these different effects is described in detail in the SO 2 

### ISA. The SO 2 

### ISA also concluded that the relations hip between short-term SO 2 

### exposure and premature mortality was “suggestive of a causal relationship” because it is difficult to attribut e the mortality risk effects to SO 2 

### alone. Although the SO 2 

### ISA stated that studies are gene rally consistent in reporting a relationship between SO 2 

### exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants.

SO 2 

### emissions also contribute to adverse 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 source of food for some recreational and subsistence fishermen and for other consumers and support several cultural services, including aesthetic and educational services and recreational fishing. Bi ological effects of acidification in terrestrial ecosystems are generally linked to al uminum toxicity, which can cause reduced root growth, which restricts the ability of the plant to take up water a nd nutrients. These direct effects can, in turn, increase the sensit ivity of these plants to stre sses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial acidification affects several im portant ecological services, incl uding declines in habitat for threatened and endangered species (c ultural), declines in forest aesthetics (cultural), declines in

### 6-13 forest productivity (provi sioning), and increases in forest so il erosion and reductions in water retention (cultural and regul ating). (U.S. EPA, 2008d)
Reducing SO 2 

### emissions and the secondary formation of PM 2.5 

### would improve the level of visibility throughout the Un ited States. Fine particles wi th significant li ght-extinction efficiencies include sulfates, nitrates, organic car bon, elemental carbon, and soil (Sisler, 1996). These suspended particles and gases degrade visi bility by scattering and absorbing light. Higher visibility impairment levels in the East are due to generally higher c oncentrations of fine particles, particularly sulfates , and higher average relative humid ity levels. In fact, particulate sulfate is the largest contributor to regional haze in th e eastern U.S. (i.e., 40% or more annually and 75% during summer). In the western U.S., particulate sulfate cont ributes 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 wellbei ng. Good visibility increa ses 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 hazardous air polluta nts that would be reduced as a result of this rule. Available emissions data s how that several different HAPs are emitted from CISWI. This rule is anticipated to reduce 560 tons of hydrochloric acid, 5.4 t ons of cadmium, 6.0 tons of lead, 280 pounds of mercury, and 230 grams of total dioxins/f urans each year in the primary approach.
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 contaminant is concentrated in higher trophic levels, including fish eat en by humans. Experimental ev idence has established 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 casual relationship between sulfur de position and increased mercury methylation in wetlands and aquatic environments (U.S. EPA, 2008d).

In addition to the role of su lfate deposition on methylation, th is proposed rule 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

### 6-14 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 mercury. Gaseous el emental mercury can be transported very long distances, even globally, to regions far from the emissions source (becoming part of the global “pool”) before deposition occurs. Inorgani c ionic and particulat e bound mercury have a shorter atmospheric lifetime and can deposit to land or water bodies closer to the emissions source. Furthermore, elemental mercury in the atmosphere can undergo transformation into ionic mercury, providing a significant pathway for deposition of emitted elemental mercury.
This source category emitted about 0.5 tons of mercury in the air in 2008 in the U.S. Based on the EPA’s National Emission Inventory, a bout 103 tons of mercury were emitted from all anthropogenic sources in th e U.S. in 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury were emitted wo rldwide 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 0.5% of the total anthropogenic mercury emissions in the U.S. and about 0.03% of the global emissions. Overall, this rule would reduce mercury emi ssions by about 280 pounds per year from current levels, and therefore, contribute to reductions in mercury exposures and health effects. Due to time and resource limitations, we were unabl e to model mercury dispersion, deposition, methylation, bioaccumulation in fish tissue, a nd human consumption of mercury-contaminated fish that would be needed in order to estimat e the human health benef its from reducing mercury emissions.
Potential exposure routes to mercury emi ssions include both direct inhalation and consumption of fish containing methylmercur y. In the U.S., the 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 se ttles into water bodies or onto land where it can either move directly or be leached into waterbodies. Once deposited, certain microorganisms can change it into methylmercury, a highly toxic form that builds up in fish, shellfish and animals that eat fish. Consumption of fish and shellfish are the main sources of methylmercury exposure to humans. Methylmercury builds up more in so me 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 fis h, including ocean specie s and local freshwater

### 6-15 fish, contain some methylmercury. For exampl e, in recent studies by EPA and the U.S. Geological Survey (USGS) of fi sh tissues, every fish sample 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 consum ption. Some of these studies al so suggest that methylmercury may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of the literature concerning methylme rcury health effects took note of two epidemiological studies that found an association between dietary exposure to methylmerc ury and adverse cardiovascular effects. 10 

### Moreover, in a study of 1,833 males in Finla nd 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 seve n 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 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

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

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370-375.

### 6-16 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 possi ble cardiovascular effects of methylmercury exposure. These studies include ep idemiological, toxicologi cal, and toxicokinetic investigations. Over a dozen review papers ha ve also been published. If there is a causal relationship between methylmercury exposure and a dverse cardiovascular effects, then reducing exposure to methylmercury would result in public health benefits 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 and to advise EPA on methodol ogies for estimating population level cardiovascular health imp acts of reduced methylmercury exposure. The report from that workshop is in preparation.

6.3.3.2 Cadmium 14 

### Breathing air with very high le vels of cadmium can severely damage the lungs and may cause death. In the United States, where proper industrial hygiene is generally practiced, inhaling very high levels of cadmium at work is expect ed to be rare and accidental. Breathing air with lower levels of cadmium over long periods of time (for years) results in a build-up of cadmium in the kidney, and if sufficien tly high, may result in kidney di sease. Lung cancer has been found in some studies of workers exposed to cadmium in the air and studies of rats that breathed in cadmium. The U.S. Department of Health and Human Services (DHHS) has determined that cadmium and cadmium compounds are known human carcinogens. The International Agency for Research on Cancer (IARC) has determined that cadmium is carcinogenic to humans. The EPA has determined that cad mium is a probable human carcinogen.

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). 2008. Public Health Statement for Cadmium. CAS# 1306-19-0. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc.gov/PHS/PHS.asp?id=46&tid=15>.

### 6-17 6.3.3.3 Lead 15 

### The main target for lead toxi city is the nervous system, bot h in adults and children. Longterm exposure of adults to lead at work has resu lted in decreased performance in some tests that measure functions of the nervous system. Lead exposure may also cause weakness in fingers, wrists, or ankles. Lead exposure also causes sma ll increases in blood pressure, particularly in middle-aged and older people. Lead exposure may also cause anemia. At high levels of exposure, lead can severely damage the brain an d kidneys in adults or children and ultimately cause death. In pregnant wome n, high levels of exposure to lead may cause miscarriage. Highlevel exposure in men can damage the or gans responsible for sperm production.
We have no conclusive proof that lead cause s cancer (is carcinogeni c) in humans. Kidney tumors have developed in rats and mice that ha d been given large doses of some kind of lead compounds. The Department of Health and Human Services (DHHS) has determined that lead and lead compounds are reason ably anticipated to be hum an carcinogens based on limited evidence from studies in humans and sufficient evidence from animal studies, and the EPA has determined that lead is a probable human carci nogen. The International Agency for Research on Cancer (IARC) has determined that inorgani c lead is probably carcinogenic to humans. IARC determined that organic lead compounds are not classifiable as to their carcinogenicity in humans based on inadequate evidence fr om studies in humans and in animals.

Children are more sensitive to the health eff ects of lead than adults. No safe blood lead level in children has been determined. Lead a ffects children in differe nt ways depending on how much lead a child swallows. A child who swallows large amounts of lead may develop anemia, kidney damage, colic (severe “stomach ache”), muscle weakness, and brain damage, which ultimately can kill the child. In some cases, the amount of lead in the child’s body can be lowered by giving the child certai n drugs that help eliminate lead from the body. If a child swallows smaller amounts of lead, such as dust c ontaining lead from paint, much less severe but still important effects on blood, de velopment, and behavior may occu r. In this case, recovery is likely once the child is removed from the source of lead exposure, but there is no guarantee that the child will completely avoid all long-term conse quences of lead exposure. At still lower levels of exposure, lead can affect a ch ild’s mental and physical growth. Fetuses exposed to lead in the womb, because their mothers had a lot of lead in their bodies, may be born prematurely and have lower weights at birth. Exposure in the womb, in infancy, or in early childhood also may slow

15 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Public Health Statement for Lead. CAS#: 7439-92-1. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Availa ble on the Internet at <http://www.atsdr.cdc.gov/ToxProfiles/phs13.html>.

### 6-18 mental development and cause lower intelligence later in childhood. There is evidence that these effects may persis t beyond childhood.
6.3.3.4 Hydrogen Chloride (HCl) 16 

### Hydrogen chloride gas is intensely irritati ng to the mucous membranes of the nose, throat, and respiratory tract. Brie f exposure to 35 ppm causes throat irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour. The grea test impact is on the upper respir atory tract; exposure to high concentrations can rapidly lead to swelling and spasm of th e throat and suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronc hioles. Patients who have massive exposures may develop an accumulation of fluid in the lung s. Exposure to hydrogen chloride can lead to Reactive Airway Dysfunction Syndrome (RADS), a chemically- or irri tant-induced type of asthma. Children may be more vulnerable to co rrosive agents than a dults because of the relatively smaller diameter of their airways. Children may al so be more vulnerable to gas exposure because of increased minute ventilation pe r kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not b een classified for carcinogenic effects.
6.3.3.5 Dioxins (Chlorinated dibenzodioxins (CDDs) 17



### 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. Sl ight increases in the risk of diabetes and abnormal glucose tolerance have been observe d in some studies of people exposed to 2,3,7,8- TCDD. We do not have enough information to know if exposure to 2,3,7,8-TCDD would result

16 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>. 17 All health effects language for this section came fro m: Agency for Toxic Substances and Disease Registry

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

### 6-19 in reproductive or developmental effects in peopl e, but animal studies 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 levels can cause a variety of effects in animals, such as weight loss, liver damage, and disruption of the endocrine system. In many species of animals, 2,3,7,8-TCDD weakens the immune system and causes a decrease in the system's ability to fight bacteria and viruses at rela tively low 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 anim al species exposed to CDDs during pregnancy had miscarriages and the offspring of animal s exposed to 2,3,7,8-TCDD during pregnancy often had severe birth defects includi ng skeletal deformities, kidney defects, and weakened immune responses. In some studies, effects were obser ved at body burdens 10 times higher than human background levels.
6.3.3.6 Furans (Chlorinated dibenzofurans (CDFs)) 18



### 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 exposures or from a normal diet. Skin and eye irritations, es pecially severe acne, darkened skin color, and swollen eyelids with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also caused vomiting and diarrhea, anemia, more 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 effect was permanent or caused by CDFs alone or CDFs and polychlor inated 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 serious ly 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.

18 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>.

### 6-20 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 a nd ambient air quality in a given year. Factors such as hours of equipment use a nd weather are constantly variable , regardless 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 analyses on the benefits resu lts to assess the sensitivity of the primary results to various data inputs a nd assumptions. We then changed e ach default input one at a time and recalculated the total moneti zed benefits to assess the percen t change from the default. We present the results of th is sensitivity analysis in Table 6-5. We indicated each input parameter, the value used as the default, and the values for the sensitivity analyses, and then we provide the total monetary benefits for each input and the percent change from the default value for the primary approach.

Above we present the estimates of the total monetized benefits, based on our interpretation of the best avai lable scientific literature a nd methods and supported by the SABHES and the NAS (NRC, 2002). The benefits estima tes are subject to a number of assumptions and uncertainties. For example, for key assu mptions underlying the estimates for premature mortality, which typically account fo r at least 90% of the total mone tized benefits, we were able to quantify include the following:

### 6-21 Table 6-5. 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) $240 N/A No Threshold (Laden) $580 N/A Threshold (Pope) $190 -21% Threshold (Laden) $420 -28%

Discount Rate (with Epidemiology Study) 3% (Pope) $240 N/A 3% (Laden) $580 N/A 7% (Pope) $210 -13% 7% (Laden) $520 -10%



### 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 control ling directly emitted fine particulates.
2. We assume that all fine particles, re gardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

### produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 

### released from diesel engines and other industrial sources, but no clear scien tific grounds exist for s upporting differential 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).

### 6-22 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 RIA can provide some evidence of th e uncertainty surrounding 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 occurr ing in any specific location, as these are all based on national or broad regional emission reduction programs and therefore represent average benef its-per-ton over the entire United States. The benefits-per-ton for emission reductions in specific locations may be ve ry different than the estimates presented here.
6.5 Comparison of Benefits and Costs Using a 3% discount rate, we estimate the total monetized benef its of the proposed CISWI NSPS and Emissions Guidelines to be $240 million to $580 million in the implementation year (2015)(Table 6-6). Using a 7% discount rate, we estimate the total monetized benefits of the CISWI NSPS and Em issions Guidelines to be $210 million to $520 million. The annualized costs are $216 million at a 7% interest rate. 19 

### Thus, net benefits are $19 million to $360 million at a 3% discount rate for th e benefits and $-2.4 million to $310 billion at a 7% discount rate. All estimates are in 2008$. Fi gures 6-5 and 6-6 show the full range of net benefits estimates (i.e., annual benefits minus annualized costs) utilizing the 14 different PM 2.5 

### mortality functions at discount rates of 3% and 7%. In additi on, the benefits from reducing 24,000 tons of carbon monoxide, 560 tons of hydrochl oric acid, 5.4 tons of cadmium, 6.0 tons of lead, 280 pounds of mercury, and 230 grams of to tal dioxins/furan each year have not been included in these estimates.

19 For more information on the annualized costs, please refer to Section 4 of this RIA. There are no estimates of costs available at a 3% discount rate.

### 6-23 Table 6-6. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the CISWI NSPS and Emissions Guide lines in 2015 (mi llions of 2008$) 1

Proposed Option
3% Discount Rate 7% Discount Rate Total Monetized Benefits $240 to $580 2 $210 to $520 2
Total Social Costs $220 3 $220 Net Benefits $12 to $350 $-10 to $300

1 All estimates are for the implementation year (2015) , 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 660 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 1,400 tons of NO x and 2,700 tons of SO 2 . The benefits from reducing 24,000 tons of carbon monoxide, 560 tons of hydrochloric acid, 5.4 tons of cadmium, 6.0 tons of lead, and 280 pounds of mercury 230 grams of total dioxins/ furans each year are not included in these estimates. In addition, the benefits fr om 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.



### Figure 6-5. Net Benefits for the Proposed CI SWI NSPS and Emissions Guidelines at 3% Discount Rate a

a Net Benefits are quantified in terms of PM 2.5 benefits for implementation year (2015). This graph shows 14

benefits estimates combined with the cost estimate. A ll combinations are treated as independent and equally probable. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced ha s a different propensity to become PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. ‐ $200 ‐ $100 $0 $100 $200 $300 $400 $500 $600

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

Cost estimate combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions

### 6-24 Figure 6-6. Net Benefits for the Proposed CI SWI NSPS and Emissions Guidelines at 7% Discount Rate a

a Net Benefits are quantified in terms of PM 2.5 benefits for implementation year (2015). This graph shows 14

benefits estimates combined with the cost estimate. A ll combinations are treated as independent and equally probable. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced ha s a different propensity to become PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. ‐ $200 ‐ $100 $0 $100 $200 $300 $400 $500 $600

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

Cost estimate combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions

### 7-1 SECTION 7 SUPPLEMENTAL ECONOMIC ANALY SES FOR ALTERNATE APPROACH

EPA also considered an economic analysis for an “alternative approach” for defining non-hazardous solid waste. Under the alternative approach, the universe of sources in the energy recovery and waste burning cement kiln subcat egories would change while the number of sources in the remaining three subcategories (i.e., incinerators, burn-off ove ns, and small, remote incinerators) does not change. This secti on provides an overview of the results.
EPA estimated compliance costs for all exis ting units to add the necessary controls, monitoring equipment, inspections, and recordk eeping and reporting requirements to comply with the CISWI standards for the alternative approach. The estimates in this RIA reflect previous estimates of engineering costs with a capital investment of $1.9 billion (2008$) with an annualized engineering cost estimate of $476 million (2008$). The revised estimates are an annualized engineering cost estimate of $480 million (2008$), which results in a slight underestimate of the economic impacts.
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 0.1%).

7.1.2 Social Cost Estimates In the near term, the Agency’s economic m odel suggests that industries are able to pass on $117 million (2008$) of the costs to U.S. households in the form of higher prices (Table 7-2). Existing U.S. industries’ surplus falls by $389 mill ion, and the net loss for U.S. stakeholders is $506 million. As U.S. prices rise, other countri es are affected through international trade relationships. Households that buy goods from the United States experience losses, while industries that sell goods to the United States benefit; the model estimates a net gain of $29 million. After accounting for intern ational trade effects, the Ag ency’s economic model projects the net surplus loss associated with the pr oposed rule is $477 million. The Agency also considered other elements of the engineering cost analysis that could not be modeled within the multimarket model (e.g., fuel savings benefits and total annualized compliance costs [unknown sources]). The net effect of the adjust ments is approximately than $1 million.

### 7-2 Table 7-1. Market-Level Price and Quanti ty Changes: 2015 (Alternative Approach)

Industry Sector Prices Production Imports Consumption Exports Energy less than 0.01% less than 0.01% less than 0.01% 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 less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Lumber, paper, and printing 0.07% -0.03% 0.07% -0.01% -0.05%
Chemicals less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01%
Plastics and Rubber less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01%
Nonmetallic Minerals 0.02% less than 0.01% less than 0.01% less than 0.01% -0.02% Primary Metals less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01%
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 less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% 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% less than 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$): 2015

Method Alternative Approach Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus − $117
Change in U.S. producer surplus − $389
Change in U.S. surplus − $506
Net change in rest of world surplus $29
Net change in total surplus − $477
Direct Compliance Costs Method

Total annualized costs, unknown sources (not modeled) less than $1 Million
Fuel savings (not modeled) $1
Change in Total Surplus − $476



### 7.1.3 Job Effects Near-term employment changes associated with the proposed rule are estimated to be less than 1,000 job losses (Table 7-3); over a long er time period, net employment effects range between 800 job losses to 1,700 job gains.
Table 7-3. Employment Changes (1,000 Jobs): 2015

Method Alternative Approach Partial equilibrium model (multiple markets) (demand effect only)

− 0.9 Estimate Derived from Morgenstern, et al. (net effect [A + B +C below]) +0.7 (

− 1.4 to +2.8)

A. Estimate Derived from Morgenst ern, et al: Demand effect − 1.7 (

− 3.6 to 0.2)

B. Estimate Derived from Morgen stern, et al: Cost effect +1.2 (+0.4 to +1.9)

C. Estimate Derived from Morgenstern, et al: Factor shift effect +1.3 (+0.0 to +2.5)
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 and monetized benefits -per-ton at discount rates of 3% and 7%. These estimates reflect EPA’s most current interpretation of the scientific literature. Higher or lower estimates of benefits are possible using other assump tions, 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 effects, and visibility impairment. The benefits from reducing hazardous air pollutants have not been monetized in this analysis, including reducing 130,000 tons of carbon monoxide , 430 tons of hydrochloric acid, 4.3 tons of cadmium, 3.4 tons of lead, and 2,400 pounds of mercury each year. Table 7-5 presents a summary of the monetized benefits, social costs, and net benefits for the alternate approach.
Table 7-4. Summary of Monetized Benefits fo r CISWI NSPS and Emission Guidelines in 2015 (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 11,962 $230,000 $560,000 $210,000 $ 500,000 $2,700 to $6,700 $2,500 to $6,000

PM 2.5 Precursors
SO 2 205 $29,000 $72,000 $27,000 $65,000 $6.0 to $15 $5.5 to $13

NO 2 522 $4,900 $12,000 $4,400 $11,000 $2.5 to $6.2 $2.3 to $5.6
Total $2,700 to $6,700 $2,500 to $6,000 *All estimates are for the analysis year (fifth year after proposal 2015), 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 prec ursor reduced has a different propensity to form PM 2.5 . The monetized benefits incorporate th e 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 CISWI NSPS and Emissions Guidelines in 2015 (millions of 2008$) (Alternate Approach) 1

Proposed Option with Alternate Approach
3% Discount Rate 7% Discount Rate Total Monetized Benefits $2,700 to $6,700 2 $2,500 to $6,000 2
Total Social Costs $480 3 $480 Net Benefits $2,300 to $6,200 $2,000 to $5,600

1 All estimates are for the implementation year (2015) , 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 12,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 520 tons of NO x and 205 tons of SO 2 . The benefits from reducing 128,000 tons of carbon monoxide, 430 tons of hydrochloric acid, 4.3 tons of cadmium, 3.4 tons of lead, 1.2 tons of mercury, and 85 grams of total 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.

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### APPENDIX A OAQPS MULTIMARKET MODEL TO ASSESS THE ECONOMIC IMPACTS OF ENVIRONMENTAL REGULATION

### A-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 considerable” 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 kit; it is designed to be used as a transparent tool that can respond quickly to requests a bout how stakeholders 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 non-en ergy material costs

– customers respond to these price increases and consumption falls

 

### Model scope

### A-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 i ndustry as a national market, th e identity represents the prepolicy market-clearing condition, 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 $19

### A-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.

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 144,100

### A-14 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 pr ices must rise to main tain the original prepolicy production levels (Q).
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 current 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.

### A-15 Figure A-1. Direct Compliance Costs Reduce Pr oduction Rates at Benchmark Prices

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

### A-16 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.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 Br oda and colleagues (2008b).

### 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. 

### 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



### 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

1 Broda et al.’s intent was to use these categories to describe or proxy for domestic market power.

### A-21 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 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, 3 

### 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.

1 See Chapter 14 of the GTAP 7 Database Documentati on for the full description of the parameters at https://www.gtap.agecon.purdue.edu/ resources/download/4184.pdf; see Table 14.2 for elasticities. 2 Detailed documentation of the entire GTAP 7 Database is available at https://www.gtap.agecon.purdue.edu/databases/v7/v7_doco. asp. The GTAP also uses a unique system of categorizing commodities that does not ma tch the NAICS or HS system exactly. 3 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 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: 

#   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,

### 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 indu stry from Ho and colleagues’ industry aggregation. Source: Ho, M. S, R. Morgenstern, and J. S. Shih. 2008. “Impact of Carbon Price Policies on US Industry.” RFF Discussion Paper 08-37. Http://Www.Rff.Org /Publications/Pages/Publicationdetails.Aspx?. Publicationid=20680. Accesse d August 2009. Table B.6.

### A-26 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.

### APPENDIX B COMPLIANCE COST ANALY SES FOR CISWI UNITSMEMORANDUM
TO: Charlene Spells, Toni Jones, Ketan Pa tel, U.S. Environmental Protection Agency
FROM: Jason Huckaby, Amber Allen, Kristen James, Eastern Research Group, Inc.
DATE: April 28, 2010
SUBJECT: Compliance Cost Analyses for CISWI Units

B ACKGROUND The U.S. Environmental Protection Agency (EPA), under section 129 of the Clean Air Act (CAA), is required to regulate emissions of nine polluta nts from Commercial and Industrial Solid Waste Incineration (CISWI) units: hydrogen chloride (HCl ), carbon monoxide (CO), lead (Pb), cadmium (Cd), mercury (Hg), particulate matter (PM), di oxins/furans (CDD/CDF), nitrogen oxides (NO x ), and sulfur dioxide (SO 2 ).
On December 1, 2000, EPA adopted new source pe rformance standards and emission guidelines for commercial and industrial solid waste incineration units established under Sections 111 and 129 of the Clean Air Act. In 2001 EPA was granted a petition for reconsideration regarding the definitions of "commercial and industrial waste" and "commercial and industrial solid waste incineration unit." In 2001, the United States Court of Appeals for the District of Columbia Circuit granted EPA’s voluntary remand, without vacatur, of the 2000 rule. In 20 05, EPA proposed and finalized the commercial and industrial solid waste incineration definition rule which revised the definition of “solid waste,” "commercial and industrial waste," and "commercial a nd industrial waste incineration unit." In 2007, the United States Court of Appeals for the District of Columbia Circuit vacated and remanded the 2005 commercial and industrial solid w aste incineration definition rule.
These proposed standards provide EPA’s response to th e voluntary remand that was granted in 2001 and the vacatur and remand of the commercial and industrial solid waste incineration definition rule in 2007. In addition, the standards re-development includes the 5-year technology review of the new source performance standards and emission guidelines require d under Section 129. The EPA has developed a series of maximum achievable control technology (MACT) floor options to support that re-development. The development of the MACT floors used to determ ine these options is discussed in more detail in a separate memorandum. 1 The purpose of this memorandum is to present for existing sources the nationwide costs and nationwide cost effectiveness as sociated with these compliance options and with alternatives to compliance. This memo is organized as follows:
I. Choosing Controls Needed for Each Unit to Meet MACT Floors II. MACT Compliance Costs A. Emission Control Costs B. Stack Testing, Monitoring, and Recordkeeping Costs 1. Stack Testing 2. Monitoring Requirements 3. Recordkeeping and Reporting C. Alternative Disposal Costs III. Cost Effectiveness IV. Beyond the Floor OptionsV. “Alternative Approach” VI. New Units VII. References Appendix A. Tables for “Proposed Approach” Appendix B. Summary Tables for Beyond-the-Floor Analysis Appendix C. Summary Tables for “Alternative Approach”

I. C HOOSING C ONTROLS N EEDED FOR E ACH U NIT TO M EET MACT F LOORS A significant portion of the total cost for industry co mpliance comes from the cost of installing new or improving existing pollution control devices for units not currently meeting the proposed limits. In order to determine the control costs, it was necessary to evaluate, for each CISWI unit, how much improvement for each pollutant would be needed to meet the proposed emissions limits.
The unit’s average pollutant concentration valu e used to calculate baseline annual emissions 2 were compared with the proposed MACT floor emissions limits, and percentages we re calculated to quantify the amount of improvement needed for the unit to meet the proposed MACT floors. Tables 1A – 1E contain the baseline pollutant concentration valu es used for each unit in each subcategory and the percentage improvement required to meet the proposed emissions limits for each unit for each pollutant. The existing CISWI units are subcategorized into five main groups: burn-off ovens, waste-burning kilns, energy recovery units, incinerators, and small, re mote units. The pollutant- and subcategory-specific limits are shown in each header row of these tables.
Control methods and cost algorithms utilized in a recent rulemaking for another waste combustion source category, Hospital, Medical and Infectious Wast e Incinerators (HMIWI) were updated and utilized generally for the CISWI source category, since mo st of these algorithms are applicable to waste combustion units found in the CISWI source category. There were some slight modifications for the energy recovery unit subcategory based on input fr om the boiler NESHAP development, since this subcategory contains units which, were they not fi ring wastes, would be cons idered boilers and process heaters. Based on these required improvements, po llutant-specific control methods were chosen as follows for units unable to meet the proposed MACT floors:
Metals (cadmium and lead) and PM: Adding fabric filters or im proving existing fabric filters.
Mercury and dioxins/furans (CDD/CDF): Adding activated carbon injection (ACI) and adjusting the carbon addition rate to meet th e amount of reduction required.
Hydrogen chloride (HCl): Adding wet scrubbers, or improving already installed wet scrubbers.
Carbon monoxide (CO): For burn-off ovens and incinerators, adding an afterburner retrofit; for wasteburning kilns, performing a tune-up. For energy r ecovery units, the control prescribed depends on the baseline CO concentration of the unit. For units in the 400 to 1,000 ppm range, adding advanced combustion controls (linkageless boiler management system) was prescribed. For units with CO concentrations over 1,000 ppm, a CO oxidation catalyst was assigned for control. No further improvement was needed for small, remote CISWI units.
Nitrogen oxides (NO x ): Adding selective non-catalytic reduction (SNCR) systems. For units requiring less than 10 percent improvement in NO x control, minor adjustments were considered sufficient.
Sulfur dioxide (SO 2 ): Adding a wet scrubber for most units. For units with dry limestone injection or spray dryer absorbers that don’t meet the proposed limit, adding lime was considered sufficient forimproving SO 2 control. For units not meeting the propo sed limit that already has a wet scrubber as a control, adding caustic was de emed necessary to improve SO 2 control.
Further descriptions of these controls and their associated costs are listed below in Section II.

II. MACT C OMPLIANCE C OSTS This section presents the nationwide costs estimated for existing CISWI for (A) the emission controls used to comply with the MACT floor; (B) the monito ring, testing, recordkeeping, and reporting activities used to demonstrate compliance; and (C) the alternativ es to compliance. Total cap ital cost for all existing CISWI units to meet the MACT floor emission limits is estimated at approximately $701 million. Total annual cost for controls for all units in all subcat egories is about $244 million, but is about $216 million for the lowest cost alternative. The existing CISWI units are subcategorized into five main groups: burnoff ovens, waste-burning kilns, energy recovery units, incinerators, and small, remote units. Tables 2A-2E present costs for emission controls, stack testing, monitoring, and reporting and recordkeeping for each unit within each subcategory, as well as costs for a lternatives to compliance where applicable. Table 3 summarizes total compliance costs, as well as the low est cost alternative (where alternative disposal methods are possible) for all units.

A. Emission Control Costs Emission control technologies and other control measur es that can be used to comply with the MACT floor options for existing CISWI units include we t scrubbers, fabric filters, selective non-catalytic reduction (SNCR), activated carbon injection (ACI), and various other control measures designed to obtain incremental emission reductions. This section pr esents the costs that were estimated for each of these control measures.
The retrofit factors for the capital co sts were assumed to be 40 percent for wet scrubbers, fabric filters, and 20 percent for SNCR and ACI. 3, 4 Downtime costs for the retrofits were assumed to be negligible. Most CISWI are expected to be outdoors with adequate space to install an emission control system without shutting down the incinerator for an extended period. It was also expected that connecting the ductwork could be performed during a scheduled downtime for maintenance, thereby minimizing expected downtime. 5
The capital and annual costs for the emission controls were estimated in units of dollars ($) and $/flow. The $/flow costs were calculated by divi ding the capital/annual control cost estimate for each unit by the average gas flow rate assigned to that unit. Ta ble 4A is a summary of the parameters used for each unit (e.g., incinerator charge rate, stack gas flow ra te, incinerator operating hour s, and concentrations). Additional information on the calculation of flue gas flow rates specifically for energy recovery units and waste-burning kilns can be found in Tables 4B and 4C.
Total capital cost for controls for all subcategories is estimated at approximately $683 million, and total annual cost for controls for all subcategories is about $234 million. Costs are on a 2008 basis, and annualized costs assumed an interest rate of 7 percent. Tables 5A-5H present a summary of the parameters and equations used in the cost algor ithms for each emission control and alternative to compliance where applicable.1. Adding a fabric filter.
Fabric filters can be installed either alone or with other add-on controls. The cost algorithm for installing a fabric filter for burn-off ovens, cement kilns, incinerators, and small remote units is presented in Table 5A and is based on algorithms in the Model Plant Description and Control Cost Report for HMIWI. 5 For energy recovery units, calculations were based on an algorithm originally utilized for HMIWI, but incorporating slight modifica tions to make them consistent with those being utilized by the boilers NEHSAP development. Calcul ations specific to fabric filter installations for energy recovery units are presented in Table 5B. The fabric filter capital costs range from approximately $722,000 to $31.3 million, and annual costs range from approximately $139,000/yr to $9.6 million/yr. Sources for specific cost data are noted below Table 5A.

2. Adding a wet scrubber.
Wet scrubbers can be installed alone or after a dry scrubber/fabric filter. The cost algorithm for installing a packed-bed wet scrubber is presented in Table 5B and is based on algorithms in the Model Plant Description and Control Cost Report for HMIWI. The packed-bed wet scrubber capital costs range from approximately $226,000 to $7.9 milli on, and annual costs range from approximately $61,000/yr to $5.7 million/yr. Sources for specific cost data are noted below Table 5B.

3. Adding a selective non-catalytic reduction (SNCR) system. In an SNCR system, a nitrogen-based reducing agent, or reagent, such as ammonia or urea, is injected into the post-combustion flue gas through 3 nozzles mounted on the wall of the combustion unit. The cost algorithm for installing an SNCR system is pre sented in Table 5C and is based on algorithms in the OAQPS Control Cost Manual

. 3 The SNCR capital costs range from approximately $48,000 to $3.9 million, and annual costs range from approxima tely $5,300/yr to $436,000/yr. Sources for specific cost data are noted below Table 5C.

4. Adding an activated carbon injection (ACI) system. Injecting activated carbon before the fabric filter has been demonstrated to improve the removal efficiency of both Hg and CDD/CDF from CISWI. The cost algorithm for installing an ACI system is presented in Table 5D and is based on algorithms in the Model Plant Description and Control Cost Report for HMIWI. 5 Adjustments to the carbon injection rate were made to account for how much reduction was required to meet the proposed limit, and whether a packed-b ed scrubber was either being added or would be improved, since those may also assist in reducing Hg emissions. The packed-bed scrubber adjustment is a ten percent Hg reduction, and is based on input from the boiler NESHAP development. The ACI factor compares the carbon grain loading originally assumed to achieve 90 percent control of mercury or 98 percen t control of CDD/CDF to the amount of reduction the unit will need to meet the proposed emission limit s. The highest factor (Hg or CDD/CDF) is then used to adjust the carbon injection rate calcula tion of the algorithm. ACI capital costs range from approximately $5,600 to $156,000, and annual cost s range from approximately $2,900/yr to $3.2 million/yr. Sources for specific cost data are noted below Table 5D.

5. Adding afterburner/secondary chamber retrofit. Afterburner, or secondary chamber, retrofits include retrofitting an in cinerator or burn-off oven with a larger secondary chamber (with a longer gas resi dence time, e.g., 2 seconds) and operating it at a higher temperature (e.g., 1800°F). The cost algorith m for installing an afterburner retrofit with an incinerator is presented in Table 5E, and the cost algo rithm for installing an afterburner retrofit with a burn-off oven is presented in Table 5F. Both are based on algorithms in the Model Plant Descriptionand Control Cost Report for HMIWI. 5 Afterburner capital costs range from approximately $99,000 to $1.0 million, and annual costs range from approxi mately $20,000/yr to $485,000/yr. Sources for specific cost data are noted below Tables 5E and 5F.

6. Incremental Controls. In some instances, it may not be necessary to insta ll a new control system to achieve the emissions reductions necessary to comply with the control op tions. An incremental reduction in emissions may be achievable by improving existing controls, such as increasing the amount of caustic used in the wet scrubber, increasing the flow of lime prior to the fabric filter, increasing wet scrubber horsepower, improving fabric filter performance, or increasing the amount of NO x reagent injected into the postcombustion flue gas. Table 5G presents the algorith ms used to determine the annual cost of these incremental controls. There are no capital costs for incremental controls. Sources for specific cost data are noted below Table 5G.

a. Improving the performance of an existing wet scrubber. One strategy to reduce PM emissions further is to increase the PM collection efficiency of the wet scrubber by increasing its pressure drop, which increases the energy demand (horsepower) of the scrubber system, specifically the fan and pump. Costs to increase scrubber horsepower were estimated based on fan and pump electric ity equations for wet scrubbers in the Model Plant Description and Control Cost Report for HMIWI 5 and range from $126,000/yr to $2.2 million/yr.

b. Improving the performance of an existing fabric filter. One strategy to reduce PM and metals emissions further is to improve the performance of the fabric filter by replacing the filter bags used to capture emitted particulate. Costs to improve fabric filter performance were estimated using the same equations for bag and cage replacement employed in costing fabric filters a nd range from $1,300/yr to $3.3 million/yr.

c. Increasing lime. Emissions of acid gases such as HCl and SO2 may be reduced by increasing the feed rate of lime prior to the fabric filter. Estimated annual co sts for increasing lime range from approximately $7,900/yr to $8,600/yr.

d. Increasing caustic. One strategy to reduce acid gas emissions further is to increase the amount of caustic used in the wet scrubber to react with and neutralize the acid ga ses in the gas stream. The addition of caustic is assumed to sufficiently reduce emissions without requiring any changes to the wet scrubber. Costs to increase the amount of caustic were estimated using the same caustic equation employed in costing packed-bed wet scrubbers and ra nge from approximately $390/yr to $1,650/yr.

7. Additional Control Options.

a. Minor adjustments for NO x control. Minor adjustments, such as air handling and distri bution adjustments in the firebox, can be made to certain units to improve NO x control. It was assumed these adjustments could be made at no additional cost.

b. Adding advanced combustion controls.The costs to add a linkageless boiler management system (LBMS) are based on a 2008 quote provided to the U.S. Department of Energy. The installed cost for a LBMS on a 20 mmBtu/hr unit was $19,127. The DOE noted that costs are relati vely fixed, regardless of the size of the unit. Therefore, this cost was used as a fixed capital cost estimate for CISWI units required to add advanced combustion controls.

c. Adding a CO catalyst. Cost estimates for adding a CO oxidation catalyst were based on the Air Pollution Control Cost Manual

. 7 Capital cost per unit ranges from $1.7 m illion to $1.9 million, and annual costs range from $794,000 to $924,000.

d. Tune-up. Cost for performing a tune-up were based on a cost estimate provided in a report by Dr. H.M. Eckerlin and E.W. Soderberg 8 . This report indicated that the initial set-up for boiler tune-up was $3,000 to $7,000 per boiler, thereafter, annual tuning costs $1,000 per boiler. An average $5,000 per boiler initial set-up costs was annualized over 5 years at a 7 percent rate, and added to the subsequent year tune-up costs. Subsequently, an estimated flat cost of $1,580 annually was applied for units requiring tune-ups.

B. Stack Testing, Monitoring, and Recordkeeping Costs
1. Monitoring Costs Initial and continuous compliance provisions for CI SWI units were selected to be as consistent as possible with comparable regulations. For energy recovery units, requirement s were developed to be consistent with proposed stack testing, monitori ng, and recordkeeping requirements for major source boilers “biomass” units, since these un its are likely to be similar in design and materials combusted, adapted to reflect the CAA section 129 pollutants. For waste-burning cement kilns, monitoring requirements were based on the Portland Cement proposed NESHAP requirements for cement kilns and adapted to reflect the CAA section 129 pollutants. For the other three subcategories, requirements were as consistent as possible with cu rrent CISWI and HMIWI provisions. This section presents the costs that were estimated for each of these requirements. The total capital cost for stack testing, monito ring, and recordkeeping and reporting for all subcategories is estimated at a pproximately $18.0 million, and the total annual cost is about $10.4 million per year. Cost estimates were based on al gorithms recently utilized in the HMIWI regulatory development. Costs were updated to a 2008 basis, a nd annualized costs assumed an interest rate of 7 percent. Tables 6A-6F present a summary of the parameters and equations used in the cost algorithms for each monitoring component, where applicable.

a. Inspections. Consistent with HMIWI regulations, it was assu med that annual control device inspections will be required for any units having control devices in place or requiring further controls to meet the MACT floors. In this context, control devices include fabric filters, afterburners, wet scrubbers, ACI systems, or SNCR systems. The cost was estimated at a flat rate of $1000 per year. See Table 6A for further details and sources.b. Parameter monitors. Monitoring of operating parameters can be u sed to indicate whether air pollution control equipment and practices are functioning properly to minimize air pollution. Based on the existing CISWI regulations and HMIWI regulations, it w as assumed that parameter monitoring will be mandatory for all units required to add fabr ic filters, wet scrubbers, SNCR systems, or ACI systems. Costs for each monitoring system were estimated as follows:

 For a fabric filter bag leak detection syst em, capital cost was estimated at $25,500 and annual cost at $9,700/yr.

 For a wet scrubber monitoring system, capital cost was estimated at $24,300 and annual cost at $5,600/yr.

 For an SNCR monitoring system, capital cost was estimated at $10,300 and annual cost at $3,200/yr.

 The cost for ACI monitoring depends on a unit’s annual operational hours. There are no capital costs for ACI monitoring. Annual costs ranged from $200 to $20,200. For default parameters and equations used for monitoring costs, see Table 6B. Sources for specific cost data are noted below the table.

c. Continuous emissions monitoring systems (CEMS). The most direct means of monitoring compliance is the use of CEMS to measure the emissions of a pollutant on a continuous basis. The fo llowing text describes the CEMS options for each subcategory of existing CISWI units that will be included in the re-developed regulation. The costs for CO, PM, and Hg CEMS are presented in Table 6B.

 For energy recovery units, it was assumed no CO CEMS is currently in place for units that did not submit CO CEMS data in response to the Boiler/CISWI ICR. For any units where CEMS data for CO were not provided, costs for installing a CO CEMS were included in monitoring cost estimates. The capital cost was estimated at $134,000 and annual cost at $41,400/yr. PM CEMS would also be required for energy recovery units having capacities greater than 250 MMBtu. The capital cost for adding PM CEMS was estimated at $158,000 and annual cost at $56,100/yr.

 For waste-burning kilns, it was assumed all units will require Hg CEMS, but that they would likely have installed these already to co mply with the requirements of the proposed Portland cement NESHAP. Capital cost was es timated at $231,000 and annual cost at $112,600/yr, but these were not applied to cement kilns.
2. Testing Costs

a. Initial Stack Testing. It was assumed that initial stack testing will be required for each pollutant that the Phase 2 testing showed did not meet the proposed emission limit. Any unit having no test data for certain pollutants will also be required to perform an in itial emissions test for those pollutants. Table 6C presents a summary of required initial stack tes ting for each unit. Costs for each required stack test were summed and multiplied by 2/3 to adjust for economies of scale when multiple pollutant tests were being performed on a unit. The annua lized costs were calculated assuming a capital recovery factor of 0.10979 (15 years at 7 percent) . The basis of these cost estimates for each stack test is summarized in Table 6D.

b. Annual Stack Testing.It was assumed that all units, to some extent, will be required to demonstrate ongoing compliance with the emissions limits for certain pollutant s. Based on existing CISWI regulations, it was assumed that all incinerators, burn-off ovens, and small, remote units will be required to conduct annual stack tests for PM, HCl, and opacity. The cost for this annual testing was estimated to be approximately $14,300/yr. Based on the Hg CEMs requirements and requirements for the Portland Cement NESHAP, it was assumed that waste-burning kilns will be required to conduct annual stack tests for NO x , SO 2 , PM, HCl, and opacity. Based on the proposed boiler NESHAP requirements, it was assumed that energy recovery units will be required to conduct annual stack tests for all section 129 pollutants except CO. The cost for this annual testing was estimated to be approximately $50,300/yr.

c. Visible emissions testing. All CISWI units except for cement kilns will likel y have ash handling operations. Therefore, these units would be required to demonstrate comp liance to a 5 percent visible emissions limit for fugitive emissions generated durin g ash handling (similar to HMIW I). We are proposing that burn-off ovens, energy recovery units, incinerators , and small, remote units will be required to conduct annual performance tests for fugitive emissions from ash handling using EPA Method 22. Costs for this annual test include a capital co st of $250 and an annual cost of $200, based on the Revised Compliance Costs and Economic Inputs for Existing HMIWI memo. 6 Further details regarding this cost estimate are included in Table 6E.

3. Recordkeeping and Reporting Costs For all units, a flat rate of $2,989 per year was estimated as the annual cost for recordkeeping and reporting. Further details regarding this cost estimate, including hourly labor assumptions, labor rates, and associated sources, are included in Table 6F.

C. Alternative Disposal Costs Certain CISWI units may have waste disposal altern atives other than combustion available to them. These alternatives may prove to be less costly than the controls and monitoring required for compliance with the proposed CISWI standards. For example, fa cilities currently using burn-off ovens may be able to utilize sand blast chambers or some alternate technology to clean their parts. Likewise, other facilities may be able to simply divert their waste to a landfill or municipal waste combustor. To attempt to quantify the alternate waste disposal co sts, for incinerators and small, re mote units, the cost of alternative waste disposal methods such as landfilling or ha uling waste to a MWC were also estimated.
For incinerators and small, remote units, unit capacity , annual operating hours, and a default tipping fee and hauling cost, were used to cal culate annual costs for landfilling th e waste that would otherwise be incinerated. Annual landfilling costs varied widely , reaching a maximum of about $165,000/yr. Table 7A summarizes the parameters and equations used to calculate these cost estimates.
An additional option for incinerators would be to haul the waste to an MWC. Unit capacity, annual operating hours, and a nationally averaged tipping fee were used to calculate annual costs, which ranged up to $2.5 million. Table 7B presents the basis for these cost estimates. In most cases, hauling waste to an MWC was found to be cheaper than complying with the proposed limits, but still more expensive than landfilling.
For burn-off ovens, sandblasting was considered as an a lternative disposal method. As shown in Table 7C, an estimated operational cost of $53.75 over 2000 hrs per year for each burn-off oven was assumed,with an additional 10 percent assumed for contingency costs. The result was an estimated flat rate of $118,250 per year to utilize an abrasive blasting service.

To calculate the overall annual cost for each altern ative disposal option, th e costs associated with operating the incinerator and the annualized capital cost of the incinerator must also be accounted for. To address this, annual incinerator operational costs were s ubtracted from the annual alternative cost plus the annualized incinerator cost. The algorithms used for calculating the operational cost and annualized capital cost of the incinerator are shown in Table 7D . Intermittent operation was assumed for incinerators burning at least 1 ton per year of waste, and batch operation was assumed for burn-off ovens and incinerators burning less than 1 ton per year of w aste. Unit-specific incinerator operational costs, annualized incinerator cost, and alternative dis posal costs are listed in Tables 1A, 1D, and 1E.

III. C OST E FFECTIVENESS The cost effectiveness of the propo sed MACT floors was calculated for each subcategory by dividing the total compliance cost (emission control, monitoring, t esting, recordkeeping, and reporting) by the total emission reduction (HCl, CO, Pb, Cd, Hg, PM, CDD/CDF, NO X , and SO 2 ) needed to meet the proposed emission limits. Note that the emission reduc tions were derived in a separate memorandum. 2 Tables 9A and 9B present the estimated cost effectiveness valu es for each subcategory, over all pollutants.

Table 9A shows the estimated costs and cost effectiv eness for all units to meet the proposed MACT floor limits. The nationwide average cost effectiveness for all units to meet the MACT floors was estimated to be $191,000/ton for burn-off ovens, $6,000/ton for w aste-burning kilns, $7,700/ton for energy recovery units, $47,000/ton for incinerators, and $1.8 million/ton fo r small, remote units. Over all subcategories, the average cost effectiveness w as estimated to be $8,200/ton.

Table 9B shows the estimated costs and cost eff ectiveness for units to choose the most inexpensive option. As previously noted, Table 8 presents the most inexpensive option for each unit. The nationwide average cost effectiveness for all units to choose the lowest cost option between complying and using an alternative disposal method was estimated as follo ws: $57,700/ton for burn-off ovens, $6,000/ton for waste-burning kilns, $7,700/ton for energy recovery units, $2,500/ton for incinerators, and -$26,600/ton for small, remote units. The negative estimate for sma ll, remote units reflects that our estimates indicate a net saving if these facilities cease combusting waste a nd divert the waste to a landfill. Over all subcategories, the average cost effec tiveness was estimated to be $7,200/ton.

IV. B EYOND THE F LOOR O PTIONS In developing this proposal, EPA considered for existing units the proposed CISWI NSPS emissions limits as a basis for the beyond-the-floor analysis fo r each subcategory. The CISWI NSPS limits are the MACT limits applicable to new CISWI units that ar e established through analysis of the best performing single source for each regulated pollutant. The deve lopment of NSPS limits for each subcategory are discussed in detail in a separate memorandum. 1 . For this proposal, potential intermediate beyond-the-floor options (i.e. emission limits between the existing s ource MACT floor and the emission limits consistent with the proposed NSPS) were not evaluated.
The beyond-the-floor analysis for each subcategory is based on an evaluation of the types of control approaches that would be necessary to achieve the N SPS level of control for the same subcategory. Specifically, for purposes of the analysis, different comb inations of available emission control techniques, including additional add-on controls, that existing unit s would have to employ to meet the NSPS limits were considered in the analysis. Alternatively, facilities with units in the incinerators, small remoteincinerators, or burn-off ovens subcategories could choose to stop using the units altogether and use alternative waste disposal options instead. To assess co sts, the effect of the proposed MACT floors was considered as a baseline, and the additional controls needed to meet the beyond-the-floor emission limits were determined.

A. Consideration of Additional Emission Controls The beyond-the-floor options were analyzed on a polluta nt-by-pollutant basis. The controls considered, and the pollutants the control is effective for, are lis ted in Section I. However, some controls are not adequate for all units to meet the beyond -the-floor limits, which are discussed below.
All Subcategories: For NO x , SNCR was evaluated as the likely control technol ogy that sources would apply to achieve the beyond-the-floor limits. The control option would be to add SNCR if there were none installed to meet the MACT floor, or to increase the reagent injecti on rate if the unit was already equipped with SNCR technology. We also considered whether selective catalytic reduction (SCR) could be utilized by sources to achieve the beyond-the-floor limits. SNCR is a proven technology for waste-combustion units, with typical effectiveness of 30 to 50%. These reductions are within the reach of the levels estimated to meet the MACT floor emission limits. However, to achieve lower reductions (i.e., greater than 50%) that the beyond-the-floor limits would require, SNCR may need to be applied in conjunction with combustion controls. 9 Feasibility of these combustion controls, such as low NO x burners or combustion chamber modifications, are unit-specific and are likely not appl icable to all existing units; therefore, compliance with the beyond-the-floor would likely require signifi cant modification at considerable cost for some existing units. In contrast, new sources can be desi gned so that the combustion chamber and air flow characteristics reduce NO x formation, which, in combination with SNCR controls, would be able to meet the new source NO x limits. SCR is typically utilized in combustion units such as industrial boilers and process heaters, gas turbines, and reci procating internal combustion engines. 10 We are not aware of any successful applications of SCR technology to wast e-combustion units. This may be due to difficulties operating SCRs in operations where there is significant PM or sulfur loading in the gas stream. These two gas stream constituents can reduce catalyst activity, and lower the resulting effectiveness of the SCR, through catalyst poisoning and blinding/plugging of active sites by ammonia sulfur salts (formed from sulfur in the flue gas with the amm onia reagent) and particulate matter. 10 It was therefore determined that controls were not demonstrated adequately for existin g CISWI units in any of the five subcategories to meet the beyond-the-floor NO x emission limits.
Waste-burning Kilns: Waste-burning kilns are not designed with secondary cham ber or afterburners, so afterburner retrofits are not an applicable control for these units. For waste- burning kilns, a significant amount of CO emissions can result from the presence of organic compounds in the raw materials and not only from incomplete combustion, so good combustion controls and practices are not as effective. Oxidation catalysts have not been applied to waste-burning kilns and may not be as effective on waste-burning kilns as they are on other sources due to pluggi ng problems. The only effective beyond -the-floor control we could identify for waste-burning kilns would be a regenerative thermal oxidizer (RTO). In the analysis for the proposed Portland Cement NESHAP, EPA notes that the additional costs and energy requirements associated with an RTO are significant, with an additional annualized cost of $3.8 million per year (see 74 FR 21153). Under the most cost effective scenario (existing unit emitting at 710 ppmv and a 98 percent CO reduction) the cost per ton of additional CO remova l would be approximately $1,500. However, at the CO levels for most facilities the cost per ton could be much higher. In addition, RTO have significant additional energy requirements, and themse lves create secondary emissions of CO, NO x , SO 2 , and PM due to their electrical demands (see 74 FR 21153). Gi ven the cost and adverse environmental and energyimpacts, we could not identify any reasonable beyond -the-floor alternative to control CO emissions from waste-burning kilns.
We expect that waste-burning kilns would install scr ubbers to meet the proposed MACT floor emission limits for HCl, and the proposed EG and NSPS limits for HCl are the same. The floor limits for wasteburning kilns are already at the quantification limits of the test method, and we are not aware of alternative methods to quantify additional reductions in HCl emissions. In addition, we are not aware of any control technologies available that would reduce HCl emission from existing cement kilns to levels below the floor levels. Therefore, we could not evaluate a beyond-the-floor option for HCl emissions from cement kilns.

The scrubbers needed to meet the CISWI MACT floor limits for HCl would also meet the CISWI MACT floor levels for SO 2 . However, it is uncertain whether it is feasible for existing waste-burning kilns to utilize additional caustic in their scrubbers, or in their existing flue gas desulfurization devices, to be able to consistently meet the 3.6 ppm beyond-the-floor emission limit for SO 2 . There are limits to the amounts of additional caustic or lime that are technically feasible, and the SO 2 content of the flue gas will vary depending on the fuel and the sulfur content of process raw materials that are charged to the wasteburning kiln. The only option for achieving additional SO 2 control is to add an additional SO 2 scrubbing device in series with the scrubber required to comply with the MACT floor limit. While we did not quantify the costs, we concluded that this level of control would pose unreasonable costs that would result in units ceasing to combust wastes in cement kilns. It was therefore determined that additional controls were not demonstrated to continu ously meet the beyond-the-floor SO 2 emission limits at existing wasteburning kilns.

For PM, it is estimated that units would install fabric filter controls or improve existing fabric filters to meet the proposed CISWI MACT floor limits for PM a nd metals. To meet the metals floor limits, highly efficient fabric filters, and possibly membrane bags , would be needed. These controls are the best technology available to control PM, and we have not identified any additional controls that are available that would enable existing waste-burning kilns to continuously meet the beyond-the-floor PM emission limit (which is considerably lower than the MACT floor limit).
Energy Recovery Units: Like waste-burning kilns, energy recovery units are not suitably designed for afterburner retrofits to control CO. The beyond-the-floor CO emission limit is 3 ppm. In comparison, the proposed MACT floor emission limit is 150 ppm. Therefore, the beyond-th e-floor emission limit is approximately 98 percent less than the MACT floor emission limit. We are unaware of any technology that is able to continuously meet this limit for all existing energy recovery un its. Variances in fuel composition and condition will have an effect on CO emissions in addition to the cont rols in place, so this limit may be achievable for the best source based on their particular unit design and fuel inputs, but not demonstrated to be achievable for any other existing units without unreasonable costs as sociated with modification of the units. As a comparison, the proposed boiler NESHAP limit vari es by combustor design, but for biomass boilers, which burn fuels and have combustor designs that are similar to some CISWI energy recovery units, the limits are in the order of 200 to 700 ppm.
For PM, we estimate that existing units would install fabric filter controls or improve existing fabric filters to meet the proposed CISWI MACT floor limits for PM and metals. As with waste-burning kilns, the fabric filters would need to be highly efficient to meet the metals floor limits, and would likely need membrane filter bags. As stated a bove, membrane filters are the best technology available to control PM and metals. As such, the fabric filters we believe will be necessary to control the metals will likely achieve a level of performance that is better than th e MACT floor limit for PM, resulting in additional PM reductions beyond the existing source fl oor level of control. For this reason, we believe that the PMemissions reductions associated with going beyond-the-floor to the new s ource limits is less than the 200 tons per year estimated based on the evaluation of the difference in PM emissions under the proposed existing source floor and the proposed new source fl oor. Furthermore, to achieve PM and metals emissions reductions greater than those required to meet the MACT floor emission limits, existing sources would likely need to install an additional particulate control device, such as a cartridge filtration system, which would require additional capital and operating expense, as well as require additional energy to power the fans for adequate draft. Wh ile we did not quantify these costs of an additional particulate control system in the table below, we believe that these additional controls would pose unreasonable costs.
B. Beyond-the-Floor Costs The methodology for estimating the incremental cost of meeting the proposed beyond-the-floor limits is essentially the same as that described for the MACT floor limits. It was assumed that the best options for each unit shown in Table 8 would serve as the baselin e for determining additional controls and costs required. Subcategory-specific considerations ar e listed below, and the following table presents the incremental costs, reductions, and cost effectiveness for units to comply with beyond-the-floor emission limits.
Burn-Off Ovens: Because a beyond-the-floor NO x limit is not proposed, the cost of SNCR and SNCR monitoring were not incorporated into the beyond-th e-floor costs for these units. However, meeting the other pollutant beyond-the-floor limits was still more e xpensive than the alternative of abrasive blasting. Hence, it was assumed that the units would shut dow n in this case, and incremental beyond-the-floor costs would be the total cost of sandblasting minus the lowest cost MACT floor costs, or about $612,000 for the whole subcategory. Unit-specific beyond-the-floor costs are listed in Table 10A.

Cement Kilns: Additional costs for cement kilns consisted of annual costs to add carbon for dioxin/furans control. As previously noted, there are technical reasons for not considering other controls for this subcategory. Unit-specific beyond-the-floor costs are listed in Table 10B.
Energy Recovery Units: As discussed above, the additional costs to meet beyond-the-floor limits for Cd, Pb and PM reflect additional fabric filter installations or improvements to existing fabric filter systems as well as additional bag leak detection system costs. Installation and operation of a secondary particulate control system are not reflected in the cost estim ate. Additional ACI and ACI monitoring costs were calculated for meeting beyond-the-floor Hg and dioxin /furan limits. Lastly, for beyond-the-floor HCl and SO2 limits, additional costs reflect the additional installation or impr ovement of scrubbers or adding lime or caustic. Unit-specific beyond-the-floor costs are listed in Table 10C.

Incinerators: The best option for these units with respect to the MACT floor baseline was to shut down and use alternative disposal rather than comply with the proposed limits. Therefore, there is no additional beyond-the-floor cost for this subcategory.

Small, Remote Units: As with burn-off ovens, it was found to be less expensive for small, remote units to shut down and use an alternative disposal method rath er than add additional controls to meet beyond-thefloor emission limits. In fact, all but two units were already assumed to be shutdown instead of complying with MACT floor limits. Incremental bey ond-the-floor costs for the subcategory would be the total cost of landfilling minus the lowest cost MACT floor costs for these two units, or about $79,000. Unit-specific beyond-the-floor costs are listed in Table 10D.Incremental Costs and Emission Reductions Expect ed for Existing Units to Comply with Beyondthe-Floor Emission Limits (Relative to the MACT Floor) Pollutants Subcategory Additional Annual Costs ($/yr) Additional Emissions Reductions (ton/year) Incremental cost effectiveness (additional costs/additional emissions reductions, $/ton) PM, Cd, Pb Energy recovery unit 2,082,013 202 10,307
Hg, CDD/CDF Energy recovery unit 18,562,287 0.03 618,742,900
Waste-burning kiln 126,944,291 0.00002 >1 Billion HCl, SO 2 Energy recovery unit 21,564,881 77 280,063

Total beyond-the-floor costs and alternative disposal costs for each unit are present in Table 11, and Table 12 presents the estimated incremental cost effectivenes s values for each subcategory, over all pollutants.

V. A LTERNATIVE A PPROACH
EPA’s solid waste definition rule proposes to defi ne which non-hazardous secondary materials that are used as fuels or ingredients in combustion units ar e solid wastes under Subtitle D of RCRA. In addition to the primary proposed approach set forth in the Solid Waste Definition rule, the rule solicits comments on an alternative approach for determining which sec ondary materials are solid waste under Subtitle D of RCRA, when combusted. To this po int, this document has focused on the universe of CISWI consistent with the definition of non-hazardous waste proposed in EPA’s concurrent notice under RCRA, or the “proposed approach.” Total capital cost for all existing CISWI units to meet the MACT floor emission limits is estimated at approximately $1.9 billion. To tal annual cost for controls for all units in all subcategories is about $508 million, but is about $4 80 million for the lowest co st alternative. Costs associated with the CISWI inventory that would r esult from RCRA’s “alternative approach,” however, are calculated using the same methodology described he rein, and relevant summary tables for “alternative approach” costs are provided in Appendix C.
The beyond-the-floor options were analyzed on a pollutant-by-pollutant basis. As in the “proposed approach,” EPA considered the NSPS limits calculate d under the alternative approach as a basis for the beyond-the-floor approach for existing units in each subcategory. However, the technical feasibility issues which precluded the use of beyond-the-floor limits that applied to the proposed approach discussed above also apply in the alternative approach. In particular, see section IV of this memorandum for a discussion of why beyond-the-floor options (e.g., NO x , CO for waste-burning kilns, etc.) are not feasible for.
In addition to the technical and costs feasibility c onsiderations discussed for the proposed approach, for the alternative approach beyond-the-floor option, ther e are concerns about the feasibility of the beyondthe-floor emission limits for HCl and SO 2 for the energy recovery unit subcategory.

HCl. The NSPS limit for HCl is 0.036 ppmvd. We do not have information that suggests that all existing energy recovery units would be able to continuously achieve this limit. Assuming units would have to install or improve existing wet scrubbers in some ca ses to achieve the MACT floor emission limit, we do not believe that these same devices could be furthe r improved to achieve an additional 99.9% reduction above that to reach the beyond-the-floor emission limit . The baseline average HCl concentration is 10 ppmvd, so achieving the beyond-the-floor limit would n ecessitate a 99.6% reduction in HCl emissions, orpossibly more, depending on the chlorine content of th e materials being combusted. We do not have the data that suggests this is consistently atta inable for all existing energy recovery units.

SO 2 . The NSPS limit for SO 2 is 0.04 ppmvd. We do not have info rmation that suggests that all existing energy recovery units would be able to continuo usly achieve this limit. The average baseline SO 2 concentration is about 75 ppmvd, so an emissions reduction of over 99.9% would be necessary for some units to achieve this emission limit. The scrubbers n eeded to meet the CISWI MACT floor limits for HCl would also meet the CISWI MACT floor levels for SO 2 . However, we are not certain that it is feasible for existing units to utilize additional caustic in their scrub bers, or in their existing flue gas desulfurization devices, to be able to consistently meet the 0.04 ppmvd beyond-the-floor emission limit for SO 2 . There are limits to the amounts of additional caustic or lime that are technically feasible and the SO 2 content of the flue gas will vary depending on the fuel and wast es that are combusted in the unit. The only option for achieving additional SO 2 control is to add an additional SO 2 scrubbing device in series with the scrubber required to comply with the MACT floor limit. While we did not quantify the costs, we concluded that this level of cont rol would pose unreasonable costs that would result in units ceasing to combust wastes in energy recovery units. Therefore, we determined that additional controls were not demonstrated to continuously meet the beyond-the-floor SO 2 emission limits at existing energy recovery units.
For the remaining pollutants where there may not be technical feasibility concerns, we developed incremental cost estimates and reductions. The met hodologies are the same as w as used in the proposed approach beyond-the-floor analysis, and are presented below.

Incremental Costs and Emission Reductions Expect ed for Existing Units to Comply with Beyondthe-Floor Emission Limits (Relative to the MACT Floor) for the Alternative Approach Pollutants Subcategory Additional Annual Costs ($/yr) Additional Emissions Reductions (ton/year) Incremental cost effectiveness (additional costs/additional emissions reductions, $/ton) PM, Cd, Pb Energy recovery unit $186,904,890 12,996 $14,382
Hg, CDD/CDF Energy recovery unit $398,773,806 0.97 $411 Million
Cement kiln $142,008,091 0.34 $418 Million

VI. N EW U NITS
Based on the results of our analysis for existing un its and our experiences with other CAA Section 129 regulations, we do not anticipate that any new CISW I units will be constructed. As discussed earlier, many existing CISWI owners and operators may find th at alternate disposal options are preferable to compliance with the proposed standards. Our experience with regulations for municipal waste combustors, HMIWI and, in fact, CISWI has show n that negative growth in the source category historically occurs upon implementation of CAA S ection 129 standards. Since CISWI rules were promulgated in 2000 and have been in effect for existing sources since 2005, many existing units have closed. At promulgation in 2000, EPA estimated 122 units in the CISWI population. In comparison, the incinerator subcategory in this proposal, which woul d contain any such units subject to the 2000 CISWI rule, has 28 units. EPA is not aware of any constr uction of new units since 2000, and therefore does not believe there are any units that are currently subject to the 2000 CISWI NSPS. The revised CISWI rule is more stringent, so this trend is expected to con tinue. The same is expect ed to be true for thesubcategories of units that would be newly affected by the proposed revised CISWI rules. Industrial or commercial operations considering waste disposal options for their facilities will likely choose not to construct new CISWI units and to use alternative waste disposal methods or alternative fuels that will not subject them to the CISWI rule. For example, tire-de rived fuel from which the metal has been removed is not considered solid waste under the proposed definiti on of solid waste. Consequently, new cement kiln owners will assess their regulatory requirements under CISWI for burning whole tires or tire-derived fuel that does not have metals removed against the cost s associated with removing the metal and complying with the applicable NESHAP instead of the CISWI ru le. Our research suggests that metal removal is routinely practiced and would most likely be a viable option for new kiln owners so that they would not be subject to the CISWI regulations. Likewise, new sources could engineer their process to minimize waste generation in the first place, or to separate w astes so that the materials sent to a combustion unit would not meet the definition of solid waste to begin w ith. For waste that is generated, cost analyses have found that alternative waste disposal is generally available and less expensive.

VII. R EFERENCES

1. “MACT Floor Analysis for the Commercial and Indust rial Solid Waste Incinerators Source Category” Memorandum from Jason Huckaby, Amber Allen and Kr isten James, Eastern Research Group, Inc. to Charlene Spells, Toni Jones and Ketan Patel, U.S. Environmental Protection Agency. April 26, 2010.
2. “Baseline Emissions and Emission Reductions Es timates for Existing CISWI Units” Memorandum from Jason Huckaby, Amber Allen and Kristen Jame s, Eastern Research Group, Inc. to Charlene Spells, Toni Jones and Ketan Patel, U.S. E nvironmental Protection Agency. April 28, 2010.

. 3. U.S. Environmental Protection Agency. October 2000. OAQPS Control Cost Manual. Section 4.2: NOX Post Combustion. Chapter 1: Selective Noncatalytic Reduction.Publication Number EPA/452/B-02-001.
4. U.S. Environmental Protection Agency. July 2002. OAQPS Control Cost Manual. Section 6: Particulate Matter Controls. Chapter 2: Wet Scrubbe rs for Particulate Matter. Publication Number EPA/452/B-02-001.
5. U.S. Environmental Protection Agency. July 1994. Medical Waste Incinerators--Background Information for Proposed Standards and Guidelines: Model Plant Description and Cost Report for New and Existing Facilities. Publication Number EPA-453/R-94- 045a. (Docket item no. II-A-112)
6. Memorandum from Thomas Holloway, RTI, to Ketan Patel, EPA. July 6, 2009. Revised Compliance Costs and Economic Inputs for Existing HMIWI

. 7. EPA Air Pollution Control Cost Manua l, Sixth Edition EPA 452/B-02-001.
8. Dr. H.M. Eckerlin and E.W. Soderberg. Industrial Extension Service. USI Boiler Efficiency Program. A Report summarizing the finding and recommendations of an evaluation of Boilers in State-Owned Facilities. February 25, 2004. Accessed online at: http://www.energync.net/programs/docs/usi/om/boilers/ssr.pdf
9. Air Pollution Control Technology Fact Sheet, Se lective Non-Catalytic Reduction (SNCR) EPA452/F-03-031
10. Air Pollution Control Technology Fact Sheet, Selective Catalytic Reduction (SCR), EPA-452/F-03- 032A PPENDIX A. T ABLES FOR “P ROPOSED A PPROACH ” MACT F LOOR C OST A NALYSES The tables referenced throughout the body of this memo are presented in this section. They are organized as follows:
1. Percent Improvement Required to Meet MACT Floor
1A: Burn-off Ovens 1B: Cement Kilns 1C: Energy Recovery Units 1D: Incinerators 1E: Small, Remote Units

2. Costs to Meet MACT Floor
2A: Burn-off Ovens 2B: Cement Kilns 2C: Energy Recovery Units 2D: Incinerators 2E: Small, Remote Units
3. Summary of MACT Compliance and Alternative Disposal Costs
4. Input Parameters for Control Cost Algorithms
5. Control Cost Algorithms
5A: Fabric Filter 5B: Packed-Bed Scrubber 5C: Selective Non-Catalytic Reduction (SNCR) 5D: Activated Carbon Injection (ACI) 5E: Afterburner Retrofit for Incinerators 5F: Afterburner Retrofit for Burn-off Ovens 5G: Incremental Controls
6. Stack Testing, Monitoring, and Recordkeeping Costs 6A: Maintenance and Inspection 6B: Monitoring 6C: Initial Stack Testing Costs by Unit and Pollutant 6D: Stack Testing Costs 6E: Visible Emissions Testing 6F: Recordkeeping and Reporting
7. Alternative Waste Disposal Algorithms
7A: Cost to Haul Waste to Landfill 7B: Cost to Haul Waste to Municipal Waste Combustor 7C: Cost to Sandblast 7D. Cost to Continue Incinerator Operation
8. Best Compliance Options by Unit
9. Cost Effectiveness of MACT Floo rs: Overall and by SubcategoryA PPENDIX B. T ABLES FOR “P ROPOSED A PPROACH ” B EYOND
- THE -F LOOR C OST A NALYSES 10. Costs to Beyond-the-Floor Emission Limits
10A: Burn-off Ovens 10B: Cement Kilns 10C: Energy Recovery Units 10D: Small, Remote Units
11. Best Beyond-the-Floor Compliance Options by Unit
12. Incremental Cost Effectiveness of Beyond-the-Floor Emission Limits: Overall and By SubcontractoryA PPENDIX C. T ABLES FOR “A LTERNATIVE A PPROACH ” MACT F LOOR C OST A NALYSES 13. Alternative Approach Costs to Meet MACT Floor
13A: Burn-off Ovens 13B: Cement Kilns 13C: Energy Recovery Units 13D: Incinerators 13E: Small, Remote Units
14. Alternative Approach Best Compliance Options by Unit 15. Alternative Approach Cost Effectiveness of MACT Floor s: Overall and by SubcategoryA PPENDIX D. T ABLES FOR “A LTERNATIVE A PPROACH ” B EYOND
- THE -F LOOR C OST A NALYSES 16. Costs to Beyond-the-Floor Emission Limits
16A: Cement Kilns !6B: Energy Recovery Units
17. Alternative Approach Best Beyond-t he-Floor Compliance Options by Unit 18. Alternative Approach Cost Effectiveness of Beyond-the-Floor Em ission Limits: Overall and by Subcategory