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 Screening 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 PM2.5 Human Health Benefits ...................................................... 6-1

6.3 Unquantified Benefits .......................................................................................... 6-9

6.3.1 Carbon Monoxide Benefits .................................................................... 6-11

6.3.2 Other SO2 Benefits ................................................................................. 6-11

6.3.3 HAP Benefits ......................................................................................... 6-13

6.4 Characterization of Uncertainty in the Monetized PM2.5 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 Compliance Cost Analyses for CISWI Units.............................................B-1 
v 

LIST OF FIGURES 

Number Page

2-1. Distribution of Value of Shipments within Wood Product Manufacturing 

(NAICS 322): 2007 .................................................................................................... 2-4

2-2. Distribution of Employment within Wood Product Manufacturing (NAICS 

322): 2007 .................................................................................................................. 2-4

2-3. Electrical Power Use Trends in the Wood Product Manufacturing Industry 

(NAICS 321): 1997–2005 .......................................................................................... 2-8

2-4. Establishment Concentration in the Wood Product Manufacturing 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 the 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 the Wood Product Manufacturing Industry 

(NAICS 321)]........................................................................................................... 2-16

2-10. Producer Price Trends in the Wood Product Manufacturing Industry (NAICS 

321) .......................................................................................................................... 2-17

2-11. Distribution of Value of Shipments within Paper Manufacturing (NAICS 

322): 2007 ................................................................................................................ 2-20

2-12. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007 ..... 2-20

2-13. Electrical Power Use Trends in the Paper Manufacturing Industry: 1997–2005 .... 2-24

2-14. Establishment Concentration in Paper 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 ................................................................................................................ 2-28

2-17. Capacity Trends in the Paper Manufacturing Industry (NAICS 322) ..................... 2-29

2-18. Industrial Production Trends in the Paper Manufacturing Industry (NAICS 

322): 1997–2009 ...................................................................................................... 2-32

2-19. International Trade Trends in the Paper Manufacturing Industry (NAICS 322) ..... 2-32

2-20. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222) ............ 2-33

2-21. Distribution of Employment within Chemical Manufacturing (NAICS 325): 

2007.......................................................................................................................... 2-35
vi 

2-22. Distribution of Total Value of Shipments within Chemical Manufacturing 

(NAICS 325): 2007 .................................................................................................. 2-36

2-23. Electric Power Use Trends in Chemical Manufacturing (NAICS 325): 1997–

2005.......................................................................................................................... 2-40

2-24. Establishment Concentration in Chemical Manufacturing (NAICS 325): 2002 ..... 2-42

2-25. Capacity Utilization Trends in Chemical 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 Chemical Manufacturing (NAICS 325) ................ 2-47

2-29. International Trade Trends in Chemical 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 PM2.5 Health Benefits using Mortality Function 

from Pope et al. (2002) .............................................................................................. 6-6

6-2. Total Monetized PM2.5 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 PM2.5 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, Social Costs, and Net Benefits for the 

CISWI NSPS and Emissions Guidelines in 2015 (millions of 2008$) ...................... 1-2

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

2-2. Industry Ratios: Wood Product Manufacturing (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, 106)................................................................................................................ 2-6

2-5. Energy Used in Wood Product Manufacturing (NAICS 321) ................................... 2-7

2-6. Demand by Sector: Wood Product Manufacturing (NAICS 321) ($2007, 106) ........ 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 Product Manufacturing (NAICS 321) ........ 2-15

2-10. Key Statistics: Paper Manufacturing (NAICS 322) ................................................. 2-18

2-11. Industry Ratios: Paper Manufacturing (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) ($106

, $2007) ..................................................................................... 2-22

2-14. Energy Used in Paper Manufacturing (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 Manufacturing Industry (NAICS 322) ($106, $2007) .... 2-25

2-17. Largest U.S. Paper and Forest Products Companies: 2006 ..................................... 2-29

2-18. Distribution of Economic Data by Enterprise Size: Paper Manufacturing 

(NAICS 322) ............................................................................................................ 2-30

2-19. Small Business Size Standards: Paper Manufacturing (NAICS 322) ...................... 2-31

2-20. Key Statistics: Chemical Manufacturing (NAICS 325)........................................... 2-34

2-21. Industry Ratios: Chemical Manufacturing (NAICS 325) ........................................ 2-34

2-22. Key Goods and Services Used in Chemical Manufacturing (NAICS 325) 

($2007, 106

).............................................................................................................. 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 Manufacturing (NAICS 325) ($2007 106) ............... 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: Chemical Manufacturing (NAICS 325) ............... 2-46

2-29. Distribution of Economic Data by Enterprise Size: Chemical Manufacturing 

(NAICS 325) ............................................................................................................ 2-47

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

4-1. Market-Level Price and Quantity Changes: 2015 (Selected Approach) .................... 4-3

4-2. Distribution of Social Costs (million, 2008$): 2015 .................................................. 4-4

5-1. Affected Sectors and Size Standards ......................................................................... 5-1

5-2. Sales Tests Using Small Companies Identified in the Combustion Survey .............. 5-3

6-1. Human Health and Welfare Effects of PM2.5 ............................................................. 6-2

6-2. Summary of Monetized Benefits Estimates for Proposed CISWI NSPS and 

Emissions Guidelines in 2015 (2008$) ...................................................................... 6-7

6-3. Summary of Reductions in Health Incidences from PM2.5 Benefits for the 

Proposed CISWI NSPS and Emissions Guidelines in 2015 ...................................... 6-7

6-4. All PM2.5 Benefits Estimates for the Proposed 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 PM2.5 Health Benefits (in millions of 

2008$) ...................................................................................................................... 6-21

6-6. Summary of the Monetized Benefits, Social 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 Changes: 2015 (Alternative Approach) ............... 7-2

7-2. Distribution of Social Costs (million, 2008$): 2015 .................................................. 7-3

7-3. Employment Changes (1,000 Jobs): 2015 ................................................................. 7-3

7-4. Summary of Monetized Benefits for CISWI NSPS and Emission Guidelines in 

2015 (2008$) (Alternate Approach)* ......................................................................... 7-4

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) ................................................................................................. 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) requirement to review the new source performance 

standards and emission guidelines every 5 years. Additionally, when revising the standards of 

performance and emission guidelines we considered the District of Columbia Circuit Court 

rulings on maximum achievable control technology standards that were issued after 

promulgation of the new source performance standards and emission guidelines for commercial 

and industrial solid waste incineration units in 2000 and a concurrently proposed definition of 

nonhazardous secondary materials as solid waste under the Resource Conservation and Recovery 

Act. EPA also proposes other amendments that EPA believes are necessary to adequately address 

air emissions from commercial and industrial solid waste incineration units and 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 economic 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 estimates 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 Agency’s economic model suggests 

the average national market-level variables (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 and 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 entities included in the 

screening analysis. 

 Benefits Analysis: In the year of full implementation (2015), EPA estimates the 

monetized PM2.5 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. All estimates are in 2008$ for the year 2015. Using alternate 

relationships between PM2.5 and premature mortality supplied by experts, higher and 

lower benefits estimates are plausible, but most of the expert-based estimates fall 

between these estimates. 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 total dioxins/furans each year. In addition, ecosystem benefits and 

visibility benefits have not been 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 million, at 3% and 7% discount rates 

respectively (Table 1-1). All estimates are in 2008$ for the year 2015. 

1.2 Organization of this Report 

The remainder of this report supports and details the methodology and the results of the 

EIA: 

 Section 2 presents the affected industry profiles. 

 Section 3 describes the engineering 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 Guidelines in 2015 (millions of 2008$)1

Proposed Option 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $240 to $580 $210 to $520 

Total Social Costs3 $220 $220 

Net Benefits $19 to $360 $-2.4 to $310 

Proposed Option with Alternate Approach 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits4 $2,700 to $6,700 $2,500 to $6,000 

Total Social Costs3 $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 PM2.5 through 

reductions of 660 tons of directly emitted PM2.5 and PM2.5 precursors such as 1,400 tons of NOx and 2,700 tons of 

SO2. 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 from reducing ecosystem effects and visibility impairment are 

not included. 

3

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

in the same social costs for both discount rates. 

4

 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2.5 through 

reductions of 12,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 520 tons of NOx and 205 tons of 

SO2. 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 aspects 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 potential 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, including, but not limited to, the housing slump, high 

input costs, low prices for lumber and other building materials, and a weak dollar. Table 2-1 

shows that revenues in this industry 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 (shipment revenue per dollar of payroll—$5.54 in 2002, $6.20 

in 2006, and $5.84 in 2007) and material inputs during 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 industry’s facilities into three categories: 

“sawmills and wood preservation”; “veneer, plywood, and engineered wood product 

manufacturing”; and “other wood product manufacturing.” 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 treating them with preservatives; and 
2-2 

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

 1997 2002 2006 2007 

Shipments ($2007, 106) $110,956 $102,721 $115,390 $101,879 

Payroll ($2007, 106) $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 Statistics for the United States and the States (1997 NAICS 

Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008). 

 U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 

00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” Accessed on 

December 27, 2009. 

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

Industry Ratios 1997 2002 2006 2007 

Total shipments ($2007, 106) $110,956 $102,721 $115,390 $101,879 

Shipments per establishment ($103) $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 Statistics for the United States and the States (1997 NAICS 

Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008). 

 U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 

00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” Accessed on 

December 27, 2009. 
2-3 

(c) treating wood sawed, planed, or shaped in other establishments with creosote 

or other preservatives to prevent decay and to protect against fire and insects. 

Sawmills may plane the rough lumber that they make with a 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 primarily engaged in one or more of the 

following manufacturing activities: (a) veneer and/or plywood, (b) engineered 

wood members, and (c) reconstituted wood products. This industry includes 

manufacturing plywood from veneer made in the same establishment or from 

veneer 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 sizes. These establishments generally use 

woodworking machinery, such as jointers, planers, lathes, and routers to shape 

wood. 

– Wood Container and Pallet Manufacturing (NAICS 32192): This industry 

comprises establishments primarily engaged in manufacturing wood pallets, wood 

box shook, wood boxes, other wood containers, and wood parts for pallets and 

containers. 

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

establishments primarily engaged in manufacturing wood products (except 

establishments operating sawmills and wood preservation facilities; and 

establishments manufacturing veneer, 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 the 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 this industry fell under other wood products (60%). 

Veneer, plywood, and engineered wood products had 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 Shipments within Wood Product Manufacturing 

(NAICS 322): 2007 

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “Sector 00: EC0700A1: All Sectors: 

Geographic Area Series: Economy-Wide Key Statistics: 2007.” <http://factfinder.census.gov>. Accessed 

on December 27, 2009. [Source for 2007 numbers] 

Figure 2-2. Distribution of Employment within Wood Product Manufacturing (NAICS 

322): 2007 

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “Sector 00: EC0700A1: All Sectors: 

Geographic Area Series: Economy-Wide Key Statistics: 2007” Release Date: 12/22/09. 

<http://factfinder.census.gov>. Accessed on December 27, 2009. [Source for 2007 numbers] 

Veneer, Plywood, &

Engineered Wood

ProductMfg (NAICS

3212), 22%

Sawmills & Wood

Preservation (NAICS

3211),

27%

Other Wood

ProductMfg (NAICS

3219),

51%

Other Wood

ProductMfg (NAICS

3219),

60%

Sawmills & Wood

Preservation (NAICS

3211), 20%

Veneer, Plywood, &

Engineered Wood

ProductMfg (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 industry has with other 

industries and people and identify the key goods and 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 the number of employees 

in this industry decreased between 2005 and 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, 106) $118,705 100% $115,390 100% $102,002 100% 

Total compensation ($2007, 106) $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, 106

) 

$13,363 11% $13,132 11% $12,086 12% 

Total production workers 431,569 432,315 417,471 

Total production hours (103

) 911,332 887,613 837,074 

Average production wages per 

hour ($2007) 

$15 $15 $14 

Total cost of materials ($2007, 103

) $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 inputs to the wood product industry accounted for 

80% of the total intermediate inputs according 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 descriptions of the various industries within wood product 

manufacturing imply that they supply each other with products in order to add value and 

distribute their products to the broader market. The top five inputs are rounded out by forestry 

and logging products, wholesale trade, management of companies and enterprises, 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, 106

) 

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 industry. 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, Figure 2-3 shows that electrical 

power use decreased rapidly during this period, including 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 Product Manufacturing (NAICS 321) 

Fuel Type 1998 2002 2006 

Total (trillion BTU) 504 375 445 

Net electricitya (million kWh) 21,170 20,985 26,723 

Residual fuel oil (million bbl) * * 1 

Distillate fuel oilb

 (million bbl) 2 2 3 

Natural gasc

 (billion cu ft) 71 56 84 

LPG and NGLd

 (million bbl) 1 1 1 

Coal (million short tons) * * Q 

Coke and breeze (million short tons) — — * 

Othere (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 electricity inputs from on-site 

cogeneration or generation from combustible fuels because that energy has already been included as generating 

fuel (for example, coal). 

b

 Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels. 

c

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

such as independent gas producers, gas brokers, marketers, and any marketing subsidiaries of utilities. 

d

 Examples of liquefied petroleum gases (LPGs) are ethane, ethylene, propane, propylene, normal butane, butylene, 

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing 

plants, including plants that fractionate raw natural gas liquids (NGLs). 

e

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

that respondents indicated was used to produce heat and power. 

* Estimate less than 0.5. 

Q = Withheld because relative standard error is greater than 50%. 

Sources: U.S. Department of Energy, Energy Information Administration. 2007. “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://www.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 Production and Capacity Utilization: Electric Power Use: 

Manufacturing and Mining.” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G321.S. 

<http://www.federalreserve.gov/datadownload/>. Accessed 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 maintenance and repair construction consume 35% of the total 

commodity output in this industry. The top 10 consumers of wood products make up 54% of the 

demand for wood products. Although many of the top consumers deal with construction, repair, 

or real estate services, other types of consumers, such as food services and drinking places, rail 

transportation, plastics and rubber products manufacturing, 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, including regulatory flexibility analyses, as well as a complete picture 

of the recent historical trends of production and pricing. 

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Manufacturing Electric Power Use for NAICS 321
2-9 

Table 2-6. Demand by Sector: Wood Product Manufacturing (NAICS 321) ($2007, 106

) 

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 drinking places 7220 $2,307 

Other miscellaneous manufacturing 3399 $1,311 

Wholesale trade 4200 $1,284 

Rail transportation 4820 $1,138 

Retail trade 4A00 $1,047 

Plastics and rubber products manufacturing 3260 $877 

General state and local government use S007 $3,116 

Owner occupied 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 dispersed 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 manufacturing industry has been experiencing 

capacity utilization increases and declines with more extreme fluctuations than those of all 

manufacturing industries combined. The decline in wood product manufacturing is similar to 

total manufacturing between 1997 and 2002. However, capacity utilization in total 

manufacturing, which peaked in 2006, started increasing at a faster rate than wood product 

manufacturing, but decreased sharply after its peak. Wood product manufacturing experienced 

its own rapid decrease in capacity utilization between 2007 and 2009, though not at the same rate 

as total manufacturing (Figure 2-5). 
2-10 

Figure 2-4. Establishment Concentration in the Wood Product Manufacturing Industry 

(NAICS 321): 2002 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: 

Manufacturing: Geographic Area Series: Industry Statistics for the States, Metropolitan and 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 Production and Capacity Utilization: Capacity Utilization.” 

Series ID: G17/CAPUTL/CAPUTL.GMF.S & G17/CAPUTL/CAPUTL.G321.S. 

<http://www.federalreserve.gov/datadownload/>. Accessed on December 15, 2009. 

Establishments by State

Less than 100

100 - 199

200 - 399

400 - 600

More than 600

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Percentage Capacity Utilization

Manufacturing Wood Products
2-11 

2.1.3.3 Employment 

California has the largest number of employees 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 directly 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 Statistics for the States, Metropolitan and Micropolitan 

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008). 

2.1.3.4 Plants and Capacity 

While the capacity of the manufacturing sector has been growing consistently since 1997, 

the wood product manufacturing industry has experienced inconsistent growth. After a small 

amount of growth in capacity between 1997 and 2001, the wood product 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 product companies produced over $75 billion in 

sales, with the top two companies—International Paper and Weyerhaeuser—generating nearly 

$22 billion each (Table 2-7). The top two companies’ revenue consists of 58% of the revenue of 

the top 10 companies in Standard & Poor’s (S&P’s) list (Benwart, 2006). Although these 

numbers do not exclusively reflect 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 business 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 employees for all sub-industries in the wood 

product manufacturing industry (Table 2-9). 

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Manufacturing Food
2-13 

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

Company Revenues ($106)

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 steady increase in production for the 

manufacturing sector since 1997 (Figure 2-8). Similar to capacity utilization trends (Figure 2-8), 

the index shows a faster rate of decline for wood products than the entire manufacturing sector. 

2.1.3.8 International Trade 

Since 1997, the wood product manufacturing industry has contributed to an increasing 

trade deficit (Figure 2-9). The value of imports 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 manufacturing industry have remained roughly the 

same since 2005. The prices for the entire manufacturing sector increased between 2003 and 

2008 but have decreased since August 2008. Producer price indices (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). 
2-14 

Table 2-8. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 32

 Enterprises with 

Variable Total 

1 to 20 

Employeesa

20 to 99 

Employees 

100 to 499 

Employees 

500 to 749 

Employees 

750 to 

Emplo

Firms 15,198 9,740 3,280 791 63 Establishments 17,052 9,758 3,482 1,271 166 

Employment 534,011 65,423 132,612 118,910 19,784 11,9Receipts ($103 ) $88,649 $8,204 $18,276 $19,717 $3,192 $1,9Receipts/firm ($103) $5,833 $842 $5,572 $24,927 $50,673 $70,4Receipts/establishment ($103) $5,199 $841 $5,249 $15,513 $19,231 $20,9Receipts/employment ($) $166,006 $125,393 $137,818 $165,814 $161,363 $159,2

a Excludes Statistics of U.S. Businesses (SUSB) employment category for zero employees. These entities only operated for a fraction o

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 Classification System Codes.” Effective August 22, 2008. 

<http://www.sba.gov/services/contractingopportunities/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.federalreserve.gov/datadownload/>. Accessed 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/scripts/user_set.asp>; (July 17, 2008). 

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$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 essential component of all business operations 

worldwide. Broadly speaking, paper and paperboard are manufactured by converting timber or 

other recycled material into products such as printing and writing papers, newsprint, tissue, and 

containerboard (Benwart, 2006). The subsector has been experiencing a decline in shipments as 

of late. From 1997 to 2007, shipments in the industry declined 7%, and 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). 

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Wood Products PPI All Manufacturing PPI
2-18 

Table 2-10. Key Statistics: Paper Manufacturing (NAICS 322) 

1997 2002 2006 2007 

Shipments ($2007, 106) $188,496 $175,983 $174,887 $175,806 

Payroll ($2007, 106) $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 Statistics for the United States and the States (1997 NAICS 

Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008). 

 U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 

00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” Accessed on 

December 28, 2009. [Source for 2007 numbers] 

Table 2-11. Industry Ratios: Paper Manufacturing (NAICS 322) 

Industry Ratios 1997 2002 2006 2007 

Total shipments ($2007, 106) $188,496 $175,983 $174,887 $175,806 

Shipments per establishment ($2007, 103) $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 Statistics for the United States and the States (1997 NAICS 

Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008). 

 U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 

00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” 

<http://factfinder.census.gov>. Accessed on December 28, 2009. [Source for 2007 numbers] 
2-19 

The U.S. Census Bureau categorizes this industry’s facilities into two categories: pulp, 

paper, and paperboard manufacturing and converted paper product manufacturing. These are 

further divided into the following types of facilities as defined by the Census Bureau (2001): 

 Pulp, Paper, and Paperboard: 

– Pulp Mills (NAICS 32211): This industry 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 recycled rags, linters, scrap paper, and straw. 

– Paper Mills (NAICS 32212): This industry comprises establishments primarily 

engaged in manufacturing paper from pulp. These establishments may 

manufacture or purchase pulp. In addition, the establishments may convert the 

paper they make. The activity of making paper classifies an establishment into 

this industry regardless of the output. 

– Paperboard Mills (NAICS 32213): This industry comprises establishments 

primarily engaged in manufacturing paperboard from pulp. These establishments 

may manufacture or purchase pulp. In addition, the establishments may also 

convert the paperboard they make. 

 Converted Paper Products: 

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

establishments primarily engaged in converting paperboard into containers 

without manufacturing paperboard. These establishments use corrugating, cutting, 

and shaping machinery to form paperboard into containers. Products made by 

these establishments include boxes; corrugated sheets, pads, and pallets; paper 

dishes; and fiber drums and reels. 

– Paper Bag and Coated and Treated Paper Manufacturing (NAICS 32222): This 

industry comprises establishments primarily engaged in one or more of the 

following manufacturing activities: cutting and coating paper and paperboard; 

cutting and laminating paper and paperboard and other flexible materials (except 

plastics film to plastics film); bags or multiwall bags or sacks of paper, metal foil, 

coated paper, or laminates or coated combinations of paper and foil with plastics 

film; laminated aluminum and other converted metal foils from purchased foils; 

and surface coating paper or paperboard. 

– Stationary Product Manufacturing (NAICS 32223): This industry comprises 

establishments primarily engaged in converting paper or paperboard into products 

used for writing, filing, art work, and similar applications. 

– Other Converted Paper Products (NAICS 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 paperboard, 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 shipments for converted paper products was slightly 

higher in 2007, 54%, than the value of shipments for pulp, paper, and paperboard products in that 

year, 46%. However, Figure 2-12 indicates that significantly more employees worked in the 

converted paper product category of the industry, 70%, thus making converted paper products 

more labor intensive. 

Figure 2-11. Distribution of Value of Shipments within Paper Manufacturing (NAICS 

322): 2007 

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder: “Sector 

31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” 

Accessed on December 28, 2009. 

Figure 2-12. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007 

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 

31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” 

<http://factfinder.census.gov>. Accessed on December 28, 2009. 

PPP (3221)

CPP (3222) 46%

54%

PPP (3221)

30%

CPP (3222)

70%
2-21 

2.2.2 Supply and Demand Characteristics 

Next, we provide a broad overview of the supply and demand sides of the paper 

manufacturing industry. We emphasize the economic interactions this industry has with other 

industries and people and identify the key goods and services used 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). Total compensation of employees represented 15% 

of the total value in 2007, down from 17% in 2005. The total number of employees dropped by 

2%, between 2005 and 2007, while shipments increased by 3% in the same period. 

Table 2-12. Costs of Goods and Services Used in the Paper Manufacturing Industry 

(NAICS 322) 

Variable 2005 Share 2006 Share 2007 Share 

Total shipments ($2007, 106) $171,477 100% $174,887 100% $176,018 100% 

Total compensation ($2007, 106) $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, 106

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

Total production workers 331,228 321,684 321,937 

Total production hours (103

) 716,963 691,134 680,732 

Average production wages per hour $21 $21 $21 

Total cost of materials ($2007, 103

) $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 inputs 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 products because the NAICS 3221 group represents the 

initial step in the paper manufacturing process; thus, its inputs include more raw resources such 

as wood products, forestry and logging products, natural gas, and electricity. This becomes 

evident when observing inputs for converted paper 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) ($106

, $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-intensive subsector. The 2008 Annual Energy Outlook predicts that 

the paper-producing subsector will be one of four subsectors experiencing positive average 

growth of delivered energy consumption between 2006 and 2030 (DOE, 2008). 

Energy generation from the recovery boiler is often insufficient for total plant needs, so 

facilities augment recovery boilers with fossil fuel–fired and wood waste–fired boilers (hogged 
2-23 

fuel) to generate steam and often electricity. Industry-wide, the use of pulp wastes, bark, and 

other papermaking residues supplies 58% of the energy requirements of pulp and paper 

companies (EPA, 2002). 

Likewise, Table 2-14 shows that total energy use decreased between 1998 and 2006 by 

14%. Figure 2-13 indicates that total electrical power use changed sporadically between 2002 

and 2004 but decreased consistently 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 electricitya (million kWh) 70,364 65,503 72,518 

Residual fuel oil (million bbl) 24 16 15 

Distillate fuel oilb

 (million bbl) 2 2 2 

Natural gasc

 (billion cu ft) 570 490 461 

LPG and NGLd

 (million bbl) 1 2 1 

Coal (million short tons) 12 11 10 

Coke and breeze (million short tons) — * — 

Othere

 (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 electricity inputs from on-site 

cogeneration or generation from combustible fuels because that energy has already been included as generating 

fuel (for example, coal). 

b

 Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels. c

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

such as independent gas producers, gas brokers, marketers, and any marketing subsidiaries of utilities. 

d

 Examples of liquefied petroleum gases (LPG) are ethane, ethylene, propane, propylene, normal butane, butylene, 

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing 

plants, including plants that fractionate raw natural gas liquids (NGLs). 

e

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

that respondents indicated was used 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 for 2006 numbers] 
2-24 

Figure 2-13. Electrical Power Use Trends in the Paper Manufacturing Industry: 1997–

2005 

Source: Federal Reserve Board. 2009. “Industrial Production 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 processes 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 about 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 3221 supplies a significant portion of NAICS 3222 

demand (37% of total commodity output). Both industries specialize in products with 

intermediate uses, with an average of 92% of sales between the two going toward this purpose. 

NAICS 3222 has a very diverse assortment of subsector groups from which it receives demand. 

Food manufacturing makes up 21% of the demand, making members of this industry the largest 

consumer of converted paper 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

108

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Index Value (Jan‐97=100)

Manufacturing Paper
2-25 

Table 2-15. Estimated Energy Sources for the U.S. Pulp and Paper Industry 

Energy Source 1972 1979 1990 1999 

Purchased steam 5.4% 6.7% 7.3% 1.5% 

Coal 9.8% 9.1% 13.7% 12.5% 

Fuel oil 22.3% 19.1% 6.4% 6.3% 

Natural gas 21.5% 17.8% 16.4% 17.6% 

Other purchased 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/resources/publications/assistance/sectors/notebooks/ 

index.html>. 

Table 2-16. Demand by Sector: Paper Manufacturing Industry (NAICS 322) ($106, $2007) 

Sector BEA Code

3221 

Pulp, Paper, and 

Paperboard 

3222 

Converted 

Paper Products Total 

Converted paper product manufacturing 3222 $30,448 $1,745 $32,193 

Food manufacturing 3110 $638 $18,782 $19,421 

Printing and related support activities 3230 $13,320 $3,874 $17,194 

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, including 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 bordering northeastern states. 

The location of establishments in the paper manufacturing industry varies a great deal by 

subsector. Wisconsin and New York have the most pulp, paper, and paperboard establishments, 

while California dominates with over 500 converted paper product establishments. Overall, the 

United States has 561 pulp, paper, and paperboard establishments and 4,956 converted paper 

product establishments. 

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 Statistics 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 manufacturing subsector has been experiencing a steady 

decline, similar to the decline of the total manufacturing sector. However, paper manufacturing 

has managed to use its capacity at a 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 Production and Capacity Utilization: Capacity Utilization.” 

Series ID: G17/CAPUTL/CAPUTL.GMF.S & G17/CAPUTL/CAPUTL.G322.S. 

<http://www.federalreserve.gov/datadownload/>. 

2.2.3.3 Employment 

Wisconsin has the largest number of employees in the paper manufacturing subsector 

with over 38,008 reported in the 2002 census followed by 29,379 in California (Figure 2-16). 

The converted paper products group has more employees per establishment, 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

100

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Percentage Capacity Utilization

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 Statistics 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 the 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—International Paper and Weyerhaeuser—generating nearly 

$22 billion each (Table 2-17). The top two companies’ revenue consists of 58% of the revenue of 

the top 10 companies in Standard & Poor’s (S&P’s) list (Benwart, 2006). Although these 

numbers do not exclusively reflect paper products, 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 Paper 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 ($106)

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

150

1997‐011997‐071998‐011998‐071999‐011999‐072000‐012000‐072001‐012001‐072002‐012002‐072003‐012003‐072004‐012004‐072005‐012005‐072006‐012006‐072007‐012007‐072008‐012008‐072009‐012009‐07

Index Value (Jan‐97=100)

Manufacturing Paper (NAICS 322)
2-30 

2.2.3.6 Size Distribution 

The primary criterion for categorizing a business 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-generating 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 paperboard group, in which large 

companies generated 92% of receipts. The number 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, subsector production rates for paper manufacturing 

have witnessed a decreasing rate of production compared 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 production trend since then. 

Table 2-18. Distribution of Economic Data by Enterprise Size: Paper Manufacturing 

(NAICS 322) 

 Enterprises with 

Variable Total 

1 to 20 

Employeesa 

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 ($106 ) $154,746 $2,218 $9,483 $17,620 $3,034 $3,951 $6,798

Receipts/firm ($103) $43,738 $1,497 $7,903 $37,017 $70,561 $179,577 $206,001

Receipts/establishment 

($103

) $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 from the Statistics of U.S. Businesses: U.S. Detail 

Employment Sizes: 2002.” <http://www.census.gov/csd/susb/download_susb02.htm>. 
2-31 

Table 2-19. Small Business Size Standards: 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 Classification System Codes.” Effective August 22, 2008. 

<http://www.sba.gov/services/contractingopportunities/sizestandardstopics/size/index.html>. 

2.2.3.8 International Trade 

Since 1997, paper manufacturing products, both pulp, paper, and paperboard products 

and converted paper products, have contributed to an increasing trade surplus in this sector 

(Figure 2-19). Imports and exports have been changing at similar 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 Production 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/scripts/user_set.asp>. 

75

85

95

105

115

125

135

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Index Value (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 been increasing at a rate consistent with all 

manufacturing products (Figure 2-20). Producer price indices (PPIs) show that producer prices 

for paper in 2007 increased by 20% since 1997, while 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 produces over 70,000 chemical substances, many 

of which are ubiquitous in American life. Broadly speaking, chemical manufacturing operates by 

converting feedstocks into chemical products that can serve as intermediate goods or final 

products such as medicine, soap, and printer ink. From 1997 to2007, shipments in the industry 

grew 42%, while employment declined by 8% (Table 2-20). While total 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). Shipments 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

140

Dec‐03

Jun‐04

Dec‐04

Jun‐05

Dec‐05

Jun‐06

Dec‐06

Jun‐07

Dec‐07

Jun‐08

Dec‐08

Jun‐09

PPI (Dec‐03=100)

Manufacturing PPI Paper PPI
2-34 

Table 2-20. Key Statistics: Chemical Manufacturing (NAICS 325) 

 1997 2002 2006 2007 

Shipments ($2007, 106) $521,251 $531,173 $675,223 $738,303 

Payroll ($2007, 106) $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 Statistics for the United States and the States (1997 NAICS 

Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008). 

 U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 

00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” 

<http://factfinder.census.gov>. Accessed on December 27, 2009. [Source for 2007 numbers] 

Table 2-21. Industry Ratios: Chemical Manufacturing (NAICS 325) 

Industry Ratios 1997 2002 2006 2007 

Total shipments ($2007, 106) $521,251 $531,173 $675,223 $738,303 

Shipments per establishment ($103) $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 Statistics for the United States and the States (1997 NAICS 

Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008). 

 U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 

00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” 

<http://factfinder.census.gov>. Accessed on December, 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 Chemical Manufacturing (NAICS 3253). 

 Pharmaceutical and Medicine Manufacturing (NAICS 3254)—Consists primarily of 

pharmaceutical preparation manufacturing. This industry group is the largest importer 

of goods within chemical manufacturing. 

 Other Chemical Manufacturing: Consists of Paint, Coating, and Adhesive 

Manufacturing (NAICS 3255); Soap, Cleaning Compound, and Toiletry 

Manufacturing (NAICS 3256); and Other Chemical Product and Preparation 

Manufacturing (NAICS 3259). 

In 2007, each of these groups generated approximately one-third of the total 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 within 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: Detailed Statistics by Industry for the U.S.: 2007.” Release 

date: October 30, 2009. Accessed on December 27, 2009. 

Bulk Chemicals,

35%

Pharmaceuticals,

31%

Other Chemicals,

34%
2-36 

Figure 2-22. Distribution of Total Value of Shipments within Chemical Manufacturing 

(NAICS 325): 2007 

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 

31:EC0731I1: Manufacturing Industry Series: Detailed Statistics by Industry for U.S.: 2007.” 

<http://factfinder.census.gov>. Accessed 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 industry has with other 

industries and people, including identifying the key goods and services used by the industry and 

the major uses and consumers of chemical manufacturing products. 

The top 10 industry groups supplying inputs to the chemical manufacturing industry in 

2002 accounted for 71% of the total intermediate inputs (Table 2-22). 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 employees 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 industry. Pharmaceuticals and other chemical 

manufacturing are categorized as non-energy-intensive industries, 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 Manufacturing (NAICS 325) 

($2007, 106

) 

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 facilitate chemical processes or provide the 

feedstock to derive value-added chemicals. In 2007, 70% of chemical manufacturing’s energy 

bill was spent on fuel used as feedstocks (O’Reilly, 2008). These fuel costs represented 2% of 

chemical manufacturing’s total value of shipments (Table 2-23). 

As a whole, chemical manufacturing has become less energy intensive over the last 10 

years. According to DOE, natural gas use by the chemical manufacturing 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 became 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, 106) $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, 106

) 

$22,643 4% $22,231 3% $23,157 3% 

Total production workers 431,502 430,880 463,802 

Total production hours (103

) 899,499 885,993 948,244 

Average production wages per 

hour 

$25 $25 $24 

Total cost of materials ($103

) $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 output and 56% of the total intermediate use of 

chemical manufacturing products. Pharmaceuticals has both a high level of demand from 

personal consumption, accounting for 67% of the total personal consumption of chemical 

manufacturing products, and a large trade deficit (Table 2-25). 
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 electricitya (million kWh) 169,233 153,104 151,646 

Residual fuel oil (million bbl) 8 7 4 

Distillate fuel oilb

 (million bbl) 2 2 2 

Natural gasc

 (billion cu ft) 1,931 1,634 1,349 

LPG and NGLd

 (million bbl) 15 9 2 

Coal (million short tons) 13 14 8 

Coke and breeze (million short tons) * * * 

Othere (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 electricity inputs from on-site 

cogeneration or generation from combustible fuels because that energy has already been included as generating 

fuel (for example, coal). 

b

 Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels. c

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

such as independent gas producers, gas brokers, marketers, and any marketing subsidiaries of utilities. 

d

 Examples of liquefied petroleum gases (LPGs) are ethane, ethylene, propane, propylene, normal butane, butylene, 

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing 

plants, including plants that fractionate raw natural gas liquids (NGLs). 

e

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

that respondents indicated was used 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. Washington, 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 Production 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 geographic, production, and market data. These data 

provide the basis for further analysis, including regulatory flexibility analyses, and give a 

complete picture of the recent historical 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 New Jersey (Figure 2-24). The composition of establishments in 

these states differs among the 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 establishments, and only 13% were pharmaceutical establishments. 

2.3.3.2 Production Capacity and Utilization 

Capacity utilization of the chemical manufacturing industry has been broadly in line with 

the manufacturing sector (Figure 2-25). In the 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 downturn in 2001 can be seen in the capacity 

utilization of both manufacturing and chemical manufacturing. 

80

85

90

95

100

105

110

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Index Value (Jan‐97=100)

Manufacturing Chemical Manufacturing
2-41 

Table 2-25. Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007 106

) 

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 manufacturing has grown 33% since 1997. This growth, 

however, is 9% less than the growth rate for the manufacturing industry as a whole 

(Figure 2-27). 
2-42 

Figure 2-24. Establishment Concentration in Chemical Manufacturing (NAICS 325): 

2002 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: 

Manufacturing: Geographic Area Series: Industry Statistics for the 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 Production and Capacity Utilization: Capacity Utilization.” 

Series ID: G17/CAPUTL/CAPUTL.GMF.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

100

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

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Jul‐00

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Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Percentage Capacity Utilization

Manufacturing Chemical Manufacturing
2-43 

Figure 2-26. Employment Concentration in Chemical Manufacturing (NAICS 325): 2002 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: 

Manufacturing: Geographic Area Series: Industry Statistics for the States, Metropolitan and Micropolitan 

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008). 

Figure 2-27. Capacity Trends in Chemical 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

150

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

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Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Index Value (Jan‐97=100)

Manufacturing Chemical Manufacturing
2-44 

2.3.3.5 Firm Characteristics 

In 2007, the top six companies by chemical sales had greater than $10 billion in sales. 

Together, their sales are greater than the next 44 highest chemical companies combined. These, 

however, are global companies, with a large portion of both sales and production coming from 

operations outside of the United States (Table 2-26). The largest chemical manufacturing 

company, Dow Chemicals, has 108 out of 150 manufacturing sites located outside of the United 

States (Dow Chemical Company, 2008). 

Table 2-26. Top Chemical Producers: 2007 

 Chemical Sales ($106) % of Total Sales % of Sales in United States 

Dow Chemical 53,513 100% 35% 

ExxonMobil 36,826 9% 38% 

DuPont 29,218 100% 38% 

Lyondella

 16,165 57% 80% 

Chevron Phillips 12,534 100% 86% 

PPG Industriesa

 10,025 90% 56% 

Huntsman Chemical 9,651 100% 50% 

Praxair 9,402 100% 43.5% 

Air Productsa

 8,820 88% 51% 

Rohm & Haasb

 7,837 88% 49% 

a Percentage of sales in the United States calculated 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 manufacturing corporations generated net income. 

Including those with and without net income, chemical manufacturers had an average before-tax 

profit margin of 10.24%. Profitability is highest for pharmaceutical and medicine corporations 

(Table 2-27). 

2.3.3.6 Size Distribution 

The primary criterion for categorizing a business 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, enterprises with fewer than 500 employees accounted for 27% 

of employment and 15% of receipts within the chemical manufacturing 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 

($103

) 

Business 

Receipts 

($103) 

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/taxstats/article/0,,id=167415,00.html>; (January, 15, 2010). 

2.3.3.7 Domestic Production 

In the late 1990s, overall manufacturing production 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 caused 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 (Figure 2-29). This change occurred because the trade deficit 

in pharmaceutical manufacturing, currently at $35 billion, overwhelmed the trade surplus of bulk 

chemicals and other chemical manufacturing combined, currently at $22 billion. 

2.3.3.9 Market Prices 

Prices of goods in chemical manufacturing have accelerated rapidly in the last 2 years, 

having outpaced overall manufacturing since 2002 (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 Manufacturing (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 and 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 Chemical Manufacturing 1,000 

325211 Plastics Material and Resin Manufacturing 750 

325212 Synthetic Rubber Manufacturing 1,000 

325221 Cellulosic Organic Fiber Manufacturing 1,000 

325222 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 Agricultural Chemical Manufacturing 500 

325411 Medicinal and Botanical Manufacturing 750 

325412 Pharmaceutical Preparation Manufacturing 750 

325413 In-Vitro Diagnostic Substance Manufacturing 500 

325414 Biological Product (except Diagnostic) Manufacturing 500 

325510 Paint and Coating 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, Plate and Chemical Manufacturing 500 

325998 All Other Miscellaneous Chemical Product and Preparation 

Manufacturing 

500 

Source: U. S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to 

North American Industry Classification System Codes.” Effective August 22, 2008. 

<http://www.sba.gov/services/contractingopportunities/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 

Employeesa

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 ($106

) $468,211 $9,631 $21,394 $39,111 $12,217 $7,324 $14,762 

Receipts/firm ($103) $50,124 $1,779 $10,838 $49,507 $128,603 $130,779 $207,913 

Receipts/establishment ($103) $35,752 $1,773 $9,689 $28,928 $48,869 $39,587 $53,485 

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

a Excludes SUSB employment category for zero employees. These entities only operated for a fraction of the year. 

Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 

2002.” <http://www.census.gov/csd/susb/download_susb02.htm>. 

Figure 2-28. Industrial Production Trends in Chemical Manufacturing (NAICS 325) 

Source: Federal Reserve Board. 2009. “Industrial Production 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

150

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

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Jul‐02

Jan‐03

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Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Index Value (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.usitc.gov/scripts/user_set.asp>. 

Figure 2-30. Producer Price Trends in Chemical Manufacturing (NAICS 325) 

Source: U.S. Bureau of Labor Statistics (BLS). 2009. 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

170

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

PPI (Jan‐98=100)

Manufacturing Chemical Manufacturing
3-1 

SECTION 3 

ENGINEERING COST ANALYSIS 

This section provides an overview of the engineering cost analysis used to estimate the 

additional private expenditures industry may make in order to comply with the rule. A detailed 

discussion of the methodology used to estimate cost impacts is presented in “Compliance Cost 

Analysis for Existing CISWI Units” in Appendix B. 

To estimate the national cost impacts of the 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 units were operated at the facility but no data were available, the 

emissions data from the unit with data were applied to the identical units. For units that did not 

report emissions data, we assigned the appropriate emission factor for the units in that 

subcategory. We then compared each unit’s baseline 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 fabric 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 injection for mercury and dioxins/furans control; 

selective non-catalytic reduction for oxides of nitrogen; and afterburner retrofits, tune-ups, 

advanced combustion controls, and oxidation catalysts for carbon monoxide controls. We also 

considered whether existing control devices could be improved to achieve the proposed limits, 

such as adding more lime to duct sorbent injection systems to meet the proposed sulfur dioxide 

limits. We included costs for testing and monitoring 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 expensive 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 operating and maintaining an incineration unit. The 

resulting total national cost impact of the proposed 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 and reporting costs. Based on the cost to 

comply with the proposed rule, availability of alternatives to incineration, 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 $224 million (2008$), which results in a slight 

overestimate of the economic impacts. 

The requirements result in industry recordkeeping and reporting burden associated with 

reviewing the amendments for all CISWI and inspections of scrubbers, fabric filters, and other 

air pollution control devices that may be used to meet the emission limits for all CISWI. 

Ongoing parametric monitoring requirements for ESPs, SNCR, and activated carbon injection 

are also required of all CISWI units. Stack testing and development 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, prior to this proposed action, have not been 

required to demonstrate compliance with certain pollutants. Visual emissions tests would be 

required for all subcategories except kilns on an annual basis. Energy 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. Kilns would be required to continuously monitor 

Hg emissions using an Hg CEMS. Any new CISWI would also be required to continuously 

monitor CO emissions. Annualized capital/startup costs and operation 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 reports, and photocopying and postage. 
4-1 

SECTION 4 

ECONOMIC IMPACT ANALYSIS 

EPA prepares an EIA to provide decision makers 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 presented 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 consumer and producer surplus). 

The Office of Air Quality Planning and Standards (OAQPS) adopted a standard market 

analysis as described in the Office’s resource manual (EPA, 1999). The approach uses a singleperiod multimarket partial equilibrium model to compare pre-policy market baselines with 

expected post-policy market outcomes. 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 social costs (changes in producer and consumer 

surplus). 

4.1 Partial Equilibrium Analysis (Multiple Markets) 

The partial equilibrium analysis develops a market model that simulates how 

stakeholders (consumers and industries) might respond to the additional regulatory program 

costs. In this section, we provide an overview of the economic model. Appendix A provides 

additional details on the behavioral assumptions, data, parameters, and model equations. 

4.1.1 Overview 

Although several tools are available to estimate social costs, current EPA guidelines 

suggest that multimarket models “...are best used when potential economic impacts and equity 

effects on related markets might be considerable” and modeling using a computable general 

equilibrium model is not available or practical (EPA, 2000, p. 146). Other guides for 

environmental economists offer similar advice (Berck and Hoffmann, 2002; Just, Hueth, and 

Schmitz, 2004). Multimarket models focus on “short-run” time horizons and measure a policy’s 

near-term or transition costs (EPA, 1999). Recent studies suggest short-run analyses can 

complement full dynamic general equilibrium analysis. For example, Morgenstern and 
4-2 

colleagues examine carbon price policies with short- and long-term time horizons (Morgenstern 

and colleagues, 2004; Ho, Morgenstern, and Shih, 2008). Aldy and Pizer (2009) assess near-term 

competitiveness effects of a domestic cap-and-trade program to address stakeholder concerns 

about shifts in economic activity and jobs to other 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 costs by changing production rates 

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

– customers respond to these price increases and consumption falls 

 Model scope 

– 100 sectors are linked with each other based on their use of energy and other nonenergy materials. For example, the construction industry is linked with the 

petroleum, cement, and steel industries and is influenced by price changes that 

occur in each sector. The links allow EPA to account for indirect effects the 

regulation has on related markets. 

– production adjustments influence employment levels 

– international trade (imports/exports) 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, and 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 quantity adjustments including the changes in 

international trade (Table 4-1). The Agency’s economic model suggests 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 Quantity 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 model 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 million, and the net loss for U.S. stakeholders is 

$228 million. As U.S. prices rise, other countries 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 international trade effects, the Agency’s economic model projects 

the net surplus loss associated with the proposed rule is $224 million. As shown in Figure 4-1, 

the surplus losses are concentrated in lumber, paper, and printing (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 Social 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 Changes by Industry (Total Surplus Change = 

$224 million, 2008$) 

4.1.2.3 Job Effects 

Precise job effect estimates cannot be estimated with certainty. Morgenstern 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 industry falls (“demand effect”); 

 pollution abatement activities require additional labor services to produce the same 

level of output (“cost effect”); and 

 post-regulation production technologies may be more or less labor intensive (i.e., 

more/less labor is required per dollar of output) (“factor-shift effect”). 

Several empirical studies, including Morgenstern et al. (2002), suggest the net 

employment decline is zero or economically small (e.g., Cole and Elliot, 2007; Berman and Bui, 

2001). However, others show the question has not been resolved in the literature (Henderson, 

1996; Greenstone, 2002). Morgenstern et al. use a six-year panel (U.S. Census data for 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 (including labor), outputs, and environmental expenditures) to 

econometrically estimate the production technologies and industry-level demand elasticities. 

Their identification strategy leverages repeat plant-level observations 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, petroleum, 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 four industry-wide estimates and 

weighting them by each industry’s share of environmental expenditures. 

EPA has most often estimated employment changes associated with plant closures due to 

environmental regulation or changes in output for the regulated industry (EPA, 1999; EPA, 

2000). This analysis goes beyond what EPA has typically 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 comprehensive “demand effect.” Secondly, parameters estimated in 

the Morgenstern paper were used to estimate all three effects (“demand,” “cost,” and “factor 

shift”). This transfer of results from the Morgenstern 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 sectors in the EPA model that show a change in output. 

We also calculated a similar “demand effect” estimate that used the Morgenstern paper. 

EPA selected this paper because the parameter estimates (expressed in jobs per million ($1987) 

of environmental compliance expenditures) provide a transparent and tractable way to transfer 

estimates for an employment effects analysis. Similar estimates were not available from other 

studies. To do this, we multiplied the point estimate for the total demand effect (−3.56 jobs per 

million ($1987) of environmental compliance expenditure) by the total environmental 

compliance expenditures used in the partial equilibrium 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 Morgenstern paper to estimate U.S. employment 

“cost” and “factor-shift” effects. Although using the Morgenstern parameters to estimate these 

“cost” and “factor-shift” employment changes is uncertain, it is helpful to compare the potential 

job gains from these effects to the job losses associated with the “demand” effect. Figure 4-2 

shows that using the Morgenstern point estimates 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 losses. The 95% confidence intervals are shown 

for all of the estimates based on the Morgenstern parameters. As shown, at the 95% confidence 

level, we cannot be certain if net employment changes are positive or negative. 

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

 

Although the Morgenstern paper provides additional information about the potential job 

effects of environmental protection programs, there are several qualifications EPA considered as 

part of the analysis. First, EPA has used the weighted average parameter estimates for a narrow 

set of manufacturing industries (pulp 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

3.0

1,000 Jobs

Base=144,100

DemandEffect:Partial Equilibrium(Multiple Markets) DemandEffect:Estimate derivedfromMorgenstern, et al.

CostEffect:  Estimate derivedfrom Morgenstern, etal. Factor ShiftEffect:Estimate derivedfrom Morgenstern, et al.

Net Effect:Estimate derivedfromMorgenstern, et al.
4-8 

reviewed source. However, EPA acknowledges the proposed rule covers a broader set of 

industries not considered in original empirical study. By transferring the 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 estimates 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 production technologies are 

fixed. As a result, the modeling system places more emphasis on the short term “demand effect” 

whereas the Morgenstern paper emphasizes other important long term responses. For example, 

positive job gains associated with “factor shift 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 used in the multi-market model. As a result, the 

demand effects are not directly comparable with the demand effects estimated by the multimarket model. Fourth, Morgenstern identifies the industry average as economically and 

statistically insignificant effect (i.e., the point estimates are small, measured imprecisely, and not 

distinguishable from zero.) EPA acknowledges this fact and has reported the 95 percent 

confidence intervals in Figure 4-2. Fifth, Morgenstern’s methodology assumes large plants bear 

most of the regulatory costs. By transferring the estimates, EPA assumes a similar distribution of 

regulatory costs by plant size and that the regulatory burden does not disproportionately fall on 

smaller plants. 
 5-1 

SECTION 5 

SMALL ENTITY ANALYSES 

The RFA as amended by SBREFA generally requires 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 agency certifies that the rule will 

not have a significant economic impact on a substantial number of small entities (SISNOSE). 

Small entities include small businesses, small governmental jurisdictions, and small not-forprofit enterprises. EPA assessed the potential small entity economic impacts using a screening 

analysis. After reviewing screening analysis results, EPA has determined the proposed rules will 

not have a SISNOSE and presumes that both proposed rules are eligible for certification under 

the RFA as amended by SBREFA. The remaining 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 identified through lists of small 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 Using Census Statistics of U.S. Businesses 

For each 3-digit NAICS code, the SUSB provides national information on the distribution 

of economic variables by industry and enterprise size (U.S. Census, 2008). The Census Bureau 

and the Office of Advocacy of the SBA supported and developed these files for use in a broad 

range of economic analyses.1 Statistics include the total number of establishments and receipts 

for all entities within an industry; however, only a 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: 

 Establishment: An establishment is a single physical location where business is 

conducted or where services or industrial operations are performed. 

 Receipts: Receipts (net of taxes) are defined as the revenue for goods produced, 

distributed, or services provided, including revenue earned from premiums, 

commissions and fees, rents, interest, dividends, and royalties. Receipts exclude all 

revenue collected for local, state, and federal taxes. 

 Enterprise: An enterprise is a business organization consisting of one or more 

domestic establishments that were specified under common ownership or control. The 

enterprise and the establishment are the same for single-establishment firms. Each 

multi-establishment company forms one enterprise—the enterprise employment and 

annual payroll are summed from the associated establishments. Enterprise size 

designations are determined by the total employment of all associated establishments. 

Because the SBA’s business size definitions (SBA, 2008) apply to an establishment’s 

“ultimate parent company,” we assumed in this analysis that the “enterprise” 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 sectors listed in Table 5-2. Although the appropriate 

SBA size definition should be applied at the parent company (enterprise) level, we can only 

compute and compare ratios for a model establishment owned by an enterprise within an SUSB 

size range (employment or receipts). Using the SUSB size range helps us account for receipt 

differences between establishments owned by large and small enterprises and also allows us to 

consider the variation in small business definitions across affected industries. Using 

establishment receipts is also a conservative approach, because an establishment’s parent 

company (the “enterprise”) may have other economic 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 SUSB 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-specific sample of small private 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 the facility was owned by a small business, and 

collected the most recent parent company sales and employment figures. 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 Companies 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-to-receipt ratios, we adjusted the engineering compliance costs to reflect 2002 dollars 

using the implicit price deflators for gross domestic product (GDP). The values used are 2002 = 92.118 and 2008 

= 108.483 (U.S. BEA, 2010). 
6-1 

SECTION 6 

HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS 

6.1 Synopsis 

In this section, we provide an estimate of the monetized benefits associated with reducing 

particulate matter (PM) for the proposed CISWI NSPS and Emissions Guidelines. For this rule, 

the PM reductions are the result of emission limits on PM, emission limits on PM2.5 precursors 

such as NOx and SO2, as well as emission limits on other pollutants. The total PM2.5 reductions 

are the consequence of the technologies installed or waste diversion to meet these multiple 

limits. These estimates reflect the monetized human health benefits of reducing cases of 

morbidity and premature mortality among populations exposed to the PM2.5 precursors reduced 

by this rulemaking. Using a 3% discount rate, we estimate the total monetized benefits of the 

proposed CISWI NSPS and Emissions Guidelines to be $240 million to $580 million in the 

implementation year (2015). Using a 7% discount rate, 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 interpretation of the scientific literature. 

Higher or lower estimates of benefits are possible 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, including benefits from 

reducing hazardous air pollutants, ecosystem effects, 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 total dioxins/furans each year. 

6.2 Calculation of PM2.5 Human Health Benefits 

This rulemaking would reduce emissions of PM2.5, SO2, and NO2. Because SOx and NO2

are also precursors to PM2.5, reducing these emissions would also reduce PM2.5 formation, human 

exposure, and the incidence of PM2.5-related health effects. For this rule, the PM reductions are 

the result of emission limits on PM, emission limits on PM2.5 precursors such as NOx and SO2, as 

well as emission limits on other pollutants. The total PM2.5 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 provide a comprehensive estimate of PM2.5-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 key assumptions are described in detail below. 
6-2 

These PM2.5 benefit-per-ton estimates provide the total monetized human health benefits (the 

sum of premature mortality and premature morbidity) of reducing one ton of PM2.5 from a 

specified source. EPA has used the benefit per-ton technique in several previous RIAs, including 

the recent NO2 NAAQS RIA (U.S. EPA, 2010b). Table 6-1 shows the quantified and 

unquantified benefits captured in those benefit-per-ton estimates. 

Table 6-1. Human Health and Welfare Effects of PM2.5 

Pollutant / 

Effect 

Quantified and Monetized 

in Primary Estimates 

Unquantified Effects 

Changes in: 

PM2.5 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 (asthmatic 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 PM2.5 benefits 

estimates utilize the concentration-response functions as reported in the epidemiology literature, 

as well as the 12 functions obtained in EPA’s expert elicitation study as a sensitivity analysis. 

 One estimate is based on the concentration-response (C-R) function developed from 

the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope 

et al. (2002), a study that EPA has previously used to generate its primary PM 

benefits estimate. 

 One estimate is based on the C-R function developed from the extended analysis of 

the Harvard Six Cities cohort, as reported by Laden et al. (2006). This study, 

published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS, has been 

used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 benefits estimates 

in RIAs completed since the PM2.5 NAAQS. 

 Twelve estimates are based on the C-R functions from EPA’s expert elicitation study 

(Roman et al., 2008) on the PM2.5 -mortality relationship and interpreted for PM 

benefits analysis in EPA’s final RIA for the PM2.5 NAAQS. For that study, twelve 

experts (labeled A through L) provided independent estimates of the PM2.5 -mortality 

concentration-response function. EPA practice has been to develop independent 

estimates of PM2.5 -mortality estimates corresponding to the concentration-response 
6-3 

function provided by each of the twelve experts, 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 anchor points in our presentation 

because, while both studies are well designed and peer reviewed, there are strengths and 

weaknesses inherent in each, which we believe argues for using both studies to generate benefits 

estimates. Previously, EPA had calculated benefits based on these two empirical studies, but 

derived the range of benefits, including the minimum and maximum results, from an expert 

elicitation of the relationship between exposure to PM2.5 and premature mortality (Roman et al., 

2008).2 Within this assessment, we include the benefits estimates derived from the concentrationresponse function provided by each of the twelve experts to better characterize 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 using these functions generally fall between results using these 

epidemiology studies (see Figure 6-2). In general, the expert elicitation results support the 

conclusion that the benefits of PM2.5 control are very likely to be substantial. 

Readers interested in reviewing the methodology for creating the benefit-per-ton 

estimates used in this analysis 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 reduced from those specific pollutant/source combinations (e.g., SO2 

emitted from electric generating units; NO2 emitted from mobile sources). Our estimate of PM2.5 

control benefits is therefore based on the total PM2.5 emissions controlled by sector and 

multiplied by this per-ton value. 

The benefit-per-ton coefficients in this analysis 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 updates: a new population dataset, an expanded 

geographic scope of the benefit-per-ton calculation, and the functions directly from the 

 

1

These two studies specify multi-pollutant models that control for SO2, 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 used 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 PM2.5 air quality levels. 

EPA strives to use the best available science to support our benefits analyses, and we 

recognize that interpretation of the science regarding air pollution and health is dynamic and 

evolving. Based on our review of the body of scientific literature, EPA applied 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 Clean Air Scientific Advisory Committee (U.S. 

EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded that the scientific literature consistently 

finds that a no-threshold log-linear model most adequately portrays the PM-mortality 

concentration-response relationship while recognizing potential uncertainty about the exact 

shape of the concentration-response function.4

 In conjunction with the underlying scientific 

literature, this document provided a basis for reconsidering the application of thresholds in PM2.5

concentration-response functions used in EPA’s RIAs. 5 

As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods 

used to estimate air quality benefits evolve over time to reflect the Agency’s most current 

interpretation of the scientific and economic literature. For a period of time (2004–2008), 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 available studies. OAR arrived 

at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of 

the wage-risk literature. The $1 million value represented the lower end of the interquartile range 

from the Mrozek and Taylor (2002) meta-analysis 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 peer-review group. 

During this time, the Agency continued work to update its guidance on valuing mortality 

risk reductions, including commissioning a report 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 Agency consulted several times with the Science Advisory Board 

Environmental Economics Advisory Committee (SAB-EEAC) on the issue. With input from the 

meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, 

appropriate meta-analytic techniques to combine estimates from unique data sources and 

different studies, including those using different methodologies (i.e., wage-risk and stated 

preference) (U.S. EPA-SAB, 2007). 

Until updated guidance is available, the Agency determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. 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 studies published between 1974 and 

1991. The mean VSL across these studies is $6.3 million (2000$).8 The Agency is committed to 

using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions 

and has made significant progress in responding to the SAB-EEAC’s specific recommendations. 

The Agency anticipates presenting results from this effort to the SAB-EEAC in Spring 2010 and 

that draft guidance will be available shortly thereafter. 

Figure 6-1 illustrates the relative breakdown of the monetized PM2.5 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 PM2.5 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 premature 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 the 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 the expert elicitation on PM mortality. Figures 6-2 

through 6-4 provide a visual representation of the range of benefits 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 specific analysis year (i.e., 2015), and most of the PM benefits occur within 

that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we 

assume 5 years of follow-up medical costs and lost wages. For premature mortality, we assume that there is a 

“cessation” lag between PM exposures and the total realization of changes in health effects. Although the 

structure of the lag is uncertain, EPA follows the advice of the SAB-HES to assume a segmented lag structure 

characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 

20 after the reduction in PM2.5 (U.S. EPA-SAB, 2004). Changes in the lag assumptions do not change the total 

number of estimated deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI 

costs after the analysis year and the valuation of premature mortalities that occur after the analysis year. As such, 

the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized benefits 

using a 3% discount rate. 

Adult Mortality ‐ Pope et

al. 93%

ChronicBronchitis 4%

AMI 2%

Acute Respiratory Symptoms

0.5%

InfantMortality 0.4%

Work Loss Days 0.2%

HospitalAdmissions, Cardio

0.2%

HospitalAdmissions, Resp

0.04%

Asthma Exacerbation 0.01%

Acute Bronchitis 0.01%

UpperResp Symp 0.00%

LowerResp Symp 0.00%

ERVisits, Resp 0.00%

Other1%
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 PM2.5 660 $230,000 $560,000 $210,000 $500,000 $150 to $370 $140 to $330 

PM2.5

Precursors 

SO2 2,659 $29,000 $72,000 $27,000 $65,000 $78.0 to $190 $71.0 to $170 

NO2 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 implementation 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 reduced has a different propensity to become PM2.5. The monetized 

benefits incorporate the conversion from precursor emissions to ambient fine particles. 

Table 6-3. Summary of Reductions in Health Incidences from PM2.5 Benefits for the 

Proposed CISWI NSPS and Emissions Guidelines in 2015a

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 equivalent health effects, but each PM2.5 precursor pollutant has a different 

propensity to form PM2.5. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. 
6-8 

Table 6-4. All PM2.5 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 Derived from Epidemiology Literature 

Pope et al. $240 $210 

Laden et al. $580 $520 

Benefit-per-ton Coefficients Derived 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. Estimates 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 mortality 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 PM2.5 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 from quantifying or monetizing the benefits from 

several important benefit categories, including benefits from reducing toxic emissions, 

ecosystem effects, and visibility impairment. The health benefits from reducing hazardous air 

pollutants (HAPs) and carbon monoxide have not been monetized in this analysis. In addition to 

being a PM2.5 precursor, SO2 emissions also contribute to adverse effects from acidic deposition 

in aquatic and terrestrial ecosystems, increased 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 from2 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 PM2.5 Precursor Pollutant and Source 

Figure 6-4. Breakdown of Monetized Benefits for the Proposed CISWI NSPS and 

Emissions Guidelines by Subcategory 

Burn‐off oven Energy recovery unit 1%

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 and the resulting ambient concentrations using 

fine-scale air quality modeling, we were unable to estimate the exposure to CO for nearby 

populations. Due to data, resource, and methodological limitations, we were unable to estimate 

the benefits associated with the 24,000 tons reductions in CO emissions that would occur as a 

result of this rule. 

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 

carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO 

emitted from these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the 

combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon 

inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia 

(reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple 

tissues and organ systems that are dependent upon concentration and duration of exposure. 

The Integrated Science Assessment for Carbon 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 coronary heart disease. Epidemiologic studies 

associate short-term CO exposure with increased risk of emergency department visits and 

hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac 

chest pain), as well as those who have experienced a heart attack. Other subpopulations 

potentially at risk include individuals with diseases such as chronic obstructive pulmonary 

disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as 

older adults or the developing young. The evidence is suggestive of a causal relationship 

between short-term exposure to CO and respiratory morbidity and mortality. The evidence is also 

suggestive of a causal relationship for birth outcomes and developmental effects following longterm exposure to CO, and for central nervous system effects linked to short- and long-term 

exposure to CO. 

6.3.2 Other SO2 Benefits 

In addition to being a precursor to PM2.5, SO2 emissions are also associated with a variety 

of respiratory health effects. Unfortunately, we were unable to estimate the health benefits 

associated with reduced SO2 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 concentrations, we were unable to estimate the exposure to SO2 for nearby 
6-12 

populations. Therefore, this analysis only quantifies and monetizes the PM2.5 benefits associated 

with the reductions in SO2 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 between respiratory health 

effects and short-term exposure to SO2 (U.S. EPA, 2008). The immediate effect of SO2 on the 

respiratory system in humans is bronchoconstriction. Asthmatics are more sensitive to the effects 

of SO2 likely resulting from preexisting inflammation associated with this disease. A clear 

concentration-response relationship has been demonstrated in laboratory studies following 

exposures to SO2 at concentrations between 20 and 100 ppb, both in terms of increasing severity 

of effect and percentage of asthmatics adversely affected. Based on our review of this 

information, we identified four short-term morbidity endpoints that the SO2 ISA identified as a 

“causal relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

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

SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 

associations to adjustment for pollutants. 

SO2 emissions also contribute to adverse welfare effects from acidic deposition, visibility 

impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which 

can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic 

ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and 

sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern United States, the 

surface waters 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. Biological effects of acidification in 

terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root 

growth, which restricts the ability of the plant to take up water and nutrients. These direct effects 

can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold 

temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial 

acidification affects several important ecological services, including declines in habitat for 

threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in 
6-13 

forest productivity (provisioning), and increases in forest soil erosion and reductions in water 

retention (cultural and regulating). (U.S. EPA, 2008d) 

Reducing SO2 emissions and the secondary formation of PM2.5 would improve the level 

of visibility throughout the United States. Fine particles with significant light-extinction 

efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). 

These suspended particles and gases degrade visibility by scattering and absorbing light. Higher 

visibility impairment levels in the East are due to generally higher concentrations of fine 

particles, particularly sulfates, and higher average relative humidity levels. In fact, particulate 

sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually 

and 75% during summer). In the western U.S., particulate sulfate contributes to 20-50% of 

regional haze (U.S. EPA, 2009c). Visibility has direct significance to people’s enjoyment of 

daily activities and their overall sense of wellbeing. Good visibility increases the quality of life 

where individuals live and work, and where they engage in recreational activities. 

6.3.3 HAP Benefits 

Due to data, resource, and methodology limitations, we were unable to estimate the 

benefits associated with the hazardous air pollutants that would be reduced as a result of this 

rule. Available emissions data show that several different HAPs are emitted from CISWI. This 

rule is anticipated to reduce 560 tons of hydrochloric acid, 5.4 tons of cadmium, 6.0 tons of lead, 

280 pounds of mercury, and 230 grams of total dioxins/furans each year in the primary approach. 

6.3.3.1 Mercury 

Mercury is a highly neurotoxic contaminant that enters the food web as a methylated 

compound, methylmercury (U.S. EPA, 2008d). The contaminant is concentrated in higher 

trophic levels, including fish eaten by humans. Experimental evidence has established that only 

inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA, 

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

deposition very likely results in methylmercury accumulation in fish (Drevnick et al., 2007; 

Munthe et al, 2007). The SO2 ISA concluded that evidence is sufficient to infer a casual 

relationship between sulfur deposition and increased mercury methylation in wetlands and 

aquatic environments (U.S. EPA, 2008d). 

In addition to the role of sulfate deposition on methylation, this 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 atmosphere and eventually deposit to land or 

water bodies. This deposition can occur locally, regionally, or globally, depending 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 and 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 elemental 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. Inorganic ionic and particulate bound mercury have a 

shorter atmospheric lifetime and can deposit to land or water bodies closer to the emissions 

source. Furthermore, elemental mercury in the atmosphere can undergo transformation into 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, about 103 tons of mercury were emitted from 

all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that 

about 2,100 tons of mercury were emitted worldwide by anthropogenic sources in 2005. We 

believe that total mercury emissions in the U.S. and globally in 2008 were about the same 

magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 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 emissions 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 unable to model mercury dispersion, deposition, 

methylation, bioaccumulation in fish tissue, and human consumption of mercury-contaminated 

fish that would be needed in order to estimate the human health benefits from reducing mercury 

emissions. 

Potential exposure routes to mercury emissions include both direct inhalation and 

consumption of fish containing methylmercury. 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 settles 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 some types of fish and shellfish than in others. The levels of 

methylmercury in fish and shellfish vary widely depending on what they eat, how long they live, 

and how high they are in the food chain. Most fish, including ocean species and local freshwater 
6-15 

fish, contain some methylmercury. For example, in recent studies by EPA and the U.S. 

Geological Survey (USGS) of fish 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 primarily 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 substantial part of their diet). It has been demonstrated that high 

levels of methylmercury in the bloodstream of unborn babies and young children may harm the 

developing nervous system, making the child less able to think and learn. Moreover, mercury 

exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people 

of all ages. 

Several studies suggest that the methylmercury content of fish may reduce these cardioprotective effects of fish consumption. Some of these studies also suggest that methylmercury 

may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of 

the literature concerning methylmercury health effects took note of two epidemiological studies 

that found an association between dietary exposure to methylmercury and adverse cardiovascular 

effects.10 Moreover, in a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al. 

(1995) observed a relationship between methylmercury exposure via fish consumption and acute 

myocardial infarction (AMI or heart attacks), coronary heart disease, cardiovascular disease, and 

all-cause mortality.11 The NRC also noted a study of 917 seven 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 data base is not as extensive for 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. 11Salonen, 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. 12Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, 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 system appears to be a target for 

methylmercury toxicity.”13 

Since publication of the NRC report there have been some 30 published papers 

presenting the findings of studies that have examined the possible cardiovascular effects of 

methylmercury exposure. These studies include epidemiological, toxicological, and toxicokinetic 

investigations. Over a dozen review papers have also been published. If there is a causal 

relationship between methylmercury exposure and adverse cardiovascular effects, then reducing 

exposure to methylmercury would result in public health benefits from reduced cardiovascular 

effects. 

In 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 methodologies for estimating population 

level cardiovascular health impacts of reduced methylmercury exposure. The report from that 

workshop is in preparation. 

6.3.3.2 Cadmium14

Breathing air with very high levels 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 expected 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 sufficiently high, may result in kidney disease. 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 cadmium is a probable human carcinogen.

 

13National 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. 14All health effects language for this section came from: 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 Lead15

The main target for lead toxicity is the nervous system, both in adults and children. Longterm exposure of adults to lead at work has resulted 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 small 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 and kidneys in adults or children and ultimately 

cause death. In pregnant women, high levels of exposure to lead may cause miscarriage. Highlevel exposure in men can damage the organs responsible for sperm production. 

We have no conclusive proof that lead causes cancer (is carcinogenic) in humans. Kidney 

tumors have developed in rats and mice that had 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 reasonably anticipated to be human 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 carcinogen. The International Agency for Research on 

Cancer (IARC) has determined that inorganic 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 from studies in humans and in animals. 

Children are more sensitive to the health effects of lead than adults. No safe blood lead 

level in children has been determined. Lead affects children in different 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 certain drugs that help eliminate lead from the body. If a child 

swallows smaller amounts of lead, such as dust containing lead from paint, much less severe but 

still important effects on blood, development, and behavior may occur. 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 consequences of lead exposure. At still lower levels 

of exposure, lead can affect a child’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 from: 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. Available 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 persist beyond childhood. 

6.3.3.4 Hydrogen Chloride (HCl) 16

Hydrogen chloride gas is intensely irritating to the mucous membranes of the nose, 

throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 

100 ppm are barely tolerable for 1 hour. The greatest impact is on the upper respiratory tract; 

exposure to high concentrations can rapidly lead to swelling and spasm of the throat and 

suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue 

coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures 

may develop an accumulation of fluid in the lungs. Exposure to hydrogen chloride can lead to 

Reactive Airway Dysfunction Syndrome (RADS), a chemically- or irritant-induced type of 

asthma. Children may be more vulnerable to corrosive agents than adults because of the 

relatively smaller diameter of their airways. Children may also be more vulnerable to gas 

exposure because of increased minute ventilation per kg and failure to evacuate an area promptly 

when exposed. Hydrogen chloride has not been classified for carcinogenic effects. 

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 levels. The most obvious health effect in people 

exposure to relatively large amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin 

disease with acne-like lesions that occur 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 that 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 reported in people exposed to relatively high concentrations of 

2,3,7,8-TCDD. Most of the effects are considered mild and were reversible. However, in some 

people these effects may last for many years. Slight increases in the risk of diabetes and 

abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-

TCDD. We do not have enough information to know if exposure to 2,3,7,8-TCDD would result 

 

16 All health effects language for this section came from: 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. Available on the Internet at 

<http://www.atsdr.cdc.gov/Mhmi/mmg173.html>. 17 All health effects language for this section came from: 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 people, but animal studies suggest that this is a 

potential health concern. 

In certain animal species, 2,3,7,8-TCDD is especially harmful and can cause death after a 

single exposure. Exposure to lower levels can cause a variety of effects in animals, 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 relatively 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 animal species exposed to CDDs during pregnancy 

had miscarriages and the offspring of animals exposed to 2,3,7,8-TCDD during pregnancy often 

had severe birth defects including skeletal deformities, kidney defects, and weakened immune 

responses. In some studies, effects were observed at body burdens 10 times higher than human 

background levels. 

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, especially severe acne, darkened skin color, and swollen eyelids 

with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also 

caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the 

nervous system, and mild changes in the liver. Children born to exposed mothers had skin 

irritation and more difficulty learning, but it is unknown if this effect was permanent or caused 

by CDFs alone or CDFs and polychlorinated biphenyls in combination. 

Many of the same effects that occurred in people accidentally exposed also occurred in 

laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomachs, 

livers, kidneys, and immune systems were seriously injured. Some animals had birth defects and 

testicular damage, and in severe cases, some animals died. These effects in animals were seen 

when they were fed large amounts of CDFs over a short time, or small amounts over several 

weeks or months. Nothing is known about the possible health effects in animals from eating 

CDFs over a lifetime. 

 

18 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). 1995. ToxFAQsTM for Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of 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 PM2.5 Benefits 

In any complex analysis, there are likely to be many sources of uncertainty. Many inputs 

are used to derive the final estimate of economic benefits, including emission inventories, air 

quality models (with their associated parameters and inputs), epidemiological estimates of 

concentration-response (C-R) functions, estimates of values, population estimates, income 

estimates, and estimates of the future state of the world (i.e., regulations, technology, and human 

behavior). For some parameters or inputs it may be possible to provide a statistical representation 

of the underlying uncertainty distribution. For other parameters or inputs, the necessary 

information is not available. 

The annual benefit estimates presented in this analysis are also inherently variable due to 

the processes that govern pollutant emissions and ambient air quality in a given year. Factors 

such as hours of equipment use and weather are constantly variable, regardless of our ability to 

measure them accurately. As discussed in the PM2.5 NAAQS RIA (Table 5.5) (U.S. EPA, 2006), 

there are a variety of uncertainties associated with these PM benefits. 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 results to assess the sensitivity of the primary 

results to various data inputs and assumptions. We then changed each default input one at a time 

and recalculated the total monetized benefits to assess the percent change from the default. We 

present the results of this sensitivity analysis in Table 6-5. 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 available scientific literature and methods and supported by the SABHES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions 

and uncertainties. For example, for key assumptions underlying the estimates for premature 

mortality, which typically account for at least 90% of the total monetized benefits, we were able 

to quantify include the following: 
6-21 

Table 6-5. Sensitivity Analyses for Monetized PM2.5 Health Benefits (in millions of 2008$) 

Total PM2.5 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. PM2.5 benefits were derived through benefit per-ton estimates, which do not reflect 

local variability in population density, meteorology, exposure, baseline health 

incidence rates, or other local factors that might lead to an over-estimate or underestimate of the actual benefits of controlling directly emitted fine particulates. 

2. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from EGUs may differ 

significantly from direct PM2.5 released from diesel engines and other industrial 

sources, but no clear scientific grounds exist for supporting differential effects 

estimates by particle type. 

3. We assume that the health impact function 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 PM2.5,

including both regions that are in attainment with fine particle standard and those that 

do not meet the standard down to the lowest modeled concentrations. 

4. To characterize the uncertainty in the relationship between PM2.5 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 PM2.5 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 PM2.5 benefits, please consult the 

PM2.5 NAAQS RIA (Table 5.5). 
6-22 

This RIA does not include the type of detailed 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 estimates 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 the 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 reductions and specific air quality and benefits 

modeling assumptions. For example, these estimates do not reflect local variability in population 

density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of 

these $/ton values to estimate benefits associated with different emission control programs (e.g., 

for reducing emissions from large stationary sources like EGUs) may lead to higher or lower 

benefit estimates than if benefits were calculated based on direct air quality modeling. Great care 

should be taken in applying these estimates to emission reductions occurring in any specific 

location, as these are all based on national or broad regional emission reduction programs and 

therefore represent average benefits-per-ton over the entire United States. The benefits-per-ton for 

emission reductions in specific locations may be very different than the estimates presented here. 

6.5 Comparison of Benefits and Costs 

Using a 3% discount rate, we estimate the total monetized benefits 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 Emissions 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 the benefits and $-2.4 million to $310 billion at 

a 7% discount rate. All estimates are in 2008$. Figures 6-5 and 6-6 show the full range of net 

benefits estimates (i.e., annual benefits minus annualized costs) utilizing the 14 different PM2.5 

mortality functions at discount rates of 3% and 7%. In addition, 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, 280 pounds of mercury, and 230 grams of total 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 Guidelines in 2015 (millions of 2008$)1

Proposed Option 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits $240 to $5802 $210 to $5202

Total Social Costs $2203 $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 PM2.5 through 

reductions of 660 tons of directly emitted PM2.5 and PM2.5 precursors such as 1,400 tons of NOx and 2,700 tons of 

SO2. 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 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. 

Figure 6-5. Net Benefits for the Proposed CISWI NSPS and Emissions Guidelines at 3% 

Discount Ratea

a

 Net Benefits are quantified in terms of PM2.5 benefits for implementation year (2015). This graph shows 14 

benefits estimates combined with the cost estimate. All 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 has a different propensity to become PM2.5. 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 from2

epidemiology functions and 12 expert functions
6-24 

Figure 6-6. Net Benefits for the Proposed CISWI NSPS and Emissions Guidelines at 7% 

Discount Ratea

a

 Net Benefits are quantified in terms of PM2.5 benefits for implementation year (2015). This graph shows 14 

benefits estimates combined with the cost estimate. All 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 has a different propensity to become PM2.5. 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 from2

epidemiology functions and 12 expert functions
7-1 

SECTION 7 

SUPPLEMENTAL ECONOMIC ANALYSES 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 subcategories would change while the number of 

sources in the remaining three subcategories (i.e., incinerators, burn-off ovens, and small, remote 

incinerators) does not change. This section provides an overview of the results. 

EPA estimated compliance costs for all existing units to add the necessary controls, 

monitoring equipment, inspections, and recordkeeping 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 quantity adjustments including the changes in 

international trade (Table 7-1). The Agency’s economic model suggests 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 model 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 million, and the net loss for U.S. stakeholders is 

$506 million. As U.S. prices rise, other countries 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 international trade effects, the Agency’s economic model projects 

the net surplus loss associated with the proposed 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 adjustments is approximately than $1 million. 
7-2 

Table 7-1. Market-Level Price and Quantity 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 Social 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 longer 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 Morgenstern, et al: Demand effect 

−1.7

(−3.6 to 0.2) 

B. Estimate Derived from Morgenstern, 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 the 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 assumptions, but most expert-derived 

estimates fall within these estimates. Data, resource, and methodological limitations prevented 

EPA from monetizing the benefits from several important benefit 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 for 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 PM2.5 11,962 $230,000 $560,000 $210,000 $500,000 $2,700 to $6,700 $2,500 to $6,000 

PM2.5 Precursors 

SO2 205 $29,000 $72,000 $27,000 $65,000 $6.0 to $15 $5.5 to $13 

NO2 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 precursor reduced has a different propensity to form PM2.5. The 

monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.

 
7-5 

Table 7-5. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

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 $6,7002 $2,500 to $6,0002

Total Social Costs $4803 $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 PM2.5 through 

reductions of 12,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 520 tons of NOx and 205 tons of 

SO2. 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. 
R-1 

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R-5 

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R-6 

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Estimates throughout the World. Journal of Risk and Uncertainty 27(1):5-76. 
APPENDIX A 

OAQPS MULTIMARKET MODEL TO ASSESS THE ECONOMIC 

IMPACTS OF ENVIRONMENTAL 

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 the costs are distributed among stakeholders (EPA, 

2000). In addition, large-scale policies require additional analysis to better understand how costs 

are passed across the economy. Although several tools are available to estimate social costs, 

current EPA guidelines suggest that multimarket models “...are best used when potential 

economic impacts and equity effects on related markets might be considerable” and modeling 

using a computable general equilibrium model is not available or practical (EPA, 2000, p. 146). 

Other guides for environmental economists offer similar advice (Berck and Hoffmann, 2002; 

Just, Hueth, and Schmitz, 2004). 

Multimarket models focus on “short-run” time horizons and measure a policy’s near term 

or transition costs (EPA, 1999). Recent studies suggest short-run analyses can complement full 

dynamic general equilibrium analysis. 

The multimarket model described in this appendix is a new addition to the Office of Air 

quality Planning and Standards’ (OAQPS’s) economic model tool kit; it is designed to be used as 

a transparent tool that can respond quickly to requests about how stakeholders in 100 U.S. 

industries might respond to new environmental policy. Next, we 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 (CO2) emissions 

– estimates of industry employment 

 Economic behavior 

– industries respond to regulatory costs by changing production rates 

– market prices rise to reflect higher energy and other non-energy 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 construction 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 account for indirect effects the regulation has 

on related markets. 

– Links come from input-output information used from OAQPS’s computable 

general equilibrium model (EMPAX) 

– production adjustments influence employment levels 

– international trade (imports/exports) behavior considered 

 Model time horizon (“short-run”) 

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

supply function 

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

(capital, labor, energy intermediates, and other intermediates) 

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

– investment and government expenditures are fixed 

A.2.1 Industry Sectors and Benchmark Data Set 

The multimarket model includes 100 industries. For the benchmark year, the model uses 

information from OAQPS’s computable general equilibrium model’s balanced social accounting 

matrix (SAM) and the following accounting identity holds (EPA, 2008): 

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

If we abstract and treat each industry as a national market, the identity represents the prepolicy market-clearing condition, or benchmark “equilibrium”; supply equals demand in each 

market. In Table A-1, we identify the 100 industries for the multimarket model; Table A-2 

provides the 2010 benchmark data set. Since the benchmark data are reported in value terms, we 

also use the common “Harberger convention” and choose units where are all prices are one in the 

benchmark equilibrium (Shoven and Whalley, 1995). 
A-3 

Table A-1. Industry Sectors Included in Multimarket Model 

Industry Label Description Representative NAICSa

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 Model (continued) 

Industry Label Description Representative NAICSa

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 Model (continued) 

Industry Label Description Representative NAICSa

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 for 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 Macroeconomic 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 release or Global Insights For multimarket industries 

with finer NAICs detail, estimates were calculated by selecting a primary NAICS supersector 

estimate (AEO or Global Insights) and distributing 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 data. 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 sectors 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. Employment 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. Employment 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 analysis 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 provides the industry with incentives to alter 

production rates at current market prices; market prices must rise to maintain the original prepolicy production levels (Q). 

The multimarket model also simultaneously considers how the policy influences other 

important production costs (via changes in energy and other intermediate material prices). As a 

result, the multimarket model can provide additional information about how policy costs are 

transmitted through the economy. As shown in Figure A-2, the indirect cost change provides the 

industry with additional incentives to alter production rates at current market prices. 

The %Δ indirect effects associated with each input are approximated using an input “use” 

ratio and the price change that occurs in the input market. 

 %Δ indirect costs = input use ratio x %Δ input price (A.4) 

The social accounting matrix provides an internally consistent estimate of the use ratio 

and describes the dollar amount of an input that is required to produce a dollar of output. Higher 

ratios suggest strong links between industries, while lower ratios suggest weaker links. Given the 

short time horizon, we assume the input use ratio is fixed and cannot adjust their input mix; this 

is a standard assumption in public and commercial input-output (IO) and SAM multiplier models 

(Berck and Hoffmann, 2002). Morgenstern and colleagues (2004) and Ho and colleagues (2008) 

also use this assumption when examining near-term effects of environmental policy. 
A-15 

Figure A-1. Direct Compliance Costs Reduce Production Rates at Benchmark Prices 

Figure A-2. Indirect Costs Further Reduce Production 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

g g gi i i Q b P t (P P )



 



       1



 (A.5) 

where 

Qg

 = with-policy supply quantity (g) 

b = calibrated scale parameter for the supply price relationship 
A-16 

Pg

 = with-policy price for output (g) 

t = direct compliance costs per unit of supply 

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

Pi

 = with-policy input (i) price 

Pi = benchmark input (i) price 

g  = price elasticity of supply for output (g) 

The key supply parameter that controls the industry production adjustments is the price 

elasticity of supply (εg).To our knowledge, there is no existing empirical work that estimates 

short-run supply elasticities for all industry groups used in the multimarket model. As a result, 

we assume the U.S. supply elasticities are a function of econometrically estimated rest-of-world 

(ROW) export supply elasticities (see discussion in the next section). We report the values 

currently available in the model in Table A-5. 

A.2.2.2 International Competition 

International competition is captured by a single ROW supply function: 

  ROW

g ε

g Pg Q  c  (A.6) 

where 

Qg

 = with-policy supply quantity (g) 

c = calibrated scale parameter for the supply and price relationship 

Pg

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

ROW

g  = price elasticity of supply of goods from the ROW to the United States (imports) 

(g) 

The key supply parameter that controls the ROW supply adjustments is the price 

elasticity of supply ( ROW

g  ). We obtained these estimates for a variety of industry groups from a 

recently published article by Broda and colleagues (2008b). 
A-17 

Table A-5. Supply Elasticities 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

ACM Accommodations 0.7 0.7 

ADM Administrative Services 0.7 0.7 

AGR Agricultural 1.0 1.0 

ALU Aluminum 0.8 0.5 

ANM Animal Foods 1.1 0.8 

APP Appliances 0.9 0.8 

ARC Aircraft 0.9 0.6 

ART Arts 0.7 0.7 

ATP Air Transportation 0.7 0.7 

BAK Baked Goods 0.8 0.7 

BEV Beverages and Tobacco 2.9 2.9 

BOI Boilers and Tanks 1.1 0.8 

CEM Cement 0.9 0.7 

CEQ Construction and Agricultural Equipment 0.8 0.6 

CHM Chemicals and Gases 1.1 0.8 

CLY Clay 0.8 0.6 

CMQ Communication Equipment 2.5 1.0 

CNS Construction 0.7 0.7 

COL Coal 2.2 2.2 

CPP Converted Paper Products 0.9 0.7 

CPU Computers 1.0 0.7 

CRU Crude Oil Extraction 3.7 3.7 

CUT Cutlery 1.4 1.1 

EDU Education 0.7 0.7 

EEQ Engines 1.2 1.0 

EGV Engraving 1.1 0.8 

ELE Electric Generation 2.0 2.0 

ELQ Electric Equipment 0.8 0.6 

FIN Finance and Insurance 0.7 0.7 

FMP Fabricated Metals 1.2 1.1 

FRG Forging and Stamping 1.6 1.5 

(continued) 
A-18 

Table A-5. Supply Elasticities (continued) 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

FRT Fertilizer 1.0 0.7 

FRU Fruits and Vegetables 1.0 0.7 

FUR Furniture 1.9 1.9 

GAS Natural Gas 12.2 12.2 

GEQ General Equipment 1.0 0.7 

GLS Glass 0.8 0.6 

GRN Grain Milling 1.7 1.5 

HLT Health Care 0.7 0.7 

HRD Hardware 1.1 0.8 

HVC HVAC Equipment 0.9 0.6 

I_S Iron and Steel 1.0 0.6 

IEQ Industrial Equipment 0.9 0.6 

INF Information 0.7 0.7 

INS Instruments 0.9 0.6 

LEA Leather 0.9 0.7 

LGT Lighting 1.1 0.7 

LIM Lime and Gypsum 0.9 0.7 

LUM Other Lumber 0.9 0.7 

M_V Motor Vehicles 1.3 0.7 

MEA Meat Products 1.2 3.9 

MED Drugs and Medicine 1.3 1.0 

MEQ Metalworking Equipment 0.7 0.5 

MGT Magnetic Recording Equipment 1.0 0.7 

MIL Dairy Products 1.1 0.9 

MIN Mining 2.2 2.2 

MNG Management 0.7 0.7 

MSC Miscellaneous Manufacturing 1.0 0.8 

MSP Machine Shops 1.1 0.8 

MVP Motor Vehicle Parts 0.9 0.6 

OCM Other Chemicals 1.1 0.6 

OEQ Other Electric Equipment 1.0 0.7 

OFD Other Food Products 1.1 0.7 

(continued) 
A-19 

Table A-5. Supply Elasticities (continued) 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

OFM Other Fabricated Metals 0.9 0.6 

OIL Refined Petroleum 1.0 0.7 

ONM Other Non-metallic Minerals 1.5 0.7 

OPM Other Primary Metals 0.7 0.5 

OSV Other Services 0.7 0.7 

OTP Other Transportation Services 0.7 0.7 

OTQ Other Transport Equipment 1.0 0.7 

PAI Paints and Adhesives 1.0 0.7 

PAP Pulp and Paper Mills 1.1 0.7 

PFS Professional Services 0.7 0.7 

PIP Pipeline Transport 2.0 2.0 

PLS Plastic 1.0 0.7 

PLY Plywood and Veneer 1.3 1.3 

PRN Printing 1.0 0.7 

PUB Public Services 0.7 0.7 

R_R Rail Cars 1.8 0.7 

REL Real Estate 0.7 0.7 

RSN Resins 1.0 0.7 

RTL Retail Trade 0.7 0.7 

RTP Railroad Transportation 0.7 0.7 

RUB Rubber 1.3 1.1 

SAW Sawmills 0.8 0.6 

SEA Seafood 1.1 0.8 

SEQ Service Industry Equipment 0.8 0.6 

SGR Sugar 1.1 0.8 

SHP Ships 1.0 0.7 

SMI Semiconductor Equipment 1.2 1.0 

SOP Soap 0.8 0.6 

TEX Textile Mills 1.0 0.7 

TKB Truck Bodies 3.2 3.1 

TPM Textile Product Mills 0.8 0.6 

TTP Freight Truck Transportation 0.7 0.7 

TVQ TV Equipment 5.8 5.4 

(continued) 
A-20 

Table A-5. Supply Elasticities (continued) 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

WAP Wearing Apparel 1.2 0.8 

WHL Wholesale Trade 0.7 0.7 

WIR Springs and Wires 1.9 0.8 

WTP Water Transportation 0.7 0.7 

Note: RTI mapped Broda et al. data for their industry aggregation to the multimarket model’s 100 industries. 

Domestic supply elasticities are typically assumed to be within one standard deviation of the sample of supply 

elasticities used for the ROW. In selected cases where this information is not available, the U.S. supply elasticity 

is set equal to the ROW. 

Source: Broda, C., N. Limao, and D. Weinstein. 2008a. “Export Supply Elasticities.” 

http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/TradeElastic

ities.html. Accessed September 2009. 

A.2.2.3 Price Elasticity of Supply: Rest of World (ROW) 

Broda and colleagues (2008) provide an empirical basis for the multimarket model supply 

elasticities. Broda et al. provide over 1,000 inverse elasticities that RTI organized to be 

comparable with the 100-sector model. The first step was to match the Harmonized Trade 

System (HS) elasticities estimated in the article to the appropriate NAICS codes. Many of the HS 

codes correspond with a detailed NAICS codes (5- and 6-digit level), while the multimarket 

sector industries typically correspond with more aggregated sectors (NAICS 2-, 3-, or 4-digit 

levels). To adapt these labels to our model, we combined the 5- and 6- digit NAICS under their 

3- and 4-digit codes and calculated an average inverse elasticity 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 set. We also restricted the elasticity sample to 

those that Broda et al. classify as “medium” and “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 less 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 relevant 3-digit elasticity. If a multimarket code 

was not covered in the medium set of elasticities, we used the low elasticity category. This 

method was sufficient for mapping 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 elasticity. Since Broda et al.’s article focused on industrial production 

goods, some of the multimarket sectors were not covered in the elasticity data. These sectors 

included mainly service industries, transportation, and energy sources. 

In order to fill these gaps, we turned to the source substitution elasticities from Purdue 

University’s Global Trade Analysis Project (GTAP).1

 Although the elasticities 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 structure2), the parameters provide us 

with some additional information about the relative trade elasticity differences between industry 

sectors. To use the GTAP information to develop assumptions about the multimarket model 

sectors with missing elasticities, we chose a base industrial sector (iron and steel) for which we 

had parameter value from Broda et al. Next, we developed industry-specific ratios for missing 

industries using the corresponding GTAP sector trade elasticities and the GTAP iron and steel 

sector. We multiplied the resulting ratio by the Broda et al. iron and steel parameter (1.0). For 

example, the GTAP trade elasticity for coal (6.10) is approximately 2.2 times the trade elasticity 

for iron and steel (2.95). As a result, the multimarket import 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 domestic supply elasticities for 

the model. We have assumed that a product’s domestic 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 foreign supply elasticities, we also 

calculated the standard deviation of each 3- and 4-digit NAICS sample. By adding the standard 

deviation to the corresponding foreign supply and then taking the inverse, we were able to 

calculate a domestic supply elasticity for each sector 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 apply 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 Documentation for the full description of the parameters at 

https://www.gtap.agecon.purdue.edu/resources/download/4184.pdf; see Table 14.2 for elasticities. 2

Detailed documentation of the entire GTAP 7 Database is available at 

https://www.gtap.agecon.purdue.edu/databases/v7/v7_doco.asp. The GTAP also uses a unique system of 

categorizing commodities that does not match 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 use (domestic and exports). Given the short time 

horizon, investment/government does not change. Intermediate use is determined by the input 

use ratios and the industry output decisions. 

Qi giQg    

 (A.7) 

Qi

 = with-policy input demand quantity (i) 

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

Qg

 = with-policy output quantity (g) 

Other final use does respond to market price changes. Following guidance in the OAQPS 

economic resource manual (OAQPS, 1999), we use a general form for the U.S. industry demand 

function: 

  g Qg a Pg

    (A.8) 

where 

Qg

 = with-policy demand quantity (g) 

a = calibrated scale parameter for the demand and price relationship 

Pg

 = with-policy price for output (g) 

g = price elasticity of demand (g) 

The key parameter that controls consumption adjustments is the price elasticity of 

demand (ηg). To approximate the response, we use demand elasticities that were simulated with a 

general equilibrium model (Ho, Morgenstern, and Shih, 2008). Table A-6 reports the values 

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 technology requires the purchase of energy and other 

intermediate goods made by other sectors included in the model. Linking the sectors in this 

manner allows the model to trace direct and indirect policy effects across different sectors. 

Therefore, it is best used when potential economic impacts and equity effects on related markets 

might be important to stakeholders not directly affected by an environmental policy. However, 
A-23 

Table A-6. U.S. Demand Elasticities 

Industry Label Industry Description 

Demand Elasticity 

ηg

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 

ηg

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 

ηg

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 industry 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. Accessed August 2009. Table B.6. 
A-26 

the model can also be run in single-market partial 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 effects associated with 

environmental policy. Production technologies are fixed; the model does not assess substitution 

among production inputs (labor, energy intermediates, and other intermediates) and assumes 

each investment cannot be changed during the time frame of the analysis. These issues are better 

addressed using other frameworks such as computable general equilibrium modeling. Similarly, 

government purchases from each sector do not adjust in response to changes in goods/service 

prices. Although, employment levels (number of jobs) adjust as production levels change, wages 

are assumed to be fixed. 
APPENDIX B 

COMPLIANCE COST ANALYSES FOR CISWI UNITS 
MEMORANDUM 

TO: Charlene Spells, Toni Jones, Ketan Patel, 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 

BACKGROUND

The U.S. Environmental Protection Agency (EPA), under section 129 of the Clean Air Act (CAA), is 

required to regulate emissions of nine pollutants from Commercial and Industrial Solid Waste 

Incineration (CISWI) units: hydrogen chloride (HCl), carbon monoxide (CO), lead (Pb), cadmium (Cd), 

mercury (Hg), particulate matter (PM), dioxins/furans (CDD/CDF), nitrogen oxides (NOx), and sulfur 

dioxide (SO2). 

On December 1, 2000, EPA adopted new source performance 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 2005, 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 and 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 waste incineration definition rule. 

 

These proposed standards provide EPA’s response to the 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 required 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 determine 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 associated 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 Options 
V. “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. CHOOSING CONTROLS NEEDED FOR EACH UNIT TO MEET MACT FLOORS

A significant portion of the total cost for industry compliance 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 value used to calculate baseline annual emissions2

 were 

compared with the proposed MACT floor emissions limits, and percentages were 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 values 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, remote 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 Waste Incinerators (HMIWI) were updated and utilized 

generally for the CISWI source category, since most 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 from the boiler NESHAP development, since this 

subcategory contains units which, were they not firing wastes, would be considered boilers and process 

heaters. Based on these required improvements, pollutant-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 improving existing fabric filters. 

Mercury and dioxins/furans (CDD/CDF): Adding activated carbon injection (ACI) and adjusting the 

carbon addition rate to meet the 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 recovery 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 (NOx): Adding selective non-catalytic reduction (SNCR) systems. For units requiring 

less than 10 percent improvement in NOx control, minor adjustments were considered sufficient. 

Sulfur dioxide (SO2): 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 for 
improving SO2 control. For units not meeting the proposed limit that already has a wet scrubber as a 

control, adding caustic was deemed necessary to improve SO2 control. 

Further descriptions of these controls and their associated costs are listed below in Section II. 

II. MACT COMPLIANCE COSTS

This section presents the nationwide costs estimated for existing CISWI for (A) the emission controls 

used to comply with the MACT floor; (B) the monitoring, testing, recordkeeping, and reporting activities 

used to demonstrate compliance; and (C) the alternatives to compliance. Total capital 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 subcategories 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 alternatives to compliance where applicable. Table 3 

summarizes total compliance costs, as well as the lowest cost alternative (where alternative disposal 

methods are possible) for all units. 

A. Emission Control Costs 

Emission control technologies and other control measures that can be used to comply with the MACT 

floor options for existing CISWI units include wet 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 presents the costs that were estimated for each of 

these control measures. 

The retrofit factors for the capital costs 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 dividing the capital/annual control cost estimate for each unit 

by the average gas flow rate assigned to that unit. Table 4A is a summary of the parameters used for each 

unit (e.g., incinerator charge rate, stack gas flow rate, incinerator operating hours, 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 algorithms 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 modifications to make them consistent with those being 

utilized by the boilers NEHSAP development. Calculations 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 million, 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 presented 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 approximately $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-bed 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 percent control of CDD/CDF to the amount of reduction 

the unit will need to meet the proposed emission limits. The highest factor (Hg or CDD/CDF) is then 

used to adjust the carbon injection rate calculation of the algorithm. ACI capital costs range from 

approximately $5,600 to $156,000, and annual costs 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 incinerator or burn-off oven with a 

larger secondary chamber (with a longer gas residence time, e.g., 2 seconds) and operating it at a 

higher temperature (e.g., 1800°F). The cost algorithm for installing an afterburner retrofit with an 

incinerator is presented in Table 5E, and the cost algorithm for installing an afterburner retrofit with a 

burn-off oven is presented in Table 5F. Both are based on algorithms in the Model Plant Description 
and Control Cost Report for HMIWI.5

 Afterburner capital costs range from approximately $99,000 to 

$1.0 million, and annual costs range from approximately $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 install a new control system to achieve the emissions 

reductions necessary to comply with the control options. 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 NOx reagent injected into the postcombustion flue gas. Table 5G presents the algorithms 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 electricity equations for wet scrubbers in the Model Plant 

Description and Control Cost Report for HMIWI5

 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 and 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 costs 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 gases 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 range from approximately $390/yr to $1,650/yr. 

7. Additional Control Options.

a. Minor adjustments for NOx control. 

Minor adjustments, such as air handling and distribution adjustments in the firebox, can be made 

to certain units to improve NOx 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 relatively 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 million 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. Soderberg8

. 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 CISWI units were selected to be as consistent as 

possible with comparable regulations. For energy recovery units, requirements were developed to be 

consistent with proposed stack testing, monitoring, and recordkeeping requirements for major source 

boilers “biomass” units, since these units 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 current CISWI and HMIWI provisions. This section 

presents the costs that were estimated for each of these requirements. 

The total capital cost for stack testing, monitoring, and recordkeeping and reporting for all 

subcategories is estimated at approximately $18.0 million, and the total annual cost is about $10.4 

million per year. Cost estimates were based on algorithms recently utilized in the HMIWI regulatory 

development. Costs were updated to a 2008 basis, and 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 assumed 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 used 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 was assumed that parameter monitoring will be 

mandatory for all units required to add fabric filters, wet scrubbers, SNCR systems, or ACI 

systems. Costs for each monitoring system were estimated as follows: 

 For a fabric filter bag leak detection system, 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 following 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 comply with the requirements of the proposed 

Portland cement NESHAP. Capital cost was estimated 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 initial emissions test for those pollutants. Table 6C 

presents a summary of required initial stack testing 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 annualized 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 pollutants. 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 NOx, SO2, 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 likely have ash handling operations. Therefore, 

these units would be required to demonstrate compliance to a 5 percent visible emissions limit for 

fugitive emissions generated during ash handling (similar to HMIWI). 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 cost 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 alternatives 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, facilities 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 costs, for incinerators and small, remote units, the cost of alternative 

waste disposal methods such as landfilling or hauling 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 calculate annual costs for landfilling the 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 alternative 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 alternative disposal option, the 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 subtracted 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 waste. Unit-specific incinerator operational costs, 

annualized incinerator cost, and alternative disposal costs are listed in Tables 1A, 1D, and 1E. 

III. COST EFFECTIVENESS

The cost effectiveness of the proposed MACT floors was calculated for each subcategory by dividing the 

total compliance cost (emission control, monitoring, testing, recordkeeping, and reporting) by the total 

emission reduction (HCl, CO, Pb, Cd, Hg, PM, CDD/CDF, NOX, and SO2) needed to meet the proposed 

emission limits. Note that the emission reductions were derived in a separate memorandum.2

 Tables 9A 

and 9B present the estimated cost effectiveness values for each subcategory, over all pollutants. 

Table 9A shows the estimated costs and cost effectiveness 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 waste-burning kilns, $7,700/ton for energy recovery 

units, $47,000/ton for incinerators, and $1.8 million/ton for small, remote units. Over all subcategories, 

the average cost effectiveness was estimated to be $8,200/ton. 

Table 9B shows the estimated costs and cost effectiveness 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 follows: $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 small, remote units reflects that our estimates indicate a 

net saving if these facilities cease combusting waste and divert the waste to a landfill. Over all 

subcategories, the average cost effectiveness was estimated to be $7,200/ton. 

IV. BEYOND THE FLOOR OPTIONS

In developing this proposal, EPA considered for existing units the proposed CISWI NSPS emissions 

limits as a basis for the beyond-the-floor analysis for each subcategory. The CISWI NSPS limits are the 

MACT limits applicable to new CISWI units that are established through analysis of the best performing 

single source for each regulated pollutant. The development 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 source 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 NSPS level of control for the same subcategory. 

Specifically, for purposes of the analysis, different combinations of available emission control techniques, 

including additional add-on controls, that existing units would have to employ to meet the NSPS limits 

were considered in the analysis. Alternatively, facilities with units in the incinerators, small remote 
incinerators, or burn-off ovens subcategories could choose to stop using the units altogether and use 

alternative waste disposal options instead. To assess costs, 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 pollutant-by-pollutant basis. The controls considered, 

and the pollutants the control is effective for, are listed 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 NOx, SNCR was evaluated as the likely control technology 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 injection 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 NOx burners or combustion chamber 

modifications, are unit-specific and are likely not applicable to all existing units; therefore, compliance 

with the beyond-the-floor would likely require significant modification at considerable cost for some 

existing units. In contrast, new sources can be designed so that the combustion chamber and air flow 

characteristics reduce NOx formation, which, in combination with SNCR controls, would be able to meet 

the new source NOx limits. SCR is typically utilized in combustion units such as industrial boilers and 

process heaters, gas turbines, and reciprocating internal combustion engines.10 We are not aware of any 

successful applications of SCR technology to waste-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 ammonia reagent) and particulate matter.10 It was therefore determined that 

controls were not demonstrated adequately for existing CISWI units in any of the five subcategories to 

meet the beyond-the-floor NOx emission limits. 

Waste-burning Kilns: 

Waste-burning kilns are not designed with secondary chamber 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 plugging 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 removal 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 themselves create secondary emissions of CO, NOx, SO2, and PM 

due to their electrical demands (see 74 FR 21153). Given the cost and adverse environmental and energy 
impacts, 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 scrubbers 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 SO2. 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 SO2. There are limits to the amounts 

of additional caustic or lime that are technically feasible, and the SO2 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 SO2 control is to add an additional SO2 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 continuously meet the beyond-the-floor SO2 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 and 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-the-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 units. Variances in fuel composition and condition will 

have an effect on CO emissions in addition to the controls 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 associated with modification of the units. As a 

comparison, the proposed boiler NESHAP limit varies 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 above, 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 the MACT floor limit for PM, resulting in additional PM 

reductions beyond the existing source floor level of control. For this reason, we believe that the PM 
emissions reductions associated with going beyond-the-floor to the new source 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 floor. 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. While 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 baseline for determining additional controls and costs 

required. Subcategory-specific considerations are 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 NOx limit is not proposed, the cost of SNCR and SNCR 

monitoring were not incorporated into the beyond-the-floor costs for these units. However, meeting the 

other pollutant beyond-the-floor limits was still more expensive than the alternative of abrasive blasting. 

Hence, it was assumed that the units would shut down 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 estimate. 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 improvement 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 rather 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 beyond-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 Expected 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, SO2 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 effectiveness values for each subcategory, over all pollutants. 

V. ALTERNATIVE APPROACH

EPA’s solid waste definition rule proposes to define which non-hazardous secondary materials that are 

used as fuels or ingredients in combustion units are 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 secondary materials are solid waste under Subtitle D of 

RCRA, when combusted. To this point, 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. Total annual cost for controls for all units in all 

subcategories is about $508 million, but is about $480 million for the lowest cost alternative. Costs 

associated with the CISWI inventory that would result from RCRA’s “alternative approach,” however, 

are calculated using the same methodology described herein, 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 calculated 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., NOx, CO for waste-burning kilns, etc.) are not feasible 

for. 

In addition to the technical and costs feasibility considerations discussed for the proposed approach, for 

the alternative approach beyond-the-floor option, there are concerns about the feasibility of the beyondthe-floor emission limits for HCl and SO2 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 cases to achieve the MACT floor emission limit, we do 

not believe that these same devices could be further 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 necessitate a 99.6% reduction in HCl emissions, or 
possibly more, depending on the chlorine content of the materials being combusted. We do not have the 

data that suggests this is consistently attainable for all existing energy recovery units. 

SO2. The NSPS limit for SO2 is 0.04 ppmvd. We do not have information that suggests that all existing 

energy recovery units would be able to continuously achieve this limit. The average baseline SO2 

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 needed to meet the CISWI MACT floor limits for HCl 

would also meet the CISWI MACT floor levels for SO2. However, we are not certain that it is feasible for 

existing units to utilize additional caustic in their scrubbers, or in their existing flue gas desulfurization 

devices, to be able to consistently meet the 0.04 ppmvd beyond-the-floor emission limit for SO2. There 

are limits to the amounts of additional caustic or lime that are technically feasible and the SO2 content of 

the flue gas will vary depending on the fuel and wastes that are combusted in the unit. The only option 

for achieving additional SO2 control is to add an additional SO2 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 energy recovery units. Therefore, we determined that additional controls were not 

demonstrated to continuously meet the beyond-the-floor SO2 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 methodologies are the same as was used in the proposed 

approach beyond-the-floor analysis, and are presented below. 

Incremental Costs and Emission Reductions Expected 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. NEW UNITS

Based on the results of our analysis for existing units and our experiences with other CAA Section 129 

regulations, we do not anticipate that any new CISWI units will be constructed. As discussed earlier, 

many existing CISWI owners and operators may find that 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 shown that negative growth in the source category 

historically occurs upon implementation of CAA Section 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 would contain any such units subject to the 2000 CISWI 

rule, has 28 units. EPA is not aware of any construction 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 continue. The same is expected to be true for the 
subcategories 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-derived fuel from which the metal has been removed is 

not considered solid waste under the proposed definition 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 costs associated with removing the metal and complying 

with the applicable NESHAP instead of the CISWI rule. 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 wastes so that the materials sent to a combustion unit 

would not meet the definition of solid waste to begin with. For waste that is generated, cost analyses have 

found that alternative waste disposal is generally available and less expensive. 

VII. REFERENCES

1. “MACT Floor Analysis for the Commercial and Industrial Solid Waste Incinerators Source Category” 

Memorandum from Jason Huckaby, Amber Allen and Kristen 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 Estimates for Existing CISWI Units” Memorandum 

from Jason Huckaby, Amber Allen and Kristen James, Eastern Research Group, Inc. to Charlene 

Spells, Toni Jones and Ketan Patel, U.S. Environmental 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 Scrubbers 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 Manual, 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, Selective Non-Catalytic Reduction (SNCR) EPA452/F-03-031 

10. Air Pollution Control Technology Fact Sheet, Selective Catalytic Reduction (SCR), EPA-452/F-03-

032 
APPENDIX A. TABLES FOR “PROPOSED APPROACH” MACT FLOOR COST ANALYSES

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 Floors: Overall and by Subcategory 
APPENDIX B. TABLES FOR “PROPOSED APPROACH” BEYOND-THE-FLOOR COST ANALYSES

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 Subcontractory
APPENDIX C. TABLES FOR “ALTERNATIVE APPROACH” MACT FLOOR COST ANALYSES

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 Floors: Overall and by Subcategory
APPENDIX D. TABLES FOR “ALTERNATIVE APPROACH” BEYOND-THE-FLOOR COST ANALYSES

16. Costs to Beyond-the-Floor Emission Limits 

16A: Cement Kilns 

!6B: Energy Recovery Units 

17. Alternative Approach Best Beyond-the-Floor Compliance Options by Unit 

18. Alternative Approach Cost Effectiveness of Beyond-the-Floor Emission Limits: Overall and by Subcategory