March 2010

Regulatory Impact Analysis:

National Emission Standards for 

Hazardous Air Pollutants for 

Industrial, Commercial, and 

Institutional Boilers and Process 

Heaters

Draft Report

Prepared for

Tom Walton

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards (OAQPS)

Air Benefit and Cost Group

(MD-C439-02)

Research Triangle Park, NC 27711

Prepared by

RTI International

3040 Cornwallis Road

Research Triangle Park, NC 27709

and

Kajal B. Kapur, Energy, Environment, and 

Economics Consulting

1 Steubin Lane

Charlottesville, VA 22911

RTI Project Number 0209897.004.074
_________________________________

RTI International is a trade name of Research Triangle Institute.

RTI Project Number

0209897.004.074

Regulatory Impact Analysis:

National Emission Standards for 

Hazardous Air Pollutants for 

Industrial, Commercial, and 

Institutional Boilers and Process 

Heaters 

Draft Report

March 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 Food 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..........................................................2-10

2.2 Wood Product Manufacturing .........................................................................2-24

2.2.1 Introduction.........................................................................................2-24

2.2.2 Supply and Demand Characteristics.....................................................2-27

2.2.3 Firm and Market Characteristics..........................................................2-34

2.3 Paper Manufacturing.......................................................................................2-49

2.3.1 Introduction.........................................................................................2-49

2.3.2 Supply and Demand Characteristics.....................................................2-53

2.3.3 Firm and Market Characteristics..........................................................2-59

2.4 Chemical Manufacturing.................................................................................2-72

2.4.1 Introduction.........................................................................................2-72

2.4.2 Supply and Demand Characteristics.....................................................2-74

2.4.3 Firm and Market Characteristics..........................................................2-79

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

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

3.2 Area Sources.................................................................................................... 3-2
iv

4 Economic Impact Analysis ....................................................................................... 4-1

4.1 Partial Equilibrium Analysis (Multiple Markets).............................................. 4-1

4.1.1 Overview.............................................................................................. 4-1

4.1.2 Economic Impact Analysis Results....................................................... 4-2

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

5.1 Small Entity Screening Analysis ...................................................................... 5-1

5.1.1 Small Businesses.................................................................................. 5-1

5.1.2 Small Government Analysis ................................................................. 5-5

5.2 Initial Regulatory Flexibility Analysis (IFRA) ................................................. 5-7

5.2.1 Reasons Why Action Is Being Considered............................................ 5-7

5.2.2 Statement of Objectives and Legal Basis of Proposed Rules................. 5-7

5.2.3 Description and Estimate of the Number of Small Entities.................... 5-7

5.2.4 Description and Compliance Costs ....................................................... 5-7

5.2.5 Description of Federal Rules that May Overlap or Conflict with 

Proposed Rules..................................................................................... 5-8

5.2.6 Description of Regulatory Alternatives that Minimize Significant 

Economic Impacts on Small Entities..................................................... 5-8

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

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

6.2 Calculation of PM2.5 Human Health Benefits.................................................... 6-1

6.2 Unquantified Benefits .....................................................................................6-13

6.2.1 Carbon Monoxide Benefits..................................................................6-14

6.2.2 Other SO2 Benefits..............................................................................6-14

6.2.3 HAP Benefits ....................................................................................6-144

6.3 Characterization of Uncertainty in the Monetized PM2.5 Benefits ....................6-24

6.4 Comparison of Benefits and Costs...................................................................6-26

References.........................................................................................................................R-1
v

Appendixes

A OAQPS Multimarket Model to Assess the Economic Impacts of 

Environmental Regulation................................................................................A-1

B Detailed economic model results by Sector ......................................................B-1
vi

LIST OF FIGURES

Number Page

2-1 Distribution of Employment within Food Manufacturing (NAICS 311): 2006........ 2-4

2-2 Distribution of Total Value of Shipments within Food Manufacturing 

(NAICS 311): 2006................................................................................................ 2-5

2-3 Electric Power Use Trends in Food Manufacturing (NAICS 311): 1997–2005 ....... 2-9

2-4 Establishment Concentration in Food Manufacturing (NAICS 311): 2002 ............2-11

2-5 Capacity Utilization Trends in Food Manufacturing (NAICS 311)........................2-13

2-6 Employment Concentration in Food Manufacturing (NAICS 311): 2002 ..............2-15

2-7 Capacity Trends in Food Manufacturing (NAICS 311) .........................................2-17

2-8 Industrial Production Trends in Food Manufacturing (NAICS 311).......................2-21

2-9 International Trade Trends in Food Manufacturing (NAICS 311)..........................2-22

2-10 Producer Price Trends in Food Manufacturing (NAICS 311) ................................2-23

2-11 Distribution of Value of Shipments within Wood Product Manufacturing 

(NAICS 322): 2007...............................................................................................2-27

2-12 Distribution of Employment within Wood Product Manufacturing (NAICS 

322): 2007 ............................................................................................................2-28

2-13 Electrical Power Use Trends in the Wood Product Manufacturing Industry 

(NAICS 321): 1997–2005 .....................................................................................2-33

2-14 Establishment Concentration in the Wood Product Manufacturing Industry 

(NAICS 321): 2002...............................................................................................2-36

2-15 Capacity Utilization Trends in the Wood Product Manufacturing Industry 

(NAICS 321) ........................................................................................................2-38

2-16 Employment Concentration in the Wood Product Manufacturing Industry 

(NAICS 321): 2002...............................................................................................2-40

2-17 Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321)........2-42

2-18 Industrial Production Trends in the Wood Product Manufacturing Industry 

(NAICS 321): 1997–2009 .....................................................................................2-46

2-19 International Trade Trends in the Wood Product Manufacturing Industry

(NAICS 321)].......................................................................................................2-47

2-20 Producer Price Trends in the Wood Product Manufacturing Industry (NAICS 

321)......................................................................................................................2-48

2-21 Distribution of Value of Shipments within Paper Manufacturing (NAICS 

322): 2007 ............................................................................................................2-52

2-22 Distribution of Employment within Paper Manufacturing (NAICS 322): 2007......2-53

2-23 Electrical Power Use Trends in the Paper Manufacturing Industry: 1997–2005.....2-57

2-24 Establishment Concentration in Paper Manufacturing Industry (NAICS 322): 

2002 .....................................................................................................................2-60
vii

2-25 Capacity Utilization Trends in the Paper Manufacturing Industry 

(NAICS 322) ........................................................................................................2-62

2-26 Employment Concentration in the Paper Manufacturing Industry (NAICS 

322): 2002 ............................................................................................................2-64

2-27 Capacity Trends in the Paper Manufacturing Industry (NAICS 322) .....................2-65

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

322): 1997–2009...................................................................................................2-69

2-29 International Trade Trends in the Paper Manufacturing Industry (NAICS 322) .....2-70

2-30 Producer Price Trends in the Paper Manufacturing Industry (NAICS 222) ............2-71

2-31 Distribution of Employment within Chemical Manufacturing (NAICS 325): 

2007 .....................................................................................................................2-75

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

(NAICS 325): 2007...............................................................................................2-76

2-33 Electric Power Use Trends in Chemical Manufacturing (NAICS 325): 1997–

2005 .....................................................................................................................2-80

2-34 Establishment Concentration in Chemical Manufacturing (NAICS 325): 2002......2-83

2-35 Capacity Utilization Trends in Chemical Manufacturing (NAICS 325) .................2-84

2-36 Employment Concentration in Chemical Manufacturing (NAICS 325): 2002........2-85

2-37 Capacity Trends in Chemical Manufacturing (NAICS 325)...................................2-86

2-37 Industrial Production Trends in Chemical Manufacturing (NAICS 325)................2-92

2-38 International Trade Trends in Chemical Manufacturing (NAICS 325)...................2-93

2-39 Producer Price Trends in Chemical Manufacturing (NAICS 325)..........................2-94

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

4-2 Distribution of Total Surplus Changes by Sector: Major Sources ........................... 4-7

4-3 Distribution of Total Surplus Changes by Sector: Area Sources............................. 4-9

4-4 Job Losses/Gains Associated with the Proposed Rules: 2013 ................................4-11

5-1 Share of NAICS/Enterprise Employment Categories (<500 employees) with 

Sales Tests Exceeding 3%...................................................................................... 5-5

6-1 Breakdown of Monetized PM2.5 Health benefits using Mortality Function 

from Pope et al. (2002) .......................................................................................... 6-7

6-2 Total Monetized PM2.5 Benefits for the Proposed Boiler Rules in 2013 .................6-12

6-3 Breakdown of Monetized benefits for the Proposed Boiler Rules by PM2.5 

Precursor Pollutant and Source .............................................................................6-13

6-4 Net Benefits for the Proposed Boiler Rules at 3% Discount Rate ..........................6-28

6-5 Net Benefits for the Proposed Boiler Rules at 7% Discount Rate ..........................6-30
viii

LIST OF TABLES

Number Page

2-1 Key Statistics: Food Manufacturing (North American Industry Classification 

System [NAICS] 311)............................................................................................ 2-1

2-2 Industry Ratios: Food Manufacturing (NAICS 311)............................................... 2-2

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

($2007).................................................................................................................. 2-6

2-4 Key Goods and Services Used in Food Manufacturing (NAICS 311) 

($2007, 106

)........................................................................................................... 2-7

2-5 Energy Used in Food Manufacturing (NAICS 311)................................................ 2-8

2-6 Demand by Sector: Food Manufacturing (NAICS 311) ($2007, 106).....................2-10

2-7 Top Publicly Held US Food Companies: 2007 ......................................................2-17

2-8 Corporate Income and Profitability for Food Manufacturing (NAICS 311) ...........2-18

2-9 Small Business Size Standards: Food Manufacturing (NAICS 311) ......................2-19

2-10 Distribution of Economic Data by Enterprise Size: Food Manufacturing 

(NAICS 311) ........................................................................................................2-20

2-11 Key Statistics: Wood Product Manufacturing (NAICS 321)..................................2-24

2-12 Industry Ratios: Wood Product Manufacturing (NAICS 321)................................2-25

2-13 Costs of Goods and Services in Wood Product Manufacturing (NAICS 321) ........2-30

2-14 Key Goods and Services Used in Wood Product Manufacturing (NAICS 321) 

($2007, 106

)..........................................................................................................2-31

2-15 Energy Used in Wood Product Manufacturing (NAICS 321) ................................2-32

2-16 Demand by Sector: Wood Product Manufacturing (NAICS 321) ($2007, 106) ......2-34

2-17 Largest US Paper and Forest Products Companies: 2006.......................................2-42

2-18 Distribution of Economic Data by Enterprise Size: Wood Product 

Manufacturing (NAICS 321).................................................................................2-44

2-19 Small Business Size Standards: Wood Product Manufacturing (NAICS 321)........2-45

2-20 Key Statistics: Paper Manufacturing (NAICS 322)................................................2-49

2-21 Industry Ratios: Paper Manufacturing (NAICS 322) .............................................2-49

2-22 Costs of Goods and Services Used in the Paper Manufacturing Industry 

(NAICS 322) ........................................................................................................2-54

2-23 Key Goods and Services Used in the Paper Manufacturing Industry 

(NAICS 322) ($106, $2007) ..................................................................................2-55

2-24 Energy Used in Paper Manufacturing (NAICS 322)..............................................2-56

2-25 Estimated Energy Sources for the US Pulp and Paper Industry..............................2-58

2-26 Demand by Sector: Paper Manufacturing Industry (NAICS 322) ($106, $2007) ....2-58

2-27 Largest US Paper and Forest Products Companies: 2006.......................................2-66
ix

2-28 Distribution of Economic Data by Enterprise Size: Paper Manufacturing 

(NAICS 322) ........................................................................................................2-67

2-29 Small Business Size Standards: Paper Manufacturing (NAICS 322) .....................2-68

2-30 Key Statistics: Chemical Manufacturing (NAICS 325)..........................................2-72

2-31 Industry Ratios: Chemical Manufacturing (NAICS 325).......................................2-72

2-32 Key Goods and Services Used in Chemical Manufacturing (NAICS 325) 

($2007, 106)..........................................................................................................2-77

2-33 Costs of Goods and Services Used in Chemical Manufacturing (NAICS 325).......2-78

2-34 Energy Used in Chemical Manufacturing (NAICS 325)........................................2-79

2-35 Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007 106)...............2-81

2-36 Top Chemical Producers: 2007 .............................................................................2-87

2-37 2007 Corporate Income and Profitability (NAICS 325).........................................2-88

2-38 Small Business Size Standards: Chemical Manufacturing (NAICS 325) ...............2-90

2-40 Distribution of Economic Data by Enterprise Size: Chemical Manufacturing 

(NAICS 325) ........................................................................................................2-91

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

3-2 Summary of Annual Costs for New and Existing Area Sources.............................. 3-3

4-1 Distribution of Social Costs Major Sources (billion, 2008$): 2013......................... 4-5

4-2 Distribution of Social Costs Area Sources (billion, 2008$): 2013........................... 4-7

5-1 Affected Sectors and Size Standards...................................................................... 5-2

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

Combustion Survey ............................................................................................... 5-3

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

6-2 Summary of Monetized benefits Estimates for Boiler MACT (for Major 

Sources) in 2013 (2008$)....................................................................................... 6-8

6-3 Summary of Monetized benefits Estimates for Boiler Area Source Rule in 

2013 (2008$) ......................................................................................................... 6-9

6-4 Summary of Reductions in Health Incidences from PM2.5 benefits for the 

Proposed Boiler Rules in 2013..............................................................................6-10

6-5 All PM2.5 Benefits Estimates for the Proposed Boiler Rules at discount rates of 

3% and 7% in 2013 (in millions of 2008$)............................................................6-11

6-6 Top HAPs by Mass from Boilers by Fuel Type.....................................................6-16

6-7 Sensitivity Analyses for Monetized PM2.5 Health benefits (in millions of 

2008$) ..................................................................................................................6-25
1-1

SECTION 1

INTRODUCTION

The U.S. Environmental Protection Agency (EPA) is proposing national emission 

standards for hazardous air pollutants (NESHAP) for new and existing industrial, commercial, 

and institutional boilers and process heaters.1 The proposed rule would require all major sources 

to meet hazardous air pollutant (HAP) emissions standards reflecting the application of the 

maximum achievable control technology (MACT). Under a separate action, EPA is also 

proposing a NESHAP for two area source categories: industrial boilers and institutional and 

commercial boilers.2

1.1 Executive Summary

The proposed emission standards for controlling mercury and polycyclic 

organic matter (POM) emissions are based on the MACT. The proposed emission standards for 

controlling other HAPs are based on EPA’s proposed determination as to what constitutes the 

generally available control technology (GACT) or management practices. As part of the 

regulatory process, EPA is required to develop a regulatory impact analysis (RIA). The RIA 

includes an economic impact analysis (EIA) and a small entity impacts analysis and documents 

the RIA methods and results. 

The key results of the RIA are as follows:

 Engineering Cost Analysis: EPA estimates the proposed major source NESHAP’s 

total annualized costs will be $5.1 billion (2008$). For the area source NESHAP, 

EPA estimates the total annualized costs will be $6.9 billion. 

 Market Analysis: Under the proposed major source NESHAP, the Agency’s 

economic model suggests the average national prices for industrial sectors could be 

0.01% higher with the NESHAP, while average annual domestic production may fall 

by about 0.01%. Because of higher domestic prices, imports rise by 0.01% per year. 

Market-level effects for the proposed area source NESHAP are smaller when 

compared to the proposed major source rule; average price, production, and import 

changes are less than 0.01%. 

 Social Cost Analysis: The estimated social cost of the proposed major source rule is 

just under $5.1 billion (2008$).$). In the near term, the Agency’s economic model 

suggests that industries are able to pass approximately $1.8 billion of the rule’s costs 

to consumers (e.g., higher market prices). Domestic industries’ surplus falls by $3.8 

billion, while other countries on net benefit from higher prices (a net increase in restof-the world [ROW] surplus of $0.2 billion). Additional costs and fuel savings for 

 1 On June 19, 2007, the U.S. Court of Appeals for the District of Columbia Circuit (DC Circuit) vacated the 

NESHAP for industrial/commercial/institutional boilers and process heaters. This action provides EPA’s 

proposed rule in response to the court’s vacatur. 2 Gas-fired boilers are not part of the area source categories of industrial boilers and institutional/commercial boilers.

Comment [A1]: Some estimates are 

$2008, and others are $2007. It would 

help to use one consistent chained year for 

comparison purposes. Otherwise, it 

becomes more difficult to make 

conclusions when comparing #s.
1-2

new and existing major sources that are not included in the economic model represent 

a net benefit of $0.3 billion. The estimated social cost of the proposed area source 

rule is approximately $6.9 billion (2008$). In the near term, the Agency’s economic 

model suggests that industries are able to pass approximately $2.3 billion of the rule’s 

costs to consumers. Domestic industries’ surplus falls by $3.3 billion and the net 

increase in ROW surplus is $0.2 billion. Additional costs and fuel savings for 

unknown, existing, and new area sources not included in the economic model results 

represent a net cost of $1.5 billion.

 Employment Changes: Near-term employment changes associated with the 

proposed major source rule are estimated to be 6,000 to 8,000 job losses; over a 

longer time period, net employment effects range between 16,000 job losses to 

32,000 job gains. For the area source rule, near-term employment changes associated 

with the proposed major source rule are estimated to be 6,000 to 12,000 job losses; 

over a longer time period, net employment effects also range between 16,000 job 

losses to 32,000 job gains.

 Small Entity Analyses: EPA performed a screening analysis for impacts on small 

entities by comparing compliance costs to sales/revenues (e.g., sales and revenue 

tests). EPA’s analysis found the tests were typically higher than 3% for small entities 

included in the screening analysis. EPA has prepared an Initial Regulatory Flexibility 

Analysis (IRFA) that discusses alternative regulatory or policy options that minimize 

the rule’s small entity impacts. It includes key information about key results from the 

Small Business Advocacy Review (SBAR) panel.

 Benefits Analysis:: In the year of full implementation (2013), EPA estimates the

annual PM2.5 co-benefits of the proposed major source rule are $16 billion to $40

billion and $15 billion to $36 billion, at 3% and 7% discount rates respectively. In the 

year of full implementation (2013), EPA estimates the annual PM2.5 co-benefits of 

this proposed area source rule are $5.1 billion to $12 billion and $4.6 billion to $11

billion, at 3% and 7% discount rates respectively. All estimates are in 2008 dollars.

Using alternate relationships between PM2.5 and premature mortality supplied by 

experts, higher and lower co-benefits estimates are plausible, but most of the expertbased estimates fall between these estimates. The benefits from reducing other air 

pollutants have not been monetized in this analysis, including reducing 420,000 tons 

of carbon monoxide, 21 tons of mercury, 48,000 tons of hydrochloric acid, and 1,200 

tons of hydrogen fluoride each year. In addition, ecosystem benefits and visibility 

benefits have not been monetized in this analysis.

 Net Benefits: The net benefits for the proposed major source rule only are $11 billion 

to $35 billion and $9.8 billion to $31 billion, at 3% and 7% discount rates, 

respectively. The net benefits for the area source rule only are $-1.8 billion to $5.5 

billion and $-2.3 to $4.3 billion, at 3% and 7% discount rates, respectively. All 

estimates are in 2008 dollars. 

1.2 Organization of this Report

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

EIA:

Comment [A2]: It should be made more 

clear throughout when discussing “annual

benefits”
1-3

 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.

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

 Appendix B provides additional economic model result tables by sector.
2-1

SECTION 2

INDUSTRY PROFILES

In this section, we provide an introduction selected industries that are affected by the 

proposed rules. The 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 Food Manufacturing

2.1.1 Introduction

Food manufacturing involves the transformation of raw agricultural and livestock 

products into processed food. Between 1997 and 2002, shipment values stagnated, falling 0.38%,

while the number of employees and payroll increased 2.71% and 7.76%, respectively (Table

2-1). This trend was partly reversed between 2002 and 2006, as shipment values rose 4.77 %%. 

The and number of employees and payroll fell 5.94% and 3.28% during this timeframe, 

respectively (Table 2-1). Shipments, payroll, and employment continued to increaseincreased

between 2006 and 2007, but there was a notable drop in the number of establishments between 

2002 and 2007 (Table 2-1). As Table 2-2 shows, payroll per employee grew 4.91% from 1997 to 

2002 and continued to increase, albeit at a slower rate of 2.83%, from 2002 to 2006. Between 

2006 and 2007, the payroll per employee declined as the growth in employees outpaced the 

increase in the annual payroll (Table 2-2).

Table 2-1. Key Statistics: Food Manufacturing (North American Industry Classification 

System [NAICS] 311) 

1997 2002 2006 2007

Shipments ($2007, 

106

)millions106) $528,928 $526,939 $552,075 $589,550

Payroll ($2007, 106)) $48,118 $51,852 $50,151 $50,467

Employees 1,466,956 1,506,781 1,417,274 1,466,683

Establishments 26,302 27,899 NA 22,055

NA = Not available.

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

Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.”

<http://factfinder.census.gov>; (July 8, 2008).

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: 

Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS 

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

Comment [A3]: It is better to say 

millions, rather than 10^6.
2-2

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 (2007 NAICS 

Basis): 2002 and 2007.” <http://factfinder.census.gov>; (January 4, 2010).

The food manufacturing industry consists of nine different industry groups, each 

distinguished by the livestock or agricultural products used as raw materials for the processed 

food products as follows:

Table 2-2. Industry RatiosDataRatios: Food Manufacturing (NAICS 311)

Industry RatiosDataRatios 1997 2002 2006 2007

Total shipments ($2007, 106)) $528,928 $526,939 $552,075 589,550

Average? Shipments per establishment 

($2007, 103)) $20,110 $18,887 NA $26,731

Average? Shipments per employee ($2007) $360,561 $349,712 $389,533 $401,961

Average? Shipments per $ of payroll ($2007) $10.99 $10.16 $11.01 $11.68

Average? Annual payroll per employee 

($2007) $32,800.97 $34,412.12 $35,385.46 $34,409.00

Average? Employees per establishment 56 54 NA 67

NA = Not available.

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

Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.”

<http://factfinder.census.gov>; (July 8, 2008). 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: 

Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS 

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

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: 

Core Business Statistics Series: Comparative Statistics for the United States and the States (2007 NAICS 

Basis): 2002 and 2007.” <http://factfinder.census.gov>; (January 4, 2010).

The food manufacturing industry consists of nine different industry groups, each 

distinguished by the livestock or agricultural products used as raw materials for the processed 

food products as follows:

 Animal Food Manufacturing (North American Industry Classification System 

[NAICS] 3111)

 Grain and Oilseed Milling (NAICS 3112)

 Sugar and Confectionery Product Manufacturing (NAICS 3113)

 Fruit and Vegetable Preserving and Specialty Food Manufacturing (NAICS 3114)

 Dairy Product Manufacturing (NAICS 3115)

 Animal Slaughtering and Processing (NAICS 3116)

Comment [A4]: This type of 

presentation of numbers should be 

changed throughout to “millions” or 

“thousands.”

Comment [A5]: One table should 

present data in a consistent fashion, so 

keep “millions” throughout the table so 

not to confuse reader. At first glance, the 

shipments/establishment do not make any 

sense until you see that they are presented 

differently than the other data.
2-3

 Seafood Product Preparation and Packaging (3117)

 Bakeries and Tortilla Manufacturing (NAICS 3118)

In 2006, Animal Slaughtering and Processing made up the largest share of both employment 

(33%) and the value of shipments (27%) in food manufacturing (Figures 2-1 and 2-2). 

Many major environmental regulations directly affect the food manufacturing industry

and/or other markets that provide key goods and services to the industry (e.g., energy). RTI’s 

multimarket model is specifically designed to analyze these types of regulations. The model

emphasizes the links among industrial sectors and provides policy makers with new insights 

about the direct and indirect effects of a regulatory program and the distribution of costs across 

the U.S. economy. 
2-4

Animal Food 

Manufacturing,

3%

Grain and Oilseed 

Milling,

4% Sugar and 

Confectionery 

Product 

Manufacturing,

5% Fruit and 

Vegetable 

Preserving and 

Specialty Food 

Manufacturing,

12%

Dairy Product 

Manufacturing,

9%

Animal 

Slaughtering and 

Processing, 33%

Seafood Product 

Preparation and 

Packaging, 3%

Bakeries and 

Tortilla 

Manufacturing,

20%

Other Food 

Manufacturing,

11%

Animal Food 

Manufacturing,

3%

Grain and Oilseed 

Milling,

4% Sugar and 

Confectionery 

Product 

Manufacturing,

5% Fruit and 

Vegetable 

Preserving and 

Specialty Food 

Manufacturing,

12%

Dairy Product 

Manufacturing,

9%

Animal 

Slaughtering and 

Processing, 33%

Seafood Product 

Preparation and 

Packaging, 3%

Bakeries and 

Tortilla 

Manufacturing,

20%

Other Food 

Manufacturing,

11%

Figure 2-1. Distribution of Employment within Food Manufacturing (NAICS 311): 2006

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

Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.”

<http://factfinder.census.gov>; (July 8, 2008). 
2-5

Animal Food 

Manufacturing,

6% Grain and Oilseed 

Milling, 10%

Sugar and 

Confectionery 

Product 

Manufacturing, 

6%

Fruit and 

Vegetable 

Preserving and 

Specialty Food 

Manufacturing,

12%

Dairy Product 

Manufacturing,

14%

Animal 

Slaughtering and 

Processing, 27%

Seafood Product 

Preparation and 

Packaging, 2%

Bakeries and 

Tortilla 

Manufacturing,

11%

Other Food 

Manufacturing,

12%

Animal Food 

Manufacturing,

6% Grain and Oilseed 

Milling, 10%

Sugar and 

Confectionery 

Product 

Manufacturing, 

6%

Fruit and 

Vegetable 

Preserving and 

Specialty Food 

Manufacturing,

12%

Dairy Product 

Manufacturing,

14%

Animal 

Slaughtering and 

Processing, 27%

Seafood Product 

Preparation and 

Packaging, 2%

Bakeries and 

Tortilla 

Manufacturing,

11%

Other Food 

Manufacturing,

12%

Figure 2-2. Distribution of Total Value of Shipments within Food Manufacturing 

(NAICS 311): 2006

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

Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.”

<http://factfinder.census.gov>; (July 8, 2008).

2.1.2 Supply and Demand Characteristics

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

manufacturing industry. We emphasize the economic interactions this industry has with other 
2-6

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

the major uses and consumers of food manufacturing products. 

2.1.2.1 Goods and Services Used in Food Manufacturing

In 2006, the cost of materials made up 57% of the value of shipments in food production. 

Total employee compensation accounted for 12% of this value, with half of that coming from 

production workers’ wages (Table 2-3). 

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

($2007)

Industry Ratios 2005 Share 2006 Share

Total shipments ($106) $563,797 100% $552,075 100%

Total compensation ($106) $64,909 12% $64,027 12%

Annual payroll $50,650 9% $50,151 9%

Fringe benefits $14,259 3% $13,877 3%

Total employees 1,440,283 1,417,274

Average compensation per employee $45,067 $45,176

Total production workers’ wages ($106

) $33,983 6% $33,670 6%

Total production workers 1,099,530 1,090,081

Total production hours (103

) 2,242,558 2,198,396

Average production wages per hour $15 $15

Total cost of materials ($103) $315,993 56% $312,847 57%

Materials, parts, packaging $286,895 51% $284,028 51%

Purchased electricity $4,513 1% $4,787 1%

Purchased fuel $5,136 1% $5,398 1%

Other $19,449 3% $18,634 3%

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

Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.”

<http://factfinder.census.gov>; (July 8, 2008).
2-7

The top 10 industry groups supplying inputs to food production accounted for 84% of the 

total intermediate inputs to the industry, with the top three industry groups (food products, 

animal products, and crop products) accounting for over half of the total intermediate inputs 

(Table 2-4). Electric power generation, transmission, and distribution accounted for 2% of the 

total intermediate inputs, whereas boilers, tanks, and shipping containers accounted for 1%.

Table 2-4. Key Goods and Services Used in Food Manufacturing (NAICS 311) 

($2007, 106

)

Sector BEA Code Food Products

Food products 3110 $91,518

Animal products 1120 $85,785

Crop products 1110 $43,109

Management of companies and enterprises 5500 $34,235

Wholesale trade 4200 $27,849

Converted paper products 3222 $18,782

Truck transportation 4840 $12,943

Plastics and rubber products 3260 $9,641

Electric power generation, transmission, and distribution 2211 $6,004

Boilers, tanks, and shipping containers 3324 $4,564

Total intermediate inputs T005 $400,067

Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 

Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.

2.1.2.2 Energy

The Department of Energy (DOE) classifies the entire food products industry as an 

energy-intensive industry to model within its Industrial Demand Module (DOE, 2008a). In 2002, 

food manufacturing accounted for 6.86% of the total fuel consumption by all manufacturing 

industries (NAICS 311–339) and 19.24% of the conventional boiler use fuel consumption by all 

manufacturing industries (DOE, Energy Information Administration, 2007).

In both 2005 and 2006, purchased electricity and fuel each accounted for 1% of the total 

value of shipments in food manufacturing (Table 2-3). In 2002, total energy consumption totaled 

1,116 TBTU, a 7% increase over 1998 (Table 2-5). Of this total fuel consumption, the largest 

share (41.72%) was consumed for indirect uses including conventional boiler use and combined 

heat and power (CHP) and/or cogeneration process (MECS Table 5.2). Between 1997 and 2005, 

while the manufacturing sector as a whole became less electricity intensive, food manufacturing
2-8

became 14.54% more electricity intensive (Figure 2-3). From 2005to 2006, the electricity 

consumption increased by nearly 9% (Table 2-5). 

Table 2-5. Energy Used in Food Manufacturing (NAICS 311)

Fuel Type 1998 2002 2006

Total (trillion BTU) 1,044 1,116 1,186

Net electricitya (million kWh) 62,457 67,521 73,440

Residual fuel oil (million bbl) 2 2 4

Distillate fuel oilb (million bbl) 3 3 3

Natural gasc (billion cu ft) 553 560 618

LPG and NGLd (million bbl) 1 1 1

Coal (million short tons) 6 8 7

Coke and breeze (million short tons) * * *

Othere (trillion BTU) 97 90 107

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. 2007. “2002 Energy Consumption by 

Manufacturers—Data Tables.” Tables 3.2 and N3.2. Washington, DC: DOE 

<http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>.

U.S. Department of Energy, Energy Information Administration. 2009a. “2006 Energy Consumption by 

Manufacturers—Data Tables.” Table 3.1. Washington, DC: DOE.

<http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html>.

2.1.2.3 Uses and Consumers 

The majority of food manufacturing’s total commodity output (58%) is sold for personal 

consumption. Of the sales for intermediate use, 42% are sold back into the food manufacturing 

industry (Table 2-6). 

Comment [A6]: It is inaccurate to make 

a claim regarding intensity when 

discussion total electricity use. The sector 

may have, in fact, become less electricity 

intensive over this period if it increased 

production/output of food products at a 

greater rate than increased electricity use. 

This should be revised to either “total 

electricity use”, or electricity intensity 

needs to be calculated (electricity used per 

unit of output) to determine if intensity 

increased.

Comment [A7]: Totals should be at the 

bottom.
2-9

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Figure 2-3. Electric Power Use Trends in Food Manufacturing (NAICS 311): 1997–2005

Source: Federal Reserve Board. 2008. “Industrial Production and Capacity Utilization: Electric Power Use: 

Manufacturing and Mining.” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G311.S. 

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

Comment [A8]: This would be more 

appropriate in Section 2.1.2.2 (Energy)
2-10

2.1.3 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.

2.1.3.1 Location

In 2002, California had the most food manufacturing establishments in the United States, 

followed by New York and Texas (see Figure 2-4). In addition, Pennsylvania, Illinois, 

Wisconsin, New Jersey, and Florida had over 1,000 establishments in their states.

Table 2-6. Demand by Sector: Food Manufacturing (NAICS 311) ($2007, 106))

Sector BEA Code Food Products

Food manufacturing 3110 $91,518

Food services and drinking places 7220 $37,291

Animal production 1120 $15,870

General state and local government services S007 $15,170

Retail trade 4A00 $13,985

Beverage manufacturing 3121 $11,703

Hospitals 6220 $9,539

Educational services 6100 $4,485

Nursing and residential care facilities 6230 $4,187

Social assistance 6240 $2,277

Total intermediate use T001 $217,570

Personal consumption expenditures F010 $301,748

Exports of goods and services F040 $28,151

Imports of goods and services F050 −$33,119

Total final uses (GDP) T004 $299,470

Total commodity output T007 $517,040

Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input-Output Accounts: 2002 

Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA.

Comment [A9]: Should this table go in 

the 2.1.2.3(Uses and Consumers)

Section?
2-11

Establishments

Fewer than 250

250 - 499

500 - 999

1,000 - 2,000

More than 2,000

Establishments

Fewer than 250

250 - 499

500 - 999

1,000 - 2,000

More than 2,000

Figure 2-4. Establishment Concentration in Food Manufacturing (NAICS 311): 2002

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

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

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

2.1.3.2 Production Capacity and Utilization

Capacity utilization of the food manufacturing industry did not fall off during the 

recession of 2001 as much as the manufacturing sector as a whole (Figure 2-5). Food 
2-12

manufacturing’s capacity utilization has remained higher than manufacturing as a whole and 

went above 85% in the spring of 2008. The effects of the recent economic downturn have not 

affected capacity utilization as sharply in the food industry relative to the overall manufacturing 

sector (Figure 2-5).
2-13

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Figure 2-5. Capacity Utilization Trends in Food Manufacturing (NAICS 311)
2-14

Source: Federal Reserve Board. 2008. “Industrial Production and Capacity Utilization: Capacity Utilization.”

Series ID: G17/CAPUTL/CAPUTL.GMF.S & G17/CAPUTL/CAPUTL.G311.S. 

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

2.1.3.3 Employment

The geographic distribution of employment in food manufacturing varies substantially 

from the distribution of establishments. In 2002, Arkansas, ranked thirty-first in number of 

establishments and had the eighth most employees (53,844) because of its national high of 199 

employees per establishment. New York, ranked second in number of establishments, had only 

the tenth most employees (50,012). North Carolina and Georgia also had greater than 50,000 

employees, despite having fewer than 600 establishments (Figure 2-6).
2-15

Employment

Fewer than 10,000

10,000 to 19,999

20,000 to 39,999

40,000 to 80,000

More than 80,000

Employment

Fewer than 10,000

10,000 to 19,999

20,000 to 39,999

40,000 to 80,000

More than 80,000

Figure 2-6. Employment Concentration in Food Manufacturing (NAICS 311): 2002 

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008). 
2-16

2.1.3.4 Plants and Capacity

Production capacity in food manufacturing only grew 17.94% between 1997 and early 

2008, a compound annual growth rate (CAGR) of 1.45%. This is substantially less than the 

42.50% growth for the manufacturing industry as a whole (Figure 2-7).

2.1.3.5 Firm Characteristics

In fiscal year 2007, the top eight food manufacturing companies each had greater than 

$10 billion in sales. These companies, however, are global, many with a large portion of both 

sales and production coming from operations outside of the United States (Table 2-7). The 

largest U.S. food manufacturing company, Kraft Foods Inc., has 50.27% of its long-lived assets 

located outside of the United States (Kraft Foods Inc., 2008).

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Figure 2-7. Capacity Trends in Food Manufacturing (NAICS 311))

Source: Federal Reserve Board. 2008. “Industrial Production and Capacity Utilization: Industrial Capacity.” Series 

ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G311.S. <http://www.federalreserve.gov/datadownload/>. 

Table 2-7. Top Publicly Held U.S. Food Companies: 2007

Sales ($106) % of Sales in United States

Kraft Foods Inc. 37,241 57.8%

Tyson Foods Inc. 26,900 90.0%

General Mills Inc. 12,442 82.9%

Sara Lee Corp. 12,278 53.8%

ConAgra Foods Inc. 12,028 89.2%

Smithfield Foods Inc.a 11,911 86.2%

Dean Foods Co. 11,822 >99.0%

Kellogg Co.a 11,776 66.1%

H.J. Heinz Co. 9,002 42.3%

Campbell Soup Co. 7,867 69.0%

a Percentage of sales in the United States is actually percentage of sales in North America.

Source: Graves, T. 2008. “Food and Nonalcoholic Beverages.” Standard and Poor’s Industry Surveys. 176(25). 

Comment [A10]: Something seems off 

with the chart – if 2002 is 100, then 

shouldn’t 2002 be 100? It is common for 

the first year of data to start as an index of 

100. Also, it is unclear why you would 

not use the actual metric for capacity 

utilization, as has been done in later 

graphics.
2-18

For the industry as a whole, the number of corporations as well as the number of 

corporations with net income in the food manufacturing industry grew between 2004 and 2005. 

Although the overall number of companies continued to grow in 2006, the number of those with 

a positive net income declined along with profit margins and total receipts (Table 2-8).

Table 2-8. Corporate Income and Profitability for Food Manufacturing (NAICS 311)

2004 2005 2006

Number of corporations 14,408 14,956 16,146

Number of corporations with net income 6,541 7,503 7,333

Total receipts (10^3) $502,149,944 $504,944,378 $484,193,319

Business receipts (10^3) $477,906,423 $465,369,666 $459,884,663

Before-tax profit margin 5.27% 10.09% 7.43%

After-tax profit margin 3.74% 7.62% 5.11%

Source: Internal Revenue Service, U.S. Department of Treasury. 2008. “Corporation Source Book: Data File 

2005.” <http://www.irs.gov/taxstats/article/0,,id=167415,00.html>; (January, 14 2009).

2.1.3.6 Size Distribution

The primary criterion for categorizing a business as small is number of employees, using 

definitions by the SBA for regulatory flexibility analyses. The data describing size standards are 

provided in Table 2-9. Over 80% of the NAICS industries within the food manufacturing 

industry use a cutoff of 500 employees. In 2002, enterprises with fewer than 500 employees 

accounted for 32% of employment and 23% of receipts within food manufacturing (Table 2-10).

2.1.3.7 Domestic Production

Between 1997 and early 2008, overall manufacturing production grew faster (34.88%) 

than the food manufacturing component (26.18%) (Figure 2-8). The food manufacturing industry 

has been less volatile, particularly during the recession of 2001 and the current economic 

downturn.

2.1.3.8 International Trade

In 2006, the United States regained a trade surplus in food manufacturing it had briefly 

lost during 2004 to 2005 (see Figure 2-9). The trade surplus in 2007 was over $4 billion. Both 

exports and imports have declined since their 2008 peak as a result of the global economic 

recession.
2-19

Table 2-9. Small Business Size Standards: Food Manufacturing (NAICS 311)

NAICS Description Employees

311111 Dog and Cat Food Manufacturing 500

311119 Other Animal Food Manufacturing 500

311211 Flour Milling 500

311212 Rice Milling 500

311213 Malt Manufacturing 500

311221 Wet Corn Milling 750

311222 Soybean Processing 500

311223 Other Oilseed Processing 1,000

311225 Fats and Oils Refining and Blending 1,000

311230 Breakfast Cereal Manufacturing 1,000

311311 Sugarcane Mills 500

311312 Cane Sugar Refining 750

311313 Beet Sugar Manufacturing 750

311320 Chocolate and Confectionery Manufacturing from Cacao Beans 500

311330 Confectionery Manufacturing from Purchased Chocolate 500

311340 Non-Chocolate Confectionery Manufacturing 500

311411 Frozen Fruit, Juice and Vegetable Manufacturing 500

311412 Frozen Specialty Food Manufacturing 500

311421 Fruit and Vegetable Canning3 3,500

311422 Specialty Canning 1,000

311423 Dried and Dehydrated Food Manufacturing 500

311511 Fluid Milk Manufacturing 500

311512 Creamery Butter Manufacturing 500

311513 Cheese Manufacturing 500

311514 Dry, Condensed, and Evaporated Dairy Product Manufacturing 500

311520 Ice Cream and Frozen Dessert Manufacturing 500

311611 Animal (except Poultry) Slaughtering 500

311612 Meat Processed from Carcasses 500

311613 Rendering and Meat By-product Processing 500

311615 Poultry Processing 500

311711 Seafood Canning 500

311712 Fresh and Frozen Seafood Processing 500

311811 Retail Bakeries 500

311812 Commercial Bakeries 500

311813 Frozen Cakes, Pies, and Other Pastries Manufacturing 500

311821 Cookie and Cracker Manufacturing 750

311822 Flour Mixes and Dough Manufacturing from Purchased Flour 500

(continued)
2-20

Table 2-9. Small Business Size Standards: Food Manufacturing (NAICS 311) (continued)

NAICS Description Employees

311823 Dry Pasta Manufacturing 500

311830 Tortilla Manufacturing 500

311911 Roasted Nuts and Peanut Butter Manufacturing 500

311919 Other Snack Food Manufacturing 500

311920 Coffee and Tea Manufacturing 500

311930 Flavoring Syrup and Concentrate Manufacturing 500

311941 Mayonnaise, Dressing and Other Prepared Sauce Manufacturing 500

311942 Spice and Extract Manufacturing 500

311991 Perishable Prepared Food Manufacturing 500

311999 All Other Miscellaneous Food Manufacturing 500

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

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

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

Table 2-10. Distribution of Economic Data by Enterprise Size: Food Manufacturing 

(NAICS 311) 

Enterprises with:

Variable Total

1 to 20

Employeesa

20 to 99 

Employees

100 to 499 

Employees

500 to 749 

Employees

750 to 999 

Employees

1,000 to 1,499 

Employees

Firms 21,384 13,645 3,935 1,247 147 63 96

Establishments 25,698 13,719 4,254 1,951 370 211 319

Employment 1,443,766 85,850 156,158 218,041 67,104 30,099 72,262

Receipts ($106) $457,521 $12,665 $32,274 $56,661 $23,103 $10,007 $21,878

Receipts/firm ($103

) $21,395 $928 $8,202 $45,438 $157,163 $158,835 $227,898

Receipts/establishment 

($103

) $17,804 $923 $7,587 $29,042 $62,440 $47,425 $68,584

Receipts/employment ($) $316,894 $147,523 $206,678 $259,862 $344,286 $332,457 $302,762

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

fraction of the year.

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

2002.” <http://www.census.gov/csd/susb/download_susb02.htm>.
2-21

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Figure 2-8. Industrial Production Trends in Food Manufacturing (NAICS 311)

Source: Federal Reserve Board. 2008. “Industrial Production and Capacity Utilization: Industrial Production.”

Series ID: G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.N & G17/IP_MAJOR_INDUSTRY_

GROUPS/IP.G311A2.N. <http://www.federalreserve.gov/datadownload/>.
2-22

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YTD

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Figure 2-9. International Trade Trends in Food Manufacturing (NAICS 311)

Source: U.S. International Trade Commission. 2009a. “U.S. Domestic Exports” & “U.S. Imports for 

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

2.1.3.9 Market Prices

Prices of goods in food manufacturing have moved generally in line with prices in overall 

manufacturing (see Figure 2-10). Both indexes increased over 31% since between early 2003 and 
2-23

early 2008, a CAGR of 5.13%. This rise was followed by a marked decline in recent years along

with the downward trend in prices throughout the economy.

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Figure 2-10. Producer Price Trends in Food Manufacturing (NAICS 311)
2-24

Source: U.S. Department of Labor; Bureau of Labor Statistics. 2010. “Producer Price Indexes.”

<http://www.bls.gov/pPI/Series Id: PCU311—311—Food Manufacturing & PCUOMFG–OMFG–Total 

Manufacturing>.

2.2 Wood Product Manufacturing

2.2.1 Introduction

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 2-11 

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 

Table 2-11. 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.

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 2-12). Shipments 

per employee grew 10.6% from 1997 to 2006 and dropped 8.9% from 2006 to 2007 (Table 2-12).

Comment [A11]: Subjective statements 

like this one should be avoided, 

particularly when they are unequivocal. 

Comment [A12]: The following 

parenthetic does not show a rise in the 

cost of labor, it shows more revenue per 

dollar of payroll.

Comment [A13]: Specify time period.
2-25

Table 2-12. 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. 

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 

(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.
2-26

 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 2-11 shows that the industry proportion of the value of shipments for other wood 

product manufacturing (51%) was greater than the value of shipments for sawmills and wood 

preservation (27%) and veneer, plywood, and engineered wood products (22%). Figure 2-12

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.
2-27

Veneer, Plywood, 

& Engineered 

Wood Product Mfg 

(NAICS 3212), 

22%

Sawmills & Wood 

Preservation 

(NAICS 3211),

27%

Other Wood 

Product Mfg 

(NAICS 3219),

51%

Veneer, Plywood, 

& Engineered 

Wood Product Mfg 

(NAICS 3212), 

22%

Sawmills & Wood 

Preservation 

(NAICS 3211),

27%

Other Wood 

Product Mfg 

(NAICS 3219),

51%

Figure 2-11. Distribution of Value of Shipments within Wood Product Manufacturing 

(NAICS 322): 2007

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

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

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

2.2.2 Supply and Demand Characteristics

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

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

industries and people and identify the key goods and services used by the industry and the major 

uses and consumers wood products. 
2-28

Other Wood 

Product Mfg 

(NAICS 3219),

60%

Sawmills & Wood 

Preservation 

(NAICS 3211), 

20%

Veneer, Plywood, 

& Engineered 

Wood Product Mfg 

(NAICS 3212),

20%

Other Wood 

Product Mfg 

(NAICS 3219),

60%

Sawmills & Wood 

Preservation 

(NAICS 3211), 

20%

Veneer, Plywood, 

& Engineered 

Wood Product Mfg 

(NAICS 3212),

20%

Figure 2-12. Distribution of Employment 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]

2.2.2.1 Goods and Services Used in Wood Product Manufacturing

In 2007, the cost of materials made up 59% of the total shipment value of goods in the 

wood product manufacturing industry (Table 2-13). Total compensation of employees 

represented 22% of the total value in 2007. Both the number of total shipments and the number 

of employees in this industry decreased between 2005 and 2007—the former by 14% and the 

latter by 3%.
2-29

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 

2-14). 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.
2-30

Table 2-13. 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]

2.2.2.1.1 Energy. The Department of Energy (DOE) categorizes wood product 

manufacturing (NAICS 321) as a nonenergy-intensive industry. The 2008 Annual Energy 

Outlook predicts that the wood product industry will be one of five (out of eight) nonenergyintensive industries experiencing positive average growth of delivered energy consumption 

between 2006 and 2030 (DOE, 2008).
2-31

Table 2-14. 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.

Table 2- shows that total energy use decreased between 1998 and 2002 by 26% and

rose by 19% then between 2002 and 2006, total energy use rose by 19%. However, Figure 2-13

shows that electrical power use decreased rapidly during this period, [1998 to 2006?], 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.

2.2.2.2 Uses and Consumers

Table 2-16 shows that three of the top four consumers of wood products are represented 

by the construction sector of the economy (NAICS 23). New residential construction, new 

nonresidential construction, and 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.

Comment [A14]: This graphic is 

inconsistent with the electricity use cited 

in Table 2-15, which shows net electricity 

use increasing b/c 1998 and 2006. 
2-32

Table 2-15. 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]
2-33

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Manufacturing Electric Power Use for NAICS 321

Figure 2-13. Electrical Power Use Trends in the Wood Product Manufacturing Industry 

(NAICS 321): 1997–2005

Source: Federal Reserve Board. 2009. “Industrial 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.
2-34

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, as well as a complete picture 

of the recent historical trends of production and pricing.

2.2.3.1 Location

As Figure 2-14 illustrates, the states with the largest number of wood product 

manufacturing establishments are dispersed throughout the country, with a significant 

concentration of establishments in the northeastern states. Other states with many establishments 

include California, Texas, and North Carolina.

Table 2-16. 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.
2-35

2.2.3.2 Production Capacity and Utilization

Capacity utilization of thetThe wood product manufacturing industry has been 

experiencingexperienceding 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 2-15).
2-36

Establishments by State

Less than 100

100 - 199

200 - 399

400 - 600

More than 600

Establishments by State

Less than 100

100 - 199

200 - 399

400 - 600

More than 600

Figure 2-14. Establishment Concentration in the Wood Product Manufacturing Industry 

(NAICS 321): 2002

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008).
2-37

2.2.3.3 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 2-17). 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 2-14, but had more than 20,000 

employees, whereas states such as Ohio and New York had fewer than 20,000 employees but 

more than 600 establishments.

2.2.3.4 Plants and Capacity

While the capacity of the manufacturing sector has been growing 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 2-17).
2-38

45

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55

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65

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Manufacturing Wood Products

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Manufacturing Wood Products

Figure 2-15. 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.

Comment [A15]: It is usually better to 

make the axis start and zero since so as to 

not overstate the changes that are 

occurring. 
2-39

2.2.3.5 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 2-17. 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 wood products, they do convey the market environment in 

which firms in this sector compete.

2.2.3.6 Size Distribution

The primary criterion for categorizing a 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 2-18). The number of 

employees in the small business cutoff is 500 employees for all subindustries in the wood 

product manufacturing industry (Table 2-19). 
2-40

Employemnt by State

Less than 2,000

2,000 - 4,999

5,000 - 9,999

10,000 - 20,000

More than 20,000

Employemnt by State

Less than 2,000

2,000 - 4,999

5,000 - 9,999

10,000 - 20,000

More than 20,000

Figure 2-16. Employment Concentration in the Wood Product Manufacturing Industry 

(NAICS 321): 2002

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008).
2-41

2.2.3.7 Domestic Production

Similar to industry capacity rates, industry production rates for wood product

manufacturing have decreased since 2006 compared to the steady increase in production for the 

manufacturing sector since 1997 (Figure 2-18). Similar to capacity utilization trends (Figure 

2-16), the index shows a faster rate of decline for wood products than the entire manufacturing 

sector.

2.2.3.8 International Trade

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

trade deficit (Figure 2-16). The value of imports has fluctuated greatly since 1997; however, 

exports have remained fairly constant, with seasonal changes, since 1997.
2-42

100

105

110

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120

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Manufacturing Wood Products

Figure 2-17. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321)

Source: Federal Reserve Board. “Industrial Production and Capacity Utilization: Industrial Capacity.” Series ID: 

G17/CAP/CAP.GMF.S & G17/CAP/CAP.G321.S. <http://www.federalreserve.gov/datadownload/>. 

Accessed on December 15, 2009.

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

Company Revenues ($106)

a
2-43

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).
2-44

Table 2-18. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 321)

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 15,198 9,740 3,280 791 63 27 30

Establishments 17,052 9,758 3,482 1,271 166 91 133

Employment 534,011 65,423 132,612 118,910 19,784 11,944 18,533

Receipts ($103 ) $88,649 $8,204 $18,276 $19,717 $3,192 $1,902 $3,118

Receipts/firm ($103

) $5,833 $842 $5,572 $24,927 $50,673 $70,453 $103,927

Receipts/establishment ($103

) $5,199 $841 $5,249 $15,513 $19,231 $20,904 $23,442

Receipts/employment ($) $166,006 $125,393 $137,818 $165,814 $161,363 $159,262 $168,231

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

Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002.”

<http://www.census.gov/csd/susb/download_susb02.htm>.
2-45

Table 2-19. Small Business Size Standards: Wood Product Manufacturing (NAICS 321)

NAICS NAICS Description Employees

321113 Sawmills 500

321114 Wood Preservation 500

321211 Hardwood Veneer and Plywood Manufacturing 500

321212 Softwood Veneer and Plywood Manufacturing 500

321213 Engineered Wood Member (except Truss) Manufacturing 500

32121 Truss Manufacturing 500

321219 Reconstituted Wood Product Manufacturing 500

321911 Wood Window and Door Manufacturing 500

321912 Cut Stock, Resawing Lumber, and Planing 500

321918 Other Millwork (including Flooring) 500

321920 Wood Container and Pallet Manufacturing 500

321991 Manufactured Home (Mobile Home) Manufacturing 500

321992 Prefabricated Wood Building Manufacturing 500

321999 All Other Miscellaneous Wood Product Manufacturing 500

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

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

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

2.2.3.9 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 2-20). Comment [A16]: Relative to what?
2-46

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Jan-07

Jul-07

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Jan-09

Jul-09

Index Value (Jan-97=100)

Manufacturing Wood Products

60

70

80

90

100

110

120

130

140

Jan-97

Jul-97

Jan-98

Jul-98

Jan-99

Jul-99

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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 Wood Products

Figure 2-18. Industrial Production Trends in the Wood Product Manufacturing Industry 

(NAICS 321): 1997–2009

Source: Federal Reserve Board. “Industrial Production and Capacity Utilization: Industrial Production.” Series ID: 

G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.S & G17/IP_MAJOR_INDUSTRY_GROUPS/

IP.G321.S. <http://www.federalreserve.gov/datadownload/>. Accessed on December 15, 2009.

Comment [A17]: These trend lines look 

very similar to the lines in the capacity 

utilization chart – total output would track 

somewhat differently from capacity 

utilization. 
2-47

0

5,000

10,000

15,000

20,000

25,000

30,000

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

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Jul-08

$1,000,000 (2007)

Wood Product Exports Wood Product Imports

0

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10,000

15,000

20,000

25,000

30,000

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Jul-97

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Jul-05

Jan-06

Jul-06

Jan-07

Jul-07

Jan-08

Jul-08

$1,000,000 (2007)

Wood Product Exports Wood Product Imports

Figure 2-19. International Trade Trends in the Wood Product Manufacturing Industry 

(NAICS 321)]

Source: U.S. International Trade Commission. 2008a. “U.S. Domestic Exports” & “U.S. Imports for 

Consumption.” <http://dataweb.usitc.gov/scripts/user_set.asp>; (July 17, 2008).
2-48

0

20

40

60

80

100

120

140

Dec-03

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PPI (Dec-03=100)

Wood Products PPI All Manufacturing PPI

0

20

40

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80

100

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Jun-04

Dec-04

Jun-05

Dec-05

Jun-06

Dec-06

Jun-07

Dec-07

Jun-08

PPI (Dec-03=100)

Wood Products PPI All Manufacturing PPI

Figure 2-20. Producer Price Trends in the Wood Product Manufacturing Industry (NAICS 

321)

Source: U.S. Bureau of Labor Statistics (BLS). 2009. “Producer Price Index.” Series ID: PCU321—321—& 

PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home.htm>. Accessed on January 8, 2010.
2-49

2.3 Paper Manufacturing

2.3.1 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 2-21). While total payroll dropped 26% over this time, annual payroll per employee 

rose 2% from 1997 to 2007 because of the decline in the number of employees (Table 2-20). 

Shipments per employee grew 28% from 1997 to 2007, with much of that growth taking place 

between 2002 and 2006 (Table 2-21).

Table 2-20. 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 2-21. Industry RatiosDataRatios: 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
2-50

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]
2-51

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
2-52

• converting pulp into pulp products, such as disposable diapers, or molded pulp 

egg cartons, food trays, and dishes.

Figure 2-21 shows that the value of shipments for converted paper products was slightly 

higher in 2007, 54%,% of the value of all paper products in 2007, while , than the value of 

shipments for pulp, paper, and paperboard products was in that year, 46%. However,

Figure 2-22 indicates that 70% of significantly industry more employees worked in the converted 

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

the labor intensive aspects of those facilities.

PPP (3221)

CPP (3222) 46%

54%

PPP (3221)

CPP (3222) 46%

54%

Figure 2-21. 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.
2-53

PPP (3221)

30%

CPP (3222)

70%

PPP (3221)

30%

CPP (3222)

70%

Figure 2-22. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007

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

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

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

2.3.2 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. 

2.3.2.1 Goods and Services Used in Paper Manufacturing

In 2007, the cost of materials made up 53% of the total shipment value of goods in the 

paper manufacturing industry (Table 2-22). Total compensation of employees represented 15% 

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

2%, between 2005 and 2007, while shipments increased by 3% in the same period. 
2-54

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 2-23). Inputs for pulp, paper, and paperboard products are notably 

different from inputs for converted paper products because the NAICS 3221 group represents the 

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

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

evident when observing inputs for converted paper products: 49% of the cost of inputs comes 

from pulp, paper, and paperboard products.

Table 2-22. Costs of Goods and Services Used in the Paper Manufacturing Industry 

(NAICS 322)

Variable 2005 Share 2006 Share 2007 Share

Total shipments ($2007, 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]
2-55

2.3.2.1.1 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).

Table 2-23. 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.

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 

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

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

companies (EPA, 2002).

Likewise, Table 2-24 shows that total energy use decreased between 1998 and 2006 by 

14%. Figure 2-24 indicates that total electrical power use changed sporadically between 2002 

and 2004 but decreased consistently and rapidly after 2004.
2-56

Table 2-24. 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]

Over the last 25 years, the pulp and paper subsector has increased the use of 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 fuelfossil fuels (Table 2-25). During the 1972–1999 period, the 

proportion of total industry power generated from the combination of woodroom wastes, spent 

liquor solids, and other self-generated methods increased from about 41% to about 56%, while 

coal, fuel oil, and natural gas use decreased from about 54% to about 36% (EPA, 2002).
2-57

90

92

94

96

98

100

102

104

106

108

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Jul-97

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Jul-05

Index Value (Jan-97=100)

Manufacturing Paper

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

Figure 2-23. Electrical Power Use Trends in the Paper Manufacturing Industry: 1997–

2005

Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Electric Power Use: 

Manufacturing and Mining.” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G322.S. 

<http://www.federalreserve.gov/datadownload/>. 
2-58

Table 2-25. Estimated Energy Sources for the U.S. Pulp and Paper Industry

Energy Source 1972 1979 1990 1999

Purchased steam 5.4% 6.7% 7.3% 1.5%

Coal 9.8% 9.1% 13.7% 12.5%

Fuel oil 22.3% 19.1% 6.4% 6.3%

Natural gas 21.5% 17.8% 16.4% 17.6%

Other purchased 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>.

2.3.2.2 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 2-26). Pulp, paper, and paperboard products have a 

large trade deficit, while converted paper products have a very small trade surplus.

Table 2-26. Demand by Sector: Paper Manufacturing Industry (NAICS 322) ($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
2-59

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.

2.3.3 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.

2.3.3.1 Location

As Figure 2-24 illustrates, California is home to the most paper manufacturing 

establishments in the United States, followed by Illinois and some 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.
2-60

Establishments

Fewer than 50

50 - 99

100 - 199

200 - 300

More than 300

Establishments

Fewer than 50

50 - 99

100 - 199

200 - 300

More than 300

Figure 2-24. Establishment Concentration in Paper Manufacturing Industry (NAICS 

322): 2002

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008).
2-61

2.3.3.2 Production Capacity and Utilization

Capacity utilization of the paper manufacturing subsector has been experiencing a steady 

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

has managed to use its capacity at a consistently higher rate than the average for manufacturing 

industries (Figure 2-25).
2-62

65

70

75

80

85

90

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)

65

70

75

80

85

90

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)

Figure 2-25. Capacity Utilization Trends in the Paper Manufacturing Industry 

(NAICS 322)

Source: 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/>.
2-63

2.3.3.3 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 2-26). 

The converted paper products group has more employees per establishment, 283, than the pulp, 

paper, and paperboard group, 67.

2.3.3.4 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 2-27).
2-64

Employment

Fewer than 3,000

3,000 - 5,999

6,000 - 11,999

12,000 - 24,000

More than 24,000

Employment

Fewer than 3,000

3,000 - 5,999

6,000 - 11,999

12,000 - 24,000

More than 24,000

Figure 2-26. Employment Concentration in the Paper Manufacturing Industry (NAICS 

322): 2002

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008).
2-65

90

100

110

120

130

140

150

1997-01

1997-071998-011998-071999-011999-072000-012000-072001-012001-072002-01

2002-072003-012003-072004-012004-072005-01

2005-072006-012006-072007-012007-072008-012008-072009-012009-07

Index Value (Jan-97=100)

Manufacturing Paper (NAICS 322)

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)

Figure 2-27. 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/>. 
2-66

2.3.3.5 Firm Characteristics

In 2006, the top 10 paper and forest product companies produced over $75 billion in 

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

$22 billion each (Table 2-27). The top two companies’ revenue consists of 58% of the revenue of 

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

numbers do not exclusively reflect paper products, they do convey the market environment in 

which firms in this sector compete.

Table 2-27. 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.

2.3.3.6 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 2-28). 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 2-29). The cutoff for all subsectors in the pulp, 

paper, and paperboard group is 750 employees, while the cutoff for most converted paper 

product groups is 500 employees.
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Table 2-28. 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>.

2.3.3.7 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 2-28). It seems that the paper manufacturing sector 

was not able to return to its former levels of growth following the 2001 recession; it has 

experienced a downward production trend since then.

2.3.3.8 International Trade

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

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

(Figure 2-29). Imports and exports have been changing at similar rates since 1999.
2-68

Table 2-29. Small Business Size Standards: Paper Manufacturing (NAICS 322)

NAICS NAICS Description Cutoff

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>.
2-69

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)

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)

Figure 2-28. 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/>. 
2-70

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

0

5,000

10,000

15,000

20,000

25,000

30,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

$1,000,000 (2007)

Year

Paper Exports Paper Imports

Figure 2-29. International Trade Trends in the Paper Manufacturing Industry (NAICS 

322)

Source: U.S. International Trade Commission. 2008b. “U.S. Total Exports” & “U.S. Imports for Consumption.”

<http://dataweb.usitc.gov/scripts/user_set.asp>.

2.3.3.9 Market Prices

Prices of goods in paper manufacturing have been increasing at a rate consistent with all 

manufacturing products (Figure 2-30). Producer price indices (PPIs) show that producer prices 

for paper in 2007 increased by 20% since 1997, while producer prices for all manufacturing 

goods increased by roughly 27%.
2-71

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

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

Figure 2-30. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222)

Source: U.S. Bureau of Labor Statistics (BLS). 2009. “Producer Price Index.” Series ID: PCU322–322– & 

PCUOMFG–OMFG–. <http://www.bls.gov/ppi/home.htm>.
2-72

2.4 Chemical Manufacturing

2.4.1 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. According to the American Chemistry Council, 

29.7% of jobs in the U.S. economy are dependent on chemical manufacturing (American 

Chemistry Council, 2007). From 1997 to2007, shipments in the industry grew 42%, while 

employment declined by 8% (Table 2-30). 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 2-31). Shipments per employee grew 54% from 1997 to 2007, with much of 

that growth taking place between 2002 and 2006 (Table 2-31). 

Table 2-30. 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 1347413,474 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 2-31. Industry RatiosDataRatios: 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
2-73

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]
2-74

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 2-31). The bulk chemicals group accounted for the biggest share 

of chemical manufacturing’s total value of shipments (Figure 2-32). 

2.4.2 Supply and Demand Characteristics

Next, we provide a broad overview of the supply and demand side of the chemical 

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

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

the major uses and consumers of chemical manufacturing products. 
2-75

Bulk Chemicals, 

35%

Pharmaceuticals, 

31%

Other Chemicals, 

34%

Bulk Chemicals, 

35%

Pharmaceuticals, 

31%

Other Chemicals, 

34%

Figure 2-31. 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.
2-76

Bulk Chemicals, 

50%

Pharmaceuticals, 

26%

Other Chemicals, 

24%

Bulk Chemicals, 

50%

Pharmaceuticals, 

26%

Other Chemicals, 

24%

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

(NAICS 325): 2007

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

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

<http://factfinder.census.gov>. Accessed on December 27, 2009.

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

2002 accounted for 71% of the total intermediate inputs (Table 2-32). Bulk chemicals’

production was the most energy intensive, using 79% of the chemical manufacturing inputs from 

petroleum and coal products, electric power generation, transmission and distribution, and 

natural gas distribution.
2-77

2.4.2.1 Goods and Services Used in Chemical Manufacturing

In2007, the cost of materials made up 49% of chemical manufacturing’s total shipment 

value (Table 2-32). Total compensation to employees represented 9% of total shipment value, 

down from 10% in 2005.

2.4.2.1.1 Energy. The Department of Energy (DOE) classifies bulk chemical 

manufacturing as an energy-intensive industry. Pharmaceuticals and other chemical 

manufacturing are categorized as nonenergy-intensive industries, grouped together with other 

industry groups under the “Balance of Manufacturing” category (DOE, 2008).

Table 2-32. 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 2-33). 

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% 
2-78

from 1998 to 2006, and electricity use fell 10% (Table 2-34). 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 2-33).).

Table 2-33. 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.

2.4.2.2 Uses and Consumers 

Products manufactured in the groups bulk chemicals, pharmaceuticals, and other 

chemicals have very different consumer profiles. Bulk chemicals is dominated by intermediate 

use, representing 93% of its total commodity output and 56% of the total intermediate use of 

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

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

manufacturing products, and a large trade deficit (Table 2-35).

Comment [A18]: None of these are 

measures of intensity, simply energy use. 

This needs to be recharacterized as such.
2-79

Table 2-34. Energy Used in Chemical Manufacturing (NAICS 325)

Fuel Type 1998 2002 2006

Total (trillion BTU) 3,704 3,769 3,159

Net electricitya (million kWh) 169,233 153,104 151,646

Residual fuel 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>.

2.4.3 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.
2-80

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

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

Figure 2-33. 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).

2.4.3.1 Location

In 2002, California had the most chemical manufacturing establishments in the United 

States, followed by Texas and New Jersey (Figure 2-34). The composition of establishments in 

these states differs among the different industry groups. Despite the fact that each group 

Comment [A19]: This chart seems to be 

misplaced in this section.
2-81

employed an approximately equal share of people in 2002, 54% of the total establishments were 

other chemicals establishments, and only 13% were pharmaceutical establishments.

2.4.3.2 Production Capacity and Utilization

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

the manufacturing sector (Figure 2-35). In the second half of 2005, the chemical manufacturing 

industry’s capacity utilization fell 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. 

Table 2-35. 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.
2-82

2.4.3.3 Employment

The geographic distribution of employment in chemical manufacturing differs largely 

among the different groups. In California, 52% of the chemical manufacturing employment 

comes from the pharmaceutical industry, while 60% of the chemical manufacturing employment 

in the Gulf Coast states comes from bulk chemicals manufacturing (Figure 2-36).

2.4.3.4 Plants and Capacity

Production capacity in chemical manufacturing has grown 33% since 1997. This growth, 

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

(Figure 2-37).
2-83

Establishments

Fewer than 100

100 - 249

250 - 474

475 - 750

More than 750

Establishments

Fewer than 100

100 - 249

250 - 474

475 - 750

More than 750

Figure 2-34. Establishment Concentration in Chemical Manufacturing (NAICS 325): 

2002

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008).
2-84

65

70

75

80

85

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 Chemical Manufacturing

65

70

75

80

85

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 Chemical Manufacturing

Figure 2-35. 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/>.
2-85

Employment

Fewer than 5,000

5,000 - 13,999

14,000 - 29,999

30,000 - 40,000

More than 40,000

Employment

Fewer than 5,000

5,000 - 13,999

14,000 - 29,999

30,000 - 40,000

More than 40,000

Figure 2-36. Employment Concentration in Chemical Manufacturing (NAICS 325): 2002 

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008).
2-86

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Figure 2-37. 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/>. 

2.4.3.5 Firm Characteristics

In 2007, the top six companies by chemical sales each 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 
2-87

from operations outside of the United States (Table 2-36). The largest chemical manufacturing 

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

States (Dow Chemical Company, 2008).

In 2007, 58%of U.S. 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 2-37).

Table 2-36. 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).
2-88

Table 2-37. 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).

2.4.3.6 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 2-38. In 2002, enterprises with fewer than 500 employees accounted for 27% 

of employment and 15% of receipts within the chemical manufacturing industry).

2.4.3.7 Domestic Production

In the late 1990s, overall manufacturing production was growing much faster than the 

chemical manufacturing component (Figure 2-37). 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.

2.4.3.8 International Trade

In the year 2000, the United States moved from having a trade surplus to a trade deficit in 

chemical manufacturing products (Figure 2-38). This change occurred because the trade deficit 

Comment [A20]: If these are the totals 

across the industry, they should indicated 

as such more explicitly and perhaps 

moved to the bottom of the table and 

bolded or separated with a line. 
2-89

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

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

2.4.3.9 Market Prices

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

having outpaced overall manufacturing since 2002 (Figure 2-39). 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.
2-90

Table 2-38. 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>.
2-91

Table 2-39. 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>.
2-92

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Manufacturing Chemical Manufacturing

Figure 2-37. 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/>.
2-93

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

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

$1,000,000 (2007)

Imports Exports

Figure 2-38. 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>.
2-94

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Manufacturing Chemical Manufacturing

90

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170

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Jul-06

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Jul-08

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Jul-09

PPI (Jan-98=100)

Manufacturing Chemical Manufacturing

Figure 2-39. 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>. 
3-1

SECTION 3

ENGINEERING COST ANALYSIS

We provide an overview of the engineering cost analysis used to estimate the additional 

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

of the methodology used to estimate cost impacts is presented in “Methodology and Results of 

Estimating the Cost of Complying with the Industrial, Commercial, and Institutional Boiler and 

Process Heater NESHAP (2010)” and “Estimation of Impacts for Industrial, Commercial, and 

Institutional Boilers Area Source NESHAP” in the Docket.

3.1 Major Sources

To estimate the national cost impacts of the proposed rule for existing sources, EPA 

developed average baseline emission factors for each fuel type/control device combination based 

on the emission data obtained and contained in the Boiler MACT emission database. If a unit 

reported emission data, we assigned its unit-specific emission data as its baseline emissions. For 

units that did not report emission data, we assigned the appropriate emission factors to each 

existing unit in the inventory database, based on the average emission factors for boilers with 

similar fuel, design, control devices. We then compared each unit’s baseline emission factors to 

the proposed MACT floor emission limit to determine if control devices were needed to meet the 

emission limits. The control analysis considered fabric filters, carbon bed 

adsorbersadsorbersabsorbers, and activated carbon injection to be the primary control devices for 

mercury control, ,. For units meeting mercury limits but requiring additional control to meet the 

PM limits, electrostatic precipitators were considered. for units meeting mercury limits but 

requiring additional control to meet the PM limits, ,. For HCl limits, wet scrubbers were 

considered.to meet the HCl limits, For CO and organic HAP control, tune-ups, replacement 

burners, and combustion controls were considered. for CO and organic HAP control , and 

carboncCarbon injection was considered for dioxin/furan control. WeEPAWe identified an 

instance where one control device could achieve reductions in multiple pollutants: , for example

a fabric filter was expected to achieve both PM and mercury control in order to avoid 

overestimating the costs. We also included costs for testing and monitoring requirements that are 

contained in the proposed rule. The resulting total national cost impact of the proposed rule is 

10.9 billion dollars in capital expenditures and 5.4 billion dollars per year in total annual costs. 

Considering estimated fuel savings resulting from work practice standards and combustion 

controls, the total annualized costs are reduced to 5.1 billion. The total capital and annual costs 

include costs for control devices, work practices, testing and monitoring. Table 7 of this 

Comment [A21]: We should be revised 

to ‘EPA’ throughout.

Comment [A22]: This number does not 

match the data in the following table.
3-2

preamble shows the capital and annual cost impacts for each subcategory. Costs include testing 

and monitoring costs, but not recordkeeping and reporting costs.

Table 3-1. Summary of Capital and Annual Costs for New and Existing Major Sources

Source Subcategory

Estimated/ 

Projected No. of 

Affected Units

Capital Costs

(106$)

Annualized Cost

(106$/yr))

Existing Units Coal units 577 4,542 1,665

Biomass units 420 2,646 880

Liquid units 831 2,245 694

Gas (NG/RG) units 11,411 2,674 1,396

Gas (other) units 196 1,367 407

Energy Audit ALL 5,067

New Units Coal units 0 0 0

Biomass units 0 0 0

Liquid units 11 12 5.8

Gas (NG/RG) units 32 43.6 18.9

Gas (other) units 2 6.3 1.7

Using the Energy Information Administration Department of Energy projections on fuel 

expenditures, the number of additional boilers that could be potentially constructed was 

estimated. The resulting total national cost impact of the proposed rule in the 3rd year is 62 

million dollars in capital expenditures and 26.4 million dollars per year in total annual costs, 

when considering a 1 percent fuel savings. 

A discussion of the methodology used to estimate cost impacts is presented in 

“Methodology and Results of Estimating the Cost of Complying with the Industrial, 

Commercial, and Institutional Boiler and Process Heater NESHAP (2010)” in the Docket.

3.2 Area Sources

To estimate the national cost impacts of the proposed rule for existing sources, EPA 

developed several model boilers and determined the cost of control for these model boilers. The 

EPA assigned a model boiler to each existing unit based on the fuel, size, and current controls.

The analysis considered all air pollution control equipment currently in operation at existing 

Comment [A23]: The sum of these 

capital costs does not equal the costs in 

preceding paragraph and it is difficult to 

match various estimates in this section. If 

there is a number cited in a paragraph, 

there should be a subtotal or total of that 

same number in a table.

Comment [A24]: These should be 

properly footnoted and the precise 

projection referenced.
3-3

boilers. Model costs were then assigned to all existing units that could not otherwise meet the 

proposed standards. The resulting total national cost impact of the proposed rule for existing 

units is $4.8 billion dollars in total annualized costs after considering fuel savings from 

efficiency improvements. The total annualized costs for installing controls, conducting an annual 

tune-up and an energy assessment, and implementing testing and monitoring requirements, is 

$5.5 billion. Table 3 of this preamble shows the total annualized cost impacts for each 

subcategory.

Table 3-2. Summary of Annual Costs for New and Existing Area Sources

Source Subcategory

Estimated/

Projected No. of Affected 

Units

Total Annualized Cost 

(106$/yr)a

Existing Units Coal 3,710 107

Biomass 10,958 2,399

Oil 168,003 2,818

Facility Energy Audit All 145

New Unitsb Coal 155 105

Biomass 200 53

Oil 6,424 1,988

a TAC does not include fuel savings from improving combustion efficiency. 

b Impacts for new units assume the number of units online in the first 3 years of this rule (2010 to 2013).

Using DOE projections on fuel expenditures, as well as the history of installation dates of 

area source boilers in the dataset, the number of additional boilers that could be potentially 

constructed was estimated. The resulting total national cost impact of the proposed rule on new 

sources by the 3rd year, 2013, is $2.2 billion dollars in total annualized costs. When accounting 

for a 1 percent fuel savings resulting from improvements to combustion efficiency, the total 

national cost impact on new sources is $2.1 billion.

Comment [A25]: Table 2?

Comment [A26]: Cite specific 

projection from EIA, which is 

independent from DOE.
4-1

SECTION 4

ECONOMIC IMPACT ANALYSIS

EPA prepares an EIAERIA 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 5). 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 prepolicypre-policy market baselines 

with expected postpolicypost-policy market outcomes. The analysis’ time horizon is the 

intermediate run;; industries have limited flexibility to adjust production technologies (e.g., alter 

capital stock quickly or switch labor and material inputs). 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 colleagues 

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

Comment [A27]: Please define here.

Comment [A28]: While there are 

certainly limits in the short-run in terms of 

capital stock, one could argue that labor 

and other inputs are components that 

allow considerably greater flexibility.
4-2

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)

– estimates of industry employment 

 Economic behavior

– industries respond to regulatory costs by changing production rates 

– market prices rise and fall rise to reflect changes in demand for higher energy and 

other nonenergy material costs

– customers respond to changes in these price prices 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

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 (Figure 4-1). Under the proposed major source NESHAP, the Agency’s 

economic model suggests the average national prices for industrial sectors could be 0.01% higher 

Comment [A29]: These citations are not 

very analogous – they cover climate and 

cap and trade/carbon taxes. This rule 

covers pollutants with shorter-term 

impacts and uses a MACT. It also 

highlights the fact that longer-term 

analysis was not completed using 

equilibrium models.

Comment [A30]: Is this the only 

response industries have? If there are 

others, they should be noted.

Comment [A31]: Some prices may 

increase, other prices may fall.

Comment [A32]: If this is modeled, 

then it should say so. If not, it should 

clarify what “considered” really means 

b/c it is unclear.

Comment [A33]: This needs to be more 

explicit – one year (20XX, multi-year 

20XX-20XX, etc.)
4-3

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

Proposed option Other option Proposed option Other option Proposed option Other option

Major Sources Area Sources Major & Area Sources

Percentage Change from Baseline

Prices Production Imports Consumption Exports
4-4

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

Proposed option Other option Proposed option Other option Proposed option Other option

Major Sources Area Sources Major & Area Sources

Percentage Change from Baseline

Prices Production Imports Consumption Exports

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

with the NESHAP, while average annual domestic production may fall by about 0.01%. Because 

of higher domestic prices, imports rise by 0.01% per year. Market-level effects for the proposed 

area source NESHAP are smaller when compared to the proposed major source rule; average 

price, production, and import changes are less than 0.01%. Industrial sector details are provided in 

Appendix B.

4.1.2.2 Social Cost Estimates Major Source Rule

In the near term, the Agency’s economic model suggests that industries are able to pass on 

$1.8 billion (2008$) of the proposed area source rule’s costs to U.S. households in the form of 

higher prices (Table 4-1). Existing U.S. industries’ surplus falls by $3.8 billion, and the net loss in 

aggregate for U.S. stakeholders is $5.6 billion. As U.S. prices rise, other countries are affected 

through international trade relationships. Households that buy The price of goods produced in

from the United States experienceincrease slightlyexperience losses, while and domestic 

production declines, replaced to a certain degree by importswhile industries that sell goods to the 
4-5

United States benefit; the model estimates a net gain of $0.2 billion. to foreign companies. After 

accounting for international trade effects, the Agency’s economic model projects the net surplus 

loss associated with the proposed rule is $5.4 billion. The loss is approximately 1.8 times smaller

than the other ($option ($9.7 billion). As shown in Figure 4-2, the surplus losses are concentrated 

in the energy (30%); other services (16%); and lumber, paper, and printing (15%).

For the selected major source rule option, 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 [existing and new major sources] and total annualized compliance costs [new 

major sources]). The net effect of the adjustments is a total surplus loss estimate of $5.1 billion.

Table 4-1. Distribution of Social Costs Major Sources (billion, 2008$): 2013

Approach Proposed Option Other Option

Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus −$1.8 −$2.8

Change in U.S. producer surplus −$3.8 −$7.3

Change in U.S. surplus −$5.6 −$10.1

Net change in rest of world surplus $0.2 $0.4

Net change in total surplus −$5.4 −$9.7

Direct Compliance Costs Method

Total annualized costs, new major sources (not modeled) Less than −$0.1 Less than −$0.1

Fuel savings, existing major sources (not modeled) $0.4 $0.3

Fuel savings, new major sources (not modeled) Less than $0.1 Less than $0.1

Change in Total Surplus −$5.1 −$9.5

4.1.2.3 Social Cost Estimates Area Source Rule

In the near term, the Agency’s economic model suggests that industries are able to pass on 

$2.3 billion (2008$) of the proposed area source rule’s costs to U.S. households in the form of 

higher prices (Table 4-2). Existing UdomesticU.S. industries’ surplus falls by $3.3 billion, and the 

net loss for U.S. stakeholders is $5.6 billion. As UdomesticU.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 less than $0.2 billion. After accounting for international trade effects, the 

Comment [A34]: There can be no such 

thing – 44% lower or almost half as much, 

perhaps.

Comment [A35]: It would be better to 

call this the “alternative option”, rather 

than “other”.

Comment [A36]: This phrase is difficult 

to understand. Is it meant to say that 

households/consumers buy less 

domestically produce products or buy 

less product in general (whether produced 

domestically or abroad)?
4-6

Agency’s economic model projects the net surplus loss in consumer/producer associated with the 

proposed rule is $5.4 billion.. The loss is 

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

Chemicals

Electronic Equipment

Energy Industries

Fabricated Metals

Food, beverages, and textiles

Lumber, paper, and printing

Machinery and Equipment

Nonmanufacturing

Nonmetallic Minerals

Other

Other Services

Plastics and Rubber

Primary Metals

Transportation Equipment

Transportation Services

Wholesale and Retail Trade

Proposed option: $5.4 Billion Other option 1: $9.7 billion

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

Chemicals

Electronic Equipment

Energy Industries

Fabricated Metals

Food, beverages, and textiles

Lumber, paper, and printing

Machinery and Equipment

Nonmanufacturing

Nonmetallic Minerals

Other

Other Services

Plastics and Rubber

Primary Metals

Transportation Equipment

Transportation Services

Wholesale and Retail Trade

Proposed option: $5.4 Billion Other option 1: $9.7 billion

Comment [A37]: You need to clarify 

what loss you are referring to here, 

producer or consumer loss.

Comment [A38]: The data in Figure 4-3 

has $6.9 billion.

Comment [A39]: This chart seems to 

overstate the impacts since at first glance 

it could appear that these are the changes 

in surplus. It would be more useful to 

show economic data (output, sales?) for 

each category with and w/o the impact of 

the rule. It is less relevant how each 

category does in relation to the others b/c 

of the vast differences across categories. 

Same comment to the next figure.
4-7

Figure 4-2. Distribution of Total Surplus Changes by Sector: Major Sources

approximately 6 times smaller than the otheralternativeother option ($($33.1 billion). As shown in 

Figure 4-3, the surplus losses are concentrated in the other services (59%); food, beverages, and 

textiles (11%); lumber, paper, and printing (11%); and nonmanufacturing industries such as 

agriculture and construction (11%).

For the selected area source rule option, the Agency also considered other elements of the 

engineering cost analysis that could not be modeled within the multimarket model (e.g., fuelsavings benefits [existing and new area sources] and total annualized compliance costs [unknown 

existing and new area sources]). The net effect of the adjustments is a total surplus loss estimate 

of $1.5 billion.

4.1.2.4 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:

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

Approach Proposed Option Other Option

Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus −$2.3 −$16.5

Change in U.S. producer surplus −$3.3 −$16.5

Change in U.S. surplus −$5.6 −$32.9

Net change in rest of world surplus $0.2 −$0.1

Net change in total surplus −$5.4 −$33.1

Direct Compliance Costs Method

Total annualized costs, unknown existing area sources (not modeled) −$0.1 −$0.3

Total annualized costs, new area sources (not modeled) −$2.2 −$2.2

Fuel savings, existing area sources (not modeled) $0.7 $0.7

Fuel savings, new area sources (not modeled) Less than $0.1 Less than $0.1

Change in Total Surplus −$6.9 −$34.8

Comment [A40]: It is not possible to be 

6 times smaller. It can be 1/6 the 

loss....also, the charts do not align well 

with the descriptive text.
4-8

 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 

 postregulationpost-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). 

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

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

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

Chemicals

Electronic Equipment

Energy Industries

Fabricated Metals

Food, beverages, and textiles

Lumber, paper, and printing

Machinery and Equipment

Nonmanufacturing

Nonmetallic Minerals

Other

Other Services

Plastics and Rubber

Primary Metals

Transportation Equipment

Transportation Services

Wholesale and Retail Trade

Proposed option: $6.9 billion Other option: $33.1 Billion

Comment [A41]: Under what 

circumstances? Please specify. Since this 

paper is heavily relied upon, a short 

description/summary of the key points and 

parameters of the paper would be 

worthwhile. 
4-9

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

Chemicals

Electronic Equipment

Energy Industries

Fabricated Metals

Food, beverages, and textiles

Lumber, paper, and printing

Machinery and Equipment

Nonmanufacturing

Nonmetallic Minerals

Other

Other Services

Plastics and Rubber

Primary Metals

Transportation Equipment

Transportation Services

Wholesale and Retail Trade

Proposed option: $6.9 billion Other option: $33.1 Billion

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

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 U.S. employment changes by assuming that the number of jobs declines 

proportionally with output changes. These results were calculated for all sectors in the EPA model 

that show a change in output. WeEPAWe also used the Morgenstern paper to estimate “demand 

effect” changes.. To do this, weEPAwe multiplied the point estimate for the total demand effect 

(−3.56 jobs per million dollars 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 19,000 jobs (−3.56 × $5.4 

billion). Demand effect results are provided in Figure 4-4.

WeEPAWe also present the results of using the Morgenstern paper to estimate 

employment “cost” and “factor-shift” effects for the U.S. 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 

Comment [A42]: Isn’t this effect the 

same one as in the preceding sentence –

changes in output/demand result in lower 

employment?

Comment [A43]: There needs to be a 

greater discussion of this number – how it 

was developed, why it is used, why EPA 

is justified in applying it to multiple 

sectors that are diverse, etc. It should be 

explicitly stated why this number applies 

(or does not apply) equally to 

food/beverage and chemicals, for 

example. 

Comment [A44]: EPA should go into 

more detail and discuss how gains are 

achieved. 
4-10

“demand” effect. Figure 4-4 shows that using the Morgenstern point estimates of parameters to 

estimate the “cost” and “factor shift”

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Proposed option Other Option Proposed option Other Option Proposed option Other Option

Major Area Major & Area Sources

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Demand Effect: Partial Equilbrium (multiple markets) Demand Effect: Literature-based estimate

Cost Effect: Literature-based estimate Factor Shift Effect: Literature-based estimate

Net Effect: Literature-based estimate
4-11

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Proposed option Other Option Proposed option Other Option Proposed option Other Option

Major Area Major & Area Sources

1,000 Jobs

Base =144,100

Demand Effect: Partial Equilbrium (multiple markets) Demand Effect: Literature-based estimate

Cost Effect: Literature-based estimate Factor Shift Effect: Literature-based estimate

Net Effect: Literature-based estimate

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

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.

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 peerreviewed 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. Second, the multimarket model only considers near term employment effects in an U.S. economy where production 
4-12

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.
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-for-profit 

enterprises. EPA assessed the potential small entity economic impacts using a screening analysis.

After reviewing screening analysis results, EPA has determined it cannot certify that the proposed 

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

certification under the RFA as amended by SBREFA. As a result, EPA has prepared and included 

an IRFA that discusses alternative regulatory or policy options that minimize the rules’ small 

entity impacts. It includes information about key results from the SBAR panel.

5.1 Small Entity Screening Analysis

5.1.1 Small Businesses

The sectors covered by the rule were identified through lists of small entities at major and 

area sources provided by the engineering analysis. Table 5-1provides a list of the sectors affected 

(3-digit NAICS) and the range of SBA size definitions.

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

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

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. 

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

conducted or where services or industrial operations are performed. 

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

Table 5-1. Affected Sectors and Size Standards

2007 

NAICS Description

Size Standard

(Effective August 22, 2008)

211 Oil and Gas Extraction 500 employees

212 Mining (except Oil and Gas) 500 employees

221 Utilities a

311 Food Manufacturing 500 to 1,000 employees

312 Beverage and Tobacco Product Manufacturing 500 to 1,000 employees

313 Textile Mills 500 to 1,000 employees

321 Wood Product Manufacturing 500 employees

322 Paper Manufacturing 500 to 750 employees

323 Printing and Related Support Activities 500 employees

324 Petroleum and Coal Products Manufacturing Typically 500 to 1,500 employees

325 Chemical Manufacturing 500 to 1,000 employees

326 Plastics and Rubber Products Manufacturing Typically 500 to 1,000 employees

327 Nonmetallic Mineral Product Manufacturing 500 to 1,000 employees

331 Primary Metal Manufacturing 500 to 1,000 employees

332 Fabricated Metal Product Manufacturing 500 to 1,500 employees

335 Electrical Equipment Manufacturing 500 to 1,000 employees

336 Transportation Equipment Manufacturing 500 to 1,000 employees

337 Furniture and Related Product Manufacturing 500 employees

339 Miscellaneous Manufacturing 500 employees

423 Merchant Wholesalers, Durable Goods 100 employees

493 Warehousing and Storage $25.5 million in annual receipts

562 Waste Management and Remediation Services Typically $7 to $14 million in annual receipts

611 Educational Services Typically $7 to $35.5 million in annual receipts

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

primarily engaged in the generation, transmission, and/or distribution of electric energy for sale and its total 

electric output for the preceding fiscal year did not exceed 4 million megawatt hours.

 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. 
5-3

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

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

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

multiestablishment 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.

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

Combustion Survey

Sample Statistic Other Option Proposed Option

Mean 11.8% 7.5%

Median 2.1% 0.7%

Maximum 100.0% 100.0%

Minimum <0.1% 0.1%

Ultimate parent company observations 50 50

Ultimate parent companies with sale tests exceeding 

3%

21 16

For each representative establishment in the SUSB data, we developed a range of facilitylevel cost numerators based on the engineering cost analysis. For major sources, we used the 

maximum and minimum small entity facility-level costs observed within each 3-digit NAICS 
5-4

code. For area sources, we were limited to two representative small entity facility-level costs 

(approximately $600,000 to $1.7 million) 1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Other option Proposed option Other option Proposed option

Major Area

Upper Bound

Lower Bound

. Using these cost data and the Census estimates of 

average establishment receipts, a substantial number of SUSB NAICS/enterprise categories have 

ratios over 3% (Figure 5-1).

 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). 
5-5

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Other option Proposed option Other option Proposed option

Major Area

Upper Bound

Lower Bound

Figure 5-1. Share of NAICS/Enterprise Employment Categories (<500 employees) with 

Sales Tests Exceeding 3%

5.1.1.2 Additional Small Business Analysis Using Sample of Small Businesses Identified in 

Combustion Facility Survey

Next, we performed a more detailed analysis that compares the Census SUSB 

representative small entity results with a firm-specific sample of major small private enterprises.

In this approach, we identified a sample of survey facility names listed as small, traced the 

ultimate parent 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 major source companies are above 5%. The median ratios are 2.1% 

(Other option) and 0.7% (proposed option).

5.1.2 Small Government Analysis

In addition to the private sector, this rule also covers sectors that include entities owned by 

small and large governments. Given the uncertainty and data limitations associated with 

identifying and appropriately classifying these entities, we computed a “revenue” test for a model 
5-6

small government, where the annualized compliance cost is a percentage of annual government 

revenues (U.S. Census, 2005a and b). 

Compliance costs were estimated for model facilities for major and area sources for 

multiple options. A summary of the compliance costs used for the small government analysis 

follows: 

Major Sources:

 Other option 1: $5.2 million (median cost small public facility) 

 Proposed option: $3.8 million (median cost small public facility) 

Area Sources:

 Other option: 

– Other public: $800,000

– Hospital: $360,000

– Schools: $20 to 300,000

– Churches: $300,000

 Proposed option: 

– Other public: $800,000

– Hospital: $1 to $360,000

– Schools: $1,000

– Churches: $1,000

From the 2002 Census (in 2008 dollars), the average revenue for small governments 

(counties and municipalities with populations fewer than 10,000) are $3 million per entity, and 

the average revenue for local governments with populations fewer than 50,000 is $7 million per 

entity. Churches are assumed to have an operation budget of $150,000.

The analysis shows that small major source public facilities would have cost-to-revenue 

ratios that exceed 10% under the both regulatory options. Small area source public facilities have 

cost-to-revenue ratios that exceed 3% for other regulatory option (with the exception of smaller 

schools). Under the proposed option, only larger hospitals and other public facilities would have 

revenue tests exceeding 3%; smaller hospitals’, schools’, and churches’ revenue tests fall below 

1%.
5-7

5.2 Initial Regulatory Flexibility Analysis (IFRA)

An IRFA illustrates how EPA considered the proposed rule’s small entity effects before a 

rule is finalized and provides information about how the objectives of the rule were achieved 

while minimizing significant economic impacts on small entities. We provide a summary of IRFA 

elements; the preambles for each rule provide additional details.

5.2.1 Reasons Why Action Is Being Considered

In 2004, EPA promulgated national emission NESHAP for new and existing 

industrial/commercial/institutional boilers and process heaters. However, in 2007, the U.S. Court 

of Appeals for the District of Columbia Circuit (DC Circuit) vacated the NESHAP for 

industrial/commercial/institutional boilers and process heaters. The proposed action provides 

EPA’s proposed rule in response to the court’s vacatur. Under authority of section 112 of the 

Clean Air Act (CAA), EPA is also proposing a NESHAP for two area source categories: 

industrial boilers and institutional/commercial boilers.

5.2.2 Statement of Objectives and Legal Basis of Proposed Rules

The proposed rules would protect air quality and promote public health by reducing 

emissions of the HAPs. Section 112(d) of the CAA requires EPA to establish NESHAPs for both 

major and area sources of HAPs that are listed for regulation under CAA section 112(c). A major 

source emits or has the potential to emit 10 tons per year (tpy) or more of any single HAP or 25 

tpy or more of any combination of HAP. An area source is a stationary source that is not a major 

source.

5.2.3 Description and Estimate of the Number of Small Entities 

The sectors covered by the rule were identified through lists of small entities at major and 

area sources included in the survey. A listing of the sectors affected (3-digit NAICS) and the 

range of SBA size definitions are provided in Table 5-1. EPA believes a substantial number of 

small entities will be affected by the proposed rules, but data limitations preclude us from 

providing precise estimates of the number of small entities affected.

5.2.4 Description and Compliance Costs 

5.2.4.1 Major Sources

A discussion of the methodology used to estimate cost impacts is presented in 

“Methodology and Results of Estimating the Cost of Complying with the Industrial, Commercial, 

and Institutional Boiler and Process Heater NESHAP” in the Docket and Section 4 of the RIA.
5-8

5.2.4.2 Area Sources

A detailed discussion of the methodology used to estimate cost impacts is presented in the 

memorandum “Estimation of Impacts for Industrial, Commercial, and Institutional Boilers Area 

Source NESHAP” in the Docket and Section 4 of the RIA.

5.2.5 Description of Federal Rules that May Overlap or Conflict with Proposed Rules

The proposed major source rule regulates source categories covering industrial boilers, 

institutional boilers, commercial boilers, and process heaters that may include combustion units 

that are already regulated by other MACT standards. Therefore, EPA proposes to exclude any 

boiler or process heater that is already or will be subject to regulation under another MACT 

standard. Boilers located at area source facilities may be regulated pursuant to CAA section 129. 

Section 129(h) states that no unit subject to standards under section 129 shall be subject to 

standards under section 112(d) of the CAA. As a result, EPA proposes to exclude any boiler that 

is subject to regulation under section 129. EPA has codified new source performance standards 

(NSPS) for industrial boilers (40 CFR 60, subparts Db and Dc), but the NSPS does not regulate 

sources of HAPs. As a result, sources subject to the NSPS will still be subject to the proposed 

rules. However, EPA has minimized the monitoring requirements, testing requirements, and 

recordkeeping requirements to avoid duplicating NSPS requirements.

5.2.6 Description of Regulatory Alternatives that Minimize Significant Economic Impacts on 

Small Entities

As required by section 609(b) of the RFA, as amended by SBREFA, EPA has conducted 

outreach to small entities and convened a SBAR Panel to obtain advice and recommendation of 

representatives of the small entities that potentially would be subject to the requirements of this 

rule. On January 22, 2009, EPA’s Small Business Advocacy Chairperson convened a Panel under 

section 609(b) of the RFA. In addition to the Chair, the Panel consisted of the Director of the 

Sector Policies and Programs Division within EPA’s Office of Air and Radiation, the Chief 

Counsel for Advocacy of the SBA, and the Administrator of the Office of Information and 

Regulatory Affairs within the Office of Management and Budget.

As part of the SBAR Panel process, we conducted outreach with representatives from 14 

various small entities that would be affected by this rule. The small entity representatives (SERs) 

included associations representing schools, churches, hotels/motels, wood product facilities, and 

manufacturers of home furnishings. We met with these SERs to discuss the potential rulemaking 

approaches and potential options to decrease the impact of the rulemaking on their 

industries/sectors. We distributed outreach materials to the SERs; these materials included 
5-9

background on the rulemaking, possible regulatory approaches, preliminary cost and economic 

impacts, and possible rulemaking alternatives. The Panel met with SERs from the industries that 

will be directly affected by this rule on February 10, 2009, to discuss the outreach materials and 

receive feedback on the approaches and alternatives detailed in the outreach packet. (EPA also 

met with SERs on November 13, 2008, for an initial outreach meeting.) The Panel received 

written comments from the SERs following the meeting in response to discussions at the meeting 

and the questions posed to the SERs by the Agency. The SERs were specifically asked to provide 

comment on regulatory alternatives that could help minimize the rules’ impact on small 

businesses.

5.2.6.1 Panel Recommendations for Small Business Flexibilities

The Panel recommended that EPA consider and seek comment on a wide range of 

regulatory alternatives to mitigate the impacts of the rulemaking on small businesses, including 

those flexibility options described below. The following section summarizes the SBAR Panel 

recommendations. EPA has proposed provisions consistent with four of the Panel’s 

recommendations.

Consistent with the RFA/SBREFA requirements, the Panel evaluated the assembled 

materials and small-entity comments on issues related to elements of the IRFA. A copy of the 

Final Panel Report (including all comments received from SERs in response to the Panel’s 

outreach meeting), as well as summaries of both outreach meetings that were held with the SERs,

is included in the docket for the proposed rules. A summary of the Panel recommendations is 

detailed below. The proposals include proposed provisions for all but one of the Panel 

recommendations.

5.2.6.1.1 Work Practice Standards. The panel recommended that EPA consider requiring 

annual tune-ups, including standardized criteria outlining proper tune-up methods targeted at 

smaller boiler operators. The panel further recommended that EPA take comment on the efficacy 

of energy assessments/audits at improving combustion efficiency and the cost of performing the 

audits, especially to smaller boiler operators.

A work practice standard, instead of MACT emission limits, may be proposed if it can be 

justified under section 112(h) of the CAA, that is, it is impracticable to enforce the emission 

standards due to technical or economic limitations. Work practice standards could reduce fuel use 

and improve combustion efficiency, which would result in reduced emissions.
5-10

In general, SERs commented that a regulatory approach to improve combustion 

efficiency, such as work practice standards, would have positive impacts with respect to the 

environment and energy use and save on compliance costs. The SERs were concerned with work 

practice standards that would require energy audits and implementation of audit findings. The 

basis of these concerns rested on the uncertainty that there is no guarantee that there are available 

funds to implement a particular audit’s findings.

5.2.6.1.2 Subcategorization. The Panel recommended that EPA allow subcategorizations 

suggested by the SERs, unless EPA finds that a subcategorization is inconsistent with the CAA. 

SERs commented that subcategorization is a key concept that could ensure that like 

boilers are compared with similar boilers so that MACT floors are more reasonable and could be 

achieved by all units within a subcategory using appropriate emission reduction strategies. SERs 

commented that EPA should subcategorize based on fuel type, boiler type, duty cycle, and 

location.

5.2.6.1.3 Health Based Compliance Alternatives (HBCA). The Panel recommended that 

EPA adopt the HBCA as a regulatory flexibility option for the boiler MACT rulemaking. The 

panel recognized, however, that EPA has concerns about its legal authority to provide an HBCA 

under the CAA, and EPA may ultimately determine that this flexibility is inconsistent with the 

CAA.

SERs commented that adopting an HBCA would perhaps be the most important step EPA 

could take to mitigate the serious financial harm the boiler MACT would otherwise inflict on 

small entities using solid fuels nationwide; therefore, HBCA should be a critical component of 

any future rule to lessen the impact on small entities. 

5.2.6.1.4 Emissions Averaging. The Panel recommended that EPA consider a provision 

for emission averaging and long averaging times for the proposed emission limits.

SERs commented that a measure EPA should consider to lessen the regulatory burden of 

complying with the boiler MACT is to allow emissions averaging at sources with multiple 

regulated units. SERs commented that another approach that can aide small entity compliance is 

to set longer averaging times (i.e., 30 days or more) rather than looking at a mere 3-run (hour) 

average for performance. Given the inherent variability in boiler performance, an annual or 

quarterly averaging period for all HAP would prevent a single spike in emissions from throwing a 

unit into noncompliance.
5-11

5.2.6.1.5 Compliance Costs. The Panel recommended that EPA carefully weigh the 

potential burden of compliance requirements and consider for small entities options, such as

emission averaging within the facility, reduced monitoring/testing requirements, or allowing more 

time for compliance. 

SERs noted that recordkeeping activities, as written in the vacated boiler MACT, would 

be especially challenging for small entities that do not have a dedicated environmental affairs 

department.
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 Boiler MACT and Boiler Area Source Rule. For these 

rules, the PM reductions are the result of emission limits on PM (as a surrogate for metals) as well 

as emission limits on other HAPs. The total PM2.5 reductions are the consequence of the 

technologies installed 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 both the proposed Boiler rules to be $22 billion to $53 

billion in the implementation year (2013). Using a 7% discount rate, we estimate the total 

monetized benefits of the proposed Boiler rules to be $20 billion to $48 billion in the 

implementation year. All estimates are in 2008$.

These estimates reflect EPA’s most current interpretation of the scientific.............. 

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 hazardous air pollutants have not been monetized in this analysis, including 

reducing 420,000 tons of carbon monoxide, 22 tons of mercury, 48,000 tons of HCl, and 1,200 

tons of HF each year. 

6.2 Calculation of PM2.5 Human Health Benefits

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

VOCs 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 these rules, the PM 

reductions are the result of emission limits on PM (as a surrogate for metals) as well as emission 

limits on other HAPs. The total PM2.5 reductions are the consequence of the technologies 

installed 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-perton” approach to estimate these benefits based on the methodology described in Fann et al. 

(2009). The key assumptions are described in detail below. These PM2.5 benefit-per-ton 

estimates provide the total monetized human health benefits (the sum of premature mortality and 
6-2

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 benefitper-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 
6-3

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

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

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.

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.

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

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.

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.

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

 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.

was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the 

4 It is important to note that uncertainty regarding the shape of the concentration-response function is conceptually 

distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is a 

discontinuity, which is a specific example of non-linearity. 5 In the Portland Cement RIA (U.S. EPA, 2009a), EPA solicited comment on the use of the no-threshold model for 

benefits analysis within the preamble of that proposed rule. The comment period for the Portland Cement 

proposed NESHAP closed on September 4, 2009 (Docket ID No. EPA–HQ–OAR–2002–0051 available at 

http://www.regulations.gov). EPA is currently reviewing those comments. 6 In this analysis, we adjust the VSL to account for a different currency year (2008$) and to account for income 

growth to 2015. After applying these adjustments to the $5.5 million value, the VSL is $7.9m.
6-5

Agency neither changed its official guidance on 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

Figure 6-1 illustrates the relative breakdown of the monetized PM2.5 health benefits.

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.

Tables 6-2 and 6-3 provide a general summary of the results by pollutant, including the 

emission reductions and monetized benefits-per-ton at discount rates of 3% and 7%.9

 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.

Table 6-4 

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. 9 To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). 

These benefits are estimated for a specific analysis year (i.e., 2013), and most of the PM benefits occur within that 

year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 

years of follow-up medical costs and lost wages. For premature mortality, we assume that there is a “cessation”
6-6

Adult Mortality - Pope et 

al. 93%

Chronic Bronchitis 4%

AMI 2%

Acute Respiratory Symptoms 

0.5%

Infant Mortality 0.4%

Work Loss Days 0.2%

Hospital Admissions, Cardio 

0.2% Hospital Admissions, Resp 

0.04%

Asthma Exacerbation 0.01%

Acute Bronchitis 0.01%

Upper Resp Symp 0.00%

Lower Resp Symp 0.00%

ER Visits, Resp 0.00%

Other 1%

 

 

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. 
6-7

Adult Mortality - Pope et 

al. 93%

Chronic Bronchitis 4%

AMI 2%

Acute Respiratory Symptoms 

0.5%

Infant Mortality 0.4%

Work Loss Days 0.2%

Hospital Admissions, Cardio 

0.2% Hospital Admissions, Resp 

0.04%

Asthma Exacerbation 0.01%

Acute Bronchitis 0.01%

Upper Resp Symp 0.00%

Lower Resp Symp 0.00%

ER Visits, Resp 0.00%

Other 1%

 

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.

provides a summary of the reductions in health incidences as a result of the pollution reductions.

In Table 6-5, 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 and 6-3 provide a 

visual representation of the range of benefits estimates and the pollutant breakdown of the 

monetized benefits.
6-8

Table 6-2. Summary of Monetized Benefits Estimates for Boiler MACT (for Major 

Sources) in 2013 (2008$)a

Pollutant

Emissions 

Reductions 

(tons)

Benefit 

per ton 

(Pope, 

3%)

Benefit 

per ton 

(Laden, 

3%)

Benefit 

per ton 

(Pope, 

7%)

Benefit 

per ton 

(Laden, 

7%)

Total Monetized 

Benefits (millions 

2008$ at 3%)

Total Monetized 

Benefits (millions 

2008$ at 7%)

Other Option

Direct PM2.5 Major 23,550 $230,000 $560,000 $210,000 $500,000 $5,400 to $13,000 $4,900 to $12,000

PM2.5 Precursors

SO2 Major 400,414 $29,000 $72,000 $27,000 $65,000 $12,000 to $29,000 $11,000 to $26,000

VOC 4,893 $1,200 $3,000 $1,100 $2,700 $6.0 to $15.0 $5.4 to $13.0

Total $17,000 to $42,000 $16,000 to $38,000

Proposal Option

Direct PM2.5 Major 22,662 $230,000 $560,000 $210,000 $500,000 $5,200 to $13,000 $4,700 to $11,000

PM2.5 Precursors

SO2 Major 384,024 $29,000 $72,000 $27,000 $65,000 $11,000 to $28,000 $10,000 to $25,000

VOC 1,436 $1,200 $3,000 $1,100 $2,700 $1.7 to $4.3 $1.6 to $3.9

Total $16,000 to $40,000 $15,000 to $36,000

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

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton 

estimates vary because each ton of precursor reduced has a different propensity to become PM2.5. The monetized 

benefits incorporate the conversion from precursor emissions to ambient fine particles. These estimates include 45 

new major sources expected to be affected by 2013.
6-9

Table 6-3. Summary of Monetized Benefits Estimates for Boiler Area Source Rule in 2013 

(2008$)a

Pollutant

Emissions 

Reductions 

(tons)

Benefit 

per ton 

(Pope, 

3%)

Benefit 

per ton 

(Laden, 

3%)

Benefit 

per ton 

(Pope, 

7%)

Benefit 

per ton 

(Laden, 

7%)

Total Monetized 

Benefits (millions 

2008$ at 3%)

Total Monetized 

Benefits (millions 

2008$ at 7%)

Other Option

Direct PM2.5 Area 27,897 $360,000 $880,000 $330,000 $790,000 $10,000 to $24,000 $9,100 to $22,000

PM2.5 Precursors

SO2 Area 2,787 $20,000 $49,000 $18,000 $44,000 $56 to $140 $51 to $120

VOC 50 $1,200 $3,000 $1,100 $2,700 $.06 to $.15 $.06 to $.13

Total $10,000 to $25,000 $9,200 to $22,000

Proposal Option

Direct PM2.5 Area 13,993 $360,000 $880,000 $330,000 $790,000 $5,000 to $12,000 $4,600 to $11,000

PM2.5 Precursors

SO2 Area 2,787 $20,000 $49,000 $18,000 $44,000 $56 to $140 $51 to $120

VOC 50 $1,200 $3,000 $1,100 $2,700 $.06 to $.15 $.06 to $.13

Total $5,100 to $12,000 $4,600 to $11,000

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

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton 

estimates vary because each ton of precursor reduced has a different propensity to become PM2.5. The monetized 

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

6,779 new area sources expected to be affected by 2013.
6-10

Table 6-4. Summary of Reductions in Health Incidences from PM2.5 Benefits for the 

Proposed Boiler Rules in 2013a

Boiler MACT (Major Sources) Boiler Area Source Rule

Other Option Proposal Option Other Option Proposal Option

Avoided Premature Mortality

Pope et al. 1,900 1,800 1,100 600

Laden et al. 4,900 4,700 2,900 1,500

Avoided Morbidity

Chronic Bronchitis 1,300 1,300 810 410

Acute Myocardial Infarction 3,100 3,000 1,900 940

Hospital Admissions, Respiratory 460 450 270 130

Hospital Admissions, Cardiovascular 990 950 570 290

Emergency Room Visits, Respiratory 1,800 1,700 840 430

Acute Bronchitis 3,100 3,000 1,900 960

Work Loss Days 250,000 240,000 160,000 81,000

Asthma Exacerbation 34,000 32,000 21,000 11,000

Acute Respiratory Symptoms 1,500,000 1,400,000 950,000 480,000

Lower Respiratory Symptoms 37,000 35,000 23,000 11,000

Upper Respiratory Symptoms 28,000 27,000 17,000 8,700

a All estimates are for the analysis year (2013) and are rounded to whole numbers with two significant figures. All 

fine particles are assumed to have 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. These estimates include 45 new major sources and 6,779 new area sources expected to be affected 

by 2013.
6-11

Table 6-5. All PM2.5 Benefits Estimates for the Proposed Boiler Rules at discount rates of 

3% and 7% in 2013 (in millions of 2008$)a

Other Option Proposal Option

3% 7% 3% 7%

Benefit-per-ton Coefficients Derived from Epidemiology Literature

Pope et al. $27,000 $25,000 $22,000 $20,000

Laden et al. $67,000 $60,000 $53,000 $48,000

Benefit-per-ton Coefficients Derived from Expert Elicitation

Expert A $71,000 $64,000 $56,000 $50,000

Expert B $54,000 $49,000 $43,000 $39,000

Expert C $54,000 $49,000 $43,000 $39,000

Expert D $38,000 $35,000 $30,000 $27,000

Expert E $87,000 $79,000 $69,000 $62,000

Expert F $49,000 $44,000 $39,000 $35,000

Expert G $32,000 $29,000 $26,000 $23,000

Expert H $41,000 $37,000 $32,000 $29,000

Expert I $53,000 $48,000 $42,000 $38,000

Expert J $44,000 $39,000 $34,000 $31,000

Expert K $11,000 $9,900 $8,500 $7,800

Expert L $39,000 $36,000 $31,000 $28,000

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. These estimates include 45 new major sources and 6,779 new area sources expected 

to be affected by 2013.
6-12

$0

$10

$20

$30

$40

$50

$60

$70

Pope et al.

Laden et al.

Billions (2008$)

Benefits estimates derived from 2 epidemiology functions and 12 expert functions

 

3% DR

7% DR

$0

$10

$20

$30

$40

$50

$60

$70

Pope et al.

Laden et al.

Billions (2008$)

Benefits estimates derived from 2 epidemiology functions and 12 expert functions

 

3% DR

7% DR

Figure 6-2. Total Monetized PM2.5 Benefits for the Proposed Boiler Rules in 2013
6-13

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.

Figure 6-3 Breakdown of Monetized Benefits for the Proposed Boiler Rules by PM2.5 

Precursor Pollutant and Source

6.2 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 thousands of tons of 

hazardous air pollutants (HAPs) and millions of tons of carbon monoxide each year 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, increase mercury 

methylation, as well as visibility impairment. 
6-14

6.2.1 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 420,000 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 long-term exposure to CO, and for central 

nervous system effects linked to short- and long-term exposure to CO. 

6.2.2 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 
6-15

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 forest productivity 
6-16

(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. 

6.2.3 HAP Benefits

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

benefits associated with the thousands tons of hazardous air pollutants that would be reduced as a 

result of this rule. Available emissions data show that several different HAPs are emitted from 

boilers, either contained within the fuel burned or formed during the combustion process. 

Although numerous HAPs may be emitted from boilers, a few HAPs account for the 

majority of the total mass of HAPs emissions. See Table 6-6 for the list of the major HAPs for 

each fuel type. This rule is anticipated to reduce 420,000 tons of carbon monoxide, 22 tons of 

mercury, 48,000 tons of HCl, and 1,200 tons of HF each year. We discuss the health effects 

associated with the top HAPs as well as mercury. 

Table 6-6. Top HAPs by Mass from Boilers by Fuel Type

Coal Gas Biomass Oil

68% HCl 44% Formaldehyde 32% Acetaldehyde 28% Nickel

5% HF 25% PAH 28% HCl 19% Manganese

3% Toluene 25% Formaldehyde
6-17

6.2.3.1 Mercury

People in the U.S. are mainly exposed to methylmercury, an organic compound, when they eat 

fish and shellfish that contain methylmercury. Whether an exposure to the various forms of mercury 

will harm a person's health depends on a number of factors. Almost all people have at least trace 

amounts of methylmercury in their tissues, reflecting methylmercury’s widespread presence in the 

environment and people’s exposure through the consumption of fish and shellfish. Mercury is a highly 

neurotoxic contaminant that enters the food web as a methylated compound, methylmercury. 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. Current evidence indicates that in watersheds where 

mercury is present, increased SOx 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, these proposed rules would 

also reduce 22 tons of mercury emissions per year. 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. 

Mercury exposure is associated with a variety of health effects.10

 10 U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional 

Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available 

on the internet at 

Mercury exists in three 

forms: elemental mercury, inorganic mercury compounds (primarily mercuric chloride), and 

organic mercury compounds (primarily methyl mercury). Each form exhibits different health 

effects. Various major sources may release elemental or inorganic mercury; environmental 

methylmercury is typically formed by biological processes after mercury has precipitated from 

the air. Acute exposure to high levels of elemental mercury in humans results in central nervous 

system (CNS) effects such as tremors, mood changes, and slowed sensory and motor nerve 

function. High inhalation exposures can also cause kidney damage and effects on the 

gastrointestinal tract and respiratory system. Chronic exposure to elemental mercury in humans 

also affects the CNS, with effects such as increased excitability, irritability, excessive shyness, 

and tremors. EPA has not classified elemental mercury with respect to cancer. Acute exposure to 

inorganic mercury by the oral route may result in effects such as nausea, vomiting, and severe 

http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf

Comment [A45]: Replaced with 

language from the EPA webpage: 

http://www.epa.gov/mercury/effects.htm

Comment [A46]: Please provide 

citation.

Comment [A47]: Would be helpful to 

put in the context of what forms of 

mercury are emitted from facilities and 

what forms will be decreased.
6-18

abdominal pain. The major effect from chronic exposure to inorganic mercury is kidney damage. 

Reproductive and developmental animal studies have reported effects such as alterations in 

testicular tissue, increased embryo resorption rates, and abnormalities of development. Mercuric 

chloride (an inorganic mercury compound) exposure has been shown to result in forestomach, 

thyroid, and renal tumors in experimental animals. EPA has classified mercuric chloride as a 

Group C, possible human carcinogen. 

6.2.3.2 Hydrogen Chloride (HCl)

Hydrogen chloride, also called hydrochloric acid, is corrosive to the eyes, skin, and 

mucous membranes. Acute inhalation exposure may cause eye, nose, and respiratory tract 

irritation and inflammation and pulmonary edema in humans. Chronic occupational exposure to 

hydrochloric acid has been reported to cause gastritis, bronchitis, and dermatitis in workers. 

Prolonged exposure to low concentrations may also cause dental discoloration and erosion.. No 

information is available on the reproductive or developmental effects of hydrochloric acid in 

humans. In rats exposed to hydrochloric acid by inhalation, altered estrus cycles have been 

reported in females and increased fetal mortality and decreased fetal weight have been reported in 

offspring. EPA has not classified hydrochloric acid for carcinogenicity. 11

6.2.3.3 Hydrogen Fluoride (HF)

Acute (short-term) inhalation exposure to gaseous hydrogen fluoride can cause severe 

respiratory damage in humans, including severe irritation and pulmonary edema. Chronic (longterm) exposure to fluoride at low levels has a beneficial effect of dental cavity prevention and 

may also be useful for the treatment of osteoporosis. Exposure to higher levels of fluoride may 

cause dental fluorosis. One study reported menstrual irregularities in women occupationally 

exposed to fluoride.. The EPA has not classified hydrogen fluoride for carcinogenicity. 12

6.2.3.4 Toluene

A substantial database examining the effects of toluene in subchronic and chronic 

occupationally exposed humans exists. The weight-of-evidence from these studies indicates 

neurologic effects (i.e., impaired color vision, impaired hearing, decreased performance in 

neurobehavioral analysis, changes in motor and sensory nerve conduction velocity, headache, 

 11 U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional 

Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available 

on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf 12 U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional 

Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available 

on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf

Comment [A48]: Citations are needed 

for all these statements. It is unclear what 

the scientific source is. Its also unclear 

why the focus is not on methylmercury.

Comment [A49]: Citations and 

information on dose levels would be 

helpful.

Comment [A50]: Citation?

Comment [A51]: Please provide 

citation.

Comment [A52]: Please provide 

citation and dose level. 

Comment [A53]: Please provide 

citation. Is this the dose level (low dose) 

that is relevant to emissions from sources 

in this rule? more clarification would help.

Comment [A54]: What levels? 

Relevance to this rulemaking?

Comment [A55]: Please provide 

citation. Unclear why only this one study 

is cited. Was it a great study? what does 

the weight of evidence show?
6-19

dizziness) as the most sensitive endpoint. Numerous case studies in humans exposed to high 

concentrations of toluene for abusive purposes have also indicated neurological effects in adults 

as critical effects of concern. Human studies indicating the potential for adverse effects from 

toluene exposure other than neurological effects are also available. None of these studies 

indicated effects at doses lower than those observed for neurological effects. Animal studies have 

also suggested respiratory irritation as a sensitive effect, but this effect in humans appears to 

occur at higher exposure concentrations than those resulting in neurologic effects.

13.14

6.2.3.5 Formaldehyde

Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on 

evidence in humans and in rats, mice, hamsters, and monkeys.15 EPA is currently reviewing more 

recently published epidemiological data. For instance, research conducted by the National Cancer 

Institute (NCI) found an increased risk of nasopharyngeal cancer and lymphohematopoietic 

malignancies such as leukemia among workers exposed to formaldehyde.16,17 In an analysis of 

the lymphohematopoietic cancer mortality from an extended follow-up of these workers, NCI 

confirmed an association between lymphohematopoietic cancer risk and peak exposures.18 A 

recent National Institute of Occupational Safety and Health (NIOSH) study of garment workers 

also found increased risk of death due to leukemia among workers exposed to formaldehyde.19 

Extended follow-up of a cohort of British chemical workers did not find evidence of an increase 

in nasopharyngeal or lymphohematopoietic cancers, but a continuing statistically significant 

excess in lung cancers was reported. 20

 13 U.S. EPA (1998). Integrated Risk Information System File of toluene. Research and Development, National 

Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0118.htm 14 U.S. EPA (1998). Integrated Risk Information System File of toluene. Research and Development, National 

Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0118.htm 15 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to 

Formaldehyde, Office of Pesticides and Toxic Substances, April 1987. 16 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Mortality from lymphohematopoetic 

malignancies among workers in formaldehyde industries. Journal of the National Cancer Institute 95: 1615-1623. 17 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from solid cancers among 

workers in formaldehyde industries. American Journal of Epidemiology 159: 1117-1130. 18 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009. 

Mortality from lymphohematopoietic malignancies among workers in formaldehyde industries: The National 

Cancer Institute cohort. J. National Cancer Inst. 101: 751-761. 19 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup. 

Environ. Med. 61: 193-200. 20 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Extended follow-up of a cohort of British chemical workers 

exposed to formaldehyde. J National Cancer Inst. 95:1608-1615.

Comment [A56]: At what dose level? Is 

this in humans or rabbits? Might suggest a 

preferred approach of taking language 

from IRIS rather than paraphrasing.

Comment [A57]: Are these exposures 

relevant for this rulemaking?

Comment [A58]: At what levels? What 

is citation?

Comment [A59]: So is this the citation 

for the entire paragraph? We note that the 

RfD, RfC and cancer assessment were 

updated in 2005. Unclear why this is 

citing 1998. Perhaps a full update is 

needed.
6-20

In the past 15 years there has been substantial research on the inhalation dosimetry for 

formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the 

Chemical Industry Institute of Toxicology), with a focus on use of rodent data for refinement of 

the quantitative cancer dose-response assessment.21,22,23 CIIT’s risk assessment of formaldehyde 

incorporated mechanistic and dosimetric information on formaldehyde. However, it should be 

noted that recent research publishedA recent publicationpublished by EPA scientists indicates that 

when two-stage modeling assumptions are varied, resulting dose-response estimates can vary by 

several orders of magnitude.24,25,26,27 These findings are not supportive of interpreting the CIIT 

model results as providing a conservative (health protective) estimate of human risk.28 EPA 

research also examined the contribution of the two-stage modeling for formaldehyde towards 

characterizing the relative weights of key events in the mode-of-action of a carcinogen. For 

example, the model-based inference in the published CIIT study that formaldehyde’s direct 

mutagenic action is not relevant to the compound’s tumorigenicity was found not to hold under 

variations of modeling assumptions.EPA is currently in the process of reevaluting the doseresponse estimates for formaldehyde.

29

Based on the developments of the last decadein the 1990’s and early 2000’sdecade, in 

2004, the working group of the IARC concluded that formaldehyde is carcinogenic to humans 

(Group 1), on the basis of sufficient evidence in humans and sufficient evidence in experimental 

animals - a higher classification than previous IARC evaluations. After reviewing the currently 

available epidemiological evidence, the IARC (2006) characterized the human evidence for 

formaldehyde carcinogenicity as “sufficient,” based upon the data on nasopharyngeal cancers; the 

 21 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologically 

motivated computational modeling of formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432-447. 22 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2004. Human respiratory 

tract cancer risks of inhaled formaldehyde: Dose-response predictions derived from biologically-motivated 

computational modeling of a combined rodent and human dataset. Tox Sci 82: 279-296. 23 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard characterization and dose-response 

assessment for carcinogenicity by the route of inhalation. CIIT, September 28, 1999. Research Triangle Park, NC. 24 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal Growth Models for Formaldehyde with 

Relevance to Other Biologically-Based Dose Response (BBDR) Models. U.S. Environmental Protection Agency, 

Washington, D.C., EPA/600/R-08/103, 2008 25 Subramaniam, R; Chen, C; Crump, K; .et .al. (2008) Uncertainties in biologically-based modeling of 

formaldehyde-induced cancer risk: identification of key issues. Risk Anal 28(4):907-923. 26 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model for formaldehydeinduced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237 27 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 28 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 29 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model for formaldehydeinduced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237
6-21

epidemiologic evidence on leukemia was characterized as “strong.”30

Formaldehyde exposure also causes a range of noncancer health effects, including 

irritation of the eyes (burning and watering of the eyes), nose and throat. Effects from repeated 

exposure in humans include respiratory tract irritation, chronic bronchitis and nasal epithelial 

lesions such as metaplasia and loss of cilia. Animal studies suggest that formaldehyde may also 

cause airway inflammation – including eosinophil infiltration into the airways. There are several 

studies that suggest that formaldehyde may increase the risk of asthma – particularly in the 

young.

EPA is reviewing the recent 

work cited above from the NCI and NIOSH, as well as the analysis by the CIIT Centers for 

Health Research and other studies, as part of a reassessment of the human hazard and doseresponse associated with formaldehyde.

31,32.33,34

6.2.3.6 Polycyclic Aromatic Hydrocarbons (PAHs) 

At least seven PAH compounds are classified by EPA as probable human carcinogens 

based on animal data, including benzo(a)anthracene35, benzo(b)fluoranthene36, 

benzo(k)fluoranthene37, benzo(a)pyrene38, chrysene39, dibenz(a,h)anthracene40

 30 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-tert-Butoxypropan-2-

ol. Monographs Volume 88. World Health Organization, Lyon, France.

, and indeno(1,2,3-

31 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological profile for Formaldehyde. 

Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. 

http://www.atsdr.cdc.gov/toxprofiles/tp111.html 32 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint 

sponsorship of the United Nations Environment Programme, the International Labour Organization, and the World 

Health Organization, and produced within the framework of the Inter-Organization Programme for the Sound 

Management of Chemicals. Geneva. 33 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological profile for Formaldehyde. 

Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. 

http://www.atsdr.cdc.gov/toxprofiles/tp111.html 34 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint 

sponsorship of the United Nations Environment Programme, the International Labour Organization, and the World 

Health Organization, and produced within the framework of the Inter-Organization Programme for the Sound 

Management of Chemicals. Geneva. 35 U.S. EPA (1997). Integrated Risk Information System File of benzo(a)anthracene. Research and Development, 

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0454.htm. 36 U.S. EPA (1997). Integrated Risk Information System File of benzo(b)fluoranthene. Research and Development, 

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0453.htm. 37 U.S. EPA (1997). Integrated Risk Information System File of benzo(k)fluoranthene. Research and Development, 

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0452.htm. 38 U.S. EPA (1998). Integrated Risk Information System File of benzo(a)pyrene. Research and Development,

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0136.htm.

Comment [A60]: Please provide 

citations for each of these sentences. 

Comment [A61]: Are these human 

studies or animal studies? please clarify.
6-22

cd)pyrene41. Recent studies haveOne research laboratory has recentlyhave found shown that 

maternal exposures to PAHs in a population of pregnant women were associated with several 

adverse birth outcomes, including low birth weight and reduced length at birth, as well as 

impaired cognitive development at age three.42,43

6.2.3.7 Acetaldehyde

 EPA has not yet evaluated these recent studies.

Acetaldehyde is classified in EPA’s IRIS database as a probable human carcinogen, based on 

nasal tumors in rats, and is considered toxic by the inhalation, oral, and intravenous routes.44 

Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS in the 11th

Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the 

IARC. 45,46

The primary noncancer effects of exposure to acetaldehyde vapors include irritation of the 

eyes, skin, and respiratory tract.

 EPA is currently conducting a reassessment of cancer risk from inhalation exposure to 

acetaldehyde.

47 In short-term (4 week) rat studies, degeneration of olfactory 

epithelium was observed at various concentration levels of acetaldehyde exposure. 48

 39U.S. EPA (1997). Integrated Risk Information System File of chrysene. Research and Development, National 

Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0455.htm

 Data from 

these studies were used by EPA to develop an inhalation reference concentration. Some 

asthmatics have been shown to be a sensitive subpopulation to decrements in functional 

40 U.S. EPA (1997). Integrated Risk Information System File of dibenz(a,h)anthracene. Research and Development, 

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0456.htm. 41 U.S. EPA (1997). Integrated Risk Information System File of indeno(1,2,3-cd)pyrene. Research and 

Development, National Center for Environmental Assessment, Washington, DC. This material is available 

electronically at http://www.epa.gov/ncea/iris/subst/0457.htm. 42 Perera, F.P.; Rauh, V.; Tsai, W-Y.; et al. (2002) Effect of transplacental exposure to environmental pollutants on 

birth outcomes in a multiethnic population. Environ Health Perspect. 111: 201-205. 43 Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D.; Diaz, D.; Hoepner, L.; Barr, D.; Tu, Y.H.; Camann, 

D.; Kinney, P. (2006) Effect of prenatal exposure to airborne polycyclic aromatic hydrocarbons on 

neurodevelopment in the first 3 years of life among inner-city children. Environ Health Perspect 114: 1287-1292. 44 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and Development, National 

Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/iris/subst/0290.htm. 45 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens 

available at: http://ntp.niehs.nih.gov/go/16183. 46 International Agency for Research on Cancer (IARC). 1999. Re-evaluation of some organic chemicals, hydrazine, 

and hydrogen peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of Chemical to Humans, Vol 

71. Lyon, France. 47 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material is available 

electronically at http://www.epa.gov/iris/subst/0290.htm. 48 Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982). Inhalation toxicity of acetaldehyde in rats. I. Acute and 

subacute studies. Toxicology. 23: 293-297.

Comment [A62]: Unclear what this 

means. What percent of the population of 

asthmatics studies showed this effect? 

What is statistically significant? More 

clarity on what the authors found would 

be helpful.
6-23

expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde inhalation. 49

6.2.3.8 Nickel

 The 

agency is currently conducting a reassessment of the health hazards from inhalation exposure to 

acetaldehyde.

Nickel is an essential element in some animal species, and it has been suggested it may be 

essential for human nutrition. Nickel dermatitis, consisting of itching of the fingers, hand and

forearms, is the most common effect in humans from chronic (long-term) skin contact with 

nickel. Respiratory effects have also been reported in humans from inhalation exposure to nickel. 

No information is available regarding the reproductive or developmental effects of nickel in 

humans, but animal studies have reported such effects. Human and animal studies have reported 

an increased risk of lung and nasal cancers from exposure to nickel refinery dusts and nickel

subsulfide. Animal studies of soluble nickel compounds (i.e., nickel carbonyl) have reported lung 

tumors. The EPA has classified nickel refinery subsulfide as a Group A, human carcinogens and 

nickel carbonyl as a Group B2, probable human carcinogen. 50

6.2.3.9 Manganese

Health effects in humans have been associated with both deficiencies and excess intakes 

of manganese. Chronic (long-term) exposure to low levels of manganese in the diet is considered 

to be nutritionally essential in humans, with a recommended daily allowance of 2 to 5 milligrams 

per day. Chronic exposure to high levels of manganese by inhalation in humans results primarily 

in CNS effects. Visual reaction time, hand steadiness, and eye-hand coordination were affected in 

chronically-exposed workers. Manganism, characterized by feelings of weakness and lethargy, 

tremors, a masklike face, and psychological disturbances, may result from chronic exposure to 

higher levels. Impotence and loss of libido have been noted in male workers afflicted with 

manganism attributed to inhalation exposures. The EPA has classified manganese in Group D, not 

classifiable as to carcinogenicity in humans.51

 49 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized acetaldehyde induces histaminemediated bronchoconstriction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1): 940-943. 50 U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional 

Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available 

on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf 51 U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional 

Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664-1665. Available 

on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf

Comment [A63]: Citations and 

information on dose levels would be 

helpful.

Comment [A64]: Please provide 

citation and dose level. Information on its 

relationship to releases from these sources 

would be helpful.

Comment [A65]: Please provide 

citations. Is there an IRIS file that can be 

cited for the non cancer effects of most 

concern?
6-24

6.2.3.10 Other Air Toxics

In addition to the compounds described above, other compounds in gaseous hydrocarbon 

and PM emissions from boilers would be affected by this rule. Information regarding the health 

effects of these compounds can be found in EPA’s IRIS database.52

6.3 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 a couple of sensitivity 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 6-7. 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. 

 52 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris
6-25

Table 6-7. 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) $22,000 N/A

No Threshold (Laden) $53,000 N/A

Threshold (Pope) $16,100 -27%

Threshold (Laden) $36,000 -32%

Discount Rate (with Epidemiology 

Study)

3% (Pope) $22,000 N/A

3% (Laden) $53,000 N/A

7% (Pope) $20,000 -9.1%

7% (Laden) $48,000 -9.4%

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 SAB-HES and the 

NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions and 

uncertainties. For example, for key assumptions underlying the estimates for premature mortality, 

which typically account for at least 90% of the total monetized benefits, we were able to quantify 

include the following: 

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 
6-26

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).

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.

6.4 Comparison of Benefits and Costs

Using a 3% discount rate, we estimate the total monetized benefits of the proposed Boiler 

MACT and Area Source Rule to be $22 billion to $53 billion in the implementation year (2013). 

Using a 7% discount rate, we estimate the total monetized benefits of the proposed Boiler MACT 

and Area Source Rule to be $209 billion to $48 billion. The combined annualized social costs of 

the proposed Boiler MACT and Area Source Rule are $12 billion at a 7% interest rate.53

 53 For more information on the annualized costs, please refer to Section 4 of this RIA.

 Thus, 
6-27

the combined net benefits are $9.5 billion to $41 billion at a 3% discount rate and $7.5 billion to 

$36 billion at a 7% discount rate. The annualized social costs of the proposed Boiler MACT only 

are $5.1 billion, and the annualized social costs of the proposed Area Source Rule are $4.9 billion 

at a 7% discount rate. The net benefits for the proposed Boiler MACT for major sources only are 

$11 billion to $35 billion and $9.8 billion to $31 billion, at 3% and 7% discount rates, 

respectively. The net benefits for the Boiler Area Source rule only are $-1.9 billion to $5.4 billion 

and $-2.4 to $4.2 billion, at 3% and 7% discount rates, respectively. All estimates are in 2008$. 

Figures 6-4 and 7-5 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 420,000 tons of carbon monoxide, 21 tons of 

mercury, 48,000 tons of HCl, and 1,200 tons of HF each year have not been included in these 

estimates. EPA believes that the benefits are likely to exceed the costs under this rulemaking 

even when taking into account uncertainties in the cost and benefit estimates.
6-28

-$10

$0

$10

$20

$30

$40

$50

$60

Pope et al.

Laden et al.

Billions (2008$)

Cost estimates combined with total monetized benefits estimates derived from 2 

epidemiology functions and 12 expert functions

 

-$10

$0

$10

$20

$30

$40

$50

$60

Pope et al.

Laden et al.

Billions (2008$)

Cost estimates combined with total monetized benefits estimates derived from 2 

epidemiology functions and 12 expert functions

 

Figure 6-4. Net Benefits for the Proposed Boiler Rules at 3% Discount Rate

a
6-29

a Net Benefits are quantified in terms of PM2.5 benefits for implementation year (2013). 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.
6-30

-$10

$0

$10

$20

$30

$40

$50

$60

Pope et al.

Laden et al.

Billions (2008$)

Cost estimates combined with total monetized benefits estimates derived from 2 

epidemiology functions and 12 expert functions

 

-$10

$0

$10

$20

$30

$40

$50

$60

Pope et al.

Laden et al.

Billions (2008$)

Cost estimates combined with total monetized benefits estimates derived from 2 

epidemiology functions and 12 expert functions

 

Figure 6-5. Net Benefits for the Proposed Boiler Rules at 7% Discount Ratea

a Net Benefits are quantified in terms of PM2.5 benefits for implementation year (2013). 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.
6-31
R-1

REFERENCES

Aldy, J., and W. Pizer. 2009. “The Competitiveness Impacts of Climate Change Mitigation 

Policies.” Pew Center on Global Climate Change.

American Chemistry Council Inc. 2007. “Snapshot of the Chemical Industry in the United 

States.” <http://www.americanchemistry.com/>.

Benwart, S.J. 2006. “Paper & Forest Products. Standard and Poor’s Industry Surveys.” 176 (28). 

Broda, C., N. Limao, and D. Weinstein. 2008a. “Export Supply Elasticities.” http://faculty.

chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/

TradeElasticities.html. Accessed September 2009.

Broda, C., N. Limao, and D.E. Weinstein. 2008b. “Optimal Tariffs and Market Power: The 

Evidence.” American Economic Review 98(5):2032-2065.

Berck, P., and S. Hoffmann. 2002. “Assessing the Employment Impacts of Environmental and 

Natural Resource Policy.” Environmental and Resource Economics 22(1):133-156.

Berman, E. and L. T. M. Bui. 2001. Environmental regulation and labor demand: Evidence from 

the south coast air basin. Journal of Public Economics 79(2):265-295.

Cole, M. A. and R. J. Elliott. 2007. Do environmental regulations cost jobs? An industry-level 

analysis of the UK. B E Journal of Economic Analysis & Policy 7(1) (Topics), Article 

28..

Dow Chemical Company. 2008. “Transforming: The Dow Chemical Company 2007 10-K and 

Stockholder Summary.” <http://www.dow.com/financial/2007ann/>.

Drevnick, P.E., D.E. Canfield, P.R. Gorski, A.L.C. Shinneman, D.R. Engstrom, D.C.G. Muir, 

G.R. Smith, P.J. Garrison, L.B. Cleckner, J.P. Hurley, R.B. Noble, R.R. Otter, and 

J.T.Oris. 2007. Deposition and cycling of sulfur controls mercury accumulation in Isle 

Royale fish. Environmental Science and Technology 41(21):7266-7272.

Dun & Bradstreet. 2007. 2007 D&B Million Dollar Directory. Pennsylvania: Dun & Bradstreet 

Inc.

Dun & Bradstreet Small Business Solutions. 2008. Small Business Database. 

<http://smallbusiness.dnb.com/default.asp?bhcd2=1107465546>.

Edison Electric Institute. “Income Statement: Q4 2008 Financial Update. Quarterly Report of the 

U.S. Shareholder-Owned Electric Utility Industry.” <http://www.eei.org>.

Fann, N., C.M. Fulcher, B.J. Hubbell. 2009. The influence of location, source, and emission type 

in estimates of the human health benefits of reducing a ton of air pollution. Air Qual 

Atmos Health (2009) 2:169-176.
R-2

Federal Reserve Board. 2008. “Industrial Production and Capacity Utilization: Capacity 

Utilization.” Series ID: G17/CAPUTL/CAPUTL.GMF.S & 

G17/CAPUTL/CAPUTL.G311.S. <http://www.federalreserve.gov/datadownload/>. 

(Food).

Federal Reserve Board. 2008. “Industrial Production and Capacity Utilization: Electric Power 

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

G17/KW/KW.G311.S. <http://www.federalreserve.gov/datadownload/>. (Food).

Federal Reserve Board. 2008. “Industrial Production and Capacity Utilization: Industrial 

Capacity.” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G311.S. 

<http://www.federalreserve.gov/datadownload/>. (Food).

Federal Reserve Board. 2008. “Industrial Production and Capacity Utilization: Industrial 

Production.” Series ID: G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.S & 

G17/IP_MAJOR_INDUSTRY_GROUPS/IP.G311.S. 

<http://www.federalreserve.gov/datadownload/>. (Food).

Federal Reserve Board. 2009. “Electric Power Use: Manufacturing and Mining.” Series ID: 

G17/KW/KW.G324.S & G17/KW/KW.GMF.S. <http://www.federalreserve.gov/

datadownload/>. (Petroleum).

Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial 

Capacity” Series ID: G17/CAP/CAP.G324.S & G17/CAP/CAP.GMF.S. 

<http://www.federalreserve.gov/datadownload/>. (Petroleum).

Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial 

Production.” Series ID: G17/IP_DURABLE_GOODS_DETAIL/IP.G324.S & 

G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.S. 

<http://www.federalreserve.gov/datadownload/>. (Petroleum).

Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Capacity 

Utilization.” Series ID: G17/CAPUTL/CAPUTL.G324.S & G17/CAPUTL/

CAPUTL.GMF.S. <http://www.federalreserve.gov/datadownload/>. (Petroleum).

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>. (Paper).

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/>. (Paper).

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/>. (Paper).
R-3

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/>. (Paper).

Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Capacity 

Utilization.” Series ID: G17/CAPUTL/CAPUTL.G2211.S. 

<http://www.federalreserve.gov/datadownload/>. Last released January 15, 2010. 

(Electric Utilities).

Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial 

Capacity.” Series ID: G17/CAP/CAP.G2211.S. 

<http://www.federalreserve.gov/datadownload/>. Last released January 15, 2010. 

(Electric Utilities).

Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial 

Production.” Series ID: G17/IP_MINING_AND_UTILITY_DETAIL/IP.G2211.S. 

<http://www.federalreserve.gov/datadownload/>. Last released January 15, 2010. 

(Electric Utilities).

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/>.

(Wood Products).

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. (Wood Products).

Federal Reserve Board. 2009. “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. 

(Wood Products).

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.G321.S. 

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

(Wood Products).

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/>. 

(Chemicals).
R-4

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/>. (Chemicals).

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/>. (Chemicals).

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/>. (Chemicals).

Gale Research Inc. 2007. Ward’s Business Directory of U.S. Private and Public Companies. 

Detroit: Gale Research.

Graves, T. 2008. “Foods and Nonalcoholic Beverages.” Standard and Poor’s Industry Surveys. 

176 (25).

Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industrial Activity: 

Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of 

Manufactures.” Journal of Political Economy 110(6):1175-1219.

Henderson, J. V. 1996. “Effects of Air Quality Regulation.” American Economic Review

86(4):789-813.

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.

Hoovers. 2007. Free Content, Company Information. <http://www.hoovers.com/free/>.

Internal Revenue Service, U.S. Department of Treasury. 2008. “Corporation Source Book: Data 

File 2005.” <http://www.irs.gov/taxstats/article/0,,id=167415,00.html>; (January, 14 

2009).

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).

Just, R.E., D.L. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy. 

Northampton, MA: Edward Elgar.

Kochi, I., B. Hubbell, and R. Kramer. 2006. “An Empirical Bayes Approach to Combining 

Estimates of the Value of Statistical Life for Environmental Policy Analysis.”

Environmental and Resource Economics 34:385-406.
R-5

Kraft Foods Inc. 2008. Form 10-K, Period ending December 31, 2007. 

<http://www.kraft.com/Investor/sec-filings-annual-report/>.

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate 

Air Pollution and Mortality.” American Journal of Respiratory and Critical Care 

Medicine 173:667-672.

Morgenstern, R. D., W. A. Pizer, and J. S. Shih. 2002. “Jobs versus the Environment: An 

Industry-Level Perspective.” Journal of Environmental Economics and Management

43(3):412-436.

Morgenstern, R.D., M. Ho, J.S. Shih, and X. Zhang. 2004. “The Near-Term Impacts of Carbon 

Mitigation Policies on Manufacturing Industries.” Energy Policy 32(16): 825-1841. 

Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.”

Journal of Policy Analysis and Management 21(2):253-270.

Munthe, J., R.A. Bodaly, B.A. Branfireun, C.T. Driscoll, C.C. Gilmour, R. Harris, M. Horvat, M. 

Lucotte, and O. Malm. 2007. “Recovery of Mercury-Contaminated Fisheries.” AMBIO: A 

Journal of the Human Environment 36:33-44.

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: The National Academies Press.

O’Reilly, R. 2008. “Chemicals.” Standard and Poor’s Industry Surveys. 176 (28).

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine 

Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.

Roman, H.A., K.D. Walker, T.L. Walsh, L. Conner, H.M. Richmond, .J. Hubbell, and P.L. 

Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in 

Ambient Fine Particulate Matter in the U.S.” Environ. Sci. Technol. 42(7):2268-2274.

Shoven, J.B., and J. Whalley. 1995. Applying General Equilibrium. New York: Cambridge 

University Press. 

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.

U.S. Bureau of Economic Analysis (BEA). 2010. Table 1.1.9. Implicit Price Deflators for Gross 

Domestic Product. Accessed January 20, 2010. 

http://www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N.

U.S. Bureau of Labor Statistics (BLS). 2009. Producer Price Index. Series ID:PCU321—321—& 

PCUOMFG—OMFG—<http://www.bls.gov/ppi/home.htm>. (Data accessed on January 

8, 2010).
R-6

U.S. Bureau of Labor Statistics (BLS). 2009. Producer Price Index. Series ID:PCU322–322– & 

PCUOMFG–OMFG–. <http://www.bls.gov/ppi/home.htm>.

U.S. Bureau of Labor Statistics (BLS). 2009. Producer Price Index. Series ID:PCU325—325—& 

PCUOMFG—OMFG—<http://www.bls.gov/ppi/home.htm>.

U.S. Bureau of Labor Statistics (BLS). 2009. “Producer Price Index.” Series ID: PCU3313–

3313– & PCUOMFG–OMFG–. <http://www.bls.gov/ppi/home.htm>.

U.S. Census Bureau. 1997. “1997 Economic Census: Sector 00: All sectors: Core Business 

Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS 

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

U.S. Census Bureau. 2001. 1997 Census NAICS Definitions.

<http://www.census.gov/epcd/ec97/industry/E322.HTM#def>.

U.S. Census Bureau. 2002. “2002 Economic Census: Sector 00: All sectors: Core Business 

Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS 

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

U.S. Census Bureau. 2002. “2002 Economic Census: Sector 22: Utilities: Geographic Area 

Series: Summary Statistics: 2002.” <http://factfinder.census.gov>; (November 10, 2008).

U.S. Census Bureau. 2005a. 2002 Census of Governments, Volume 4, Number 3, Finances of 

County Governments: 2002 GC02(4)-3. U.S. Washington, DC: Government Printing 

Office. Table 12.

U.S. Census Bureau. 2005b. 2002 Census of Governments, Volume 4, Number 4, Finances of 

Municipal and Township Governments: 2002 GC02(4)-4. Washington, DC: Government 

Printing Office. Table 13.

U.S. Census Bureau. 2008a. “Exclusions from the Economic Census.”

<http://www.census.gov/econ/census02/guide/excluded.htm>; (January 14, 2009).

U.S. Census Bureau. 2008b. “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>.

U.S. Census Bureau. 2009a. “2002 NAICS Definition.” <http://www.census.gov/cgi-bin/

sssd/naics/naicsrch?code=324&search=2002%20NAICS%20Search>. (January 9, 2009).

U.S. Census Bureau. 2009b. “Sector 00: All Sectors: Core Business Statistics Series: 

Comparative Statistics for the United States and the States (1997 NAICS Basis): 2002 

and 1997.”

U.S. Census Bureau. 2009c. 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>.
R-7

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>.

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 January 6, 2010.

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. 

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.

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. http://factfinder.census.gov. Accessed on December 27, 2009.

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 27, 

2009.

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.

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All 

sectors: Core Business Statistics Series: Comparative Statistics for the United States and 

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

2008.

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All 

sectors: Core Business Statistics Series: Comparative Statistics for the United States and 

the States (2007 NAICS Basis): 2002 and 2007.” <http://factfinder.census.gov>; (January 

4, 2010). 

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 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).
R-8

U.S. Department of Energy, Energy Information Administration (EIA). 1996–2008. “Petroleum 

Supply Annuals, Volume 1.”

<http://www.eia.doe.gov/oil_gas/petroleum/data_publications/petroleum_supply_annual/

psa_volume1/psa_volume1.htmls>. Accessed on December 28, 2009.

U.S. Department of Energy, Energy Information Administration (EIA). 1999. U.S. 

Environmental Protection Agency (EPA). 2008. “Regulatory Impact Analysis for the 

Petroleum Refineries NSPS.” <http://www.epa.gov/ttn/ecas/regdata/RIAs/ 

finalpetroleumrefineriesnspsria43008.pdf>.

U.S. Department of Energy, Energy Information Administration. 2007. “2002 Energy 

Consumption by Manufacturers—Data Tables.” Tables 3.2, N3.2 and 5.2. Washington, 

DC: DOE. <http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>.

U.S. Department of Energy, Energy Information Administration. 2007a. “2006 Annual Electric 

Generator Report—Generator (Existing) File.”

U.S. Department of Energy, Energy Information Administration. 2007b. “2006 Energy 

Consumption by Manufacturers—Data Tables.” Table 3.1. 

http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html. 

U.S. Department of Energy, Energy Information Administration. 2007c. “State Electricity 

Profiles 2007.” DOE/EIA-0348(01)/2. p. 314. 

<http://www.eia.doe.gov/cneaf/electricity/st_profiles/sep2007.pdf>. (Data accessed on 

January 6, 2010)

U.S. Department of Energy, Energy Information Administration. 2008. “Assumptions to the 

Annual Energy Outlook 2008: With Projections for 2030.”

<http://www.eia.doe.gov/oiaf/aeo/assumption/>.

U.S. Department of Energy, Energy Information Administration. 2009a. “2006 Energy 

Consumption by Manufacturers—Data Tables.” Table 3.1. Washington, DC: DOE. 

<http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html>.

U.S. Department of Energy, Energy Information Administration. 2009b. “Current and Historical 

Monthly Retail Sales, Revenues and Average Revenue per Kilowatt hour by State and by 

Sector (Form EIA-826).”

<http://www.eia.doe.gov/cneaf/electricity/epm/table5_5_b.html>; (January 21, 2010 and 

February 16, 2010).

U.S. Department of Energy, Energy Information Administration. 2009c. “Electric Power Annual 

2007: Table 6.3.” DOE/EIA-0348(2007).

U.S. Department of Energy, Energy Information Administration. 2009d. “Official Energy 

Statistics from the U.S. Government: Glossary.” <http://www.eia.doe.gov/glossary/

index.html>; (January 29, 2009).
R-9

U.S. Department of Energy, Energy Information Administration (EIA). 2009e. “Refinery 

Capacity Report 2009.”

<http://www.eia.doe.gov/oil_gas/petroleum/data_publications/refinery_capacity_data/ref

capacity.html >. Released on June 25, 2009.

U.S. Department of Energy, Energy Information Administration. 2009f. “Status of Electricity 

Restructuring by State.” <http://www.eia.doe.gov/cneaf/electricity/page/restructuring/

restructure_elect.html>; (February 3, 2009).

U.S. Department of Energy, Energy Information Administration. 2010a. “Annual Energy 

Outlook 2010 Reference Case Table A8.”

<http://www.eia.doe.gov/oiaf/aeo/pdf/appa.pdf>.

U.S. Department of Energy, Energy Information Administration. 2010b. “Electric Power Annual 

2008: Table 1.1.” Accessed on January 29, 2010.

U.S. Department of Energy, Energy Information Administration. 2010c. “Electric Power Annual 

2008: Table 2.1.” Accessed on January 29, 2010.

U.S. Department of Energy, Energy Information Administration. 2010d. “Electric Power Annual 

2008: Table 8.1, Table 8.3, Table 8.4, Table 8.5, Table 8.6.” DOE/EIA-0348 (2008).

U.S. Department of Labor, Occupational Safety and Health Administration (OSHA). 2003. 

OSHA Technical Manual, Section IV: Chapter 2, Petroleum Refining Processes. TED 01-

00-015. Washington, DC: U.S. DOL. 

<http://www.osha.gov/dts/osta/otm/otm_iv/otm_iv_2.html>. As obtained on October 23, 

2006. 

U.S. Department of Labor; Bureau of Labor Statistics. 2010. Producer Price Indexes. 

<http://www.bls.gov/pPI/Series Id: PCU311—311—Food Manufacturing & 

PCUOMFG–OMFG–Total Manufacturing>.

U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. 1999. 

OAQPS Economic Analysis Resource Document. 

http://www.epa.gov/ttn/ecas/analguid.html.

U.S. Environmental Protection Agency. September 2000. Guidelines for Preparing Economic 

Analyses. EPA 240-R-00-003. Washington, DC: EPA. http://yosemite.epa.gov/ee/

epa/eed.nsf/webpages/Guidelines.html. Accessed August 2009.

U.S. Environmental Protection Agency. 2002. “Profile of the Pulp and Paper Industry.” Sector 

Notebook Project. <http://www.epa.gov/Compliance/resources/publications/assistance/

sectors/notebooks/index.html>.

U.S. Environmental Protection Agency. 2008. Regulatory Impact Analysis for the Petroleum 

Refineries NSPS, Chapter 3. Available at< http://www.epa.gov/ttn/ecas/regdata/RIAs/ 

finalpetroleumrefineriesnspsria43008.pdf>.
R-10

U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. March 

2008. EMPAX-CGE Model Documentation.

http://pubweb.epa.gov/ttn/ecas/models/empax_model_documentation.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic 

Analyses. EPA 240-R-00-003. National Center for Environmental Economics, Office of 

Policy Economics and Innovation. Washington, DC. September. Available at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>.

U.S. Environmental Protection Agency. 2006. Regulatory Impact Analysis, 2006 National 

Ambient Air Quality Standards for Particulate Matter, Chapter 5. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf >.

U.S. Environmental Protection Agency (U.S. EPA). 2008. Guidelines for Preparing Economic 

Analyses: External Review Draft. National Center for Environmental Economics, Office 

of Policy Economics and Innovation. Washington, DC. September. Available at 

<http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE-0516-01.pdf/$File/EE-0516-

01.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for 

Sulfur Oxides—Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Guidelines for Preparing Economic 

Analyses: External Review Draft. National Center for Environmental Economics, Office 

of Policy Economics and Innovation. Washington, DC. Available at 

<http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE-0516-01.pdf/$File/EE-0516-

01.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008d. Integrated Science Assessment for 

Oxides of Nitrogen and Sulfur–Ecological Criteria National (Final Report). National 

Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139.

December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.

U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: 

National Emission Standards for Hazardous Air Pollutants from the Portland Cement 

Manufacturing Industry. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. April. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf >.

U.S. Environmental Protection Agency (U.S. EPA). 2009b. Proposed SO2 NAAQS Regulatory 

Impact Analysis (RIA). Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. November. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/pso2full11-16-09.pdf >.
R-11

U.S. Environmental Protection Agency (U.S. EPA). 2009c. Integrated Science Assessment for 

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

Environmental Assessment—RTP Division. December. Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Integrated Science Assessment for 

Carbon Monoxide. National Center for Environmental Assessment, Research Triangle 

Park, NC. EPA/600/R-09/019F. January. Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=218686>.

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Final Regulatory Impact Analysis 

(RIA) for the NO2 National Ambient Air Quality Standards (NAAQS). Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. January. Available at < 

http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2007. SAB 

Advisory on EPA’s Issues in Valuing Mortality Risk Reduction. EPA-SAB-08-001.

October. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$F

ile/sab-08-001.pdf >.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009a. 

Review of EPA’s Integrated Science Assessment for Particulate Matter (First External 

Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

e/73ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-

unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009b.

Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: 

Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-

009. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

e/723FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>.

U.S. International Trade Commission. 2008a. “U.S. Domestic Exports” & “U.S. Imports for 

Consumption.” <http://dataweb.usitc.gov/scripts/user_set.asp>.

U.S. International Trade Commission. 2008b. “U.S. Total Exports” & “U.S. Imports for 

Consumption.” <http://dataweb.usitc.gov/scripts/user_set.asp>.

U.S. International Trade Commission. 2009. “U.S. Domestic Exports” & “U.S. Imports for 

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

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>.
R-12

Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market 

Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76.
APPENDIX A

OAQPS MULTIMARKET MODEL TO ASSESS THE ECONOMIC 

IMPACTS OF ENVIRONMENTAL 

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. For example, Morgenstern and 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 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
A-2

– market prices rise to reflect higher energy and other nonenergy 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. 

– 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

A.2.2 Economic Behavior

Table A-4 

reports the baseline employment for each of the 100 sectors.

A.2.2.1 U.S. Supply

In a postpolicy scenario, indus try 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).

 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

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

Price

Quantity

Supply

Q

Supply′

P

Q′

Price

Quantity

Supply

Q

Supply′

P

Q′

Figure A-1. Direct Compliance Costs Reduce Production Rates at Benchmark Prices

Price

Quantity

Supply

Q

Supply′

P

Q′

Supply′′

Q′′
A-16

Price

Quantity

Supply

Q

Supply′

P

Q′

Supply′′

Q′′

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

α

(A.5)

where

Qg

′ = with-policy supply quantity (g)

b = calibrated scale parameter for the supply price relationship

Pg

′ = with-policy price for output (g)

t = direct compliance costs per unit of supply

αgi = input use ratio (g using input i)

Pi

′ = with-policy input (i) price

Pi = benchmark input (i) price

g ε = 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 
A-17

(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 ε = 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 ε ). We obtained these estimates for a variety of industry groups from a 

recently published article by Broda and colleagues (2008b). 

A.2.2.3 Price Elasticity of 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

 1 Broda et al.’s intent was to use these categories to describe or proxy for domestic market power.
A-18

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-19

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-20

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-21

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.

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 

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

 1 See Chapter 14 of the GTAP 7 Database Documentation for the full description of the parameters at 

), 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 

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.
A-22

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, 1

A.2.2.5 Demand

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. 

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 ′ = α ′ (A.7)

Qi

′ = with-policy input demand quantity (i)

αgi = 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:

 1 No standard deviations were calculated for the 3- and 4-digit codes that had only one observation (i.e., Broda et 

al.’s model used the exact 3- or 4-digit code).
A-23

( ) g

g Pg Q a η ′ = ′ (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)

ηg = 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, 

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.
A-24

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-25

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-26

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.
APPENDIX B

DETAILED ECONOMIC MODEL RESULTS BY SECTOR
B-1

Table B-1. Prices (Percentage Change from Benchmark): Industry Detail

Major Area

Opt 4 Opt 1 Opt 2a Opt 1

Energy 0.14% 0.18% 0.00% −0.01%

Nonmanufacturing 0.01% 0.01% 0.04% 0.01%

Manufacturing

Food, beverages, and textiles 0.02% 0.05% 0.04% 0.15%

Lumber, paper, and printing 0.29% 0.48% 0.38% 0.31%

Chemicals 0.02% 0.07% −0.01% −0.02%

Plastics and rubber 0.01% 0.06% −0.01% −0.04%

Nonmetallic minerals 0.01% 0.02% −0.01% −0.02%

Primary metals 0.04% 0.08% 0.00% −0.01%

Fabricated metals 0.01% 0.03% −0.01% −0.02%

Machinery and equipment 0.01% 0.02% 0.00% −0.01%

Electronic equipment 0.00% 0.00% 0.00% −0.01%

Transportation equipment 0.00% 0.01% 0.00% 0.00%

Other 0.01% 0.02% 0.02% 0.01%

Wholesale and retail trade −0.01% −0.01% 0.00% 0.01%

Transportation services 0.01% 0.01% 0.00% 0.00%

Other services 0.00% 0.00% 0.01% 0.08%
B-2

Table B-2. Production (Percentage Change from Benchmark): Industry Detail

Major Area

Opt 4 Opt 1 Opt 2a Opt 1

Energy −0.07% −0.10% 0.00% −0.01%

Nonmanufacturing −0.01% −0.02% −0.02% −0.04%

Manufacturing

Food, beverages, and textiles −0.02% −0.05% −0.03% −0.11%

Lumber, paper, and printing −0.12% −0.21% −0.16% −0.16%

Chemicals −0.04% −0.11% 0.00% −0.01%

Plastics and rubber −0.02% −0.06% −0.01% −0.02%

Nonmetallic minerals −0.01% −0.02% −0.01% −0.01%

Primary metals −0.04% −0.09% 0.00% 0.00%

Fabricated metals −0.01% −0.02% 0.00% −0.01%

Machinery and equipment −0.02% −0.03% 0.00% 0.00%

Electronic equipment −0.01% −0.01% 0.00% 0.00%

Transportation equipment −0.01% −0.02% 0.00% 0.00%

Other −0.02% −0.04% −0.04% −0.03%

Wholesale and retail trade 0.00% −0.01% −0.01% −0.02%

Transportation services −0.02% −0.04% 0.00% −0.01%

Other services 0.00% −0.01% −0.01% −0.04%
B-3

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

Major Area

Opt 4 Opt 1 Opt 2a Opt 1

Energy −0.03% −0.05% 0.00% −0.01%

Nonmanufacturing −0.01% −0.01% −0.01% −0.04%

Manufacturing

Food, beverages, and textiles −0.01% −0.03% −0.02% −0.08%

Lumber, paper, and printing −0.07% −0.12% −0.05% −0.06%

Chemicals −0.03% −0.08% 0.00% −0.01%

Plastics and rubber −0.01% −0.04% −0.01% −0.02%

Nonmetallic minerals −0.01% −0.02% −0.01% −0.02%

Primary metals −0.02% −0.05% 0.00% −0.01%

Fabricated metals −0.01% −0.02% 0.00% −0.01%

Machinery and equipment −0.01% −0.01% 0.00% −0.01%

Electronic equipment 0.00% −0.01% 0.00% 0.00%

Transportation equipment 0.00% −0.01% 0.00% 0.00%

Other −0.01% −0.01% −0.01% −0.02%

Wholesale and retail trade 0.00% −0.01% 0.00% −0.02%

Transportation services −0.01% −0.02% 0.00% −0.01%

Other Services 0.00% −0.01% −0.01% −0.04%
B-4

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

Major Area

Opt 4 Opt 1 Opt 2a Opt 1

Energy 0.18% 0.27% 0.00% −0.01%

Nonmanufacturing 0.01% 0.01% 0.03% 0.01%

Manufacturing

Food, beverages, and textiles 0.03% 0.07% 0.05% 0.16%

Lumber, paper, and printing 0.30% 0.49% 0.39% 0.31%

Chemicals 0.02% 0.08% −0.01% −0.02%

Plastics and rubber 0.01% 0.06% −0.01% −0.04%

Nonmetallic minerals 0.00% 0.01% 0.00% −0.01%

Primary metals 0.04% 0.07% 0.00% −0.01%

Fabricated metals 0.01% 0.03% −0.01% −0.02%

Machinery and equipment 0.01% 0.02% 0.00% −0.01%

Electronic equipment 0.00% 0.00% 0.00% −0.01%

Transportation equipment 0.00% 0.01% 0.00% 0.00%

Other 0.02% 0.03% 0.03% 0.02%

Wholesale and retail trade 0.00% −0.01% 0.00% 0.00%

Transportation services 0.01% 0.01% 0.00% 0.00%

Other services 0.00% 0.00% 0.00% 0.01%
B-5

Table B-5. Exports (Percentage Change from Benchmark): Industry Detail

Major Area

Opt 4 Opt 1 Opt 2a Opt 1

Energy −0.01% −0.02% 0.00% 0.00%

Nonmanufacturing −0.01% −0.01% −0.03% −0.01%

Manufacturing

Food, beverages, and textiles −0.02% −0.03% −0.03% −0.09%

Lumber, paper, and printing −0.20% −0.33% −0.26% −0.21%

Chemicals −0.02% −0.07% 0.01% 0.02%

Plastics and rubber −0.01% −0.06% 0.01% 0.04%

Nonmetallic minerals −0.01% −0.02% 0.01% 0.02%

Primary metals −0.04% −0.07% 0.00% 0.01%

Fabricated metals 0.00% −0.02% 0.00% 0.01%

Machinery and equipment −0.02% −0.03% 0.01% 0.02%

Electronic equipment 0.00% 0.00% 0.00% 0.02%

Transportation equipment −0.01% −0.02% 0.00% 0.01%

Other −0.01% −0.02% −0.01% 0.01%

Wholesale and retail trade 0.00% 0.01% 0.00% 0.00%

Transportation services −0.01% −0.01% 0.00% 0.00%

Other services 0.00% 0.00% −0.01% −0.06%