April 2010 

Regulatory Impact Analysis: 

National Emission Standards for 

Hazardous Air Pollutants for 

Industrial, Commercial, and 

Institutional Boilers and Process 

Heaters 

Draft Report

Prepared for 

Tom Walton 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Benefit and Cost Group 

(MD-C439-02) 

Research Triangle Park, NC 27711 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709

and 

Kajal B. Kapur, Energy, Environment, and 

Economics Consulting 

1 Steubin Lane 

Charlottesville, VA 22911 

RTI Project Number 0209897.004.074 
_________________________________ 

RTI International is a trade name of Research Triangle Institute. 

 RTI Project Number 

 0209897.004.074 

 

Regulatory Impact Analysis: 

National Emission Standards for 

Hazardous Air Pollutants for 

Industrial, Commercial, and 

Institutional Boilers and Process 

Heaters 

Draft Report

April 2010

Prepared for 

Tom Walton 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Benefit and Cost Group 

(MD-C439-02) 

Research Triangle Park, NC 27711 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709

and 

Kajal B. Kapur, Energy, Environment, and 

Economics Consulting 

1 Steubin Lane 

Charlottesville, VA 22911 
iii 

CONTENTS 

Section Page

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

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

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

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

2.1 Food Manufacturing ............................................................................................. 2-1

2.1.1 Introduction .............................................................................................. 2-1

2.1.2 Supply and Demand Characteristics ........................................................ 2-4

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

2.2 Wood Product Manufacturing............................................................................ 2-16

2.2.1 Introduction ............................................................................................ 2-16

2.2.2 Supply and Demand Characteristics ...................................................... 2-19

2.2.3 Firm and Market Characteristics ............................................................ 2-24

2.3 Paper Manufacturing .......................................................................................... 2-33

2.3.1 Introduction ............................................................................................ 2-33

2.3.2 Supply and Demand Characteristics ...................................................... 2-37

2.3.3 Firm and Market Characteristics ............................................................ 2-43

2.4 Chemical Manufacturing ................................................................................... 2-51

2.4.1 Introduction ............................................................................................ 2-51

2.4.2 Supply and Demand Characteristics ...................................................... 2-54

2.4.3 Firm and Market Characteristics ............................................................ 2-58

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

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

3.2 Area Sources ........................................................................................................ 3-2
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 Governmental Jurisdictions and Not-for--Profit Enterprises ......... 5-5

5.2 Initial Regulatory Flexibility Analysis (IFRA) .................................................... 5-6

5.2.1 Reasons Why Action Is Being Considered .............................................. 5-6

5.2.2 Statement of Objectives and Legal Basis of Proposed Rules .................. 5-6

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......................................................................................... 5-7

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

6.3 Unquantified Benefits ........................................................................................ 6-12

6.3.1 Carbon Monoxide Benefits .................................................................... 6-12

6.3.2 Other SO2 Benefits ................................................................................. 6-13

6.3.3 HAP Benefits ......................................................................................... 6-15

6.4 Characterization of Uncertainty in the Monetized PM2.5 Benefits ..................... 6-27

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

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

7.1 Economic Impact Analysis Results ..................................................................... 7-1

7.1.1 Market-Level Results ............................................................................... 7-1
v 

7.1.2 Social Cost Estimates ............................................................................... 7-1

7.1.3 Job Effects ................................................................................................ 7-1

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

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

Appendixes 

A: OAQPS Multimarket Model to Assess the Economic Impacts of 

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

B: Detailed Economic Model Results by Sector ......................................................B-1

C: Major Sources Cost and Emissions Memorandum ..............................................C-1

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

LIST OF FIGURES 

Number Page

2-1. Distribution of Employment within Food Manufacturing (NAICS 311): 2006 ........ 2-3

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

(NAICS 311): 2006 .................................................................................................... 2-3

2-3. Electric Power Use Trends in Food Manufacturing (NAICS 311): 1997–2005 ........ 2-7

2-4. Establishment Concentration in Food Manufacturing (NAICS 311): 2002 .............. 2-8

2-5. Capacity Utilization Trends in Food Manufacturing (NAICS 311) .......................... 2-9

2-6. Employment Concentration in Food Manufacturing (NAICS 311): 2002 .............. 2-10

2-7. Capacity Trends in Food Manufacturing (NAICS 311) .......................................... 2-11

2-8. Industrial Production Trends in Food Manufacturing (NAICS 311) ....................... 2-15

2-9. International Trade Trends in Food Manufacturing (NAICS 311) .......................... 2-15

2-10. Producer Price Trends in Food Manufacturing (NAICS 311) ................................. 2-16

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

(NAICS 322): 2007 .................................................................................................. 2-19

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

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

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

(NAICS 321): 1997–2005 ........................................................................................ 2-24

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

(NAICS 321): 2002 .................................................................................................. 2-26

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

(NAICS 321) ............................................................................................................ 2-27

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

(NAICS 321): 2002 .................................................................................................. 2-28

2-17. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321) ....... 2-29

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

(NAICS 321): 1997–2009 ........................................................................................ 2-32

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

(NAICS 321)]........................................................................................................... 2-32

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

321) .......................................................................................................................... 2-33

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

322): 2007 ................................................................................................................ 2-36

2-22. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007 ..... 2-36

2-23. Electrical Power Use Trends in the Paper Manufacturing Industry: 1997–2005 .... 2-41

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

2002.......................................................................................................................... 2-43
vii 

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

(NAICS 322) ............................................................................................................ 2-44

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

322): 2002 ................................................................................................................ 2-45

2-27. Capacity Trends in the Paper Manufacturing Industry (NAICS 322) ..................... 2-46

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

322): 1997–2009 ...................................................................................................... 2-49

2-29. International Trade Trends in the Paper Manufacturing Industry (NAICS 322) ..... 2-50

2-30. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222) ............ 2-50

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

2007.......................................................................................................................... 2-53

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

(NAICS 325): 2007 .................................................................................................. 2-53

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

2005.......................................................................................................................... 2-58

2-34. Establishment Concentration in Chemical Manufacturing (NAICS 325): 2002 ..... 2-60

2-35. Capacity Utilization Trends in Chemical Manufacturing (NAICS 325) ................. 2-61

2-36. Employment Concentration in Chemical Manufacturing (NAICS 325): 2002 ....... 2-61

2-37. Capacity Trends in Chemical Manufacturing (NAICS 325) ................................... 2-62

2-38. Industrial Production Trends in Chemical Manufacturing (NAICS 325) ................ 2-66

2-39. International Trade Trends in Chemical Manufacturing (NAICS 325) ................... 2-67

2-40. Producer Price Trends in Chemical Manufacturing (NAICS 325) .......................... 2-67

4-1. Market-Level Changes by Source and Option ........................................................... 4-3

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

4-3. Distribution of Total Surplus Changes by Sector: Area Sources ............................... 4-6

4-4. Job Losses/Gains Associated with the Proposed Rules: 2013 ................................... 4-8

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

Sales Tests Exceeding 3% ......................................................................................... 5-4

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

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

6-2. Total Monetized PM2.5 Benefits for the Proposed Major and Area Source 

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

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

Precursor Pollutant and Source ................................................................................ 6-11

6-4. Breakdown of Monetized Benefits for the Proposed Boiler MACT (Major 

Sources) by Fuel Type ............................................................................................. 6-11

6-5. Breakdown of Monetized Benefits for the Proposed Boiler Area Source Rule 

by Fuel Type ............................................................................................................ 6-12

6-6. Net Benefits for the Proposed Major and Area Source Boiler Rules at 3% 

Discount Rate ........................................................................................................... 6-33

6-7. Net Benefits for the Proposed Major and Area Source Boiler Rules at 7% 

Discount Rate ........................................................................................................... 6-34 
viii 

LIST OF TABLES 

Number Page

1-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler MACT (Major Sources) in 2013 (millions of 2008$) ..................................... 1-3

1-2. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler Area Source Rule in 2013 (millions of 2008$) ............................................... 1-4

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

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

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

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

($2007) ....................................................................................................................... 2-5

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

($2007, millions) ........................................................................................................ 2-5

2-5. Energy Used in Food Manufacturing (NAICS 311) .................................................. 2-6

2-6. Demand by Sector: Food Manufacturing (NAICS 311) ($2007, millions) ............... 2-7

2-7. Top Publicly Held U.S. Food Companies: 2007 ...................................................... 2-11

2-8. Corporate Income and Profitability for Food Manufacturing (NAICS 311) ........... 2-12

2-9. Small Business Size Standards: Food Manufacturing (NAICS 311) ....................... 2-13

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

(NAICS 311) ............................................................................................................ 2-14

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

2-12. Industry Data: Wood Product Manufacturing (NAICS 321) ................................... 2-17

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

($2007) ..................................................................................................................... 2-21

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

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

2-15. Energy Used in Wood Product Manufacturing (NAICS 321) ................................. 2-23

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

millions) ................................................................................................................... 2-25

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

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

Manufacturing (NAICS 321) ................................................................................... 2-30

2-19. Small Business Size Standards: Wood Product Manufacturing (NAICS 321) ........ 2-31

2-20. Key Statistics: Paper Manufacturing (NAICS 322) ................................................. 2-34

2-21. Industry Data: Paper Manufacturing (NAICS 322) ................................................. 2-34

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

(NAICS 322) ............................................................................................................ 2-37
ix 

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

(NAICS 322) ($millions, $2007) ............................................................................. 2-39

2-24. Energy Used in Paper Manufacturing (NAICS 322) ............................................... 2-40

2-25. Estimated Energy Sources for the U.S. Pulp and Paper Industry ............................ 2-41

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

$2007) ...................................................................................................................... 2-42

2-27. Largest U.S. Paper and Forest Products Companies: 2006 ..................................... 2-46

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

(NAICS 322) ............................................................................................................ 2-47

2-29. Small Business Size Standards: Paper Manufacturing (NAICS 322) ...................... 2-48

2-30. Key Statistics: Chemical Manufacturing (NAICS 325)........................................... 2-51

2-31. Industry Data: Chemical Manufacturing (NAICS 325) ........................................... 2-52

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

($2007, millions) ...................................................................................................... 2-54

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

($2007) ..................................................................................................................... 2-55

2-34. Energy Used in Chemical Manufacturing (NAICS 325) ......................................... 2-57

2-35. Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007 millions) ....... 2-58

2-36. Top Chemical Producers: 2007 ................................................................................ 2-63

2-37. 2007 Corporate Income and Profitability (NAICS 325) .......................................... 2-63

2-38. Small Business Size Standards: Chemical Manufacturing (NAICS 325) ............... 2-65

2-39. Distribution of Economic Data by Enterprise Size: Chemical Manufacturing 

(NAICS 325) ............................................................................................................ 2-66

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 .......................... 4-4

4-2. Distribution of Social Costs Area Sources (billion, 2008$): 2013 ............................ 4-5

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

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

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

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

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

Sources) in 2013 (2008$) ........................................................................................... 6-7

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

2013 (2008$) .............................................................................................................. 6-7

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

Proposed Major and Area Source Boiler Rules in 2013 ............................................ 6-8

6-5. All PM2.5 Benefits Estimates for the Proposed Major and Area Source Boiler 

Rules at discount rates of 3% and 7% in 2013 (in millions of 2008$) ...................... 6-9

6-6. Top HAPs by Mass from Boilers by Fuel Type ...................................................... 6-15

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

2008$) ...................................................................................................................... 6-27
x 

6-8. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler MACT (Major Sources) in 2013 (millions of 2008$) ................................... 6-31

6-9. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler Area Source Rule in 2013 (millions of 2008$) ............................................. 6-32

7-1. Market-Level Price and Quantity Changes: 2013 (Alternate Definition) .................. 7-2

7-2. Distribution of Social Costs (million, 2008$): 2013 .................................................. 7-3

7-3. Employment Changes: 2013 ...................................................................................... 7-3

7-4. Summary of Monetized Benefits for the Boiler MACT for Existing Major 

Sources in 2013 (2008$) (Alternate Approach) ......................................................... 7-4

7-5. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler MACT (Major Sources) in 2013 (millions of 2008$) ..................................... 7-5
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 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. 

1.1 Executive Summary 

The key results of the RIA are as follows: 

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

total annualized costs will be $2.9 billion (2008$). For the area source NESHAP, 

EPA estimates the total annualized costs will be $0.5 billion. 

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

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

0.01% higher with the NESHAP, while average annual 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 $2.9 billion (2008$). In the near term, the Agency’s economic model 

suggests that industries are able to pass approximately $0.8 billion of the rule’s costs 

to consumers (e.g., higher market prices). Domestic industries’ surplus falls by $2.5 

billion, while other countries on net benefit from higher prices (a net increase in restof-the world [ROW] surplus of $0.1 billion). Additional costs and fuel savings for 

 

1

 On June 19, 2007, the U.S. Court of Appeals for the District of 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. 
1-2 

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

a net benefit of $0.4 billion. The estimated social cost of the proposed area source 

rule is approximately $0.5 billion (2008$). In the near term, the Agency’s economic 

model suggests that industries are able to pass approximately $0.3 billion of the rule’s 

costs to consumers. Domestic industries’ surplus falls by $0.3 billion and the net 

increase in ROW surplus is less than $0.1 billion. Additional costs and fuel savings 

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

results represent a net benefit of $0.1 billion. 

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

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

longer time period, net employment effects range between 6,000 job losses to 12,000 

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

the proposed major source rule are estimated to be less than 1,000 job losses; over a 

longer time period, net employment effects also range between 1,000 job losses to 

3,000 job gains.

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

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

tests). EPA’s analysis found the tests were 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 

PM2.5 co-benefits of the proposed major source rule are $17 billion to $41 billion and 

$15 billion to $37 billion, at 3% and 7% discount rates respectively. In the year of full 

implementation (2013), EPA estimates the PM2.5 co-benefits of this proposed area 

source rule are $1.0 billion to $2.4 billion and $910 million to $2.2 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 expert-based estimates fall 

between these estimates. The benefits from reducing other air pollutants have not 

been monetized in this analysis, including reducing 370,000 tons of carbon monoxide, 

37,000 tons of HCl, and 1,000 tons of HF, 8.3 tons of mercury, 3,400 tons of other 

metals, and 1,200 grams of dioxins/furans each year from major and area sources. In 

addition, ecosystem benefits and visibility benefits have not been monetized in this 

analysis.

 Net Benefits: The net benefits for the proposed major source rule only are $14 billion 

to $38 billion and $12 billion to $34 billion, at 3% and 7% discount rates, 

respectively in 2013. The net benefits for the area source rule only are $500 million to 

$1.9 billion and $410 million to $1.7 billion in 2013, at 3% and 7% discount rates, 

respectively. All estimates are in 2008 dollars. 

 
1-3 

Table 1-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler MACT (Major Sources) in 2013 (millions of 2008$)1

Proposed Option 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $17,000 to $41,000 $15,000 to $37,000 

Total Social Costs3 $2,900 $2,900 

Net Benefits $14,000 to $38,000 $12,000 to $34,000 

Option 1N and 1E 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits4 $17,000 to $41,000 $15,000 to $37,000 

Total Social Costs3 $12,000 $12,000 

Net Benefits $5,000 to $30,000 $3,400 to $26,000 

Proposed Option with Alternate Solid Waste Definition 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits5 $3,100 to $7,700 $2,800 to $6,900 

Total Social Costs3 $2,200 $2,200 

Net Benefits $930 to $5,500 $640 to $4,700 

1

All estimates are for the implementation year (2013), and are rounded to two significant figures. 2

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

reductions of 29,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 1,700 tons of VOC and 340,000 

tons of SO2. The benefits from reducing 340,000 tons of carbon monoxide, 37,000 tons of HCl, 1,000 tons of HF, 

and 7.5 tons of mercury, 3,200 tons of other metals, and 720 grams of dioxins/furans each year are not included in 

these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not 

included. 

3

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

in the same social costs for both discount rates. 4

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

reductions of 29,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 6,700 tons of VOC and 350,000 

tons of SO2. The benefits from reducing 390,000 tons of carbon monoxide, 42,000 tons of HCl, 8,600 tons of HF, 

and 8.1 tons of mercury, 3,200 tons of other metals, and 760 grams of dioxins/furans each year are not included in 

these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not 

included. 

5

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

reductions of 8,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 4,700 tons of VOC and 44,000 

tons of SO2. The benefits from reducing 280,000 tons of carbon monoxide, 5,100 tons of HCl, 1,100 tons of HF, 

and 7.1 tons of mercury, 1,600 tons of other metals, and 290 grams of dioxins/furans each year are not included in 

these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not 

included. 
1-4 

Table 1-2. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler Area Source Rule in 2013 (millions of 2008$)1

Proposed Option 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $1,000 to $2,400 $910 to $2,200 

Total Social Costs3 $500 $500 

Net Benefits $500 to $1,900 $410 to $1,700 

Option 1N and 1E 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits4 $8,300 to $20,000 $7,500 to $18,000 

Total Social Costs3 $35,000 $35,000 

Net Benefits $-27,000 to $-15,000 $-28,000 to $-17,000 

1

All estimates are for the implementation year (2013), and are rounded to two significant figures. 2

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

reductions of 2,700 tons of directly emitted PM2.5 and PM2.5 precursors such as 1,200 tons of VOC and 1,500 tons 

of SO2. The benefits from reducing 39,000 tons of carbon monoxide, 130 tons of HCl, 5 tons of HF, and 0.75 tons 

of mercury, 250 tons of other metals, and 470 grams of dioxins/furans each year are not included in these 

estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included. 

3

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

in the same social costs for both discount rates. 

4

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

reductions of 23,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 2,100 tons of VOC and 1,700 

tons of SO2. The benefits from reducing 58,000 tons of carbon monoxide, 140 tons of HCl, 6.4 tons of HF, and 

1.5 tons of mercury, 6,200 tons of other metals, and 530 grams of dioxins/furans each year are not included in 

these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not 

included. 

1.2 Organization of this Report 

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

EIA: 

 Section 2 presents the affected industry profiles. 

 Section 3 describes the engineering cost analysis. 

 Section 4 describes the economic impact analysis. 

 Section 5 describes the small entity analyses. 

 Section 6 presents the benefits estimates. 

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

solid waste definition 

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

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

SECTION 2 

INDUSTRY PROFILES 

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

proposed rules. The industries were selected based on high facility population counts within 3-

digit NAICs industries reported in the combustion facility survey. The purpose is to give the 

reader a general understanding of economic aspects and industry trends to provide additional 

context for the economic impact analysis. 

2.1 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 reversed between 2002 and 2006, as shipment values rose 4.77 % and 

number of employees and payroll fell 5.94% and 3.28% respectively (Table 2-1). Shipments, 

payroll, and employment continued to increase between 2006 and 2007, but there was a notable 

drop in the number of establishments between 2002 and 2007 (Table 2-1). As Table 2-2 shows, 

payroll per employee grew 4.91% from 1997 to 2002 and continued to increase, albeit at a 

slower rate of 2.83%, from 2002 to 2006. Between 2006 and 2007, the payroll per employee 

declined as the growth in employees outpaced the increase in the annual payroll (Table 2-2). 

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

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

food products as follows: 

 Animal Food Manufacturing (North 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) 

 Seafood Product Preparation and Packaging (3117) 

 Bakeries and Tortilla Manufacturing (NAICS 3118) 
2-2 

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

System [NAICS] 311) 

1997 2002 2006 2007 

Shipments ($2007, millions) $528,928 $526,939 $552,075 $589,550 

Payroll ($2007, millions) $48,118 $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). 

 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). 

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

Industry Data 1997 2002 2006 2007 

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

Shipments per establishment ($2007, thousands) $20,110 $18,887 NA $26,731 

Average 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). 

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). 
2-3 

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). 

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

(NAICS 311): 2006 

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

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

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

Animal Food

Manufacturing,

3%

Grain and Oilseed

Milling,

4% Sugar and

Confectionery

Product

Manufacturing,

5%

Fruit and Vegetable

Preserving and

Specialty Food

Manufacturing,

12%

Dairy Product

Manufacturing,

9%

Animal Slaughtering

and Processing, 33%

Seafood Product

Preparation and

Packaging, 3%

Bakeries and Tortilla

Manufacturing,

20%

Other Food

Manufacturing,

11%

Animal Food

Manufacturing,

6% Grain and Oilseed

Milling, 10%

Sugar and

Confectionery

Product

Manufacturing,

6%

Fruit and Vegetable

Preserving and

Specialty Food

Manufacturing,

12%

Dairy Product

Manufacturing,

14%

Animal Slaughtering

and Processing, 27%

Seafood Product

Preparation and

Packaging, 2%

Bakeries and Tortilla

Manufacturing,

11%

Other Food

Manufacturing,

12%
2-4 

Many major environmental regulations 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. 

2.1.2 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 

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). 

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

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

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

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

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

2.1.2.2 Energy 

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

energy-intensive industry to model within its Industrial Demand Module (DOE, 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). 
2-5 

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

($2007) 

Industry Ratios 2005 Share 2006 Share 

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

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

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

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

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

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

Total production workers’ wages ($millions) $33,983 6% $33,670 6% 

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

Total production hours (thousands) 2,242,558 2,198,396 

Average production wages per hour $15 $15 

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

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

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

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

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

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

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

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

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

($2007, millions) 

Sector BEA Code Food Products 

Food products 3110 $91,518 

Animal products 1120 $85,785 

Crop products 1110 $43,109 

Management of companies and enterprises 5500 $34,235 

Wholesale trade 4200 $27,849 

Converted paper products 3222 $18,782 

Truck transportation 4840 $12,943 

Plastics and rubber products 3260 $9,641 

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

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

Total intermediate inputs T005 $400,067 

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

Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA. 
2-6 

In both 2005 and 2006, purchased electricity 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 used less electricity, food manufacturing used more 

electricity (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 

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 

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

a

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

resources, minus quantities sold and transferred out. It does not include 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>. 
2-7 

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/>. 

2.1.2.3 Uses and Consumers 

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

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

industry (Table 2-6). 

2.1.3 Firm and Market 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. 

90

95

100

105

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Jun‐97

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Index Value (Jan‐97=100)

Manufacturing Food
2-8 

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

Sector BEA Code Food Products 

Food manufacturing 3110 $91,518 

Food services and drinking places 7220 $37,291 

Animal production 1120 $15,870 

General state and local government services S007 $15,170 

Retail trade 4A00 $13,985 

Beverage manufacturing 3121 $11,703 

Hospitals 6220 $9,539 

Educational services 6100 $4,485 

Nursing and residential care facilities 6230 $4,187 

Social assistance 6240 $2,277 

Total intermediate use T001 $217,570 

Personal consumption expenditures F010 $301,748 

Exports of goods and services F040 $28,151 

Imports of goods and services F050 −$33,119 

Total final uses (GDP) T004 $299,470 

Total commodity output T007 $517,040 

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

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

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

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

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

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

Establishments

Fewer than 250

250 - 499

500 - 999

1,000 - 2,000

More than 2,000
2-9 

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 

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). 

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

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). 

0

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30

35

40

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Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Percentage Capacity Utilization

Manufacturing Food
2-10 

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

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

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

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

2.1.3.4 Plants and Capacity 

Production 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). 

Employment

Fewer than 10,000

10,000 to 19,999

20,000 to 39,999

40,000 to 80,000

More than 80,000
2-11 

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

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

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

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

 Sales ($millions) % of Sales in United States 

Kraft Foods Inc. 37,241 57.8% 

Tyson Foods Inc. 26,900 90.0% 

General Mills Inc. 12,442 82.9% 

Sara Lee Corp. 12,278 53.8% 

ConAgra Foods Inc. 12,028 89.2% 

Smithfield Foods Inc.a

 11,911 86.2% 

Dean Foods Co. 11,822 >99.0% 

Kellogg Co.a

 11,776 66.1% 

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

Campbell Soup Co. 7,867 69.0% 

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

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

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Index Value (January 1997 = 100)

Manufacturing Food
2-12 

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

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

Although the overall number of 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 (thousands) $502,149,944 $504,944,378 $484,193,319 

Business receipts (thousands) $477,906,423 $465,369,666 $459,884,663 

Before-tax profit margin 5.27% 10.09% 7.43% 

After-tax profit margin 3.74% 7.62% 5.11% 

Source: Internal Revenue Service, U.S. Department of Treasury. 2008. “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. 
2-13 

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

NAICS Description Employees 

311111 Dog and Cat Food Manufacturing 500 

311119 Other Animal Food Manufacturing 500 

311211 Flour Milling 500 

311212 Rice Milling 500 

311213 Malt Manufacturing 500 

311221 Wet Corn Milling 750 

311222 Soybean Processing 500 

311223 Other Oilseed Processing 1,000 

311225 Fats and Oils Refining and Blending 1,000 

311230 Breakfast Cereal Manufacturing 1,000 

311311 Sugarcane Mills 500 

311312 Cane Sugar Refining 750 

311313 Beet Sugar Manufacturing 750 

311320 Chocolate and Confectionery 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) 
2-14 

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

NAICS Description Employees 

311823 Dry Pasta Manufacturing 500 

311830 Tortilla Manufacturing 500 

311911 Roasted Nuts and Peanut Butter Manufacturing 500 

311919 Other Snack Food Manufacturing 500 

311920 Coffee and Tea Manufacturing 500 

311930 Flavoring Syrup and Concentrate Manufacturing 500 

311941 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 ($millions) $457,521 $12,665 $32,274 $56,661 $23,103 $10,007 $21,878

Receipts/firm ($thousands) $21,395 $928 $8,202 $45,438 $157,163 $158,835 $227,898

Receipts/establishment 

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

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

a

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

fraction of the year. 

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

2002.” <http://www.census.gov/csd/susb/download_susb02.htm>. 
2-15 

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

Source: Federal Reserve Board. 2008. “Industrial 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/>. 

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

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

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

60

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Manufacturing Food, Beverage, and Tobacco

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1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2008

YTD

2009

YTD

Actual Dollars ($billion)

U.S. Domestic Exports (FAS Value) U.S. Imports for Consumption (CIF Value)
2-16 

2.1.3.9 Market Prices 

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

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

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

with the downward trend in prices throughout the economy. 

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

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

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

Manufacturing>. 

2.2 Wood Product Manufacturing 

2.2.1 Introduction 

According to a report by 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. 

0

20

40

60

80

100

120

140

160

180

200

19901991199219931994199519961997199819992000200120022003200420052006200720082009

Index Values (December 1984=Base)

Manufacturing Food
2-17 

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

 1997 2002 2006 2007 

Shipments ($2007, 

millions) 

$110,956 $102,721 $115,390 $101,879 

Payroll ($2007, millions) $17,959 $18,528 $18,623 $17,439 

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. 

While total payroll dropped 3% over from 1997 to 2007, annual payroll per employee rose 

6.5% because of the decline in the number of employees (Table 2-12). Shipments per employee 

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

Table 2-12. Industry Data: Wood Product Manufacturing (NAICS 321) 

Industry Data 1997 2002 2006 2007 

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

Shipments per establishment ($thousands) $25,613 $5,953 NA $6,855 

Average Shipments per employee ($2007) $194,648 $189,014 $215,243 $196,053 

Average Shipments per $ of payroll ($2007) $6.18 $5.54 $6.20 $5.84 

Average Annual payroll per employee 

($2007) 

$31,504 $34,093 $34,738 $33,558 

Average Employees per establishment 33 31 NA 35 

NA = Not available. 

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

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

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

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

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

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

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

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

December 27, 2009. 
2-18 

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. 

 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). 
2-19 

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

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

preservation (27%) and veneer, 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. 

2.2.2 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. 

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] 

Veneer, Plywood, &

Engineered Wood

ProductMfg (NAICS

3212), 22%

Sawmills & Wood

Preservation (NAICS

3211),

27%

Other Wood

ProductMfg (NAICS

3219),

51%
2-20 

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%. 

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. 

Other Wood

ProductMfg (NAICS

3219),

60%

Sawmills & Wood

Preservation (NAICS

3211), 20%

Veneer, Plywood, &

Engineered Wood

ProductMfg (NAICS

3212),

20%
2-21 

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

($2007) 

Industry Ratios 2005 Share 2006 Share 2007 Share 

Total shipments (millions) $118,705 100% $115,390 100% $102,002 100% 

Total compensation (millions) $23,327 20% $23,306 20% $22,513 22% 

Annual payroll millions $18,884 16% $18,623 16% $17,444 17% 

Fringe benefits $4,442 4% $4,683 4% $5,069 5% 

Total employees 538,890 536,094 524,212 

Average compensation per employee $43,286 $43,473 $42,947 

Total production workers’ wages 

(millions) 

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

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

Total production hours (thousands) 911,332 887,613 837,074 

Average production wages per 

hour ($2007) 

$15 $15 $14 

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

Materials, parts, packaging $65,319 55% $63,499 55% $54,462 53% 

Purchased electricity $1,530 1% $1,625 1% $1,446 1% 

Purchased fuel $810 1% $835 1% $843 1% 

Other $4,149 3% $3,933 3% $3,931 4% 

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

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

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

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

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

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

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

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

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

between 2006 and 2030 (DOE, 2008). 
2-22 

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

($2007, millions) 

Description BEA Commodity Code Wood Products 

Wood products 3210 $20,989 

Forestry and logging products 1130 $18,914 

Wholesale trade 4200 $5,417 

Management of companies and enterprises 5500 $2,853 

Truck transportation 4840 $2,542 

Electric power generation, transmission, and distribution 2211 $1,388 

Other fabricated metal products 332B $1,310 

Nonmetallic mineral products 3270 $1,110 

Real estate 5310 $799 

All other administrative and support services 561A $748 

Architectural and structural metal products 3323 $725 

Rail transportation 4820 $723 

Other inputs $14,650 

Total intermediate inputs T005 $72,169 

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

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

Table 2-15 shows that total energy use between 1998 and 2002 Figure 2-13 shows that 

electrical power use decreased, since 2000. 

2.2.2.2 Uses and Consumers 

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

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

nonresidential construction, and maintenance and repair construction consume 35% of the total 

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

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

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

transportation, plastics and rubber products manufacturing, and other, use these products. 
2-23 

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

Fuel Type 1998 2002 2006 

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

Residual fuel oil (million bbl) * * 1 

Distillate fuel 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 

Total (trillion BTU) 504 375 445 

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

resources, minus quantities sold and transferred out. It does not include electricity inputs from on-site 

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

fuel (for example, coal). 

b

 Distillate fuel oil includes Nos. 1, 2, and 4 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] 
2-24 

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. 

2.2.3 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. 

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 Electric Power Use for NAICS 321
2-25 

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

millions) 

Sector BEA Code 3210 Wood Products 

New residential construction 2302 $19,997 

New nonresidential construction 2301 $11,854 

Furniture and related product manufacturing 3370 $8,197 

Maintenance and repair construction 2303 $4,048 

Motor vehicle body, trailer and parts 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. 

2.2.3.2 Production Capacity and Utilization 

Capacity utilization of the wood product manufacturing industry has been experiencing 

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

manufacturing industries 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). 
2-26 

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). 

2.2.3.3 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). 

Establishments by State

Less than 100

100 - 199

200 - 399

400 - 600

More than 600
2-27 

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. 

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). 

0

5

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35

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45

50

55

60

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70

75

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100

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Percentage Capacity Utilization

Manufacturing Wood Products
2-28 

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

(NAICS 321): 2002 

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

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

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

2.2.3.7 Domestic Production 

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

manufacturing have decreased 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. 

Employemnt by State

Less than 2,000

2,000 - 4,999

5,000 - 9,999

10,000 - 20,000

More than 20,000
2-29 

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

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

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

Accessed on December 15, 2009. 

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

Company Revenues ($millions)a

International Paper 21,995 

Weyerhaeuser 21,896 

Smurfit-Stone 7,157 

MeadWestvaco 6,530 

Temple-Inland 5,558 

Bowater 3,530 

Grief Inc. 2,628 

Louisiana-Pacific 2,235 

Packaging Corp. 2,187 

Plum Creek 1,627 

a

 Includes revenues from operations other than paper and forest products in certain cases. 

Source: Benwart, S.J. 2006. “Paper & Forest Products.” Standard and Poor’s Industry Surveys. 176(28). 

 

100

105

110

115

120

125

130

135

140

145

150

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

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 Wood Products
2-30 

Table 2-18. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 32

 Enterprises with 

Variable Total 

1 to 20 

Employeesa

20 to 99 

Employees 

100 to 499 

Employees 

500 to 749 

Employees 

750 to 

Emplo

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

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

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

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

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

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

NAICS NAICS Description Employees 

321113 Sawmills 500 

321114 Wood Preservation 500 

321211 Hardwood Veneer and Plywood Manufacturing 500 

321212 Softwood Veneer and Plywood Manufacturing 500 

321213 Engineered Wood Member (except Truss) Manufacturing 500 

32121 Truss Manufacturing 500 

321219 Reconstituted Wood Product Manufacturing 500 

321911 Wood Window and Door Manufacturing 500 

321912 Cut Stock, Resawing Lumber, and Planing 500 

321918 Other Millwork (including Flooring) 500 

321920 Wood Container and Pallet Manufacturing 500 

321991 Manufactured Home (Mobile Home) Manufacturing 500 

321992 Prefabricated Wood Building Manufacturing 500 

321999 All Other Miscellaneous Wood Product Manufacturing 500 

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

North American Industry 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 (Figure 2-20). 
2-32 

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

(NAICS 321): 1997–2009 

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

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

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

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

(NAICS 321)] 

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

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

60

70

80

90

100

110

120

130

140

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Jan-06

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Index Value (Jan-97=100)

Manufacturing Wood Products

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

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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
2-33 

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

321) 

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

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

2.3 Paper Manufacturing 

2.3.1 Introduction 

The paper manufacturing subsector is an 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). 

0

20

40

60

80

100

120

140

Dec‐03

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
2-34 

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

 1997 2002 2006 2007 

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

Payroll ($2007, millions) $27,983 $24,561 $21,188 $20,804 

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 Data: Paper Manufacturing (NAICS 322) 

Industry Data 1997 2002 2006 2007 

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

Shipments per establishment ($2007, thousands) $32,123 $32,026 NA $36,603 

Average Shipments per employee ($2007) $328,233 $359,614 $422,381 $421,712 

Average Shipments per $ of payroll ($2007) $6.74 $7.17 $8.25 $8.45 

Average Annual payroll per employee ($2007) $48,727 $50,189 $51,174 $49,904 

Average Employees per establishment 98 89 NA 87 

NA = Not available. 

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

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

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

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

Core Business Statistics Series: Comparative 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] 
2-35 

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

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

further divided into the following 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 
2-36 

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

egg cartons, food trays, and dishes. 

Figure 2-21 shows that the value of shipments for converted paper products was 54% of 

the value of all paper products in 2007, while the value of shipments for pulp, paper, and 

paperboard products was 46%. Figure 2-22 indicates that 70% of industry employees worked in 

the converted paper product category of the industry due to the labor intensive aspects of those 

facilities. 

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. 

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. 

PPP (3221)

CPP (3222) 46%

54%

PPP (3221)

30%

CPP (3222)

70%
2-37 

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. 

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. 

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). 

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. Industry wide, the use of pulp wastes, bark, and 

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

companies (EPA, 2002). 

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

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

and 2004 but decreased consistently and rapidly after 2004. 
2-38 

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

(NAICS 322) 

Variable 2005 Share 2006 Share 2007 Share 

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

Total compensation ($2007, millions) $28,846 17% $27,791 16% $27,150 15% 

Annual payroll $21,792 13% $21,188 12% $20,804 12% 

Fringe benefits $7,054 4% $6,603 4% $6,346 4% 

Total employees 426,748 414,049 417,367 

Average compensation per employee $67,596 $67,121 $65,051 

Total production workers wages ($2007, 

millions) 

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

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

Total production hours (thousands) 716,963 691,134 680,732 

Average production wages per hour $21 $21 $21 

Total cost of materials ($2007, thousands) $91,897 54% $92,452 53% $94,029 53% 

Materials, parts, packaging $77,494 45% $78,202 45% $79,984 45% 

Purchase electricity $3,788 2% $3,841 2% $3,780 2% 

Purchased fuel ($2007) $5,537 3% $5,509 3% $5,511 3% 

Other $5,078 3% $4,901 3% $4,755 3% 

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

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

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

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

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

<http://factfinder.census.gov>. Accessed on December 28, 2009. [Source for 2007 numbers] 
2-39 

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

(NAICS 322) ($millions, $2007) 

Description BEA Code 

NAICS 3221 

Pulp, Paper, and 

Paperboard 

NAICS 3222 

Converted 

Paper Products Total 

Pulp, paper, and paperboard 3221 $4,155 $30,448 $34,603 

Wholesale trade 4200 $3,916 $6,356 $10,273 

Management of companies and enterprises 5500 $3,154 $3,838 $6,993 

Forestry and logging products 1130 $5,389 $0 $5,389 

Basic chemicals 3251 $3,734 $263 $3,997 

Electric power generation, transmission, 

and distribution 

2211 $2,690 $913 $3,603 

Wood products 3210 $3,450 $33 $3,484 

Converted paper products 3222 $1,415 $1,745 $3,159 

Natural gas distribution 2212 $2,680 $345 $3,026 

Truck transportation 4840 $1,428 $1,571 $2,999 

Total intermediate inputs T005 $47,835 $62,690 $110,525 

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

Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA. 
2-40 

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

Fuel Type 1998 2002 2006 

Net 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 

Total (trillion BTU) 2,744 2,361 2,354 

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

resources, minus quantities sold and transferred out. It does not include electricity inputs from on-site 

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

fuel (for example, coal). 

b

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

c

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

such as independent gas 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 changed its energy generation 

methods from fossil fuels to a greater use of processes such as increases in the use of wood 

wastes in place of fuel (Table 2-25). During the 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). 
2-41 

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/>. 

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>. 

90

92

94

96

98

100

102

104

106

108

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Index Value (Jan‐97=100)

Manufacturing Paper
2-42 

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) ($millions, 

$2007) 

Sector BEA Code 

3221 

Pulp, Paper, and 

Paperboard 

3222 

Converted 

Paper Products Total 

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

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

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

General state and local government 

services 

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

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

Newspaper, periodical, book, and 

directory publishers 

5111 $4,851 $168 $5,018 

Plastics and rubber products 

manufacturing 

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

Wholesale trade 4200 $990 $2,619 $3,609 

Food services and drinking places 7220 $1,510 $2,597 $4,107 

Total intermediate use T001 $76,729 $80,862 $157,591 

Personal consumption expenditures F010 $11,882 $9,295 $21,177 

Exports of goods and services F040 $7,724 $5,799 $13,523 

Imports of goods and services F050 −$15,284 −$5,720 −$21,005 

Total final uses (GDP) T004 $4,996 $9,607 $14,604 

Total commodity output T007 $81,725 $90,469 $172,195 

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

Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA. 
2-43 

2.3.3 Firm and Market Characteristics 

This section describes geographic, production, and market data. These data provide the 

basis for further analysis, 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. 

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). 

Establishments

Fewer than 50

50 - 99

100 - 199

200 - 300

More than 300
2-44 

2.3.3.2 Production Capacity and Utilization 

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

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

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

industries (Figure 2-25). 

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

(NAICS 322) 

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

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

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

2.3.3.3 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. 

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Percentage Capacity Utilization

Manufacturing Paper (NAICS 322)
2-45 

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

322): 2002 

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

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

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

2.3.3.4 Plants and 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). 

2.3.3.5 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. 

Employment

Fewer than 3,000

3,000 - 5,999

6,000 - 11,999

12,000 - 24,000

More than 24,000
2-46 

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/>. 

Table 2-27. Largest U.S. Paper and Forest Products Companies: 2006 

Company Revenues ($millions)a

International Paper 21,995 

Weyerhaeuser 21,896 

Smurfit-Stone 7,157 

MeadWestvaco 6,530 

Temple-Inland 5,558 

Bowater 3,530 

Grief Inc. 2,628 

Louisiana-Pacific 2,235 

Packaging Corp. 2,187 

Plum Creek 1,627 

a

 Includes revenues from operations other than paper and forest products in certain cases. 

Sources: Benwart, S.J. 2006. “Paper & Forest Products. Standard and Poor’s Industry Surveys.” 176(28). 

 U.S. and international sales data from company reports. 

90

100

110

120

130

140

150

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

Index Value (Jan‐97=100)

Manufacturing Paper (NAICS 322)
2-47 

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. 

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

Receipts/firm 

($thousands) $43,738 $1,497 $7,903 $37,017 $70,561 $179,577 $206,001

Receipts/establishment 

($thousands) $27,902 $1,491 $7,461 $23,338 $36,556 $57,256 $49,261

Receipts/employment 

($) $311,994 $195,850 $181,203 $224,742 $228,250 $316,157 $291,974

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

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

Employment Sizes: 2002.” <http://www.census.gov/csd/susb/download_susb02.htm>. 
2-48 

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

NAICS NAICS Description Employees 

322110 Pulp Mills 750 

322121 Paper (except Newsprint) Mills 750 

322122 Newsprint Mills 750 

322130 Paperboard Mills 750 

322211 Corrugated and Solid Fiber Box Manufacturing 500 

322212 Folding Paperboard Box Manufacturing 750 

322213 Setup Paperboard Box Manufacturing 500 

322214 Fiber Can, Tube, Drum, and Similar Products Manufacturing 500 

322215 Non-Folding Sanitary Food Container Manufacturing 750 

322221 Coated and Laminated Packaging Paper Manufacturing 500 

322222 Coated and Laminated Paper Manufacturing 500 

322223 Coated Paper Bag and Pouch Manufacturing 500 

322224 Uncoated Paper and Multiwall Bag Manufacturing 500 

322225 Laminated Aluminum Foil Manufacturing for Flexible, Packaging 

Uses 

500 

322226 Surface-Coated Paperboard Manufacturing 500 

322231 Die-Cut Paper and Paperboard Office Supplies, Manufacturing 500 

322232 Envelope Manufacturing 500 

322233 Stationery, Tablet, and Related Product Manufacturing 500 

322291 Sanitary Paper Product Manufacturing 500 

322299 All Other Converted Paper Product Manufacturing 500 

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

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

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

2.3.3.7 Domestic Production 

Similar to 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. 
2-49 

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/>. 

2.3.3.8 International Trade 

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

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

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

2.3.3.9 Market Prices 

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

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

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

goods increased by roughly 27%. 

75

85

95

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115

125

135

Jan‐97

Jul‐97

Jan‐98

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Manufacturing Paper (NAICS 322)
2-50 

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

322) 

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

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

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

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

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

0

5,000

10,000

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1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

$1,000,000 (2007)

Year

Paper Exports Paper Imports

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Index Value (Jan‐97=100)

Manufacturing Paper (NAICS 322)
2-51 

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. 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). 

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). 
2-52 

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

 1997 2002 2006 2007 

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

Payroll ($2007, millions) $49,961 $51,317 $46,981 $49,648 

Employees 882,645 853,224 747,134 814,024 

Establishments 13474 13,475 NA 12,937 

NA = Not available. 

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

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

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

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

Core Business Statistics Series: Comparative 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 Data: Chemical Manufacturing (NAICS 325) 

Industry Data 1997 2002 2006 2007 

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

Shipments per establishment ($thousands) $38,686 $39,419 NA $57,069 

Shipments per employee ($2007) $590,556 $622,548 $903,750 $906,979 

Shipments per $ of payroll ($2007) $10.43 $10.35 $14.37 $14.87 

Annual payroll per employee ($2007) $56,603 $60,145 $62,882 $60,991 

Employees per establishment 66 63 NA 63 

NA = Not available. 

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

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

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

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

Core Business Statistics Series: Comparative 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] 
2-53 

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. 

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. 

Bulk Chemicals,

35%

Pharmaceuticals,

31%

Other Chemicals,

34%

Bulk Chemicals,

50%

Pharmaceuticals,

26%

Other Chemicals,

24%
2-54 

2.4.2 Supply and Demand Characteristics 

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

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

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

the major uses and consumers of chemical manufacturing products. 

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

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

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

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

natural gas distribution. 

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

($2007, millions) 

Good or Service BEA Code 

Bulk 

Chemicals Pharmaceuticals 

Other 

Chemicals Total 

Basic chemicals 3251 $59,495 $4,772 $14,021 $78,288

Management of companies and enterprises 5500 $15,071 $19,380 $16,396 $50,846

Pharmaceuticals and medicines 3254 $0 $25,125 $0 $25,125

Wholesale trade 4200 $9,428 $8,367 $6,077 $23,872

Scientific research and development services 5417 $6,172 $6,139 $5,554 $17,865

Petroleum and coal products 3240 $10,066 $398 $3,432 $13,896

Plastics and rubber products 3260 $2,675 $1,132 $5,556 $9,363

Resins, rubber, and artificial fibers 3252 $4,048 $0 $4,949 $8,996

Electric power generation, transmission, and 

distribution 

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

Natural gas distribution 2212 $6,390 $154 $390 $6,934

Total intermediate use T005 $167,699 $82,403 $91,833 $341,935

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

Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA. 
2-55 

2.4.2.1 Goods and Services Used in Chemical Manufacturing 

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

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

down from 10% in 2005. 

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

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

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

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

Fuel used in chemical production can either facilitate chemical processes or provide the 

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

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

chemical manufacturing’s total value of shipments (Table 2-33). 

As a whole, chemical manufacturing use less energy over the last 10 years. According to 

DOE, natural gas use by the chemical manufacturing industry dropped 30% from 1998 to 2006, 

and electricity use fell 10% (Table 2-34). From 1997 to 2005, when data ceased to be available, 

chemical manufacturing used less electricity relative to the manufacturing sector as a whole 

(Figure 2-33). 

2.4.2.2 Uses and Consumers 

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

chemicals have very different consumer profiles. Bulk chemicals is dominated by 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). 
2-56 

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

($2007) 

Variable 2005 Share 2006 Share 2007 Share 

Total shipments $646,895 100% $675,223 100% $722,494 100% 

Total compensation (millions) $62,669 10% $61,683 9% $63,591 9% 

Annual payroll $48,159 7% $46,981 7% $48,780 7% 

Fringe benefits $14,510 2% $14,702 2% $14,811 2% 

Total employees 756,078 747,134 801,567 

Average compensation per 

employee 

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

Total production workers’ wages 

(millions) 

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

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

Total production hours 

(thousands) 

899,499 885,993 948,244 

Average production wages per 

hour 

$25 $25 $24 

Total cost of materials 

($thousands) 

$299,859 46% $318,945 47% $357,055 49% 

Materials, parts, packaging $247,851 38% $260,934 39% $291,656 40% 

Purchase electricity $8,291 1% $8,490 1% $8,936 1% 

Purchased fuel $14,568 2% $13,667 2% $14,227 2% 

Other $29,148 5% $35,855 5% $42,236 6% 

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

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

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

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

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

Accessed on December, 27, 2009. 
2-57 

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

Fuel Type 1998 2002 2006 

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

Net 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 

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

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

resources, minus quantities sold and transferred out. It does not include electricity inputs 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>. 
2-58 

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 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. 

2.4.3.1 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 

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

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

80

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Index Value (Jan‐97=100)

Manufacturing Chemical Manufacturing
2-59 

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

Sector 

BEA 

Code 

Bulk 

Chemicals Pharmaceuticals 

Other 

Chemicals Total 

Plastics and rubber products manufacturing 3260 $39,353 $0 $3,057 $42,410

Basic chemical manufacturing 3251 $33,972 $0 $1,675 $35,647

Pharmaceutical and medicine 

manufacturing 

3254 $4,778 $25,125 $462 $30,365

Resin, rubber, and artificial fibers 

manufacturing 

3252 $28,249 $0 $1,076 $29,325

Ambulatory health care services 6210 $2,716 $22,900 $934 $26,550

General state and local government 

services 

S007 $7,150 $10,586 $8,807 $26,543

Hospitals 6220 $2,936 $15,390 $394 $18,720

Other chemical product and preparation 

manufacturing 

3259 $8,021 $0 $2,680 $10,701

Textile mills 3130 $9,568 $0 $930 $10,498

Soap, cleaning compound, and toiletry 

manufacturing 

3256 $3,886 $0 $6,289 $10,176

Total intermediate use T001 $212,996 $83,279 $82,107 $378,382

Personal consumption expenditures F010 $4,449 $123,746 $55,882 $184,077

Exports of goods and services F040 $47,121 $15,683 $13,136 $75,940

Imports of goods and services F050 −$38,732 −$67,950 −$10,906 −$117,588

Total final uses (GDP) T004 $15,733 $73,485 $58,023 $147,241

Total commodity output T007 $228,729 $156,765 $140,129 $525,623

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

Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA. 
2-60 

Figure 2-34. Establishment 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). 

2.4.3.2 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. 

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). 

Establishments

Fewer than 100

100 - 249

250 - 474

475 - 750

More than 750
2-61 

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/>. 

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). 

0

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Percentage Capacity Utilization

Manufacturing Chemical Manufacturing

Employment

Fewer than 5,000

5,000 - 13,999

14,000 - 29,999

30,000 - 40,000

More than 40,000
2-62 

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 had greater than $10 billion in sales. 

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

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

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

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). 

100

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120

130

140

150

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Jul‐97

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Jul‐98

Jan‐99

Jul‐99

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Jul‐00

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Index Value (Jan‐97=100)

Manufacturing Chemical Manufacturing
2-63 

Table 2-36. Top Chemical Producers: 2007 

Chemical Sales 

($millions) % of Total Sales % of Sales in United States 

Dow Chemical 53,513 100% 35% 

ExxonMobil 36,826 9% 38% 

DuPont 29,218 100% 38% 

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). 

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

Industry 

Number of 

Corporations

Number of 

Corporations 

with Net 

Income 

Total 

Receipts 

($thousands) 

Business 

Receipts 

($thousands) 

BeforeTax Profit 

Margin 

After-Tax 

Profit 

Margin 

Basic chemical 1,244 757 $195,022,700 $178,019,490 5.07% 4.10% 

Resin, synthetic rubber, 

and artificial synthetic 

fibers and filaments 

1,067 648 $44,692,366 $40,078,009 8.06% 6.33% 

Pharmaceutical and 

medicine 

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

Paint, coating, and 

adhesive 

1,411 1,260 $51,778,868 $49,486,744 5.39% 4.02% 

Soap, cleaning compound, 

and toilet preparation 

1,862 463 $150,506,485 $139,836,602 9.07% 7.51% 

Other chemical product 

and preparation 

2,946 1,773 $89,014,032 $84,062,534 6.71% 5.27% 

Chemical manufacturing 9,564 5,512 $912,353,710 $808,897,810 10.24% 7.89% 

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

2007.” <http://www.irs.gov/taxstats/article/0,,id=167415,00.html>; (January, 15, 2010). 
2-64 

2.4.3.6 Size Distribution 

The primary criterion for categorizing a business as small is number of 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 2-38). Following the recession of 2001, however, 

the components have moved broadly in line with one another, except for the drop in chemical 

manufacturing production 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 2-39). This change occurred because the trade deficit 

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

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

2.4.3.9 Market Prices 

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

having outpaced overall manufacturing since 2002 (Figure 2-40). Much of this recent 

acceleration seen in the industry PPI is due to the bulk chemicals segment, largely reflecting the 

rapid increase in fertilizer prices. 
2-65 

Table 2-38. Small Business Size Standards: Chemical 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>. 
2-66 

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

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

Receipts/establishment 

($thousands) $35,752 $1,773 $9,689 $28,928 $48,869 $39,587 $53,485 

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

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

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

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

Figure 2-38. Industrial Production Trends in Chemical Manufacturing (NAICS 325) 

Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial Production.” 

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

GROUPS/IP.G325.S. <http://www.federalreserve.gov/datadownload/>. 

100

110

120

130

140

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 Chemical Manufacturing
2-67 

Figure 2-39. International Trade Trends in Chemical Manufacturing (NAICS 325) 

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

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

Figure 2-40. Producer Price Trends in Chemical Manufacturing (NAICS 325) 

Source: U.S. Bureau of Labor Statistics (BLS). 2009. Producer Price Index. Series ID: PCU325—325—

&PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home.htm>. 

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

$1,000,000 (2007)

Imports Exports

90

100

110

120

130

140

150

160

170

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

PPI (Jan‐98=100)

Manufacturing Chemical Manufacturing
3-1 

SECTION 3 

ENGINEERING COST ANALYSIS 

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 Appendices C and D. 

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 adsorbers, and 

activated carbon injection to be the primary control devices for mercury control, electrostatic 

precipitators for units meeting mercury limits but requiring additional control to meet the PM 

limits, wet scrubbers to meet the HCl limits, tune-ups, replacement burners, and combustion 

controls for CO and organic HAP control, and carbon injection for dioxin/furan control. We 

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

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

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

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

10.0 billion dollars in capital expenditures and 3.2 billion dollars per year in total annual costs. 

Considering estimated fuel savings resulting from work practice standards and combustion 

controls, the total annualized costs are reduced to 2.9 billion. The total capital and annual costs 

include costs for control devices, work practices, testing and monitoring. Table 3-1 of this shows 

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

costs, but not recordkeeping and reporting costs. 
3-2 

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

Source Subcategory 

Estimated/ 

Projected No. of 

Affected Units 

Capital Costs 

(million) 

Annualized Cost 

(million per yr) 

Existing Units Coal units 578 $4,500 $1,600 

Biomass units 420 $2,000 $600 

Liquid units 826 $1,400 $500 

Gas (NG/RG) units 11,532 $60 $30 

Gas (other) units 199 $1,600 $500 

Energy Audit ALL 26 

New Units Coal units 0 0 0 

Biomass units 0 0 0 

Liquid units 11 12 6.1 

Gas (NG/RG) units 33 0.2 0.01 

Gas (other) units 2 5.5 1.7 

Using Department of Energy 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 17 million dollars in capital expenditures and 6.2 

million dollars per year in total annual costs, when considering a 1 percent fuel savings. 

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

“Methodology and Results of Estimating the 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 

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

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

units is $279 million dollars in total annualized costs after considering fuel savings from 

efficiency improvements. The total annualized costs for installing controls, conducting an annual 
3-3 

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

$696 million. Table 3-2 of this preamble shows the total annualized cost impacts for each 

subcategory. 

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

Source Subcategory 

Estimated/ 

Projected No. of Affected 

Units 

Total Annualized Cost 

(million per yr)a 

Existing Units Coal 3,710 $160 

Biomass 10,958 $47 

Oil 168,003 $436 

Facility Energy Audit All $52 

New Unitsb

 Coal 155 $54 

Biomass 200 $13 

Oil 6,424 $244 

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

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

EPA also estimated the number of additional boilers that could be potentially constructed. 

The resulting total national cost impact of the proposed rule on new sources by the 3rd year, 

2013, is $311 million dollars in 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 $260 million. 
4-1 

SECTION 4 

ECONOMIC IMPACT ANALYSIS 

EPA prepares an RIA to provide decision makers with a measure of the social costs of 

using resources to comply with a program (EPA, 2000). The social costs can then be compared 

with estimated social benefits (as presented in Section 6). As noted in EPA’s (2000) Guidelines 

for Preparing Economic Analyses, several tools are available to estimate social costs and range 

from simple direct compliance cost methods to the development of a more complex market 

analysis that estimates market changes (e.g., price and consumption) and economic welfare 

changes (e.g., changes in consumer and producer surplus). 

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

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

expected post-policy market outcomes. The analysis’ time horizon is the intermediate run; some 

production factors are fixed and some are variable and is distinguished from the very short run 

where all factors are fixed and producers cannot adjust inputs or outputs (EPA, 1999, 5-6). The 

intermediate time horizon allows us to capture important transitory stakeholder outcomes. Key 

measures in this analysis include industry-level changes in price levels, production and 

consumption, jobs, international trade, and social costs (changes in producer and consumer 

surplus). 

4.1 Partial Equilibrium Analysis (Multiple Markets) 

The partial equilibrium analysis develops a market model that simulates how stakeholders 

(consumers and industries) might respond to the additional regulatory program costs. In this 

section, we provide an overview of the economic model. Appendix A provides additional details 

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

4.1.1 Overview 

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

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

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

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

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

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

near-term or transition costs (EPA, 1999). The multimarket model contains the following 

features: 
4-2 

 Industry sectors and benchmark data set 

– 100 industry sectors 

– a single benchmark year (2010) 

– estimates of industry employment 

 Economic behavior 

– industries respond to regulatory costs by changing production rates 

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

costs and changes in demand 

– customers respond to these price increases and consumption falls 

 Model scope 

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

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

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

regulation has on related markets. 

– production adjustments influence employment levels 

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

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

– 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 

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 
4-3 

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

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 

$0.8 billion (2008$) of the proposed area source rule’s costs to U.S. households in the form of 

higher prices (Table 4-1). Existing U.S. industries’ surplus falls by $2.5 billion and the net loss in 

aggregate is $3.3 billion. As U.S. prices rise, other countries are affected through international 

trade relationships. The price of goods produced in the United States increase slightly and 

domestic production declines, replaced to a certain degree by imports; the model estimates a net 

gain of $0.1 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 $3.2 

billion. As shown in Figure 4-2, the surplus losses are concentrated in other services (20 percent); 

lumber, paper, and printing (19 percent); and chemicals (16 percent) 

‐0.10%

‐0.09%

‐0.08%

‐0.07%

‐0.06%

‐0.05%

‐0.04%

‐0.03%

‐0.02%

‐0.01%

0.00%

0.01%

0.02%

0.03%

0.04%

0.05%

0.06%

0.07%

0.08%

0.09%

0.10%

Primary Option

Option 1E and

Option 1N Primary Option

Option 1E and

Option 1N Primary Option

Option 1E and

Option 1N

Major Sources Area Sources Major & Area Sources

Percentage Change from Baseline

Prices Production Imports Consumption Exports
4-4 

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

Approach Primary Option 

 Option 1E and 

Option 1N 

Partial Equilibrium Model (Multiple Markets) 

Change in U.S. consumer surplus −$0.8 −$3.8 

Change in U.S. producer surplus −$2.5 −$8.9

Change in U.S. surplus −$3.3 −$12.7 

Net change in rest of world surplus $0.1 $0.5

Net change in total surplus −$3.2 −$12.2 

Direct Compliance Costs Method 

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

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

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

Change in Total Surplus −$2.9 −$11.9 

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

20.4%

19.1%

16.0%

11.8%

9.7%

5.6%

4.1%

3.3%

2.3%

1.4%

1.5%

1.3%

1.2%

1.1%

0.7%

0.5%

24.8%

9.9%

13.4%

17.8%

6.0%

4.5%

3.0%

7.2%

1.5%

1.8%

1.4%

4.7%

0.8%

1.6%

0.9%

0.6%

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

Other Services

Lumber, paper, and printing

Chemicals

Energy Industries

Food, beverages, and textiles

Primary Metals

Nonmanufacturing

Transportation Equipment

Machinery and Equipment

Plastics and Rubber

Other

Transportation Services

Wholesale and Retail Trade

Fabricated Metals

Electronic Equipment

Nonmetallic Minerals

Primary Option:  $3.2 Billion Surplus Change Option 1E and Option 1N:  $12.2 billion Surplus Change
4-5 

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 $2.9 billion. 

4.1.2.3 Social Cost Estimates Area Source Rule 

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

$0.3 billion (2008$) of the proposed area source rule’s costs to U.S. households in the form of 

higher prices (Table 4-2). Existing U.S. industries’ surplus falls by $0.3 billion and the net loss 

for U.S. stakeholders is $0.6 billion. As U.S. prices rise, other countries are affected through 

international trade relationships. Households that buy U.S. exports pay higher prices and purchase 

fewer U.S. produced goods. Other countries that that sell goods to the United States benefit; the 

model estimates a net rest of the world gain of less than $0.1 billion. After accounting for 

international trade effects, the Agency’s economic model projects the net surplus (consumer and 

producer) loss associated with the proposed rule is $0.6 billion. As shown in Figure 4-3, the 

surplus losses are concentrated in the other services (86 percent ). 

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

Approach Primary Option 

Option 1E and 

Option 1N 

Partial Equilibrium Model (Multiple Markets) 

Change in U.S. consumer surplus −$0.3 −$16.5 

Change in U.S. producer surplus −$0.3 −$16.5

Change in U.S. surplus −$0.6 −$33.1 

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

Net change in total surplus −$0.6 −$33.2 

Direct Compliance Costs Method 

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

Total annualized costs, new area sources (not modeled) −$0.3 −$2.3 

Fuel savings, existing area sources (not modeled) $0.4 $0.4 

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

Change in Total Surplus −$0.5 −$35.3 
4-6 

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

The Agency also considered other elements of the engineering cost analysis that could not 

be modeled within the multimarket model (e.g., fuel-savings benefits [existing and new area 

sources] and total annualized compliance costs [unknown existing and new area sources]). The 

net effect of the adjustments is a total surplus loss estimate of $0.5 billion. 

4.1.2.4 Job Effects 

Precise job effect estimates cannot be estimated with certainty. Morgenstern et al. (2002) 

identify three economic mechanisms by which pollution abatement activities can indirectly 

influence jobs: 

 higher production costs raise market prices, higher prices reduce consumption, and 

employment within an industry falls (“demand effect”); 

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

level of output (“cost effect”); and 

86.2%

5.2%

2.8%

1.9%

2.6%

0.2%

0.4%

0.2%

0.1%

0.1%

0.1%

0.1%

0.1%

0.0%

0.0%

0.0%

85.0%

6.1%

2.9%

1.7%

2.8%

0.2%

0.5%

0.2%

0.1%

0.1%

0.1%

0.1%

0.1%

0.0%

0.0%

0.0%

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

Other Services

Food, beverages, and textiles

Nonmanufacturing

Lumber, paper, and printing

Wholesale and Retail Trade

Other

Transportation Services

Chemicals

Energy Industries

Primary Metals

Plastics and Rubber

Nonmetallic Minerals

Fabricated Metals

Electronic Equipment

Machinery and Equipment

Transportation Equipment

Primary option:  $0.6 billion Surplus Change Option 1E and Option 1N:  $33.2 Billion Surplus Change
4-7 

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

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

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

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

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

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

estimate the production technologies and industry-level demand elasticities. Their identification 

strategy leverages repeat plant-level observations over time and uses plant-level and year fixed 

effects (e.g., plant and time dummy variables). After estimating their model, Morgenstern show 

and compute the change in employment associated with an additional $1 million ($1987) in 

environmental spending. Their estimates cover four manufacturing industries (pulp and paper, 

plastics, petroleum, and steel) and Morgenstern, et al. present results separately for the cost, factor 

shift, and demand effects, as well as the net effect. They also estimate and report an industry-wide 

average parameter that combines the four industry-wide estimates and weighting them by each 

industry’s share of environmental expenditures. 

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

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

shift”). This transfer of results from the Morgenstern study is uncertain but avoids ignoring the 

“cost effect” and the “factor-shift effect.” 

We calculated “demand effect” employment changes by assuming that the number of jobs 

declines proportionally with multi-market model’s simulated output changes. These results were 

calculated for all sectors in the EPA model that show a change in output. 

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

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

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

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

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

million ($1987) of environmental compliance expenditure) by the total environmental compliance 
4-8 

expenditures used in the partial equilibrium model. For example, the jobs effect estimate for the 

Major Source Rule is estimated to be 7,000 jobs (−3.56× $3.2 billion × 0.60).1

 Demand effect 

results are provided in Figure 4-4. 

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

We also present the results of using the Morgenstern paper to estimate employment “cost” 

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

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

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

that using the Morgenstern point estimates of parameters to estimate the “cost” and “factor shift” 

employment gains may be greater than the employment losses using either of the two ways of 

estimating “demand” employment losses. The 95% confidence intervals are shown for all of the 

 

1

 Since Morgenstern’s analysis reports environmental expenditures in $1987, we make an inflation adjustment the 

engineering cost analysis using GDP implicit price deflator (64.76/108.48) = 0.60) 

‐5 ‐19

‐1

‐62

‐7

‐81

‐7

‐26

‐1

‐71

‐8

‐96

5

18

1

48

6

66

5

19

1

53

6

73

3 11

1

31

4

42

‐250

‐225

‐200

‐175

‐150

‐125

‐100

‐75

‐50

‐25

0

25

50

75

100

125

150

175

200

225

250

Primary Option

Option 1E and

Option 1N Primary Option

Option 1E and

Option 1N Primary Option

Option 1E and

Option 1N

Major Sources Area Sources Major & Area Sources

1,000 Jobs

Base =144,100

DemandEffect:  Partial Equilbrium(multiple markets) Demand Effect:  Estimate derivedfrom Morgenstern, etal.

CostEffect:   Estimate derived fromMorgenstern, et al. Factor ShiftEffect:   Estimate derived fromMorgenstern, et al.

NetEffect:   Estimate derivedfrom Morgenstern, etal.
4-9 

estimates based on the Morgenstern parameters. As shown, at the 95% confidence level, we 

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

Although the Morgenstern paper provides 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. In addition, EPA 

assumes also that Morgenstern et al.’s estimates derived from the 1979-1991 still applicable for 

policy taking place in 2013, almost 20 years later. Second, the multi-market model only considers 

near term employment effects in a U.S. economy where production technologies are fixed. As a 

result, the modeling system places more emphasis on the short term “demand effect” whereas the 

Morgenstern paper emphasizes other important long term responses. For example, positive job 

gains associated with “factor shift effects” are more plausible when production choices become 

more flexible over time and industries can substitute labor for other production inputs. Third, the 

Morgenstern paper estimates rely on sector demand elasticities that are different from the demand 

elasticity parameters used in the multi-market model. As a result, the demand effects are not 

directly comparable with the demand effects estimated by the multi-market model. Fourth, 

Morgenstern identifies the industry average as economically and statistically insignificant effect 

(i.e., the point estimates are small, measured imprecisely, and not distinguishable from zero.) EPA 

acknowledges this fact and has reported the 95 percent confidence intervals in Figure 4-4. Fifth, 

Morgenstern’s methodology assumes large plants bear most of the regulatory costs. By 

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

that the regulatory burden does not disproportionately fall on smaller plants. 
5-1 

SECTION 5 

SMALL ENTITY ANALYSES 

The RFA as amended by SBREFA generally requires an agency to prepare a regulatory 

flexibility analysis of any rule subject to notice and comment rulemaking requirements under the 

Administrative Procedure Act or any other statute, unless the agency certifies that the rule will not 

have a significant economic impact on a substantial number of small entities (SISNOSE). Small 

entities include small businesses, small governmental jurisdictions, and small not-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 5-1 provides a list of the sectors affected 

(3-digit NAICS) and the range of SBA size definitions. 

5.1.1.1 Representative Small Business Analysis Using Census Statistics of U.S. Businesses 

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

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

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

range of economic analyses.1

 Statistics include the total number of establishments and receipts for 

all entities within an industry; however, only a subset of entities will be covered by the proposed 

rule. SUSB also provides statistics by enterprise employment and receipt size. 

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

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

conducted or where services or industrial operations are performed. 

 

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. 
5-3 

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

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

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

revenue collected for local, state, and federal taxes. 

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

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

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

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

annual payroll are summed from the associated establishments. Enterprise size 

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

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

“ultimate parent company,” we assumed in this analysis that the “enterprise” definition above is 

consistent with the concept of ultimate parent company that is typically used for SBREFA 

screening analyses, and the terms are used interchangeably. 

The analysis generated a set of establishment sales tests (represented as cost-to-receipt 

ratios) for NAICS codes associated with sectors listed in Table 5-2. Although the appropriate 

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

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

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

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

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

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

“enterprise”) may have other economic resources that could be used to cover the costs of the 

regulatory program. 

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 

code. For area sources, we were limited to two representative small entity facility-level costs 

(approximately $26,000,000 to $1.4 million) 1. Using these cost data and the Census estimates of 

average establishment receipts, a substantial number of SUSB 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). 
5-4 

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 below 

one percent for the primary option and above 5 percent for Option 1E. 

63%

98%

25%

100%

17%

78%

8%

65%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

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

Major Area

Upper Bound

Lower Bound
5-5 

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

Combustion Survey 

Sample Statistic Primary Option Option 1E 

Mean 4.9% 15.6% 

Median 0.4% 5.3% 

Maximum 72.9% 100.0% 

Minimum <0.1% <0.1% 

Ultimate parent company observations 50 50 

Ultimate parent companies with sale tests 

exceeding 3% 

14 30 

5.1.2 Small Governmental Jurisdictions and Not-for--Profit Enterprises 

In addition to the private sector, this rule also covers sectors that include entities owned by 

small and large governments and not-for-profit enterprises. Given the uncertainty and data 

limitations associated with identifying and appropriately classifying these entities, we computed a 

“revenue” test, where the annualized compliance cost is a percentage of annual revenues (U.S. 

Census, 2005a and b). 

Compliance costs were estimated for model facilities for major and area sources for 

multiple options. A summary of the compliance costs used for the small entity analysis follows: 

Major Sources: 

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

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

Area Sources: 

 Primary option: 

– Other public: $3.0 million 

– Hospital: $11,300 

– Schools: $4,500 

– Churches: $2,200 
5-6 

 Option 1E and Option 1N: 

– Other public: $2.9 million 

– Hospital: $141,000 

– Schools: $346,000 

– Churches: $45,000 

From the 2002 Census (in 2008 dollars), 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. The following small area source 

facilities would have cost-to-revenue ratios exceeding 1 percent: 

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

 Option 1E and Option 1N: other public (ratio > 10 percent), hospitals (ratio > 3 

percent), schools (ratio > 10 percent), and churches (ratio >10 percent). 

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 
5-7 

major and area sources of HAPs that are listed for regulation under CAA section 112(c). A major 

source emits or has the potential to emit 10 tons per year (tpy) or more of any single HAP or 25 

tpy or more of any combination of HAP. An area 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. 

5.2.4.2 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. 
5-8 

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 

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 
5-9 

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. 

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. 
5-10 

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. 

5.2.6.1.5 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 Rule 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 the proposed Major and Area Source Boiler Rules to be 

$18 billion to $43 billion in the implementation year (2013). Using a 7% discount rate, we 

estimate the total monetized benefits of the proposed Major and Area Source Boiler Rules to be 

$16 billion to $39 billion in the implementation year. All estimates are in 2008$. 

These estimates reflect EPA’s most current interpretation of the scientific literature. 

Higher or lower estimates of benefits are possible using other assumptions; examples of this are 

provided in Figure 6-2. Data, resource, and methodological limitations prevented EPA from 

monetizing the benefits from several important benefit categories, including benefits from 

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

reducing hazardous air pollutants have not been monetized in this analysis, including reducing 

370,000 tons of carbon monoxide, 37,000 tons of HCl, 1,000 tons of HF, 8.3 tons of mercury, 

3,400 tons of other metals, and 1,200 grams of dioxins/furans each year. 

6.2 Calculation of 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-per-ton” 

approach to estimate these benefits based on the methodology described in Fann et al. (2009). The 

key assumptions are described in detail below. These PM2.5 benefit-per-ton estimates provide the 

total monetized human health benefits (the sum of premature mortality and premature morbidity) 
6-2 

of reducing one ton of PM2.5 from a specified source. EPA has used the benefit per-ton technique 

in several previous RIAs, including the recent NO2 NAAQS RIA (U.S. EPA, 2010b). Table 6-1 

shows the quantified and unquantified benefits captured in those benefit-per-ton estimates. 

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

Pollutant / 

Effect 

Quantified and Monetized in Primary 

Estimates Unquantified Effects Changes in: 

PM2.5 Adult premature mortality 

Bronchitis: chronic and acute 

Hospital admissions: respiratory and 

cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial infarction) 

Lower and upper respiratory illness 

Minor restricted-activity days 

Work loss days 

Asthma exacerbations (asthmatic population) 

Infant mortality 

Subchronic bronchitis cases 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than chronic 

bronchitis 

Non-asthma respiratory emergency room visits 

Visibility 

Household soiling 

Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the PM2.5 benefits 

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

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

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

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

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

estimate. 

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

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

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

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

completed since the PM2.5 NAAQS. 

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

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

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

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

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

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

function provided by each of the twelve experts, to better characterize the degree of 

variability in the expert responses. 

The effect coefficients are drawn from epidemiology studies examining two large 

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six 

Cities cohort (Laden et al., 2006).1 These are logical choices for anchor points in our presentation 

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

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

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

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

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

2008).2 Within this assessment, we include the benefits estimates derived from the concentrationresponse function provided by each of the twelve experts to better characterize the uncertainty in 

the concentration-response function for mortality and the degree of variability in the expert 

responses. Because the experts used these cohort studies to inform their concentration-response 

functions, benefits estimates using these functions generally fall between results using these 

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

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

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

used in this analysis should consult Fann et al. (2009). As described in the documentation for the 

benefit per-ton estimates cited above, national per-ton estimates are developed for selected 

pollutant/source category combinations. The per-ton values calculated therefore apply only to 

tons reduced from those specific pollutant/source combinations (e.g., SO2 emitted from electric 

generating units; NO2 emitted from mobile sources). Our estimate of PM2.5 control benefits is 

therefore based on the total PM2.5 emissions controlled by sector and multiplied by this per-ton 

value. 

The benefit-per-ton coefficients in this analysis were derived using modified versions of 

the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically, 

this analysis uses the benefit-per-ton method first applied in the Portland Cement NESHAP RIA 

(U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an expanded 

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

 

1

 These two studies specify multi-pollutant models that control for SO2, among other pollutants. 2

 Please see the Section 5.2 of the Portland Cement RIA in Appendix 5A for more information regarding the change 

in the presentation of benefits estimates. 
6-4 

epidemiology studies without an adjustment for an assumed threshold.3

 Removing the threshold 

assumption is a key difference between the method used in this analysis of PM benefits and the 

methods used in RIAs prior to Portland Cement, and we now calculate incremental benefits down 

to the lowest modeled PM2.5 air quality levels. 

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

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

evolving. Based on our review of the body of scientific literature, EPA applied the no-threshold 

model in this analysis. EPA’s Integrated Science Assessment for Particulate Matter (U.S. EPA, 

2009b), which was recently reviewed by EPA’s Clean Air Scientific Advisory Committee (U.S. 

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

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

concentration-response relationship while recognizing potential uncertainty about the exact shape 

of the concentration-response function.4

 In conjunction with the underlying scientific literature, 

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

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

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

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

interpretation of the scientific and economic literature. For a period of time (2004-2008), the 

Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical life 

(VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived at a 

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

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

from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million value 

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$)6

 was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the 

 

3

 The benefit-per-ton estimates have also been updated since the Cement RIA to incorporate a revised VSL, as 

discussed on the next page. 4 It is important to note that uncertainty regarding the shape of the concentration-response function is conceptually 

distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is a 

discontinuity, which is a specific example of non-linearity. 5

 In the Portland Cement RIA (U.S. EPA, 2009a), EPA solicited comment on the use of the no-threshold model for 

benefits analysis within the preamble of that proposed rule. The comment period for the Portland Cement 

proposed NESHAP closed on September 4, 2009 (Docket ID No. EPA–HQ–OAR–2002–0051 available at 

http://www.regulations.gov). EPA is currently reviewing those comments. 6

 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 The Agency is committed to using 

scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has 

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

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

draft guidance will be available shortly thereafter. 

Figure 6-1 illustrates the relative breakdown of the monetized PM2.5 health benefits. 

 

7

 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008), EPA retained the VSL endorsed by the SAB 

with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the 

near future. Therefore, this report does not represent final agency policy. 8

 In this analysis, we adjust the VSL to account for a different currency year (2008$) and to account for income 

growth to 2015. After applying these adjustments to the $6.3 million value, the VSL is $9.1m. 
6-6 

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

from Pope et al. (2002)a

a

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

Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult 

mortality would be 97%. This chart shows the breakdown using a 3% discount rate, and the results would be 

similar if a 7% discount rate was used. 

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 Table 6-4 

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 of the proposed option. 

 

 

9

 To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). 

These benefits are estimated for a specific analysis year (i.e., 2013), and most of the PM benefits occur within that 

year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 

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

lag between PM exposures and the total realization of changes in health effects. Although the structure of the lag 

is uncertain, EPA follows the advice of the SAB-HES to assume a segmented lag structure characterized by 30% 

of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction 

in PM2.5 (U.S. EPA-SAB, 2004). Changes in the lag assumptions do not change the total number of estimated 

deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis 

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

using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate. 

Adult Mortality ‐ Pope et

al. 93%

ChronicBronchitis 4%

AMI 2%

Acute Respiratory Symptoms

0.5%

InfantMortality 0.4%

Work Loss Days 0.2%

HospitalAdmissions, Cardio

0.2%

HospitalAdmissions, Resp

0.04%

AsthmaExacerbation 0.01%

Acute Bronchitis 0.01%

UpperResp Symp 0.00%

LowerResp Symp 0.00%

ERVisits, Resp 0.00%

Other1%
6-7 

Table 6-2. Summary of Monetized Benefits Estimates for Boiler MACT (for Major 

Sources) in 2013 (2008$)a

Pollutant 

Emissions 

Reductions 

(tons) 

Benefit 

per ton 

(Pope, 

3%) 

Benefit 

per ton 

(Laden, 

3%) 

Benefit 

per ton 

(Pope, 

7%) 

Benefit 

per ton 

(Laden, 

7%) 

Total Monetized 

Benefits (millions 

2008$ at 3%) 

Total Monetized 

Benefits (millions 

2008$ at 7%) 

Option 1E and 1N Direct PM2.5 Major 29,336 $230,000 $560,000 $210,000 $500,000 $6,700 to $16,000 $6,100 to $15,000

PM2.5 Precursors 

SO2 Major 347,114 $29,000 $72,000 $27,000 $65,000 $10,000 to $25,000 $9,300 to $23,000

VOC 6,679 $1,200 $3,000 $1,100 $2,700 $8.1 to $20.0 $7.4 to $18.0 

 Total $17,000 to $41,000 $15,000 to $37,000

Primary Options Direct PM2.5 Major 29,020 $230,000 $560,000 $210,000 $500,000 $6,600 to $16,000 $6,000 to $15,000

PM2.5 Precursors 

SO2 Major 339,996 $29,000 $72,000 $27,000 $65,000 $10,000 to $25,000 $9,100 to $22,000

VOC 1,786 $1,200 $3,000 $1,100 $2,700 $2.2 to $5.3 $2.0 to $4.8 

 Total $17,000 to $41,000 $15,000 to $37,000

a

 All estimates are for the 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 46 

new major sources expected to be affected by 2013. 

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%) 

Option 1E and 1N 

Direct PM2.5 Area 22,920 $360,000 $880,000 $330,000 $790,000 $8,200 to $20,000 $7,500 to $18,000

PM2.5 Precursors 

SO2 Area 1,745 $20,000 $49,000 $18,000 $44,000 $35 to $86 $32 to $77 

VOC 2,119 $1,200 $3,000 $1,100 $2,700 $2.6 to $6.3 $2.3 to $5.7 

 Total $8,300 to $20,000 $7,500 to $18,000

Primary Options 

Direct PM2.5 Area 2,682 $360,000 $880,000 $330,000 $790,000 $960 to $2,400 $880 to $2,100 

PM2.5 Precursors 

SO2 Area 1,539 $20,000 $49,000 $18,000 $44,000 $31 to $76 $28 to $68 

VOC 1,179 $1,200 $3,000 $1,100 $2,700 $1.4 to $3.5 $1.3 to $3.2 

 Total $1,000 to $2,400 $910 to $2,200 

a

 All estimates are for the 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. 
6-8 

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

Proposed Major and Area Source Boiler Rules in 2013a

Boiler MACT (Major Sources) Boiler Area Source Rule 

Options 1E and 1N Primary Options Options 1E and 1N Primary Options

Avoided Premature Mortality 

Pope et al. 1,900 1,900 930 110 

Laden et al. 4,900 4,800 2,400 300 

Avoided Morbidity 

Chronic Bronchitis 1,300 1,300 670 81 

Acute Myocardial Infarction 3,000 3,000 1,500 190 

Hospital Admissions, Resp 460 450 220 27 

Hospital Admissions, Cardio 980 960 460 57 

Emergency Room Visits, Resp 1,800 1,800 690 85 

Acute Bronchitis 3,000 3,000 1,600 190 

Work Loss Days 250,000 250,000 130,000 16,000 

Asthma Exacerbation 33,000 33,000 17,000 2,100 

Acute Respiratory Symptoms 1,500,000 1,500,000 780,000 95,000 

Lower Respiratory Symptoms 36,000 36,000 19,000 2,300 

Upper Respiratory Symptoms 27,000 27,000 14,000 1,700 

a

 All estimates are for the analysis year (2013) and are rounded to whole numbers with two significant figures. All 

fine particles are assumed to have 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 46 new major sources and 6,779 new area sources expected to be affected 

by 2013. 
6-9 

Table 6-5. All PM2.5 Benefits Estimates for the Proposed Major and Area Source Boiler 

Rules at discount rates of 3% and 7% in 2013 (in millions of 2008$)a

 Options 1E and 1N Proposal Options 

 3% 7% 3% 7% 

Benefit-per-ton Coefficients Derived from Epidemiology Literature 

Pope et al. $25,000 $23,000 $18,000 $16,000 

Laden et al. $62,000 $56,000 $43,000 $39,000 

Benefit-per-ton Coefficients Derived from Expert Elicitation 

Expert A $8,100 $7,300 $46,000 $41,000 

Expert B $6,200 $5,600 $35,000 $32,000 

Expert C $6,200 $5,600 $35,000 $31,000 

Expert D $4,400 $4,000 $25,000 $22,000 

Expert E $10,000 $9,100 $57,000 $51,000 

Expert F $5,700 $5,100 $32,000 $29,000 

Expert G $3,700 $3,400 $21,000 $19,000 

Expert H $4,700 $4,200 $26,000 $24,000 

Expert I $6,100 $5,500 $34,000 $31,000 

Expert J $5,000 $4,500 $28,000 $25,000 

Expert K $1,200 $1,100 $6,900 $6,300 

Expert L $4,500 $4,100 $25,000 $23,000 

a

 All estimates are rounded to two significant figures. 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. 
6-10 

Figure 6-2. Total Monetized PM2.5 Benefits for the Proposed Major and Area Source 

Boiler Rules in 2013 

a

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

Pope et al. study and the Laden et al. study, as well as 12 effect coefficients derived from EPA’s expert elicitation 

on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the 

estimates are based in part on the concentration-response function provided in those studies. 

$0

$10

$20

$30

$40

$50

$60

Pope et al.

Laden et al.

Billions(2008$)

Benefits estimates derived from2 epidemiology functions and 12 expert functions

3% DR

7% DR
6-11 

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

Precursor Pollutant and Source 

Figure 6-4. Breakdown of Monetized Benefits for the Proposed Boiler MACT (Major 

Sources) by Fuel Type 

PM2.5 Major

38% PM2.5 Area

5%

SO2 Major

57%

SO2 Area

0.2%

VOC

0.02%

Biomass

27%

Coal

65%

Liquid

8%
6-12 

Figure 6-5. Breakdown of Monetized Benefits for the Proposed Boiler Area Source Rule 

by Fuel Type 

6.3 Unquantified Benefits 

The monetized benefits estimated in this RIA only reflect the portion of benefits 

attributable to the health effect reductions associated with ambient fine particles. Data, resource, 

and methodological limitations prevented EPA 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. 

6.3.1 Carbon Monoxide Benefits 

Carbon monoxide (CO) exposure is associated with a variety of health effects. Without 

knowing the location of the emission reductions and the resulting ambient concentrations using 

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

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

Biomass

22%

Coal

66%

Gas

1%

Liquid

11%
6-13 

the benefits associated with the 370,000 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. 

6.3.2 Other SO2 Benefits 

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

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

associated with reduced SO2 exposure in this analysis because we do not have air quality 

modeling data available. Without knowing the location of the emission reductions and the 

resulting ambient concentrations, we were unable to estimate the exposure to SO2 for nearby 

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 Integrated Science Assessment (ISA) for Sulfur Dioxide 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. 
6-14 

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 

(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, 
6-15 

particularly sulfates, and higher average relative humidity levels. In fact, particulate sulfate is the 

largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually and 75% 

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

(U.S. EPA, 2009c). Visibility has direct significance to people’s enjoyment of daily activities and 

their overall sense of wellbeing. Good visibility increases the quality of life where individuals live 

and work, and where they engage in recreational activities. 

6.3.3 HAP Benefits 

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

benefits associated with the 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 370,000 tons of carbon monoxide, 37,000 tons of 

HCl, 1,000 tons of HF, 8.3 tons of mercury, and 3,400 tons of other metals, 1,200 grams of 

dioxins/furans each year from major and area sources. We discuss the health effects associated 

with these top HAPs as well as the HAPs for which we have emission reduction estimates. 

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

Coal Gas Biomass Oil 

68% HCl 44% Formaldehyde 32% Acetaldehyde 28% Nickel 

5% HF 25% PAH 28% HCl 19% Manganese 

 3% Toluene 25% Formaldehyde 

6.3.3.1 Mercury 

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

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

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

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

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

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

Munthe et al, 2007). The SO2 ISA concluded that evidence is sufficient to infer a casual 
6-16 

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 mercury emissions. Mercury is emitted to the air from various man-made and natural 

sources. These emissions transport through the atmosphere and eventually deposit to land or 

water bodies. This deposition can occur locally, regionally, or globally, depending on the form of 

mercury emitted and other factors such as the weather. The form of mercury emitted varies 

depending on the source type and other factors. Available data indicate that the mercury 

emissions from these sources are a mixture of gaseous elemental mercury, inorganic ionic 

mercury, and particulate bound mercury. Gaseous elemental mercury can be transported very long 

distances, even globally, to regions far from the emissions source (becoming part of the global 

“pool”) before deposition occurs. Inorganic ionic and particulate bound mercury have a shorter 

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

Furthermore, elemental mercury in the atmosphere can undergo transformation into ionic 

mercury, providing a significant pathway for deposition of emitted elemental mercury. 

Major and area source boilers emitted about 16 tons of mercury in the air in 2008 in the 

U.S. Based on the EPA’s National Emission Inventory, about 103 tons of mercury were emitted 

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

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

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

magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 16% of the 

total anthropogenic mercury emissions in the U.S. and about 0.8% of the global emissions. 

Overall, this rule would reduce mercury emissions by about 8.3 tons per year from current levels, 

and therefore, contribute to reductions in mercury exposures and health effects. Due to time and 

resource limitations, we were unable to model mercury dispersion, deposition, methylation, 

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

be needed in order to estimate the human health benefits from reducing mercury emissions. 

Potential exposure routes to mercury emissions include both direct inhalation and 

consumption of fish containing methylmercury. The primary route of human exposure to mercury 

emissions from industrial sources is generally indirectly through the consumption of fish 

containing methylmercury. As described above, mercury that has been emitted to the air 

eventually settles into water bodies or onto land where it can either move directly or be leached 

into waterbodies. Once deposited, certain microorganisms can change it into methylmercury, a 

highly toxic form that builds up in fish, shellfish and animals that eat fish. Consumption of fish 
6-17 

and shellfish are the main sources of methylmercury exposure to humans. Methylmercury builds 

up more in some types of fish and shellfish than in others. The levels of methylmercury in fish 

and shellfish vary widely depending on what they eat, how long they live, and how high they are 

in the food chain. Most fish, including ocean species and local freshwater fish, contain some 

methylmercury. For example, in recent studies by EPA and the U.S. Geological Survey (USGS) 

of fish tissues, every fish sampled contained some methylmercury. 

The majority of fish consumed in the U.S. are ocean species. The methylmercury 

concentrations in ocean fish species are primarily influenced by the global mercury pool. 

However, the methylmercury found in local fish can be due, at least partly, to mercury emissions 

from local sources. Research shows that most people’s fish consumption does not cause a 

mercury-related health concern. However, certain people may be at higher risk because of their 

routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families 

who rely heavily on fish for a substantial part of their diet). It has been demonstrated that high 

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

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

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

all ages. 

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

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

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

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

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

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

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

all-cause mortality.11 The NRC also noted a study of 917 seven year old children in the Faroe 

Islands, whose initial exposure to methylmercury was in utero although post natal exposures may 

have occurred as well. At seven years of age, these children exhibited an increase in blood 

 

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

Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. 

Washington, DC. pp.168-173. 11Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid peroxidation, and the risk of 

myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men.” Circulation, 91 (3):645-

655. 
6-18 

pressure and a decrease in heart rate variability.12 Based on these and other studies, NRC 

concluded in 2000 that, while “the data base is not as extensive for cardiovascular effects as it is 

for other end points (i.e. neurologic effects) the cardiovascular system appears to be a target for 

methylmercury toxicity.”13 

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

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

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

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

methylmercury exposure and adverse cardiovascular effects, then reducing exposure to 

methylmercury would result in public health benefits from reduced cardiovascular effects. 

In early 2010, EPA sponsored a workshop in which a group of experts were asked to 

assess the plausibility of a causal relationship between methylmercury exposure and 

cardiovascular health effects and to advise EPA on methodologies for estimating population level 

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

workshop is in preparation. 

6.3.3.2 Hydrogen Chloride (HCl) 14

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

and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 100 

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

to high concentrations can rapidly lead to swelling and spasm of the throat and suffocation. Most 

seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and 

narrowing of the bronchioles. Patients who have massive exposures may develop an accumulation 

of fluid in the lungs. Exposure to hydrogen chloride can lead to Reactive Airway Dysfunction 

Syndrome (RADS), a chemically- or irritant-induced type of asthma. Children may be more 

vulnerable to corrosive agents than adults because of the relatively smaller diameter of their 

airways. Children may also be more vulnerable to gas exposure because of increased minute 

 

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

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

Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. 

Washington, DC. p. 229. 14 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). Medical Management Guidelines for Hydrogen Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S. 

Department of Health and Human Services, Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/Mhmi/mmg173.html>. 
6-19 

ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has 

not been classified for carcinogenic effects. 

6.3.3.3 Chlorine gas (Cl2)

 15

Chlorine gas is irritating and corrosive to the eyes, skin, and respiratory tract. Exposure to 

chlorine may cause burning of the eyes, nose, and throat; cough as well as constriction and edema 

of the airway and lungs can occur. 

6.3.3.4 Hydrogen cyanide (HCN) 16

Hydrogen cyanide is highly toxic by all routes of exposure and may cause abrupt onset of 

profound central nervous system, cardiovascular, and respiratory effects, leading to death within 

minutes. Exposure to lower concentrations of hydrogen cyanide may produce eye irritation, 

headache, confusion, nausea, and vomiting followed in some cases by coma and death. Hydrogen 

cyanide acts as a cellular asphyxiant. By binding to mitochondrial cytochrome oxidase, it prevents 

the utilization of oxygen in cellular metabolism. The central nervous system and myocardium are 

particularly sensitive to the toxic effects of cyanide. 

6.3.3.5 Hydrogen Fluoride (HF) 17

Acute (short-term) inhalation exposure to gaseous hydrogen fluoride can cause severe 

respiratory damage in humans, including severe irritation and pulmonary edema. Chronic (longterm) exposure to fluoride at low levels has a 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. 

 

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

(ATSDR). 2007. Medical Management Guidelines for Chlorine (CAS 7782-50-5; UN 1017). Atlanta, GA: U.S. 

Department of Health and Human Services, Public Health Service. Available on the Internet at 

http://www.atsdr.cdc.gov/MHMI/mmg172.html#bookmark02. 16 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). 2007. Medical Management Guidelines for Hydrogen Cyanide (HCN) (CAS#: 7782-50-5). Atlanta, GA: 

U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at 

http://www.atsdr.cdc.gov/Mhmi/mmg8.html#bookmark02. 17 All health effects language for this section came from: U.S. EPA, “National Emission Standards for Hazardous Air 

Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule,” 68 Federal 

Register 8 (January 13, 2003). pp. 1664-1665. Available on the internet at 

http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf
6-20 

6.3.3.6 Toluene18

Toluene is found in evaporative as well as exhaust emissions from motor vehicles. Under 

the 2005 Guidelines for Carcinogen Risk Assessment, there is inadequate information to assess 

the carcinogenic potential of toluene because studies of humans chronically exposed to toluene 

are inconclusive, toluene was not carcinogenic in adequate inhalation cancer bioassays of rats and 

mice exposed for life, and increased incidences of mammary cancer and leukemia were reported 

in a lifetime rat oral bioassay. 

The central nervous system (CNS) is the primary target for toluene toxicity in both 

humans and animals for acute and chronic exposures. CNS dysfunction (which is often reversible) 

and narcosis have been frequently observed in humans acutely exposed to low or moderate levels 

of toluene by inhalation; symptoms include fatigue, sleepiness, headaches, and nausea. Central 

nervous system depression has been reported to occur in chronic abusers exposed to high levels of 

toluene. Symptoms include ataxia, tremors, cerebral atrophy, nystagmus (involuntary eye 

movements), and impaired speech, hearing, and vision. Chronic inhalation exposure of humans to 

toluene also causes irritation of the upper respiratory tract, eye irritation, dizziness, headaches, 

and difficulty with sleep. 

Human studies have also reported developmental effects, such as CNS dysfunction, 

attention deficits, and minor craniofacial and limb anomalies, in the children of women who 

abused toluene during pregnancy. A substantial database examining the effects of toluene in 

subchronic and chronic occupationally exposed humans exists. The weight of evidence from these 

studies indicates neurological effects (i.e., impaired color vision, impaired hearing, decreased 

performance in neurobehavioral analysis, changes in motor and sensory nerve conduction 

velocity, headache, dizziness) as the most sensitive endpoint. 

6.3.3.7 Formaldehyde 

Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on 

evidence in humans and in rats, mice, hamsters, and monkeys.19 EPA is currently reviewing 

recently published epidemiological data. For instance, research conducted by the National Cancer 

Institute (NCI) found an increased risk of nasopharyngeal cancer and lymphohematopoietic 

 

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

108-88-3)” Environmental Protection Agency, Integrated Risk Information System (IRIS), Office of Health and 

Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH. Available on the 

Internet at <http://www.epa.gov/iris/subst/0118.htm>. 19 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to 

Formaldehyde, Office of Pesticides and Toxic Substances, April 1987. 
6-21 

malignancies such as leukemia among workers exposed to formaldehyde.20,21 In an analysis of the 

lymphohematopoietic cancer mortality from an extended follow-up of these workers, NCI 

confirmed an association between lymphohematopoietic cancer risk and peak exposures.22 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.23

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.24

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.25,26,27 CIIT’s risk assessment of formaldehyde 

incorporated mechanistic and dosimetric information on formaldehyde. However, it should be 

noted that recent research published by EPA indicates that when two-stage modeling assumptions 

are varied, resulting dose-response estimates can vary by several orders of magnitude.28,29,30,31

These findings are not supportive of interpreting the CIIT model results as providing a 

 

20 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. 21 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. 22 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009. 

Mortality from lymphohematopoietic malignancies among workers in formaldehyde industries: The National 

Cancer Institute cohort. J. National Cancer Inst. 101: 751-761. 23 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup. 

Environ. Med. 61: 193-200. 24 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. 25 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologically 

motivated computational modeling of formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432-447. 26 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, 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. 27 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard characterization and dose-response 

assessment for carcinogenicity by the route of inhalation. CIIT, September 28, 1999. Research Triangle Park, NC. 28 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal Growth Models for Formaldehyde with 

Relevance to Other Biologically-Based Dose Response (BBDR) Models. U.S. Environmental Protection Agency, 

Washington, D.C., EPA/600/R-08/103, 2008 29 Subramaniam, R; Chen, C; Crump, K; .et .al. (2008) Uncertainties in biologically-based modeling of 

formaldehyde-induced cancer risk: identification of key issues. Risk Anal 28(4):907-923. 30 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model for formaldehydeinduced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237 31 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 
6-22 

conservative (health protective) estimate of human risk.32 EPA research also examined the 

contribution of the two-stage modeling for 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.33

Based on the developments of the last decade, 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 epidemiologic evidence on leukemia was 

characterized as “strong.”34 EPA is reviewing the recent work cited above from the NCI and 

NIOSH, as well as the analysis by the CIIT Centers for Health Research and other studies, as part 

of a reassessment of the human hazard and dose-response associated with formaldehyde. 

Formaldehyde exposure also causes a range of noncancer health effects, including 

irritation of the eyes (burning and watering of 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.35,36

 

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

ol. Monographs Volume 88. World Health Organization, Lyon, France. 35 Agency for Toxic Substances and Disease Registry (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 36 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint 

sponsorship of the United Nations Environment Programme, the International Labour Organization, and the World 

Health Organization, and produced within the framework of the Inter-Organization Programme for the Sound 

Management of Chemicals. Geneva. 
6-23 

6.3.3.8 Polycyclic Aromatic Hydrocarbons (PAHs) 

At least seven PAH compounds are classified by EPA as probable human carcinogens 

based on animal data, including benzo(a)anthracene37, benzo(b)fluoranthene38, 

benzo(k)fluoranthene39, benzo(a)pyrene40, chrysene41, dibenz(a,h)anthracene42, and indeno(1,2,3-

cd)pyrene43. Recent studies have found that maternal exposures to PAHs in a population of 

pregnant women were associated with several adverse birth outcomes, including low birth weight 

and reduced length at birth, as well as impaired cognitive development at age three.44,45 EPA has 

not yet evaluated these recent studies. 

6.3.3.9 Acetaldehyde 

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.46

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 

 

37 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. 38 U.S. EPA (1997). Integrated Risk Information System File of benzo(b)fluoranthene. Research and Development, 

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0453.htm. 39 U.S. EPA (1997). Integrated Risk Information System File of benzo(k)fluoranthene. Research and Development, 

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0452.htm. 40 U.S. EPA (1998). Integrated Risk Information System File of benzo(a)pyrene. Research and Development, 

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0136.htm. 41U.S. EPA (1997). Integrated Risk Information System File of chrysene. Research and Development, National 

Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0455.htm 42 U.S. EPA (1997). 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. 43 U.S. EPA (1997). Integrated Risk Information System File of indeno(1,2,3-cd)pyrene. Research and Development, 

National Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/ncea/iris/subst/0457.htm. 44 Perera, F.P.; Rauh, V.; Tsai, W-Y.; et al. (2002) Effect of transplacental exposure to environmental pollutants on 

birth outcomes in a multiethnic population. Environ Health Perspect. 111: 201-205. 45 Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D.; Diaz, D.; Hoepner, L.; Barr, D.; Tu, Y.H.; Camann, 

D.; Kinney, P. (2006) Effect of prenatal exposure to airborne polycyclic aromatic hydrocarbons on 

neurodevelopment in the first 3 years of life among inner-city children. Environ Health Perspect 114: 1287-1292. 46 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and Development, National 

Center for Environmental Assessment, Washington, DC. This material is available electronically at 

http://www.epa.gov/iris/subst/0290.htm. 
6-24 

IARC.47,48 EPA is currently conducting a reassessment of cancer risk from inhalation exposure to 

acetaldehyde. 

The primary noncancer effects of exposure to acetaldehyde vapors include irritation of the 

eyes, skin, and respiratory tract.49 In short-term (4 week) rat studies, degeneration of olfactory 

epithelium was observed at various concentration levels of acetaldehyde exposure.50 Data from 

these studies were used by EPA to develop an inhalation reference concentration. Some 

asthmatics have been shown to be a sensitive subpopulation to decrements in functional 

expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde inhalation.51 The 

agency is currently conducting a reassessment of the health hazards from inhalation exposure to 

acetaldehyde. 

6.3.3.10 Nickel52

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 Group A, human carcinogens and 

nickel carbonyl as a Group B2, probable human carcinogen. 

 

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

available at: http://ntp.niehs.nih.gov/go/16183. 48 International Agency for Research on Cancer (IARC). 1999. Re-evaluation of some organic chemicals, hydrazine, 

and hydrogen peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of Chemical to Humans, Vol 

71. Lyon, France. 49 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material is available 

electronically at http://www.epa.gov/iris/subst/0290.htm. 50 Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982). Inhalation toxicity of acetaldehyde in rats. I. Acute and 

subacute studies. Toxicology. 23: 293-297. 51 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized acetaldehyde induces histaminemediated bronchoconstriction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1): 940-943. 52 All health effects language for this section came from: U.S. EPA, “National Emission Standards for Hazardous Air 

Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule,” 68 Federal 

Register 8 (January 13, 2003). pp. 1664-1665. Available on the internet at 

http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf
6-25 

6.3.3.11 Manganese53

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. 

6.3.3.12 Dioxins (Chlorinated dibenzodioxins (CDDs) 54

A number of effects have been observed in people exposed to 2,3,7,8-TCDD levels that 

are at least 10 times higher than background levels. The most obvious health effect in people 

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

disease with acne-like lesions that occur mainly on the face and upper body. Other skin effects 

noted in people exposed to high doses of 2,3,7,8-TCDD include skin rashes, discoloration, and 

excessive body hair. Changes in blood and urine that may indicate liver damage also are seen in 

people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglobin, lipids, 

sugar, and protein have been reported in people exposed to relatively high concentrations of 

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

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

glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-TCDD. We do 

not have enough information to know if exposure to 2,3,7,8-TCDD would result in reproductive 

or developmental effects in people, but animal studies suggest that this is a potential health 

concern. 

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

single exposure. Exposure to lower levels can cause a variety of effects in animals, such as weight 

 

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

Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule,” 68 Federal 

Register 8 (January 13, 2003). pp. 1664-1665. Available on the internet at 

http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf 54 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). 1999. ToxFAQs for Chlorinated Dibenzo-p-dioxins (CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6). Atlanta, 

GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at 

http://www.atsdr.cdc.gov/tfacts104.html. 
6-26 

loss, liver damage, and disruption of the endocrine system. In many species of animals, 2,3,7,8-

TCDD weakens the immune system and causes a decrease in the system's ability to fight bacteria 

and viruses at relatively low levels (approximately 10 times higher than human background body 

burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused reproductive damage and 

birth defects. Some animal species exposed to CDDs during pregnancy had miscarriages and the 

offspring of animals exposed to 2,3,7,8-TCDD during pregnancy often had severe birth defects 

including skeletal deformities, kidney defects, and weakened immune responses. In some studies, 

effects were observed at body burdens 10 times higher than human background levels. 

6.3.3.13 Furans (Chlorinated dibenzofurans (CDFs)) 55

Most of the information on the adverse health effects comes from studies in people who 

were accidentally exposed to food contaminated with CDFs. The amounts that these people were 

exposed to were much higher than are likely from environmental exposures or from a normal diet. 

Skin and eye irritations, especially severe acne, darkened skin color, and swollen eyelids with 

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

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

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

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

CDFs alone or CDFs and polychlorinated biphenyls in combination. 

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

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

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

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

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

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

CDFs over a lifetime. 

6.3.3.14 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.56

 

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

(ATSDR). 1995. ToxFAQsTM for Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of Health and 

Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc.gov/tfacts32.html>. 56 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris 
6-27 

6.4 Characterization of Uncertainty in the Monetized PM2.5 Benefits 

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

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

quality models (with their associated parameters and inputs), epidemiological estimates of 

concentration-response (C-R) functions, estimates of values, population estimates, income 

estimates, and estimates of the future state of the world (i.e., regulations, technology, and human 

behavior). For some parameters or inputs it may be possible to provide a statistical representation 

of the underlying uncertainty distribution. For other parameters or inputs, the necessary 

information is not available. 

The annual benefit estimates presented in this analysis are also inherently variable due to 

the processes that govern pollutant emissions and ambient air quality in a given year. Factors such 

as hours of equipment use and weather are constantly variable, regardless of our ability to 

measure them accurately. As discussed in the PM2.5 NAAQS RIA (Table 5.5) (U.S. EPA, 2006), 

there are a variety of uncertainties associated with these PM benefits. Therefore, the estimates of 

annual benefits should be viewed as representative of the magnitude of benefits expected, rather 

than the actual benefits that would occur every year. 

We performed 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 for the proposed option. 

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: 
6-28 

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) $18,000 N/A 

No Threshold (Laden) $43,000 N/A 

 Threshold (Pope) $13,810 -23% 

Threshold (Laden) $31,000 -28% 

Discount Rate 

(with Epidemiology Study) 

3% (Pope) $18,000 N/A 

3% (Laden) $43,000 N/A 

7% (Pope) $16,000 -11% 

 7% (Laden) $39,000 -9% 

1. PM2.5 benefits were derived through benefit per-ton estimates, which do not reflect 

local variability in population density, meteorology, exposure, baseline health 

incidence rates, or other local factors that might lead to an over-estimate or underestimate of the actual benefits of controlling directly emitted fine particulates. 

2. We assume that all fine particles, regardless of their chemical composition, are equally 

potent in causing premature mortality. This is an important assumption, because PM2.5 

produced via transported precursors emitted from EGUs may differ significantly from 

direct PM2.5 released from diesel engines and other industrial sources, but no clear 

scientific grounds exist for supporting differential effects estimates by particle type. 

3. We assume that the health impact function for fine particles is linear down to the 

lowest air quality levels modeled in this analysis. Thus, the estimates include health 

benefits from reducing fine particles in areas with varied concentrations of PM2.5,

including both regions that are in attainment with fine particle standard and those that 

do not meet the standard down to the lowest modeled concentrations. 

4. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality (which typically accounts for 85% to 95% of total monetized benefits), we 

include a set of twelve estimates based on results of the expert elicitation study in 

addition to our core estimates. Even these multiple characterizations omit the 

uncertainty in air quality estimates, baseline incidence rates, populations exposed and 

transferability of the effect estimate to diverse locations. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the 

overall uncertainty in the PM2.5 estimates. This information should be interpreted 

within the context of the larger uncertainty surrounding the entire analysis. For more 

information on the uncertainties associated with PM2.5 benefits, please consult the 

PM2.5 NAAQS RIA (Table 5-5). 
6-29 

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 NAAQS RIA can provide some evidence of the uncertainty surrounding the benefits 

results presented in this analysis. 

It is important to note that the monetized benefit-per-ton estimates used here reflect 

specific geographic patterns of emissions reductions and specific air quality and benefits 

modeling assumptions. For example, these estimates do not reflect local variability in population 

density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of 

these $/ton values to estimate benefits associated with different emission control programs (e.g., 

for reducing emissions from large stationary sources like EGUs) may lead to higher or lower 

benefit estimates than if benefits were calculated based on direct air quality modeling. Great care 

should be taken in applying these estimates to emission reductions occurring in any specific 

location, as these are all based on national or broad regional emission reduction programs and 

therefore represent average benefits-per-ton over the entire United States. The benefits- per-ton 

for emission reductions in specific locations may be very different than the estimates presented 

here. 

6.5 Comparison of Benefits and Costs 

Using a 3% discount rate, we estimate the total combined monetized benefits of the 

proposed Boiler MACT and Area Source Rule to be $18 billion to $45 billion in the 

implementation year (2013). Using a 7% discount rate, we estimate the total monetized benefits 

of the proposed Boiler MACT and Area Source Rule to be $17 billion to $40 billion. The 

combined annualized social costs of the proposed Boiler MACT and Area Source Rule are $3.4 

billion at a 7% interest rate.57 Thus, the combined net benefits are $15 billion to $41 billion at a 

3% discount rate and $13 billion to $37 billion at a 7% discount rate. All estimates are in 2008$ 

for the year 2013. 

The annualized social costs of the proposed Boiler MACT only are $2.9 billion, and the 

annualized social costs of the proposed Area Source Rule are $500 million at a 7% discount rate. 

The net benefits for the proposed Boiler MACT for major sources only are $14 billion to $38 

 

57 For more information on the annualized social costs, please refer to Section 4 of this RIA. 
6-30 

billion and $12 billion to $34 billion, at 3% and 7% discount rates, respectively. The net benefits 

for the Boiler Area Source rule only are $500 million to $1.9 billion and $410 million to $1.7 

billion, at 3% and 7% discount rates, respectively. All estimates are in 2008$ for the year 2013. 

Tables 6-8 and 6-9 show a summary of the monetized benefits, social costs, and net 

benefits for the proposed options and the alternative options for the Boiler MACT for Major 

Sources and for the Boiler Area Source Rule, respectively. Figures 6-4 and 6-5 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 370,000 tons of carbon monoxide, 37,000 tons of HCl, 1,000 tons of HF, 8.3 tons of 

mercury, 3,400 tons of other metals, and 1,200 grams of dioxins/furans each year from major and 

area sources have not been included in these 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. 
6-31 

Table 6-8. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler MACT (Major Sources) in 2013 (millions of 2008$)1

Proposed Option 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $17,000 to $41,000 $15,000 to $37,000 

Total Social Costs3 $2,900 $2,900 

Net Benefits $14,000 to $38,000 $12,000 to $34,000 

Option 1N and 1E 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits4 $17,000 to $41,000 $15,000 to $37,000 

Total Social Costs3 $12,000 $12,000 

Net Benefits $5,000 to $30,000 $3,500 to $26,000 

1

All estimates are for the implementation year (2013), and are rounded to two significant figures. 2

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

reductions of 29,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 1,700 tons of VOC and 340,000 

tons of SO2. The benefits from reducing 340,000 tons of carbon monoxide, 37,000 tons of HCl, 1,000 tons of HF, 

and 7.5 tons of mercury, 3,200 tons of other metals, and 720 grams of dioxins/furans each year are not included in 

these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not 

included. 

3

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

in the same social costs for both discount rates. 

4

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

reductions of 29,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 6,700 tons of VOC and 350,000 

tons of SO2. The benefits from reducing 390,000 tons of carbon monoxide, 42,000 tons of HCl, 8,600 tons of HF, 

and 8.1 tons of mercury, 3,200 tons of other metals, and 760 grams of dioxins/furans each year are not included in 

these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not 

included. 

 
6-32 

Table 6-9. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler Area Source Rule in 2013 (millions of 2008$)1

Proposed Option 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $1,000 to $2,400 $910 to $2,200 

Total Social Costs3 $500 $500 

Net Benefits $500 to $1,900 $410 to $1,700 

Option 1N and 1E 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits4 $8,300 to $20,000 $7,500 to $18,000 

Total Social Costs3 $35,000 $35,000 

Net Benefits $-27,000 to $-15,000 $-28,000 to $-17,000 

1

All estimates are for the implementation year (2013), and are rounded to two significant figures. 2

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

reductions of 2,700 tons of directly emitted PM2.5 and PM2.5 precursors such as 1,200 tons of VOC and 1,500 tons 

of SO2. The benefits from reducing 40,000 tons of carbon monoxide, 130 tons of HCl, 5 tons of HF, and 0.75 tons 

of mercury, 250 tons of other metals, and 470 grams of dioxins/furans each year are not included in these 

estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included. 

3

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

in the same social costs for both discount rates. 

4

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

reductions of 23,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 2,100 tons of VOC and 1,700 tons 

of SO2. The benefits from reducing 58,000 tons of carbon monoxide, 140 tons of HCl, 6.4 tons of HF, and 1.5 tons 

of mercury, 6,200 tons of other metals, and 530 grams of dioxins/furans each year are not included in these 

estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are not included. 
6-33 

Figure 6-6. Net Benefits for the Proposed Major and Area Source Boiler Rules at 3% 

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. 

$0

$10

$20

$30

$40

$50

Pope et al.

Laden et al.

Billions(2008$)

Cost estimates combined with total monetized benefits estimates derived from2

epidemiology functions and 12 expert functions
6-34 

Figure 6-7. Net Benefits for the Proposed Major and Area Source 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. 

$0

$10

$20

$30

$40

$50

Pope et al.

Laden et al.

Billions(2008$)

Cost estimates combined with total monetized benefits estimates derived from2

epidemiology functions and 12 expert functions
7-1 

SECTION 7 

SUPPLEMENTAL ECONOMIC ANALYSES FOR NON-HAZARDOUS SOLID WASTE 

DEFINITION 

EPA also considered an economic analysis for an alternate approach for defining nonhazardous solid waste. Under the alternative approach, the universe of sources in the energy 

recovery and waste burning cement kiln subcategories would change while the number of sources 

in the remaining three subcategories (i.e., incinerators, burn-off ovens, and small, remote 

incinerators) does not change. This section provides an overview of the results. 

7.1 Economic Impact Analysis Results 

7.1.1 Market-Level Results 

Market-level impacts include price and quantity adjustments including the changes in 

international trade (Table 7-1). The Agency’s economic model suggests the average national 

market-level variables (prices, production-levels, consumption, and international trade) will not 

significantly change (e.g., are less than 0.5%). 

7.1.2 Social Cost Estimates 

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

$0.7 billion (2008$) of the costs to U.S. households in the form of higher prices (Table 7-2). 

Existing U.S. industries’ surplus falls by $2.0 billion, and the net loss for U.S. stakeholders is 

$2.7 billion. Households that buy U.S. exports pay higher prices and purchase fewer U.S. 

produced goods. Other countries that that sell goods to the United States benefit; the model 

estimates a net rest of the world gain of $0.1 billion. After accounting for international trade 

effects, the Agency’s economic model projects the net surplus loss associated with the proposed 

rule is $2.6 billion. The Agency also considered other elements of the engineering cost analysis 

that could not be modeled within the multimarket model (e.g., 0.4 billion in fuel savings) which 

reduces the social cost estimate to 2.2 billion. 

7.1.3 Job Effects 

Near-term employment changes associated with the proposed rule are estimated to be less 

than 5,000 job losses; over a longer time period, net employment effects could range between 

4,000 job losses to 9,000 job gains. Additional details and caveats associated with these estimates 

are present in section 4. 
7-2 

Table 7-1. Market-Level Price and Quantity Changes: 2013 (Alternate Definition) 

Industry Sector Prices Production Imports Consumption Exports 

Energy 0.04% -0.01% 0.07%

 less than 

0.01% 

 less than 

0.01%

Nonmanufacturing less than 

0.01%

 less than 

0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

Manufacturing 

Food, beverages, and textiles 0.02% -0.02% 0.02% -0.01% -0.01%

Lumber, paper, and printing 0.14% -0.06% 0.14% -0.03% -0.09%

Chemicals 0.02% -0.03% 0.02% -0.02% -0.02%

Plastics and Rubber less than 

0.01% -0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

Nonmetallic Minerals less than 

0.01%

 less than 

0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

Primary Metals 0.04% -0.04% 0.04% -0.02% -0.04%

Fabricated Metals less than 

0.01%

 less than 

0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

Machinery and Equipment 0.01% -0.01%

 less than 

0.01%

 less than 

0.01% -0.02%

Electronic Equipment less than 

0.01%

 less than 

0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

Transportation Equipment less than 

0.01%

 less than 

0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

Other less than 

0.01% -0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

Wholesale and Retail Trade less than 

0.01%

 less than 

0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

Transportation Services less than 

0.01%

 less than 

0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

Other Services less than 

0.01%

 less than 

0.01%

 less than 

0.01%

 less than 

0.01% 

 less than 

0.01%

 
7-3 

Table 7-2. Distribution of Social Costs (million, 2008$): 2013 

Method Alternate Definition 

Partial Equilibrium Model (Multiple Markets) 

Change in U.S. consumer surplus −$0.7 

Change in U.S. producer surplus −$2.0

Change in U.S. surplus −$2.7 

Net change in rest of world surplus $0.1

Net change in total surplus −$2.6 

Direct Compliance Costs Method 

Fuel savings (not modeled) $0.4 

Change in Total Surplus −$2.2 

Table 7-3. Employment Changes: 2013 

Method 1,000 Jobs 

Partial equilibrium model (multiple markets) 

(demand effect only) −4.2 

Estimate Derived from Morgenstern, et al. (net effect [A 

+ B +C below]) 

+2.4 

(−4.4 to +9.2)

A. Estimate Derived from Morgenstern, et al: Demand 

effect 

−5.5 

(−11.6 to 0.6) 

B. Estimate Derived from Morgenstern, et al: Cost effect 

+3.7 

(+1.2 to +6.2) 

C. Estimate Derived from Morgenstern, et al: Factor shift 

effect 

+4.1 

(+0.1 to +8.2) 

Note: Totals may not add due to independent rounding. 95 percent confidence intervals are shown in parenthesis. 
7-4 

7.2 Benefits Analysis Results 

Table 7-4 provides a general summary of the alternate approach results by pollutant, 

including the emission reductions and monetized benefits-per-ton at discount rates of 3% and 7%. 

These estimates reflect EPA’s most current interpretation of the scientific literature. Higher or 

lower estimates of benefits are possible using other assumptions, but most expert-derived 

estimates fall within these estimates. Data, resource, and methodological limitations prevented 

EPA from monetizing the benefits from several important benefit categories, including benefits 

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

from reducing hazardous air pollutants have not been monetized in this analysis, including 

reducing 280,000 tons of carbon monoxide, 5,100 tons of HCl, 1,100 tons of HF, and 7.1 tons of 

mercury, and 290 grams of dioxins/furans from major sources each year. Tables 7-5 shows a 

summary of the monetized benefits, social costs, and net benefits for the alternative definition for 

the Boiler MACT for existing major sources. 

Table 7-4. Summary of Monetized Benefits for the Boiler MACT for Existing Major 

Sources in 2013 (2008$) (Alternate Approach)* 

Pollutant 

Emissions 

Reductions 

(tons) 

Benefit 

per ton 

(Pope, 

3%) 

Benefit 

per ton 

(Laden, 

3%) 

Benefit 

per ton 

(Pope, 

7%) 

Benefit 

per ton 

(Laden, 

7%) 

Total Monetized 

Benefits (millions 

2008$ at 3%) 

Total Monetized 

Benefits 

(millions 2008$ 

at 7%) 

Direct PM2.5 Major 8,040 $230,000 $560,000 $210,000 $500,000 $1,800 to $4,500 $1,700 to $4,100

PM2.5 Precursors 

SO2 Major 44,092 $29,000 $72,000 $27,000 $65,000 $1,300 to $3,200 $1,200 to $2,900

VOC 4,703 $1,200 $3,000 $1,100 $2,700 $5.7 to $14.0 $5.2 to $13.0 

 Total $3,100 to $7,700 $2,800 to $6,900

*All estimates are for the implementation year (2013), and are rounded to two significant figures so numbers may not 

sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates 

vary because each ton of precursor reduced has a different propensity to form PM2.5. The monetized benefits 

incorporate the conversion from precursor emissions to ambient fine particles.

 
7-5 

Table 7-5. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler MACT (Major Sources) in 2013 (millions of 2008$)1

Proposed Option with Alternate Solid Waste Definition 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $3,100 to $7,700 $2,800 to $6,900 

Total Social Costs3 $2,200 $2,200 

Net Benefits $930 to $5,500 $640 to $4,700 

1

All estimates are for the implementation year (2013), and are rounded to two significant figures. 2

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

reductions of 8,000 tons of directly emitted PM2.5 and PM2.5 precursors such as 4,700 tons of VOC and 44,000 tons of 

SO2. The benefits from reducing 280,000 tons of carbon monoxide, 5,100 tons of HCl, 1,100 tons of HF, and 7.1 tons 

of mercury, 1,600 tons of other metals, and 290 grams of dioxins/furans each year from major sources are not 

included in these estimates. In addition, the benefits from reducing ecosystem effects and visibility impairment are 

not included. 
R-1 

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APPENDIX A 

OAQPS MULTIMARKET MODEL TO ASSESS THE ECONOMIC 

IMPACTS OF ENVIRONMENTAL 

REGULATION 
A-1 

A.1 Introduction 

An economic impact analysis (EIA) provides information about a policy’s effects (i.e., 

social costs); emphasis is also placed on how the costs are distributed among stakeholders (EPA, 

2000). In addition, large-scale policies require additional analysis to better understand how costs 

are passed across the economy. Although several tools are available to estimate social costs, 

current EPA guidelines suggest that multimarket models “...are best used when potential 

economic impacts and equity effects on related markets might be considerable” and modeling 

using a computable general equilibrium model is not available or practical (EPA, 2000, p. 146). 

Other guides for environmental economists offer similar advice (Berck and Hoffmann, 2002; 

Just, Hueth, and Schmitz, 2004). 

Multimarket models focus on “short-run” time horizons and measure a policy’s near term 

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

dynamic general equilibrium analysis. 

The multimarket model described in this appendix is a new addition to the Office of Air 

Quality Planning and Standards’ (OAQPS’s) economic model tool kit; it is designed to be used 

as a transparent tool that can respond quickly to requests about how stakeholders in 100 U.S. 

industries might respond to new environmental policy. Next, we provide an overview of the 

model, data, and parameters. 

A.2 Multimarket Model 

The multimarket model contains the following features: 

 Industry sectors and benchmark data set 

– 100 industry sectors 

– a single benchmark year (2010) 

– estimates of industry carbon dioxide (CO2) emissions 

– estimates of industry employment 

 Economic behavior 

– industries respond to regulatory costs by changing production rates 

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

– customers respond to these price increases and consumption falls 

 Model scope 
A-2 

 100 sectors are linked with each other based on their use of energy and other 

nonenergy materials. For example, the construction industry is linked with the 

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

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

on related markets. 

– Links come from input-output information used from OAQPS’s computable 

general equilibrium model (EMPAX) 

– production adjustments influence employment levels 

– international trade (imports/exports) behavior considered 

 Model time horizon (“short-run”) 

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

supply function 

– firms cannot alter input mixes; there is no substitution among production inputs 

(capital, labor, energy intermediates, and other intermediates) 

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

– investment and government expenditures are fixed 

A.2.1 Industry Sectors and Benchmark Data Set 

The multimarket model includes 100 industries. For the benchmark year, the model uses 

information from OAQPS’s computable general equilibrium model’s balanced social accounting 

matrix (SAM) and the following accounting identity holds (EPA, 2008): 

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

If we abstract and treat each industry as a national market, the identity represents the 

prepolicy market-clearing condition, or benchmark “equilibrium”; supply equals demand in each 

market. In Table A-1, we identify the 100 industries for the multimarket model; Table A-2 

provides the 2010 benchmark data set. Since the benchmark data are reported in value terms, we 

also use the common “Harberger convention” and choose units where are all prices are one in the 

benchmark equilibrium (Shoven and Whalley, 1995). 
A-3 

Table A-1. Industry Sectors Included in Multimarket Model 

Industry Label Description Representative NAICSa

Energy Industries 

COL Coal 2121 

CRU Crude Oil Extraction 211111 (exc. nat gas) 

ELE Electric Generation 2211 

GAS Natural Gas 211112 2212 4862 

OIL Refined Petroleum 324 

Nonmanufacturing 

AGR Agricultural 11 

MIN Mining 21 less others 

CNS Construction 23 

Manufactured Goods 

Food, beverages, and textiles 

ANM Animal Foods 3111 

GRN Grain Milling 3112 

SGR Sugar 3113 

FRU Fruits and Vegetables 3114 

MIL Dairy Products 3115 

MEA Meat Products 3116 

SEA Seafood 3117 

BAK Baked Goods 3118 

OFD Other Food Products 3119 

BEV Beverages and Tobacco 312 

TEX Textile Mills 313 

TPM Textile Product Mills 314 

WAP Wearing Apparel 315 

LEA Leather 316 

Lumber, paper, and printing 

SAW Sawmills 3211 

PLY Plywood and Veneer 3212 

LUM Other Lumber 3219 

PAP Pulp and Paper Mills 3221 

CPP Converted Paper Products 3222 

PRN Printing 323 

Chemicals 

CHM Chemicals and Gases 3251 

RSN Resins 3252 

FRT Fertilizer 3253 

MED Drugs and Medicine 3254 

PAI Paints and Adhesives 3255 

SOP Soap 3256 

OCM Other Chemicals 3259 

(continued) 
A-4 

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

Industry Label Description Representative NAICSa

Plastics and Rubber 

PLS Plastic 3261 

RUB Rubber 3262 

Nonmetallic Minerals 

CLY Clay 3271 

GLS Glass 3272 

CEM Cement 3273 

LIM Lime and Gypsum 3274 

ONM Other Non-Metallic Minerals 3279 

Primary Metals 

I_S Iron and Steel 3311 3312 33151 

ALU Aluminum 3313 331521 331524 

OPM Other Primary Metals 3314 331522 331525 331528 

Fabricated Metals 

FRG Forging and Stamping 3321 

CUT Cutlery 3322 

FMP Fabricated Metals 3323 

BOI Boilers and Tanks 3324 

HRD Hardware 3325 

WIR Springs and Wires 3326 

MSP Machine Shops 3327 

EGV Engraving 3328 

OFM Other Fabricated Metals 3329 

Machinery and Equipment 

CEQ Construction and Agricultural 

Equipment 

3331 

IEQ Industrial Equipment 3332 

SEQ Service Industry Equipment 3333 

HVC HVAC Equipment 3334 

MEQ Metalworking Equipment 3335 

EEQ Engines 3336 

GEQ General Equipment 3339 

Electronic Equipment 

CPU Computers 3341 

CMQ Communication Equipment 3342 

TVQ TV Equipment 3343 

SMI Semiconductor Equipment 3344 

INS Instruments 3345 

MGT Magnetic Recording Equipment 3346 

LGT Lighting 3351 

APP Appliances 3352 

(continued) 
A-5 

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

Industry Label Description Representative NAICSa

Electronic Equipment (continued)

ELQ Electric Equipment 3353 

OEQ Other Electric Equipment 3359 

Transportation Equipment 

M_V Motor Vehicles 3361 

TKB Truck Bodies 3362 

MVP Motor Vehicle Parts 3363 

ARC Aircraft 3364 

R_R Rail Cars 3365 

SHP Ships 3366 

OTQ Other Transport Equipment 3369 

Other 

FUR Furniture 337 

MSC Miscellaneous Manufacturing 339 

Services 

Wholesale and Retail Trade 

WHL Wholesale Trade 42 

RTL Retail Trade 44-45 

Transportation Services 

ATP Air Transportation 481 

RTP Railroad Transportation 482 

WTP Water Transportation 483 

TTP Freight Truck Transportation 484 

PIP Pipeline Transport 486 

OTP Other Transportation Services 485 487 488 

Other Services 

INF Information 51 

FIN Finance and Insurance 52 

REL Real Estate 53 

PFS Professional Services 54 

MNG Management 55 

ADM Administrative Services 56 

EDU Education 61 

HLT Health Care 62 

ART Arts 71 

ACM Accommodations 72 

OSV Other Services 81 

PUB Public Services 92 

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

EMPAX-modeling system, which relies on the commodity code system used in IMPLAN. 
A-6 

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

Industry 

Label Industry Description Output Imports Consumption

Investment 

and 

Government Exports 

ACM Accommodations $828 $6 $816 $17 $1 

ADM Administrative Services $795 $37 $771 $61 Less than $1 

AGR Agricultural $314 $53 $333 $5 $29 

ALU Aluminum $65 $17 $70 $4 $8 

ANM Animal Foods $45 Less than $1 $36 Less than $1 $9 

APP Appliances $25 $19 $35 $6 $3 

ARC Aircraft $217 $60 $58 $120 $98 

ART Arts $252 Less than $1 $246 $3 $3 

ATP Air Transportation $154 $28 $91 $32 $59 

BAK Baked Goods $61 $3 $61 $2 Less than $1 

BEV Beverages and Tobacco $133 $54 $186 Less than $1 $1 

BOI Boilers and Tanks $27 $2 $19 $9 $2 

CEM Cement $52 Less than $1 $47 $3 $2 

CEQ Construction and 

Agricultural 

Equipment 

$70 $24 $47 $33 $14 

CHM Chemicals and Gases $284 $75 $300 $10 $49 

CLY Clay $8 $4 $10 $1 $2 

CMQ Communication 

Equipment 

$73 $40 $47 $56 $11 

CNS Construction $983 $77 $594 $465 Less than $1 

COL Coal $44 $2 $42 Less than $1 $4 

CPP Converted Paper 

Products 

$52 $2 $43 $6 $6 

CPU Computers $145 $76 $132 $52 $37 

CRU Crude Oil Extraction $67 $189 $255 Less than $1 Less than $1 

CUT Cutlery $11 $5 $9 $5 $2 

EDU Education $970 Less than $1 $257 $701 $13 

EEQ Engines $36 $14 $30 $6 $13 

EGV Engraving $21 Less than $1 $9 $5 $7 

ELE Electric Generation $317 Less than $1 $287 $31 Less than $1 

ELQ Electric Equipment $33 $16 $23 $17 $10 

FIN Finance and Insurance $2,015 $106 $1,972 $43 $106 

FMP Fabricated Metals $66 $1 $58 $7 $2 

FRG Forging and Stamping $20 Less than $1 $17 $1 $2 

FRT Fertilizer $42 $5 $33 $4 $10 

(continued) 
A-7 

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

Industry 

Label Industry Description Output Imports Consumption

Investment 

and 

Government Exports 

FRU Fruits and Vegetables $74 $12 $76 $4 $5 

FUR Furniture $66 $38 $84 $17 $2 

GAS Natural Gas $139 $32 $160 $6 $6 

GEQ General Equipment $54 $32 $47 $25 $14 

GLS Glass $30 Less than $1 $18 $2 $10 

GRN Grain Milling $77 $9 $74 $2 $10 

HLT Health Care $1,863 Less than $1 $1,823 $20 $20 

HRD Hardware $8 $4 $5 $4 $3 

HVC HVAC Equipment $34 $9 $26 $10 $6 

I_S Iron and Steel $125 $42 $143 $10 $13 

IEQ Industrial Equipment $26 $14 $16 $14 $11 

INF Information $1,305 $77 $1,217 $154 $11 

INS Instruments $112 $44 $71 $65 $20 

LEA Leather $4 $26 $29 Less than $1 $1 

LGT Lighting $12 $11 $16 $5 $1 

LIM Lime and Gypsum $7 Less than $1 $1 Less than $1 $5 

LUM Other Lumber $41 $2 $32 $9 $2 

M_V Motor Vehicles $272 $190 $313 $106 $43 

MEA Meat Products $174 $9 $169 $5 $10 

MED Drugs and Medicine $258 $102 $316 $18 $27 

MEQ Metalworking 

Equipment 

$24 $11 $16 $14 $4 

MGT Magnetic Recording 

Equipment 

$15 $2 $13 $2 $2 

MIL Dairy Products $87 $3 $84 $4 $2 

MIN Mining $53 $2 $30 $15 $11 

MNG Management $469 Less than $1 $378 Less than $1 $92 

MSC Miscellaneous 

Manufacturing 

$178 $129 $189 $73 $46 

MSP Machine Shops $38 $2 $32 $5 $2 

MVP Motor Vehicle Parts $220 $75 $226 $17 $52 

OCM Other Chemicals $45 $2 $23 $9 $15 

OEQ Other Electric 

Equipment 

$31 $16 $28 $7 $11 

OFD Other Food Products $92 $7 $90 $2 $7 

OFM Other Fabricated 

Metals 

$56 $28 $50 $22 $12 

(continued) 
A-8 

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

Industry 

Label Industry Description Output Imports Consumption

Investment 

and 

Government Exports 

OIL Refined Petroleum $415 $106 $462 $12 $47 

ONM Other Non-Metallic 

Minerals 

$13 $5 $15 $1 $2 

OPM Other Primary Metals $40 $27 $52 $2 $12 

OSV Other Services $2,321 Less than $1 $1,479 $598 $244 

OTP Other Transportation 

Services 

$245 Less than $1 $202 $22 $22 

OTQ Other Transport Equip $23 $10 $14 $13 $5 

PAI Paints and Adhesives $36 $1 $28 $3 $6 

PAP Pulp and Paper Mills $131 $21 $133 $5 $14 

PFS Professional Services $2,103 $84 $1,715 $461 $10 

PIP Pipeline Transport $37 $83 $20 $98 $1 

PLS Plastic $145 $14 $139 $4 $15 

PLY Plywood and Veneer $19 $8 $25 $1 $1 

PRN Printing $51 $1 $34 $11 $6 

PUB Public Services $1,099 $22 $355 $766 Less than $1 

R_R Rail Cars $11 $2 $6 $6 $2 

REL Real Estate $2,719 $2 $2,559 $131 $31 

RSN Resins $107 $23 $98 $6 $26 

RTL Retail Trade $1,440 $53 $1,412 $82 Less than $1 

RTP Railroad Transportation $70 Less than $1 $42 $18 $11 

RUB Rubber $38 $20 $36 $15 $6 

SAW Sawmills $29 $9 $36 $1 $1 

SEA Seafood $13 $3 $14 $1 $1 

SEQ Service Industry 

Equipment 

$29 $23 $22 $24 $6 

SGR Sugar $34 $6 $36 $2 $3 

SHP Ships $36 $6 $13 $29 Less than $1 

SMI Semiconductor 

Equipment 

$141 $69 $157 $12 $41 

SOP Soap $82 $5 $74 $3 $9 

TEX Textile Mills $29 $9 $31 $1 $6 

TKB Truck Bodies $58 $12 $34 $32 $5 

TPM Textile Product Mills $27 $19 $37 $7 $2 

TTP Freight Truck 

Transportation 

$301 Less than $1 $211 $39 $51 

(continued) 
A-9 

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

Industry 

Label Industry Description Output Imports Consumption 

Investment 

and 

Government Exports 

TVQ TV Equipment $19 $37 $50 $3 $3 

WAP Wearing Apparel $25 $94 $117 $1 Less than $1

WHL Wholesale Trade $1,309 $22 $1,021 $172 $138 

WIR Springs and Wires $5 $2 $1 $3 

WTP Water Transportation $45 $14 $12 $19 

 
A-10 

A.2.1.2 Employment Data 

The model includes employment forecasts for each of the 100 sectors. Employment 

estimates are based on data from three sources: the AEO 2009 estimates of employment (AEO 

supplemental Table 126 and indicators of Macroeconomic Activity), and Global Insights, Inc., 

and the Bureau of Labor Statistics (BLS) 2008 end-of year-employment (Current Employment 

Statistics—CES [National]). Typically, 3-digit NAICS sectors’ employment estimates are either 

directly reported in the updated AEO 2009 release or Global Insights For multimarket industries 

with finer NAICs detail, estimates were calculated by selecting a primary NAICS supersector 

estimate (AEO or Global Insights) and distributing total employment from the larger NAICS 

supersectors across more detailed NAICS sectors within the super-sector. The distributions were 

determined using observed 2008 BLS employment data. In the last step, In order to match 

aggregate U.S. employment numbers reported in the AEO 2009 release (140.1 million), a single 

adjustment factor was applied to all sectors that use Global Insights’ supersector data.1

 Table A-4 

reports the baseline employment for each of the 100 sectors. 

A.2.2 Economic Behavior 

A.2.2.1 U.S. Supply 

In a postpolicy scenario, industry responds to changes in the new market-clearing “net” 

price for the good or service sold: 

 %Δ”net” price = %Δ market price − %Δ direct costs − %Δ indirect costs (A.2) 

The %Δ direct costs are approximated using the engineering cost analysis and baseline 

value of output. For example, a $1 billion increase in compliance costs for the electricity sector 

(ELE) would be represented in the model as follows: 

 %Δ direct costs = $1/$317= 0.03% (A.3) 

As shown in Figure A-1, the cost change provides the industry with incentives to alter 

production rates at current market prices; market prices must rise to maintain the original 

prepolicy production levels (Q). 

 

 

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. 

Figure A-1. Direct Compliance Costs Reduce Production Rates at Benchmark Prices 
A-15 

Figure A-2. Indirect Costs Further Reduce Production Rates at Benchmark Prices 

Following guidance in the OAQPS economic resource manual (OAQPS, 1999), we use a 

general form for the U.S. industry supply function: 

g ε n

i

g g gi i i Q b P t (P P )



 



       1



 (A.5) 

where 

Qg

 = with-policy supply quantity (g) 

b = calibrated scale parameter for the supply price relationship 

Pg

 = with-policy price for output (g) 

t = direct compliance costs per unit of supply 

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

Pi

 = with-policy input (i) price 

Pi = benchmark input (i) price 

g  = price elasticity of supply for output (g) 

The key supply parameter that controls the industry production adjustments is the price 

elasticity of supply (εg).To our knowledge, there is no existing empirical work that estimates 

short-run supply elasticities for all industry groups used in the multimarket model. As a result, 

we assume the U.S. supply elasticities are a function of econometrically estimated rest-of-world 

(ROW) export supply elasticities (see discussion in the next section). We report the values 

currently available in the model in Table A-5. 
A-16 

A.2.2.2 International Competition 

International competition is captured by a single ROW supply function: 

  ROW

g ε

g Pg Q  c  (A.6) 

where 

Qg

 = with-policy supply quantity (g) 

c = calibrated scale parameter for the supply and price relationship 

Pg

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

ROW

g  = price elasticity of supply of goods from the ROW to the United States (imports) 

(g) 

The key supply parameter that controls the ROW supply adjustments is the price 

elasticity of supply ( ROW

g  ). We obtained these estimates for a variety of industry groups from a 

recently published article by Broda and colleagues (2008b). 

A.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-17 

Table A-5. Supply Elasticities 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

ACM Accommodations 0.7 0.7 

ADM Administrative Services 0.7 0.7 

AGR Agricultural 1.0 1.0 

ALU Aluminum 0.8 0.5 

ANM Animal Foods 1.1 0.8 

APP Appliances 0.9 0.8 

ARC Aircraft 0.9 0.6 

ART Arts 0.7 0.7 

ATP Air Transportation 0.7 0.7 

BAK Baked Goods 0.8 0.7 

BEV Beverages and Tobacco 2.9 2.9 

BOI Boilers and Tanks 1.1 0.8 

CEM Cement 0.9 0.7 

CEQ Construction and Agricultural Equipment 0.8 0.6 

CHM Chemicals and Gases 1.1 0.8 

CLY Clay 0.8 0.6 

CMQ Communication Equipment 2.5 1.0 

CNS Construction 0.7 0.7 

COL Coal 2.2 2.2 

CPP Converted Paper Products 0.9 0.7 

CPU Computers 1.0 0.7 

CRU Crude Oil Extraction 3.7 3.7 

CUT Cutlery 1.4 1.1 

EDU Education 0.7 0.7 

EEQ Engines 1.2 1.0 

EGV Engraving 1.1 0.8 

ELE Electric Generation 2.0 2.0 

ELQ Electric Equipment 0.8 0.6 

FIN Finance and Insurance 0.7 0.7 

FMP Fabricated Metals 1.2 1.1 

FRG Forging and Stamping 1.6 1.5 

(continued) 
A-18 

Table A-5. Supply Elasticities (continued) 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

FRT Fertilizer 1.0 0.7 

FRU Fruits and Vegetables 1.0 0.7 

FUR Furniture 1.9 1.9 

GAS Natural Gas 12.2 12.2 

GEQ General Equipment 1.0 0.7 

GLS Glass 0.8 0.6 

GRN Grain Milling 1.7 1.5 

HLT Health Care 0.7 0.7 

HRD Hardware 1.1 0.8 

HVC HVAC Equipment 0.9 0.6 

I_S Iron and Steel 1.0 0.6 

IEQ Industrial Equipment 0.9 0.6 

INF Information 0.7 0.7 

INS Instruments 0.9 0.6 

LEA Leather 0.9 0.7 

LGT Lighting 1.1 0.7 

LIM Lime and Gypsum 0.9 0.7 

LUM Other Lumber 0.9 0.7 

M_V Motor Vehicles 1.3 0.7 

MEA Meat Products 1.2 3.9 

MED Drugs and Medicine 1.3 1.0 

MEQ Metalworking Equipment 0.7 0.5 

MGT Magnetic Recording Equipment 1.0 0.7 

MIL Dairy Products 1.1 0.9 

MIN Mining 2.2 2.2 

MNG Management 0.7 0.7 

MSC Miscellaneous Manufacturing 1.0 0.8 

MSP Machine Shops 1.1 0.8 

MVP Motor Vehicle Parts 0.9 0.6 

OCM Other Chemicals 1.1 0.6 

OEQ Other Electric Equipment 1.0 0.7 

OFD Other Food Products 1.1 0.7 

(continued) 
A-19 

Table A-5. Supply Elasticities (continued) 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

OFM Other Fabricated Metals 0.9 0.6 

OIL Refined Petroleum 1.0 0.7 

ONM Other Non-metallic Minerals 1.5 0.7 

OPM Other Primary Metals 0.7 0.5 

OSV Other Services 0.7 0.7 

OTP Other Transportation Services 0.7 0.7 

OTQ Other Transport Equipment 1.0 0.7 

PAI Paints and Adhesives 1.0 0.7 

PAP Pulp and Paper Mills 1.1 0.7 

PFS Professional Services 0.7 0.7 

PIP Pipeline Transport 2.0 2.0 

PLS Plastic 1.0 0.7 

PLY Plywood and Veneer 1.3 1.3 

PRN Printing 1.0 0.7 

PUB Public Services 0.7 0.7 

R_R Rail Cars 1.8 0.7 

REL Real Estate 0.7 0.7 

RSN Resins 1.0 0.7 

RTL Retail Trade 0.7 0.7 

RTP Railroad Transportation 0.7 0.7 

RUB Rubber 1.3 1.1 

SAW Sawmills 0.8 0.6 

SEA Seafood 1.1 0.8 

SEQ Service Industry Equipment 0.8 0.6 

SGR Sugar 1.1 0.8 

SHP Ships 1.0 0.7 

SMI Semiconductor Equipment 1.2 1.0 

SOP Soap 0.8 0.6 

TEX Textile Mills 1.0 0.7 

TKB Truck Bodies 3.2 3.1 

TPM Textile Product Mills 0.8 0.6 

TTP Freight Truck Transportation 0.7 0.7 

TVQ TV Equipment 5.8 5.4 

(continued) 
A-20 

Table A-5. Supply Elasticities (continued) 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

WAP Wearing Apparel 1.2 0.8 

WHL Wholesale Trade 0.7 0.7 

WIR Springs and Wires 1.9 0.8 

WTP Water Transportation 0.7 0.7 

Note: RTI mapped Broda et al. data for their industry aggregation to the multimarket model’s 100 industries. 

Domestic supply elasticities are typically assumed to be within one standard deviation of the sample of supply 

elasticities used for the ROW. In selected cases where this information is not available, the U.S. supply elasticity 

is set equal to the ROW. 

Source: Broda, C., N. Limao, and D. Weinstein. 2008a. “Export Supply Elasticities.” 

http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/TradeElastic

ities.html. Accessed September 2009. 

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

), 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 

 

1

 See Chapter 14 of the GTAP 7 Database Documentation for the full description of the parameters at 

https://www.gtap.agecon.purdue.edu/resources/download/4184.pdf; see Table 14.2 for elasticities. 2

 Detailed documentation of the entire GTAP 7 Database is available at 

https://www.gtap.agecon.purdue.edu/databases/v7/v7_doco.asp. The GTAP also uses a unique system of 

categorizing commodities that does not match the NAICS or HS system exactly. 
A-21 

sector. We multiplied the resulting ratio by the Broda et al. iron and steel parameter (1.0). For 

example, the GTAP trade elasticity for coal (6.10) is approximately 2.2 times the trade elasticity 

for iron and steel (2.95). As a result, the multimarket import 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

 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. 

A.2.2.5 Demand 

Uses for industry output are divided into three groups: investment/government use, 

domestic intermediate uses, and other final use (domestic and exports). Given the short time 

horizon, investment/government does not change. Intermediate use is determined by the input 

use ratios and the industry output decisions. 

Qi giQg    

 (A.7) 

Qi

 = with-policy input demand quantity (i) 

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

Qg

 = with-policy output quantity (g) 

Other final use does respond to market price changes. Following guidance in the OAQPS 

economic resource manual (OAQPS, 1999), we use a general form for the U.S. industry demand 

function: 

 

1

 No standard deviations were calculated for the 3- and 4-digit codes that had only one observation (i.e., Broda et 

al.’s model used the exact 3- or 4-digit code). 
A-22 

  g Qg a Pg

    (A.8) 

where 

Qg

 = with-policy demand quantity (g) 

a = calibrated scale parameter for the demand and price relationship 

Pg

 = with-policy price for output (g) 

g = price elasticity of demand (g) 

The key parameter that controls consumption adjustments is the price elasticity of 

demand (ηg). To approximate the response, we use demand elasticities that were simulated with a 

general equilibrium model (Ho, Morgenstern, and Shih, 2008). Table A-6 reports the values 

currently available in the model. 

A.2.2.6 Model Scope 

The multimarket model includes 100 sectors covering energy, manufacturing, and service 

applications. Each sector’s production technology requires the purchase of energy and other 

intermediate goods made by other sectors included in the model. Linking the sectors in this 

manner allows the model to trace direct and indirect policy effects across different sectors. 

Therefore, it is best used when potential economic impacts and equity effects on related markets 

might be important to stakeholders not directly affected by an environmental policy. However, 

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-23 

Table A-6. U.S. Demand Elasticities 

Industry Label Industry Description 

Demand Elasticity 

ηg

ACM Accommodations −0.7 

ADM Administrative Services −0.7 

AGR Agricultural −0.8 

ALU Aluminum −1.0 

ANM Animal Foods −0.6 

APP Appliances −2.6 

ARC Aircraft −2.5 

ART Arts −0.7 

ATP Air Transportation −0.8 

BAK Baked Goods −0.6 

BEV Beverages and Tobacco −0.6 

BOI Boilers and Tanks −0.5 

CEM Cement −0.8 

CEQ Construction and Agricultural Equipment −1.7 

CHM Chemicals and Gases −1.0 

CLY Clay −0.8 

CMQ Communication Equipment −2.6 

CNS Construction −0.8 

COL Coal −0.1 

CPP Converted Paper Products −0.7 

CPU Computers −2.6 

CRU Crude Oil Extraction −0.3 

CUT Cutlery −0.5 

EDU Education −0.7 

EEQ Engines −1.7 

EGV Engraving −0.5 

ELE Electric Generation −0.2 

ELQ Electric Equipment −2.6 

FIN Finance and Insurance −0.7 

FMP Fabricated Metals −0.5 

FRG Forging and Stamping −0.5 

FRT Fertilizer −1.0 

FRU Fruits and Vegetables −0.6 

FUR Furniture −0.7 

(continued) 
A-24 

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

Industry Label Industry Description 

Demand Elasticity 

ηg

GAS Natural Gas −0.3 

GEQ General Equipment −1.7 

GLS Glass −0.8 

GRN Grain Milling −0.6 

HLT Health Care −0.7 

HRD Hardware −0.5 

HVC HVAC Equipment −1.7 

I_S Iron and Steel −1.0 

IEQ Industrial Equipment −1.7 

INF Information −0.7 

INS Instruments −2.6 

LEA Leather −1.1 

LGT Lighting −2.6 

LIM Lime and Gypsum −0.8 

LUM Other Lumber −0.7 

M_V Motor Vehicles −2.5 

MEA Meat Products −0.6 

MED Drugs and Medicine −1.0 

MEQ Metalworking Equipment −1.7 

MGT Magnetic Recording Equipment −2.6 

MIL Dairy Products −0.6 

MIN Mining −0.6 

MNG Management −0.7 

MSC Miscellaneous Manufacturing −1.7 

MSP Machine Shops −0.5 

MVP Motor Vehicle Parts −2.5 

OCM Other Chemicals −1.0 

OEQ Other Electric Equipment −2.6 

OFD Other Food Products −0.6 

OFM Other Fabricated Metals −0.5 

OIL Refined Petroleum −0.1 

ONM Other Non-metallic Minerals −0.8 

OPM Other Primary Metals −1.0 

OSV Other Services −0.7 

OTP Other Transportation Services −0.8 

(continued) 
A-25 

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

Industry Label Industry Description 

Demand Elasticity 

ηg

OTQ Other Transport Equip −2.5 

PAI Paints and Adhesives −1.0 

PAP Pulp and Paper Mills −0.7 

PFS Professional Services −0.7 

PIP Pipeline Transport −0.8 

PLS Plastic −1.0 

PLY Plywood and Veneer −0.7 

PRN Printing −0.7 

PUB Public Services −0.7 

R_R Rail Cars −2.5 

REL Real Estate −0.7 

RSN Resins −1.0 

RTL Retail Trade −0.7 

RTP Railroad Transportation −0.8 

RUB Rubber −1.0 

SAW Sawmills −0.7 

SEA Seafood −0.6 

SEQ Service Industry Equipment −1.7 

SGR Sugar −0.6 

SHP Ships −2.5 

SMI Semiconductor Equipment −2.6 

SOP Soap −1.0 

TEX Textile Mills −1.1 

TKB Truck Bodies −2.5 

TPM Textile Product Mills −1.1 

TTP Freight Truck Transportation −0.8 

TVQ TV Equipment −2.6 

WAP Wearing Apparel −2.4 

WHL Wholesale Trade −0.7 

WIR Springs and Wires −0.5 

WTP Water Transportation −0.8 

Note: RTI assigned an elasticity using the most similar industry from Ho and colleagues’ industry aggregation. 

Source: Ho, M. S, R. Morgenstern, and J. S. Shih. 2008. “Impact of Carbon Price Policies on US Industry.” RFF 

Discussion Paper 08-37. Http://Www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?. 

Publicationid=20680. Accessed August 2009. Table B.6. 
APPENDIX B 

DETAILED ECONOMIC MODEL RESULTS BY SECTOR 
B-1 

Table B-1. Prices (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Primary 

Option Option 1E 

Primary 

Option Option 1N 

Energy 0.04% 0.18% 0.00% —0.01%

Nonmanufacturing 0.00% 0.01% 0.00% 0.02%

Manufacturing            

Food, beverages, and textiles 0.02% 0.05% 0.00% 0.14%

Lumber, paper, and printing 0.22% 0.42% 0.01% 0.31%

Chemicals 0.02% 0.07% 0.00% —0.02%

Plastics and rubber 0.01% 0.08% 0.00% —0.04%

Nonmetallic minerals 0.01% 0.06% 0.00% —0.02%

Primary metals 0.04% 0.10% 0.00% —0.01%

Fabricated metals 0.01% 0.07% 0.00% —0.02%

Machinery and equipment 0.01% 0.02% 0.00% —0.01%

Electronic equipment 0.00% 0.00% 0.00% —0.01%

Transportation equipment 0.00% 0.02% 0.00% 0.00%

Other 0.01% 0.03% 0.00% 0.01%

Wholesale and retail trade 0.00% —0.01% 0.00% 0.01%

Transportation services 0.00% 0.02% 0.00% 0.00%

Other services 0.00% 0.00% 0.00% 0.08%
B-2 

Table B-2. Production (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Primary 

Option Option 1E 

Primary 

Option Option 1N 

Energy —0.01% —0.12%    0.00% —0.01%

Nonmanufacturing —0.01% —0.02%    0.00% —0.04%

Manufacturing              

Food, beverages, and textiles —0.02% —0.05%    0.00% —0.11%

Lumber, paper, and printing —0.10% —0.18%    0.00% —0.16%

Chemicals —0.04% —0.12%    0.00% —0.01%

Plastics and rubber —0.02% —0.08%    0.00% —0.02%

Nonmetallic minerals —0.01% —0.03%    0.00% —0.01%

Primary metals —0.04% —0.13%    0.00% 0.00%

Fabricated metals —0.01% —0.04%    0.00% —0.01%

Machinery and equipment —0.01% —0.04%    0.00% 0.00%

Electronic equipment 0.00% —0.02%    0.00% 0.00%

Transportation equipment —0.01% —0.06%    0.00% 0.00%

Other —0.02% —0.06%    0.00% —0.04%

Wholesale and retail trade 0.00% —0.01%    0.00% —0.02%

Transportation services 0.00% —0.07%    0.00% —0.01%

Other services 0.00% —0.01%    0.00% —0.04%
B-3 

Table B-3. Consumption (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Primary 

Option Option 1E 

Primary 

Option Option 1N 

Energy —0.01% —0.05%    0.00% —0.01%

Nonmanufacturing —0.01% —0.01%    0.00% —0.04%

Manufacturing              

Food, beverages, and textiles —0.01% —0.03%    0.00% —0.08%

Lumber, paper, and printing —0.06% —0.11%    0.00% —0.06%

Chemicals —0.02% —0.08%    0.00% —0.01%

Plastics and rubber —0.01% —0.05%    0.00% —0.02%

Nonmetallic minerals —0.01% —0.02%    0.00% —0.02%

Primary metals —0.02% —0.07%    0.00% —0.01%

Fabricated metals —0.01% —0.03%    0.00% —0.01%

Machinery and equipment —0.01% —0.02%    0.00% —0.01%

Electronic equipment 0.00% —0.01%    0.00% 0.00%

Transportation equipment 0.00% —0.03%    0.00% 0.00%

Other —0.01% —0.02%    0.00% —0.02%

Wholesale and retail trade 0.00% —0.01%    0.00% —0.02%

Transportation services 0.00% —0.02%    0.00% —0.01%

Other Services 0.00% —0.01%    0.00% —0.04%
B-4 

Table B-4. Imports (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Primary 

Option Option 1E 

Primary 

Option Option 1N 

Energy 0.08% 0.28%    0.00% —0.01%

Nonmanufacturing 0.00% 0.01%    0.00% 0.01%

Manufacturing              

Food, beverages, and textiles 0.03% 0.06%    0.00% 0.16%

Lumber, paper, and printing 0.23% 0.42%    0.01% 0.32%

Chemicals 0.02% 0.08%    0.00% —0.02%

Plastics and rubber 0.01% 0.09%    0.00% —0.04%

Nonmetallic minerals 0.00% 0.02%    0.00% —0.01%

Primary metals 0.04% 0.10%    0.00% —0.01%

Fabricated metals 0.01% 0.07%    0.00% —0.02%

Machinery and equipment 0.01% 0.02%    0.00% —0.01%

Electronic equipment 0.00% 0.00%    0.00% —0.01%

Transportation equipment 0.00% 0.03%    0.00% 0.00%

Other 0.01% 0.05%    0.00% 0.02%

Wholesale and retail trade 0.00% —0.01%    0.00% 0.00%

Transportation services 0.00% 0.02%    0.00% 0.00%

Other services 0.00% 0.00%    0.00% 0.01%
B-5 

Table B-5. Exports (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Primary 

Option Option 1E 

Primary 

Option Option 1N 

Energy —0.01% —0.02%    0.00% 0.00%

Nonmanufacturing 0.00% —0.01%    0.00% —0.01%

Manufacturing              

Food, beverages, and textiles —0.01% —0.03%    0.00% —0.09%

Lumber, paper, and printing —0.16% —0.29%    0.00% —0.22%

Chemicals —0.02% —0.07%    0.00% 0.02%

Plastics and rubber —0.01% —0.08%    0.00% 0.04%

Nonmetallic minerals 0.00% —0.05%    0.00% 0.02%

Primary metals —0.04% —0.10%    0.00% 0.01%

Fabricated metals 0.00% —0.03%    0.00% 0.01%

Machinery and equipment —0.02% —0.03%    0.00% 0.02%

Electronic equipment 0.00% 0.01%    0.00% 0.02%

Transportation equipment —0.01% —0.06%    0.00% 0.01%

Other —0.01% —0.03%    0.00% 0.01%

Wholesale and retail trade 0.00% 0.01%    0.00% 0.00%

Transportation services 0.00% —0.02%    0.00% 0.00%

Other services 0.00% 0.00%    0.00% —0.06%
APPENDIX C 

MAJOR SOURCES COST AND EMISSIONS MEMORANDUM 
C-1 

MEMORANDUM 

TO: Jim Eddinger, U.S. Environmental Protection Agency, OAQPS/SPPD 

FROM: Susan McClutchey, Amanda Singleton, and Graham Gibson, ERG 

DATE: 15 April 2010 

SUBJECT: Methodology for Estimating Cost and Emissions Impacts for Industrial, 

Commercial, Institutional Boilers and Process Heaters National Emission 

Standards for Hazardous Air Pollutants – Major Source 

1.0 INTRODUCTION 

The purpose of this memorandum is to discuss the methodology used to estimate the 

costs, emission reductions, and secondary impacts from industrial, commercial, and institutional 

boilers at major sources of hazardous air pollutants (HAP). These impacts were calculated for 

existing units and new units projected to be operational by the year 2013, three years after the 

rule is expected to be promulgated. The results of the impacts analysis are presented for both the 

most stringent regulatory option evaluated and the regulatory option contained in the proposed 

rule. The development of the maximum achievable control technology (MACT) floor level of 

control, projection of new units, and a detailed description of the cost equations used to estimate 

costs for various control technologies is presented in other memoranda.1,2,3 This memorandum is 

organized as follows: 

1.0 Introduction 

2.0 Overview of Regulatory Options 

3.0 Estimating Cost Impacts 

4.0 Methodology for Estimating Emission Reductions 

5.0 Methodology for Estimating Secondary Impacts 

6.0 References 
C-2 

2.0 OVERVIEW OF REGULATORY OPTIONS 

Five control options were considered for existing boilers and process heaters at major 

sources of HAP. A description of the six options is included in this section. 

2.1 Existing Units 

 Option 1E represents the option where all boilers, regardless of fuel type or size, must 

meet PM, HCl, mercury, CO, and D/F numerical emission limits. 

 Option 2E uses the same control device and testing, and monitoring cost estimation logic 

outlined above for option 1E, except that it does not estimate ACI for units exceeding the 

MACT floor emission limit for dioxin/furan. Instead, it is estimated that most units, when 

testing for dioxin/furan will be below detection levels without installing any additional 

control devices. 

 Option 3E uses the same control device and testing, and monitoring cost estimation logic 

outlined above for option 4E below, but it does not include the cost of an annual energy 

audit. 

 Option 4E represents the primary proposed option discussed in the preamble. In this 

option all boilers and process equal to or greater than or equal to 10 mmBtu/hr must meet 

PM, HCl, mercury, CO, and D/F numerical emission limits, except for units in the natural 

gas/refinery gas and Metallurgical Process Furnace subcategories. All boilers and process 

heaters less than 10 mmBtu/hr must meet a work practice standard of a biennial boiler 

tune-up. All large units greater than or equal to 10 mmBtu/hr in the natural gas/refinery 

gas and Metallurgical Process Furnace subcategories must meet a work practice standard 

of an annual boiler tune-up. All major source facilities with a boiler or process heater 

must conduct an energy audit. 

 Option 5E uses the same control device and testing, and monitoring cot estimation logic 

outlined above for option 4E above for all units except for refinery gas units. Units firing 

refinery gas will have a separate numerical emission limit for mercury. 

 A revised definition of solid waste is being proposed parallel with this proposed 

rulemaking effort. The solid waste definitional rulemaking proposal includes both a 

primary and alternative definition of solid waste for consideration during the public 

comment period. Options 1E through 5E analyze the regulatory impacts with respect to 

the primary proposed definition of solid waste. However, to compare the impacts of both 

definitions of solid waste, the cost and emission impacts of regulatory option 4E 

discussed above were applied to the inventory of units that were not classified as waste 

burning units under the alternative solid waste definition. 
C-3 

2.2 New Units 

Three control options were considered for existing boilers and process heaters at major 

sources of HAP. A description of the three options is included in this section. 

 Option 1N represents the option where all new boilers, regardless of fuel type or size, 

must meet PM, HCl, mercury, CO, and D/F numerical emission limits. 

 Option 2N uses the same control device and testing, and monitoring cost estimation logic 

outlined above for option 1N, except that it does not estimate ACI for units exceeding the 

MACT floor emission limit for dioxin/furan. Instead, it is estimated that most units, when 

testing for dioxin/furan will be below detection levels without installing any additional 

control devices. 

 Option 4N represents the primary proposed option discussed in the preamble. In this 

option all boilers and process, regardless of size, meet PM, HCl, mercury, CO, and D/F 

numerical emission limits, except for units in the natural gas/refinery gas and 

Metallurgical Process Furnace subcategories. All units in the natural gas/refinery gas and 

Metallurgical Process Furnace subcategories must meet a work practice standard of an 

annual boiler tune-up. 

3.0 ESTIMATING COST IMPACTS 

For each option, the cost impacts analysis compares the baseline emissions for each unit 

to the corresponding MACT floor emission limit for the unit’s subcategory. A control device was 

applied to the unit if its baseline emissions exceeded their applicable MACT floor emission limit. 

A comparison of the overall capital and annualized costs of the proposed options 4E and Option 

4N are presented in Table 1. The detailed equations used to estimate the control, testing, 

monitoring, and work practice costs are discussed in another memorandum.2 The following logic 

was used to apply control, testing, and monitoring costs to each boiler or process heater: 

3.1 Option 1E/1N 

Control Cost Impacts 

Mercury Control 

 A new fabric filter installation was expected to achieve the Hg emission limits in the 

proposed rule. Where baseline Hg emissions were found to be greater than the MACT 

floor, the cost of a fabric filter was estimated for an individual boiler or process heater, 

unless the unit already had a fabric filter installed. A new fabric filter was estimated to be 

installed at 12,138 existing boilers and process heaters. In the case of a unit with a fabric 
C-4 

filter emitting Hg above the MACT floor emission limit, the incremental Hg removal 

efficiency required to meet the MACT floor was calculated, and then the costs to install 

activated carbon injection (ACI) technology on the boiler were estimated. If the unit had 

a packed bed scrubber and did not meet the MACT floor for Hg, a fabric filter was 

installed if no other control was in place. Incremental ACI equipment was installed for 

155 existing boilers and process heaters. 

 Wet scrubbers—the technology selected for the cost analysis to reduce emissions of 

hydrogen chloride (HCl)—are also capable of achieving modest reductions in Hg. 

Literature suggests that these scrubbers can achieve a 10-percent reduction in Hg 

emissions. If a scrubber was being installed for HCl, and baseline Hg emissions were 

within 10 percent of the MACT floor, the wet scrubber was expected to achieve this level 

of emission reduction without installing a fabric filter. 

Particulate Matter Control 

 When baseline particulate (PM) emissions exceeded the MACT floor, the cost of an ESP 

was estimated, unless a fabric filter had already been included in the cost analysis for Hg 

reduction. ESP technology was estimated to be installed at 397 existing boilers and 

process heaters. 

 Wet scrubbers are also capable of achieving a modest reduction in PM. Literature 

suggests that these scrubbers can achieve an 85-percent reduction in PM emissions. If a 

scrubber was being installed for HCl, and baseline PM emissions were within 85 percent 

of the MACT floor for PM, the wet scrubber was expected to achieve this level of 

emission reduction without installing an ESP. 

Hydrogen Chloride Control 

 When HCl baseline emissions were greater than the MACT floor, the cost of adding a 

packed bed scrubber was estimated. If the boiler already reported to have a scrubber 

installed and the baseline emissions still exceeded the floor, the incremental required HCl 

removal efficiency was calculated and the then the cost to increase the sorbent injection 

rate in the scrubber was estimated in the cost analysis. Wet scrubbers were identified to 

be necessary to control HCl emissions at 13,269 existing boilers and process heaters 
C-5 

Dioxin/Furan Control 

 Where dioxin/furans exceeded the MACT floor, the required removal efficiencies of 

dioxin/furans was calculated, and the costs to install ACI were included in the cost 

analysis. If ACI had was previously included in the cost analysis for incremental Hg 

control, the ACI costs based on the highest required removal efficiency were used in the 

cost analysis. For example, if a boiler required 90 percent reduction in Hg and 98 percent 

reduction in dioxin/furan, the higher ACI costs associated with the 98 percent reduction 

of dioxin/furan were used in the cost analysis. ACI technology was estimated to be 

required at 2,538 existing boilers and process heaters for either Hg or dioxin/furan 

control. 

Carbon Monoxide and Organic HAP Control 

 Organic HAP and carbon monoxide can be controlled by either improving the 

combustion efficiency of the unit, or installing an oxidation catalyst on the exhaust of a 

combustion unit. The control strategy necessary to meet the MACT floor emission limit 

will vary depending on the magnitude between the baseline emissions and the CO MACT 

floor. A step function was used to delineate what type of control strategy should be 

analyzed in the cost impacts analysis: 

o Most boilers (other than Dutch ovens and PC-coal boilers discussed below) are 

designed to operate with CO emissions at or near 400 parts per million (ppm). A 

boiler tune-up, was estimated in the cost impacts analysis if the unit’s baseline 

emissions exceeded the floor for carbon monoxide (CO), but were less than or equal 

to 400 ppm @ 3% O2. 

o If the baseline CO emissions were between 400 and 1000 ppm @ 3% O2 for boilers 

and process heaters designed to burn liquid and gaseous fuels, the cost of a 

replacement low-NOx burner was estimated to achieve the MACT floor emission 

limits. 

o Since stokers, fuel cells, or fluidized bed unit do not have replaceable burners, a 

linkageless boiler management system (LBMS) was the technology estimated to 

achieve the MACT floor when baseline CO emissions exceeded the floor and were 

between 400 and 1000 ppm @ 3% O2. 
C-6 

o For boilers and process heaters designed to burn gas and liquids, as well as stokers, 

fuel cells, and fluidized bed boilers and process heaters design to burn solid fuels, the 

cost impacts analysis estimated that a CO Oxidation catalyst would be required to 

meet MACT floor limits if a unit’s baseline CO emissions were above 1,000 ppm @ 

3% O2 and exceeded the MACT floor. 

o Similar to stokers, Dutch oven and suspension-fired boilers and process heaters do 

not have replaceable burners. The design of the Dutch oven/suspension burner 

combustion system and the high moisture content of the fuels used in Dutch ovens 

typically cause elevated CO levels compared to other combustor designs. A tune-up 

was estimated in the cost analysis for any Dutch oven/suspension burner with a 

baseline of less than 5,000 ppm @ 3% O2. LBMS costs were estimated for any Dutch 

oven boiler with baseline CO between 5,000 – 12,500 ppm @ 3% O2. If the baseline 

CO emissions at Dutch oven/suspension-fired units exceeded 12,500 ppm @ 3% O2,

the cost of adding catalytic oxidation was estimated for the cost impacts analysis. 

o For PC-boilers, a tune-up was estimated in the cost analysis for any unit with a 

baseline of less than 1200 ppm @ 3% O2. The cost of a replacement LNB was 

estimated if CO emissions were between 1200 – 3000 ppm @ 3% O2 and catalytic 

oxidation was estimated if CO baseline was greater than 3000 ppm @ 3% O2.

Option 1E of the cost impacts analysis estimated 11,013 existing boilers and process 

heaters would meet the CO MACT floor with a tune-up, an additional 1,073 existing units would 

install a LBMS, 1 existing boiler would replace their existing burner with a LNB, and 482 

existing units would install a CO oxidation catalyst. 

Testing and Monitoring Cost Impacts 

Testing and monitoring requirements varied depending on the equipment installed on the 

unit to control emissions, the design capacity of the unit, and the fuel category the unit was 

assigned to. 

Testing Costs 

All boilers and process heaters designed to burn solid and gaseous fuels were expected to 

conduct an annual compliance test for PM, HCl, Hg, D/F, and CO. The cost to conduct stack 
C-7 

tests for these five pollutants was estimated to be $44,000 per year. Combustion units greater 

than 100 mmBtu/hr were expected to install CO CEMS in lieu of conducting a CO stack test and 

the cost to conduct tests on PM, HCl, Hg, and D/F was estimated to be $37,000 per year. 

Boilers and process heaters designed to burn liquid fuels were expected to conduct an 

annual compliance test for PM, D/F, and CO. In lieu of a stack test boilers designed to burn 

liquid fuels were expected to conduct fuel analysis, or report fuel analyses received from a fuel 

supplier for chlorine and Hg. Conducting stack tests for PM, D/F, and CO and fuel analysis for 

chlorine and Hg was estimated to be $16,000 per year. Combustion units greater than 100 

mmBtu/hr were expected to install CO CEMS in lieu of conducting a CO stack test and the cost 

to conduct tests on PM and D/F and fuel analysis for chlorine and Hg was estimated to be 

$10,000 per year. Although other fuels are eligible to comply with the proposed rule through fuel 

analysis in lieu of stack testing, this cost estimate conservatively assumed that only units 

designed to fire liquid fuels would use this compliance alternative. The methods and data sources 

used to estimate testing and monitoring costs are discussed in other memoranda.2 

Small boilers often exhaust to small diameter stacks that do not have any test ports or test 

platforms installed. For these small units, we estimated the additional costs to these costs to 

construct or rent scaffolding and install test ports. The costs include installation of 4 test ports, 90 

degrees opposed to each other, and five weeks rental of temporary scaffolding. EPA estimates 

that these small sources would incur an additional $185 million to install test ports and rent 

temporary scaffolding. Many establishments in each industry, commercial, or institutional sector 

are associated with multiple (as many as a 700) small units. A summary of the costs by fuel 

category is shown in Table 1 below. 
C-8 

Table 1. Cost Estimate for Renting Scaffolding and Constructing Test Ports at Small 

Boilers and Process Heaters 

Fuel Category 

Number of Small Boilers and 

Process Heaters (less than 10 

mmBtu/hr) Port Costs ($2008) 

Renting Temporary 

Scaffolding ($2008) 

Total Costs 

($2008) 

Coal 3 32,944 42,000 74,944 

Biomass 26 285,517 364,000 649,517 

Natural Gas/ 

Refinery Gas 7138 78,385,476 99,932,000 178,317,476 

Other Process 

Gases 2 21,963 28,000 49,963 

Liquid 238 2,613,581 3,332,000 5,945,581 

Total 7,407 81,339,482 103,698,000 185,037,482 

Monitoring Costs 

Various monitor configurations were installed based on the size of the unit and the 

pollution control devices expected to be installed to achieve the MACT floor emission limits. For 

units expected to install packed bed wet scrubbers, an annualized cost of $5,600 for a scrubber 

parametric monitor was included in the cost analysis. For units expected to install a fabric filter, 

an annualized cost of $9,700 for a bag leak detection monitor was included in the cost analysis. 

If a unit was expected to install ACI, the cost to monitor the carbon injection rate was included in 

the analysis, based on the unit’s hours of operation. For units greater than 100 mmBtu/hr, an 

annualized cost of $53,300 for a CO CEMS was included in the cost analysis, and for units 

greater than 250 mmBtu/hr an annualized cost of $56,100 was used in the cost analysis. For units 

that did not install a PM CEMS and did not install a scrubber to meet HCl limits, an annualized 

cost of $14,660 for an opacity monitor was included in the cost analysis. No PM CEMS or 

opacity monitors were assumed for boilers and process heaters designed to gaseous fuels. 

Fuel Savings Impacts 

This cost analysis includes an estimate of energy savings for every unit that is expected to 

install controls to improve combustion, or conduct an annual tune-up or energy audit. The 

Department of Energy has conducted energy assessments at selected manufacturing facilities and 

reports that facilities can reduce fuel/energy use by 10 to 15 percent by using best practices to 

increase their energy efficiency. Many best practices are considered pollution prevention because 

they reduce the amount of fuel combusted which results in a corresponding reduction in 
C-9 

emissions from the fuel combustion. Further boiler tune-ups have been shown to improve the 

efficiency of a boiler between 1 and 5 percent, depending on the age of the unit and the time 

lapse since the previous tune-up. Other combustion controls such as upgrading burners and 

installation of an LBMS are also expected to improve the efficiency of the unit, thus reducing 

fuel consumption. This cost analysis assumes an annual fuel savings of 1 percent. The energy 

savings is estimated using the Equation 1: 

Annual Fuel Savings (mmBtu/yr) = DC * CF * Ophours* EG (Equation 1)

Where: 

DC = unit design capacity (mmBtu/hr) 

CF = capacity factor, 90% of design capacity 

Ophours = annual operating hours reported in 2008 survey (hours/year) 

EG = Efficiency gain, estimated to be 1% 

After the fuel savings for each boiler and process heater was calculated, the both 

industrial and commercial prices for coal, #2 distillate fuel oil, #6 residual fuel oil, and natural 

gas were obtain from the EIA.3

 The EIA data reported fuel prices as $/ton for coal, $/thousand 

cubic feet for natural gas, and cents per gallon for fuel oil. The higher heating values were 

obtained from Table C-1 of the EPA Mandatory Reporting Rule (40 CFR part 98 subpart C) and 

the higher heating values were used to convert the fuel prices to a standard unit of measure, $ per 

mmBtu. Using the NAICS code reported by each facility and the fuel category assigned to each 

combustion unit, the appropriate fuel price was multiplied by the calculated fuel savings. This 

cost analysis only estimates the fuel savings from units in the coal, liquid and natural gas and 

other gaseous fuel categories. A fuel savings was not estimated for units in the biomass fuel 

category since the price of biomass fuels is variable, and often biomass is an on-site industrial 

byproduct instead of a purchased fuel. 

3.2 Option 2E/2N 

Option 2E uses the same control device and testing, and monitoring cost estimation logic 

outlined above for option 1E, except that it does not estimate ACI for units exceeding the MACT 

floor emission limit for dioxin/furan. Instead, it is estimated that most units, when testing for 

dioxin/furan will be below detection levels without installing any additional control devices. 
C-10 

3.3 Option 3E 

Option 3E follows the same logic for estimating control costs as option 2E outlined 

above, with the exception of small units (less than 10 mmBtu/hr) and units in the natural 

gas/refinery gas and natural gas-fired metallurgical process furnace subcategories. In option 3E, 

the only cost estimated for small units is the cost of an annual tune-up for each boiler or process 

heater. For all units firing natural gas or refinery gas, regardless of the size of the unit, the only 

cost estimated is the cost of an annual tune-up for each boiler or process heater. 

Unlike option 2E, there are no testing and monitoring costs are associated with small 

units or units of any size designed to burn natural gas, refinery gas, or natural gas-fired 

metallurgical process furnaces. 

3.4 Option 4E 

Option 4E includes control device and testing/monitoring cost estimation logic identical 

to Option 3E outlined above, except for the addition of the estimated cost of one facility-wide 

energy audit for each facility, annualized over 5 years. As discussed in the memorandum for 

Estimating Control Costs from Major Source Boilers and Process Heaters, the cost of an energy 

audit ranges from $75,000 for industrial-scale energy audits to between $2,000 and $5,000 per 

energy audit for institutional and commercial-scale audits.2 The cost of each type of audit was 

annualized over 5 years at 7 percent to obtain an annualized cost estimate. For the cost impacts 

analysis, 1,608 facilities are expected to conduct an audit, 197 facilities are commercial or 

institutional and 1,411 facilities are industrial. 

3.5 Option 5E 

For refinery gas units, option 5E includes a cost estimate to install a carbon adsorption 

system to filter the refinery gas prior to combustion. Carbon adsorption is expected to remove 

trace contaminants in the gas, such as Hg. When this option was initially considered, the baseline 

emission factor for mercury from refinery gas units was estimated to be 1.07E-07 lb per mmBtu. 

Since that analysis was completed, the baseline emissions from refinery gas units were reviewed 

and an outlier from a 1990 test, on the order of 10-5 was identified to be biasing the baseline 

emission averages toward a higher average. This test occurred at Facility ID: CAExxonMobilTorrance (unit ID 75-F-1) and only one test run was available. Several additional mercury tests at 

other units firing refinery gas at the same facility in the same year were reported two be two 
C-11 

orders of magnitude lower than the test reported for 75-F-1. After removing the outlier 

emissions, the baseline emissions of mercury from refinery gas were comparable to those of 

natural gas, and a combined average mercury baseline emission factor of 7.28E-07 lb/mmBtu 

was used for both refinery gas and natural gas. 

For analyzing the cost impacts of option 5E EPA estimated one carbon adsorption system 

for each facility with refinery gas boilers and process heaters. EPA expected that a centralized 

adsorption system would provide economies of scale and would treat all the gas prior to the 

refinery gas being used in the combustion equipment. The cost impacts assumption estimates that 

73 different facilities would install a carbon adsorption system to treat refinery gas. Since the 

revised estimate of mercury baseline emissions estimated from refinery gas units had decreased 

substantially as a result of adjusting the baseline emission factor, the cost to install carbon 

adsorption was no longer cost effective and the beyond the floor mercury control option from 

refinery gas units was not included in the proposal. However, the estimated costs of installing 

carbon adsorbers at refineries are included in Appendix A for reference. 

3.6 Option 4N 

Option 4N follows the same logic for estimating control costs as option 2E/2N outlined 

above, with the exception of units in the natural gas/refinery gas and natural gas-fired 

metallurgical process furnace subcategories. In option 4N, the only cost estimated for units firing 

natural gas or refinery gas, regardless of the size of the unit, the only cost estimated is the cost of 

an annual tune-up for each boiler or process heater. 

Unlike option 2E/2N, there are no testing and monitoring costs are associated units of any 

size designed to burn natural gas, refinery gas, or natural gas-fired metallurgical process 

furnaces. 

3.7 Impacts Considering the Alternative Solid Waste Definition 

The impacts of the alternative solid waste definition follows the identical logic for 

estimating control costs as option 4E discussed in Section 3.4 above. Under this option, there 

are13,275 boilers identified as not burning waste materials, compared to a total of 13,555 boilers 

and process heaters under the primary definition of solid waste. The remainders of these units are 

analyzed under the parallel rulemaking effort for Commercial Industrial Solid Waste Incinerators 

if the units are located in the private sector. Should the alternative definition of solid waste 
C-12 

become the final promulgated definition, the additional waste burning units located in the public 

sector will be analyzed under the forthcoming Other Solid Waste Incinerator rulemaking. 

3.8 Summary of Cost Impacts 

Option 4E is the proposed option for existing boilers and process heaters and option 4N is 

the proposed option for new boilers and process heaters. Tables 1 and 2 summarize the costs of 

the proposed option for new and existing units. Appendix A of this memorandum provides a 

detailed summary of the costs according to boiler size, boiler fuel category, and individual 

control device costs. Appendix A also includes a summary of the costs on existing units under 

the alternative definition of solid waste. 

Table 1: Summary of Costs of Proposed Options 

Costs shown in $106

 (2008) with capital recovery estimated at 7% 

Type of 

Unit Option 

Number 

of Units TAC 

TAC 

considering 

fuel savings 

Testing & 

Monitoring 

TAC 

Control 

TAC 

Control 

TCI 

New 4N 46 $7.9 $6.2 $0.6 $7.2 $17.3 

Existing 4E 13,555 $3,248 $2,871 $136 $3,086 $9,489 

Table 2: Summary of Costs by Control Type for Existing Units under Option 4E 

Costs shown in $106 (2008) with capital recovery estimated at 7% 

Number of 

Boilers 

Fabric Filter ESP Wet Scrubber 

Increased 

Caustic Rate

Combustion 

Controls and 

Oxidation 

Catalysts 

Activated Carbon 

Injection 

Energy 

Audit 

TCI TAC TCI TAC TCI TAC TAC TCI TAC TCI TAC TAC 

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

4.0 METHODOLOGY FOR ESTIMATING EMISSION 

REDUCTIONS 

This section discusses the methodology used to estimate emission reductions from boilers 

and process heaters at both existing and new facilities and it presents a summary of the results 

for the regulatory options 1E/1N and 4E/4N. 

4.1 Emission Reductions from Existing Boilers and Process Heaters 

The emission reductions analysis for existing combustion units was done for each boiler 

and process heater in the major source inventory. There are a total of 13,555 boilers and process 

heaters at major sources that reported data in the 2008 questionnaire (ICR No. 2286.01). Each 

combustion unit was assigned a unit-specific or average baseline emission factor, depending on 

the availability of emission data reported for the unit. A detailed discussion of the procedures and 

results of the baseline emissions analysis is presented in another memorandum.4

Emission Reductions for Option 1E 

Emission reductions for PM, HCl, Hg, CO, and dioxins/furans were calculated on a ton 

per year basis by subtracting the baseline emissions assigned to each unit from the MACT floor 

emission limits corresponding to each unit’s subcategory. A detailed discussion of the 

procedures and results of the MACT floor analysis is presented in another memorandum.1 For 

each combustion unit, a percent reduction in CO was calculated. It was assumed that each 

combustion unit would achieve an identical percent reduction from baseline emissions for THC 

and VOC as was achieved for CO. Similarly, a percent reduction was also calculated for HCl. It 

was assumed that each combustion unit would achieve an identical percent reduction from 

baseline emissions for HF as was achieved for HCl. A combustion unit is required to install a 

scrubber for HCl control if it is not currently meeting the HCl floor limit, and if it doesn’t 

already have a scrubber installed. For units required to install a scrubber, it was assumed that the 

scrubber will achieve a 95 percent reduction from baseline for SO2. To calculate emission 

reductions for SO2, baseline emissions were multiplied by a factor of 0.95. A percent reduction 

in PM was calculated in order to estimate total metals reductions. It was assumed that each 

combustion unit would achieve an identical percent reduction from baseline emissions for each 

non-Hg metallic HAP as was achieved for PM. PM2.5 emissions were assumed to be a fraction 
C-14 

of total filterable PM emissions based on fuel and control device configuration installed on the 

unit. The methods used to derive the contribution of PM2.5 to overall filterable PM are presented 

in other memoranda.4

 To calculate emission reductions for PM2.5, the emission reductions for 

PM were multiplied by the applicable PM2.5 fraction. Emission reductions for all pollutants for 

which there was no floor value were calculated on a ton per year basis. 

To convert emission reductions from an emission rate on a heat input basis to an annual 

emission rate, Equation 2 was used: 

Annual Emission Rate (tpy) = ERHI * 0.0005 * Ophours (Equation 2)

Where:

ERHI = emission rate (lb/mmBtu) 

0.0005 = conversion factor, lbs per ton 

Ophours = annual operating hours reported in 2008 survey (hours/year) 

To convert emission reductions from a concentration basis to an annual emission rate, 

Equations 3 and 4 were used: 

Annual Emission Rate (tpy) = ERC * 0.000001 * ρair * QS * 60 * Ophours * 0.0005 * 

(20.946 – O2) / (20.946 – Std O2) (Equation 3)

Where:

ERC = emission concentration (ppm @ 3% O2) 

0.000001 = conversion factor, ppm to parts 

ρair = density of air, 0.0749 lb/dscf 

QS = exhaust flowrate (dscfm) 

60 = conversion factor, minutes to hours 

Ophours = annual operating hours reported in 2008 survey (hours/year) 

0.0005 = conversion factor, lb per ton 

20.946 = percentage of oxygen in ambient air 

O2 = percentage of oxygen assumed in exhaust gas 

Std. O2 = 3 percent oxygen in standardized emission concentration for proposed rule. 
C-15 

Annual Emission Rate (tpy) = ERC * 0.0283 * QS * 60 * Ophours * 0.000000001 * 0.0022 

* 0.0005 * (20.946 – O2) / (20.946 – Std O2) (Equation 4) 

Where:

ERC = emission concentration (ng/dscm @ 7% O2) 

0.0283 = conversion factor, dry standard cubic meter per dry std. cubic foot 

QS = exhaust flowrate (dscfm) 

60 = conversion factor, minutes per hour 

Ophours = annual operating hours reported in 2008 survey (hours/year) 

0.000000001 = conversion factor, ng to g 

0.0022 = conversion factor, g per lb 

0.0005 = conversion factor, lb per ton 

20.946 = percentage of oxygen in ambient air 

O2 = percentage of oxygen assumed in exhaust gas 

Std O2 = 7 percent oxygen in standardized emission concentration for proposed rule. 

Converting concentrations to an annual emission rate required an oxygen concentration 

and exhaust flowrate estimated for each specific fuel type. The development of these 

assumptions and estimates is presented in other memoranda.2

 All conversions required the 

annual operating hours for each combustion unit reported in the 2008 survey. If no operating 

hours were reported, the unit was assumed to operate for 8,400 hours per year (two weeks of 

downtime). 

Emission Reductions for Option 4E 

The same calculations discussed for estimating emission reductions for option 1E were 

applied to all units except small units (less than 10 mmBtu/hr) and units of any size firing natural 

gas or refinery gas. For small units firing any type of fuel and units in the natural gas/refinery gas 

and natural gas metallurgical process furnace subcategories, the emission reductions were based 

on a one percent gain in efficiency expected from the annual tune-up work practice standard. 

Efficiency gains reduce fuel use, and in turn, emissions of hazardous air pollutants. A one 

percent reduction in all types of emissions was estimated by multiplying the baseline emissions 

for each unit by a factor of 0.01. 

Emission Reductions under the Alternative Solid Waste Definition 

The same calculations discussed for estimating emission reductions for option 4E were 

applied to all units that were identified as not burning waste under the alternative solid waste 

definition. In this case, the baseline emissions were compared to the MACT floor emission limits 

calculated for units not burning solid waste under the alternative definition. A discussion of the 
C-16 

methodology used to calculate the MACT floor emission limits under the alternative solid waste 

definition is discussed in another memorandum.1

Incremental Emission Reductions if Energy Audit Findings are Implemented 

We evaluated the potential additional reductions and fuel savings that could occur as a 

result of implementing certain cost-effective energy efficiency improvements identified during 

the audit. Using the Department of Energy references of a 10 to 15 percent improvement in 

efficiency, a range of incremental emission reductions was estimated considering efficiency 

gains between 5 and 10 percent. We applied the control efficiency ranges of 5 and 10 percent 

reduction to the emissions remaining after MACT level of controls were installed on the unit to 

identify the potential for additional emission reductions if the findings of an energy audit were 

implemented. Table 3 summarizes the potential additional emission reductions that could occur 

under a beyond the floor option of implementing any findings in the audit with a short term 

payback. The fuel savings were estimated using Equation 1 described in 3.1 of this memo was 

used to estimate fuel savings. In this case the efficiency gain term was modified to be between 5 

and 10 percent. 

A summary of the estimated emission reductions at existing units for options 1E, the 

proposed option 4E, as well as the reductions from units considering the alternative proposed 

solid waste definition are located in Appendix B. 
C-17 

Table 3. Emission Reductions and Energy Savings Resulting from Implementing Cost 

Effective Findings of an Energy Audit 

 Assuming a Five Percent Efficiency Gain From Implementing Audit Findings 

Emission 

Reductions and 

Energy Savings 

Per Facility 

HCl 

Emission 

Reductions 

(tpy) 

Filterable 

PM 

Emission 

Reductions 

(tpy) 

Non-Hg 

Metals 

Emission 

Reductions 

(tpy) 

Hg 

Emission 

Reductions 

(tpy) 

VOC 

Emission 

Reductions 

(tpy) 

Energy 

Saved 

(mmBtu/yr) 

Annual Fuel 

Savings ($/yr) 

Min 1.23E-07 1.23E-06 1.49E-07 2.55E-10 2.13E-10 2.05E-01 $ - 

Max 6.85E+00 2.02E+01 4.98E+00 2.95E-03 2.80E+01 6.31E+06 $ 58,771,608 

Average 1.76E-01 8.55E-01 5.07E-02 8.53E-05 2.84E-01 1.69E+05 $ 1,290,803 

Median 1.57E-02 1.95E-01 2.97E-03 1.68E-05 5.17E-02 4.50E+04 $ 255,301 

TOTAL 

(all 1,608 facilities) 282 1,375 81 0 457 271,172,354 2,075,611,566 

 Assuming a 10 Percent Efficiency Gain From Implementing Audit Findings 

Emission 

Reductions and 

Energy Savings 

Per Facility 

HCl 

Emission 

Reductions 

(tpy) 

Filterable 

PM 

Emission 

Reductions 

(tpy) 

Non-Hg 

Metals 

Emission 

Reductions 

(tpy) 

Hg 

Emission 

Reductions 

(tpy) 

VOC 

Emission 

Reductions 

(tpy) 

Energy 

Saved 

(mmBtu/yr) 

Annual Fuel 

Savings ($/yr) 

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

Max 1.37E+01 4.04E+01 9.97E+00 5.89E-03 5.60E+01 1.26E+07 $ 117,543,216 

Average 3.51E-01 1.71E+00 1.01E-01 1.71E-04 5.69E-01 3.37E+05 $ 2,581,606 

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

TOTAL 

(all 1,608 facilities) 565 2,749 163 0 914 542,344,709 4,151,223,131 

* Although the energy savings in mmBtu/yr are calculated for all fuels, additional fuel savings in $/yr consider only 

units in the coal, liquid and gas 1 fuel types. Biomass and process gases are industrial byproducts and were not 

assigned a purchase price. 

4.2 Emission Reductions from New Boilers and Process Heaters 

Based on industrial and commercial fuel consumption projections from the EIA, there are 

46 new boilers and process heaters expected to come on-line by 2013.5 a discussion of the 

methodology used to project new boilers and process heaters is discussed in another 

memorandum.3

The New Source Performance Standards for Industrial, Commercial and Institutional 

Boilers (40 CFR part 60, subparts Db, Dc) (NSPS), was reviewed to identify the expected 

baseline level of control for projected new units. It was determined that new boilers and process 

heaters larger than 30 mmBtu/hr and combusting biomass would install an ESP. This technology 
C-18 

selection is based on the analysis used to establish the PM NSPS limit for biomass boilers. New 

coal units larger than 75 mmBtu/hr would have a fabric filter and wet scrubber installed, while 

coal new units between 30 and 75 mmBtu/hr would only have a fabric filter installed and would 

meet the SO2 limits in the NSPS by using coals with a low sulfur content. New units larger than 

30 mmBtu/hr and combusting liquid fuel would have a fabric filter installed. All new units less 

than 30 mmBtu/hr would have no add-on controls and liquid fuels were expected to meet the 

NSPS So2 limits using low sulfur fuel oils. Gas-fired units of all sizes were not expected to 

install controls to meet any of the NSPS limits. For this impacts analysis, it was assumed that all 

new solid fuel units would be stokers, since stoker boilers are the most common type of solid 

fuel boilers and all new units would have NOx control installed as a baseline control, regardless 

of fuel. 

Emission Reductions for Option 1N 

After an appropriate baseline level of control was determined for each model unit, an 

average baseline emission factor calculated for existing units within the same fuel category and 

having the same level of control was assigned to each model boiler. The NSPS specifies PM and 

SO2 limits for new solid- and liquid-fired combustion units based on heat input. It was assumed 

that all new solid and liquid units would be constructed to meet these limits, so they were used as 

baseline emission values where applicable. The baseline emissions for each unit were subtracted 

from the new source MACT floor emission limit corresponding to each unit’s subcategory. The 

same calculations discussed in Section 3.1 of this memo were used to estimate the reductions for 

new units. 

Emission Reductions for Option 4N 

Similar to the methods discussed in Section 4.1 of this memorandum, the emission 

reductions for new units under regulatory option 4N were calculated by subtracting the baseline 

emissions assigned to each unit from the MACT floor emission limits corresponding to each 

unit’s subcategory, except for units firing natural gas or refinery gas. For any size natural gas or 

refinery gas-fired units, the emission reductions were estimated base on a 1 percent reduction in 

emissions, as a result of implementing a tune-up work practice. A summary of the estimated 

emission reductions at existing units for both option 1 and option 4 are located in Appendix B-1. 
C-19 

5.0 SECONDARY IMPACTS 

Secondary impacts include the solid waste, water, wastewater, and electricity required to 

operate air pollution control devices, as well as the additional energy savings resulting from 

improved combustion controls or work practices required by the NESHAP. This section 

documents the inputs and equations used to estimate these secondary impacts, and it summarizes 

the impacts at existing units under proposed regulatory option 4 and new units under proposed 

regulatory option 1. Table 4-1 summarizes the cost, emission, and secondary impacts of this 

proposed NESHAP. Appendices C-1 and C-2 present a detailed breakdown of the secondary 

waste, water, and energy impacts from each subcategory of new and existing boilers and process 

heaters, respectively. 

Table 4-1. Summary of Secondary Impacts 

Impact New Units 

(proposed regulatory Option 4N)

Existing Units 

(proposed regulatory Option 4E)

Water (gal/yr) 197,200 2.4 billion

Wastewater (gal/yr) 142,300 730 million

Solid Waste (tons/yr) 149,800 81,400

Purchased Electricity (kW-hr/yr) 11.2 2.9 billion

Energy Savings* (trillion Btu/yr) 0.1 41.7

* Energy savings is calculated for units in the coal, liquid and gas subcategories. 

The secondary impacts were calculated using algorithms and assumptions described in 

another memorandum.2

 These algorithms and assumptions were applied to the existing boiler 

and process heaters, where the baseline emissions for each unit exceeded the proposed MACT 

floor emission limit except for small units (<10 mmBtu/hr) and units firing natural gas or 

refinery gas. A one percent energy savings was calculated for all units, including the small and 

gas-fired units since these units are expected to conduct a tune-up. For new units, the algorithms 

and assumptions were applied to model units representing units expected to come online between 

2010 and 2013, when the baseline emissions for each model exceeded the proposed MACT floor 

emission limit for new units except for units firing natural gas and refinery gas. Natural gas and 

refinery gas are not required to meet a numerical emission limit, and therefore not expected to 
C-20 

incur any secondary waste, water, or electricity impacts from these controls. A 1 percent energy 

savings from natural gas and refinery gas units are included in the energy savings estimate in 

Table 4-1 since these units are expected to conduct a tune-up. The methodology used to assign 

baseline emission factors to new and existing units are discussed in another memorandum.4

5.1 Wastewater and Water Impacts 

The water required to create a slurry in the packed scrubber and the wastewater generated 

by the effluent of a packed bed scrubber was calculated for every unit expected to install a 

scrubber to meet the HCl limits in the proposed rule. Both the water and wastewater calculations 

required the use of several constants and variables. The constants including the density of gas, 

moles of salt needed per mole of hydrogen chloride in the exhaust gas, the molecular weight of 

the salt used, the fraction of the waste stream treated, operating hours per year and the molecular 

weight of the gas. The data sources for these constants are provided in another memorandum.2

The variables used to estimate the quantity of water required and wastewater generated were 

calculated based on characteristics reported for each existing unit in the 2008 survey and for the 

characteristics assigned to each new model unit. The variables included: exhaust flow rate from 

the combustion unit to the control device in actual cubic feet per minute, the inlet loading of 

hydrogen chloride to the control device (mole fraction), and the efficiency of the control device 

in removing hydrogen chloride from the exhaust gas (percent reduction). The calculations used 

to estimate each variable are provided in another memorandum.2 The total national water and 

wastewater amounts in Table 4.1 were determined by adding the per unit water and wastewater 

estimates for all new and existing units, respectively. 

5.2 Solid Waste Impacts 

Solid waste is generated from collecting dust and fly ash in fabric filters or ESP control 

devices, spent carbon associated with ACI or the installation of a carbon bed adsorber, or spent 

caustic from increasing the caustic injection rate. Solid waste impacts were estimated for every 

unit expected to install a fabric filter, ACI or carbon bed adsorber to meet mercury emission 

limits, or install an ESP to meet PM emission limits. The total national solid waste amounts in 

Table 4.1 were determined by adding the per unit solid waste estimates for all new and existing 

units, respectively. To estimate the solid waste contribution from each of these control devices, 

the variables were calculated based on characteristics reported for each existing unit in the 2008 
C-21 

survey and for the characteristics assigned to each new model unit. The calculations used to 

estimate each variable and the quantity of solid waste generated are provided in another 

memorandum.2

The solid waste (dust, fly ash) generated by the use of an electrostatic precipitator was 

calculated when an electrostatic precipitator was determined to be necessary to meet the 

NESHAP emission limits for PM. Estimates of the solid waste collected in an ESP was based on 

several variables including: exhaust flow rate from the combustion unit to the control device 

(acfm); the inlet loading of particulate matter to the control device (gr/acfm); operating hours 

(hr/year) and the efficiency of the control device required to meet the PM emission limits in the 

proposed NESHAP. 

The solid waste generated from the collection of dust and fly ash in a fabric filter was 

calculated when a fabric filter was determined to be necessary to meet the proposed NESHAP 

emission limits for particulate matter and/or mercury. The calculation required the use of three 

variables, including: exhaust flow rate from the combustion unit to the control device (dscfm); 

operating hours (hr/year) and the inlet loading of particulate matter to the control device 

(gr/acfm). 

For this analysis, the spent carbon collected from units with ACI is assumed to be 

disposed of instead of being re-generated. The amount of spent carbon created from ACI was 

calculated when ACI was expected to be necessary to meet the proposed NESHAP emission 

limits for mercury or dioxin/furan. The calculation required the use of six variables, including: 

exhaust flow rate from the combustion unit to the control device (dscfm); operating hours 

(hr/year), required removal efficiency for mercury and dioxin/furan, and an adjustment factor 

based required removal efficiency of mercury or dioxin /furan. 

The solid waste generated by the use of increased caustic was calculated for those units 

where additional caustic was expected to achieve the proposed NESHAP emission limits for 

HCl. The calculation required the use of three variables, including: exhaust flow rate from the 

combustion unit to the control device (dscfm); operating hours (hr/year), and the required 

removal efficiency for HCl. 
C-22 

5.3 Electricity Impacts 

The amount of electricity required to operate a control device was calculated for a packed 

scrubber, electrostatic precipitator, and fabric filter, CO oxidation catalyst and the fans for the 

ductwork associated with this equipment. These impacts were assessed for every unit that was 

estimated to require hydrogen chloride and/or particulate matter control. Electricity requirements 

are one output of the cost algorithms used in the analyses, so no additional calculations were 

necessary. For some units, an electrical demand from multiple control devices was estimated. 

The total national electricity demand in Table 4.1 was determined by adding the per unit solid 

waste estimates for all new and existing units, respectively. To estimate the electricity demand 

from each of these control devices, a set of variables were calculated based on characteristics 

reported for each existing unit in the 2008 survey and for the characteristics assigned to each new 

model unit. The constants, variables, and calculations used to estimate each variable and the 

electricity demand to operate the control devices are provided in another memorandum.2

5.4 Energy Savings Impacts 

The energy savings from combustion controls such as low NOx burners or linkageless 

boiler management systems, and work practice standards, including a tune-up, and implementing 

the energy audit findings with a short-term payback can improvements in efficiency, thereby 

reducing fuel consumption. Although these combustion improvements have been documented to 

achieve efficiency gains between 5 and 10 percent from the baseline operating conditions, this 

secondary impacts analysis estimates a 1 percent efficiency gain, to be conservative and 

consistent with the assumptions made in Section 3.1 of this memorandum. Quantifying the exact 

gains in efficiency from each of these work practice standards is difficult, and may depend on the 

baseline operating efficiency of each unit. 

Section 3.1 discusses the fuel savings impacts in terms of annualized cost savings to each 

boiler or process heater, and the national energy savings presented in Table 4.1 of this section 

follows the same methodology as was discussed in Section 3.1 and reflect the savings from 

boilers in the coal, gas, and liquid fuel categories only. 

5.5 Estimating Secondary Impacts for Regulatory Options 4E/4N 

Regulatory Options 4E for existing and 4N for new units are both described in detail in 

Section 3 of this memorandum. For the secondary impacts analysis at existing units under option 
C-23 

4E, the water, wastewater, solid waste, and electricity impacts were only assessed for large units, 

(those greater than or equal to 10 mmBtu/hr) that are in the coal, biomass, liquids, or other 

process gas subcategories. Secondary impacts of water, wastewater, solid waste, and electricity 

were not assessed for natural gas or refinery gas units or units in the natural gas metallurgical 

process furnace subcategory. Energy savings were estimated for all units firing anything other 

than biomass since all units were expected to conduct an energy audit. 

For new units under option 4N, the water, wastewater, solid waste, and electricity 

impacts were assessed for any size unit firing coal, biomass, liquid, or other process gases. 

Secondary impacts of water, wastewater, solid waste, and electricity were not assessed for 

natural gas or refinery gas units or units in the natural gas metallurgical process furnace 

subcategory. A one percent energy savings was estimated for all units firing coal, liquids or 

gases that were estimated to require a tune-up or the installation of combustion control 

equipment to meet the CO limits from new boilers. Both tune-ups and combustion controls 

improve the efficiency of the unit, thereby reducing energy consumption. 

6.0 REFERENCES 

1. Singleton, Amanda. ERG. MACT Floor Methodology for Major Source Boilers and 

Process Heaters. April 2010. 

2. Singleton, Amanda and S. McClutchey. ERG. Methodology for Estimating Control Costs 

for the Major Source Industrial, Commercial and Institutional Boilers and Process 

Heaters National Emission Standards for Hazardous Air Pollutants. April 2010. 

3. Gibson, Graham. ERG. New Unit Analysis for Industrial, Commercial, and Institutional 

Boilers and Process Heaters at Major Sources. April 2010. 

4. U.S. Department of Energy, EIA. 2008 Delivered Fuel Prices to Industrial and 

Commercial Clients. http://www.eia.doe.gov/

5. Gibson, G. ERG. Development of Baseline Emission Factors for Boilers and Process 

Heaters at Commercial, Industrial, and Institutional Facilities. April 2010. 

6. U.S. Department of Energy, EIA. Annual Energy Outlook 2009. Table 2. Energy 

Consumption by Sector and Source. http://www.eia.doe.gov/
APPENDIX D 

AREA SOURCES COST AND EMISSIONS MEMORANDUM 
D-1 

MEMORANDUM 

TO: Jim Eddinger, U.S. Environmental Protection Agency, OAQPS/SPPD 

FROM: Graham Gibson, Susan McClutchey, and Amanda Singleton, ERG 

DATE: April 2010 

SUBJECT: DRAFT Methodology for Estimating Impacts from Industrial, Commercial, 

Institutional Boilers at Area Sources of Hazardous Air Pollutant Emissions 

1.0 INTRODUCTION 

The purpose of this memorandum is to discuss the methodology used to estimate the 

costs, emission reductions, and secondary impacts from industrial, commercial, and institutional 

boilers at area sources of hazardous air pollutants (HAP). These impacts were calculated for 

existing units and new units projected to be operational by the year 2013, three years after the 

rule is expected to be promulgated. The results of the impacts analysis are presented for both the 

most stringent regulatory option evaluated and the regulatory option contained in the proposed 

rule. The development of the maximum achievable control technology (MACT) floor and 

Generally Achievable Control Technology (GACT) level of control, projection of new units, and 

a detailed description of the cost equations used to estimate costs for various control technologies 

is presented in other memoranda.1,2,3 This memorandum is organized as follows: 

1.0 Introduction 

2.0 Overview of Regulatory Options 

3.0 Estimating Cost Impacts 

4.0 Methodology for Estimating Emission Reductions 

5.0 Methodology for Estimating Secondary Impacts 

6.0 References 
D-2 

2.0 OVERVIEW OF REGULATORY OPTIONS 

Three control options were considered for existing boilers at area sources of HAP. A 

description of the three options is described below. 

2.1 Existing Units 

 Option 1E represents the option where all boilers, regardless of fuel type or size, must 

meet mercury and CO numerical emission limits based on MACT and PM numerical 

emission limits based on GACT. PM GACT was identified to be a multiclone for existing 

units. 

 Option 2E represents the same emission limits as discussed in 1E above for large units 

(equal to or greater than 10 mmBtu/hr). Small units are exempt from numerical limits and 

instead are required to meet a work practice standard of a biennial tune-up. All facilities 

are required to conduct an energy audit. 

 Option 3E represents the primary proposed option discussed in the preamble. In this 

option, all coal boilers equal to or greater than 10 mmBtu/hr must meet mercury and CO 

numeric emission limits based on MACT. All biomass and liquid boilers equal to or 

greater than 10 mmBtu/hr must meet a CO numerical emission limit, based on MACT. 

All facilities with a large boiler are required to conduct an energy audit. Small boilers are 

exempt from numeric emission limits for all pollutants, but are required to meet a work 

practice standard of a biennial tune-up. There are no numerical emission limits for PM 

under this option for any size or type of unit. 

2.2 New Units 

Three control options were considered for new boilers at area sources of HAP. A detailed 

description of the three options is described below. 

 Option 1N represents the option where all boilers, regardless of fuel type or size, must 

meet mercury and CO limits based on MACT and PM numerical emission limits based 

on GACT. GACT for new units is based on PM limits in the New Source Performance 

Standards (NSPS) for Industrial, Commercial, and Institutional Boilers (40 CFR part 60 

subparts Db, Dc). 

 Option 2N represents the same emission limits as discussed in 1N above for large units 

(equal to or greater than 10 mmBtu/hr). Small units are exempt from numerical emission 

limits and instead are required to meet a work practice standard of a biennial tune-up. 

 Option 3N represents the primary proposed option discussed in the preamble. In this 

option, all coal boilers, regardless of size, must meet mercury and CO limits based on 

MACT and PM numerical emission limits based on GACT. All biomass and liquid 
D-3 

boilers, regardless of size, must meet CO limits based on MACT and PM numerical 

emission limits based on GACT. 

3.0 ESTIMATING COST IMPACTS 

For each option, a percentage of units in each model unit were assumed to require control 

devices in order to meet the limit when the baseline emissions for the model unit exceeded the 

MACT floor emission limit applicable to each model. A detailed description of the three options 

is described below. A summary table comparing the overall capital and annualized costs of 

option 3E for existing units and option 3N for new units is presented in Table 1. The equations 

used to estimate the control, testing, monitoring, and work practice costs are discussed in another 

memorandum.3 The following logic was used to apply control, testing, and monitoring costs to 

each boiler: 

3.1 Option 1E 

Control Cost Impacts 

Mercury Control 

A new fabric filter installation was expected to achieve the mercury emission limits in the 

proposed rule. Where baseline mercury emissions were found to be greater than the MACT floor, 

the cost of a fabric filter was estimated for a portion of the boilers represented by the model unit. 

Based on the data used in the MACT floor analysis, a fraction of units in each subcategory 

meeting the MACT floor for Hg was estimated. For boilers designed to burn biomass one of the 

boilers was meeting the floor and the other unit was not meeting the floor. In the cost impacts 

analysis, 50 percent of the biomass units were estimated to install a fabric filter to meet the floor. 

For boilers designed to burn coal, 44 percent of units were achieving the proposed MACT floor 

emission limit for coal, so a new fabric filter was estimated to be installed at 56 percent of the 

existing coal-fired boilers. For liquid fuel units, there were no area source boilers with emission 

test data available for mercury. In the absence of other information about the distribution of units 

that would require a fabric filter to be installed, this cost impacts analysis assumes that 50 

percent of the liquid fuel units would install a fabric filter to meet the mercury limit. 
D-4 

CO/Organic HAP Control 

Organic HAP and carbon monoxide can be controlled by either improving the 

combustion efficiency of the unit, or installing an oxidation catalyst on the exhaust of a 

combustion unit. The control strategy necessary to meet the MACT floor emission limit will vary 

depending on the magnitude between the baseline emissions and the CO MACT floor. 

Most boilers (other than Dutch ovens and PC-coal boilers) are designed to operate with 

CO emissions at or near 400 parts per million (ppm). A boiler tune-up was estimated in the cost 

impacts analysis if the unit’s baseline emissions exceeded the floor for carbon monoxide (CO), 

but were less than or equal to 400 ppm @ 3% O2. The combustor design of the boilers in the area 

source inventory is not known and this impacts analysis assumes that all areas source boilers 

firing solid fuels have a stoker combustor design since this is the predominant combustor design 

in the major source boiler inventory. The baseline emissions for area source boilers in all fuel 

categories are less than 400 ppm, and so it is assumed that combustion controls, either a basic 

tune-up or a more advanced burner replacement or installation of a linkageless boiler 

management system, can achieve the proposed MACT floor emission limits. No oxidation 

catalysts are estimated to be required to meet the MACT floor emission limits. 

Based on the emission test data used to calculate the MACT floor for CO, 29 percent of 

units burning coal are meeting the MACT floor and these units would be expected to install a 

linkageless boiler management system to comply with the CO limits. For units burning biomass, 

72 percent of the units are exceeding the MACT floor emission limits, and so 72 percent of the 

units are estimated to install a linkageless boiler management system. For units burning liquids, 

86 percent of the units are exceeding the floor and this cost impacts analysis assumes these units 

will install a new low NOx replacement burner in order to meet the CO limits. The units not 

expected to install these advanced controls are expected to conduct an annual tune-up to maintain 

in compliance with the proposed CO limit overtime. 

Particulate Matter Control 

For all units that were not expected to install a fabric filter for mercury control, the cost 

impacts analysis for this option assumes that the unit would install a multiclone to achieve the 

GACT emission limits for PM. Based on the current MACT floor analysis, 44 percent of coal 

units and 50 percent of liquid units would install a multiclone. Existing biomass units not 
D-5 

expected to install a fabric filter would also install a multiclone. Base on the current MACT floor 

analysis, 50 percent of existing biomass units would install a multiclone. 

Testing and Monitoring Cost Impacts 

Testing and monitoring requirements varied depending on the equipment installed on the 

unit to control emissions, the design capacity of the model unit, and the fuel category of the 

model unit. 

Testing Costs 

All boilers designed to burn solid fuels were expected to conduct an annual compliance 

test for PM, Hg, and CO. The cost to conduct stack tests for these three pollutants was estimated 

to be $15,000 per year. Boilers greater than 100 mmBtu/hr were expected to install CO CEMS in 

lieu of conducting a CO stack test and the cost to conduct tests on PM and Hg was estimated to 

be $12,000 per year. 

Boilers designed to burn liquid fuels were expected to conduct an annual compliance test 

for PM and CO. In lieu of a stack test boilers designed to burn liquid fuels were expected to 

conduct fuel analysis, or report fuel analyses received from a fuel supplier for chlorine and Hg. 

Conducting stack tests for PM and CO was estimated to be $13,000 per year and the cost to 

conduct fuel analysis for Hg was estimated to be $600 per year. Combustion units greater than 

100 mmBtu/hr were expected to install CO CEMS in lieu of conducting a CO stack test and the 

cost to conduct tests on PM and fuel analysis for Hg was estimated to be $8,600 per year. 

Although solid fuels are eligible to comply with the proposed rule through fuel analysis in lieu of 

stack testing, when the mercury content of the fuel is below the MACT floor emission limit, this 

cost estimate conservatively assumed that only units designed to fire liquid fuels would use this 

compliance alternative. The methods and data sources used to estimate testing and monitoring 

costs are discussed in other memoranda.3 

Monitoring Costs 

Various monitor configurations were installed based on the size of the unit and the 

pollution control devices expected to be installed to achieve the MACT floor emission limits. For 

units expected to install a fabric filter, an annualized cost of $9,700 for a bag leak detection 

monitor was included in the cost analysis. For units greater than 100 mmBtu/hr, an annualized 
D-6 

cost of $53,300 for a CO CEMS was included in the cost analysis. For units that did not install a 

bag leak detector, an annualized cost of $14,660 for an opacity monitor was included in the cost 

analysis. 

Fuel Savings Impacts 

This cost analysis includes an estimate of energy savings for every unit that is expected to 

install controls to improve combustion, or conduct an annual tune-up or energy audit. The 

Department of Energy has conducted energy assessments at selected manufacturing facilities and 

reports that facilities can reduce fuel/energy use by 10 to 15 percent by using best practices to 

increase their energy efficiency. Many best practices are considered pollution prevention because 

they reduce the amount of fuel combusted which results in a corresponding reduction in 

emissions from the fuel combustion. Further boiler tune-ups have been shown to improve the 

efficiency of a boiler between 1 and 5 percent, depending on the age of the unit and the time 

lapse since the previous tune-up. Other combustion controls such as upgrading burners and 

installation of an LBMS are also expected to improve the efficiency of the unit, thus reducing 

fuel consumption. This cost analysis assumes an annual fuel savings of 1 percent. The energy 

savings is estimated using Equation 1: 

Annual Fuel Savings (mmBtu/yr) = DC * CF * Ophours * EG (Equation 1)

Where: 

DC = unit design capacity (mmBtu/hr) 

Ophours = annual operating hours, assumed 8400 (hours/year) 

EG = Efficiency gain, estimated to be 1% 

CF = annual average capacity factor, 0.5 for liquid and 0.65 for coal and biomass 

After the fuel savings for each boiler was calculated, the both industrial and commercial 

prices for coal, #2 distillate fuel oil, and #6 residual fuel oil were obtained from the EIA.3

 The 

EIA data reported fuel prices as $/ton for coal, and cents per gallon for fuel oil. The higher 

heating values were obtained from Table C-1 of the EPA Mandatory Reporting Rule (40 CFR 

part 98 subpart C) and the higher heating values were used to convert the fuel prices to a 

standard unit of measure, $ per mmBtu. Using the distribution of SIC codes reported in the 13-

state boiler inspector inventory, the model units were distributed to an industrial or commercial 

sector, and then the appropriate fuel price was multiplied by the calculated fuel savings. This 

cost analysis only estimates the fuel savings from units in the coal and liquid fuel categories. A 
D-7 

fuel savings was not estimated for units in the biomass fuel category since the price of biomass 

fuels is variable, and often biomass is an on-site industrial byproduct instead of a purchased fuel. 

3.2 Option 2E 

Option 2E follows the same logic for estimating control costs as option 1E outlined 

above, with the exception of small units (less than 10 mmBtu/hr). In option 2E, the only cost 

estimated for small units is the cost of an annual tune-up for each boiler. No testing and 

monitoring costs were included in option 2E for small units. Option 2E also includes the cost of 

an energy audit at every area source facility, approximately 91,339 facilities. As discussed in the 

memorandum for Estimating Control Costs from Major Source Boilers and Process heaters, the 

cost of an energy audit ranges from $75,000 for industrial-scale energy audits to between $2,000 

and $5,000 per energy audit for institutional and commercial-scale audits.4

 The facility’s 

classification of either an industrial or commercial facility was assigned using the distribution of 

SIC codes in the 13-state boiler inspector inventory. The cost of each type of audit was 

annualized over 5 years at 7 percent to obtain an annualized cost estimate. 

3.3 Option 3E 

The proposed option 3E includes control device and testing/monitoring cost estimation 

for mercury and CO from large coal units. As mentioned in option 1E, 56 percent of large coal 

units, or 321 boilers are expected to install a fabric filter in order to meet the mercury limit. In 

addition, 29 percent of large coal units are expected to install advanced combustion controls in 

order to meet the CO limit. This analysis uses the cost of a linkageless boiler management 

system to estimate the costs of advanced combustion control. The remaining 71 percent of large 

coal units are expected to meet the CO limit with a tune-up. The testing and monitoring costs for 

large coal units include a test for CO and mercury, as well as a bag leak detection system for the 

321 boilers that are expected to install a fabric filter. There are no proposed numerical PM 

emission limits under this option, and so no additional testing costs for PM or opacity monitoring 

costs were assessed in the cost impacts analysis for this option. 

Under option 3E liquid and biomass boilers are not subject to numerical emission limits 

for mercury and there are no costs included in the impacts analysis to install fabric filters or 

conduct mercury fuel analysis or stack testing. Large liquid and biomass boilers are subject to 

numerical emission limits for CO. This cost impacts analysis estimates that all the biomass and 
D-8 

liquid fuel units can meet the CO emission limits by conducting an annual tune-up. These large 

units must also conduct testing and monitoring activities for CO to demonstrate compliance with 

the numerical emission limits. 

Option 3E exempts small boilers from numerical emission limits. Instead these units must 

conduct a work practice standard of a biennial tune-up. The cost impacts analysis does not 

include any additional testing and monitoring requirements for these small boilers. 

Finally, option 3E proposes that all facilities with large boilers conduct an energy audit. 

For this cost impacts analysis one large boiler per facility was assumed, or 13,268 facilities 

estimated to conduct an audit. Similar to the discussion under option 2E, the cost of the audit 

ranged from $75,000 for industrial-scale energy audits to between $2,000 and $5,000 per energy 

audit for institutional and commercial-scale audits.2

 

3.4 Option 1N 

New area source boilers are subject to an NSPS (40 CFR part 60 subparts Db, Dc) to 

regulate emissions of PM, NOx and SO2. The cost impacts analysis considered controls that 

would likely be installed to comply with the NSPS and includes an estimate of any additional 

control, testing and monitoring costs that would not be already conducted to meet the 

requirements of the NSPS. Based on a review of the NSPS, this analysis assumes all biomass 

boilers greater than 30 mmBtu/hr will have an ESP control installed as the baseline to meet the 

NSPS PM limits; all coal boilers greater than 75 will have an FF and wet scrubber installed to 

meet PM and SO2 limits; all coal boilers between 30 and 75 will have a Fabric Filter and use low 

sulfur coal to meet PM and SO2 limits, and all liquid boilers greater than 30 will have an FF 

installed to meet PM limits. The NSPS does not regulate PM for units less than 30 mmBtu/hr. 

Mercury Control 

A new fabric filter installation was expected to achieve the mercury emission limits in the 

proposed rule. Where baseline mercury emissions were found to be greater than the MACT floor, 

the cost of a fabric filter was estimated for a portion of the boilers represented by the model unit. 

All new boilers, regardless of size or fuel, were expected to install a fabric filter in order to meet 

the mercury limits under this option. Comparing these mercury control requirement to the 

expected controls under the NSPS, all biomass boilers are expected to install a fabric filter to 
D-9 

meet the mercury limit and all liquid and coal boilers less than or equal to 30 mmBtu/hr are 

expected to install a fabric filter to meet the mercury limit. 

CO/Organic HAP Control 

New boilers are expected to be equipped with new and efficient burners, and it was 

assumed that an annual tune-up could achieve the CO numeric emission limit for all sizes and 

types of boilers. Other advanced combustion controls were not considered as a control alternative 

for new boilers. As mentioned under 1E, the control strategy necessary to meet the MACT floor 

emission limit will vary depending on the magnitude between the baseline emissions and the CO 

MACT floor. 

Particulate Matter Control 

Under this option all units are expected to install a fabric filter for mercury control, which 

has a co-benefit of reducing PM emissions, as well as other non-mercury metallic HAP. No 

additional control costs were estimated for PM control at new boilers. 

Testing and Monitoring Cost Impacts 

Testing and monitoring requirements varied depending on the equipment installed on the 

unit to control emissions, the design capacity of the model unit, and the fuel category of the 

model unit. 

Testing Costs 

All boilers designed to burn solid fuels were expected to conduct an annual compliance 

test for PM, Hg, and CO. The cost to conduct stack tests for these three pollutants was estimated 

to be $15,000 per year. Combustion units greater than 100 mmBtu/hr were expected to install 

CO CEMS in lieu of conducting a CO stack test and the cost to conduct tests on PM and Hg was 

estimated to be $12,000 per year. 

Boilers designed to burn liquid fuels were expected to conduct an annual compliance test 

for PM and CO. In lieu of a stack test boilers designed to burn liquid fuels were expected to 

conduct fuel analysis, or report fuel analyses received from a fuel supplier for chlorine and Hg. 

Conducting stack tests for PM and CO was estimated to be $13,000 per year and the cost to 

conduct fuel analysis for Hg was estimated to be $600 per year. Combustion units greater than 

100 mmBtu/hr were expected to install CO CEMS in lieu of conducting a CO stack test and the 
D-10 

cost to conduct tests on PM and fuel analysis for Hg was estimated to be $8,600 per year. 

Although solid fuels are eligible to comply with the proposed rule through fuel analysis in lieu of 

stack testing, when the mercury content of the fuel is below the MACT floor emission limit, this 

cost estimate conservatively assumed that only units designed to fire liquid fuels would use this 

compliance alternative. The methods and data sources used to estimate testing and monitoring 

costs are discussed in other memoranda.3 

Monitoring Costs 

Various monitor configurations were installed based on the size of the unit and the 

pollution control devices expected to be installed to achieve the MACT floor emission limits. For 

units expected to install a fabric filter, an annualized cost of $9,700 for a bag leak detection 

monitor was included in the cost analysis. For units greater than 100 mmBtu/hr, an annualized 

cost of $53,300 for a CO CEMS was included in the cost analysis. For units that did not install a 

bag leak detector, an annualized cost of $14,660 for an opacity monitor was included in the cost 

analysis. 

3.5 Option 2N 

Option 2N follows the same logic for estimating control costs as option 1N outlined 

above, with the exception of small units (less than 10 mmBtu/hr). In option 2N, the only cost 

estimated for small units is the cost of an annual tune-up for each boiler. No testing and 

monitoring costs were included in option 2N for small units 

3.6 Option 3N 

The proposed option 3N includes identical requirement for coal units as outlined under 

option 1N. Under option 3N liquid and biomass boilers are not subject to numerical emission 

limits for mercury and there are no costs included in the impacts analysis to install fabric filters 

for mercury control or conduct mercury fuel analysis or stack testing. Liquid and biomass boilers 

are subject to numerical emission limits for CO. This cost impacts analysis estimates that all the 

biomass and liquid fuel units can meet the CO emission limits by conducting an annual tune-up. 

These units must also conduct testing and monitoring activities for CO to demonstrate 

compliance with the numerical emission limits. 
D-11 

Option 3N also includes a numerical PM emission limit for coal, biomass, and liquid 

boilers, based on the NSPS limits applicable to each of these categories. Since all coal units are 

subject to mercury emission limits, they are expected to meet a PM GACT limit of 0.051 

lb/mmBtu without any additional control requirements. The NSPS PM limit for biomass is 0.1 

lb/mmBtu, which is based on the performance of an electrostatic precipitator (ESP). Since 

biomass units greater than 30 mmBtu/hr are already subject to this limit under the NSPS, this 

cost analysis applies the costs for an ESP to units less than or equal to 30 mmBtu/hr. The NSPS 

PM limit for liquids is also 0.051 lb/mmBtu. Based on the calculated average baseline emission 

factors analysis distillate liquids are expected to meet that limit without any additional control.5

However residual liquid units are expected to install a fabric filter to meet the PM emission limit. 

Under option 3N, all boilers less than or equal to 30 mmBtu/hr are estimated to incur 

costs to test for PM and CO, at an estimate cost of $14,000 per year. Boilers greater than 30 

mmBtu/hr will incur PM stack testing costs under the NSPS. Coal boilers are estimated to incur 

additional costs to test for mercury and the cost to conduct tests for PM, CO, and Hg is estimated 

to be $19,000 per year. 

3.7 Summary of Cost Impacts 

Option 3E is the proposed option for existing boilers and option 3N is the proposed 

option for new boilers. Since new boilers and their exhaust stacks can be designed to allow for 

stack testing, the tune-up work practice standard does not apply to new small units. Table 1 

summarizes the costs of the proposed option for new and existing boilers at area sources of HAP. 

Appendix A of this memorandum provides a detailed summary of the costs for each model unit. 

Table 1: Summary of Costs of Proposed Options 

Costs shown in $106

 (2008) with capital recovery estimated at 7% 

Type of 

Unit Option 

Number 

of 

Boilers TAC 

TAC 

considering 

fuel savings 

Testing & 

Monitoring 

TAC 

Control 

TAC 

Control 

TCI 

New 3N 6,779 $311 $260 $193 $117 $343 

Existing 3E 182,671 $696 $279 $94 $550 $1,792 
D-12 

4.0 METHODOLOGY FOR ESTIMATING EMISSION 

REDUCTIONS 

This section discusses the methodology used to estimate emission reductions from boilers 

at both existing and new facilities and it presents a summary of the results for the regulatory 

options 1E/1N and 3E/3N.

4.1 Emission Reductions from Existing Boilers 

Each model area source boiler was assigned baseline emissions based on the calculated 

baseline averages for existing major source combustion units in the same size and fuel 

subcategory. The development of area source model units and the procedures and results of the 

baseline emissions analysis is presented in other memoranda.5-6 

Emission Reductions for Option 1E 

Emission reductions for all pollutants were calculated on a ton per year basis. Emission 

reductions of Hg and CO were calculated by subtracting the baseline emissions assigned to each 

model unit from the MACT floor (or GACT) emission limits corresponding to the subcategory 

for each model boiler. A detailed discussion of the procedures and results of the MACT floor 

analysis is presented in another memorandum.1

For all units expected to install a fabric filter to meet the mercury MACT floor emission 

limits, this fabric filter achieves a co-benefit of reducing emissions of PM and non-mercury 

metallic HAP. To calculate the PM emission reductions from units expected to install a fabric 

filter, the baseline emissions assigned to each model boiler were subtracted from the calculated 

average baseline emission factor corresponding to a fabric filter level of control in the same fuel 

category. For example, the PM baseline emission factor for uncontrolled or multiclone-equipped 

biomass boilers is 0.26 lb/mmBtu, and the calculated baseline emission factors for biomass 

boilers equipped with a fabric filter is 0.01 lb/mmBtu. The emission reductions were estimated 

using the difference of these two factors, or 0.25 lb/mmBtu. The methodology used to calculate 

average baseline emission factors for different fuel and control configurations is discussed in 

another memorandum.5

 It was assumed that the remaining boilers that did not install a fabric 

filter will install a cyclone or multiclone to reduce PM emissions. Multiclones were identified as 

a GACT level of control for PM. Emission reductions for units expected to install a multiclone 
D-13 

were estimated by multiplying the baseline emissions of each model unit by the expected PM 

control efficiency of a multiclone, a 75 percent reduction. These control efficiencies for various 

control devices are detailed in another memorandum.5

 Model units with a design capacity greater 

than 10 mmBtu/hr were expected to already have a multiclone installed as a baseline level of 

control, so no additional PM emission reductions were estimated from these units, unless the unit 

installed a fabric filter for mercury control. 

To estimate the reductions in other non-mercury metallic HAP, the percent reduction in 

filterable PM was calculated for each model boiler expected to install a fabric filter for mercury 

control. This percent reduction was multiplied by the baseline emissions for each of the nonmercury metallic HAP. Since fabric filters capture fine particulate, this analysis assumes that 

each model boiler would achieve an identical percent reduction from baseline emissions for each 

non-mercury metallic HAP as was achieved for PM. For model boilers that were expected to 

install a multiclone to meet the PM GACT limit, a 10 percent reduction was estimated for nonmercury metallic HAP. 

PM2.5 emissions comprise a fraction of total filterable PM emissions depending on the 

fuel combusted and control device configuration installed on the unit. The methods used to 

derive the contribution of PM2.5 to overall filterable PM are presented in other memoranda.5

 To 

calculate emission reductions for PM2.5 for each model boiler, the emission reductions for PM 

were multiplied by the applicable PM2.5 fraction. 

For any boiler conducting a tune-up or installing advanced combustion controls such as a 

replacement burner or linkageless boiler management system, a one percent gain in combustion 

efficiency was estimated, resulting in an estimated one percent emissions reduction of all 

pollutants. Efficiency gains reduce fuel use, and in turn, emissions of hazardous air pollutants.8

 A 

one percent reduction in emissions for these pollutants was estimated by multiplying the baseline 

emissions for each unit by a factor of 0.01. 

To convert emission reductions from an emission rate on a heat input basis to an annual 

emission rate, Equation 2 was used: 
D-14 

Annual Emission Rate (tpy) = ERHI * 0.0005 * Ophours * CF (Equation 2)

Where:

ERHI = emission rate (lb/mmBtu) 

0.0005 = conversion factor, lbs per ton 

Ophours = annual operating hours, assumed 8760 (adjusted using capacity factor) 

CF = annual average capacity factor, 0.5 for liquid and 0.65 for coal and biomass 

To convert emission reductions from a concentration basis to an annual emission rate, 

Equations 3 and 4 were used: 

Annual Emission Rate (tpy) = ERC * 0.000001 * ρair * QS * 60 * Ophours * 0.0005 * 

(20.946 – O2) / (20.946 – Std O2) * CF (Equation 3)

Where:

ERC = emission concentration (ppm @ 3% O2) 

0.000001 = conversion factor, ppm to parts 

ρair = density of air, 0.0749 lb/dscf 

QS = exhaust flowrate (dscfm) 

60 = conversion factor, minutes to hours 

Ophours = annual operating hours reported in 2008 survey (hours/year) 

0.0005 = conversion factor, lb per ton 

20.946 = percentage of oxygen in ambient air 

O2 = percentage of oxygen assumed in exhaust gas 

Std. O2 = 3 percent oxygen in standardized emission concentration for proposed rule. 

CF = annual average capacity factor, 0.5 for liquid and 0.65 for coal and biomass 

Annual Emission Rate (tpy) = ERC * 0.0283 * QS * 60 * Ophours * 0.000000001 * 0.0022 

* 0.0005 * (20.946 – O2) / (20.946 – Std O2) * CF (Equation 4) 

Where:

ERC = emission concentration (ng/dscm @ 7% O2) 

0.0283 = conversion factor, dscm to dscf 

QS = exhaust flowrate (dscfm) 

60 = conversion factor, minutes to hours 

Ophours = annual operating hours reported in 2008 survey (hours/year) 

0.000000001 = conversion factor, ng to g 

0.0022 = conversion factor, g per lb 

0.0005 = conversion factor, lb per ton 

20.946 = percentage of oxygen in ambient air 

O2 = percentage of oxygen assumed in exhaust gas 

Std O2 = 7 percent oxygen in standardized emission concentration for proposed rule. 

CF = annual average capacity factor, 0.5 for liquid and 0.65 for coal and biomass 

Converting concentrations to an annual emission rate required an oxygen concentration 

and exhaust flowrate estimated for each specific fuel type. The development of these 

assumptions and estimates is presented in another memorandum.4
D-15 

Emission Reductions for Option 3E 

The same calculations discussed for estimating emission reductions for option 1E were 

applied to all large coal boilers. For small model boilers, the emission reductions were based on a 

one percent gain in efficiency expected from the biennial tune-up work practice standard. For 

large biomass and liquid units no add-on controls for PM or mercury are expected since these 

units are not subject to numerical emission limits for PM or Hg. Instead, a similar one percent 

gain in efficiency is expected to occur as a result of conducting an annual tune-up or installing 

advanced combustion controls necessary to meet the CO numerical limit in each category. 

Efficiency gains reduce fuel use, and in turn, emissions of hazardous air pollutants. A one 

percent reduction in all types of emissions was estimated by multiplying the baseline emissions 

for each unit by a factor of 0.01. A summary of the estimated emission reductions at existing 

units for both option 1 and option 2a are located in Appendix B-1. 

4.2 Emission Reductions from New Boilers 

Based on industrial and commercial fuel consumption projections from the EIA and a 

history of boiler installation dates in the boiler inspector inventory, there are 6,779 new area 

source boilers expected to come on-line by 2013.7 These new projected boilers are expected to 

fire biomass, coal, and liquid fuels. An average (mean) design capacity of area source boilers 

firing similar fuel type, in the same size category, and in the same sector (industrial or 

commercial) was estimated to develop new model units representative of the existing boiler 

inventory. New model units were assigned baseline emissions in the same manner as existing 

area source model units. The projection of new model area source boilers and the procedures and 

results of the baseline emissions analysis is presented in other memoranda.2

As discussed in Section 3.4, the NSPS for Industrial, Commercial and Institutional 

Boilers (40 CFR part 60, subparts Db, Dc) (NSPS) were reviewed to identify the expected 

baseline level of control for projected new units. Then, the average baseline emission factor 

corresponding to the expected level of control and fuel category was assigned to each new model 

boiler. New biomass boilers larger than 30 mmBtu/hr were expected to install an ESP; new coal 

boilers larger than 75 mmBtu/hr were expected to install a fabric filter and wet scrubber; new 

coal boilers between 30 and 75 mmBtu/hr would only have a fabric filter installed and were 

expected to meet the SO2 limits in the NSPS by using coals with a low sulfur content; new 
D-16 

boilers larger than 30 mmBtu/hr and combusting liquid fuels were expected to install a fabric 

filter. All new boilers less than 30 mmBtu/hr would have no add-on controls. For this impacts 

analysis, it was assumed that all new solid fuel units would be stokers, since stoker boilers are 

the most common type of solid fuel boilers and all new units would have NOx control installed 

as a baseline control, regardless of fuel. Based on the EIA fuel projections, all new coal boilers 

are projected to be less than 10 mmBtu/hr and the only 49 model boilers firing biomass are 

expected to exceed 30 mmBtu/hr. 

Emission Reductions for Option 1N 

After an appropriate baseline level of control was determined for each model unit, an 

average baseline emission factor was calculated for existing units within the same fuel category 

and having the same level of control was assigned to each model boiler. The NSPS specifies PM 

and SO2 limits for new solid- and liquid-fired combustion units based on heat input. It was 

assumed that all new solid and liquid units would be constructed to meet these limits and those 

limits were used as baseline emission values, where applicable. For units less than 30 mmBtu/hr, 

the baseline emissions for PM were estimated assuming the unit was uncontrolled and the target 

PM emission limit from the NSPS was used as the GACT level of control. The baseline 

emissions for each unit were subtracted from the new source MACT floor for Hg and CO and 

GACT emission limit for PM corresponding to each unit’s subcategory. The same calculations 

discussed in Section 4.1 of this memo were used to estimate the reductions for new units. 

Emission Reductions for Option 3N 

For new coal boilers, the emission reductions were calculated using the same methods 

discussed for Option 1N above. For new biomass boilers less than 30 mmBtu/hr, emission 

reductions for PM were calculated by subtracting the PM NSPS emission limits from a baseline 

emission factor representing uncontrolled units. Since an ESP is not expected to be very effective 

at capturing mercury emissions, mercury emissions reductions from all biomass units were 

estimated based on a one percent efficiency improvement, resulting from annual tune-ups or 

other combustion controls expected to occur in order to demonstrate compliance with CO 

emission limits. For new biomass boilers greater than or equal to 30 mmBtu/hr, there were no 

estimated additional PM or non-Hg metallic HAP emission reductions since these larger biomass 

boilers are already expected to be in compliance with a PM NSPS limit using an ESP. 
D-17 

New residual liquid boilers less than 30 mmBtu/hr were expected to install a fabric filter 

to meet the PM emission limit. Since a fabric filter is effective at capturing fine particulate, 

additional emission reductions for mercury were calculated by subtracting the average baseline 

emission factor for heavy liquid boilers equipped with a fabric filter from the average baseline 

emission factor corresponding to an uncontrolled heavy liquid unit. 

The average baseline emission factor for PM at uncontrolled distillate liquid units is less 

than the NSPS emission limit for liquid units. As a result, no additional PM, Hg, or non-Hg 

metallic HAP emission reductions were estimated from installing additional PM controls. 

Instead, these reductions were estimated based on a one percent efficiency improvement, 

resulting from annual tune-ups or other combustion controls expected to occur in order to 

demonstrate compliance with CO emission limits. 

Under this proposed option, new small units do not qualify for the same tune-up work 

practice standards that apply to existing units since it is expected that new units can be designed 

to allow for stack test diameters that would be compatible with EPA test methods. As a result, 

new A summary of the estimated emission reductions at existing units for both option 1N and the 

proposed option 3N are located in Appendix B-2. 

Incremental Emission Reductions if Energy Audit Findings are Implemented 

We evaluated the potential additional reductions and fuel savings that could occur as a 

result of implementing certain cost-effective energy efficiency improvements identified during 

the audit. Using the Department of Energy references of a 10 to 15 percent improvement in 

efficiency, a range of incremental emission reductions was estimated considering efficiency 

gains between 5 and 10 percent. We applied the control efficiency ranges of 5 and 10 percent 

reduction to the emissions remaining after MACT level of controls were installed on the unit to 

identify the potential for additional emission reductions if the findings of an energy audit were 

implemented. Table 3 summarizes the potential additional emission reductions that could occur 

under a beyond the floor option of implementing any findings in the audit with a short term 

payback. The fuel savings were estimated using Equation 1 described in 3.1 of this memo that 

was used to estimate fuel savings. In this case the efficiency gain term was modified to be 

between 5 and 10 percent. A summary of the estimated incremental emission reduction and fuel 

savings is shown in Table 3. 
D-18 

A summary of the estimated emission reductions at existing area source boilers for 

options 1E, 2E, and the proposed option 3E are located in Appendix B-1. A summary of the 

estimated emission reductions at new area source boilers for options 1N, 2N, and the proposed 

option 3N are located in Appendix B-2. 

Table 3. Emission Reductions and Energy Savings Resulting from Implementing Cost 

Effective Findings of an Energy Audit at Facilities with Large (≥10 mmBtu/hr) Boilers 

 Assuming a Five Percent Efficiency Gain From Implementing Audit Findings 

Emission 

Reductions and 

Energy Savings Per 

Facility 

HCl 

Emission 

Reductions 

(tpy) 

Filterable 

PM 

Emission 

Reductions 

(tpy) 

Non-Hg 

Metals 

Emission 

Reductions 

(tpy) 

Hg 

Emission 

Reductions 

(tpy) 

VOC 

Emission 

Reductions 

(tpy) 

Energy 

Saved 

(mmBtu/yr) 

Annual Fuel 

Savings ($/yr) 

Min 3.92E-03 3.01E-02 1.12E-03 9.21E-07 3.59E-04 3.85E+03 $0 

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

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

Median 3.92E-03 3.01E-02 2.39E-02 3.45E-06 4.20E-04 5.54E+03 $125,613 

TOTAL 

(13,268 facilities with 

a large boiler) 

2.29E+02 2.31E+03 2.63E+02 6.16E-02 2.47E+01 8.16E+07 $1,264,161,683 

 Assuming a 10 Percent Efficiency Gain From Implementing Audit Findings 

Emission 

Reductions and 

Energy Savings Per 

Facility 

HCl 

Emission 

Reductions 

(tpy) 

Filterable 

PM 

Emission 

Reductions 

(tpy) 

Non-Hg 

Metals 

Emission 

Reductions 

(tpy) 

Hg 

Emission 

Reductions 

(tpy) 

VOC 

Emission 

Reductions 

(tpy) 

Energy 

Saved 

(mmBtu/yr) 

Annual Fuel 

Savings ($/yr) 

Min 7.84E-03 6.02E-02 2.25E-03 1.84E-06 7.18E-04 7.69E+03 $0 

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

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

Median 7.84E-03 6.02E-02 4.79E-02 6.89E-06 8.40E-04 1.11E+04 $251,226 

TOTAL 

(13,268 facilities with 

a large boiler)

4.58E+02 4.62E+03 5.26E+02 1.23E-01 4.94E+01 1.63E+08 $2,528,323,367 

* Although the energy savings in mmBtu/yr are calculated for all fuels, additional fuel savings in $/yr consider only 

units in the coal and liquid categories. Biomass is often an industrial byproducts and was not assigned a purchase 

price.
D-19 

5.0 METHODOLOGY FOR ESTIMATING SECONDARY 

IMPACTS 

Secondary impacts include the solid waste and electricity required to operate air pollution 

control devices, as well as the additional energy savings resulting from improved combustion 

controls or work practices required by the NESHAP. This section documents the inputs and 

equations used to estimate these secondary impacts, and it summarizes the impacts at existing 

units under proposed regulatory option 3E and new units under proposed regulatory option 3N. 

Table 5-1 summarizes the secondary impacts of this proposed NESHAP. Appendices C-1 and C2 present a detailed breakdown of the secondary waste and energy impacts from each 

subcategory of existing and new boilers, respectively. 

Table 5-1: Summary of Secondary Impacts 

Impact New Units 

(proposed regulatory Option 4N)

Existing Units 

(proposed regulatory Option 4E)

Solid Waste (tons/yr) 1,800 14,300

Purchased Electricity (kW-hr/yr) 22 million 206 million

Energy Savings* (tBtu/yr) 2.34 19.6

* Energy savings is calculated for units in the coal and liquid subcategories. 

The secondary impacts were calculated using algorithms and assumptions described in 

another memorandum.3

 These algorithms and assumptions were applied to the existing boilers, 

where the baseline emissions for each unit exceeded the proposed MACT floor emission limit. 

For new units, the algorithms and assumptions were applied to model units representing units 

expected to come online between 2010 and 2013, when the baseline emissions for each model 

exceeded the proposed MACT floor or GACT emission limit for new units. The methodology 

used to assign baseline emission factors to new and existing units are discussed in another 

memorandum.5

5.1 Solid Waste Impacts 

Solid waste is generated from collecting dust and fly ash in fabric filters or ESP control 

devices. Solid waste impacts were estimated for every unit expected to install a fabric filter to 

meet mercury emission limits, or install an ESP to meet PM emission limits. The total national 
D-20 

solid waste amounts in Table 5-1 were determined by adding the per unit solid waste estimates 

for all new and existing units, respectively. To estimate the solid waste contribution from each of 

these control devices, the variables were calculated based on characteristics reported for each 

model unit. The calculations used to estimate each variable and the quantity of solid waste 

generated are provided in another memorandum.3

The solid waste (dust, fly ash) generated by the use of an electrostatic precipitator was 

calculated when an electrostatic precipitator was determined to be necessary to meet the GACT 

emission limits for PM. Estimates of the solid waste collected in an ESP was based on several 

variables including: exhaust flow rate from the combustion unit to the control device (acfm); the 

inlet loading of particulate matter to the control device (gr/acfm); operating hours (hr/year) and 

the efficiency of the control device required to meet the PM emission limits in the proposed 

NESHAP. 

The solid waste generated from the collection of dust and fly ash in a fabric filter was 

calculated when a fabric filter was determined to be necessary to meet the proposed NESHAP 

emission limits for particulate matter and/or mercury. The calculation required the use of three 

variables, including: exhaust flow rate from the combustion unit to the control device (dscfm); 

operating hours (hr/year) and the inlet loading of particulate matter to the control device 

(gr/acfm). 

5.2 Electricity Impacts 

The amount of electricity required to operate a control device was calculated for an 

electrostatic precipitator and fabric filter. These impacts were assessed for every unit that was 

estimated to require particulate matter control. Electricity requirements are one output of the cost 

algorithms used in the analyses, so no additional calculations were necessary. For some units, an 

electrical demand from multiple control devices was estimated. The total national electricity 

demand in Table 5-1 was determined by adding the per unit solid waste estimates for all new and 

existing units, respectively. To estimate the electricity demand from each of these control 

devices, a set of variables were calculated based on characteristics assigned to each model unit. 

The constants, variables, and calculations used to estimate each variable and the electricity 

demand to operate the control devices are provided in another memorandum.3

 
D-21 

5.3 Energy Savings Impacts 

The energy savings from combustion controls such as low NOx burners or linkageless 

boiler management systems, and work practice standards, including a tune-up, and implementing 

the energy audit findings with a short-term payback can improvements in efficiency, thereby 

reducing fuel consumption. Although these combustion improvements have been documented to 

achieve efficiency gains between 5 and 10 percent from the baseline operating conditions, this 

secondary impacts analysis estimates a 1 percent efficiency gain, to be conservative and 

consistent with the assumptions made in Section 3.1 of this memorandum. Quantifying the exact 

gains in efficiency from each of these work practice standards is difficult, and may depend on the 

baseline operating efficiency of each unit. 

Section 3.1 discusses the fuel savings impacts in terms of annualized cost savings to each 

boiler, and the national energy savings presented in Table 5-1 of this section follows the same 

methodology as was discussed in Section 3.1 and reflect the savings from boilers in the coal and 

liquid fuel categories only. 

5.4 Estimating Secondary Impacts for Regulatory Options 3E/3N 

Regulatory Options 3E for existing and 3N for new units are both described in detail in 

Section 2 of this memorandum. For the secondary impacts analysis at existing units under option 

3E, the waste and electricity impacts were only assessed for large units (those greater than or 

equal to 10 mmBtu/hr) that are in the coal subcategory. Secondary impacts of solid waste and 

electricity were not assessed for the liquid and biomass subcategories because these boilers were 

not subject to PM or Hg numerical emission limits and were not expected to install add-on 

controls. Energy savings were estimated for all units firing anything other than biomass since all 

units were expected to conduct a tune-up or install combustion controls. 

For new units under option 3N, the solid waste and electricity impacts were assessed for 

any size unit firing coal, liquid, or biomass. A one percent energy savings was estimated for all 

units firing coal or liquids that were estimated to require a tune-up to meet the CO limits from 

new boilers. Both tune-ups and combustion controls improve the efficiency of the unit, thereby 

reducing energy consumption.
D-22 

6.0 REFERENCES 

1. Singleton, A., ERG. MACT Floor Analysis for the Industrial, Commercial, Institutional 

Boilers National Emission Standards for Hazardous Air Pollutants – Area Source. April, 

2010. 

2. Singleton, A., ERG. New Unit Analysis Industrial, Commercial, and Institutional Boilers 

National Emission Standards for Hazardous Air Pollutants – Area Source. April, 2010. 

3. Singleton, A., ERG. Methodology for Estimating Control Costs for Industrial, 

Commercial, Institutional Boilers National Emission Standards for Hazardous Air 

Pollutants – Area Source. April, 2010. 

4. Singleton, A., ERG. Methodology for Estimating Control Costs for Industrial, 

Commercial, Institutional Boilers and Process Heaters National Emission Standards for 

Hazardous Air Pollutants – Major Source. April, 2010. 

5. Gibson, G. and A. Singleton, ERG. Development of Baseline Emission Factors for 

Boilers and Process Heaters at Commercial, Industrial and Institutional Facilities. April, 

2010. 

6. Singleton, A., ERG. Methodology for Developing Model Units for Industrial, 

Commercial, and Institutional Boilers National Emission Standards for Hazardous Air 

Pollutants – Area Source. April, 2010. 

7. U.S. Department of Energy. Energy Information Administration. Annual Energy Outlook 

2006 (Early Release) with Projections to 2030. Figure 3. Energy Consumption by Fuel, 

1980-2030 (quadrillion Btu). 

8. Delta Institute. Sector-Based Based Pollution Prevention: Toxic Reductions through 

Energy Efficiency and Conservation Among Industrial Boilers. July 2002. Located online 

at http://www.delta-institute.org/publications/boilers/SectorBasedP2.pdf