February 2011 

Regulatory Impact Analysis: 

National Emission Standards for 

Hazardous Air Pollutants for 

Industrial, Commercial, and 

Institutional Boilers and Process 

Heaters

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Economics Group 

Risk and Benefits Group 

Air Quality Modeling Group 

Research Triangle Park, NC 27711 
 

 

Regulatory Impact Analysis: 

National Emission Standards for 

Hazardous Air Pollutants for 

Industrial, Commercial, and 

Institutional Boilers and Process 

Heaters

February 2011

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Economics Group 

Risk and Benefits Group 

Air Quality Modeling Group 

Research Triangle Park, NC 27711 
iii 

CONTENTS 

Section Page

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

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

1.2 Organization of this Report .................................................................................. 1-3 

1.3 Section 1 References ............................................................................................ 1-6 

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 

2.1.2.1 Goods and Services Used in Food Manufacturing ..................... 2-4 

2.1.2.2 Energy ........................................................................................ 2-4 

2.1.2.3 Uses and Consumers .................................................................. 2-7 

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

2.1.3.1 Location ...................................................................................... 2-7 

2.1.3.2 Production Capacity and Utilization .......................................... 2-9 

2.1.3.3 Employment ............................................................................... 2-9 

2.1.3.4 Plants and Capacity .................................................................. 2-10 

2.1.3.5 Firm Characteristics ................................................................. 2-10 

2.1.3.6 Size Distribution ....................................................................... 2-12 

2.1.3.7 Domestic Production ................................................................ 2-12 

2.1.3.8 International Trade ................................................................... 2-12 

2.1.3.9 Market Prices ............................................................................ 2-16 

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

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

2.2.2 Supply and Demand Characteristics ...................................................... 2-20 

2.2.2.1 Goods and Services Used in Wood Product Manufacturing .... 2-20 

2.2.2.2 Uses and Consumers ................................................................ 2-22 

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

2.2.3.1 Location .................................................................................... 2-24 

2.2.3.2 Production Capacity and Utilization ........................................ 2-25 
iv 

2.2.3.3 Employment ............................................................................. 2-26 

2.2.3.4 Plants and Capacity .................................................................. 2-26 

2.2.3.5 Firm Characteristics ................................................................. 2-27 

2.2.3.6 Size Distribution ....................................................................... 2-27 

2.2.3.7 Domestic Production ................................................................ 2-28 

2.2.3.8 International Trade ................................................................... 2-28 

2.2.3.9 Market Prices ............................................................................ 2-31 

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

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

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

2.3.2.1 Goods and Services Used in Paper Manufacturing .................. 2-37 

2.3.2.2 Uses and Consumers ................................................................ 2-42 

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

2.3.3.1 Location .................................................................................... 2-43 

2.3.3.2 Production Capacity and Utilization ........................................ 2-44 

2.3.3.3 Employment ............................................................................. 2-44 

2.3.3.4 Plants and Capacity .................................................................. 2-45 

2.3.3.5 Firm Characteristics ................................................................. 2-45 

2.3.3.6 Size Distribution ....................................................................... 2-47 

2.3.3.7 Domestic Production ................................................................ 2-48 

2.3.3.8 International Trade ................................................................... 2-49 

2.3.3.9 Market Prices ............................................................................ 2-49 

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

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

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

2.4.2.1 Goods and Services Used in Chemical Manufacturing ............ 2-55 

2.4.2.2 Uses and Consumers ................................................................ 2-55 

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

2.4.3.1 Location .................................................................................... 2-58 

2.4.3.2 Production Capacity and Utilization ........................................ 2-60 

2.4.3.3 Employment ............................................................................. 2-60 

2.4.3.4 Plants and Capacity .................................................................. 2-60 

2.4.3.5 Firm Characteristics ................................................................. 2-62 

2.4.3.6 Size Distribution ....................................................................... 2-64 

2.4.3.7 Domestic Production ................................................................ 2-64 

2.4.3.8 International Trade ................................................................... 2-64 

2.4.3.9 Market Prices ............................................................................ 2-64 
v 

2.5 Section 2 References .......................................................................................... 2-68 

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

3.1 Major Sources ...................................................................................................... 3-2 

3.2 Area Sources ........................................................................................................ 3-5 

3.3 Section 3 References ............................................................................................ 3-6 

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 

4.1.2.1 Market-Level Results ................................................................. 4-2 

4.1.2.2 Social Cost Estimates Major Source Rule .................................. 4-3 

4.1.2.3 Social Cost Estimates Area Source Rule .................................... 4-5 

4.1.2.4 Job Effects .................................................................................. 4-6 

4.2 Section 4 References .......................................................................................... 4-14 

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

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

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

5.1.1.1 Representative Small Business Analysis Using Census 

Statistics of U.S. Businesses ....................................................... 5-1 

5.1.1.2 Additional Small Business Analysis Using Sample of 

Small Businesses Identified in Combustion Facility 

Survey ......................................................................................... 5-4 

5.1.2 Small Governmental Jurisdictions and Not-for-Profit Enterprises .......... 5-5 

5.2 Final Regulatory Flexibility Analysis (FRFA): Major Sources ........................... 5-6 

5.2.1 Need for Rule and Objectives .................................................................. 5-6 

5.2.2 Summary of the Significant Issues raised by the Public Comments 

and Agency Assessment .......................................................................... 5-6 

5.2.3 Description of and Estimate of the Number of Small entities to 

Which the Rules Will Apply .................................................................... 5-7 

5.2.4 Description of the Projected Reporting, Record keeping and Other 

Compliance Requirements of the Rule .................................................... 5-9 
vi 

5.2.5 Description of the steps the Agency has Taken to Minimize the 

Significant Economic Impact on Small Entities .................................... 5-11 

5.3 Final Regulatory Flexibility Analysis (FRFA): Area Sources ........................... 5-12 

5.3.1 Need for Rule and Objectives ................................................................ 5-12 

5.3.2 Summary of the Significant Issues raised by the Public Comments 

and Agency Assessment ........................................................................ 5-12 

5.3.3 Description of and Estimate of the Number of Small entities to 

Which the Rules Will Apply .................................................................. 5-13 

5.3.4 Description of the Projected Reporting, Record keeping and Other 

Compliance Requirements of the Rule .................................................. 5-15 

5.3.5 Description of the steps the Agency has Taken to Minimize the 

Significant Economic Impact on Small Entities .................................... 5-16 

5.4 Section 5 References .......................................................................................... 5-16 

6 Air Quality Modeling of Emissions Reductions ........................................................... 6-1 

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

6.2 Photochemical Model Background ...................................................................... 6-2 

6.3 Model Domain and Grid Resolution .................................................................... 6-3 

6.4 Emissions Input Data ........................................................................................... 6-4 

6.4.1 2005 Baseline Emissions ......................................................................... 6-4 

6.4.2 Future Year Baseline Emissions .............................................................. 6-5 

6.4.3 Future Year Sector Contribution Approach ............................................. 6-7 

6.5 Model Results ...................................................................................................... 6-9 

6.5.1 Impacts of Sector on Total Mercury Deposition .................................... 6-10 

6.5.2 Impacts of Sector on Future Annual PM2.5 Levels ............................... 6-10 

6.5.3 Impacts of Sector on Future 24-hour PM2.5 Levels .............................. 6-11 

6.5.4 Impacts of Sector on Future Visibility Levels ....................................... 6-11 

6.5.5 Impacts of Sector on Future Ozone Levels ............................................ 6-12 

6.6 Section 6 References .......................................................................................... 6-12 

7 Benefits of Emissions Reductions ................................................................................ 7-1 

7.1 Synopsis ............................................................................................................... 7-1 

7.2 Calculating Benefits ............................................................................................. 7-2 
vii 

7.2.1 Methodology Improvements since Proposal ............................................ 7-4 

7.2.2 Benefits Analysis Approach for PM2.5 and Ozone ................................. 7-5 

7.2.3 Health Impact Analysis (HIA) ................................................................. 7-6 

7.2.4 Estimating PM2.5-related Premature Mortality ..................................... 7-10 

7.2.5 Economic Valuation of Health Impact .................................................. 7-12 

7.2.6 Calculating Boiler Sector-specific Benefit-per-ton Estimates ............... 7-14 

7.3 Health Benefits Results ...................................................................................... 7-20 

7.4 Energy Disbenefits ............................................................................................. 7-34 

7.4.1 Social Cost of Carbon and Greenhouse Gas Disbenefits ....................... 7-34 

7.5 Unquantified or Nonmonetized Benefits ........................................................... 7-37 

7.5.1 Other SO2 Benefits ................................................................................ 7-37 

7.5.2 Carbon Monoxide Benefits .................................................................... 7-39 

7.5.3 Visibility Benefits .................................................................................. 7-40 

7.5.4 Ozone Vegetation Benefits .................................................................... 7-40 

7.5.5 Direct HAP Benefits .............................................................................. 7-40 

7.5.5.1 Mercury .................................................................................... 7-44 

7.5.5.2 Hydrogen Chloride (HCl) ......................................................... 7-49 

7.5.5.3 Chlorine Gas (Cl2) ................................................................... 7-50 

7.5.5.4 Hydrogen Cyanide (HCN) ........................................................ 7-50 

7.5.5.5 Hydrogen Fluoride (HF) ........................................................... 7-50 

7.5.5.6 Toluene ..................................................................................... 7-50 

7.5.5.7 Formaldehyde ........................................................................... 7-51 

7.5.5.8 Polycyclic Aromatic Hydrocarbons (PAHs) ............................ 7-53 

7.5.5.9 Acetaldehyde ............................................................................ 7-54 

7.5.5.10 Nickel ....................................................................................... 7-55 

7.5.5.11 Manganese ................................................................................ 7-55 

7.5.5.12 Dioxins (Chlorinated dibenzodioxins (CDDs) ......................... 7-56 

7.5.5.13 Furans (Chlorinated dibenzofurans (CDFs)) ............................ 7-57 

7.5.5.14 Other Air Toxics ....................................................................... 7-57 

7.6 Limitations and Uncertainties ............................................................................ 7-57 

7.6.1 Monte Carlo Analysis ............................................................................ 7-58 

7.6.2 LML Assessment for PM2.5 .................................................................. 7-59 

7.6.3 Alternate Concentration-response Functions for PM Mortality ............ 7-59 

7.6.4 Alternate Concentration-response Functions for Ozone Mortality ........ 7-60 
viii 

7.6.5 Qualitative Assessment of Uncertainty and Other Analysis 

Limitations ............................................................................................. 7-60 

7.7 Section 7 References .......................................................................................... 7-62 

8 Comparison of Monetized Benefits and Costs.............................................................. 8-1 

8.1 Boiler MACT ....................................................................................................... 8-1 

8.2 Boiler Area Source Rule ...................................................................................... 8-1 

8.3 Section 8 References ............................................................................................ 8-5 

Appendixes

A OAQPS Multimarket Model to Assess the Economic Impacts of 

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

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

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
x 

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 U.S. Surplus Changes by Sector: Major Sources ................................. 4-4

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

4-4. Employment Impacts Using Morgenstern, Pizer, Shih (2002) (1,000 FTEs) ............. 4-10

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

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

6-1. Map of the photochemical modeling domains. The black outer box denotes the 

36 km national modeling domain; the red inner box is the 12 km western U.S. 

grid; and the blue inner box is the 12 km eastern U.S. grid. ......................................... 6-3 

6-2. Locations of boilers in the NEI point inventory for the future baseline (2016) ........... 6-9

7-1. Total Monetized PM2.5 and Ozone Benefits for the Final Boiler MACT and 

Boiler Area Source Rule in 2014 .................................................................................. 7-2

7-2. Illustration of BenMAP Approach ................................................................................ 7-7

7-3. Data Inputs and Outputs for the BenMAP Model for a PM2.5 Analysis ....................... 7-8

7-4. Change in Ambient PM2.5 Levels from SO2 Emissions from Point Source 

Boilers ......................................................................................................................... 7-17

7-5. Change in Ambient PM2.5 Levels from PM Emissions from Point Source Boilers .... 7-18
xi 

7-6. Change in Ambient PM2.5 Levels from SO2 Emissions from Non-point Source 

Boilers ......................................................................................................................... 7-18

7-7. Change in Ambient PM2.5 Levels from PM Emissions from Non-point Source 

Boilers ......................................................................................................................... 7-19

7-8. Breakdown of Monetized PM2.5 Health Benefits Estimates using Mortality 

Function from Pope et al. (2002) ................................................................................ 7-28

7-9. Breakdown of Monetized Ozone Health Benefits Estimates using Mortality 

Function from Bell et al. (2004) ................................................................................. 7-29

7-10. Total Monetized PM2.5 and OzoneBenefits Estimates for the Final Boiler 

MACT and Boiler Area Source Rule in 2014 ............................................................. 7-30

7-11.Breakdown of Monetized PM2.5 Benefits Estimates by Precursor for the Final 

Boiler MACT in 2014 ................................................................................................. 7-31

7-12.Breakdown of Monetized PM2.5 Benefits Estimates by Precursor for the Final 

Boiler Area Source Rule in 2014 ................................................................................ 7-31

7-13.Breakdown of Monetized PM2.5 Benefits Estimates by Subcategory for the Final 

Boiler MACT in 2014 ................................................................................................. 7-32

7-14.Breakdown of Monetized PM2.5 Benefits Estimates by Subcategory for the Final 

Boiler Area Source Rule in 2014 ................................................................................ 7-32

7-15. Percentage of Population Exposed to Baseline Air Quality Levels for Final 

Boiler MACT and Boiler Area Source Rule ............................................................... 7-33

7-16.Cumulative Percentage of Population Exposed to Baseline Air Quality Levels 

for Final Boiler MACT and Boiler Area Source Rule ................................................ 7-34

7-17. Estimated County Level Carcinogenic Risk from HAP exposure from outdoor 

sources (2002 NATA) ................................................................................................. 7-42

7-18. Estimated County Level Noncancer (Respiratory) Risk from HAP exposure 

from outdoor sources (2002 NATA) ........................................................................... 7-42

7-19. Percentage Reduction in Total Mercury Deposition in the Eastern U.S. .................... 7-48

7-20. Percentage Reduction in Total Mercury Deposition in the Western U.S. .................. 7-49

8-1. Net Benefits for the Final Major and Area Source Boiler Rules at 3% Discount 

Rate ............................................................................................................................... 8-4

8-2. Net Benefits for the Final Major and Area Source Boiler Rules at 7% Discount 

Rate ............................................................................................................................... 8-5
xii 

LIST OF TABLES 

Number Page

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

Boiler MACT (Major Sources) in 2014 (millions of 2008$) ........................................ 1-4

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

Boiler Area Source Rule in 2014 (millions of 2008$) .................................................. 1-5

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

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

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) .................. 2-8

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) ............................................................................................................................. 2-38
xiii 

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).............................................. 2-52

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) ........................................................................................................................ 2-56

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

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

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 ............. 3-3

3-2. Summary of Annual Costs for New and Existing Area Sources .................................. 3-6

4-1. Distribution of Social Costs Major Sources (billion, 2008$): 2014 ............................. 4-4

4-2. Distribution of Social Costs Area Sources (billion, 2008$): 2014 ............................... 4-5

4-3. Employment Impacts Using Morgenstern, Pizer, Shih (2002) (FTE) .......................... 4-9

4-4. Cost Approach Jobs Impacts in FTEs ......................................................................... 4-12

4-5. Employment Impacts .................................................................................................. 4-13

4-6. Aggregate Employment in FTEs ................................................................................ 4-13

4-7. Aggregate Employment Distributed to Regulated Entities and Others in FTEs ........ 4-14

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

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

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

5-3. Information Collection Effort for Facilities with Combustion Units: Major 

Sources .......................................................................................................................... 5-8

5-4. Estimated Affected Facilities Using 13 State Boiler Inspector Inventory: Area 

Sources ........................................................................................................................ 5-14
xiv 

6-1. Geographic Elements of Domains Used in Photochemical Modeling .......................... 6-4 

6-2. Control Strategies and/or Growth Assumptions Included in the 2016 Projection ........ 6-6

6-3. Estimated Future Year (2016) Controllable Boiler Sector Emissions .......................... 6-8

7-1. Human Health and Welfare Effects of Air Pollutants Affected ................................... 7-9

7-2. Summary of Monetized Benefits Estimates for Final Boiler MACT in 2014 

(millions of 2008$) ..................................................................................................... 7-21

7-3. Summary of Monetized Benefits Estimates for Final Boiler Area Source Rule in 

2014 (millions of 2008$) ............................................................................................ 7-21

7-4. Summary of Estimated Reductions in Health Incidences from PM2.5 for the 

Final Boiler MACT in 2014 (95th percentile confidence interval) ............................. 7-22

7-5. Summary of Estimated Reductions in Health Incidences from PM2.5 for the 

Final Boiler Area Source Rule in 2014 (95th percentile confidence interval) ............ 7-23

7-6. Summary of Monetized Benefits Estimates from PM2.5 for the Final Boiler 

MACT in 2014 (95th percentile confidence interval) .................................................. 7-24

7-7. Summary of Monetized Benefits Estimates from PM2.5 for the Final Boiler Area 

Source Rule in 2014 (95th percentile confidence interval) ......................................... 7-25

7-8. Summary of Monetized Benefits Estimates from Ozone for the Final Boiler 

MACT in 2014 (95th percentile confidence interval) .................................................. 7-26

7-9. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2014 ........................ 7-36

7-10. Monetized SCC-Derived Disbenefits of CO2 Emission Increases in 2014 

(millions of 2008$) ..................................................................................................... 7-37

7-11. Top HAPs by Mass from Boilers by Fuel Type ......................................................... 7-44

8-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler MACT in 2014 (millions of 2008$) ................................................................... 8-2

8-2 Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler Area Source Rule in 2014 (millions of 2008$) .................................................. 8-3
1-1 

SECTION 1

INTRODUCTION 

The U.S. Environmental Protection Agency (EPA) is promulgating two rules for national 

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

commercial, and institutional boilers and process heaters.1

 One rule requires all major sources to 

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

maximum achievable control technology (MACT). In the other rule, EPA is promulgating a 

NESHAP for two area source categories: industrial boilers and institutional and commercial 

boilers.2 The emission standards for controlling mercury and polycyclic organic matter (POM) 

emissions are based on the MACT. The emission standards for controlling other HAPs are based 

on EPA’s 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 major source NESHAP’s total 

annualized costs will be $1.4 billion (2008$). For the area source NESHAP, EPA 

estimates the total annualized costs will be $0.5 billion. 

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

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

(less than 0.01% higher). Average annual domestic production may also fall by 

less than 0.01%. Because of higher domestic prices, imports rise by less than 

0.01% per year. Market-level effects for the area source NESHAP are smaller 

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

changes are less than 0.01%. 

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

under $1.5 billion (2008$). The Agency’s economic model suggests that 

industries are able to pass approximately $0.5 billion of the rule’s costs to 

consumers (e.g., higher market prices). Domestic industries’ surplus falls by $1.4 

billion, while other countries on net benefit from higher prices (a net increase in 

rest-of-the world [ROW] surplus of less than $0.1 billion). Additional costs and 

fuel savings for new and existing major sources that are not included in the 

 

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 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 

economic model represent a net benefit of $0.4 billion.1

 The estimated social cost 

of the area source rule is approximately $0.5 billion (2008$). The Agency’s 

economic model suggests that industries are able to pass approximately $0.2 

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 less than $0.1 billion. 

 Employment Changes: The estimated employment changes range between 

−3100 to 6,500 employees, with a central estimate of +1,700 employees for the 

major source NESHAP. The estimated employment changes range between 

−1,000 to 2,000 employees, with a central estimate of +500 employees for the 

area source NESHAP. 

 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. Pursuant to section 603 of the 

RFA, EPA prepared an initial regulatory flexibility analysis (IRFA) for the 

proposed rule and convened a Small Business Advocacy Review Panel to obtain 

advice and recommendations of representatives of the regulated small entities. A 

detailed discussion of the Panel’s advice and recommendations is found in the 

final Panel Report (Docket ID No. EPA-HQ-OAR-2002-0058-0797). A summary 

of the Panel’s recommendations is also presented in the preamble to the proposed 

rule at 75 FR 32044-32045 (June 4, 2010). In the proposed rule, EPA included 

provisions consistent with four of the Panel’s recommendations. As required by 

section 604 of the RFA, we also prepared a final regulatory flexibility analysis 

(FRFA) the final rule (see Section 5). 

 Benefits Analysis:

– The benefits from reducing some air pollutants have not been monetized in this 

analysis, including reducing a combined 113,000 tons of carbon monoxide, 

30,000 tons of HCl, 830 tons of HF, 2,900 pounds of mercury, 3,000 tons of other 

metals, and 23 grams of dioxins/furans (TEQ) each year. We assess the benefits 

of these emission reductions qualitatively in this analysis. 

– We have monetized the benefits from reducing PM (as a surrogate for metal 

HAP), as well as the co-benefits that result from the HAP emissions reductions 

(e.g., the pollution control equipment for HCl also reduces sulfur dioxide, a precursor to PM2.5). Thus all monetized benefits reported reflect improvements in 

ambient PM2.5 and ozone concentrations. As such, although the monetized 

benefits likely underestimate the total benefits, the extent of the underestimate is 

unclear. 

– Using a 3% discount rate, we estimate the total monetized benefits of the Boiler 

MACT to be $22 billion to $54 billion in the implementation year (2014). Using a 

7% discount rate, we estimate the total monetized benefits of the Boiler MACT to 

 

1

 See additional details in Chapter 3 and Cost Appendices. 
1-3 

be $20 billion to $49 billion in the implementation year. Using alternate 

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

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

fall between these estimates. 

– Using a 3% discount rate, we estimate the total monetized benefits of the Boiler 

Area Source Rule to be $210 million to $520 million in the implementation year 

(2014). Using a 7% discount rate, we estimate the total monetized of the Boiler 

Area Source Rule to be $190 million to $470 million in the implementation year. 

– Using a 3% discount rate, we estimate the total monetized benefits of the 

combined Boiler MACT and Boiler Area Source Rule to be $22 billion to $58 

billion in the implementation year (2014). Using a 7% discount rate, we estimate 

the total monetized benefits of the combined Boiler MACT and Boiler Area 

Source Rule to be $20 billion to $50 billion in the implementation year. All 

estimates are in 2008$. 

 Net Benefits: For the Boiler MACT, the net benefits are $21 billion to $53 billion 

at a 3% discount rate for the benefits and $19 million to $48 billion at a 7% 

discount rate. For the Boiler Area Source Rule, the net benefits are −$280 million 

to $30 million at a 3% discount rate for the benefits and −$300 million to −$20 

million at a 7% discount rate. These results are shown in Tables 1-1 and 1-2. 

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 describes the air quality modeling performed by EPA. 

 Section 7 presents the benefits estimates. 

 Section 8 presents the net benefits. 

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

 Appendix B provides additional economic model result tables by sector. 
1-4 

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

Boiler MACT (Major Sources) in 2014 (millions of 2008$)a

3% Discount Rate 7% Discount Rate 

Selected 

Total Monetized Benefitsb $22,000 to $54,000 $20,000 to $49,000 

Total Social Costs3 $1,500 1,500 

Net Benefits $20,500 to $52,500 $18,500 to $47,500 

Non-monetized Benefits 112,000 tons of carbon monoxide 

30,000 tons of HCl 

820 tons of HF 

2,800 pounds of mercury 

2,700 tons of other metals 

23 grams of dioxins/furans (TEQ) 

Health effects from SO2 exposure 

Ecosystem effects 

Visibility impairment 

Alternative 

Total Monetized Benefitsb $18,000 to $43,000 $16,000 to $39,000 

Total Social Costsb $1,900 $1,900 

Net Benefits $16,100 to $41,100 $14,100 to $37,100 

Non-monetized Benefits 112,000 tons of carbon monoxide 

 22,000 tons of HCl 

 620 tons of HF 

 2,400 pounds of mercury 

 2,600 tons of other metals 

23 grams of dioxins/furans (TEQ) 

Health effects from SO2 exposure 

Ecosystem effects 

 Visibility impairment 

a

 All estimates are for the implementation year (2014), and are rounded to two significant figures. These results 

include units anticipated to come online and the lowest cost disposal assumption. 

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

reductions of directly emitted PM2.5 and PM2.5 precursors such as SO2, as well as reducing exposure to ozone 

through reductions of VOCs. It is important to note that the monetized benefits include many but not all health 

effects associated with PM2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. 

(2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in 

causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect 

estimates by particle type. These estimates include energy disbenefits associated with the increased emissions 

from additional energy usage valued at $22 million for the selected option and $37 million for the alternative 

option. Ozone benefits are valued at $3.6 to $15 million for both options. 

c 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. 
1-5 

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

Boiler Area Source Rule in 2014 (millions of 2008$)a

 3% Discount Rate 7% Discount Rate 

Final MACT/GACT Approach: Selected 

Total Monetized Benefitsb $210 to $520 $190 to $470 

Total Social Costsc $490 $490 

Net Benefits −$280 to $30 −$300 to −$20 

Non-monetized Benefits 1,100 tons of carbon monoxide 

340 tons of HCl 

8 tons of HF 

90 pounds of mercury 

320 tons of other metals 

<1 gram of dioxins/furans (TEQ) 

Health effects from SO2 exposure 

Ecosystem effects 

Visibility impairment 

Proposed MACT Approach: Alternative 

Total Monetized Benefitsb $200 to $490 $180 to $440 

Total Social Costsc $850 $850 

Net Benefits −$650 to −$360 −$670 to −$410 

Non-monetized Benefits 1,100 tons of carbon monoxide 

 340 tons of HCl 

 8 tons of HF 

 90 pounds of mercury 

 320 tons of other metals 

 <1 gram of dioxins/furans (TEQ) 

 Health effects from SO2 exposure 

 Ecosystem effects 

 Visibility impairment 

a

 All estimates are for the implementation year (2014), and are rounded to two significant figures. These results 

include units anticipated to come online and the lowest cost disposal assumption. 

b

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

reductions of directly emitted PM2.5 and PM2.5 precursors such as SO2. It is important to note that the monetized 

benefits include many but not all health effects associated with PM2.5 exposure. Benefits are shown as a range 

from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. These estimates include energy disbenefits 

associated with the increased emissions from additional energy usage valued at less than $1 million. 

c

 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. 
1-6 

1.3 Section 1 References 

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate 

Air Pollution and Mortality.” American Journal of Respiratory and Critical Care 

Medicine 173:667-672. 

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine 

Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141. 
2-1 

SECTION 2

INDUSTRY PROFILES 

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

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 

Animal Food

Manufacturing,

3%

Grain and Oilseed

Milling,

4% Sugar and

Confectionery

Product

Manufacturing,

5%

Fruit and Vegetable

Preserving and

Specialty Food

Manufacturing,

12%

Dairy Product

Manufacturing,

9%

Animal Slaughtering

and Processing, 33%

Seafood Product

Preparation and

Packaging, 3%

Bakeries and Tortilla

Manufacturing,

20%

Other Food

Manufacturing,

11%

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

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

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

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

Animal Food

Manufacturing,

6% Grain and Oilseed

Milling, 10%

Sugar and

Confectionery

Product

Manufacturing,

6%

Fruit and Vegetable

Preserving and

Specialty Food

Manufacturing,

12%

Dairy Product

Manufacturing,

14%

Animal Slaughtering

and Processing, 27%

Seafood Product

Preparation and

Packaging, 2%

Bakeries and Tortilla

Manufacturing,

11%

Other Food

Manufacturing,

12%

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

(NAICS 311): 2006 

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

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

<http://factfinder.census.gov>; (July 8, 2008). 
2-4 

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, 2008). 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, 2007a). 
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 2005 to 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. 2007a. “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 

90

95

100

105

110

115

120

Jan‐97

Jun‐97

Nov‐97

Apr‐98

Sep‐98Feb‐99

Jul‐99

Dec‐99

May‐00

Oct‐00

Mar‐01

Aug‐01

Jan‐02

Jun‐02

Nov‐02

Apr‐03

Sep‐03Feb‐04

Jul‐04

Dec‐04

May‐05

Oct‐05

Index Value (Jan‐97=100)

Manufacturing Food

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

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

Manufacturing and Mining.” <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. 
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. 

Establishments

Fewer than 250

250 - 499

500 - 999

1,000 - 2,000

More than 2,000

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

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008). 
2-9 

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). 

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

Percentage Capacity Utilization

Manufacturing Food

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

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

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

2.1.3.3 Employment 

The geographic distribution of employment in food manufacturing varies substantially 

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

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

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

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

employees, despite having fewer than 600 establishments (Figure 2-6). 
2-10 

Employment

Fewer than 10,000

10,000 to 19,999

20,000 to 39,999

40,000 to 80,000

More than 80,000

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

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

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

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

2.1.3.4 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). 
2-11 

0

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40

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Jul‐97

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Index Value (January 1997 = 100)

Manufacturing Food

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

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

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

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

 Sales ($millions) % of Sales in North America 

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). 
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. 2008a. “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 

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70

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120

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Jul‐97

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Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Index Value (2002=100)

Manufacturing Food, Beverage, and Tobacco

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

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

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

0

10

20

30

40

50

60

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)

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

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

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

0

20

40

60

80

100

120

140

160

180

200

19901991199219931994199519961997199819992000200120022003200420052006200720082009

Index Values (December 1984=Base)

Manufacturing Food

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. 
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 planning 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. 

Veneer, Plywood, &

Engineered Wood

ProductMfg (NAICS

3212), 22%

Sawmills & Wood

Preservation (NAICS

3211),

27%

Other Wood

ProductMfg (NAICS

3219),

51%

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

(NAICS 322): 2007 

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

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

on December 27, 2009. [Source for 2007 numbers] 
2-20 

Other Wood

ProductMfg (NAICS

3219),

60%

Sawmills & Wood

Preservation (NAICS

3211), 20%

Veneer, Plywood, &

Engineered Wood

ProductMfg (NAICS

3212),

20%

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

322): 2007 

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

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

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

2.2.2 Supply and Demand Characteristics 

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

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

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

uses and consumers wood products. 

2.2.2.1 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. 
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. 

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. 2007a. “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 

85

90

95

100

105

110

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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

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

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. 
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 

Establishments by State

Less than 100

100 - 199

200 - 399

400 - 600

More than 600

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

(NAICS 321): 2002 

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

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

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

2.2.3.3 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 2-16). The states with the highest number of employees do not directly correlate with the 

states with the highest number of establishments. States such as Indiana, Georgia, Arkansas, and 

Oregon had fewer than 600 establishments, as shown in Figure 2-14, but had more than 20,000 

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

more than 600 establishments. 

2.2.3.4 Plants and Capacity 

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

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

amount of growth in capacity between 1997 and 2001, the wood product manufacturing 

industry’s capacity dipped between 2002 and 2005 but has been growing at a slow rate since then 

though it started to dip again in 2008 and 2009 (Figure 2-17). 
2-27 

0

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Percentage Capacity Utilization

Manufacturing Wood Products

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

(NAICS 321) 

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

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

2.2.3.5 Firm Characteristics 

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

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

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

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

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

which firms in this sector compete. 

2.2.3.6 Size Distribution 

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

using definitions by the SBA for regulatory flexibility analyses. According to SUSB reports for 

2002, small companies were the recipients of the majority of receipts in 2002; 53% of receipts 

were generated by companies with fewer than 500 employees (Table 2-18). The number of 

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

product manufacturing industry (Table 2-19). 
2-28 

Employemnt by State

Less than 2,000

2,000 - 4,999

5,000 - 9,999

10,000 - 20,000

More than 20,000

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

(NAICS 321): 2002 

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

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

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

2.2.3.7 Domestic Production 

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

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

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

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

manufacturing sector. 

2.2.3.8 International Trade 

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

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

exports have remained fairly constant, with seasonal changes, since 1997. 
2-29 

100

105

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145

150

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Index Value (Jan‐97=100)

Manufacturing Wood Products

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

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

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

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). 
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 

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Index Value (Jan-97=100)

Manufacturing Wood Products

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

(NAICS 321): 1997–2009 

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

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

0

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$1,000,000 (2007)

Wood Product Exports Wood Product Imports

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

(NAICS 321)] 

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

<http://dataweb.usitc.gov/scripts/user_set.asp>. 
2-33 

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PPI (Dec‐03=100)

Wood Products PPI All Manufacturing PPI

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

(NAICS 321) 

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

PPP (3221)

CPP (3222) 46%

54%

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

322): 2007 

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

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

Accessed on December 28, 2009. 

PPP (3221)

30%

CPP (3222)

70%

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

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

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

<http://factfinder.census.gov>. Accessed on December 28, 2009. 
2-37 

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. 2007a. “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 

90

92

94

96

98

100

102

104

106

108

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Jul‐03

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Jul‐05

Index Value (Jan‐97=100)

Manufacturing Paper

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

2005 

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

Manufacturing and Mining.” <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>. 
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. 

Establishments

Fewer than 50

50 - 99

100 - 199

200 - 300

More than 300

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

322): 2002 

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008). 
2-44 

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). 

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)

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

(NAICS 322) 

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

<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. 
2-45 

Employment

Fewer than 3,000

3,000 - 5,999

6,000 - 11,999

12,000 - 24,000

More than 24,000

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

322): 2002 

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

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

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

2.3.3.4 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. 
2-46 

90

100

110

120

130

140

150

1997‐01

1997‐07

1998‐01

1998‐07

1999‐01

1999‐07

2000‐01

2000‐07

2001‐01

2001‐07

2002‐01

2002‐07

2003‐01

2003‐07

2004‐01

2004‐07

2005‐01

2005‐07

2006‐01

2006‐07

2007‐01

2007‐07

2008‐01

2008‐07

2009‐01

2009‐07

Index Value (Jan‐97=100)

Manufacturing Paper (NAICS 322)

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

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

<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. 
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.” 

<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%. 
2-50 

0

5,000

10,000

15,000

20,000

25,000

30,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

$1,000,000 (2007)

Year

Paper Exports Paper Imports

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

322) 

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

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

75

85

95

105

115

125

135

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

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Jul‐02

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Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Index Value (Jan‐97=100)

Manufacturing Paper (NAICS 322)

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

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

PCUOMFG–OMFG–. <http://www.bls.gov/ppi/home.htm>. 
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 to 2007, 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). 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 

Bulk Chemicals,

35%

Pharmaceuticals,

31%

Other Chemicals,

34%

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

2007 

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

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

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

Bulk Chemicals,

50%

Pharmaceuticals,

26%

Other Chemicals,

24%

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

(NAICS 325): 2007 

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

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

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

In 2007, 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. 2007a. “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 

80

85

90

95

100

105

110

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Index Value (Jan‐97=100)

Manufacturing Chemical Manufacturing

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

1997–2005 

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

Manufacturing and Mining.” <http://www.federalreserve.gov/datadownload/>. 

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. 
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 

Establishments

Fewer than 100

100 - 249

250 - 474

475 - 750

More than 750

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

2002 

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

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

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

2.4.3.2 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). 
2-61 

0

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Jul‐05

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Jan‐07

Jul‐07

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Jul‐08

Jan‐09

Jul‐09

Percentage Capacity Utilization

Manufacturing Chemical Manufacturing

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

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

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

Employment

Fewer than 5,000

5,000 - 13,999

14,000 - 29,999

30,000 - 40,000

More than 40,000

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

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008). 
2-62 

100

110

120

130

140

150

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

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Jan‐00

Jul‐00

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Jul‐02

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Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Index Value (Jan‐97=100)

Manufacturing Chemical Manufacturing

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

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

<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). 
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. 

Source: 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. 2008b. “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 and Table 2-39. 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>. 

100

110

120

130

140

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

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Jul‐01

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Index Value (Jan‐97=100)

Manufacturing Chemical Manufacturing

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

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

<http://www.federalreserve.gov/datadownload/>. 
2-67 

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

$1,000,000 (2007)

Imports Exports

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

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

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

90

100

110

120

130

140

150

160

170

Jan‐98

Jul‐98

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Jul‐99

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Jul‐00

Jan‐01

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Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

PPI (Jan‐98=100)

Manufacturing Chemical Manufacturing

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

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

&PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home.htm>. 
2-68 

2.5 Section 2 References 

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

Dow Chemical Company. 2008. “Transforming: The Dow Chemical Company 2007 10-K and 

Stockholder Summary.” <http://www.dow.com/financial/2007ann/>. 

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

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

Graves, T. 2008. “Food and Nonalcoholic Beverages.” Standard and Poor’s Industry Surveys

176(25). 

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

File 2005.” <http://www.irs.gov/taxstats/article/0,,id=167415,00.html>. 

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

File 2007.” <http://www.irs.gov/taxstats/article/0,,id=167415,00.html>. 

Kraft Foods Inc. 2008. “Form 10-K, Period ending December 31, 2007.” 

<http://www.kraft.com/Investor/sec-filings-annual-report/>. 

O’Reilly, R. 2008. “Chemicals.” Standard and Poor’s Industry Surveys. 176(28). 

Standard & Poors, 2008. Standard & Poor’s netAdvantage [electronic resource]. New York: 

Standard & Poors. 

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

2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: 

BEA. U.S. Bureau of Labor Statistics. 2009a. “Producer Price Index.” Series ID: 

PCU321—321—& PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home.htm>. 

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

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

U.S. Bureau of Labor Statistics. 2009c. “Producer Price Index.” Series ID: PCU325—325—

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

U.S. Census Bureau. 2001. “1997 Census NAICS Definitions.”

<http://www.census.gov/epcd/ec97/industry/E322.HTM#def>.

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>. 

U.S. Census Bureau. “Sector 00: All Sectors: Core Business Statistics Series: Comparative 

Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997” 

(generated by RTI International using American FactFinder). 

<http://factfinder.census.gov>. Accessed July 8, 2008. 
2-69 

U.S. Census Bureau. “Sector 00: All Sectors: Core Business Statistics Series: Comparative 

Statistics for the United States and the States (2007 NAICS Basis): 2002 and 2007” 

(generated by RTI International using American FactFinder). 

<http://factfinder.census.gov>. Accessed January 4, 2010. 

U.S. Census Bureau. “Sector 00: EC0700A1: All Sectors: Geographic Area Series: EconomyWide Key Statistics: 2007” (generated by Kapur Energy and Environment using 

American FactFinder). <http://factfinder.census.gov>. Accessed December 27, 2009. 

U.S. Census Bureau. “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics 

for Industry Groups and Industries: 2006 and 2005” (generated by RTI International 

using American FactFinder). <http://factfinder.census.gov>. Accessed July 8, 2008. 

U.S. Census Bureau. “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics 

by Industry for the United States: 2007” (generated by Kapur Energy and Environment 

using American FactFinder). <http://factfinder.census.gov>. Accessed December 27, 

2009. 

U.S. Census Bureau. “Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for 

the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002” 

(generated by RTI International using American FactFinder). 

<http://factfinder.census.gov>. Accessed July 23, 2008. 

U.S. Department of Energy, Energy Information Administration. 2007a. “2002 Energy 

Consumption by Manufacturers—Data Tables” (Tables 3.2 and N3.2). 

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

U.S. Department of Energy, Energy Information Administration. 2007b. “2006 Energy 

Consumption by Manufacturers—Data Tables” (Table 3.1). 

<http://www.eia.doe.gov/emeu/mecs/mecs2006/ 

2006tables.html>. 

U.S. Department of Energy, Energy Information Administration. 2008. “Assumptions to the 

Annual Energy Outlook 2008: With Projections for 2030.” 

<http://www.eia.doe.gov/oiaf/aeo/assumption/>. 

U.S. Department of Labor, Bureau of Labor Statistics. 2010. “Producer Price Indexes.” 

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

PCUOMFG–OMFG–Total Manufacturing>. 

U.S. Environmental Protection Agency. 2002. “Profile of the Pulp and Paper Industry. Sector 

Notebook Project.” <http://www.epa.gov/Compliance/resources/publications/assistance/ 

sectors/notebooks/index.html>. 

U.S. International Trade Commission. 2008. “U.S. Domestic Exports” and “U.S. Imports for 

Consumption.” <http://dataweb.usitc.gov/scripts/user_set.asp>. 
2-70 

U.S. Small Business Administration. 2008. “Table of Small Business Size Standards Matched to 

North American Industry Classification System Codes.” <http://www.sba.gov/services/ 

contractingopportunities/sizestandardstopics/size/index.html>. 
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. 

Several provisions in the rule and the data used to generate regulatory impacts analysis 

between proposal and the final rule. As a result, the costs of the RIA analysis decreased from the 

proposal to final analysis. 

The major regulatory provisions resulting in a reduction in the cost estimates were: 

 Consolidating the biomass and coal subcategories into a single solid fuel 

subcategory for fuel-based HAP and HAP surrogates (Hg, HCl, PM). This 

provision has reduced the number of biomass units expected to require HCl 

control in order to meet the emission limits. 

 Creating a hybrid grate/suspension category for combustion based HAP and HAP 

surrogates to address boilers that express unique design features to handle the 

combustion of fuels with a very high moisture content. This increased the CO 

emission level for this subcategory which resulting in less units needing CO 

catalyst or advanced combustion controls in order to meet the limit. 

 Creating a limited use subcategory. This subcategory removes the control, testing 

and monitoring costs for 134 large boilers firing solid and liquid fuels that operate 

less than 876 hours per year. 

 Creating a fuel gas specification to allow additional gaseous fuels to qualify for 

the gas 1 subcategory. Based on the data in the record, most fuels are expected to 

meet the mercury specification and will qualify for a work practice standard 

instead of meeting emission limits. The number of gas 2 units estimated to meet 

emission limits at proposal was and this has been reduced to an estimated 71 units 

in the final rule, compared with an estimated 199 gas 2 units in the proposed 

analysis. 

 Removing the CO CEMS monitoring requirement for units with a heat input 

capacity. Although this change in monitoring is offset in part by O2 parameter 

monitoring requirement. 

 Removing duplicative monitoring requirements for PM CEMS, opacity 

monitoring and bag leak detection monitors. The proposal had included 

requirements to measure more than one of these parameters and this duplication 

has been removed from the final rule. 
3-2 

 Reducing testing frequency for dioxin/furan emissions. The final rule requires a 

one-time initial test instead of annual testing. 

The major data analysis elements resulting in a reduction in cost were: 

 As discussed in detail in the “Handling and Processing of Corrections and New 

Data in the EPA ICR Databases” memorandum, several new data submissions and 

corrections to existing entries were received from the public comment process. 

These new data incorporated into the revised MACT floor, baseline emission, and 

emission reduction analysis. 

 The criteria for considering data in the MACT floor analysis were revised to 

reduce the low bias impact from tests conducted during co-firing configurations. 

In the final rule, the test had to be fired with at least 90 percent of its heat input 

belonging to the same subcategory for existing sources and 100 percent of its heat 

input belonging to the same subcategory for new sources. At proposal, data were 

considered relevant for consideration in the MACT floor analysis if at least 10 

percent of its heat input belonged to the same subcategory. This modified the 

make-up of the top performing units and the resulting. 

 The CO emission data analysis was revised to incorporate measurement error 

resulting from calibration error, system bias, and drift requirements using the 

reported instrument calibration span as the basis for estimating the error. This 

increased CO emission levels in several cases in order to better incorporate this 

error in the final standard. 

 Several elements of the MACT floor analysis statistical approach were 

incorporated from public commenters, which resulted in increased emission limits 

in some cases. The memorandum “Revised MACT Floor Analysis (2011) for the 

Industrial, Commercial, and Institutional Boilers and Process Heaters National 

Emission Standards for Hazardous Air Pollutants—Major Source” details the 

specific statistical modifications that were made in the final analysis. 

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, but similar units at the same facility reported data, we 

assigned the average emission factors from similar units at the same facility to the baseline 

emissions of the unit. For all other 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 
3-3 

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 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 and dry injection fabric filter combinations to meet the HCl 

limits, tune-ups, replacement burners, combustion controls, and CO oxidation catalyst for CO 

and organic HAP 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 5.1 billion dollars in capital expenditures and 1.8 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 1.4 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. 

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

Source Subcategory 

Estimated/ 

Projected No. of 

Affected Units 

Capital 

Costs 

(million) 

Annualized 

Cost 

(million per yr)a 

Annualized 

Cost 

(million per yr)b

Existing Units Solid units 1,014 $2,182 $873 $846 

Liquid units 713 $2,656 $833 $828 

Non-Continental 

Liquid Units 

27 $86 $24 $21 

Gas 1 units 10,797 $70 $31 $(325) 

Gas 1 Metallurgical 

Furnaces 

694 $4.5 $2.0 $(6) 

Gas (other) units 118 $79 $39 $37 

 Limited Use 477 $3.1 $1.3 $(25) 

Energy Audit 

New Units 

ALL 27 $27 

Total Existing Costs 13,840 $5,081 $1,803 $1,403 

Solid units 0 0 0 0 

Liquid units 13 $21 $6.1 $6.1 

Gas 1 units 34 $0.2 $0.1 0 

 Total New Costs 47 $21.2 $6.1 $6.1 

a

 Does not include fuel savings. 

b

 Includes Fuel Savings. 
3-4 

Using Department of Energy (DOE) projections on fuel expenditures, the number of 

additional boilers that could be potentially constructed was estimated. A detailed description of 

how the DOE data was used to project new units expected to be constructed is discussed in the 

memorandum, “Revised New Unit Analysis Industrial, Commercial, and Institutional Boilers and 

Process Heaters National Emission Standards for Hazardous Air Pollutants—Major Source

(Gibson et al., 2011).” This analysis consisted of two steps, (1) calculating the predicted percent 

change in fuel consumption in the industrial and commercial/institutional sectors between the 

baseline year (2008) of the major source boiler and process heater inventory and the target years 

of the projection (2013 and 2015); and (2) calculating the expected fuel consumption in future in 

boilers and heaters and predicting the number of new units required to fire a particular fuel. The 

resulting total national cost impact of the proposed rule in the 3rd year is 21 million dollars in 

capital expenditures and 6 million dollars per year in total annual costs, when considering a 1 

percent fuel savings. 

The fuel savings are based on an estimated savings of 1 percent from tuning the boiler to 

adjust oxygen levels and inspecting the flame pattern, consistent with manufacturer’s 

specifications. For units with an automatic air to fuel ratio control, it also includes inspection and 

calibration of that system. It is primarily the tune-ups that are expected to obtain these fuel 

savings, but approximately 440 units are estimated to retrofit their units with linkageless boiler 

management systems in order to reduce CO emissions and improve boiler efficiency. Fuel 

savings of one percent was selected for the basis of the fuel savings analysis. The fuel savings 

was estimated for units firing gaseous, liquid, and coal fuels only. Biomass fuels were not 

considered in the fuel savings analysis since it is difficult to establish a market value for the 

various biomass fuel streams 

The estimated fuel savings between proposal and promulgation are similar. At proposal, 

fuel savings from existing units was estimated to be $376 million, this assumed that 12,887 units 

would install combustion controls or conduct a tune-up in order to meet the CO emission limit or 

comply with a work practice standard. At the final rule, the fuel savings was estimated to be 

$428 million, based on 13,279 units installing combustion controls or conducting a work practice 

standard. The difference resulted from two main changes: 

 First, more units were added to the inventory, based on comments received from 

additional facilities that were left out of the analysis at the proposal. Many of 

these additional units will conduct a tune-up or install combustion controls in 

order to meet the work practice or CO emission limit. 
3-5 

 Second, the baseline emission and emission limits have been modified, which 

have in turn increased some of the limits for CO. As a result, the assumptions 

associated with how many units will conduct a tune-up or install combustion 

controls in order to meet the emission limits have changed. At proposal, 458 units 

were estimated to install a linkageless boiler management system, one boiler was 

expected to install a replacement burner, and 12,428 boilers were estimated to 

conduct a tune-up. In the final analysis, 440 units were estimated to install a 

linkageless boiler management system, 1 unit was expected to install a 

replacement LNB, and 12,838 boilers were expected to conduct a tune-up. 

 Third, the treatment of gas 2 units has been modified to use a fuel gas 

specification. This is expected to increase the number of gas units eligible for a 

work practice standard instead of an emission limit. As a result of this change 

more of these units are expected to conduct a tune-up to fulfill the requirements of 

the work practice standard. 

Although the age of the boiler can affect combustion efficiency, periodic tune-ups are expected 

to improve the combustion units of both new and existing units. Even new units are susceptible 

of being operated at air-to-fuel ratios that are not optimal. EPA adopted the same fuel savings 

assumptions for both new and existing units. The analysis estimates that if the annual tune-up 

provisions are incorporated, a savings of one percent would be achieved on an annual basis when 

compared to a new boiler installation that does not conduct routine tune-ups. The analysis 

projects the construction of 6,424 new liquid fired boilers at area sources, as opposed to 155 new 

coal units and 200 biomass units, respectively. All of these boilers are expected to conduct a 

tune-up in order to meet the requirements of the final rule. Liquid-fired units have the greatest 

energy cost on a per mmBtu basis of any of the subcategories, on which the fuel savings are 

based. For residual fuels, the energy cost was calculated to be $12.96/mmBtu. Distillate fuels 

were found to have an energy cost of either $22.66 for commercial units, units or $23.23 for 

industrial units. The combination of the large number of projected units in the liquid subcategory 

and the high energy cost resulted in the fuel savings shown. 

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

Methodology for Estimating Cost and Emission Impacts for Industrial, Commercial, and 

Industrial Boilers and Process Heaters National Emission Standards for Hazardous Air 

Pollutants—Major Source (2011)” in an Appendix. 

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. 
3-6 

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 final rule for existing units is 

$487 million dollars in total annualized costs after considering fuel savings from efficiency 

improvements. The total annualized costs for installing controls, conducting an annual tune-up 

and an energy assessment, and implementing testing and monitoring requirements, is $535 

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 $37 

Biomass 10,958 $24 

Oil 168,003 $374 

Facility Energy Audit All $52 

New Unitsb

 Coal 155 $0.4 

Biomass 200 $2.6 

Oil 6,424 $45 

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 final rule on new sources by the 3rd year, 2013, is 

$48 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 ($3.6 million). The size of the fuel savings estimate resulted primarily from the large 

number of new liquid fired boilers, for which the cost of energy is the highest on a per mmBtu 

basis. This large energy cost created to a correspondingly high cost savings with the increased 

combustion efficiency. 

3.3 Section 3 References 

Gibson et al., 2011. Revised New Unit Analysis Industrial, Commercial, and Institutional Boilers 

and Process Heaters National Emission Standards for Hazardous Air Pollutants—Major Source.
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 (2010) 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 short 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 

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 impacts on related markets 

might be considerable” and modeling using a computable general equilibrium model is not 

available or practical (EPA, 2010, p. 9-21). 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). Note that the multimarket model is not designed to directly estimate employment impacts. 

Job effects are discussed later, in section 4.1.2.4. The multimarket model contains the following 

features: 
4-2 

 Industry sectors and benchmark data set 

– 100 industry sectors 

– a single benchmark year (2010) 

– industry employment data 

 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 (2014) 

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

function 

– firms cannot alter input mixes; there is no substitution among intermediate 

production inputs 

– 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 major source NESHAP, the Agency’s economic model 

suggests the average national price increases for industrial sectors are less than 0.01%, while 

average annual domestic production may fall by less than 0.01%. Because of higher domestic 

prices, imports slightly rise. Market-level effects for the area source NESHAP are smaller when 

compared to the major source rule; average price, production, and import changes are less than 

0.01%. Industrial sector details are provided in Appendix B. 
4-3 

‐0.0200%

‐0.0150%

‐0.0100%

‐0.0050%

0.0000%

0.0050%

0.0100%

0.0150%

0.0200%

Selected Option Alternative Option

Final MACT/GACT

Approach

Proposed MACT

Approach Selected Option Alternative Option

Major Sources Area Sources Major & Area Sources

Market‐Level Economic Vaiables: Percentage Change from Baseline

Prices Production Imports Consumption Exports

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

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 

$0.5 billion (2008$) of the major source rule’s costs to U.S. households in the form of higher 

prices (Table 4-1). Existing U.S. industries’ surplus falls by $1.4 billion and the net U.S. loss in 

aggregate is $1.9 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 less than $0.1 billion to foreign companies. As shown in Figure 4-2, the U.S. surplus 

losses are concentrated in other services (23 percent), lumber, paper, and printing (16 percent), 

energy industries (15 percent), and chemicals (13 percent). 
4-4 

Table 4-1. Distribution of Social Costs Major Sources (billion, 2008$): 2014 

Method Selected Option Alternative Option 

Partial Equilibrium Model (Multiple Markets) 

Change in U.S. consumer surplus −$0.530 −$0.600 

Change in U.S. producer surplus −$1.360 −$1.730

Change in U.S. surplus −$1.890 −$2.330 

Direct Compliance Costs Method (Not Modeled) 

Total annualized costs, new major sourcesa

 −$0.006 −$0.920 

Fuel savings, existing major sources $0.430 $0.400 

Fuel savings, new major sourcesa

 Less than $0.001 Less than $0.001

Net Change in U.S. Surplusb

 −$1.470 −$1.940 

Net change in rest of world surplus $0.060 $0.090

Net change in total surplus −$1.410 −$1.850 

a Estimates for the Alternative option are assumed to be the same as the Selected option. 

b U.S. surplus changes add the partial equilibrium model estimates and the direct compliance estimates not included 

in the partial equilibrium model. For example, the selected option’s net change in U.S. surplus is $1.890 + (0.430 – 

0.006) = –$1.470 billion. 

22.5%

16.2%

15.1%

12.6%

8.7%

5.3%

5.0%

3.0%

2.4%

2.1%

2.0%

1.4%

1.2%

1.0%

0.8%

0.5%

22.0%

28.4%

10.7%

9.2%

8.3%

3.1%

3.3%

5.2%

2.0%

1.8%

0.6%

1.3%

1.9%

0.8%

0.7%

0.6%

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

Other Services

Lumber, paper, and printing

Energy Industries

Chemicals

Food, beverages, and textiles

Primary Metals

Transportation Equipment

Nonmanufacturing

Plastics and Rubber

Transportation Services

Machinery and Equipment

Wholesale and Retail Trade

Other

Fabricated Metals

Electronic Equipment

Nonmetallic Minerals

Selected Option:  $1.9 Billion U.S. Surplus Change

Alternative Option:  $2.3 Billion  U.S. Surplus Change

Figure 4-2. Distribution of U.S. Surplus Changes by Sector: Major Sources 
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 U.S. surplus loss estimate of $1.5 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 

$0.2 billion (2008$) of the 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 $0.5 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 $0.01 billion. As shown in Figure 4-3, the U.S. 

surplus losses are concentrated in the other services (82 percent ). Other services include 

information, finance and insurance, real estate, professional services, management, administrative 

services, education, health care, arts, accommodations, and public services. 

Table 4-2. Distribution of Social Costs Area Sources (billion, 2008$): 2014 

Method 

Final MACT/GACT 

Approach 

Proposed MACT 

Approach 

Partial Equilibrium Model (Multiple Markets) 

Change in U.S. consumer surplus −$0.240 −$0.300 

Change in U.S. producer surplus −$0.250 −$0.330

Change in U.S. surplus −$0.490 −$0.630 

Direct Compliance Costs Method (Not Modeled) 

Total annualized costs, unknown existing area sources −$0.003 −$0.008 

Total annualized costs, new area sources −$0.050 −$0.270 

Fuel savings, existing and new area sources) $0.050 $0.050

Net Change in U.S. Surplus a

 −$0.490 −$0.850 

Net change in rest of world surplus $0.004 $0.005 

Net change in total surplus −$0.480 −$0.850 

a

 U.S. surplus changes add the partial equilibrium model estimates and the direct compliance estimates not included 

in the partial equilibrium model. For example, the Final MACT/GACT net change in U.S. surplus is $0.490 + 

(0.050 – 0.050–0.003) = –$0.490 billion. 
4-6 

81.6%

4.9%

2.6%

4.7%

4.2%

0.5%

0.3%

0.6%

0.1%

0.2%

0.1%

0.1%

0.1%

0.0%

0.0%

0.0%

79.3%

6.7%

3.1%

4.9%

4.0%

0.6%

0.3%

0.5%

0.2%

0.2%

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

Wholesale and Retail Trade

Nonmanufacturing

Lumber, paper, and printing

Transportation Services

Chemicals

Other

Plastics and Rubber

Energy Industries

Nonmetallic Minerals

Primary Metals

Fabricated Metals

Machinery and Equipment

Transportation Equipment

Electronic Equipment

Final MACT/GACT Approach:  $0.4 Billion U.S. Surplus Change

Proposed MACT Approach:  $0.6 Billion U.S. Surplus Change

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

a

 Other services include information, finance and insurance, real estate, professional services, management, 

administrative services, education, health care, arts, accommodations, and public services. 

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 

4.1.2.4.1 Background

In addition to estimating this rule’s social costs and benefits, EPA has estimated the 

employment impacts of the final rule based on Morgenstern, Pizer and Shih (2002). A standalone analysis of jobs is not included in a standard cost-benefit analysis. Executive Order 13563 

however, states, “Our regulatory system must protect public health, welfare, safety, and our 

environment while promoting economic growth, innovation, competitiveness, and job creation” 

(emphasis added). Therefore, we have provided this analysis to inform the discussion of job 
4-7 

impacts. EPA continues to explore the relevant theoretical and empirical literature and to seek 

public comments in order to ensure that such estimates are as accurate as possible. 

 From an economic perspective labor is an input into producing goods and services; if 

regulation requires that more labor be used to produce a given amount of output, that additional 

labor is reflected in an increase in the cost of production. Moreover, when the economy is near 

full employment, jobs created in one industry as a result of regulation displace jobs in other 

industries. On the other hand, in periods of high unemployment, an increase in labor demand due 

to regulation may have a stimulative effect that results in a net increase in overall employment. 

With significant numbers of workers unemployed, the opportunity costs associated with 

displacing jobs in other sectors are likely to be much smaller. 

For this reason, this RIA looks carefully at a subset of the employment consequences of 

this final rule. It is important to note that EPA has estimated only a portion of the employment 

effects -- namely, those associated with the direct impacts on employment in the regulated 

industry. A full analysis would include estimates of the direct impacts on other industries (e.g. 

suppliers of pollution control equipment) as well as the indirect and induced effects on 

employment throughout the economy as a whole in response to changes in output and factor 

prices. 

We expect that the rule’s direct impact on employment will be small. The Agency’s 

analysis does not include all the direct effects of this regulation. For example, EPA is currently 

exploring ways to quantify the job impacts in the pollution control sector that result from these 

and future regulations. Furthermore, we have not quantified the rule’s indirect or induced 

impacts. What follows is an overview of the various ways that environmental regulation can 

affect employment, followed by a discussion of the estimated impacts of this rule. An 

environmental regulation can affect the demand for labor in several ways: 

 Direct Effects:

– Increased prices for industry output may reduce the demand for labor:

Environmental regulations increase production costs causing firms to increase 

prices; higher prices reduce consumption (and production), thus reducing demand 

for labor within the regulated industry. The extent of this effect will depend on the 

extent of the price increase and the elasticity of the demand curve.

– Regulated firms demand labor workers to operate and maintain pollution 

controls within those firms. Once pollution control equipment is installed, 

regulated firms may hire workers to operate and maintain it, just as they would hire 
4-8 

workers to produce more output. The extent of this effect will depend in part on 

whether the operation and maintenance of pollution controls are labor intensive 

– Increased demand for pollution control equipment and services: When a 

regulation requiring emission reductions is promulgated, affected sources must 

immediately place orders for pollution control equipment and services. Filling 

these orders will require a scale-up in manufacturing of pollution control 

equipment, performance of engineering analyses and significant expenditures for 

assembly and installation of such equipment. These activities will be job-creating 

during the period before firms must comply with the rule, at which point all 

pollution control equipment must be installed and operating. 

. 

Indirect and Induced Effects: 

– Environmental regulations create employment in many basic industries. In 

addition to the increase in employment in the environmental protection industry 

(increased orders for pollution control equipment), environmental regulations also 

create employment in industries that provide intermediate goods to the environmental 

protection industry. For example, capital expenditures to reduce air pollution involve 

the purchase of abatement equipment. The equipment manufacturers, in turn, order 

steel, tanks, vessels, blowers, pumps, and chemicals to manufacture and install the 

equipment. On the other hand, demand for labor will decrease in sectors that 

supply inputs for, or demand the outputs of the regulated industry. None of these 

impacts is accounted for in the current analysis. We also do not estimate 

employment impacts “induced” by increased output of the environmental 

protection sector, or decreased output of the regulated sectors. 

4.1.2.4.2 Methodology and Results

The estimated impacts of the final rule on employment in affected sources are based on an 

empirically derived relationship reported in Morgenstern, Pizer and Shih (2002), a peer-reviewed, 

published study. Estimates of the employment impacts of the capital investments and other nonrecurring requirements of the rule are derived from the cost analysis developed for the regulation.1

Morgenstern, Pizer and Shih (2002): Overview of Conceptual Approach 

The fundamental insight of Morgenstern, Pizer and Shih (2002) is that environmental 

regulations can be understood as requiring regulated firms to add a new output (environmental 

quality) to their product mixes. Although legally compelled to satisfy this new demand, regulated 

firms have to finance this additional production with the proceeds of sales of their other (market) 

products. Satisfying this new demand requires additional inputs, including labor, and may alter 

the relative proportions of labor and capital used by regulated firms in their production processes. 

 

1

 Richard D. Morgenstern, William A. Pizer, and Jhih-Shyang Shih, Journal of Environmental Economics and 

Management | May 2002 | Vol. 43, no. 3 | pp. 412-436. 
4-9 

Thus, Morgenstern et al., decompose the overall effect of a regulation on employment into 

the following three subcomponents: 

 The “Demand Effect”: higher production costs raise market prices,reducing 

consumption (and production), thereby reducing demand for labor within the 

regulated industry 2;

 The “Cost Effect”: As production costs increase, plants use more of all inputs, 

including labor, to maintain a given level of output. For example, in order to 

reduce pollutant emissions while holding output levels constant, regulated firms 

may require additional labor; 

 The “Factor-Shift Effect”: Regulated firms’ production technologies may be more 

or less labor intensive after complying with a regulation (i.e., more/less labor is 

required per dollar of output). 

Decomposing the overall employment impact of environmental regulation into three 

subcomponents clarifies the conceptual relationship between environmental regulation and 

employment in regulated sectors, and permitted Morgenstern, et al. to provide an empirical 

estimate of the net impact. For present purposes, the net effect is of particular interest, and is the 

focus of our analysis. 

Morgenstern, Pizer and Shih (2002): Empirical Results 

Morgenstern et al. empirically estimate a model for four highly polluting, regulated industries 

(pulp and paper, plastics, petroleum refining and steel) to examine the effect of higher abatement 

costs from regulation on employment. They conclude that increased abatement expenditures 

generally do not cause a significant change in employment. More specifically, their results show 

that, on average across the four industries, each additional $1 million spending on pollution 

abatement results in a (statistically insignificant) net increase of 1.55 (+/- 2.24) jobs.3

 “In plastics 

and petroleum, [Morgenstern et al] find that increased regulation raises employment by a small 

but statistically significant amount: 6.9 and 2.2 jobs per million dollars of regulatory expense, 

respectively. In pulp and paper and steel, the estimates are even smaller and insignificantly 

different from zero.”4

 By applying these estimates to pollution abatement costs, we estimated the 

 

2

 The Morgenstern et al. results rely on industry demand and supply elasticities to determine cost pass-through and 

reductions in output. 3

 These results are similar to Berman and Bui, who find that while sharply increased air quality regulation in Los 

Angeles to reduce NOx emissions resulted in large abatement costs they did not result in substantially reduced 

employment. "Environmental regulation and labor demand: evidence from the South Coast Air Basin." Journal of 

Public Economics 79(2): 265-295. 4

 Morgenstern, Pizer and Shih, p. 413. 
4-10 

net employment effect for major and areas sources to range from -4,100 to +8,500 jobs in the 

directly affected sectors with a central estimate of +2,200 (Table 4-3). 5, 6 

 

5

 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) 6

 Net employment effect = 1.55× $2,400 million × 0.60 
4-11 

Table 4-3. Employment Impacts Using Morgenstern, Pizer, Shih (2002) (FTE) 

Demand 

Effect Cost Effect 

Factor Shift 

Effect 

Net 

 Effect 

Change in Full-Time 

Jobs per Million 

Dollars of 

Environmental 

Expenditurea

−3.56 2.42 2.68 1.55 

Standard Error 2.03 1.35 0.83 2.24 

EPA estimate for 

Major Sources Rule 

−3,900 

−8,200 to +500 

2,600 

+900 to +4,400 

2,900 

 0 to +5,800 

1,700 

−3,100 to +6,500 

EPA estimate for Area 

Source Rule 

−1,200 

−2,600 to +100 

800 

+300 to +1,400 

900 

 0 to +1,800 

500 

−1,000 to +2,000 

EPA estimate for both 

Rules b 

−5,100 

−10,800 to +600 

3,500 

+1,100 to +5,800 

3,900 

 0 to 7,700 

2,200 

−4,100 to +8,500 

a Estimates from Morgenstern, Pizer, and Shih (2002) expressed in 2010 dollars using the GDP price deflator (see 

footnote 7). 

b

Totals may not add due to independent rounding. 
4-12 

‐3.9 ‐4.8

‐1.2 ‐0.9

‐5.1 ‐5.7

2.6 3.2

0.8 0.6

3.5 3.9

2.9

3.6

0.9 0.7

3.9 4.3

1.7 2.1

0.5 0.4

2.2 2.5

‐15

‐14

‐13

‐12

‐11

‐10

‐9

‐8

‐7

‐6

‐5

‐4

‐3

‐2

‐1

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Selected Option Alternative Option

Final MACT/GACT

Approach

Proposed MACT

Approach Selected Option Alternative Option

Major Sources Area Sources Major & Area Sources

1,000 Jobs

Base =144,100

Demand Effect:  Estimate derived from Morgenstern, etal. CostEffect:   Estimate derivedfromMorgenstern, et al.

Factor ShiftEffect:   Estimate derivedfromMorgenstern, et al. NetEffect:   Estimate derivedfrom Morgenstern, etal.

Figure 4-3. Employment Impacts Using Morgenstern, Pizer, Shih (2002) (1,000 FTEs) 

Limitations of the Analysis 

Although the Morgenstern et al. paper provides information about the potential job effects of 

environmental protection programs, there are several caveats associated with using those 

estimates to analyze the final rule. First, the Morgenstern et al. estimates presented in Table 4-3 

and used in EPA’s analysis represent the weighted average parameter estimates for a set of 

manufacturing industries (pulp and paper, plastics, petroleum, and steel). This set of industries 

only partially overlaps with the sectors affected by this rule. Second, relying on Morgenstern et al. 

implicitly assumes that estimates derived from 1979–1991 data are still applicable. Third, the 

methodology used in Morgenstern et al. assumes that regulations affect plants in proportion to 

their total costs. In other words, each additional dollar of regulatory burden affects a plant by an 

amount equal to that plant's total costs relative to the aggregate industry costs. By transferring the 
4-13 

estimates, EPA assumes a similar distribution of regulatory costs by plant size and that the 

regulatory burden does not disproportionately fall on smaller or larger plants. Further, 

Morgenstern et al. does not include most indirect effects and all induced effects. 

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 rules will 

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

as amended by SBREFA. 

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 Bureau, 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 rule. SUSB also provides statistics by enterprise employment and receipt size. 

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

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

conducted or where services or industrial operations are performed. 

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

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

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

revenue collected for local, state, and federal taxes. 

 

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 

 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. 

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

Combustion Survey 

Sample Statistic Proposal Selected Option Alternative Option 

Mean 4.9% 4.0% 3.8% 

Median 0.4% 0.2% 0.4% 

Maximum 72.9% 59.8% 31.4% 

Minimum <0.01% <0.01% <0.01% 

Ultimate parent company observations 50 50 50 

Ultimate parent companies with sale tests exceeding 3% 14 8 13 

For each representative establishment in the SUSB data, we developed a range of facilitylevel cost numerators based on the engineering cost analysis. For major sources, we used the 

maximum and minimum small entity facility-level costs observed within each 3-digit NAICS 
5-4 

code. For area sources, we were limited to two representative small entity facility-level costs 

(approximately $27,000,000 to $43,000).2

 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). 

68%

65%

25% 25%

21%

17%

8% 8%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Selected Alternative Final MACT/GACT

Approach

Proposed MACT Approach

Major Area

Upper Bound

Lower Bound

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

Sales Tests Exceeding 3% 

5.1.1.2 Additional Small Business Analysis Using Sample of Small Businesses Identified in 

Combustion Facility Survey 

Next, we performed a more detailed analysis that compares the Census SUSB 

representative small entity results with a firm-specific sample of major small private enterprises. 

In this approach, we identified a sample of survey facility names listed as small, traced the 

ultimate parent company name to verify the facility was owned by a small business, and collected 

 

2

 Prior to computing the cost-to-receipt ratios, we adjusted the engineering compliance costs to reflect 2002 dollars 

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

= 108.483 (U.S. BEA, 2010). 
5-5 

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 3%. The median ratios are below 

one percent. 

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: 

 Selected option: $1.1 million (median cost small public facility) 

 Alternative Option: $0.9 million (median cost small public facility) 

Area Sources3: 

 Final MACT/GACT Approach and Proposed MACT Approach: 

– Other Public: $45,000 

– Hospital: $11,300 

– Schools: $4,500 

– Churches: $2,200 

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. 

Representative small major public entities would have cost-to-revenue ratios above 10 

percent. The following types of representative small area source facilities would have cost-torevenue ratios exceeding 1 percent but below 3 percent: 

 

3

 Graham Gibson, Susan McClutchey, and Amanda Singleton, ERG. (January, 2011). Methodology for Estimating 

Impacts from Industrial, Commercial, Institutional Boilers at Area Sources of Hazardous Air Pollutant Emissions. 
5-6 

 Final MACT/GACT Approach: other public (ratio > 1.7 percent) and churches 

(ratio = 1.5 percent) 

 Proposed MACT Approach: other public (ratio > 1.7 percent) and churches (ratio = 

1.5 percent) 

5.2 Final Regulatory Flexibility Analysis (FRFA): Major Sources 

Pursuant to section 603 of the RFA, EPA prepared an initial regulatory flexibility analysis 

(IRFA) for the proposed rule and convened a Small Business Advocacy Review Panel to obtain 

advice and recommendations of representatives of the regulated small entities. A detailed 

discussion of the Panel’s advice and recommendations is found in the final Panel Report (Docket 

ID No. EPA-HQ-OAR-2002-0058-0797). A summary of the Panel’s recommendations is also 

presented in the preamble to the proposed rule at 75 FR 32044-32045 (June 4, 2010). In the 

proposed rule, EPA included provisions consistent with four of the Panel’s recommendations. As 

required by section 604 of the RFA, we also prepared a final regulatory flexibility analysis 

(FRFA) the final rule. 

5.2.1 Need for Rule and Objectives 

The rule is intended to reduce emissions of HAP as required under section 112 of the 

CAA. Section II.A of the final rule’s preamble describes the reasons that EPA is finalizing this 

action. 

5.2.2 Summary of the Significant Issues raised by the Public Comments and Agency 

Assessment 

Many significant issues were raised during the public comment period, and EPA’s 

responses to those comments are presented in section V of this preamble or in the response to 

comments document contained in the docket. Significant changes to the rule that resulted from the 

public comments are described in section IV of the final rule’s preamble. 

The primary comments on the IRFA were provided by SBA, with the remainder of the 

comments generally supporting SBA’s comments. Those comments included the following: EPA 

should have adopted a health-based compliance alternative (HBCA) which provides alternative 

emission limits for threshold chemicals; EPA should have adopted additional subcategories, 

including the following: subcategories based on fuel type (including coal rank, bagasse, biomass 

by type, and oil by type), unit design type (e.g., process heater, fluidized bed, stoker, fuel cell, 

suspension burner), duty cycle, geographic location, boiler size, burner type (with and without 

low-NOx burners), and hours of use (limited use); EPA should have minimized facility 

monitoring and reporting requirements; EPA should not have proposed the energy audit 
5-7 

requirement; EPA’s proposed emissions standards are too stringent; and, EPA should provide 

more flexibility for emissions averaging. 

In response to the comments on the IRFA and other public comments, EPA made the 

following changes to the final rule. EPA adopted additional subcategories, including a limited-use 

subcategory for units that operate less than 10 percent of the operating hours in a year, a noncontinental liquid unit subcategory for units with the unique challenges faced by remote island 

locations, and a combination suspension/grate boiler subcategory. EPA also consolidated the 

subcategories for units combusting various types of solid fuels, which will simplify compliance 

and will allow units to combust varying percentages of different solid fuels without triggering 

subcategory changes. EPA also decreased monitoring and testing costs by eliminating the CO 

CEMS requirement for units greater than 100 mmBtu/hr and changing the dioxin testing 

requirement to a one-time test. The final rule also includes work practice standards for additional 

subcategories, including limited-use units, new small units, and units combusting gaseous fuels 

that are demonstrated to have similar contaminant levels to natural gas. Finally, EPA is finalizing 

emission limits that are less stringent than the proposed limits for most of the category/pollutant 

combinations. The emission limit changes are largely due to the changes in subcategories, data 

corrections, and incorporation of new data into the floor calculations. Additional details on the 

changes discussed in this paragraph are included in sections IV and V of the final rule’s preamble. 

5.2.3 Description of and Estimate of the Number of Small entities to Which the Rules Will 

Apply 

Based on the distribution of major source facilities with affected boilers or process heaters 

reported in the 2008 survey entitled “Information Collection Effort for Facilities with Combustion 

Units. (ICR No. 2286.01),” there are 1,639 existing facilities with affected boilers or process 

heaters. Of these, 94 percent are located in the private sector and the remaining 6 percent are 

located in the public sector. Table 5-3 summarizes the types of small entities expected to be 

affected by the major source rule. The number of small entities is based on the facilities which 

responded to the ICR by confirming small entity status. If that question was not answered, or if 

the facility answered that their small entity status was unknown, it was assumed that those 

facilities were not small entities. The summary in Table 5-3 is for major source facilities which 

have at least one boiler or process heater expected to be covered by the rule. Table 5-4 

summarizes the EPA estimates of the number of area source facilities expected to be affected by 

the major source rule (84,700 total). EPA does not have sufficient information to estimate the 

number of small entities expected to be covered by the area source rule. 
5-8 

Table 5-3. Information Collection Effort for Facilities with Combustion Units: Major 

Sources 

NAICS NAICS Description 

Total 

Number of 

Facilities 

Total SelfReported Facilities 

Owned by Small 

Businesses 

111 Crop Production 1 0 

113 Forestry and Logging 1 0 

115 Support Activities for Agriculture and Forestry 1 0 

211 Oil and Gas Extraction 24 3 

212 Mining (Except Oil and Gas) 14 1 

221 Utilities 183 23 

311 Food Manufacturing 110 7 

312 Beverage and Tobacco Product Manufacturing 5 0 

313 Textile Mills 14 1 

314 Textile Product Mills 1 0 

316 Leather and Allied Product Manufacturing 3 1 

321 Wood Product Manufacturing 183 18 

322 Paper Manufacturing 186 14 

323 Printing and Related Support Activities 33 5 

324 Petroleum and Coal Products Manufacturing 84 8 

325 Chemical Manufacturing 220 17 

326 Plastics and Rubber Products Manufacturing 89 11 

327 Nonmetallic Mineral Product Manufacturing 41 2 

331 Primary Metal Manufacturing 57 6 

332 Fabricated Metal Product Manufacturing 46 8 

333 Machinery Manufacturing 13 0 

334 Computer and Electronic Product Manufacturing 2 0 

335 Electrical Equipment, Appliance, and Component Manufacturing 12 0 

336 Transportation Equipment Manufacturing 100 7 

337 Furniture and Related Product Manufacturing 45 8 

339 Miscellaneous Manufacturing 15 1 

423 Durable Goods Merchant Wholesalers 1 1 

424 Nondurable Goods Merchant Wholesalers 1 0 

441 Motor Vehicle and Parts Dealers 1 0 

481 Air Transportation 7 0 

482 Rail Transportation 1 0 

486 Pipeline Transportation 60 0 

488 Support Activities for Transportation 3 0 

493 Warehousing and Storage 5 1 

(continued) 
5-9 

Table 5-3. Information Collection Effort for Facilities with Combustion Units: Major 

Sources (continued) 

NAICS NAICS Description 

Total 

Number of 

Facilities 

Total SelfReported Facilities 

Owned by Small 

Businesses 

531 Real Estate 1 0 

541 Professional, Scientific, and Technical Services 8 0 

561 Administrative and Support Services 1 0 

562 Waste Management and Remediation Services 7 2 

611 Educational Services 29 2 

622 Hospitals 4 0 

623 Nursing and Residential Care Facilities 1 0 

811 Repair and Maintenance 1 0 

921 Executive, Legislative, and Other General Government Support 2 0 

928 National Security and International Affairs 23 0 

As discussed in Section 5.1 of this RIA, using these cost data and the Census estimates of 

average establishment receipts, a substantial number of SUSB NAICS/enterprise categories have 

ratios over 3%. EPA determined 8 of 50 small ultimate parent companies owning major source 

facilities listed in Table 5-3 will experience an impact of over 3 percent of revenues. 

Representative small major public entities would have cost-to-revenue ratios above 10 percent. 

5.2.4 Description of the Projected Reporting, Record keeping and Other Compliance 

Requirements of the Rule 

The information collection activities in this ICR include initial and annual stack tests, fuel 

analyses, operating parameter monitoring, continuous O2 monitoring for all units greater than 10 

mmBtu/hr, continuous emission monitoring for PM at units greater than 250 mmBtu/hr, certified 

energy audits, annual or biennial tune-ups (depending on the size of the combustion equipment), 

preparation of a startup, shutdown, malfunction plan (SSMP), preparation of a site-specific 

monitoring plan and a site-specific fuel monitoring plan, one-time and periodic reports, and the 

maintenance of records. 

For sources that can demonstrate compliance through fuel analysis, the regulation requires 

an initial fuel analysis and monthly fuel analyses. Sources must conduct additional fuel analyses if 

they burn a new type of fuel. For sources that are demonstrating that their gaseous fuels other than 

natural gas and refinery gas meet the specifications for H2S and Hg contained in the final rule, 

they must conduct either an initial or monthly fuel analysis to remain in the gas 1 subcategory. If 
5-10 

the content of these constituents are not going to exceed the specifications, these units may 

conduct an initial testing and include a statement that the gas will not exceed the specification in 

the initial Notification of Compliance Status. If the gaseous fuel constituents will vary, the unit is 

required to conduct monthly testing and maintain records to demonstrate that the gaseous fuels 

meet the specifications. 

An initial performance test must be completed for particulate matter, mercury, hydrogen 

chloride, carbon monoxide, and dioxin and furans for affected sources with applicable emission 

limits. During the initial performance test, the owner or operator must establish maximum or 

minimum values for each operating parameter. Thereafter, the owner or operator must, in some 

cases, conduct annual stack tests for particulate matter, mercury, hydrogen chloride, carbon 

monoxide, and dioxin and furans and must continuously monitor the operating parameters. If a 

source is required to use PM CEMS, performance testing is not required for particulate matter. 

Following the initial performance test, the owner or operator must submit a report that 

documents the performance test results and the values for their required operating parameters. 

All existing units will be required to conduct an initial certified energy audit by qualified 

personnel which includes a visual inspection of the boiler system, establishing operating 

characteristics, identifying major energy consuming systems and energy savings potential, 

reviewing available engineering plans, and listing major energy conservation measures. A signed 

certification that an audit has been completed should be submitted to the Agency for each energy 

audit. 

All new and existing small and limited use units, and all large units firing natural gas, 

refinery gas, or other gas 1 fuels meeting the fuel spec can demonstrate compliance by conducting 

a tune-up of the boiler. Small and limited use units are requested to conduct a tune-up biennially 

and large natural gas, refinery gas, or other gas 1 units will conduct a tune-up annually. Any large 

natural gas, refinery gas, or other gas 1 unit will also submit a notification of alternative fuel use 

if the unit fires alternative fuels during periods of gas curtailment or gas supply emergencies. 

For all units other than small and limited use boilers and process heaters and units firing 

natural gas a semiannual report is required that documents the values for the operating 

parameters; any deviation; the results of any annual stack tests; the results of any fuel analysis and 

emissions calculations; fuel usage, and if no deviation occurred, a statement that no deviations 

occurred. 
5-11 

As specified in the ICI Boiler and Process Heater NESHAP, owners or operators of boilers 

and process heaters must keep records of certain parameters and information for a period of five 

years. Owners or operators must maintain records of the initial performance test, annual stack 

tests, fuel analyses, and any subsequent stack tests or fuel analyses. Owners or operators must 

also maintain records of the monitoring data for the operating parameters and daily fuel usage. 

Owners or operators must also maintain records for boiler or process heater malfunctions 

and any deviations from the operating parameters. Records must also be maintained of all 

monitoring device calibration data. 

The Agency expects that persons with knowledge of .pdf software, spreadsheet and 

relational database programs will be necessary in order to prepare the report or record. Based on 

experience with previous emission stack testing, we expect most facilities to contract out 

preparation of the reports associated with emission stack testing, including creation of the 

Electronic Reporting Tool submittal, which will minimize the need for in depth knowledge of 

databases or spreadsheet software at the source. We also expect affected sources will need to 

work with web-based applicability tools and flowcharts to determine the requirements applicable 

to them, knowledge of the heat input capacity and fuel use of the combustion units at each facility 

will be necessary in order to develop the reports and determine initial applicability to the rule. 

Affected facilities will also need skills associated with vendor selection in order to identify 

service providers that can help them complete their compliance requirements, as necessary. 

5.2.5 Description of the steps the Agency has Taken to Minimize the Significant Economic 

Impact on Small Entities 

While EPA did make significant changes based on public comment, EPA did not finalize a 

HBCA or HBELs and is maintaining, but clarifying, the energy assessment requirement. The 

discussion of the HBCA decision is included in section V of this preamble. Some changes to the 

energy assessment requirement that will reduce costs for small entities include a the following 

provisions: the energy assessment for facilities with affected boilers and process heaters using less 

than 0.3 trillion Btu per year heat input will be one day in length maximum. The boiler system 

and energy use system accounting for at least 50 percent of the energy output will be evaluated to 

identify energy savings opportunities, within the limit of performing a one-day energy 

assessment; and the energy assessment for facilities with affected boilers and process heaters 

using 0.3 to 1.0 trillion Btu per year will be 3 days in length maximum. The boiler system and any 

energy use system accounting for at least 33 percent of the energy output will be evaluated to 

identify energy savings opportunities, within the limit of performing a 3-day energy assessment. 

While EPA did not make major adjustments to the emissions averaging provisions, the change to 
5-12 

a solid fuel subcategory will enable all solid fuel-fired units at a facility to use the emissions 

averaging provision for Hg, PM, and HCl. 

As required by section 212 of SBREFA, EPA also is preparing a Small Entity Compliance 

Guide to help small entities comply with this rule. Small entities will be able to obtain a copy of 

the Small Entity Compliance guide at the following web site: 

http://www.epa.gov/ttn/atw/boiler/boilerpg.html. 

5.3 Final Regulatory Flexibility Analysis (FRFA): Area Sources 

Pursuant to section 603 of the RFA, EPA prepared an initial regulatory flexibility analysis 

(IRFA) for the proposed rule and convened a Small Business Advocacy Review Panel to obtain 

advice and recommendations of representatives of the regulated small entities. A detailed 

discussion of the Panel’s advice and recommendations is found in the final Panel Report (Docket 

ID No. EPA-HQ-OAR-2002-0058-0797). A summary of the Panel’s recommendations is also 

presented in the preamble to the proposed rule at 75 FR 32044-32045 (June 4, 2010). In the 

proposed rule, EPA included provisions consistent with four of the Panel’s recommendations. As 

required by section 604 of the RFA, we also prepared a final regulatory flexibility analysis 

(FRFA) the final rule. 

5.3.1 Need for Rule and Objectives 

The rule is intended to reduce emissions of HAP as required under section 112 of the 

CAA. Section II.A of the final rule’s preamble describes the reasons that EPA is finalizing this 

action. 

5.3.2 Summary of the Significant Issues raised by the Public Comments and Agency 

Assessment 

Many significant issues were raised during the public comment period, and EPA’s 

responses to those comments are presented in section V of this preamble or in the response to 

comments document contained in the docket. Significant changes to the rule that resulted from the 

public comments are described in section IV of the final rule’s preamble. 

The primary comments on the IRFA were provided by SBA, with the remainder of the 

comments generally supporting SBA’s comments. Those comments applicable to the proposal 

regarding area source boilers included the following: EPA should have adopted additional 

subcategories, including the following: unit design type (e.g., fluidized bed, stoker, fuel cell, 

suspension burner), duty cycle, geographic location, boiler size, burner type (with and without 

low-NOx burners), and hours of use (limited use); EPA should have minimized facility 
5-13 

monitoring and reporting requirements; EPA should not have proposed the energy audit 

requirement; and EPA’s proposed emissions standards are too stringent. 

In response to the comments on the IRFA and other public comments, EPA made the 

following changes to the final rule. EPA is promulgating management practice standards 

requiring the implementation of a boiler tune-up program for area source boilers in the biomass 

and oil subcategories instead of the proposed CO emission limits. This change will significantly 

reduce the monitoring and testing costs for existing and new biomass-fired and oil-fired area 

source boilers. EPA also decreased monitoring and testing costs for coal-fired area source boilers 

by eliminating the CO CEMS requirement for boilers greater than 100 MMBtu/h. The final rule 

also includes work practice standards or management practice standards, instead of emission 

limits, for new area source boilers less than 10 MMBtu/h. Finally, EPA is finalizing emission 

limits that are less stringent than the proposed limits. The emission limit changes are largely due 

to the changes in data corrections and incorporation of new data into the floor calculations. 

Additional details on the changes discussed in this paragraph are included in sections IV and V of 

the final rule’s preamble. 

5.3.3 Description of and Estimate of the Number of Small entities to Which the Rules Will 

Apply 

Table 5-4 summarizes the EPA estimates of the number of area source facilities expected 

to be affected by the major source rule (84,700 total). EPA does not have sufficient information to 

estimate the number of small entities expected to be covered by the area source rule. 

As discussed in Section 5.1 of this RIA, using these cost data and the Census estimates of 

average establishment receipts, a substantial number of SUSB NAICS/enterprise categories have 

ratios over 3%. The following types of representative small area source public facilities would 

have cost-to-revenue ratios exceeding 1 percent but below 3 percent: other public facilities (ratio 

> 1.7 percent) and churches (ratio = 1.5 percent). 
5-14 

Table 5-4. Estimated Affected Facilities Using 13 State Boiler Inspector Inventory: Area 

Sources 

SIC Total Number of Affected Facilities in SIC Code 

01 0 

02 247 

07 0 

09 0 

14 83 

16 0 

17 247 

20 5,733 

23 83 

24 2,676 

26 0 

40 329 

41 0 

42 83 

43 0 

44 0 

45 0 

47 0 

48 741 

50 165 

51 247 

52 0 

53 494 

54 0 

55 801 

56 0 

57 0 

58 905 

59 288 

60 329 

64 0 

(continued) 
5-15 

Table 5-4. Estimated Affected Facilities Using 13 State Boiler Inspector Inventory: Area 

Sources (continued) 

SIC Total Number of Affected Facilities in SIC Code 

65 2,878 

70 4,893 

72 2,138 

73 165 

75 1,606 

76 0 

79 1,151 

80 15,293 

81 0 

82 33,303 

83 0 

84 165 

86 3,330 

87 666 

91 to 98 5,098 

Unknown 576 

5.3.4 Description of the Projected Reporting, Record keeping and Other Compliance 

Requirements of the Rule 

The information collection activities in this ICR include initial and annual stack tests, fuel 

analyses, operating parameter monitoring, continuous O2 monitoring for all coal-fired area source 

boilers greater than 10 MMBtu/h, certified energy assessments for area source facilities having a 

boiler greater than 10 MMBtu/h, biennial tune-ups, preparation of a startup, shutdown, 

malfunction plan (SSMP), preparation of a site-specific monitoring plan and a site-specific fuel 

monitoring plan, one-time and periodic reports, and the maintenance of records. Based on 13 

states’ inventories of boilers, there are an estimated 92,000 existing facilities with affected 

boilers. It is estimated that 53 percent are located in the private sector and the remaining 47 

percent are located in the public sector. Of these, only about 0.3 percent of the area source 

facilities are subject to emission limits and the testing and monitoring requirements in the final 

rule. A table included in the FRFA summarizes the types and number of each type of small 

entities expected to be affected by the area source rule. 
5-16 

The Agency expects that persons with knowledge of .pdf software, spreadsheet and 

relational database programs will be necessary in order to prepare the report or record. Based on 

experience with previous emission stack testing, we expect most facilities to contract out 

preparation of the reports associated with emission stack testing, including creation of the 

Electronic Reporting Tool submittal which will minimize the need for in depth knowledge of 

databases or spreadsheet software at the source. We also expect affected sources will need to 

work with web-based applicability tools and flowcharts to determine the requirements applicable 

to them, knowledge of the heat input capacity and fuel use of the combustion units at each facility 

will be necessary in order to develop the reports and determine initial applicability to the rule. 

Affected facilities will also need skills associated with vendor selection in order to identify 

service providers that can help them complete their compliance requirements, as necessary. 

5.3.5 Description of the steps the Agency has Taken to Minimize the Significant Economic 

Impact on Small Entities 

While EPA did make significant changes based on public comment, EPA is maintaining, 

but clarifying, the energy assessment requirement. Some changes to the energy assessment 

requirement that will reduce costs for small entities include a the following provisions: the energy 

assessment for facilities with affected boilers using less than 0.3 trillion Btu per year heat input 

will be one day in length maximum. The boiler system and energy use system accounting for at 

least 50 percent of the energy output will be evaluated to identify energy savings opportunities, 

within the limit of performing a one-day energy assessment; and the energy assessment for 

facilities with affected boilers using 0.3 to 1.0 trillion Btu per year will be 3 days in length 

maximum. The boiler system and any energy use system accounting for at least 33 percent of the 

energy output will be evaluated to identify energy savings opportunities, within the limit of 

performing a 3-day energy assessment. In addition, the final rule allows facilities to use a 

previously completed energy assessment to satisfy the energy assessment requirement. 

As required by section 212 of SBREFA, EPA also is preparing a Small Entity Compliance 

Guide to help small entities comply with this rule. Small entities will be able to obtain a copy of 

the Small Entity Compliance guide at the following web site: 

http://www.epa.gov/ttn/atw/boiler/boilerpg.html. 

5.4 Section 5 References 

U.S. Bureau of Economic Analysis. 2010. “Implicit Price Deflators for Gross Domestic Product” 

(Table 1.1.9). <http://www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N>. 
5-17 

U.S. Census Bureau. 2005a. 2002 Census of Governments, Volume 4, Number 3, Finances of 

County Governments: 2002 GC02(4)-3 (Table 12).Washington, DC: U.S. Government 

Printing Office. 

U.S. Census Bureau. 2005b. 2002 Census of Governments, Volume 4, Number 4, Finances of 

Municipal and Township Governments: 2002 GC02(4)-4 (Table 13). Washington, DC: 

U.S. Government Printing Office. 

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>. 

U.S. Small Business Administration. 2008. “Table of Small Business Size Standards Matched to 

North American Industry Classification System Codes.” 

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

size/index.html>. 

6-1 

SECTION 6

AIR QUALITY MODELING OF EMISSIONS REDUCTIONS 

6.1 Synopsis 

This section describes the air quality modeling performed by EPA in support of the final 

boiler MACT rule. A national scale air quality modeling analysis was performed to estimate the 

impact of the sector emissions changes on future year: annual and 24-hour PM2.5 concentrations, 

8-hr maximum ozone, total mercury deposition, as well as visibility impairment. Air quality 

benefits are estimated with the Comprehensive Air Quality Model with Extensions (CAMx) 

model. CAMx simulates the numerous physical and chemical processes involved in the 

formation, transport, and destruction of ozone, particulate matter and air toxics. In addition to the 

CAMx model, the modeling platform includes the emissions, meteorology, and initial and 

boundary condition data which are inputs to this model. 

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, it is important to note that the inventories used in the air quality modeling and 

the benefits modeling are different than the final boiler sector inventories presented in the RIA. 

At the time of proposal, we did not have the results of the ICR; those results have been 

incorporated into this final analysis as explained below. The boiler ICR does not have the 

emissions release point information such as stack height, exit velocity, exit temperature, etc. 

necessary for photochemical modeling so the ICR emissions data was matched with the National 

Emissions Inventory (NEI). Since States and local agencies have different criteria for emissions 

that get inventoried as a point source with stack information and area sources (no specific stack 

information) the ability to match ICR emissions to NEI varies from State to State depending on 

the level of information provided in the ICR and NEI. 

Photochemical grid models use state of the science numerical algorithms to estimate 

pollutant formation, transport, and deposition over a variety of spatial scales that range from 

urban to continental. Emissions of precursor species are injected into the model where they react 

to form secondary species such as ozone and then transport around the modeling domain before 

ultimately being removed by deposition or chemical reaction. Photochemical model source 

apportionment tracks the formation and transport of primarily and secondarily formed pollutants 

from emissions sources and allows the estimation of contributions at receptors. This type of 

emissions apportionment is useful to understand what types of sources or regions are 

contributing to pollutants estimated by photochemical grid models. 
6-2 

The 2005-based CAMx modeling platform was used for the air quality modeling for this 

rule. This platform represents a structured system of connected modeling-related tools and data 

that provide a consistent and transparent basis for assessing the air quality response to projected 

changes in emissions. The base year of data used to construct this platform includes emissions 

and meteorology for 2005. The platform is intended to support a variety of regulatory and 

research model applications and analyses. This modeling platform and analysis is described 

below. Additional information about the photochemical modeling is available as part of the 

modeling technical assessment document to support this source sector rule (USEPA, 2010). 

6.2 Photochemical Model Background 

CAMx version 5.20 is a freely available computer model that simulates the formation and 

fate of photochemical oxidants, ozone, primary and secondary PM concentrations, and air toxics, 

over regional and urban spatial scales for given input sets of meteorological conditions and 

emissions. CAMx includes numerous science modules that simulate the emission, production, 

decay, deposition and transport of organic and inorganic gas-phase and particle-phase pollutants 

in the atmosphere (Baker and Scheff, 2007; Nobel et al., 2001; Russell, 2008). CAMx is applied 

with ISORROPIA inorganic chemistry (Nenes et al., 1999), a semi-volatile equilibrium scheme 

to partition condensable organic gases between gas and particle phase (Strader et al., 1999), 

Regional Acid Deposition Model (RADM) aqueous phase chemistry (Chang et al., 1987), and 

Carbon Bond 05 (CB05) gas-phase chemistry module (ENVIRON, 2008; Gery et al., 1989). 

Mercury oxidation pathways are represented for both the gas and aqueous phases in addition to 

aqueous phase reduction reactions (ENVIRON, 2008). 

CAMx contains a variety of ozone source apportionment tools, including the original 

ozone source apportionment tool (OSAT) and the anthropogenic pre-cursor culpability 

assessment (APCA) tool (ENVIRON, 2008). Ozone source apportionment tracers are treated 

using the standard model algorithms for vertical advection, vertical diffusion, and horizontal 

diffusion. Horizontal advective fluxes for each of the regular model species that make up 

nitrogen oxides (NOx) and volatile organic compounds (VOC) are combined and normalized by 

a concentration based weighted mean. Separate ozone tracers are used in CAMx to track ozone 

formation that happens under NOx and VOC limited conditions. 

Particulate matter source apportionment technology (PSAT) implemented in CAMx

estimates the contribution from specific emissions source groups to PM2.5 and all forms of 

mercury using reactive tracers (ENVIRON, 2008; Wagstrom et al., 2008). The tracer species are 

estimated with source apportionment algorithms rather than by the host model routines. PSAT 
6-3 

tracks contribution to PM2.5 sulfate, nitrate, ammonium, secondary organic aerosol, and inert 

primarily emitted species. Non-linear processes like gas and aqueous phase chemistry are solved 

for bulk species and then apportioned to the tagged species. Emissions of nitrogen oxides are 

tracked through all intermediate nitrogen species to particulate nitrate ion. Ammonia emissions 

are tracked to particulate ammonium ion. This modeling assessment used the PSAT approach to 

estimate source contribution to PM2.5 species and mercury and the APCA method to estimate 

source contribution to modeled ozone. 

6.3 Model Domain and Grid Resolution 

The modeling analyses were performed for a domain covering the continental United 

States, as shown in Figure 6-1. This domain has a parent horizontal grid of 36 km with two finerscale 12 km grids over portions of the eastern and western U.S. The model extends vertically 

from the surface to 100 millibars (approximately 15 km) using a sigma-pressure coordinate 

system. Air quality conditions at the outer boundary of the 36 km domain were taken from a 

global model and vary in time and space. The 36 km grid was only used to establish the 

incoming air quality concentrations along the boundaries of the 12 km grids. Only the finer grid 

data were used in determining the impacts of the emission standard program changes. Table 6-1 

provides some basic geographic information regarding the photochemical model domains. 

Figure 6-1. Map of the photochemical modeling domains. The black outer box denotes 

the 36 km national modeling domain; the red inner box is the 12 km western 

U.S. grid; and the blue inner box is the 12 km eastern U.S. grid. 
6-4 

Table 6-1. Geographic Elements of Domains Used in Photochemical Modeling 

Photochemical Modeling Configuration 

 National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid 

Map Projection Lambert Conformal Projection 

Grid Resolution 36 km 12 km 12 km 

Coordinate Center 97 deg W, 40 deg N 

True Latitudes 33 deg N and 45 deg N 

Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14 

Vertical extent 14 Layers: Surface to 100 millibar level (see Table II-3) 

6.4 Emissions Input Data 

6.4.1 2005 Baseline Emissions 

The emissions data used in the 2005 base year are from the EPA’s 2005-based v4.1 

modeling platform. This platform is based on the 2005 National Emissions Inventory (NEI), 

version 2. Emissions were processed to photochemical model inputs with the Sparse Matrix 

Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 2000). 

This platform includes criteria pollutants and precursors: particulate matter less than 10 

microns (PM10), PM2.5, nitrogen oxides (NOX), sulfur dioxide (SO2), carbon monoxide (CO), 

volatile organic compounds (VOC), ammonia (NH3) and hazardous air pollutants (HAP): 

hydrogen chloride, chlorine and mercury. Additionally, for some sectors, HAP emissions of 

benzene, formaldehyde, acetaldehyde and methanol are used from the inventory for chemical 

speciation VOC. For this rule, mercury emissions were added to the v4.1 platform to reflect the 

needs for the rule development will not be used for other rules. The mercury emissions included 

in this platform are primarily from the 2005 National Air Toxics Assessment (NATA) inventory, 

which was updated from the 2005 NEI v2 in order to incorporate updated data for particular 

source categories such as cement and hazardous waste incineration, and also revised from 

comments from state and local inventory providers as a result of NATA review. 

This inventory was further modified to remove sources that were found to have shut 

down prior to 2005 and to update the gold mine emissions per information collected during the 

Gold Mine Ore and Production NESHAP. In addition, mercury emissions were revised for the 

boiler sector to allow for greater consistency with the Information Collection Request (ICR) data 

collected for this rule. In particular, we used the unit-specific ICR mercury emissions for all ICR 
6-5 

facilities that could be mapped to the NATA Inventory.1

 We used the NEI to add important 

emissions release point information necessary for photochemical grid modeling such as 

geographic coordinates, stack coordinates, stack release height, exit temperature, exit velocity 

because the ICR lacks this information. 

The replacement of the NATA mercury with the ICR mercury is described in more detail 

in the air quality modeling TSD. ICR emissions were not used directly for any other pollutants 

used in the v4.1 platform; however they informed numerous corrections and updates to the 

inventory including the removal of duplicates and facilities that had shut down prior to 2005, and 

the inclusion of control information. 

The stationary inventory used in the v4.1 platform is separated into modeling sectors such 

as EGU point (ptipm), non-EGU point (ptnonipm), and stationary emissions not included in the 

point source inventories (nonpt). This nonpoint category is generally referred to as the “area” 

source inventory, and this category is not a direct representation of sources classified as area 

sources for the purposes of National Emissions Standards for Hazardous Air Pollutants 

(NESHAP) and Maximum Available Control Technology (MACT) standards. 

6.4.2 Future Year Baseline Emissions 

The 2016 baseline emissions are intended to represent the emissions associated with 

growth and controls in 2016. The projections used for this effort are unique to this project and 

are not associated with a particular modeling platform. 

The EGU point source (ptipm) emissions estimates for the future year reference were 

created by the Integrated Planning Model (IPM) version 3.02 for criteria pollutants, hydrochloric 

acid, and mercury in 2015.2

 For the non-EGU point (ptnonipm) and nonpoint (nonpt) sectors, 

both control and growth factors were applied to a subset of the 2005 v4.1 platform data to create 

the 2016 reference case. The 2014 projection factors developed for the Transport Rule proposal

(see http://www.epa.gov/ttn/chief/emch/index.html#transport) were further enhanced and 

updated for these 2016 baseline projections. 

The projected inventory incorporates emissions projections for the proposed Transport 

Rule, cement kiln NESHAP, RICE NESHAP, gold mine NESHAP, changes to boiler emissions 

 

1

 The “NEI_UNIQUE_ID” field was used to map the ICR facilities to the NATA inventory. We used 7032 units’ Hg 

emissions out of 7,738 total units in the ICR. These 7032 units from the ICR sum to 4.66 tons. ICR emissions 

that were not included sum to 0.177 tons. 2

 The 2015 IPM run represents the average of 2014 to 2016. 
6-6 

based on the ICR database developed for this rule, and known consent decrees. A complete list 

of rules included in the future point source baseline inventory is shown in Table 6-2. 

Table 6-2. Control Strategies and/or Growth Assumptions Included in the 2016 

Projection 

Projections Carried Forward from the proposed Transport Rulea,b 

Description Pollutants 

MACT rules, national, VOC: national applied by SCC, MACT VOC 

Consent Decrees and Settlements, including refinery consent decrees, and settlements for: (1) 

Alcoa, TX and (2) Premcor (formerly MOTIVA), DE 

All 

Municipal Waste Combustor Reductions—plant level PM 

Hazardous Waste Combustion PM 

Hospital/Medical/Infectious Waste Incinerator Regulations under Section 129d/11 1d NOX, PM, SO2

Large Municipal Waste Combustors—growth applied to specific plants All 

MACT rules, plant-level, VOC: Auto Plants VOC 

MACT rules, plant-level, PM & SO2: Lime Manufacturing PM, SO2

MACT rules, plant-level, PM: Taconite Ore PM 

Municipal Waste Landfills: project factor of 0.25 applied All 

Livestock Emissions Growth from year 2002 to 2016 NH3, PM 

Residential Wood Combustion Growth and Changeouts from year 2005 to year 2016 All 

Gasoline Stage II growth and control from year 2005 to year 2016 VOC 

Portable Fuel Container MSAT2 inventory growth and control from year 2005 to year 2016 VOC 

Additional Projections Used In Boiler MACT modelingc 

Emission Reductions resulting from controls put on specific boiler units (not due to MACT) 

after 2005, identified through analysis of the control data gathered from the ICR from the ICI 

Boiler NESHAP. 

NOX, SO2, HCL 

NESHAP: Portland Cement (09/09/10)—plant level based on Industrial Sector Integrated 

Solutions (ISIS) policy emissions in 2013. The ISIS results are from the ISIS-Cement model 

runs for the NESHAP and NSPS analysis of July 28, 2010. 

HG, NOX, SO2, 

PM, HCL 

NESHAP: Gold Mine Ore Processing and Production Area Source Category (based on 

proposed rule 04-15-10)—finalized 12/2010 

HG 

(continued) 
6-7 

Table 6-2. Control Strategies and/or Growth Assumptions Included in the 2016 

Projection (continued) 

Projections Carried Forward from the proposed Transport Rulea,b 

New York SIP reductions VOC, NOX

Additional plant and unit closures All 

NESHAP: Reciprocating Internal Combustion Enginesd NOX, CO, PM 

a

 They were only changed in that the projection year was 2015 or 2016, rather than 2012 / 2014. 

b

 We inadvertently did not apply closures that had been applied for the Transport Rule proposal; emissions from 

these plants sum to 3300 tons VOC, 178 tons PM2.5, 1982 tons SO2, 1639 tons NOX, 6 tons NH3 and 379 tons CO. 

At the state level, the largest impact is in West Virginia (717 tons NOX, which is 2% of emissions in ptnonipm) 

and 1604 tons SO2, which is 7% of the ptnonipm sector. When considering emissions from other sectors, the 

percentages will be much smaller. All other errors are under 500 tons (less than 1% of the ptnonipm sector). 

c

 We inadvertently did not apply LaFarge and SaintGobain consent decrees, since one of the LaFarge facilities was 

already covered in the cement ISIS projections, the reductions missed were lower than estimated by the consent 

decree were on the order of 20,000 tons SO2, 15,000 tons NOX, 400 tons HCL, 200 tons PM2.5.

d

 Note that SO2 reductions are expected to occur to due fuel sulfur limits but were excluded from the projection. 

They were expected to reduce SO2 by 27,000 tons, nationwide. 

The 2016 onroad emissions reflect control program implementation through 2016 and 

include the Light-Duty Vehicle Tier 2 Rule, the Onroad Heavy-Duty Rule, and the Mobile 

Source Air Toxics (MSAT) final rule and the category 3 marine diesel engines Clean Air Act and 

International Maritime Organization standards which includes the establishment of emission 

control areas for these ships. Emission reductions and increases from the Renewable Fuel 

Standard version 2 (RFS2) are not included. The future baseline case nonroad mobile emissions 

reductions for these years include reductions to locomotives, various nonroad engines including 

diesel engines and various marine engine types, fuel sulfur content, and evaporative emissions 

standards. 

6.4.3 Future Year Sector Contribution Approach 

The 2016 reference scenario for the boilers affected by the rule includes 2005 emissions 

estimates with some emissions removed from the inventory because of shut downs. The length of 

time required to conduct emissions and photochemical modeling precluded the use of the final 

facility-specific emissions estimates based on controls implemented for this rule. A 2016 

“control” or emissions adjustment scenario was developed by tracking the total contribution from 

potentially controllable boiler sector emissions from the 2016 baseline inventory. This total 

contribution estimate, essentially a “zero-out,” of the sector creates a policy space where 

potential control impacts would be maximized at all locations. Since emissions reductions at 

controllable sources are not 100% (100% ~ total contribution), the boiler sector air quality 
6-8 

contribution estimates from the 2016 source apportionment model simulation are adjusted based 

on nation-wide estimates of control percentages by pollutant to create a final 2016 “control” 

emissions scenario. 

The 2016 estimated controllable emissions for the boiler sector are shown in Table 6-3. 

Boiler sector emissions are contained in several different general classes of emissions used for 

emissions modeling: non-point or area (nonpt), point EGU (ptipm), and point non-EGU 

(ptnonipm). These totals are the sum of emissions in the eastern and western U.S. modeling 

domains so the non-point (area) category contains some double-counting of emissions where the 

model domains overlap in the central United States (see Figure 6-2). 

Table 6-3. Estimated Future Year (2016) Controllable Boiler Sector Emissions 

 Sector VOC NOX SO2 PM2.5 NH3 HG0 HG2

PM2.5

HG 

Total (TPY) Non-point—Boiler (nonpt) 14,107 394,459 1,149,402 108,386 8,489 0 0 0 

Total (TPY) EGU Point—Boiler (ptipm) 57 0 54,337 826 0 0 0 0 

Total (TPY) Non-EGU Point—Boiler 

(ptnonipm) 

15,451 215,809 492,676 28,838 706 1 2 1 

Pct of Sector Non-point—Boiler (nonpt) 0 19 82 9 5 

Pct of Sector EGU Point—Boiler (ptipm) 0 0 1 0 0 

Pct of Sector Non-EGU Point—Boiler 

(ptnonipm) 

1 9 26 6 0 10 7 12 

Figure 6-2 shows the locations of boilers in the NEI point source inventory used in the 

modeling analysis. Non-point boilers in the NEI do not have stack location information and are 

not shown. These boilers are spatially distributed in the modeling domain using spatial 

surrogates appropriate for this sector. 

Boiler emissions in the non-point/area (nonpt) modeling sector are used as the basis of 

estimating air quality impacts and health benefits for the area source rule. These emissions are 

based on SCC codes shown in Table II-5. Boiler emissions in the point non-EGU (ptnonipm) and 

point EGU (ptipm) are used to estimate impacts of the major source rule. Facilities were 

identified for the major source rule based on meeting several criteria: (1) NEI facility ID and 

process level fuel type matched to a facility in the boiler ICR database, (2) NEI source had a 

boiler SCC code, and (3) unit design capacity was less than 25 MW. 
6-9 

Figure 6-2. Locations of boilers in the NEI point inventory for the future baseline (2016) 

Major source mercury emissions were identified as the units added from the boiler ICR 

database plus units in the NATA Hg inventory that were not in the ICR but were considered part 

of the Boiler MACT Universe. A percent emissions reduction was estimated based on the rule 

proposal unit-specific facility-fuel combination in the ICR database. Each facility/process 

matching the facility-fuel combinations that had emission reductions larger than 1% based on the 

proposed rule were tracked for source contribution.

6.5 Model Results 

As part of the analysis for this rulemaking, the modeling system was used to calculate 

daily and annual PM2.5 concentrations, 8-hr maximum ozone, annual total mercury deposition 

levels and visibility impairment. Model predictions are used to estimate future-year design 

values of PM2.5 and ozone. The annual PM2.5 design value3 determines whether a monitor 

location is attaining the annual PM2.5 NAAQS. Specifically, we compare a 2016 reference 

scenario, a scenario without the boiler sector controls, to a 2016 control scenario which includes 

the adjustments to the boiler sector. This is done by calculating the simulated air quality ratios 

 

3

 The full details involved in calculating the annual PM2.5 design value are provided in appendix N of 40 CFR part 

50. 
6-10 

between any particular future year simulation and the 2005 base. These predicted ratios are then 

applied to ambient base year design values. The design value projection methodology used here 

followed EPA guidance for such analyses (USEPA, 2007). Additionally, the raw model outputs 

are also used in a relative sense as inputs to the health and welfare impact functions of the 

benefits analysis. Only model predictions for mercury deposition were analyzed using absolute 

model changes, although percent changes between the control case and two future baselines are 

also estimated. 

The 36 km and both 12 km modeling domains were modeled for the entire year of 2005 

and projected year 2016. Data from the entire year were utilized when looking at the estimation 

of PM2.5, total mercury deposition, and visibility impacts from the regulation. Data from April 

through October is used to estimate ozone impacts. All air quality impacts are based on 

improvements in future year pollution based on emissions changes from this source sector. 

6.5.1 Impacts of Sector on Total Mercury Deposition 

This section summarizes the results of our modeling of total mercury deposition impacts 

in the future based on changes to boiler emissions. Available data indicate that the mercury 

emissions from these sources are a mixture of gaseous elemental mercury (25%), inorganic 

divalent mercury (reactive gas phase mercury) (50%), and particulate bound mercury (25%). 

Model results for the eastern and central United States indicate that estimated total mercury 

deposition (wet and dry forms) reductions from this sector would be 1,393 μg/m2

 (1.5% of total 

mercury deposition from all sources); approximately 51% in dry form and 49% in wet form. The 

chemical composition of this estimated reduction is approximately 60% reactive gas phase (HG2) 

and 40% particulate. A reduction of 75 μg/m2 (0.5% of total mercury deposition from all 

sources) is estimated for the western United States; approximately 56% in dry form and 44% in 

wet form. The chemical composition of the reductions in the western model domain is 

approximately 63% reactive gas phase (HG2) and 37% particulate. These reductions are related 

to changes in emissions at major boiler sources using the ptnonipm sector as a surrogate. 

6.5.2 Impacts of Sector on Future Annual PM2.5 Levels 

This section summarizes the results of our modeling of annual average PM2.5 air quality 

impacts in the future due to estimated reductions in emissions from this sector. Specifically, we 

compare a 2016 reference scenario to a 2016 control scenario. The modeling assessment 

indicates a decrease up to 0.81 μg/m3

 in annual PM2.5 design values is possible given an area’s 

proximity to controlled sources and the amount of reduced sulfur dioxide and primary PM2.5

emissions from major sources under the recommended solid fuel category option. The estimated 
6-11 

mean reduction over all monitor locations is 0.15 μg/m3

 for major source reductions under the 

recommended solid fuel category. A decrease up to 0.66 μg/m3 in annual PM2.5 design values is 

possible given an area’s proximity to controlled sources and the amount of reduced sulfur 

dioxide and primary PM2.5 emissions from major sources under the alternative option. The 

estimated mean reduction over all monitor locations is 0.12 μg/m3

 for major source reductions 

under the alternative option. Area source reductions show a decrease up to 0.01 μg/m3 in annual 

PM2.5 design values is possible given an area’s proximity to controlled area sources and the 

amount of reduced sulfur dioxide and primary PM2.5 emissions. 

6.5.3 Impacts of Sector on Future 24-hour PM2.5 Levels 

This section summarizes the results of our modeling of 24-hr average PM2.5 air quality 

impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 

2016 reference scenario to a 2016 control scenario. The modeling assessment indicates an 

estimated average decrease of 0.50 μg/m3

 in 24-hr average PM2.5 design values over all monitor 

locations in the United States is possible given the amount of reduced sulfur dioxide and primary 

PM2.5 emissions from major sources under the recommended solid fuel category option. An 

estimated average decrease of 0.39 μg/m3 in 24-hr average PM2.5 design values over all monitor 

locations in the United States is possible given the amount of reduced sulfur dioxide and primary 

PM2.5 emissions from major sources under the alternative option. An estimated decrease up to 

0.03 μg/m3 in 24-hr average PM2.5 design values at monitor locations in the United States is 

possible given an area’s proximity to controlled sources and the amount of reduced sulfur 

dioxide and primary PM2.5 emissions from area sources. 

6.5.4 Impacts of Sector on Future Visibility Levels 

Air quality modeling conducted for this final rule was used to project visibility conditions 

in 138 mandatory Class I federal areas across the U.S. in 2016 (USEPA, 2007). The level of 

visibility impairment in an area is based on the light-extinction coefficient and a unitless 

visibility index, called a “deciview,” which is used in the valuation of visibility. The deciview 

metric provides a scale for perceived visual changes over the entire range of conditions, from 

clear to hazy. Under many scenic conditions, the average person can generally perceive a change 

of one deciview. Higher deciview values are indicative of worse visibility. Thus, an 

improvement in visibility is a decrease in deciview value. 

The modeling assessment indicates an estimated average visibility improvement of 0.51 

deciviews in annual 20% worst visibility days over all Class I area monitors based on controls 

for major sources under the recommended solid fuel category option. An estimated average 
6-12 

visibility improvement of 0.42 deciviews in annual 20% worst visibility days over all Class I 

area monitors based on controls for major sources under the alternative option. An improvement 

in visibility up to 0.03 deciviews at Class I monitor locations in the United States is possible 

given an area’s proximity to controlled sources and the amount of reduced sulfur dioxide and 

primary PM2.5 emissions from area sources. 

6.5.5 Impacts of Sector on Future Ozone Levels 

This section summarizes the results of our modeling of 8-hr maximum ozone air quality 

impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 

2016 reference scenario to a 2016 control scenario. The modeling assessment indicates a 

decrease of less than 0.01 ppb in ozone design values is possible given an area’s proximity to 

controlled sources and the amount of reduced VOC emissions under all major and area source 

options. The full details involved in calculating design value are given in appendix P of 40 CFR 

part 50. Projected air quality benefits are estimated using procedures outlined by United States 

Environmental Protection Agency modeling guidance (USEPA, 2007). 

6.6 Section 6 References 

Baker, K., Scheff, P., 2007. Photochemical model performance for PM2.5 sulfate, nitrate, 

ammonium, and precursor species SO2, HNO3, and NH3 at background monitor locations 

in the central and eastern United States. Atmospheric Environment 41, 6185-6195. 

Chang, J.S., Brost, R.A., Isaksen, I.S.A., Madronich, S., Middleton, P., Stockwell, W.R., 

Walcek, C.J. 1987. A 3-dimensional eulerian acid deposition model—physical concepts 

and formulation. J. Geophys. Res.-Atmos. 92, 14681-14700. 

ENVIRON, 2008. User’s Guide Comprehensive Air Quality Model with Extensions. ENVIRON 

International Corporation, Novato. 

Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics 

mechanism for urban and regional scale computer modeling. J. Geophys. Res.-Atmos. 94, 

12925-12956. 

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S., 2000. Emission 

inventory development and processing for the Seasonal Model for Regional Air Quality 

(SMRAQ) project. Journal of Geophysical Research-Atmospheres 105, 9079-9090. 

Nenes, A., Pandis, S.N., Pilinis, C., 1999. Continued development and testing of a new 

thermodynamic aerosol module for urban and regional air quality models. Atmospheric 

Environment 33, 1553-1560. 

Nobel, C.E., McDonald-Buller, E.C., Kimura, Y., Allen, D.T., 2001. Accounting for spatial 

variation of ozone productivity in NOx emission trading. Environmental Science & 

Technology 35, 4397-4407. 
6-13 

Russell, A.G., 2008. EPA Supersites Program-related emissions-based particulate matter 

modeling: Initial applications and advances. J. Air Waste Manage. Assoc. 58, 289-302. 

Strader, R., Lurmann, F., Pandis, S.N., 1999. Evaluation of secondary organic aerosol formation 

in winter. Atmospheric Environment 33, 4849-4863.USEPA, 2007. Guidance on the Use 

of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for 

Ozone, PM2.5, and Regional Haze, RTP. 

USEPA, 2010. Air Quality Modeling Technical Support Document: Boiler Source Sector Rules 

(EPA-454/R-10-006), Research Triangle Park, North Carolina. 

Wagstrom, K.M., Pandis, S.N., Yarwood, G., Wilson, G.M., Morris, R.E., 2008. Development 

and application of a computationally efficient particulate matter apportionment algorithm 

in a three-dimensional chemical transport model. Atmospheric Environment 42, 5650-

5659. 

7-1 

SECTION 7

BENEFITS OF EMISSIONS REDUCTIONS 

7.1 Synopsis 

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

exposure to particulate matter (PM) and ozone for the final Boiler MACT and Boiler Area Source 

Rule. The PM2.5 reductions are the result of emission limits on PM as well as emission limits on 

other pollutants, including hazardous air pollutants (HAPs). The total PM2.5 reductions are the 

consequence of the technologies installed to meet these multiple limits. The latter are often 

referred to as “co-benefits.” Ozone reductions are the result of reductions in emissions of VOCs, 

which are precursors to ozone formation. These benefit estimates include the number of cases of 

avoided morbidity and premature mortality among populations exposed to PM2.5 and ozone, as 

well as the monetized value of those avoided cases. Because we were unable to monetize the 

direct benefits associated with reducing HAPs, the monetized benefits estimate is an 

underestimate of the total benefits. The extent of this underestimate, whether small or large, is 

unknown. Using a 3% discount rate, we estimate the total combined monetized benefits of the 

final Boiler MACT and Boiler Area Source Rule to be $22 billion to $54 billion in the 

implementation year (2014). Using a 7% discount rate, we estimate the total combined monetized 

benefits of the final Boiler MACT and Boiler Area Source Rule to be $20 billion to $49 billion in 

the implementation year. Higher or lower estimates of benefits are possible using other 

assumptions; examples of this are provided in Figure 7-1. All estimates are in 2008$ and include 

any energy disbenefits associated with the increased emissions from additional energy usage. 

These monetized estimates reflect EPA’s current interpretation of the scientific literature 

(U.S. EPA, 2009c). In addition, these estimates incorporate an array of improvements introduced 

since the proposal, including boiler sector-specific air quality modeling data, lowest measured 

level (LML) assessment, mercury deposition maps, ozone benefits, and energy disbenefits. 

Methodological and time limitations under the court-ordered schedule prevented EPA from 

monetizing the benefits from several important benefit categories, including direct benefits from 

reducing hazardous air pollutants, ecosystem effects, and visibility impairment. The direct 

benefits from reducing other air pollutants have not been monetized in this analysis, including 

reducing a combined 113,000 tons of carbon monoxide, 30,000 tons of HCl, 830 tons of HF, 

2,900 pounds of mercury, 3,000 tons of other metals, and 23 grams of dioxins/furans (TEQ) each 

year. We assess the direct benefits of these emission reductions qualitatively in this analysis. 
7-2 

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

Pope et al.

Laden et al.

Millions(2008$)

Benefits estimates derived from2 epidemiology functions and 12 expert functions

Benefits  3%

Benefits 7%

Figure 7-1. Total Monetized PM2.5 and Ozone Benefits for the Final Boiler MACT and 

Boiler Area Source Rule in 2014a

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

Pope et al. (2002) study and the Laden et al. (2006) 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. These 

estimates do not include the direct benefits from reducing HAP emissions, but they do include the energy 

disbenefits associated with the increased emissions from additional energy usage. Due to methodology and time 

limitations under the court-ordered schedule, we were unable to monetize the benefits associated with several 

categories of benefits, including direct exposure to HAPs and SO2, as well as ecosystem effects, and visibility 

effects. These benefits reflect existing boilers and new boilers anticipated to come online by 2014. 

7.2 Calculating Benefits 

The benefit categories associated with the emission reduction anticipated for these rules 

can be broadly categorized as those benefits directly attributable to reduced exposure to HAPs, 

the PM co-benefits associated with reducing HAPs (i.e., PM reduced by introducing technology 

designed to reduce HAPs), and those attributable to exposure to other pollutants. Several of the 

HAPs that would be reduced by these rules have been classified as known or probable human 
7-3 

carcinogens.1 As a result, one benefit of the proposed regulation is a reduction in the risk of 

cancer. Other benefit categories include potential reduced incidence of neurological effects and 

irritations of the lungs and skin, reduced mortality and other morbidity effects associated with PM 

and SO2. In addition to health impacts occurring as a result of reductions in HAPs and other 

pollutant emissions, there are welfare impacts which can also be identified. In general, welfare 

impacts include effects on vegetation, visibility impairment, and acidification of water bodies. We 

were unable to monetize the direct benefits associated with reducing HAPs in this analysis. In 

Section 7.5.5 of this RIA, we provide a full qualitative discussion of the direct health benefits 

associated with the reductions in emissions of HAPs anticipated by these rules, including a full 

discussion of the complexity associated with monetizing HAP benefits. We also provide maps of 

reduced mercury deposition in that section. Therefore, all monetized benefits provided in this 

analysis only reflect improvements in ambient PM2.5 and ozone concentrations. Thus, the 

monetized benefits estimate is an underestimate of the total benefits. The extent of this 

underestimate, whether small or large, is unknown. In addition to pollutants we do not directly 

monetize, these rulemakings are expected to reduce emissions of PM2.5, SO2, and VOCs. Because 

SO2 is also a precursor to PM2.5, reducing SO2 emissions will also reduce PM2.5 formation, human 

exposure, and therefore reduce estimated incidence of PM2.5-related health effects. The estimated 

PM reductions are the result of emission limits on PM as well as emission limits on other 

pollutants, including hazardous air pollutants for these rules. The total PM2.5 reductions are the 

consequence of the technologies installed to meet these multiple limits. In addition, these rules are 

expected to result in reductions in VOCs, which will result in changes in ambient ozone 

concentrations. 

In implementing these rules, emission controls may lead to reductions in ambient PM2.5 

below the National Ambient Air Quality Standards (NAAQS) for PM in some areas and assist 

other areas with attaining the PM NAAQS. Because the PM NAAQS RIAs also calculate PM 

benefits, there are important differences worth noting in the design and analytical objectives of 

each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a new air 

quality standard nationwide based on an array of emission control strategies for different sources. 

In short, NAAQS RIAs hypothesize, but do not predict, the control strategies that States may 

choose to enact when implementing a NAAQS. The setting of a NAAQS does not directly result 

in costs or benefits, and as such, the NAAQS RIAs are merely illustrative and are not intended to 

be added to the costs and benefits of other regulations that result in specific costs of control and 

 

1

 As described in Section 7.5, formaldehyde, several PAHs, and acetaldehyde are classified as probable human 

carcinogens. Different nickel compounds are classified as human carcinogens and probable human carcinogens. 
7-4 

emission reductions. However, some costs and benefits estimated in this RIA account for the 

same air quality improvements as estimated in the illustrative PM2.5 NAAQS RIA. 

By contrast, the emission reductions for this rule are from a specific class of wellcharacterized sources. In general, EPA is more confident in the magnitude and location of the 

emission reductions for these rules. It is important to note that emission reductions anticipated 

from these rules do not result in emission increases elsewhere (other than potential energy 

disbenefits). Emission reductions achieved under these and other promulgated rules will 

ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the 

incremental costs and benefits associated with attaining the NAAQS. EPA remains forward 

looking towards the next iteration of the 5-year review cycle for the NAAQS, and as a result does 

not issue updated RIAs for existing NAAQS that retroactively update the baseline for NAAQS 

implementation. For more information on the relationship between the NAAQS and rules such as 

analyzed here, please see Section 1.2.4 of the SO2 NAAQS RIA (U.S. EPA, 2010h). 

7.2.1 Methodology Improvements since Proposal 

This benefits analysis for the final Boiler MACT and Area Source Rule incorporates an 

array of policy and technical improvements since the proposal analysis in 2010 (U.S. EPA, 

2010c), including: 

1. Boiler sector-specific air quality modeling data. The benefits estimates for this final 

analysis are derived from air quality data modeled using CAMx that reflect the 

emissions from the boiler sector and their contribution to ambient PM2.5 and ozone 

levels. These data provide a different and likely superior representation of the 

geographic distribution of emissions and associated ambient concentrations 

specifically from boilers than the generic benefit-per-ton estimates used in the 

proposal that reflected emissions for the entire non-EGU (non-electrical generating 

unit) category. For more information regarding the modeling inputs and assumptions, 

please see Section 6 of this RIA. For more information regarding the derivation of 

boiler sector-specific benefit-per-ton estimates and how they are superior to the 

estimates used in the proposal, please see Section 7.2.6 of this RIA. 

2. Lowest Measured Level (LML) assessment. Consistent with the rationale outlined in 

the Cement NESHAP proposal RIA (U.S. EPA, 2009a), EPA has recently changed its 

approach to characterizing the uncertainty associated with benefits estimated at lower 

air quality levels. Specifically, EPA now estimates PM-related mortality without 

assuming an threshold at 10 μg/m3

 in the concentration-response function. Consistent 

with recent scientific advice, we are replacing the previous threshold sensitivity 

analysis with a new LML assessment. We discuss this assessment in more detail in 

Section 7.2.4 and provide the results of this LML assessment in Section 7.3. 
7-5 

3. Mercury deposition. The air quality modeling data provide an estimate of the reduction 

in mercury deposition associated with the mercury emission reductions anticipated as a 

result of these rules. We provide maps of the reduced mercury deposition in Section 

7.5.4.1. EPA did not model mercury 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. 

4. Ozone benefits. The air quality modeling data provide an estimate of the change in 

ambient ozone concentrations associated with the VOC emission reductions 

anticipated as a result of the Boiler MACT. We provide a table of the ozone-related 

health benefits in Section 7.3. 

5. Energy disbenefits. For this final analysis, we include an estimate of the increased CO2

emissions associated with the additional electricity required to operate control devices. 

We provide the results of this analysis in Section 7.4. 

6. Improved characterization of uncertainty. For this final analysis, we characterize 

uncertainty using four methods (Monte Carlo methods, LML assessment, alternate 

concentration-response functions, and qualitative descriptions). While the proposal 

incorporated some of these approaches, the Monte Carlo methods and LML 

assessment are an improvement for the final analysis. Confidence intervals reflecting 

the standard errors in the underlying epidemiology and economics literature are 

provided for incidence and valuation results in Tables 7-4 to 7-8. 

7.2.2 Benefits Analysis Approach for PM2.5 and Ozone 

We follow a “damage-function” approach in calculating total benefits of the modeled 

changes in environmental quality. This approach estimates changes in individual health and 

welfare endpoints and assigns values to those changes assuming independence of the individual 

values. Total benefits are calculated simply as the sum of the values for all non-overlapping 

health and welfare endpoints. The “damage-function” approach is the standard method for 

assessing costs and benefits of environmental quality programs and has been used in several 

recent published analyses (Levy et al., 2009; Hubbell et al., 2009; Tagaris et al., 2009). 

To assess economic value in a damage-function framework, the changes in environmental 

quality must be translated into effects on people or on the things that people value. For changes in 

PM2.5 or ozone, a health impact analysis (HIA) must first be conducted to convert air quality 

changes into effects that can be assigned dollar values. For this RIA, the health impacts analysis is 

limited to those health effects that are directly linked to ambient levels of air pollution and 

specifically to those linked to PM2.5 and ozone. We also provide qualitative discussions of the 

impact of changes in other environmental and ecological effects, including the benefits associated 

with decreasing deposition of sulfur to terrestrial and aquatic ecosystems, but we are unable to 
7-6 

place an economic value on these changes due to time limitations under the court-ordered 

schedule. 

We note at the outset that EPA rarely has the time or resources to perform extensive new 

research to measure directly either the health outcomes or their values for regulatory analyses. 

Thus, similar to Kunzli et al. (2001) and other recent health impact analyses, our estimates are 

based on the best available methods of benefits transfer. Benefits transfer is the science and art of 

adapting primary research from similar contexts to obtain the most accurate measure of benefits 

for the environmental quality change under analysis. Adjustments are made for the level of 

environmental quality change, the socio-demographic and economic characteristics of the affected 

population, and other factors to improve the accuracy and robustness of benefits estimates. 

7.2.3 Health Impact Analysis (HIA) 

The HIA quantifies the potential changes in the incidence of adverse health impacts 

resulting from estimated changes in human exposure to air pollution. HIAs are a well-established 

approach for estimating the retrospective or prospective change in adverse health impacts 

resulting from population-level changes in exposure to pollutants (Levy et al., 2009). Analysts 

have applied the HIA approach to estimate human health impacts resulting from hypothetical 

changes in pollutant levels (Hubbell et al., 2005; Davidson et al., 2007; Tagaris et al., 2009). For 

this analysis, we used the environmental Benefits Mapping and Analysis Program (BenMAP), 

which is a PC-based tool that can systematize health impact analyses by applying a database of 

key input parameters, including health impact functions and population projections.2

The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMxgenerated projections of PM2.5 and ozone air quality and estimating the change in the spatial 

distribution of the ambient air quality; (2) determining the subsequent change in population-level 

exposure; (3) calculating health impacts by applying concentration-response relationships drawn 

from the epidemiological literature (Hubbell et al., 2009) to this change in population exposure. 

A typical health impact function might look as follows: 

where y0 is the baseline incidence rate for the health endpoint being quantified (for example, a 

health impact function quantifying changes in mortality would use the baseline, or background, 

 

2

 For this analysis, we used BenMAP version 3.0 (Abt Associates, 2008). This model is available for free download 

on the Internet at <http://www.epa.gov/air/benmap>. 
7-7 

mortality rate for the given population of interest); Pop is the population affected by the change in 

air quality; x is the change in air quality; and β is the effect coefficient drawn from the 

epidemiological study. For this analysis, we systematize the HIA calculation process using 

BenMAP’s library of existing air quality monitoring data, population data and health impact 

functions. Figure 7-2 provides a simplified overview of this approach, and Figure 7-3 identifies 

the data inputs and outputs for the BenMAP model for a PM2.5 analysis. 

Figure 7-2. Illustration of BenMAP Approach 
7-8 

Figure 7-3. Data Inputs and Outputs for the BenMAP Model for a PM2.5 Analysis 

The benefits estimates in this analysis were derived using modified versions of the health 

impact functions used in the PM NAAQS Regulatory Impact Analysis (RIA) (U.S. EPA, 2006) 

and the Ozone NAAQS RIA (U.S. EPA, 2010a).3 While many of the functions are identical to 

those used in the PM NAAQS RIA, we have updated a few of the underlying assumptions over 

the last few years. For a detailed description of the underlying functions, studies, baseline 

incidence rates, and population data used in this analysis, please refer to Chapter 5 of the recently 

proposed Transport Rule (U.S. EPA, 2010e). Table 7-1 identifies which human health and welfare 

endpoints are included in the monetized benefits and which endpoints remain unquantified. In 

summary, the estimate of monetized PM benefits includes premature mortality and eleven 

morbidity endpoints, and the estimate of monetized ozone benefits includes premature mortality 

and five morbidity endpoints. 

 

3

 All of the RIAs cited in this analysis are available on EPA’s website at http://www.epa.gov/ttn/ecas/ria.html. 

Census 

Population Data 

Baseline and PostControl PM2.5

Concentrations 

PM2.5 Health 

Functions 

Economic 

Valuation 

Functions 

Population 

Projections 

PM2.5 Incremental 

Air Quality Change 

PM2.5 Related 

Health Impacts 

Woods & Poole 

Population 

Projections 

Background 

Incidence and 

Prevalence Rates 

Monetized PM2.5-

related Benefits 

Blue identifies a user-selected input within the BenMAP model 

Green identifies a data input generated outside of the BenMAP model 
7-9 

Table 7-1. Human Health and Welfare Effects of Air Pollutants Affected 

Pollutant/Effect Quantified and Monetized Unquantified 

PM: health a

Premature mortality based on cohort 

study estimates b

Low birth weight 

Pre-term births 

Premature mortality based on expert 

elicitation estimates 

Pulmonary function 

Nonfatal cardiovascular outcomes other than 

myocardial infarctions 

Chronic bronchitis 

Hospital admissions: respiratory and 

cardiovascular 

Chronic respiratory diseases other than chronic 

bronchitis 

Emergency room visits for asthma Non-asthma respiratory emergency room visits 

Nonfatal heart attacks (myocardial 

infarctions) 

UVb exposure (+/−)

c

Lower and upper respiratory illness 

Minor restricted activity days 

Work loss days 

Asthma exacerbations (among 

asthmatic populations 

Respiratory symptoms (among 

asthmatic populations) 

 Infant mortality 

PM: welfare 

 Visibility in Class I areas 

Household soiling 

Visibility in residential and non-class I areas 

 UVb exposure (+/−)

c

Global climate impacts c

Ozone: health 

Premature mortality based on shortterm study estimates 

Chronic respiratory damage 

Premature mortality due to long-term exposures 

Hospital admissions: respiratory Premature aging of the lungs 

Emergency room visits for asthma Non-asthma respiratory emergency room visits 

Minor restricted activity days UVb exposure (+/−)

c

 School loss days 

Ozone: welfare 

Decreased outdoor worker 

productivity 

Yields for: 

– Commercial forests 

– Fruits and vegetables, and 

– Other commercial and noncommercial crops 

 Damage to urban ornamental plants 

Recreational demand from damaged forest 

aesthetics 

 Ecosystem functions 

 UVb exposure (+/−)

c

SO2: health 

 Respiratory hospital admissions 

 Asthma emergency room visits 

 Asthma exacerbation 

 Acute respiratory symptoms 

 Premature mortality 

 Pulmonary function 

(continued) 
7-10 

Table 7-1. Human Health and Welfare Effects of Air Pollutants Affected (continued) 

Pollutant/Effect Quantified and Monetized Unquantified 

SO2: welfare 

Commercial fishing and forestry from acidic 

deposition 

Recreation in terrestrial and aquatic ecosystems 

from acid deposition 

 Increased mercury methylation 

Mercury: health 

 Incidence of neurological disorders 

 Incidence of learning disabilities 

 Incidences in developmental delays 

 Potential cardiovascular effects including: 

– Altered blood pressure regulation 

– Increased heart rate variability 

– Incidences of heart attack 

Potential reproductive effects 

Mercury: 

environment 

 Impact on birds and mammals (e.g., reproductive 

effects, cardiovascular effects) 

Mercury: welfare Impacts to commercial., subsistence and 

recreational fishing 

HAPs Health effects associated with HAP exposure 

a

 In addition to primary economic endpoints, there are a number of biological responses that have been associated 

with PM health effects including morphological changes and altered host defense mechanisms. The public health 

impact of these biological responses may be partly represented by our quantified endpoints. 

b

 Cohort estimates are designed to examine the effects of long-term exposures to ambient pollution, but relative risk 

estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion 

of this issue). While some of the effects of short-term exposure are likely to be captured by the cohort estimates, 

there may be additional premature mortality from short term PM exposure not captured in the cohort estimates 

included in the primary analysis. 

c

 May result in benefits or disbenefits. 

7.2.4 Estimating PM2.5-related Premature Mortality 

Consistent with all RIAs since the proposal RIA for 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 characterization of uncertainty. 

 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 

benefits estimate. When calculating the estimate, EPA applied the effect 

coefficient as reported in the study without an adjustment for assumed 

concentration threshold of 10 μg/m3 as was done in recent (2006–2009) Office of 

Air and Radiation RIAs. 

 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 current body of scientific 
7-11 

literature, EPA now estimates PM-related mortality without applying an assumed 

concentration threshold. EPA’s Integrated Science Assessment for Particulate 

Matter (U.S. EPA, 2009c), 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 loglinear model most adequately portrays the PM-mortality concentration-response 

relationship while recognizing potential uncertainty about the exact shape of the 

concentration-response function. Since then, the Health Effects Subcommittee 

(U.S. EPA-SAB, 2010) of EPA’s Council concluded, “The HES fully supports 

EPA’s decision to use a no-threshold model to estimate mortality reductions. This 

decision is supported by the data, which are quite consistent in showing effects 

down to the lowest measured levels. Analyses of cohorts using data from more 

recent years, during which time PM concentrations have fallen, continue to report 

strong associations with mortality. 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. When calculating the estimate, EPA applied the effect coefficient 

as reported in the study without an adjustment for assumed concentration threshold 

of 10 μg/m3 as was done in recent (2006–2009) RIAs. 

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

study (IEc, 2006; Roman et al., 2008) on the PM2.5-mortality relationship and 

interpreted for 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 function provided by each of the twelve experts, to better 

characterize the degree of variability in the expert responses. 

Therefore, there is no evidence to support a truncation of the CRF [concentration-response 

function].” 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. For a summary of these scientific review statements and the panel members 

commenting on thresholds since 2002, please consult the Technical Support Document (TSD) 

Summary of Expert Opinions on the Existence of a Threshold (U.S. EPA, 2010d). 

Consistent with the recent scientific advice summarized above, we are replacing the 

previous threshold sensitivity analysis with a new “Lowest Measured Level” (LML) assessment. 

This approach summarizes the distribution of avoided PM mortality impacts according to the 

baseline PM2.5 levels experienced by the population receiving the PM2.5 mortality benefit. In the 

results section, we identify on the figures the lowest air quality levels measured in each of the 
7-12 

primary cohort studies that estimate PM-related mortality. This information allows readers to 

determine the portion of PM-related mortality benefits occurring above or below the LML of each 

study; in general, our confidence in the estimated PM mortality decreases as we consider air 

quality levels further below the LML in the two epidemiological studies. While an LML 

assessment provides some insight into the level of uncertainty in the estimated PM mortality 

benefits, EPA does not view the LML as a threshold and continues to quantify PM-related 

mortality impacts using a full range of modeled air quality concentrations. Unlike an assumed 

threshold, which is a modeling assumption that reduces the magnitude of the estimated health 

impacts, the LML is a characterization of the fraction of benefits that are more uncertain. It is 

important to emphasize that just because we have greater confidence in the benefits above the 

LML, this does not mean that we have no confidence that benefits occur below the LML. 

Analyses of these cohorts using data from more recent years, during which time PM 

concentrations have fallen, continue to report strong associations with mortality. As we model 

mortality impacts among populations exposed to levels of PM2.5 that are successively lower than 

the LML of each study, our confidence in the results diminishes. As air pollution emissions 

continue to decrease over time, there will be more people in areas where we do not have 

published epidemiology studies. However, each successive cohort study has shown evidence of 

effects at successively lower levels of PM2.5. As more cohort studies follow large populations 

over time, we will likely have more studies with lower LML as air quality levels continue to 

improve. Even in the absence of a definable threshold, we have more confidence in the benefits 

estimates above the LML of the large cohort studies. To account for the uncertainty in each of the 

studies that we base our mortality estimates on, we provide the LML for each of the cohort 

studies. However, the finding of effects at the lowest LML from recent studies indicates that 

confidence in PM2.5-related mortality effects down to at least 7.5 μg/m3 is high. 

In implementing these rules, emission controls may lead to reductions in ambient PM2.5 

below the PM NAAQS in some areas. While benefits occurring below the standard may be 

somewhat more uncertain than those occurring above the standard, EPA considers them to be 

legitimate components of the total benefits estimate. Furthermore, given that the epidemiological 

literature in most cases has not provided estimates based on threshold models, there would be 

additional uncertainties imposed by assuming thresholds or other non-linear concentrationfunctions for the purposes of benefits analysis. 
7-13 

7.2.5 Economic Valuation of Health Impacts 

These models assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality because the scientific evidence is not yet sufficient 

to allow differentiation of effect estimates by particle type (U.S. EPA, 2009c). Directly emitted 

PM, SO2, and VOC are the dominant PM2.5 precursors affected by this rule. Even though we 

assume that all fine particles have equivalent health effects, the benefit-per-ton estimates 

(described in detail in Section 7.2.6) vary between precursors because each ton of precursor 

reduced has a different propensity to form PM2.5 and a different pattern of transport, resulting 

geographic distribution of exposure. When more people are exposed, the benefits per ton are 

greater. For example, SO2 tends to have a lower benefit-per-ton estimate than direct PM2.5

because sulfate particles formed from SO2 emissions can transport many miles, meaning that 

higher exposures may occur over areas with low populations. On the other hand, to the extent that 

direct PM2.5 emissions occur in high density population areas, exposures will tend to be higher 

there, leading to higher monetized health benefits for direct PM2.5 than for SO2 emissions. 

Economic Valuation of Health Impacts 

After quantifying the change in adverse health impacts, the final step is to estimate the 

economic value of these avoided impacts. Please refer to Table 5-11 in the recently proposed 

Transport Rule (U.S. EPA, 2010e) for a detailed description of the underlying valuation functions 

and the monetized unit values for each endpoint incorporated into this analysis.4 The monetized 

mortality benefits dominate the total benefits estimates. 

As is the nature of 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 (2004–2006), the Office of Air and Radiation (OAR) 

valued mortality risk reductions using a value-of-a-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 

 

4

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., 2014), 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. 
7-14 

$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) meta-analysis of 43 studies. The mean 

estimate of $5.5 million (2000$)5

 was also consistent with the mean VSL of $5.4 million 

estimated in the Kochi et al. (2006) meta-analysis. However, the 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)6

 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 $6.3 million (2000$).7

 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. 

 

5

After adjusting the VSL to account for a different currency year (2008$) and to account for income growth to 2015, 

the $5.5 million VSL is $7.9 million. 6

In the (draft) update of the Economic Guidelines (U.S. EPA, 2008b), 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. 7

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 $9.1 million. 
7-15 

7.2.6 Calculating Boiler Sector-specific Benefit-per-ton Estimates 

Benefit per-ton (BPT) estimates can be used to quantify the health impacts and monetized 

human health benefits of an incremental change in air pollution precursor emissions. In situations 

when we are unable to specifically model a regulatory option because of data or resource 

constraints, this approach can provide a reliable estimate of the benefits of emission reduction 

scenarios. EPA has used the BPT technique in previous RIAs, including the recent Ozone 

NAAQS RIA (U.S. EPA, 2010a) and Federal Transport Rule RIA (U.S. EPA, 2010e). For this 

analysis, the emissions inventories used in the air quality modeling and benefits modeling are 

slightly different from the final boiler sector emission inventories reflected in the rules’ emission 

memos. 8 These discrepancies exist because emissions and air quality modeling decisions are 

made early in the analytical process, and the sheer number of boilers (over 180,000 area boilers 

and 13,000 major boilers) made it impossible to reconcile the data completely. Part of the 

discrepancy is because the emissions inventory categorizes boilers as “point” and “non-point” 

instead of “major source” or “area source.” We assume that the boilers in the point source 

inventory are appropriate surrogates for the major source boilers and that the boilers in the nonpoint source inventory are appropriate surrogates for the area source boilers. Furthermore, the 

emission inventories used for air quality modeling do not reflect emissions from new sources 

anticipated to come online by the analysis year. To address concerns about the discrepancies 

between these inventories, we utilized the air quality modeling to derive boiler sector-specific 

BPT estimates, which are then multiplied by the tons of emissions reduced for each regulatory 

option. 

To derive the BPT estimates for this analysis, we: 

1. Quantified the PM2.5 -related human health and monetized benefits of the SO2 emission 

reductions for major source boilers. We first quantified the health impacts and 

monetized benefits of total PM2.5 mass formed from the SO2 reductions, allowing us to 

isolate the PM air quality impacts from SO2 reductions alone. This procedure allowed 

us to develop PM2.5 BPT estimates that quantified the PM2.5-related benefits of 

incremental changes in SO2 emissions. 

2. Divided the health impacts and monetized benefits by the emission reduction. This 

calculation yields BPT estimates for PM-related SO2. The resulting BPT estimates 

 

8

Technical details regarding the emissions inventory used in the air quality modeling are available in the Air Quality 

Modeling TSD (U.S. EPA, 2010f). Technical details regarding the emission reductions estimated for this rule are 

available in these docket memos: “Revised Development of Baseline Emission Factors for Boilers and Process 

Heaters at Commercial, Industrial, and Institutional Facilities” and “Revised Methodology for Estimating Cost and 

Emissions Impacts for Industrial, Commercial, Institutional Boilers and Process Heaters National Emission 

Standards for Hazardous Air Pollutants.” 
7-16 

were then multiplied by the projected SO2 emission reductions to produce an estimate 

of the PM-related health impacts and monetized benefits. 

This process was repeated for directly emitted PM2.5 from major sources and for PM2.5 and 

SO2 emissions from area source boilers. This process was also repeated using the 2.5th percentile 

and 97.5th percentile benefits to provide confidence intervals for the BPT estimates.9 To calculate 

the total benefits, we simply multiply the BPT estimate by the emission reductions of directly 

emitted PM2.5 and SO2 for each regulatory option. Deriving boiler sector-specific BPT estimates 

provides equivalent benefits results as scaling the air quality data directly to correspond to the 

inventory differences, but it provides greater flexibility to examine alternate regulatory scenarios. 

The BPT estimates used in this analysis are different from the BPT estimates used in the 

proposal analysis. While these boiler sector-specific BPT estimates are methodologically 

consistent with the BPT estimates in Fann, Fulcher, and Hubbell (2009) that were used in the 

proposal analysis, the BPT estimates applied in this final analysis provide a different and likely 

superior representation of the health impacts and monetized benefits from boilers compared to the 

proposal. This is because the BPT used in the proposal lumped emissions from major boilers 

together with other emission sources in the non-EGU category and area boilers together with 

other emission sources in the area category. For example, the non-EGU category includes 

emissions from many different large stationary sources, including boilers, metal production, 

mineral products, chemical manufacturing, petroleum industry, pulp and paper production, and oil 

and gas production, and waste disposal, which all have different geographic distributions across 

the country. Because the geographic distribution of boilers is not likely to exactly match the 

average distribution of all of these sources, ambient air quality and the associated benefits for 

reducing boiler emissions are going to be different than for the entire non-EGU category. In 

addition to the location of the emission sources, the geographic differences also affect the benefits 

to due to geographic differences in local air chemistry, meteorology, population density, and 

baseline health incidence. 

In addition, the air quality modeling for these rules reflected higher spatial resolution (12 

km by 12 km nationally) than the previous BPT estimates and reflects an updated baseline 

emissions inventory.10 We believe that these updates result in benefits estimates that are more 

 

9

These confidence intervals only reflect the standard errors within the epidemiology studies and valuation functions, 

but they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. 10For more information on the changes in the emissions inventory, please see Table 2-2 of U.S. EPA, (2010g), which 

provides a list of the differences between the 2002 and 2005 base inventory. In addition to including consent 

decrees and several recent mobile source rules, the emissions platform used for this analysis also removed 

duplicates and plant closures and an updated version of IPM to project EGU emissions. 
7-17 

accurate than the proposal. For comparison, the BPT for directly emitted PM2.5 in this analysis is 

lower for major and area sources than the proposal, whereas the BPT for SO2 is higher in this 

analysis than the proposal. In addition, we have updated the analysis year used in this final 

analysis to be consistent with the implementation timeline, but this revision would not have a 

significant impact on the benefits results. In the proposal, the BPT estimates reflected emissions 

inventories, income growth, and population growth for 2015 because we did not have BPT 

estimates available for the proposal analysis year (2013). For this final analysis, the emissions 

inventories are for 2016, but income growth and population growth are for the final analysis year 

(2014). Because the projected emissions inventory does not assume growth in the boiler sector, 

the boiler benefits are not likely to change due to a 2016 inventory instead of a 2014 inventory. 

We provide the boiler-specific BPT estimates in Section 7.3. We also provide maps of the total 

ambient PM2.5 impacts from the modeled point and non-point boilers in Figures 7-4 through 7-7. 

Figure 7-4. Change in Ambient PM2.5 Levels from SO2 Emissions from Point Source Boilers 

No change

0.01 - 0.10

0.10 - 0.50

0.50 - 1.00

1.00 - 3.05
7-18 

Figure 7-5. Change in Ambient PM2.5 Levels from PM Emissions from Point Source Boilers 

Figure 7-6. Change in Ambient PM2.5 Levels from SO2 Emissions from Non-point Source 

Boilers 

No change

0.01 - 0.10

0.10 - 0.50

0.50 - 1.00

1.00 - 3.05

No change

0.01 - 0.10

0.10 - 0.50

0.50 - 1.00

1.00 - 3.05
7-19 

Figure 7-7. Change in Ambient PM2.5 Levels from PM Emissions from Non-point Source 

Boilers 

Although VOCs are also precursors to PM2.5, we have not monetized their contribution to 

the PM2.5 benefits in this analysis for several reasons. Analysis of organic carbon measurements 

suggest only a fraction of secondarily formed organic carbon aerosols are of anthropogenic origin. 

The current state of the science of secondary organic carbon formation indicates that 

anthropogenic VOC contribution to secondary organic carbon aerosols is often lower than the 

biogenic (natural) contribution. Given that a fraction of secondarily formed organic carbon 

aerosols from anthropogenic VOC emissions and the extremely small amount of VOC emissions 

from this sector relative to the entire VOC inventory, it is unlikely this sector has a large 

contribution to ambient secondary organic carbon aerosols. Photochemical models typically 

estimate secondary organic carbon aerosols from anthropogenic VOC emissions to be less than 

0.1 μg/m3

. Given the resource requirements to apply source apportionment technology in the 

photochemical model for secondary organic carbon aerosols and that only a very small portion of 

secondary organic carbon aerosols (i.e., from anthropogenic sources) would be apportioned, this 

option was not employed to estimate the impact of this sector’s VOC emissions on secondary 

organic carbon aerosols. Lastly, the contribution of VOC reductions to total monetized PM2.5 

benefits would likely be very small for these rules. For example, while not an appropriate source 

No change

0.01 - 0.10

0.10 - 0.50

0.50 - 1.00

1.00 - 3.05
7-20 

for this application, if the BPT for VOC emissions from industrial point sources and EGUs from 

Fann, Fulcher, and Hubbell (2009) were applied to the boiler rule, the VOC-related benefits 

would be less than $6 million for the Boiler MACT. 

The differences between the VOC emissions inventories are comparatively small, and 

VOCs are only reduced by 20% for both of the Boiler MACT options. In addition, the complex 

non-linear chemistry of ozone formation introduces uncertainty to the development and 

application of a BPT estimate. Therefore, we used the scaled air quality modeling results directly 

to estimate the ozone benefits associated with VOC reductions. As the ozone-related benefits are 

very small, we do not believe that the differences in the VOC emissions inventory contribute 

much uncertainty to the overall benefits results. Because the VOC emission reductions for the 

Boiler Area Source Rule are less than 1%, we do not include the ozone-related benefits associated 

with that rule, as the benefits would be very small. 

7.3 Health Benefits Results 

The health benefits have increased since the proposal analysis for two reasons: (1) 

emission reductions of SO2 have increased, and (2) the BPT for SO2 has increased. The decreases 

in directly emitted PM2.5 emissions and lower BPT for PM2.5 since the proposal do not offset the 

increase in benefits from SO2 emission reductions. 

Tables 7-2 and 7-3 provide a summary of the monetized PM2.5 benefits (including the 

indirect PM co-benefits) using the anchor points of Pope et al. (2002) and Laden et al. (2006) at 

discount rates of 3% and 7% for the final Boiler MACT and Boiler Area Source Rule, 

respectively. Tables 7-4 and 7-5 provide the reductions in health incidences as a result of the 

reduction in ambient PM2.5 levels for the final Boiler MACT and Boiler Area Source Rule, 

respectively. Tables 7-6 and 7-7 provide the total monetized PM2.5 benefits derived from Pope et 

al. (2002) and Laden et al. (2006) as well as the expert elicitation for the final Boiler MACT and 

Boiler Area Source Rule, respectively. Table 7-8 provides the estimated reductions in health 

incidences and the monetized benefits associated with estimated reductions in ambient ozone 

concentrations. 
7-21 

Table 7-2. Summary of Monetized Benefits Estimates for Final Boiler MACT in 2014 

(millions of 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 

of 2008$ at 3%) 

Total Monetized 

Benefits (millions 

of 2008$ at 7%) 

Selected 

Option Direct PM2.5 29,007 $72,000 $180,000 $65,000 $160,000 $2,100 to $5,100 $1,900 to $4,600 

SO2 439,901 $46,000 $110,000 $42,000 $100,000 $20,000 to $49,000 $18,000 to $45,000

 Total $22,000 to $54,000 $20,000 to $49,000

Alternative 

Option Direct PM2.5 28,139 $72,000 $180,000 $65,000 $160,000 $2,000 to $5,000 $1,800 to $4,500 

SO2 337,514 $46,000 $110,000 $42,000 $100,000 $15,000 to $38,000 $14,000 to $34,000

 Total $17,000 to $43,000 $16,000 to $39,000

 a All estimates are for the implementation year (2014), and are rounded to two significant figures so numbers may 

not sum across columns. These models assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow 

differentiation of effect estimates by particle type. 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 do not include benefits from reducing HAP 

emissions, VOC emissions and ozone exposure, nor energy disbenefits associated with the increased emissions 

from additional energy usage described in the next section. These benefits reflect existing boilers and 47 new 

boilers anticipated to come online by 2014. 

Table 7-3. Summary of Monetized Benefits Estimates for Final Boiler Area Source Rule in 

2014 (millions of 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 of 

2008$ at 3%) 

Total 

Monetized 

Benefits 

(millions of 

2008$ at 7%) 

Proposed 

MACT 

Approach Direct PM2.5 590 $120,000 $290,000 $110,000 $260,000 $69 to $170 $63 to $150 

SO2 3,197 $41,000 $100,000 $37,000 $91,000 $130 to $320 $120 to $290 

 Total $200 to $490 $180 to $440 

Final 

MACT/ 

GACT 

Approach Direct PM2.5 678 $120,000 $290,000 $110,000 $260,000 $79 to $190 $72 to $180 

SO2 3,197 $41,000 $100,000 $37,000 $91,000 $130 to $320 $120 to $290 

 Total $210 to $520 $190 to $470 

a

 All estimates are for the implementation year (2014), and are rounded to two significant figures so numbers may 

not sum across columns. These models assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow 

differentiation of effect estimates by particle type. 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 do not include benefits from reducing HAP 

emissions, nor energy disbenefits associated with the increased emissions from additional energy usage described 

in the next section. 
7-22 

Table 7-4. Summary of Estimated Reductions in Health Incidences from PM2.5 for the 

Final Boiler MACT in 2014 (95th percentile confidence interval) a

 Selected Option Alternative Option 

Avoided Premature Mortality 

Pope et al. (2002) 2,500 2,000 

(1,600 – 8,100) (1,400 – 7,100) 

Laden et al. (2006) 6,500 5,100 

(6,200 – 19,000) (5,500 – 16,000) 

Woodruff (Infant mortality) 

10 8 

(0 – 58) (0 – 51) 

Avoided Morbidity 

Chronic Bronchitis 1,600 1,300 

(340 – 6,000) (300 – 5,200) 

Acute Myocardial Infarction 4,000 3,100 

(2,400 – 13,000) (2,100 – 11,000) 

Hospital Admissions, Respiratory 

610 480 

(520 – 1,800) (460 – 1,600) 

Hospital Admissions, Cardiovascular 1,300 1,000 

(1,800 – 3,000) (1,600 – 2,700) 

Emergency Room Visits, Respiratory 2,400 1,900 

(2,600 – 6,800) (2,300 – 6,000) 

Acute Bronchitis 3,700 2,900 

(0 – 15,000) (0 – 13,000) 

Work Loss Days 310,000 250,000 

(520,000 – 690,000) (460,000 – 600,000) 

Asthma Exacerbation 41,000 32,000 

(5,800 – 250,000) (5,000 – 220,000) 

Minor Restricted Activity Days 1,900,000 1,500,000 

(3,000,000 – 4,200,000) (2,600,000 – 3,700,000) 

Lower Respiratory Symptoms 44,000 35,000 

(37,000 – 130,000) (32,000 – 120,000) 

Upper Respiratory Symptoms 34,000 26,000 

(16,000 – 110,000) (14,000 – 99,000) 

a

 All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These 

models assume that all fine particles, regardless of their chemical composition, are equally potent in causing 

premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates 

by particle type. These estimates do not include benefits from reducing HAP emissions, VOC emissions and ozone 

exposure, nor energy disbenefits associated with the increased emissions from additional energy usage described in 

the next section. These confidence intervals only reflect the standard errors within the epidemiology studies, but 

they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. These benefits 

reflect existing boilers and 47 new boilers anticipated to come online by 2014. 
7-23 

Table 7-5. Summary of Estimated Reductions in Health Incidences from PM2.5 for the 

Final Boiler Area Source Rule in 2014 (95th percentile confidence interval)a

Proposed MACT Approach 

Final MACT/GACT 

Approach 

Avoided Premature Mortality 

Pope et al. (2002) 

23 24 

(8 – 38) (8 – 40) 

Laden et al. (2006) 

58 61 

(29 – 87) (31 – 91) 

Woodruff (Infant mortality) 

0 0 

(0 – 0) (0 – 0) 

Avoided Morbidity 

Chronic Bronchitis 

16 17 

(2 – 30) (2 – 31) 

Acute Myocardial Infarction 

38 40 

(12 – 64) (12 – 67) 

Hospital Admissions, Respiratory 

6 6 

(3 – 9) (3 – 9) 

Hospital Admissions, Cardiovascular 

12 13 

(8 – 14) (9 – 15) 

Emergency Room Visits, Respiratory 

20 21 

(11 – 28) (11 – 30) 

Acute Bronchitis 

37 38 

(0 – 77) (0 – 81) 

Work Loss Days 3,100 3,200 

(2,600 – 3,500) (2,800 – 3,700) 

Asthma Exacerbation 

400 420 

(29 – 1,200) (31 – 1,300) 

Minor Restricted Activity Days 18,000 19,000 

(15,000 – 21,000) (16,000 – 22,000) 

Lower Respiratory Symptoms 

430 460 

(190 – 680) (200 – 710) 

Upper Respiratory Symptoms 

330 350 

(83 – 580) (87 – 610) 

a

 All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These 

models assume that all fine particles, regardless of their chemical composition, are equally potent in causing 

premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates 

by particle type. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits 

associated with the increased emissions from additional energy usage described in the next section. These 

confidence intervals only reflect the standard errors within the epidemiology studies, but they do not reflect other 

sources of uncertainty inherent within the boiler-specific BPT estimates. These benefits reflect existing boilers and 

6,779 new boilers anticipated to come online by 2014. 
7-24 

Table 7-6. Summary of Monetized Benefits Estimates from PM2.5 for the Final Boiler 

MACT in 2014 (95th percentile confidence interval)a

 Selected Option Alternative Option 

 3% 7% 3% 7% 

Based on Epidemiology Literature 

Pope et al. (2002) $22,000 $20,000 $17,000 $16,000 

($1,900 – $68,000) ($1,600 – $62,000) ($1,500 – $53,000) ($1,300 – $49,000) 

Laden et al. (2006) $54,000 $49,000 $43,000 $39,000 

($4,900 – $160,000) ($4,300 – $140,000) ($3,800 – $120,000) ($3,400 – $110,000) 

Based on Expert Elicitation 

Expert A $58,000 $52,000 $45,000 $41,000 

($3,400 – $190,000) ($3,000 – $170,000) ($2,700 – $150,000) ($2,400 – $140,000) 

Expert B $44,000 $39,000 $34,000 $31,000 

($1,700 – $180,000) ($1,500 – $160,000) ($1,300 – $140,000) ($1,200 – $130,000) 

Expert C $44,000 $40,000 $35,000 $31,000 

($2,600 – $170,000) ($2,300 – $150,000) ($2,100 – $130,000) ($1,800 – $120,000) 

Expert D $31,000 $28,000 $24,000 $22,000 

($2,100 – $100,000) ($1,800 – $91,000) ($1,600 – $78,000) ($1,400 – $71,000) 

Expert E $72,000 $65,000 $56,000 $51,000 

($6,200 – $210,000) ($5,500 – $190,000) ($4,900 – $170,000) ($4,400 – $150,000) 

Expert F $40,000 $36,000 $31,000 $28,000 

($3,900 – $120,000) ($3,500 – $110,000) ($3,100 – $92,000) ($2,700 – $84,000) 

Expert G $26,000 $24,000 $21,000 $19,000 

($250 – $95,000) ($160 – $86,000) ($190 – $74,000) ($120 – $68,000) 

Expert H $33,000 $30,000 $26,000 $24,000 

($300 – $130,000) ($210 – $120,000) ($240 – $100,000) ($160 – $92,000) 

Expert I $44,000 $39,000 $34,000 $31,000 

($2,500 – $140,000) ($2,200 – $130,000) ($1,900 – $110,000) ($1,700 – $100,000) 

Expert J $36,000 $32,000 $28,000 $25,000 

($2,700 – $140,000) ($2,400 – $120,000) ($2,100 – $110,000) ($1,900 – $97,000) 

Expert K $8,400 $7,800 $6,600 $6,100 

($250 – $54,000) ($160 – $50,000) ($190 – $43,000) ($120 – $39,000) 

Expert L $29,000 $27,000 $23,000 $21,000 

($1,200 – $110,000) ($1,000 – $100,000) ($930 – $89,000) ($790 – $81,000) 

a

 All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These 

models assume that all fine particles, regardless of their chemical composition, are equally potent in causing 

premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates 

by particle type. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits 

associated with the increased emissions from additional energy usage described in the next section. These 

confidence intervals only reflect the standard errors within the epidemiology studies and valuation functions, but 

they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. These benefits 

reflect existing boilers and 47 new boilers anticipated to come online by 2014. 
7-25 

Table 7-7. Summary of Monetized Benefits Estimates from PM2.5 for the Final Boiler Area 

Source Rule in 2014 (95th percentile confidence interval)a

 Proposed MACT Approach Final MACT/GACT Approach 

 3% 7% 3% 7% 

Based on Epidemiology Literature 

Pope et al. (2002) $200 $180 $210 $190 

($17 – $610) ($15 – $560) ($18 – $650) ($16 – $590) 

Laden et al. (2006) $490 $440 $520 $470 

($44 – $1,400) ($39 – $1,300) ($46 – $1,500) ($41 – $1,400) 

Based on Expert Elicitation 

Expert A $520 $470 $550 $490 

($30 – $1,700) ($27 – $1,500) ($32 – $1,800) ($28 – $1,600) 

Expert B $390 $360 $410 $370 

($15 – $1,600) ($14 – $1,500) ($16 – $1,700) ($14 – $1,500) 

Expert C $400 $360 $420 $380 

($24 – $1,500) ($21 – $1,400) ($25 – $1,600) ($22 – $1,400) 

Expert D $280 $250 $300 $270 

($19 – $900) ($17 – $820) ($20 – $950) ($17 – $860) 

Expert E $640 $580 $680 $610 

($56 – $1,900) ($50 – $1,700) ($59 – $2,000) ($52 – $1,800) 

Expert F $360 $330 $380 $340 

($35 – $1,100) ($31 – $960) ($37 – $1,100) ($33 – $1,000) 

Expert G $240 $220 $250 $230 

($2.1 – $850) ($1.6 – $780) ($2.2 – $900) ($1.6 – $820) 

Expert H $300 $270 $310 $280 

($2.6 – $1,200) ($2.0 – $1,000) ($2.7 – $1,200) ($2.1 – $1,100) 

Expert I $390 $350 $410 $370 

($22 – $1,300) ($20 – $1,200) ($23 – $1,400) ($21 – $1,200) 

Expert J $320 $290 $340 $300 

($24 – $1,200) ($22 – $1,100) ($25 – $1,300) ($23 – $1,200) 

Expert K $77 $71 $81 $75 

($2.1 – $490) ($1.6 – $450) ($2.2 – $520) ($1.6 – $480) 

Expert L $270 $240 $280 $250 

($11 – $1,000) ($10 – $930) ($12 – $1,100) ($10 – $970) 

a

 All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These 

models assume that all fine particles, regardless of their chemical composition, are equally potent in causing 

premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates 

by particle type. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits 

associated with the increased emissions from additional energy usage described in the next section. These 

confidence intervals only reflect the standard errors within the epidemiology studies and valuation functions, but 

they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. These benefits 

reflect existing boilers and 6,779 new boilers anticipated to come online by 2014. 
7-26 

Table 7-8. Summary of Monetized Benefits Estimates from Ozone for the Final Boiler 

MACT in 2014 (95th percentile confidence interval)a

Avoided Premature Mortality Incidence 

Valuation 

(millions of 2008$) 

NMMAPS 

Bell et al. (2004) 

0 $3.3 

(0 – 1) ($0.27 – $9.8) 

Schwartz (2005) 

1 $5.0 

(0 – 1) ($0.41 – $15) 

Huang and Bell (2005) 

1 $5.5 

(0 – 1) ($0.46 – $16) 

Meta-analyses 

Bell et al. (2005) 

1 $11 

(1 – 2) ($0.94 – $31) 

Ito et al. (2005) 

2 $15 

(1 – 2) ($1.4 – $41) 

Levy et al. (2005) 

2 $15 

(1 – 2) ($1.4 – $41) 

Avoided Morbidity 

Adult Hospital Admissions, Respiratory 

3 $0.07 

(0 – 5) ($0.01 – $0.12) 

Infant Hospital Admissions, Respiratory 

2 $0.03 

(1 – 4) ($0.01 – $0.04) 

Emergency Room Visits, Respiratory 

2 < $0.01 

(0 – 5) ($0.00 – $0.01) 

School Loss Days 

810 $0.08 

(330 – 1,200) ($0.03 – $0.11) 

Worker Productivity N/A 

$0.02 

($0.02 – $0.02) 

Minor Restricted Activity Days 2,300 $0.15 

(1,100 – 3,600) ($0.06 – $0.28) 

a

 All estimates are for the analysis year (2014) and are rounded to two significant figures. Health effects associated 

with ozone exposure are assumed to occur within the analysis year. Therefore, the monetized benefits are the same 

for any discount rate. These confidence intervals reflect the standard errors within the epidemiology studies and 

valuation functions. These benefits reflect existing boilers and 47 new boilers anticipated to come online by 2014. 
7-27 

Figures 7-8 and 7-9 illustrate the relative breakdown of the estimated monetized health 

benefits associated with changes in PM2.5 and ozone, respectively. Figure 7-10 shows the total 

combined monetized benefits for the final Boiler MACT and Area Source Rule at discount rates 

of 3% and 7%. Figures 7-11 and 7-12 provide a breakdown of the estimated monetized PM2.5 

benefits by precursor pollutant for the final Boiler MACT and Boiler Area Source Rule, 

respectively. Figures 7-13 and 7-14 provide a breakdown of the monetized PM2.5 benefits by 

subcategory for the final Boiler MACT and Boiler Area Source Rule, respectively. 

This analysis shows that the majority of the population is exposed to PM levels at or 

above the lowest LML of the cohort studies (Figures 7-15 and 7-16), increasing our confidence in 

the PM mortality analysis. Because we used BPT estimates, we are unable to provide an estimate 

of the mortality impacts that occur at various PM levels. Figure 7-15 shows a bar chart of the 

percentage of the adult population exposed to each PM2.5 level in the baseline. Figure 7-16 shows 

a cumulative distribution function of the same data. Both figures identify the LML for each of the 

major cohort studies. 

Using the Pope et al. (2002) study, approximately 79% of the population is exposed to 

annual mean PM2.5 levels at or above the LML of 7.5 μg/m3. Using the Laden et al. (2006) study, 

34% of the population is exposed to annual mean PM2.5 levels at or above the LML of 10 μg/m3. 

As we model mortality impacts among populations exposed to levels of PM2.5 that are 

successively lower than the LML of the lowest cohort study, our confidence in the results 

diminishes. However, the analysis above confirms that the great majority of the impacts occur at 

or above the lowest cohort study’s LML. It is important to emphasize that we have high 

confidence in PM2.5-related effects down to the lowest LML of the major cohort studies. 
7-28 

Figure 7-8. Breakdown of Monetized PM2.5 Health Benefits Estimates 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. 
7-29 

Figure 7-9. Breakdown of Monetized Ozone Health Benefits Estimates using Mortality 

Function from Bell et al. (2004)a

a

 This pie chart breakdown is illustrative, using the results based on Bell et al. (2004) as an example. Using the Levy 

et al. (2005) function for premature mortality, the percentage of total monetized benefits due to mortality would be 

98%. 
7-30 

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

Pope et al.

Laden et al.

Millions(2008$)

Benefits estimates derived from2 epidemiology functions and 12 expert functions

Benefits  3%

Benefits 7%

Figure 7-10. Total Monetized PM2.5 and Ozone Benefits Estimates for the Final Boiler 

MACT and Boiler Area Source Rule in 2014a

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

Pope et al. (2002) study and the Laden et al. (2006) 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. These 

estimates do not include benefits from reducing HAP emissions, but they do include the energy disbenefits 

associated with the increased emissions from additional energy usage. Due to methodology and time limitations 

under the court-ordered schedule, we were unable to monetize the benefits associated with several categories of 

benefits, including direct exposure to HAPs and SO2, as well as ecosystem effects, and visibility effects. These 

benefits reflect existing boilers and new boilers anticipated to come online by 2014. 
7-31 

Figure 7-11. Breakdown of Monetized PM2.5 BenefitsEstimates by Precursor for the Final 

Boiler MACT in 2014a

Figure 7-12. Breakdown of Monetized PM2.5 BenefitsEstimates by Precursor for the Final 

Boiler Area Source Rule in 2014a
7-32 

Figure 7-13. Breakdown of Monetized PM2.5 BenefitsEstimates by Subcategory for the 

Final Boiler MACT in 2014a

Figure 7-14. Breakdown of Monetized PM2.5 BenefitsEstimates by Subcategory for the 

Final Boiler Area Source Rule in 2014a
7-33 

0%

5%

10%

15%

20%

25%

1 2 3 4 5 6 7 7.5 8 9 10 11 12 13 14 15 16 17 18 19 20

Baseline Annual Mean PM2.5 Level(μg/m3)

LML of Laden et

al. (2006) study

LML of Pope et

al. (2002) study

Figure 7-15. Percentage of Population Exposed to Baseline Air Quality Levels for Final 

Boiler MACT and Boiler Area Source Rulea

a Approximately 79% of the population is exposed to baseline exposure to annual mean PM2.5 levels at or above 7.5 

μg/m3

, which is the lowest air quality level considered in the ACS cohort study by Pope et al. (2002).
7-34 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 7.5 8 9 10 11 12 13 14 15 16 17 18 19 20

Baseline Annual MeanPM2.5 Level(μg/m3)

LML of Pope et

al. (2002) study

LML of Laden et al. (2006)

study

Figure 7-16. Cumulative Percentage of Population Exposed to Baseline Air Quality Levels 

for Final Boiler MACT and Boiler Area Source Rulea 

a Approximately 79% of the population is exposed baseline exposure to annual mean PM2.5 levels at or above 7.5 

μg/m3

, which is the lowest air quality level considered in the ACS cohort study by Pope et al. (2002).

7.4 Energy Disbenefits 

In this section, we provide an estimate of the energy disbenefits associated with the 

increased emissions from additional energy usage.11 Electricity usage associated with the 

operation of control devices is anticipated to increase emissions of pollutants from electric utility 

boilers (EGU boilers) that supply electricity to the non-EGU boiler facilities. We estimate 

emission increases of 910,000 tpy CO2 for major boilers and 22,000 tpy CO2 for area boilers. 

7.4.1 Social Cost of Carbon and Greenhouse Gas Disbenefits 

EPA has assigned a dollar value to reductions in carbon dioxide (CO2) emissions using 

recent estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized 

damages associated with an incremental increase in carbon emissions in a given year. It is 

 

11As we use the term “energy disbenefits” in this analysis, we are not referring to the cost of purchasing additional 

electricity or fuel. 
7-35 

intended to include (but is not limited to) changes in net agricultural productivity, human health, 

property damages from increased flood risk, and the value of ecosystem services due to climate 

change. The SCC estimates used in this analysis were developed through an interagency process 

that included EPA and other executive branch entities, and concluded in February, 2010. EPA 

first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking to 

establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel 

Economy Standards; see the rule’s preamble for discussion about application of SCC (75 FR 

25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a complete 

discussion of the methods used to develop these SCC estimates.12 

The interagency group selected four SCC values for use in regulatory analyses, which we 

have applied in this analysis: $5, $21, $35, and $65 per metric ton of CO2 emissions13 in 2010, in 

2007 dollars. The first three values are based on the average SCC from three integrated 

assessment models, at discount rates of 2.5, 3, and 5 percent, respectively. SCCs at several 

discount rates are included because the literature shows that the SCC is quite sensitive to 

assumptions about the discount rate, and because no consensus exists on the appropriate rate to 

use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all 

three models at a 3 percent discount rate. It is included to represent higher-than-expected impacts 

from temperature change further out in the tails of the SCC distribution. Low probability, high 

impact events are incorporated into all of the SCC values through explicit consideration of their 

effects in two of the three models as well as the use of a probability density function for 

equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high 

temperature outcomes, which in turn lead to higher projections of damages. 

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that the interagency group estimated the growth rate of the SCC 

directly using the three integrated assessment models rather than assuming a constant annual 

 

12Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for Regulatory 

Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with 

participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, 

Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection 

Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, 

Office of Science and Technology Policy, and Department of Treasury (February 2010). Also available at 

http://www.epa.gov/otaq/climate/regulations.htm 13The interagency group decided that these estimates apply only to CO2 emissions. Given that warming profiles and 

impacts other than temperature change (e.g., ocean acidification) vary across GHGs, the group concluded 

“transforming gases into CO2-equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, 

would not result in accurate estimates of the social costs of non-CO2 gases” (SCC TSD, pg 13). 
7-36 

growth rate. This helps to ensure that the estimates are internally consistent with other modeling 

assumptions. The SCC estimates for the analysis years of 2014, in 2005 dollars are provided in 

Table 7-9. 

Table 7-9. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2014a

Discount Rate and Statistic SCC estimate (2008$) 

5% (Average) $5.7 

3% (Average) $24.2 

2.5% (Average) $39.1 

3% (95th percentile) $73.9 

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

When attempting to assess the incremental economic impacts of carbon dioxide emissions, 

the analyst faces a number of serious challenges. A recent report from the National Academies of 

Science (NRC, 2009) points out that any assessment will suffer from uncertainty, speculation, and 

lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and 

future emissions on the climate system, (3) the impact of changes in climate on the physical and 

biological environment, and (4) the translation of these environmental impacts into economic 

damages. As a result, any effort to quantify and monetize the harms associated with climate 

change will raise serious questions of science, economics, and ethics and should be viewed as 

provisional. 

The interagency group noted a number of limitations to the SCC analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. The limited amount of research linking climate impacts to economic damages makes the 

interagency modeling exercise even more difficult. The interagency group hopes that over time 

researchers and modelers will work to fill these gaps and that the SCC estimates used for 

regulatory analysis by the Federal government will continue to evolve with improvements in 

modeling. Additional details on these limitations are discussed in the SCC TSD. 

In light of these limitations, the interagency group has committed to updating the current 

estimates as the science and economic understanding of climate change and its impacts on society 

improves over time. Specifically, the interagency group has set a preliminary goal of revisiting 
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the SCC values within two years or at such time as substantially updated models become 

available, and to continue to support research in this area. 

Applying the global SCC estimates to the estimated increases in CO2 emissions for the 

range of policy scenarios, we estimate the dollar value of the climate-related disbenefits captured 

by the models for each analysis year. For internal consistency, the annual disbenefits are 

discounted back to NPV terms using the same discount rate as each SCC estimate (i.e., 5%, 3%, 

and 2.5%) rather than 3% and 7%.14 These estimates are provided in Table 7-10. 

Table 7-10. Monetized SCC-Derived Disbenefits of CO2 Emission Increases in 2014 (millions 

of 2008$)a

Discount Rate and 

Statistic 

Selected Option 

(Major) 

Alternative Option 

(Major) 

Proposed MACT 

Approach (Area) 

Final MACT/GACT 

Approach (Area) 

Tons of CO2 911,048 1,524,572 24,936 22,191 

5% (Average) $5.2 $8.7 $0.1 $0.1 

3% (Average) $22 $37 $0.6 $0.5 

2.5% (Average) $36 $60 $1.0 $0.9 

3% (95th percentile) $67 $113 $1.8 $1.6 

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

7.5 Unquantified or Nonmonetized Benefits 

The monetized benefits estimated in this RIA only reflect the portion of benefits 

attributable to the health impacts associated with exposure to 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 direct health benefits from reducing 

HAPs 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. In addition to health 

effects, ozone is associated with adverse vegetation effects to forests and crops. 

7.5.1 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 

 

14It is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be discounted at 

rates that differ from those used to develop the SCC estimates. 
7-38 

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, 

2008a). According to summary of the ISA in EPA’s risk and exposure assessment (REA) for the 

SO2 NAAQS “the immediate effect of SO2 on the respiratory system in humans is 

bronchoconstriction” (U.S. EPA, 2009b). In addition, the REA summarized from the ISA that 

“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 (U.S. 

EPA, 2009b). 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, respiratoryrelated 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 (U.S. EPA, 2008c). 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. 
7-39 

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) 

7.5.2 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 methodological and time limitations under the court-ordered schedule, we 

were unable to estimate the benefits associated with the 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, 2010b) 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. 
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7.5.3 Visibility Benefits 

Reducing SO2 and PM emissions would improve the level of visibility throughout the 

United States (U.S. EPA, 2009c). Fine particles with significant light-extinction efficiencies 

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

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

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

particularly sulfates, and higher average relative humidity levels. In fact, particulate sulfate is the 

largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually and 75% 

during summer). In the western U.S., particulate sulfate contributes to 20–50% of regional haze 

(U.S. EPA, 2009c). Visibility has direct significance to people’s enjoyment of daily activities and 

their overall sense of wellbeing. Good visibility increases the quality of life where individuals live 

and work, and where they engage in recreational activities. Due to time limitations under the 

court-ordered schedule, we were unable to estimate the monetized benefits associated with 

visibility improvements. Previous analyses (U.S. EPA, 2006; U.S. EPA, 2010e) show that 

visibility benefits are a significant welfare benefit category. As shown in Section 6.5.4 of this 

RIA, an average visibility improvement of 0.51 deciviews in annual 20% worst visibility days 

over all Class I area monitors is anticipated as a result of these rules. 

7.5.4 Ozone Vegetation Benefits 

Exposure to ozone has been associated with a wide array of vegetation and ecosystem 

effects in the published literature (U.S. EPA, 2010a). Sensitivity to ozone is highly variable across 

species, with over 65 plan species identified as “ozone-sensitive,” many of which occur in state 

and national parks and forests. These effects include those that damage or impair the intended use 

of the plant or ecosystem. Such effects are considered adverse to the public welfare and can 

include reduced growth and/or biomass production in sensitive plant species, including forest 

trees, reduced crop yields, visible foliar injury, reduced plant vigor (e.g., increased susceptibility 

to harsh weather, disease, insect pest infestation, and competition), species composition shift, and 

changes in ecosystems and associated ecosystem services. 

7.5.5 Direct HAP Benefits 

Americans are exposed to ambient concentrations of air toxics at levels which have the 

potential to cause adverse health effects.15 The levels of air toxics to which people are exposed 

vary depending on where people live and work and the kinds of activities in which they engage. 

In order to identify and prioritize air toxics, emission source types and locations that are of 

 

15U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 
7-41 

greatest potential concern, U.S. EPA conducts the National-Scale Air Toxics Assessment 

(NATA). The most recent NATA was conducted for calendar year 2002, and was released in June 

2009.16 NATA for 2002 includes four steps: 

1. Compiling a national emissions inventory of air toxics emissions from outdoor sources 

2. Estimating ambient concentrations of air toxics across the United States 

3. Estimating population exposures across the United States 

4. Characterizing potential public health risk due to inhalation of air toxics including 

both cancer and noncancer effects 

Noncancer health effects can result from chronic,17 subchronic,18 or acute19 inhalation 

exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory 

effects as well as effects on the immune and reproductive systems. According to the 2002 NATA, 

nearly the entire U.S. population was exposed to an average concentration of air toxics that has 

the potential for adverse noncancer respiratory health effects.20 Figures 7-17 and 7-18 depict 

estimated county-level carcinogenic risk and noncancer respiratory hazard from the assessment. 

Results from the 2002 NATA suggest that acrolein is the primary driver for noncancer respiratory 

risk.21 Large reductions in HAP emissions may not necessarily translate into significant reductions 

in health risk because toxicity varies by pollutant and whether or not there are exposures at or 

above levels of concern is not known. For example, acetaldehyde mass emissions are more than 

double acrolein emissions on a national basis, according to EPA’s 2005 National Emissions 

Inventory (NEI). However, the Integrated Risk Information System (IRIS) reference 

concentration (RfC) for acrolein is considerably lower than that for acetaldehyde, suggesting that 

acrolein could be potentially more toxic than acetaldehyde. Thus, it is important to 

 

16U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 17Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database 

(http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than 

approximately 10% of the life span in humans (more than approximately 90 days to 2 years in typically used 

laboratory animal species). 18Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an 

organism. 19Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 

20The NATA modeling framework has a number of limitations which prevent its use as the sole basis for setting 

regulatory standards. These limitations and uncertainties are discussed on the 2002 NATA website. Even so, this 

modeling framework is very useful in identifying air toxic pollutants and sources of greatest concern, setting 

regulatory priorities, and informing the decision making process. U.S. EPA. (2009) 2002 National-Scale Air 

Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 21 Details about the overall confidence of certainty ranking of the individual pieces of NATA assessments including 

both quantitative (e.g., model-to-monitor ratios) and qualitative (e.g., quality of data, review of emission 

inventories) judgments can be found at http://www.epa.gov/ttn/atw/nata/roy/page16.html. 
7-42 

Figure 7-17. Estimated County Level Carcinogenic Risk from HAP exposure from outdoor 

sources (2002 NATA) 

Figure 7-18. Estimated County Level Noncancer (Respiratory) Risk from HAP exposure 

from outdoor sources (2002 NATA) 
7-43 

account for the toxicity and exposure, as well as the mass of the targeted emissions when 

designing reduction strategies to maximize health benefits. 

Due to methodology and time limitations under the court-ordered schedule, we were 

unable to estimate the benefits associated with the hazardous air pollutants that would be reduced 

as a result of these rules. In a few previous analyses of the benefits of reductions in HAPs, EPA 

has quantified the benefits of potential reductions in the incidences of cancer and non-cancer risk 

(e.g., U.S. EPA, 1995). In those analyses, EPA relied on unit risk factors (URF) developed 

through risk assessment procedures.22 These URFs are designed to be conservative, and as such, 

are more likely to represent the high end of the distribution of risk rather than a best or most likely 

estimate of risk. As the purpose of a benefit analysis is to describe the benefits most likely to 

occur from a reduction in pollution, use of high-end, conservative risk estimates would 

overestimate the benefits of the regulation. While we used high-end risk estimates in past 

analyses, advice from the EPA’s Science Advisory Board (SAB) recommended that we avoid 

using high-end estimates in benefit analyses (U.S. EPA, 2002). Since this time, EPA has 

continued to develop better methods for analyzing the benefits of reductions in HAPs. 

In the Second Prospective 812 Analysis, EPA conducted a case study analysis of the 

health effects associated with reducing exposure to benzene in Houston from implementation of 

the Clean Air Act (IEc, 2009). While reviewing the draft report, EPA’s Advisory Council on 

Clean Air Compliance Analysis concluded that “the challenges for assessing progress in health 

improvement as a result of reductions in emissions of hazardous air pollutants (HAPs) are 

daunting...due to a lack of exposure-response functions, uncertainties in emissions inventories and 

background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of 

tracking health progress for diseases, such as cancer, that have long latency periods” (U.S. EPASAB, 2008). 

In 2009, EPA convened a workshop to address the inherent complexities, limitations, and 

uncertainties in current methods to quantify the benefits of reducing HAPs. Recommendations 

from this workshop included identifying research priorities, focusing on susceptible and 

vulnerable populations, and improving dose-response relationships (Gwinn et al., 2011). 

In summary, monetization of the benefits of reductions in cancer incidences requires 

several important inputs, including central estimates of cancer risks, estimates of exposure to 

 

22The unit risk factor is a quantitative estimate of the carcinogenic potency of a pollutant, often expressed as the 

probability of contracting cancer from a 70-year lifetime continuous exposure to a concentration of one μg/m3 of a 

pollutant. 
7-44 

carcinogenic HAPs, and estimates of the value of an avoided case of cancer (fatal and non-fatal). 

Due to methodology and time limitations under the court-ordered schedule, we do not have 

sufficient information on emissions from specific sources and thus are unable to model changes in 

population exposures to ambient concentrations of HAPs. For this reason, we did not attempt to 

quantify or monetized the health benefits of reductions in HAPs in this analysis.23 Instead, we 

provide a qualitative analysis of the health effects associated with the HAPs anticipated to be 

reduced by these rules. EPA remains committed to improving methods for estimating HAP 

benefits by continuing to explore additional concepts of benefits, including changes in the 

distribution of risk. 

Available emissions data show that boilers emit several different HAPs, 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 7-11 for the list of the major HAPs for each fuel type. These rules are 

anticipated to reduce a combined 113,000 tons of carbon monoxide, 30,000 tons of HCl, 830 tons 

of HF, 2,900 pounds of mercury, 3,000 tons of other metals, and 23 grams of dioxins/furans 

(TEQ) each year. We discuss the health effects associated with these top HAPs as well as the 

HAPs for which we have emission reduction estimates. 

Table 7-11. 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 

7.5.5.1 Mercury 

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

compound, methylmercury (U.S. EPA, 2008c). 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, 

 

23Due to time limitations under the court-ordered schedule for this rule, we were unable to model mercury 

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. However, it is important 

to emphasize, that we generally have more data and accepted methods to estimate mercury benefits than we have 

for other HAPs. 
7-45 

2008c). 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 NOx SOX Ecological ISA concluded that evidence is sufficient to infer a 

casual relationship between sulfur deposition and increased mercury methylation in wetlands and 

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

In addition to the role of sulfate deposition on methylation, these 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 (25%), inorganic divalent mercury (reactive 

gas phase mercury) (50%), and particulate bound mercury (25%) (U.S. EPA, 2010f). 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 

divalent 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 divalent mercury, providing a significant pathway 

for deposition of emitted elemental mercury. 

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 

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 from 291 streams across the country contained some 

methylmercury (Scudder, 2009). 
7-46 

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 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 cardiovascular effects.24

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), death from coronary heart disease or cardiovascular disease.25

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 postnatal exposures may have occurred as well. 

At seven years of age, these children exhibited an increase in blood pressure and a decrease in 

heart rate variability.26 Based on these and other studies, NRC concluded in 2000 that, “Although 

the data base is not as extensive as it is for other end points (i.e., neurologic effects) the 

cardiovascular system appears to be a target for methylmercury toxicity in humans and 

 

24National 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. 25Salonen, 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. 26Sorensen, 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. 
7-47 

animals.”27 NRC also stated that “additional studies are needed to better characterize the effect of 

methylmercury exposure on blood pressure and cardiovascular function at various stages of life.” 

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 were 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. 

Baseline emissions for major and area source boilers in the U.S. are about 11 tons of 

mercury in the air.28 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 11% of 

the total anthropogenic mercury emissions in the U.S. and about 0.5% of the global emissions. 

Overall, the major and area source rules would reduce mercury emissions by about 1.5 

tons (2,900 pounds, or a 13% reduction) per year from current levels, and therefore, contribute to 

reductions in mercury exposures and health effects. Due to time limitations under the courtordered schedule for this rule, we were unable to model mercury 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. However, we were able 

to model the change in mercury deposition using CAMx for the final Boiler MACT and Boiler 

Area Source Rule.29 These modeling results indicate significantly reduced total mercury 

 

27National 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. 28See “Revised Development of Baseline Emission Factors for Boilers and Process Heaters at Commercial, 

Industrial, and Institutional Facilities,” which available in the docket. 29See Section 6 of this RIA and U.S. EPA (2010f) for more information on the air quality modeling conducted for 

these rules. 
7-48 

deposition (wet and dry forms) in some areas, including reducing total deposition from all sources 

by an average of 1.4% in the East and 0.5% in the West in the analysis year. This modeling 

indicates that mercury deposition reductions tend to be greatest nearest the sources. Figure 7-19 

shows the percentage reduction in total mercury deposition as a result of the final Boiler MACT 

and Boiler Area Source Rule in the Eastern U.S., and Figure 7-20 shows the percentage reduction 

in mercury deposition in the Western U.S. based on the air quality modeling conducted for these 

rules. 

Figure 7-19. Percentage Reduction in Total Mercury Deposition in the Eastern U.S. 
7-49 

Figure 7-20. Percentage Reduction in Total Mercury Deposition in the Western U.S. 

7.5.5.2 Hydrogen Chloride (HCl) 30

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

and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 100 

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

to high concentrations can rapidly lead to swelling and spasm of the throat and suffocation. Most 

seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and 

narrowing of the bronchioles. Patients who have massive exposures may develop an accumulation 

of fluid in the lungs. Exposure to hydrogen chloride can lead to Reactive Airway Dysfunction 

Syndrome (RADS), a chemically- or irritant-induced type of asthma. Children may be more 

vulnerable to corrosive agents than adults because of the relatively smaller diameter of their 

airways. Children may also be more vulnerable to gas exposure because of increased minute 

ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has 

not been classified for carcinogenic effects. 

 

30All 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>. 
7-50 

7.5.5.3 Chlorine Gas (Cl2)

 31

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. 

7.5.5.4 Hydrogen Cyanide (HCN) 32

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. 

7.5.5.5 Hydrogen Fluoride (HF) 33

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. 

7.5.5.6 Toluene34

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 

 

31All 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. 32All 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. 33All 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 34All 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>. 
7-51 

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, and dizziness) as the most sensitive endpoint. 

7.5.5.7 Formaldehyde 

Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on 

evidence in humans and in rats, mice, hamsters, and monkeys.35 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 

malignancies such as leukemia among workers exposed to formaldehyde.36,37 In an analysis of the 

lymphohematopoietic cancer mortality from an extended follow-up of these workers, NCI 

 

35U.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. 36Hauptmann, 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. 37Hauptmann, 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. 
7-52 

confirmed an association between lymphohematopoietic cancer risk and peak exposures.38 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.39

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.40

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.41,42,43 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.44,45,46,47

These findings are not supportive of interpreting the CIIT model results as providing a 

conservative (health protective) estimate of human risk.48 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 

 

38Beane 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. 39Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup. 

Environ. Med. 61: 193-200. 40Coggon, 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. 41Conolly, 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. 42Conolly, 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. 43Chemical 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. 44U.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, 2006 45Subramaniam, R; Chen, C; Crump, K; et al. (2006) Uncertainties in biologically-based modeling of formaldehydeinduced cancer risk: identification of key issues. Risk Anal 28(4):907-923. 46Subramaniam, 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 47Crump, K; Chen, C; Fox, J; et al. (2006) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 48Crump, K; Chen, C; Fox, J; et al. (2006) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 
7-53 

to the compound’s tumorigenicity was found not to hold under variations of modeling 

assumptions.49

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.”50 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.51, 52

7.5.5.8 Polycyclic Aromatic Hydrocarbons (PAHs) 

At least seven PAH compounds are classified by EPA as probable human carcinogens 

based on animal data, including benzo(a)anthracene,53 benzo(b)fluoranthene,54

 

49Subramaniam, 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 50International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-tert-Butoxypropan-2-

ol. Monographs Volume 88. World Health Organization, Lyon, France. 51Agency 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 52WHO (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. 53U.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. 54U.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. 
7-54 

benzo(k)fluoranthene,55 benzo(a)pyrene,56 chrysene,57 dibenz(a,h)anthracene,58 and indeno(1,2,3-

cd)pyrene.59 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.60, 61 EPA has 

not yet evaluated these recent studies. 

7.5.5.9 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.62

Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS in the 11th

Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the 

IARC.63, 64 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.65 In short-term (4 week) rat studies, degeneration of olfactory 

 

55U.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. 56U.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. 57U.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 58U.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. 59U.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. 60Perera, 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. 61Perera, 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. 62U.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. 63U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens 

available at: http://ntp.niehs.nih.gov/go/16183. 64International 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. 65U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material is available 

electronically at http://www.epa.gov/iris/subst/0290.htm. 
7-55 

epithelium was observed at various concentration levels of acetaldehyde exposure.66 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.67 The 

agency is currently conducting a reassessment of the health hazards from inhalation exposure to 

acetaldehyde. 

7.5.5.10 Nickel68

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. 

7.5.5.11 Manganese69

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 

 

66Appleman, 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. 67Myou, 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. 68All 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 69All 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
7-56 

manganism attributed to inhalation exposures. The EPA has classified manganese in Group D, not 

classifiable as to carcinogenicity in humans. 

7.5.5.12 Dioxins (Chlorinated dibenzodioxins (CDDs) 70

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 

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. 

 

70All 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. 
7-57 

7.5.5.13 Furans (Chlorinated dibenzofurans (CDFs)) 71

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. 

7.5.5.14 Other Air Toxics 

In addition to the compounds described above, other compounds in gaseous hydrocarbon 

and PM emissions would be affected by these rules, including metal and organic HAPs. 

Information regarding the health effects of these compounds can be found in EPA’s IRIS 

database.72

7.6 Limitations and Uncertainties 

The National Research Council (NRC) (2002) concluded that EPA’s general methodology 

for calculating the benefits of reducing air pollution is reasonable and informative in spite of 

inherent uncertainties. To address these inherent uncertainties, NRC highlighted the need to 

conduct rigorous quantitative analysis of uncertainty and to present benefits estimates to 

decisionmakers in ways that foster an appropriate appreciation of their inherent uncertainty. In 

response to these comments, EPA’s Office of Air and Radiation (OAR) is developing a 

 

71All 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>. 72U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris 
7-58 

comprehensive strategy for characterizing the aggregate impact of uncertainty in key modeling 

elements on both health incidence and benefits estimates. Components of that strategy include 

emissions modeling, air quality modeling, health effects incidence estimation, and valuation. 

In this analysis, we use three methods to assess uncertainty quantitatively: Monte Carlo 

analysis, LML assessment, and alternate concentration-response functions for PM- and ozonerelated mortality. We also provide a qualitative assessment for those aspects that we are unable to 

address quantitatively in this analysis. Each of these analyses is described in the following 

sections. 

This analysis includes many data sources as inputs, including emission inventories, air 

quality data from models (with their associated parameters and inputs), population data, health 

effect estimates from epidemiology studies, and economic data for monetizing benefits. Each of 

these inputs may be uncertain and would affect the benefits estimate. When the uncertainties from 

each stage of the analysis are compounded, small uncertainties can have large effects on the total 

quantified benefits. In this analysis, we are unable to quantify the cumulative effect of all of these 

uncertainties, but we provide the following analyses to characterize many of the largest sources of 

uncertainty. 

7.6.1 Monte Carlo Analysis 

Similar to other recent RIAs, we used Monte Carlo methods for characterizing random 

sampling error associated with the concentration response functions and economic valuation 

functions. Monte Carlo simulation uses random sampling from distributions of parameters to 

characterize the effects of uncertainty on output variables, such as incidence of morbidity. 

Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated 

health impact and monetized benefits. The reported standard errors in the epidemiological studies 

determined the distributions for individual effect estimates, as shown in Tables 7-4 and 7-5. The 

confidence intervals around the monetized benefits in Tables 7-6 and 7-7 incorporate the 

epidemiology standard errors as well as the distribution of the valuation function. These 

confidence intervals do not reflect other sources of uncertainty inherent within the boiler-specific 

BPT estimates, nor do they reflect 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 

benefits estimates. 
7-59 

7.6.2 LML Assessment for PM2.5

PM2.5 mortality benefits are the largest benefit category that we monetized in this analysis. 

To better characterize the uncertainty associated with mortality impacts that are estimated to 

occur in areas with low baseline levels of PM2.5, we included the LML assessment. We have more 

confidence in the mortality impacts among populations exposed to levels of PM2.5 above the 

lowest LML of the large cohort studies, and our confidence in the results diminish as we model 

that are lower than the LML. While an LML assessment provides some insight into the level of 

uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold 

and continues to quantify PM-related mortality impacts using a full range of modeled air quality 

concentrations. It is important to emphasize that just because we have greater confidence in the 

benefits above the LML, this does not mean that we have no confidence that benefits occur below 

the LML. In Section 7.3, we provide the results of the LML assessment in Figures 7-15 and 7-16. 

7.6.3 Alternate Concentration-response Functions for PM Mortality 

PM2.5 mortality benefits are the largest benefit category that we monetized in this analysis. 

To better understand the concentration-response relationship between PM2.5 exposure and 

premature mortality, EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006). 

In general, the results of the expert elicitation support the conclusion that the benefits of PM2.5 

control are very likely to be substantial. In previous RIAs, EPA presented benefits estimates using 

concentration response functions derived from the PM2.5 Expert Elicitation as a range from the 

lowest expert value (Expert K) to the highest expert value (Expert E). However, this approach did 

not indicate the agency’s judgment on what the best estimate of PM benefits may be, and EPA’s 

Science Advisory Board described this presentation as misleading. Therefore, we began to present 

the cohort-based studies (Pope et al., 2002; and Laden et al., 2006) as our core estimates in the 

proposal RIA for the Portland Cement NESHAP (U.S. EPA, 2009a). Using alternate relationships 

between PM2.5 and premature mortality supplied by experts, higher and lower benefits estimates 

are plausible, but most of the expert-based estimates fall between the two epidemiology-based 

estimates (Roman et al., 2008). 

In this analysis, we present the results derived from the expert elicitation as indicative of 

the uncertainty associated with a major component of the health impact functions, and we provide 

the independent estimates derived from each of the twelve experts to better characterize the 

degree of variability in the expert responses. In this section, we provide the results using the 

concentration-response functions derived from the expert elicitation in both tabular (Tables 7-7 

and 7-8) and graphical form (Figure 7-10). Please note that these results are not the direct results 
7-60 

from the studies or expert elicitation; rather, the estimates are based in part on the concentrationresponse function provided in those studies. 

7.6.4 Alternate Concentration-response Functions for Ozone Mortality 

While particulate matter is the criteria pollutant most clearly associated with premature 

mortality, recent research suggests that short-term repeated ozone exposure also likely contributes 

to premature death. In 2008 the National Research Council (NRC) of the National Academy of 

Science (NAS) (NRC, 2008) issued a series of recommendations to the EPA regarding the 

quantification and valuation of ozone-related short-term mortality. Chief among these was that 

“...short-term exposure to ambient ozone is likely to contribute to premature deaths” and the 

committee recommended that “ozone-related mortality be included in future estimates of the 

health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatest 

emphasis be placed on the multicity and NMMAPS studies without exclusion of the metaanalyses” (NRC, 2008). 

In view of the findings of the NAS panel, we include used estimates of short-term ozone 

mortality from the Bell et al. (2004) NMMAPS analysis, the Schwartz (2005) multi-city study, the 

Huang and Bell (2005) multi-city study as well as effect estimates from the three meta-analyses 

(Bell et al., 2005; Levy et al., 2005; Ito et al., 2005). 

7.6.5 Qualitative Assessment of Uncertainty and Other Analysis Limitations 

Although we strive to incorporate as many quantitative assessments of uncertainty, there 

are several aspects for which we are only able to address qualitatively. These aspects are 

important factors to consider when evaluating the relative benefits of the attainment strategies for 

each of the alternative standards: 

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, the key assumptions underlying the estimates for premature mortality, 

which typically account for at least 90% of the total monetized benefits, include the following: 

1. 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 the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. 
7-61 

2. 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. 

3. 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). 

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 assume that emissions are reduced equally 

across all boilers and that the geographic distribution of boiler emissions will remain constant 

through the analysis year. As we did not model fuel subcategories, we assume that each fuel type 

is equally represented in the emissions and geographic distribution. Use of these $/ton values to 

estimate benefits associated with a specific fuel type (e.g., for reducing emissions from liquid 

fuel) may lead to higher or lower benefit estimates than if benefits were calculated based on direct 

air quality modeling of that fuel subcategory. Great care should be taken in applying these 

estimates to emission reductions occurring in any specific location, as these are all based on 

national emission reduction assumptions and therefore represent an average benefits-per-ton over 

the entire United States. The benefits- per-ton for variable emission reductions in specific 

locations may be very different from the estimates presented here. Despite our inability to fully 

characterize and quantify these relatively small uncertainties, we believe that, on net, the air 

quality impacts and associated monetized benefits are representative of the magnitude of benefits 

anticipated from these rules. 

As previously described, we strive to monetize as many of the benefits anticipated from 

these rules as possible, but the monetized benefits estimated in this RIA inevitably only reflect the 

portion of benefits. Specifically, only the benefits attributable to the health impacts associated 

with exposure to ambient fine particles and ozone have been monetized in this analysis. 

Methodological and time limitations under the court-ordered schedule for this rule prevented EPA 
7-62 

from quantifying or monetizing the benefits from several important benefit categories, including 

benefits from reducing toxic emissions, ecosystem effects, and visibility impairment. Data 

limitations include limited monitoring for HAPs, incomplete emissions inventories for HAPs, and 

limited photochemical air quality modeling for non-mercury HAPs. Resource limitations include 

limited staff and extramural funding in conjunction with a heavy regulatory workload, which led 

EPA to not model these endpoints. Methodological limitations include an absence of 

concentration-response functions for many HAP health effects, with issues such as exposure 

misclassification, small number of cases, confounding, and extrapolation of toxicological effects 

down to ambient levels. Despite our inability to monetize all of the benefit categories, the total 

combined monetized benefits still exceed the costs by a substantial margin. 

This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA (U.S. EPA, 2006). However, the results of the Monte Carlo analyses of the health 

and welfare benefits presented in Chapter 5 of the PM RIA can provide some evidence of the 

uncertainty surrounding the benefits results presented in this analysis. 

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8-1 

SECTION 8

COMPARISON OF MONETIZED BENEFITS AND COSTS 

8.1 Boiler MACT 

Because we were unable to monetize the benefits associated with reducing HAPs, all 

monetized benefits reflect improvements in ambient PM2.5 and ozone concentrations. This results 

in an underestimate of the monetized benefits. Using a 3% discount rate, we estimate the total 

monetized benefits of the major source rule to be $22 billion to $54 billion in the implementation 

year (2014) (Table 8-1). Using a 7% discount rate, we estimate the total monetized benefits to be 

$20 billion to $49 billion. The annualized costs are $1.5 billion at a 7% interest rate. The net 

benefits are $21 billion to $53 billion at a 3% discount rate for the benefits and $19 million to $48 

billion at a 7% discount rate. All estimates are in 2008$. The benefits from reducing other air 

pollutants have not been monetized in this analysis, including reducing 112,000 tons of carbon 

monoxide, 30,000 tons of HCl, 820 tons of HF, 2,800 pounds of mercury, 2,700 tons of other 

metals, and 23 grams of dioxins/furans (TEQ) each year. 

8.2 Boiler Area Source Rule 

Because we were unable to monetize the benefits associated with reducing HAPs, all 

monetized benefits reflect improvements in ambient PM2.5 and ozone concentrations. This results 

in an underestimate of the monetized benefits. Using a 3% discount rate, we estimate the total 

monetized benefits of the area source rule to be $210 million to $520 million in the 

implementation year (2014) (Table 8-2). Using a 7% discount rate, we estimate the total 

monetized benefits to be $190 million to $470 million. The annualized costs are $490 million at a 

7% interest rate. The net benefits are −$280 million to $30 million at a 3% discount rate for the 

benefits and −$300 million to −$20 million at a 7% discount rate. All estimates are in 2008$. The 

benefits from reducing other air pollutants have not been monetized in this analysis, including 

reducing 1,100 tons of carbon monoxide, 340 tons of HCl, 8 tons of HF, 90 pounds of mercury, 

320 tons of other metals, and less than 1 gram of dioxins/furans (TEQ) each year. 
8-2 

Table 8-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler MACT in 2014 (millions of 2008$)a

 3% Discount Rate 7% Discount Rate 

Selected 

Total Monetized Benefitsb $22,000 to $54,000 $20,000 to $49,000 

Total Social Costsc $1,500 $1,500 

Net Benefits $20,500 to $52,500 $18,500 to $47,500 

Non-monetized Benefits 112,000 tons of carbon monoxide 

30,000 tons of HCl 

820 tons of HF 

2,800 pounds of mercury 

2,700 tons of other metals 

23 grams of dioxins/furans (TEQ) 

Health effects from SO2 exposure 

Ecosystem effects 

Visibility impairment 

Alternative 

Total Monetized Benefitsb $18,000 to $43,000 $16,000 to $39,000 

Total Social Costsb $1,900 $1,900 

Net Benefits $16,100 to $41,100 $14,100 to $37,100 

Non-monetized Benefits 112,000 tons of carbon monoxide 

22,000 tons of HCl 

620 tons of HF 

2,400 pounds of mercury 

2,600 tons of other metals 

23 grams of dioxins/furans (TEQ) 

Health effects from SO2 exposure 

Ecosystem effects 

 Visibility impairment 

a

 All estimates are for the implementation year (2014), and are rounded to two significant figures. These results 

include units anticipated to come online and the lowest cost disposal assumption. 

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

reductions of directly emitted PM2.5 and PM2.5 precursors such as SO2, as well as reducing exposure to ozone 

through reductions of VOCs. It is important to note that the monetized benefits include many but not all health 

effects associated with PM2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. 

(2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in 

causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect 

estimates by particle type. These estimates include energy disbenefits associated with the increased emissions from 

additional energy usage valued at $22 million for the selected option and $37 million for the alternative option. 

Ozone benefits are valued at $3.6 to $15 million for both options. 

c 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. 
8-3 

Table 8-2 Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

Boiler Area Source Rule in 2014 (millions of 2008$)a

3% Discount Rate 7% Discount Rate 

Final MACT/GACT Approach 

Total Monetized Benefitsb $210 to $520 $190 to $470 

Total Social Costsc $490 $490 

Net Benefits −$280 to $30 −$300 to −$20 

Non-monetized Benefits 1,100 tons of carbon monoxide 

340 tons of HCl 

8 tons of HF 

90 pounds of mercury 

320 tons of other metals 

<1 gram of dioxins/furans (TEQ) 

Health effects from SO2 exposure 

Ecosystem effects 

Visibility impairment 

Proposed MACT Approach 

Total Monetized Benefitsb $200 to $490 $180 to $440 

Total Social Costsc $850 $850 

Net Benefits −$650 to −$360 −$670 to −$410 

Non-monetized Benefits 1,100 tons of carbon monoxide 

 340 tons of HCl 

 8 tons of HF 

 90 pounds of mercury 

 320 tons of other metals 

 <1 gram of dioxins/furans (TEQ) 

 Health effects from SO2 exposure 

 Ecosystem effects 

 Visibility impairment 

a

 All estimates are for the implementation year (2014), and are rounded to two significant figures. These results 

include units anticipated to come online and the lowest cost disposal assumption. 

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

reductions of directly emitted PM2.5 and PM2.5 precursors such as SO2. It is important to note that the monetized 

benefits include many but not all health effects associated with PM2.5 exposure. Benefits are shown as a range from 

Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their chemical 

composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient 

to allow differentiation of effect estimates by particle type. These estimates include energy disbenefits associated 

with the increased emissions from additional energy usage valued at less than $1 million. 

c 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. 
8-4 

Figures 8-1 and 8-2 show the full range of net benefits estimates (i.e., annual benefits 

minus annualized costs) quantified in terms of PM2.5 benefits for the implementation year (2014). 

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

Pope et al.

Laden et al.

Millions (2008$)

Cost estimate combinedwith total monetized benefits estimates derived from2epidemiology functions and 12 expert

functions

Figure 8-1. Net Benefits for the Final Major and Area Source Boiler Rules at 3% 

Discount Ratea

a

 Net benefits are quantified in terms of PM2.5 benefits for the implementation year (2014). This graph shows 14 

benefits estimates combined with the cost estimate. These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. The monetized benefits incorporate the 

conversion from precursor emissions to ambient fine particles. 
8-5 

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

Pope et al.

Laden et al.

Millions (2008$)

Cost estimate combinedwith total monetized benefits estimates derived from2epidemiology functions and 12 expert

functions

Figure 8-2. Net Benefits for the Final Major and Area Source Boiler Rules at 7% 

Discount Ratea

a

 Net benefits are quantified in terms of PM2.5 benefits for the implementation year (2014). This graph shows 14 

benefits estimates combined with the cost estimate. These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. The monetized benefits incorporate the 

conversion from precursor emissions to ambient fine particles. 

8.3 Section 8 References 

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air 

Pollution and Mortality.” American Journal of Respiratory and Critical Care Medicine 

173:667-672. 

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. 

“Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate 

Air Pollution.” Journal of the American Medical Association 287:1132-1141. 

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 intermediate 

production inputs 

– 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: 

 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 (EPA, 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 
A-16 

(ROW) export supply elasticities (see discussion in the next section). We report the values 

currently available in the model in Table A-5. 

A.2.2.2 International Competition 

International competition is captured by a single ROW supply function: 

  ROW

g ε

g Pg Q  c  (A.6) 

where 

Qg

 = with-policy supply quantity (g) 

c = calibrated scale parameter for the supply and price relationship 

Pg

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

ROW

g  = price elasticity of supply of goods from the ROW to the United States (imports) 

(g) 

The key supply parameter that controls the ROW supply adjustments is the price 

elasticity of supply ( ROW

g  ). We obtained these estimates for a variety of industry groups from a 

recently published article by Broda and colleagues (2008b). 

A.2.2.3 Price Elasticity of Supply: Rest of World (ROW) 

Broda and colleagues (2008a and 2008b) 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 (EPA, 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. 
A-26 

A.3 Appendix A References 

Berck, P., and S. Hoffmann. 2002. “Assessing the Employment Impacts of Environmental and 

Natural Resource Policy.” Environmental and Resource Economics 22(1):133-156. 

Broda, C., N. Limao, and D.E. Weinstein. 2008a. “Optimal Tariffs and Market Power: The 

Evidence.” American Economic Review 98(5):2032-2065. 

Broda, C., N. Limao, and D. Weinstein. 2008b. “Export Supply Elasticities.” <http://faculty. 

chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/ 

TradeElasticities.html.> 

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>. 

Just, R.E., D.L. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy. 

Northampton, MA: Edward Elgar. 

Morgenstern, R.D., M. Ho, J.S. Shih, and X. Zhang. 2004. “The Near-Term Impacts of Carbon 

Mitigation Policies on Manufacturing Industries.” Energy Policy 32(16): 1825-1841. 

Shoven, J.B., and J. Whalley. 1995. Applying General Equilibrium. New York: Cambridge 

University Press. 

U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. 1999. 

OAQPS Economic Analysis Resource Document. 

http://www.epa.gov/ttn/ecas/analguid.html. 

U.S. Environmental Protection Agency. 2000. “Guidelines for Preparing Economic Analyses” 

(EPA 240-R-00-003). 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>. 
APPENDIX B 

DETAILED ECONOMIC MODEL RESULTS BY SECTOR 

B-1 

Table B-1. Prices (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Selected 

Option 

Alternative 

Option 

Selected 

Option 

Alternative 

Option 

Energy 0.0279% 0.0228% −0.0001% −0.0001% 

Nonmanufacturing 0.0007% 0.0054% 0.0013% 0.0016% 

Manufacturing 

Food, beverages, and textiles 0.0106% 0.0118% 0.0016% 0.0030% 

Lumber, paper, and printing 0.1006% 0.2427% 0.0133% 0.0162% 

Chemicals 0.0082% 0.0038% −0.0003% −0.0005% 

Plastics and rubber 0.0183% 0.0147% −0.0006% −0.0008% 

Nonmetallic minerals 0.0033% 0.0041% −0.0004% −0.0005% 

Primary metals 0.0221% 0.0139% −0.0002% −0.0002% 

Fabricated metals 0.0036% 0.0018% −0.0003% −0.0004% 

Machinery and equipment 0.0051% 0.0004% −0.0002% −0.0003% 

Electronic equipment −0.0004% −0.0006% −0.0001% −0.0001% 

Transportation equipment 0.0025% 0.0020% −0.0001% −0.0001% 

Other 0.0035% 0.0079% 0.0005% 0.0006% 

Wholesale and retail trade −0.0018% −0.0024% 0.0000% 0.0001% 

Transportation services −0.0010% −0.0022% −0.0001% −0.0002% 

Other services 0.0001% 0.0000% 0.0012% 0.0014% 
B-2 

Table B-2. Production (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Selected 

Option 

Alternative 

Option 

Selected 

Option 

Alternative 

Option 

Energy −0.0087% −0.0084% −0.0002% −0.0002% 

Nonmanufacturing −0.0029% −0.0046% −0.0007% −0.0011% 

Manufacturing 

Food, beverages, and textiles −0.0121% −0.0136% −0.0013% −0.0022% 

Lumber, paper, and printing −0.0442% −0.0999% −0.0057% −0.0071% 

Chemicals −0.0168% −0.0150% −0.0001% −0.0002% 

Plastics and rubber −0.0153% −0.0155% −0.0003% −0.0004% 

Nonmetallic minerals −0.0038% −0.0054% −0.0003% −0.0004% 

Primary metals −0.0233% −0.0167% −0.0001% −0.0001% 

Fabricated metals −0.0047% −0.0047% −0.0002% −0.0003% 

Machinery and equipment −0.0071% −0.0029% −0.0001% −0.0001% 

Electronic equipment −0.0026% −0.0034% −0.0001% −0.0001% 

Transportation equipment −0.0059% −0.0049% 0.0000% 0.0000% 

Other −0.0077% −0.0162% −0.0013% −0.0016% 

Wholesale and retail trade −0.0012% −0.0015% −0.0002% −0.0004% 

Transportation services −0.0030% −0.0035% −0.0002% −0.0003% 

Other services −0.0011% −0.0014% −0.0006% −0.0008% 
B-3 

Table B-3. Consumption (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Selected 

Option 

Alternative 

Option 

Selected 

Option 

Alternative 

Option 

Energy −0.0057% −0.0059% −0.0002% −0.0002% 

Nonmanufacturing −0.0026% −0.0039% −0.0006% −0.0009% 

Manufacturing 

Food, beverages, and textiles −0.0063% −0.0074% −0.0009% −0.0016% 

Lumber, paper, and printing −0.0273% −0.0566% −0.0018% −0.0024% 

Chemicals −0.0114% −0.0109% −0.0002% −0.0003% 

Plastics and rubber −0.0094% −0.0104% −0.0003% −0.0005% 

Nonmetallic minerals −0.0032% −0.0045% −0.0003% −0.0004% 

Primary metals −0.0117% −0.0088% −0.0001% −0.0002% 

Fabricated metals −0.0036% −0.0036% −0.0002% −0.0003% 

Machinery and equipment −0.0034% −0.0018% −0.0001% −0.0001% 

Electronic equipment −0.0014% −0.0016% −0.0001% −0.0001% 

Transportation equipment −0.0033% −0.0027% 0.0000% 0.0000% 

Other −0.0025% −0.0050% −0.0004% −0.0006% 

Wholesale and retail trade −0.0012% −0.0015% −0.0002% −0.0003% 

Transportation services −0.0026% −0.0031% −0.0002% −0.0003% 

Other Services −0.0011% −0.0014% −0.0006% −0.0008% 
B-4 

Table B-4. Imports (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Selected 

Option 

Alternative 

Option 

Selected 

Option 

Alternative 

Option 

Energy 0.0509% 0.0413% −0.0001% −0.0002% 

Nonmanufacturing 0.0010% 0.0037% 0.0008% 0.0010% 

Manufacturing 

Food, beverages, and textiles 0.0134% 0.0155% 0.0018% 0.0032% 

Lumber, paper, and printing 0.1034% 0.2467% 0.0136% 0.0166% 

Chemicals 0.0093% 0.0045% −0.0004% −0.0006% 

Plastics and rubber 0.0197% 0.0158% −0.0006% −0.0009% 

Nonmetallic minerals 0.0012% 0.0022% −0.0001% −0.0001% 

Primary metals 0.0208% 0.0132% −0.0002% −0.0002% 

Fabricated metals 0.0042% 0.0023% −0.0003% −0.0004% 

Machinery and equipment 0.0045% 0.0004% −0.0002% −0.0003% 

Electronic equipment −0.0003% −0.0003% −0.0001% −0.0002% 

Transportation equipment 0.0036% 0.0029% 0.0000% −0.0001% 

Other 0.0059% 0.0137% 0.0011% 0.0013% 

Wholesale and retail trade −0.0012% −0.0016% 0.0000% 0.0001% 

Transportation services −0.0007% −0.0014% 0.0000% −0.0001% 

Other services −0.0002% −0.0004% 0.0001% 0.0001% 
B-5 

Table B-5. Exports (Percentage Change from Benchmark): Industry Detail 

 Major Area 

 

Selected 

Option 

Alternative 

Option 

Selected 

Option 

Alternative 

Option 

Energy −0.0041% −0.0033% 0.0000% 0.0000% 

Nonmanufacturing −0.0004% −0.0040% −0.0010% −0.0013% 

Manufacturing 

Food, beverages, and textiles −0.0069% −0.0076% −0.0010% −0.0019% 

Lumber, paper, and printing −0.0702% −0.1690% −0.0093% −0.0113% 

Chemicals −0.0081% −0.0038% 0.0003% 0.0005% 

Plastics and rubber −0.0181% −0.0145% 0.0006% 0.0008% 

Nonmetallic minerals −0.0027% −0.0034% 0.0003% 0.0004% 

Primary metals −0.0211% −0.0133% 0.0002% 0.0002% 

Fabricated metals −0.0018% −0.0009% 0.0002% 0.0002% 

Machinery and equipment −0.0084% −0.0006% 0.0004% 0.0005% 

Electronic equipment 0.0010% 0.0015% 0.0003% 0.0004% 

Transportation equipment −0.0062% −0.0049% 0.0001% 0.0002% 

Other −0.0034% −0.0069% −0.0002% −0.0002% 

Wholesale and retail trade 0.0013% 0.0018% 0.0000% −0.0001% 

Transportation services 0.0008% 0.0018% 0.0001% 0.0002% 

Other services −0.0001% −0.0001% −0.0009% −0.0011%