## 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 Plann ing 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
In stitutional 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 De mand 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 C apacity and Utilization

................................ ....... 2

- 6 0
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 Dis tribution

................................ ................................ ..... 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 Effec ts

................................ ................................ ............... 4

- 6

4.2 Section 4 References

................................ ............ Error! Bookmark not defined.

5 Small Entity Analyses

................................ ................................ .............................. 5

- 13

5.1 Small Entity Screening Analysis

................................ ................................ ..... 5

- 13
5.1.1 Small Businesses

................................ ................................ ................. 5

- 13
5.1.1.1 Representative Small Business Analysis Using Census Statistics of U.S. Businesses

................................ ................... 5

- 13
5.1.1.2 Additional Small Business Analysis Using Sample of Small Businesses Identified in Combustion Facil ity Survey

................................ ................................ .................... 5

- 16
5.1.2 Small Governmental Jurisdictions and Not

- for

- Profit Enterprises

........ 5

- 17

5.2 Final Regulatory Flexibility Analysis (FRFA): Major Sources

........................ 5

- 18
5.2.1 Nee d for Rule and Objectives

................................ .............................. 5

- 18
5.2.2 Summary of the Significant Issues raised by the Public Comments and Agency Assessment

................................ ................................ ...... 5

- 18
5.2.3 Description of and Estimate of the Number of Small entities to W hich the Rules Will Apply

................................ ................................ 5

- 19
5.2.4 Description of the Projected Reporting, Record keeping and Other Compliance Requirements of the Rule

................................ ................. 5

- 21

### vi 5.2.5 Description of the steps the Agency has Taken to Minimize the Significant Economic Impact on Small Entities

................................ ... 5

- 23

5.3 Final Regulatory Flexibility Analysis (FRFA): Area Sources

.......................... 5

- 24
5.3.1 Need for Rule and Objectives

................................ .............................. 5

- 24
5.3.2 Summary of the Significant Issues raised by the Public Comments and Agency Assessment

................................ ................................ ...... 5

- 24
5.3.3 Description of and Estimate of the Number of Small entities to Which the Rules Will Apply

................................ ................................ 5

- 25
5.3.4 Description of the Projected Reporting, Record keeping and Other Compliance Requirements of the Rule

................................ ................. 5

- 27
5.3.5 Description of the steps the Agency has Taken to Minimize the Significant Economic Impact on Small E ntities

................................ ... 5

- 28

5.4 Section 5 References

................................ ................................ ....................... 5

- 28

6 Air Quality Modeling of Emissions Reductions

................................ ........................ 6

- 1

6.1 Synopsis

................................ ................................ ................................ .......... 6

- 1

6.2 Photochemical Model B ackground

................................ ................................ ... 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

...... Error! Boo kmark not defined.
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 A ir 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 Alternat e 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 Li mitations

................................ ................................ .......................... 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. Distr ibution 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 Foo d 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 Concentr ation 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 Manu facturing 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 3 22): 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) Error! Bookmark not defin

5

- 1. Share of NAI CS/Enterprise Employment Categories (<500 employees) with Sales Tests Exceeding 3%

................................ ................................ ........................ 5

- 16

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 1 2 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 PM 2 .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 PM 2.5 

### Analy sis

...................... 7

- 8 7

- 4. Change in Ambient PM 2.5 

### Levels from SO 2 

### Emissions from Point Source Boilers

................................ ................................ ................................ ..................... 7

- 17 7

- 5. Change in Ambient PM 2.5 

### Levels from PM Emissions from Point Source Boilers

.... 7

- 18

### xi 7

- 6. Change in Ambient PM 2.5 

### Levels from SO 2 

### Emissions from Non

- point Source Boilers

................................ ................................ ................................ ..................... 7

- 18 7

- 7. Change in Ambient PM 2.5 

### Levels from PM Emissions from Non

- point Source Boilers

................................ ................................ ................................ ..................... 7

- 19 7

- 8. Breakdown of Monetized PM 2.5 

### 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 PM 2.5 

### and Ozone Benefits Estimates for the Final Boiler MACT and Boiler Area Source Rule in 2014

................................ ........................... 7

- 30 7

- 11. Breakdown of Monetized PM 2.5 

### Benefits Estimates by Precursor for the Final Boiler MACT in 2014

................................ ................................ .............................. 7

- 31 7

- 12. Breakdown of Monetized PM 2.5 

### Benefits Estimates by Precursor for the Final Boiler Area Source Rule in 2014

................................ ................................ ............. 7

- 31 7

- 13. Breakdown of Monetized PM 2.5 

### Benefits Es timates by Subcategory for the Final Boiler MACT in 2014

................................ ................................ .............................. 7

- 32 7

- 14. Breakdown of Monetized PM 2.5 

### Benefits Estimates by Subcategory for the Final Boiler Area Source Rule in 2014

................................ ................................ ............. 7

- 32 7

- 15. Percenta ge 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 A rea 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 expos ure 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. Sum mary 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 Se rvices 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 Stan dards: 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. K ey 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 Ma nufacturing (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

- 3 0 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. Ind ustry 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 Manufact uring 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 Eco nomic 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 32 5)

................................ ............ 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 Sta ndards: 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 an d 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) Error! Bookmark not defined. 4

- 4. Cost Approach Jobs Impacts in FTEs

............................ Error! Bookmark not defined. 4

- 5. Employment Impacts

................................ .................... Error! Bookmark not defined. 4

- 6. Aggregate Employment in FTEs

................................ ... Error! Bookmark not defined. 4

- 7. Aggregate Employment Distributed to Regulated Entities and Others in FTEs Error! Bookmark not de

5

- 1. Affected Sectors and Size Standards

................................ ................................ ........ 5

- 14 5

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

................................ ................................ ................................ . 5

- 15 5

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

................................ ................................ ................................ .................... 5

- 20 5

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

................................ ................................ ................................ .................... 5

- 26

### xiv 6

- 1. Geographic Elements of Domains Used in Pho tochemical 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. Su mmary 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 PM 2.5 

### for the Final Boiler MACT in 2014 (95 th 

### percentile c onfidence interval)

............................ 7

- 22 7

- 5. Summary of Estimated Reductions in Health Incidences from PM 2.5 

### for the Final Boiler Area Source Rule in 2014 (95 th 

### percentile confidence interval)

............. 7

- 23 7

- 6. Summary of Monetized Benefits Estimates from PM 2.5 

### for the Final Boiler MACT in 2014 (95 th 

### percentile confidence interval)

................................ ................ 7

- 24 7

- 7. Summary of Monetized Benefits Estimates from PM 2.5 

### for the Final Boiler Area Source Rule in 2014 (95 th 

### percentile confidence interval)

................................ ........ 7

- 25 7

- 8. Summary of Monetized Benefits Estimates from Ozone for the Final Boiler MACT in 2014 (95 th 

### percentile confidence interval)

................................ ................ 7

- 26 7

- 9. Social Cost of Car bon (SCC) Estimates (per tonne of CO 2 

### ) for 2014

....................... 7

- 36 7

- 10. Monetized SCC

- Derived Disbenefits of CO 2 

### 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 B enefits 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 (N ESHAP) 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 t echnology (MACT). In the other rule, EPA is promulgating a NESHAP for two area source categories: industrial boilers and institutional and commercial
boilers. 2



### 1.1 Executive Summary 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 d evelop a regulatory impact analysis (RIA). The RIA includes an economic impact analysis (EIA) and a small entity impacts analysis and documents the RIA methods and results.

The key results of the RIA are as follows:

 

### Engineering Cost Analy sis: EPA estimates the major source NESHAP ’ s tot al 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 econom ic model suggests t he 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; avera ge price, production, and import changes are less than 0.01%.

 

### Social Cost Analysis: The estimated social cost of the majo r 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 economic model represent a net benefit of $0.4 billion

. 3

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/institutiona l boilers and process heaters. This act ion provides EPA ’ s rule in response to the court ’ s vacatur. 

### The estimated social cost of the area source rule is

2 G as

- fired boilers are not part of the area source categories of industrial boilers and institutional/commercial boilers.
3 See additional details in Chapter 3 and Cost Appendices.

### 1

- 2 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 ’ surpl us 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 billi on.

 

### Employment Changes: The annual estimated employment changes of the major source rule are +2,000 to +3,000 employees within the regulated industries. The one

time employment changes of the major source rule are +11,000 to +14,000 employees The annual e stimated employment changes of the area source rule are +1,000 employees within the regulated industries and +3,000 employees for the pollution control or engineering services industries . The one

- time employment changes of the

area source rule are 6,000 t o 15,000 employees.

 

### 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 sm all 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 recommendatio ns 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 (Jun e 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 flexibil ity 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 fo r 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 pre

cursor to PM 2.5 

### ). Thus all monetized benefits reported reflect improvements in ambient PM 2.5 

### 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 b illion to $54 billion in the implementation year (2014). Using a 7% discount rate, we estimate the total monetized benefits of the Boiler MACT to be $20 billion to $49 billion in the implementation year. Using alternate relationships between PM 2.5 

### and prem ature mortality supplied by experts, higher

### 1

- 3 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 esti mate 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 eco nomic 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 Benefits b $22,000 to $54,000 $20,000 to $49,000
Total Social Costs 3 $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 SO 2 exposure Ecosystem effects Visibility impairment
Alternative
Total Monetized Benefits b $18,000 to $43,000 $1 6,000 to $39,000
Total Social Costs b $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 SO 2 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 PM 2.5 through

reductions of directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 , 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 PM 2.5 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 Benefits b $210 to $520 $190 to $470
Total Social Costs c $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 SO 2 exposure Ecosystem effects Visibility impairment Proposed MACT Approach: Alternative

Total Monetized Benefits b $200 to $490 $180 to $440
Total Social Costs c $850 $850
Net Benefits −$650 to −$360 −$670 to −$410

Non

- monetized Benefits 1,100 tons of carbon monoxide
340 tons o f HCl
8 tons of HF 90 pounds of mercury
320 tons of other metals <1 gram of dioxins/furans (TEQ)
Health effects from SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementation year (2014), and are ro unded 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 PM 2.5 through

reduction s of directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 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 Particulat e 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 Manufa cturing

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% a nd 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 betwee n 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 livest ock 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 Man ufacturing (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 Tort illa 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) $48,1 18 $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; us ing 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 Shipm ents 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 establishm ent 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 Ba sis): 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 Statist ics 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%) a nd 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 Dema nd 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 servi ces 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 acc ounted 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 industr y 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% $552,075 100% Total compensation ($mil lions) $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 Manufa ctures: 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 Prod ucts 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

Plasti cs 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% in crease 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 electricity a (million kWh) 62,457 67,521 73,440
Residual fuel oil (million bbl) 2 2 4

Distillate fuel oil b (million bbl) 3 3 3 Natural gas c (billion cu ft) 553 560 618

LPG and NGL d (million bbl) 1 1 1
Coal (million s hort tons) 6 8 7
Coke and breeze (million short tons) * * *

Other e (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 in cludes 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 mar keting 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 proces sing plants, including plants that fractionate raw natural gas liquids (NGLs).

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

that respondents indicated was used to produce heat and power.

* Estimate less than 0.5.

Sources: U.S. Department of Energy, Energy Information Administration. 2007 a

. “ 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.e ia.doe.gov/emeu/mecs/mecs2006/2006tables.html>

.

### 2

- 7 90 95 100 105 110 115 120Jan-97Jun-97Nov-97Apr-98Sep-98Feb-99Jul-99Dec-99May-00Oct-00Mar-01Aug-01Jan-02Jun-02Nov-02Apr-03Sep-03Feb-04Jul-04Dec-04May-05Oct-05Index Value (Jan-97=100)

Manufacturing Food



### Figure 2

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

– 2005

Source: Federal Reserve Board. 200 9

. “ Industrial Production and Capacity Utilization: Electric Power Use: Manufacturing and Mining. ” <http://www.fede ralreserve.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 Characteris tics
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 pr icing.

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 an d 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 int ermediate 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 Concentr ation 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 Statis tical 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 d uring 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 economi c 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 100Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Percentage Capacity Utilization

Manufacturing Food



### Figure 2

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

Source: Federal Reserve Board. 200 9

. “ I ndustrial 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

- fi rst 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 Amer ican 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 20 40 60 80 100 120 140 160Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index Value (January 1997 = 100)

Manufacturing Food



### Figure 2

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

Source: Federal Reserve Board. 200 9

. “ 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 S ales 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 C o. 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. ” St andard 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 compan ies 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 cor porations 14,408 14,956 16,146 Number of corporations with net income 6,541 7,503 7,333 Total receipts (thousands) $502,149,944 $504,944,378 $484,193,319 Business receipts (thousands) $477,906,423 $465,369,666 $459,884,663
Before

- tax profit margin 5.27 % 10.09% 7.43%
After

- tax profit margin 3.74% 7.62% 5.11%

Source: Internal Revenue Service, U.S. Department of Treasury. 2008 a

. “ 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 m anufacturing (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, particula rly 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 Manufacturi ng 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 Re fining 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 Confectio nery 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
31 1422 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 Manufactur ing 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 Ca nning 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 Doug h 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 Nu ts 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
31 1942 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 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees

Firms 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. Busines ses (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 Si zes: 2002. ” < http://www.census.gov/csd/susb/download_susb02.htm >.

### 2

- 15 60 70 80 90 100 110 120Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index Value (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 YTDActual 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 Com mission. 200 8

. “ 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 wi th 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 th e economy.

0 20 40 60 80 100 120 140 160 180 20019901991199219931994199519961997199819992000200120022003200420052006200720082009Index 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

curren t 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 predictabl e, 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.

Sourc es: 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 Stat istics: 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 20 06 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 esta blishment 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 S tates (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 Sector s: 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 primar ily 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 plan n i ng 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 oth er establishments, and manufacturing plywood faced with non

wood 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 gener ally 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, woo d 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 manufactu ring (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 Product Mfg (NAICS 3212), 22%

Sawmills & Wood Preservation (NAICS 3211),

27% Other Wood Product Mfg (NAICS 3219),

51%



### Figure 2

- 11. Distribution of Value of Shipments within Wood Product Manufacturing (NAICS 322): 2007

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

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

### 2

- 20 Other Wood Product Mfg (NAICS 3219),

60% Sawmills & Wood Preservation (NAICS 3211), 20%

Veneer, Plywood, &

Engineered Wood Product Mfg (NAICS 3212),

20%



### Figure 2

- 12. Distribution of Employment within Wood Product Manufacturing (NAICS 322): 2007

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “ Sector 00: EC0 700A1: 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 a nd 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 product s.

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 v alue 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 m anufacturing 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 enter prises, 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 52 4,212
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 Industr y 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: Detaile d 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 predi cts that the wood product industry will be one of five (out of eight) non

- energy

intensive 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 550 0 $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 su pport services 561A $748
Architectural and structural metal products 3323 $725
Rail transportation 4820 $723
Other inputs $14,650
Total intermediate inputs T005 $72,169

Source: U.S. Bureau of Economic Analysis (BEA). 2008

. “ 2002 Benchmark Input

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



### Table 2

- shows that total energy use between 1998 and 2002. Figure 2

- 13 shows that electrical power use decreased, since 2000.

2.2.2.2

104B 

### 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 mainte nance 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 s ervices, 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 electricity a (million kWh) 21,170 20,985 26,723
Residual fuel oil (million bbl) * * 1

Distillate fuel oil b (million bbl) 2 2 3 Natural gas c (billion cu ft) 71 56 84

LPG and NGL d (million bbl) 1 1 1
Coal (million short tons) * * Q

Coke and breeze (million short tons)

— — *

Other e (trillion BTU) 341 229 228
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 fue l 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 util ities.

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 plan ts that fractionate raw natural gas liquids (NGLs).

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

that respondents indicated was used to produce heat and power.

* Estimate less than 0.5.

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

Sources: U.S. Department of Energy, Energy Information Administration. 2007 a

. “ 2002 Energy Consumption by Manufac turers

— 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 110Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Index Value (Jan-97=100)

Manufacturing Electric Power Use for NAICS 321



### Fig ure 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: Manufactur ing 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 signific ant 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 manufa cturing 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. Washin gton, 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 c ombined. 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 Micropo litan 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
indust ry 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, Ge orgia, 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 manufacturin g 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 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Percentage Capacity Utilization

Manufacturing Wood Products



### Figure 2

- 15. Capacity Utilization Trends in the Wood Product Manufacturing Industry (NAICS 32 1)

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 prod uct 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 Stan dard & 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 catego rizing 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 gener ated 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 th e 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 Micropoli tan 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

m anufacturing 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 tha n 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

- 29 100 105 110 115 120 125 130 135 140 145 150Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index 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://ww w.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 Produc ts. ” Standard and Poor ’ s Industry Surveys . 176(28).2-30 

### Table 2

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

Enterprises with

Variable Total 1 to 20 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 7 49 Employees 750 to 999 Employees 1,000 to 1,499 Employees

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

Establishments 17,052 9,758 3,482 1,271 166 91 133
Employment 534,011 65,423 132,612 118,910 19,784 11,944 18,533 Receipts ($thousands ) $88,649 $8,204 $18 ,276 $19,717 $3,192 $1,902 $3,118 Receipts/firm ($thousands) $5,833 $842 $5,572 $24,927 $50,673 $70,453 $103,927
Receipts/establishment ($thousands) $5,199 $841 $5,249 $15,513 $19,231 $20,904 $23,442 Receipts/employment ($) $166,006 $125,393 $137,818 $1 65,814 $161,363 $159,262 $168,231

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

Source: U.S. Census Bureau. 2008

. “ Firm Siz e 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

3 21991 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 Mar ket 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 (PP Is) show that producer prices for wood products increased by 6% from 2004 to 2007 (Figure 2

- 20).

### 2

- 32 60 70 80 90 100 110 120 130 140Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index Value (Jan-97=100)

Manufacturing Wood Products



### 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 5,000 10,000 15,000 20,000 25,000 30,000Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08$1,000,000 (2007)

Wood Product Exports Wood Product Imports



### 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 0 20 40 60 80 100 120 140Dec-03Jun-04Dec-04Jun-05Dec-05Jun-06Dec-06Jun-07Dec-07Jun-08PPI (Dec-03=100)

Wood Products PPI All Manufacturing PPI



### Figure 2

- 20. Producer Price Trends in the Wood Product Manufacturing Industry (NAICS 321)

Sour ce: U.S. Bureau of Labor Statistics (BLS). 2009 a

. “ 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 Introd uction
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 pape rs, 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). Whi le 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
betw een 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 Sta tistics 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 FactF inder; “ 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, mi llions) $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: An nual 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 f or 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 de fined 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 mad e 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 mac hinery 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 3 2229): 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 val ue 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) 46% CPP (3222) 54%



### Figure 2

- 21. Dis tribution 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 Indu stry 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 su pplying 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 b ecomes 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

- intensiv e 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 generati on 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%

Tota l 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 h ours (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 electri city $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: EC073 1I1: 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 Us ed 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 $3,91 6 $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

Sourc e: 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 electricity a (million kWh) 70,364 65,503 72,518
Residual fuel oil (million bbl) 24 16 15

Distillate fuel oil b (million bbl) 2 2 2
Natural gas c (billion cu ft) 570 490 461

LPG and NGL d (milli on bbl) 1 2 1
Coal (million short tons) 12 11 10
Coke and breeze (million short tons)

— *

—
Other e (trillion BTU) 1,476 1,276 1,303
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 produ cers, 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 prod uced 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 indic ated was used to produce heat and power.

* Estimate less than 0.5.

Sources: U.S. Department of Energy, Energy Information Administration. 2007 a

. “ 2002 Energy Consumption by Manufact urers

— 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. [S ource 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 197 2

– 1999 period, the proportion of total industry power generated from the combination of woodroom wastes, spent liquor solids, and
other self

- generated methods increased from about 41% to about 56%, while coal, fuel oil, and natural gas use decreased from a bout 54% to about 36% (EPA, 2002 ).

### 2

- 41 90 92 94 96 98 100 102 104 106 108Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Index 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: Manu facturing 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 ener gy

— — — 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 322 1 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 pr oducts (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

Gene ral 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 good s 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 I nput

- 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, f ollowed 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 Californ ia 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 Manufactu ring 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, C ounties, 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 100Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Percentage Capacity Utilization

Manufacturing Paper (NAICS 322)


### Figure 2

- 25. Capacity Utilization T rends 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, t han 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: G eographic 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 Capa city
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 ma nufacturing
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

— Internationa l 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 1501997-011997-071998-011998-071999-011999-072000-012000-072001-012001-072002-012002-072003-012003-072004-012004-072005-012005-072006-012006-072007-012007-072008-012008-072009-012009-07Index 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 21,99 5
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 product s 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 categor izing 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

var ies 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 Da ta by Enterprise Size: Paper Manufacturing (NAICS 322)

Enterprises with

Variable Total 1 to 20 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees

Firms 3,538 1,482 1,200 476 43 22 33

E stablishments 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 $179,57 7 $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 ent ities 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 Manufac turing 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 Rel ated 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

. “ Tab le 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 indus try 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

So urce: 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, bot h 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 ma nufacturing 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 in creased 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 135Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index 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). 2009 b

. “ 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 energ y

- 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 an d 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
Shipme nts ($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; generat ed 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 Bu reau; 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://factfind er.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 $738,303 Shipm ents per establishment ($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; “ Sect or 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 Manufac turing (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 FactFin der; “ 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 t he 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 consum ers 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, us ing 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, mil lions )

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 T00 5 $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. Washing ton, 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 oth er 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 ov er 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 e lectricity 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 inte rmediate 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 consu mption 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

em ployee $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 pe r

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 t he 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 electricity a (million kWh) 169,233 153,104 151,646

Residual fuel oi l (million bbl) 8 7 4

Distillate fuel oil b (million bbl) 2 2 2
Natural gas c (billion cu ft) 1,931 1,634 1,349

LPG and NGL d (million bbl) 15 9 2
Coal (million short tons) 13 14 8
Coke and breeze (million short tons) * * *

Other e (trillion BTU) 748 1,1 58 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 fr om 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 in cludes 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 Othe r 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 Adm inistration. 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 fo r 2006 numbers] U.S. Department of Energy, Energy Information Administration. 2007 a

. “ 2002 Energy Consumption by Manufacturers

— Data Tables. ” Tables 3.2 and N3.2. Washington, DC: DO E. <http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>.

### 2

- 58 80 85 90 95 100 105 110Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Index 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 Product ion 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
Stat es, 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 es tablishments 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

Plast ics 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 manufacturin g 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 prepa ration 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 consum ption expenditures F010 $4,449 $123,746 $55,882 $184,077 Exports of goods and services F040 $47,121 $15,683 $13,136 $75,940 Imports of goods and services F050

− $38,732

− $67,950

− $10,906

− $117,588
Total final uses (GDP) T004 $15,733 $73,485 $58,023 $147, 241 Total commodity output T007 $228,729 $156,765 $140,129 $525,623

Source: U.S. Bureau of Economic Analysis (BEA). 2008

. “ 2002 Benchmark Input

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

### 2

- 60 Figure 2

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

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 31: Manufacturing: Geograph ic 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 an d 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 chemi cal 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 ch emicals 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 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Percentage Capacity Utilization

Manufacturing Chemical Manufacturing



### Figure 2

- 35. C apacity Utilization Trends in Chemical Manufacturing (NAICS 325)

Source: Federal Reserve Board. 2009

. “ Industrial Production and Capacity Utilization: Capacity Utilization. ” <http://www.federalreserve.gov/datadownload/>.



### Figure 2

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

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for the State s, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002. ” <http://factfinder.census.gov>; (July 23, 2008)

.

### 2

- 62 100 110 120 130 140 150Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index Value (Jan-97=100)

Manufacturing Chemical Manufacturing



### Figure 2

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

So urce: 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 sal es 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. chemica l 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 Ta ble 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% Lyondell a 16,165 57% 80% Chevron Phillips 12,534 100% 86%

PPG Industries a 10,025 90% 56%

Huntsman Chemical 9,651 100% 50%

Praxair 9,402 100% 43.5%

Air Products a 8,820 88% 51%

Rohm & Haas b 7,837 88% 49%

a Percentage of sales in the United States calculated from total sales, not chemical sales.

b Percentage of sa les 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)

Ind ustry Number of Corporations Number of Corporations with Net Income Total Receipts ($thousands) Business Receipts ($thousands) Before

Tax Profit

Margin After

- Tax Profit Margin

Basic chemical 1,244 757 $195,022,700 $178,019,490 5.07% 4.10%
Resin, synth etic 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 $808,897,81 0 10.24% 7.89%

Source: Internal Revenue Service, U.S. Department of Treasury. 2008 b

. “ 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 T able 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 t han 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 I nternational 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 b illion, 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 manuf acturing 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 Al cohol 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 Noncellul osic 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 Manuf acturing 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

32591 0 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 2 2, 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 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees

Firms 9,341 5,413 1,974 790 95 56 71

Establishments 13,096 5,433 2,208 1,352 250 185 276
Employment 827,430 34,838 78,090 113,326 28,025 18,119 28,33 8
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 140Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index Value (Jan-97=100)

Manufacturing Chemical Manufacturing



### Figure 2

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

Source: Federa l 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 32 5)

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 170Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09PPI (Jan-98=100)

Manufacturing Chemical Manufacturing



### Figure 2

- 40. Producer Price Trends in Chem ical Manufacturing (NAICS 325)

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

— 325

— &PCUOMFG

— OMFG

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

### 2

- 68 2.5 Section 2 References

Ben wart, 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 Prod uction 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. Sta ndard & 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.go v/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/E 322.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.g ov>. 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 FactFind er). <http://factfinder.census.gov>. Accessed January 4, 2010.

U.S. Census Bureau. “ Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy

Wide 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 FactFin der). <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 ” (gene rated 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 Manufact urers

— 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 A nnual 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 & P CUOMFG

– 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. Internati onal 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 C lassification System Codes. ” <http://www.sba.gov/services/ contractingopportunities/sizestandardstopics/size/index.html>.

### 3

- 1 SECTION 3
ENGINEERING COST ANA LYSIS

We provide an overview of the engineering cost analysis used to estimate the additional private expenditur es 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
betwe en 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 categ ory 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 un its 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 pract ice 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 an alysis.

 

### Removing the CO CEMS monitoring requirement for units with a heat input capacity. Although this change in monitoring is offset in part by O 2 

### parameter monitoring requirement.

 

### Removing duplicative monitoring requirements for PM CEMS, opacity monito ring 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 requi res a one

time initial t est 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 consi dering 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 instr ument 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 incorpor ated 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 Hazardo us 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 fact ors 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 tha t 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 d ata, 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 p roposed 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 pr ecipitators 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 cat alyst 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 als o 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. Co nsidering 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 monito ring.
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 Sources a

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

- Contine ntal 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 ex penditures, 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 predi cted 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) calculat ing 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 exp enditures 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, cons istent 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 propo sal 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 wer e 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 C O 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 control s 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. I n 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 requi rements 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

- t o

- 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 e nergy 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

a nd 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 P rocess 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 th e 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 co sts 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 saving s 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 ann ualized 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 1 0,958 $24

Oil 168,003 $374
Facility Energy Audit All $52

New Units b 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 dol lars 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 fro m 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 e t 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 ANAL YSIS

EPA prepares an RIA to provide decision makers wi th 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 co nsumption) 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 single

period 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 var iable 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 measu res 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 migh t 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 multimar ket 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 y ear (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 th ese price increases and consumption falls

 

### Model scope

– 100 sectors are linked with each other based on their use of energy and other non

energy 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 domest ic price changes

 

### Model time horizon ( “ short run ” ) for a single period (201 4 )

– 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 productio n 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 (F igure 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 domesti c 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 SourcesMarket-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. househol ds 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
loss es 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
Change in U.S. surplus −$1.730

−$1.890 −$2.330
Direct Compliance Costs Method (Not Modeled)
Total annualized costs, new major sources a −$0.006 −$0.920
Fuel savings, existing major sources $0.430 $0.400
Fuel savings, new major sources a Less than $0.001
Net Change in U.S. Surplus b Less than $0.001

−$1.470 −$1.940
Net change in rest of world surplus $0.060
Net change in total surplus $0.090

−$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 complian ce 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 relations hips. 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 Fig ure 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, accommo dations , 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

Change in U.S. surplus −$0.330

−$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 sour ces −$0.050 −$0.270
Fuel savings, existing and new area sources) $0.050

Net Change in U.S. Surplus a $0.050

−$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 t he 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. Distri bution 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

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 stand

alone 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
environm ent while promoting economic growth, innovation, competitiveness, and 4.1.2.4 .1 Background

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 lite rature 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 amo unt 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 displacin g 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

-- name ly, 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 effe cts 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 ind ustry 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,
regu lated 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 dema nd 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 ma nufacturing 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 protectio n industry (increased orders for pollution cont rol equipment), environmental regulations also create employment in industries that provide intermediate goods to the environmental protection industry. For example, capit 

### a l expenditures to reduce air polluti on 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. 1 

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

s upply inputs for, or demand the outputs of the regulated industry. None of these impacts is accounted for in the current analysis. 2 

### We also do not estimate employment impacts “induced” by increased output of the environmental protection sector, or decreas ed output of the regulated sectors.

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

- review ed, published study . Estimates of the employment impacts of the capital investments and other non

recurri ng requirements of the rule are derived from the cost analysis developed for the regul a t ion. 4.1.2.4.2 Methodology and Results

3 

### Morgenstern, Pizer and Shih (2002): Overview of Conceptu al 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

3 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.
3 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.
3 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 quality) to their product mixes

. Although legally compelled to sa tisfy 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.

Thus, Morgenstern et al., decompose the overall effect of a regulation on employment into the following three sub components:

 

### The “Demand Effect”: higher production costs raise marke t prices,reducing consumption (and production), thereby reducing demand for labor within the regulated industry 4

 

### The “Cost Effect”: As production costs increase, plants use more of all inputs, including labor, to maintain a given level of output. For ex ample, 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 r egulated 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 incr eased 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 (statisticall y insignificant) net increase of 1.5 5 (+/

- 2.24) jobs. 5

4 The Morgenstern et al. results rely on industry demand and supply elasticities to determine cost pass

- thro ugh and reductions in output.. 

### “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 ex pense,

5 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

- 10 respectively. In pulp and paper and steel, the estimates are even smaller and insignificantly different from zero.” 6 

### By applying these estimates to [[EPA please provide clarification

– we think this should be pollution abatement costs]] w e estimat ed the 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). 7
, 8

6 Morgenstern, Pizer and Shih, p. 413.
7 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)
8 Net employment effect = 1 .5 5 × $ 2,4 00 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 Expenditure a −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 b oth 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 E stimates from Morgenstern, Pizer, and Shih (2002) e xpressed 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 Sources1,000 JobsBase =144,100

Demand Effect: Estimate derived from Morgenstern, et al. Cost Effect: Estimate derived from Morgenstern, et al.

Factor Shift Effect: Estimate derived from Morgenstern, et al. Net Effect: Estimate derived from Morgenstern, et al.



### Figure 4

- 3. Employment Impacts Using Morgenstern, Pizer, Shih (2002) ( 1,000 FTE s )

Limitations of the Analysis

Although the Morgenstern et al. paper provides information a bout 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 repres ent 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 a l. 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 r egulatory burden affects a plant by an amount equal to that plant's total costs relative to the aggregate industry costs. By transferring the

### 5

- 13 estimates, EPA assumes a similar distribution of regulatory costs by plant size and that the regulatory burden doe s not disproportionately fall on smaller or larger plants . Further, Morgenstern et al. does not include most indirect effects and all induced effects.

SECTION 5
SMALL ENTITY ANALYSE S

The RFA as amended by SBREFA generally requires an agency to prepare a regulator y 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 num ber 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 Sma ll 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. 9



### The Census Bureau ’ s definitions used in the SUSB are as follows: Statistics include the total number of establishments and receipts for all entities within an industry; h owever, only a subset of entities will be covered by the rule. SUSB also provides statistics by enterprise employment and receipt size.

 

### Establishmen t: An establishment is a single physical lo cation where business is conducted or where services or industrial operations are performed.

9 Se e http://www.census.gov/csd/sus b/ and http://www.sba.gov/advo/research/data.html for ad ditional details.

### 5

- 14  

### Receipts : Receipts (net of taxes) are defined as the revenue for goods produced, distributed, or services provided, including revenue earned from premiums, commiss ions and fees, rents, interest, dividends, and royalties. Receipts exclude all revenue collected for local, state, and federal taxes.

Table 5

- 1. Affected Sectors and Size Standards

2007 NAICS Description Size Standard (Effective August 22, 2008)

211 Oil a nd 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 Manufacturi ng 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 P roduct 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

- 15  

### Enterprise : An enterprise is a business organization consisting of one or more domestic establishments that were specified under common own ership 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. Enterpris e 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 t he “ 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 establishmen t 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 smal l 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

reg ulatory 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 facility

level 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

- 16 code. For area sources, we were limited to two representative small entity facility

- level costs (approximatel y $27,000,000 to $43,000). 10

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 

### 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).

Figure 5

- 1. Share of NAICS/Enterprise Employment Cate gories (<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 sma ll 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 busines s, and collected

10 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

- 17 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 a nd 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 facili ties 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 co st small public facility)

Area Sources 11

 

### 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 (counti es 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.

Re presentative 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

- to

revenue ratios exceeding 1 percent but below 3 percent:

11 Graham Gibson, Susan McClutchey, and Amanda Singleton, ERG

. (January, 2011 ) . Methodology for Est imating Impacts from Industrial, Commercial, Institutional Boilers at Area Sources of Hazardous Air Pollutant Emissions.

### 5

- 18  

### Final MACT/GACT Appro ach : 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

- NO x 

### burners), a nd hours of use (limited use); EPA should have minimized facility monitoring and reporting requirements; EPA should not have proposed the energy audit

### 5

- 19 requirement; EPA ’ s proposed emissions standards are too stringent; and, EPA should provide
more flexibili ty 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 p ercent of the operating hours in a year, a non

continental 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 t he 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 emissio n 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 sec tor. 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 c overed 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 t o be covered by the area source rule.

### 5

- 20 Table 5

- 3. Information Collection Effort for Fac ilities with Combustion Units: Major Sources

NAICS NAICS Description Total Number of Facilities Total Self

Reported Facilities

Owned by Small Businesses

111 Crop Product ion 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 Manu facturing 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 a nd 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 Manufacturi ng 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 Man ufacturing 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

- 21 Table 5

- 3. Information Collection Effort for Facilities with Combustion Units: Major Sources (continued)

NAICS NAICS Description Total Number of Facilities Total Self

Reported 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 Educ ational 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 discu ssed 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 m ajor 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 O ther Compliance Requirements of the Rule

The information collection activities in this ICR include initial and annual stack tests, fuel analyses, operating parameter monitoring, continuous O 2 

### monitoring for all units greater than 10 mmBtu/hr, continuous em ission 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

- specif ic 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 H 2 

### S 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

- 22 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 specific ations.

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 operat or 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 perform ance 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 operatin g 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 t o 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 a nd 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 altern ative 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 do cuments 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

- 23 As specif ied 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 tes t, 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 f or 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
relationa l 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 flowchart s 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 facil ities 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

- 24 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. Sma ll 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 pr epared 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 r ule, 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 r educe 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 result ed 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

r egarding 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, burn er type (with and without
low

- NO x 

### burners), and hours of use (limited use); EPA should have minimized facility

### 5

- 25 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 boile rs 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 a nd 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 ca lculations. 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 discuss ed 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 fa cilities 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

- 26 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

- 27 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 O 2 

### 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

sta tes ’ 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 a rea source rule.

### 5

- 28 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 m ost 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 requi rements, 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 req uirement. 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 b e 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 ene rgy 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 e nergy 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/boil erpg.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

- 29 U.S. Census Bureau. 2005a. 2002 Census of Governm ents, 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 MODEL ING OF EMISSIONS RED UCTIONS

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 chan ges on future year: annual and 24

- hour PM 2.5 

### 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 (CAM x 

### )
model. CAM x 

### simu lates the numerous physical and chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and air toxics. In addition to the
CAM x 

### 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 m odeling 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 t he 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

cont ributing to pollutants estimated by photochemical grid models.

### 6

- 2 The 2005

- based CAM x 

### 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 o f 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 so urce sector rule (USEPA, 2010 ).

6.2 Photochemical Model Background

CAM x 

### version 5.20 is a freely available computer model that simulates the formation and fate of photochemical oxidants, ozone, primary and secondary PM concentrations, a nd air toxics, over regional and urban spatial scales for given input sets of meteorological conditions and
emissions. CAM x 

### includes numerous science modules that simulate the emission, production, decay, deposition and transport of organic and inorganic g as

- phase and particle

- phase pollutants in the atmosphere (Baker and Scheff, 2007 ; Nobel et al., 2001 ; Russell, 2008 ). CAM x 

### 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 chem istry (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).

CAM x 

### contains a variety of ozone source apportionment tools, including the original ozone source apportionment tool (OSAT) and the anthropogenic pre

- cursor culpability

assessm ent (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 regu lar model species that make up nitrogen oxides (NO x 

### ) and volatile organic compounds (VOC) are combined and normalized by a concentration based weighted mean. Separate ozone tracers are used in CAM x 

### to track ozone formation that happens under NO x 

### and VOC li mited conditions.

Particulate matter source apportionment technology (PSAT) implemented in CAM x 

### estimates the contribution from specific emissions source groups to PM 2.5 

### 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 PM 2.5 

### sulfate, nitrate, ammonium, secondary organic aerosol, an d 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 parti culate nitrate ion. Ammonia emissions are tracked to particulate ammonium ion. This modeling assessment used the PSAT approach to
estimate source contribution to PM 2.5 

### 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 finer

scale 12 km grids over portions of the ea stern 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 i n 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

p rovides 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 Co nformal 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 proces sed 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
micr ons (PM 10 

### ), PM 2.5 

### , nitrogen oxides (NO X 

### ), sulfur dioxide (SO 2 

### ), carbon monoxide (CO), volatile organic compounds (VOC), ammonia (NH 3 

### ) 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 . T he 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 haza rdous 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 rul e. 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



### 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 inclu sion of control information. We used the NEI to add important emissions release point information necessary for photochemical grid modeling such as

geo graphic coordinates, stack coordinates, stack release height, exit temperature, exit velocity
because the ICR lacks this 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 nonp oint 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 merc ury 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 to tal 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 fu ture point source baseline inventory is shown in Table 6

- 2.

Table 6

- 2. Control S trategies and/or Growth Assumptions Included in the 2016 P rojection

Projections Carried Forward from the proposed Transport Rule a , b

Description Pollutants

MACT rules, nationa l, 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 Wa ste Combustion PM
Hospital/Medical/Infectious Waste Incinerator Regulations under Section 129d/11 1d NO X , PM, SO 2 Large Municipal Waste Combustors

— growth applied to specific plants All
MACT rules, plant

- level, VOC: Auto Plants VOC
MACT rules, plant

- lev el, PM & SO 2 : Lime Manufacturing PM, SO 2 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 NH 3 , PM
Residential Wood Combustion Growth and Changeo uts 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 modeling c

Em ission 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. NO X , SO 2 , HCL

NESHAP: Portland Cement (09/09/10)

— plant l evel 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, NO X , SO 2 , PM, HCL

NESHAP: Gold Mine Ore Processing and Produ ction 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 Rule a,b

New York SIP reductions VOC, NO X Additional plant and unit closures All

NESHAP: Reciprocating Internal Combustion Engines d NO X , CO, PM

a They were only changed in that the projection year was 2015 or 2016, rather than 2012 / 2014.

b We inadvertentl y did not apply closures that had been applied for the Transport Rule proposal; emissions from

these plants sum to 3300 tons VOC, 178 tons PM 2.5 , 1982 tons SO 2 , 1639 tons NO X , 6 tons NH 3 and 379 tons CO. At the state level, the largest impact is in West Vi rginia (717 tons NO X , which is 2% of emissions in ptnonipm) and 1604 tons SO 2 , 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 SO 2 , 15,000 tons NO X , 400 tons HCL, 200 tons PM 2.5

. d Note that SO 2 reductions are expected to occur to due fuel sulfur limits but were excluded from the projection.

They were expected to reduce SO 2 by 27,000 tons, nationwide

.



### T he 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 A ct 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 Contribut ion 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 modelin g 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 sect or 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 201 6 “ 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 cent ral United States (see Figure 6

- 2 ).

Table 6

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

Sector VOC NO X SO 2 PM 2.5 NH 3 HG 0 HG 2 PM 2.5 HG

Total (TPY) Non

- point

— Boiler (nonpt) 14,107 394,459 1,149,402 108,386 8,489 0 0 0

Tot al (TPY) EGU Point

— Boiler (ptipm) 57 0 54,337 826 0 0 0 0
Total (TPY) Non

- EGU Poi n t

— 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 so urce 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 th e 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 th an 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 PM 2.5 

### concentrations, 8

- hr maximum ozone, annual total mercury depo sition levels and visibility impairment. Model predictions are used to estimate future

- year design
values of PM 2.5 

### and ozone. The annual PM 2.5 

### design value 3

3 The full details involved in calculating the annual PM 2.5 design val ue are provided in appendix N of 40 CFR part 50. 

### determines whether a monitor location is attaining the annual PM 2.5 

### 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

### 6

- 10 between any particular future year simul ation 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 PM 2.5 

### , total mercury de position, 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 mi xture 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/m 2 

### (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 (HG 2 

### ) and 40% particulate. A reduction of 75 μg/m 2 

### (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 (HG 2 

### ) 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 PM 2.5 

### Levels

This se ction summarizes the results of our modeling of annual average PM 2.5 

### 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 as sessment indicates a decrease up to 0.81 μg/m 3 

### in annual PM 2.5 

### design values is possible given an area ’ s proximity to controlled sources and the amount of reduced sulfur dioxide and primary PM 2.5 

### emissions from major sources under the recommended solid fue l category option. The estimated

### 6

- 11 mean reduction over all monitor locations is 0.15 μg/m 3 

### for major source reductions under the recommended solid fuel category. A decrease up to 0.66 μg/m 3 

### in annual PM 2.5 

### design values is possible given an area ’ s proximity to controlled sources and the amount of reduced sulfur dioxide and primary PM 2.5 

### emissions from major sources under the alternative option. The
estimated mean reduction over all monitor locations is 0.12 μg/m 3 

### for major source reductions
under the alternat ive option. Area source reductions show a decrease up to 0.01 μg/m 3 

### in annual
PM 2.5 

### design values is possible given an area ’ s proximity to controlled area sources and the amount of reduced sulfur dioxide and primary PM 2.5 

### emissions.

6.5.3 Impacts of Sector on Fu ture 24

- hour PM 2.5 

### Levels
This section summarizes the results of our modeling of 24

- hr average PM 2.5 

### 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 sce nario. The modeling assessment indicates an
estimated average decrease of 0.50 μg/m 3 

### in 24

- hr average PM 2.5 

### design values over all monitor locations in the United States is possible given the amount of reduced sulfur dioxide and primary
PM 2.5 

### emissions fro m major sources under the recommended solid fuel category option. An
estimated average decrease of 0.39 μg/m 3 

### in 24

- hr average PM 2.5 

### design values over all monitor locations in the United States is possible given the amount of reduced sulfur dioxide and pr imary
PM 2.5 

### emissions from major sources under the alternative option. A n estimated decrease up to 0.03 μg/m 3 

### in 24

- hr average PM 2.5 

### 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 PM 2.5 

### 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 are as 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 pro vides 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 m ajor 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 PM 2.5 

### emissions from area sources.

6.5.5 Impact s 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 con trol 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 d etails 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 Refere nces Baker, K., Scheff, P., 2007. Photochemical model performance for PM 2.5 

### sulfate, nitrate, ammonium, and precursor species SO 2 

### , HNO 3 

### , and NH 3 

### 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

- At mospheres 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 NO x 

### emission trading. Environmental Science & Technology 35, 4397

- 4407.

### 6

- 13 Russell, A.G., 2008. EPA Supersites Program

- related emissions

- based particulate matter mode ling: 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, PM 2.5 

### , 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. Atmospheri c Environment 42, 5650

- 5659.

### 7

- 1 SECTION 7
BENEFITS OF EMISSION S 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 Sou rce Rule. The PM 2.5 

### reductions are the result of emission limits on PM as well as emission limits on other pollutants, including hazardous air pollutants (HAPs). The total PM 2.5 

### reductions are the consequence of the technologies installed to meet these mul tiple 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 prema ture mortality among populations exposed to PM 2.5 

### 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 benef its 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 EP A ’ 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.
M ethodological and time limitations under the court

- ordered schedule prevented EPA from monetizing the benefits from several important benefit categori es, 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 from 2 epidemiology functions and 12 expert functions Benefits 3%
Benefits 7%



### Figure 7

- 1. Total Monetized PM 2.5 

### and Ozone Benefits for the Final Boiler MACT and Boiler Area Source Rule in 2014 a

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 elicitatio n; 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 incr eased 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 SO 2 , as we ll 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 p ollutants. Several of the HAPs that would be reduced by these rules have been classified as known or probabl e human

### 7

- 3 carcinogens. 1



### In implementing these rules, emission controls may lead to reductions in ambient PM 2.5 

### 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, t here are important differences worth noting in the design and analytical objectives of
each RIA. The NAAQS RIAs illustrate the potential costs and benef its 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 As a result, one benefit of the proposed regulation is a reduction in the risk of

cancer. Other benefit categories include pot ential reduced incidence of neurological effects and irritations of the lungs and skin, reduced mortality and other morbidity effects associated with PM
and SO 2 

### . 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 associ ated 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 PM 2.5 

### and ozone concentrations. Thus, the
monetized bene fits 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 PM 2.5 

### , SO 2 

### , and VOCs . Because
SO 2 

### is also a precursor to PM 2.5, 

### reducing SO 2 

### emissions will also reduce PM 2.5 

### formation, human exposure, and the refore reduce estimated incidence of PM 2.5 

### - 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 PM 2.5 

### reductions are the consequence of the technologies installed to meet these multiple limits. In addition, these rules are expected to resu lt in reductions in VOCs, which will result in changes in ambient ozone
concentrations.

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 car cinogens.

### 7

- 4 em ission reductions. However, some costs and benefits estimated in this RIA account for the same air quality improvements as estimated in the illustrative PM 2.5 

### NAAQS RIA.

By contrast, the emission reductions for this rule are from a specific class of well

characterized 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 atta ining 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 infor mation on the relationship between the NAAQS and rules such as analyzed here, please see Section 1.2.4 of the SO 2 

### 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 fina l analysis are derived from air quality data modeled using CAM x 

### that reflect the emissions from the boiler sector and their contribution to ambient PM 2.5 

### 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 in formation 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, pl ease 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 μ g/m 3 

### 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 th e 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 t issue, 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 increa sed CO 2 

### 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 fi nal 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 PM 2.5 

### and Ozone

We follow a “ dam age

- 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
val ues. 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 u sed 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 chang es in environmental quality must be translated into effects on people or on the things that people value. For changes in
PM 2.5 

### 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 PM 2.5 

### and ozone. We also provide qualitative discussions of the imp act 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 limitation s 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. (20 01) 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 i mprove 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 ana lysis, 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 projectio ns. 2



### The HIA approach used in this analysis involves three basic steps: (1) utilizing CAM x 

### generated projections of PM 2.5 

### 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 ty pical health impact function might look as follows:

where y 0 

### 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 backgroun d,

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 imp act 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 PM 2.5 

### analysis.

Figure 7

- 2. Illustration of BenMAP Approach

### 7

- 8 Figure 7

- 3. Data Inputs and Outputs for the BenMAP Model for a PM 2.5 

### 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

3 All of the RIAs cited in this analysis are availa ble on EPA’s website at 

### 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 de scription 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 hum an 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 ozo ne benefits includes premature mortality and five morbidity endpoints.

http://www.epa.gov/ttn/ecas/ria.html

. 

### Census Population Data

Baseline and Post

Con trol PM 2.5 

### Concentrations

PM 2.5 

### Health

Functions

Economic Valuation Functions Population Projections

PM 2.5 

### Incremental Air Quality Change

PM 2.5 

### Related Health Impacts Woods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized PM 2.5 

### -

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 cardiov ascular 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 short

term study estimates Chronic respiratory damage Premature mortality due to long

- t erm 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 Decrease d 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

SO 2 : 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 A ffected (continued)

Pollutant/Effect Quantified and Monetized Unquantified

SO 2 : welfare Commercial fishing and forestry from acidic deposition Recreation in terrestrial and aquatic ecosystems from acid deposition Increased mercury methylation

Mer cury: 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

– Incidence s 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 im pact 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 PM 2.5 

### - related Premature Mortality
Consistent with all RIAs since the proposal RIA for the Portland Cement NESHAP (U.S. EPA, 2009a ), the PM 2.5 

### 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 characteriz ation 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 previou sly 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/m 3 

### 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 literature, EPA now estimates PM

- related mortality without applying an assumed concentration

### 7

- 11 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 log

- linear model most adequately p ortrays 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 ) o f 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. Analys es 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 Citie s cohort, as reported by Laden et al. (2006) . This study, published after the completion of the Staff Paper for the 2006 PM 2.5 

### NAAQS, has been used as an alternative estimate in the PM 2.5 

### NAAQS RIA and PM 2.5 

### benefits estimates i n RIAs completed since the PM 2.5 

### 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/m 3 

### as was done in recent (2006

– 2009) RIAs.

 

### Twelve estimat es are based on the C

- R functions from EPA ’ s expert elicitation study (IEc, 2006 ; Roman et al., 2008 ) on the PM 2.5 

### - mortality relationship and interpreted for benefits analysis in EPA ’ s final RIA for the PM 2.5 

### NA AQS. For that study, twelve experts (labeled A through L) provided independent estimates of the PM 2.5 

### - mortality concentration

- response function. EPA practice has been to develop independent estimates of PM 2.5 

### - mortality estimates corresponding to the conce ntration

- 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 conjuncti on with the underlying scientific literature, this document provided a basis for reconsidering the application of thresholds in PM 2.5 

### concentration

- response functions used in
EPA ’ s RIAs. For a summary of these scientific review statements and the panel mem bers 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 abov e, 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 PM 2.5 

### levels experienced by the populat ion receiving the PM 2.5 

### mortality benefit. In the results section, we identify on the figures the lowest air quality levels measured in each of the 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

### 7

- 12 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 concentrat ions. 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, contin ue to report strong associations with mortality. As we model mortality impacts among populations exposed to levels of PM 2.5 

### that are successively lower than the LML of each study, our confidence in the results diminishes. As air pollution emissions
continu e 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 PM 2.5 

### . 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 PM 2.5 

### - related mortality effects down to at least 7.5 μg/m 3 

### is high.

In implementing these rules, emission controls may lead to reductions in ambient PM 2.5 

### 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
add itional uncertainties imposed by assuming thresholds or other non

- linear concentration

functions for the purposes of benefits analysis.

7.2.5 Economic Valuation of Health Impacts
These models assume that all fine particles, regardless of their chemical compositi on, 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

### 7

- 13 PM, SO 2 

### , and VOC are the dominant PM 2.5 

### 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 propens ity to form PM 2.5 

### and a different pattern of transport, resulting geographic distribution of exposure. When more people are exposed, the benefits per ton are
greater. For example, SO 2 

### tends to have a lower benefit

- per

- ton estimate than direct PM 2.5 

### because sulfate particles formed from SO 2 

### 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 PM 2.5 

### emissions occur in high density population areas, exposures w ill tend to be higher there, leading to higher monetized health benefits for direct PM 2.5 

### than for SO 2 

### 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 incorpor ated into this analysis. 4



### 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 interpretat ion 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 availa ble studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta

- analyses of the wage

- risk literature. The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) meta

- analysis of 33 studies. The $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta

- analysis of 43 studies. The mean The monetized mortality benefits dominate the total benefits estimates.

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 wa ges. 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 PM 2.5 (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 bene fits using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate.

### 7

- 14 estimate of $5.5 million (2000$) 5



### During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissionin g 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 Economi cs 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 differen t studies, including those using different methodologies (i.e., wage

- risk and stated preference) (U.S.
EPA

- SAB, 2007 ). 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 subjecte d the interim estimate to a scientific peer

- review process through the Science Advisory Board (SAB) or other peer

- review

group.

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

- reviewed estimate applied cons istently 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 Agen cy 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



### 7.2.6 Calculating Boiler Sector

- specific Benefit

- per - ton Estimates 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.

Benefit per

- ton (BPT) estimates can be used to quantify the health impacts and monetized
human hea l th benefits of an incremental change in air pollution precursor emissions. In situations when we are unable to spe cifically model a regulatory option because of data or resource constraints, this approach can provide a reliable estimate of the benefits of emission reduction

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 $7. 9 million.
6 In the (draft) update of the Economic Guidelines (U.S. EPA, 200 8b ), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming i n the near future. Therefore, this report does not represent final agency policy.
7 In this analysis, we adjust the VSL to account for a different currency year (2008$) and to account for income growth to 2015. After applying these adjustments to the $6.3 m illion value, the VSL is $ 9.1 million.

### 7

- 15 scenarios. EPA has used the BPT technique in previous RIAs, including the recent Ozone NAAQS RI A (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



### To derive the BPT estimates for this analysis, we: 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 non

point 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.

1. Quantified the PM 2.5 

### - related human health and monetized benefits of the SO 2 

### emission reductions for major source boilers . We first quantified the health impacts and monetized benefits of total PM 2.5 

### mass formed from the SO 2 

### reductions, allowing us to isolate the PM air quality impacts from SO 2 

### reductions alone. This procedure allowed us to develop PM 2.5 

### BPT estimate s that quantified the PM 2.5 

### - related benefits of incremental changes in SO 2 

### emissions.

2. Divided the health impacts and monetized benefits by the emission reduction. This calculation yields BPT estimates for PM

- related SO 2 

### . The resulting BPT estimates

wer e then multiplied by the projected SO 2 

### emission reductions to produce an estimate of the PM

- related health impacts and monetized benefits.

This process was repeated for directly emitted PM 2.5 

### from major sources and for PM 2.5 

### and
SO 2 

### emissions from area so urce boilers. This process was also repeated using the 2.5 th 

### percentile

8 Technical details regarding the emissions inventory used in the air quality modeling are available in the Air Quality

Modeling TSD (U.S. EPA, 201 0f ). Technical details regarding the emission r eductions 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 a nd Emissions Impacts for Industrial, Commercial, Institutional Boilers and Process Heaters National Emission Standards for Hazardous Air Pollutants

. ”

### 7

- 16 and 97.5 th 

### percentile benefits to provide confidence intervals for the BPT estimates. 9



### 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 e stimates 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 a nd 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 qualit y 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 differ ences in local air chemistry, meteorology, population density, and
baseline health incidence. To calculate the total benefits, we simply multiply the BPT estimate by the emission reductions o f directly
emitted PM 2.5 

### and SO 2 

### 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 fle xibility to examine alternate regulatory scenarios.

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 reflect s an updated baseline emissions inventory. 10

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. 

### We believe that these updates result in benefits estimates that are more accurate than the proposal. For comparison, the BPT for directly emitted PM 2.5 

### in this analysis is lower for major and area sources than th e proposal, whereas the BPT for SO 2 

### 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 i mpact on the benefits results. In the proposal, the BPT estimates reflected emissions

10 For 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 u pdated version of IPM to project EGU emissions.

### 7

- 17 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 PM 2.5 

### impacts from the modeled point and non

- point boilers in Figures 7

- 4 through 7

- 7.

Figure 7

- 4. Change in Ambient PM 2.5 

### Levels from SO 2 

### Emissions from Point Source Boilers

### 7

- 18 Figure 7

- 5. Change in Ambient PM 2.5 

### Levels from PM Emissions from Point Source Boilers

Figure 7

- 6. Change in Ambient PM 2.5 

### Levels from SO 2 

### Emissions from Non

- point Source Bo ilers

### 7

- 19 Figure 7

- 7. Change in Ambient PM 2.5 

### Levels from PM Emissions from Non

- point Source Boilers

Although VOCs are also precursors to PM 2.5 

### , we have not monetized their contribution to
the PM 2.5 

### 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 second ary 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 relat ive 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/m 3 

### . 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) wo uld 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 PM 2.5 

### benefits would likely be very small for these r ules. For example, while not an appropriate source

### 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, t he 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

- lin ear 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

- re lated 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 no t 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 SO 2 

### have increased, and (2) the BPT for SO 2 

### has increased. The decreases in directly emitted PM 2.5 

### emissions and lower BPT for PM 2.5 

### since the proposal do not offset the
increase in benefits from SO 2 

### emission reductions.

Tables 7

- 2 and 7

- 3 provide a summary of the monetized PM 2. 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 PM 2.5 

### levels for the final Boiler MACT and Boiler Area Source Rule, respectively. Tables 7

- 6 and 7

- 7 provide the total monetized PM 2. 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 estimate d 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%)SelectedOption Direct P M 2.5 29,007 $72,000 $180,000 $65,000 $160,000 $2,100 to $5,100 $1,900 to $4,600

SO 2 439,901 $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 $49,000AlternativeOption Direct PM 2.5 28,139 $72,000 $180,000 $65,000 $160,000 $2,000 to $5,000 $1,800 to $4,500

SO 2 337,514 $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 implemen tation 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 scien tific 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 PM 2.5 . 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 usag e 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 E missions 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%)ProposedMACTApproach Direct PM 2.5 590 $120,000 $290,000 $110,000 $260,000 $69 to $170 $63 to $150

SO 2 3,197 $41,000 $100,000 $37,000 $91,000 $130 to $320 $120 to $290

Total $200 to $490 $180 to $440FinalMACT/GACTApproach Direct PM 2.5 678 $120,000 $ 290,000 $110,000 $260,000 $79 to $190 $72 to $180

SO 2 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 significan t 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 differe ntiation of effect estimates by particle type. The benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine p articles. 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 I ncidences from PM 2.5 

### for the Final Boiler MACT in 2014 (95 th 

### 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

– 11,00 0)

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 Bronchiti s 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 d ifferentiation 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 describe d 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 boil ers and 47 new boilers anticipated to come online by 2014.

### 7

- 23 Table 7

- 5. Summary of Estimated Reductions in Health Incidences from PM 2.5 

### for the Final Boiler Area Source Rule in 2014 (95 th 

### percentile confidence interval) a

Proposed MACT Approach Final MACT/GA CT 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

– 3 1)

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 Bronch itis 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 Sym ptoms 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 PM 2.5 

### for the Final Boiler MACT in 2014 (95 th 

### 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

– $190,00 0) ($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) ($2,3 00

– $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

– $190,0 00) ($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 $2 7,000 $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 re ducing 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 funct ions, 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 PM 2.5 

### for the Final Boiler Area Source Rule in 2014 (95 th 

### 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 t he 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 (95 th 

### 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 (1,1 00

– 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 PM 2.5 

### 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 PM 2.5 

### 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 PM 2.5 

### b enefits 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), increasin g 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

p ercentage of the adult population exposed to each PM 2.5 

### 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% o f the population is exposed to
annual mean PM 2.5 

### levels at or above the LML of 7.5 μg/m 3 

### . Using the Laden et al. (2006) study, 34% of the population is exposed to annual mean PM 2.5 

### levels at or above the LML of 10 μg/m 3



### . As w e m odel mortality impacts among populations exposed to levels of PM 2.5 

### 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 o ccur at or above the lowest cohort study’s LML. It is important to emphasize that we have high
confidence in PM 2.5 

### - related effects down to the lowest LML of the major cohort studies.

### 7

- 28 Figure 7

- 8

. Breakdown of Monetized PM 2.5 

### Health Benefits Estimates us ing 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 prema ture 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 from 2 epidemiology functions and 12 expert functions Benefits 3%
Benefits 7%



### Figure 7

- 10

. Total Monetized PM 2.5 

### and Ozone Benefits Estimates for the Final Boiler MACT and Boiler Area Source Rule in 2014 a

a Th is 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 coeffici ents 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 we re unable to monetize the benefits associated with several categories of benefits, including direct exposure to HAPs and SO 2 , 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 PM 2.5 

### Benefits Estimates by Precursor for the Final Boiler MACT in 2014 a



### Figure 7

- 12

. Breakdown of Monetized PM 2.5 

### Benefits Estimates by Precursor for the Final Boiler Area Source Rule in 2014 a

### 7

- 32 Fi gure 7

- 13

. Breakdown of Monetized PM 2.5 

### Benefits Estimates by Subcategory for the Final Boiler MACT in 2014 a



### Figure 7

- 14

. Breakdown of Monetized PM 2.5 

### Benefits Estimates by Subcategory for the Final Boiler Area Source Rule in 2014 a

### 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 PM 2.5 Level (μg/m 3 ) LML of Laden et al. (2006) study LML of Pope et al. (2002) study



### Figure 7

- 15

. Perce ntage of Population Exposed to Baseline Air Quality Level s for Final Boiler MACT and Boiler Area Source Rule a

a Approximately 79% of the population is exposed to baseline exposure to annual mean PM 2.5 levels at or above 7.5

μg/m 3 , which is the lowest air q uality 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 Mean PM 2.5 Level (μg/m 3 ) 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 Level s for Final Boiler MACT and Boiler Area Source Rule a

a Approximately 79% of the population is exposed baseline exposure to annual mean PM 2.5 levels at or above 7.5

μg/m 3 , which is the lowest air quality level considered in the ACS cohort study by Pope et al. (2002)

.


### 7.4 Energy Disbenefits

In this sect ion, we provide an estimate of the energy disbenefits associated with the increased emissions from additional energy usage. 11



### 7.4.1 Social Cost of Carbon and Greenhouse Gas Disbenefits 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 CO 2 

### for major boilers and 22,000 tpy CO 2 

### for area boilers.

EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 

### ) 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

11 As 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 Vehi cle 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 me thods 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 CO 2 

### emissions 13



### The SCC increases ove r 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 u sing the three integrated assessment models rather than assuming a constant annual in 2010, in
2007 dollars. The first t hree 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 assumpti ons 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 model s 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.

12 Docket ID EPA

- HQ

- OAR

- 2009

- 0472

- 114577, Technical Support Document: S ocial 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 T reasury (February 2010)

. Also available at http://www.epa.gov/otaq/climate/regulations.htm
13 The interagency group decided that these estimates apply only to CO 2 emissions. Given that warming profiles and impacts other than temperature change ( e.g., ocean acidification) vary across GHGs, the group concluded

“ transforming gases into CO 2 - equivalents using GWP, and then multiplying the carbon

- equivalents by the SCC, would not result in accurate estimates of the social costs of non

- CO 2 gases ” (SCC T SD, 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 dol lars are provided in
Table 7

- 9.

Table 7

- 9. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 

### ) for 2014 a

Discount Rate and Statistic SCC estimate (2008$)
5% (Average) $5.7
3% (Average) $24.2
2.5% (Average) $39.1
3% (95 th 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 ch allenges. 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 mone tize 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 whic h the integrated assessment models capture catastrophic and non

catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk
aversi on. 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 es timates 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 t o 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

### 7

- 37 the SCC values within two years or at s uch 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 CO 2 

### emissions for the range of policy scenarios, we estimate the dollar value of t he 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



### Table 7

- 10. Monetized SCC

- Derived Disbenefits of CO 2 

### Emission Increases in 2014 (millions of 2008$) a 

### These estimates are provided in Table 7

- 10.

Discount Rate and Statistic Selected Option (Major) Alternative Option (Major) Proposed MACT Approach (Area) Final MACT/GACT Approach (Area)

Tons of CO 2 911,048 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% (95 th 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 associat ed 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 effe cts, and visibility impairment. The direct health benefits from reducing HAPs have not been monetized in this analysis. In addition to being a PM 2.5 

### precursor, SO 2 

### emissions also contribute to adverse effects from acidic deposition in aquatic and terrestri al 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 SO 2 

### Benefits
In addition to being a precursor to PM 2.5 

### , SO 2 

### emissi ons are also associated with a variety of respiratory health effects. Unfortunately, we were unable to estimate the health benefits

14 It is possible that other benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at rates that differ from those used to develop the SCC estimates.

### 7

- 38 associated with reduced SO 2 

### exposure in this analysis because we do not have air quality modeling data available. Without kn owing the location of the emission reductions and the resulting ambient concentrations, we were unable to estimate the exposure to SO 2 

### for nearby populations. Therefore, this analysis only quantifies and monetizes the PM 2.5 

### benefits associated
with the red uctions in SO 2 

### 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 ef fects and short

- term exposure to SO 2 

### (U.S. EPA,
2008a ). According to summary of the ISA in EPA ’ s risk and exposure assessment (REA) for the
SO 2 

### NAAQS “ the immediate effect of SO 2 

### on the respiratory system in humans is
bronchoconstri ction ” (U.S. EPA, 2009b ). In addition, the REA summarized from the ISA that

“ asthmatics are more sensitive to the effects of SO 2 

### likely resulting from preexisting inflammation
associated with this disease. ” A clear concentration

- res ponse relationship has been demonstrated in laboratory studies following exposures to SO 2 

### at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversely affected (U.S. EPA, 2009b ). Based on our review of this information, we identified four short

- term morbidity endpoints that the SO 2 

### ISA identified as a “ causal relationship ” : asthma exacerbation, respiratory

related emergency department visits, and respiratory

- related h ospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in
the SO 2 

### ISA. The SO 2 

### ISA also concluded that the relationship between short

- term SO 2 

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

### alone. Although the SO 2 

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

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

SO 2 

### emissions also contribute to adverse 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, i ncluding aesthetic and educational services and recreational fishing. B iological 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 a ffects 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 concentration s 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 reductio ns 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 rea ctions, relative to carbon dioxide (CO 2 

### ), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (r educed 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 i ncreased 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
includ e 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 betw een 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 t o short

- and long

- term exposure to CO.

### 7

- 40 7.5.3 Visibility Benefits
Reducing SO 2 

### and PM emissions would improve the level of visibility throughout the United States (U.S. EPA, 2009c ). Fine particles with significant light

- extinction efficie ncies 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%
d uring 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. Goo d 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
visibi lity 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 imp rovement 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 eff ects 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, redu ced 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 Direc t HAP Benefits
Americans are exposed to ambient concentrations of air toxics at levels which have the potential to cause adverse health effects. 15

15 U.S. EPA. (2009 ) 2002 National

- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 

### The levels of air toxics to which people are exposed vary depending on where people live and work and the kind s of activities in which they engage. In order to identify and prioritize air toxics, emission source types and locations that are of

### 7

- 41 greatest potential concern, U.S. EPA conducts the National

- Scale Air Toxics Assessment (NATA). The most recent NATA was co nducted for calendar year 2002, and was released in June
2009. 16



### 1

. Compiling a national emissions inventory of air toxics emissions from outdoor sources NATA for 2002 includes four steps:

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 

### o r acute 19 

### 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 expos ed 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

16 U.S. EPA. (2009 ) 2002 National

- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 

### Large reductions in HAP emissions may not necessarily translate into significant reductions in health risk because toxicity varies by pollutant and whether or not th ere 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 Inf ormation 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

17 Chronic exposure is defined in the glossary of the Integrated Risk Infor mation (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 specie s).
18 Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an organism.
19 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less.
20 The NATA modeling framework ha s 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 polluta nts 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 over all 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.go v/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 h azardous 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



### 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 impl ementation 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 i n 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

trac king health progress for diseases, such as cancer, that have long latency periods ” (U.S. EPA

SAB, 2008 ). These URFs are designed to be conservative, and as such, are more likely to represent the high end of the distributio n 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, E PA has continued to develop better methods for analyzing the benefits of reductions in HAPs.

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

22 The 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/m 3 of a pollutant.

### 7

- 44 carcinogenic HAPs, and estimates of the value of an avoided case of ca ncer (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 concentra tions of HAPs. For this reason, we did not attempt to quantify or monetized the health benefits of reductions in HAPs in this analysis. 23



### 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 HAP s 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. 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.

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% P AH 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 high er 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,

23 Due 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 esti mate 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 NO x 

### SO X 

### Ecological I SA 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 depositio n 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 f rom 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 v ery 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 emi ssions 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 de scribed 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 to xic 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 studi es 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 met hylmercury 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 su bstantial 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

exposu re 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 cardio

protective effects of fish consumption. Some of these studies also sugges t 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 alth ough 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

24 National Research Council (NRC). 2000

. Toxicological Effects of Methylmercury. Committee on the Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. pp.168

- 173. 

### Based on these and other studies, NRC concluded in 2000 that, “ Although
the data ba se 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

25 Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995

. “ Intake of mercury from fish lipid peroxidation, and the risk of myocardial infarction and coronary, cardiovascular and any death in Eastern Finnis h men. ” Circulation, 91 (3):645

- 655.
26 Sorensen, 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



### Since publication of the NRC report there have been some 30 published papers presenting the findings of studies that have examined the possible c ardiovascular 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. 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. ”

In early 2010, EPA sponsored a workshop in which a group of experts were asked to assess the plausibili ty 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 i s in preparation.

Baseline emissions for major and area source boilers in the U.S. are about 11 tons of mercury in the air. 28



### 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 court

ordered schedule for this rule , we were unable to model mercury methylat ion, 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 usi ng CAM x 

### for the final Boiler MACT and Boiler
Area Source Rule. Based on the EPA ’ s National Emission Inventory, about 103 tons of mercury were emitted from all anthropogenic sources in the U.S. i n 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. The refore, 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.

29

27 National Research Council (NRC). 2000

. Toxicological Effects of Methylmercury. Committee on the Toxicological Effects of Methylmercury, Board on Env ironmental Studies and Toxicology. National Academies Press. Washington, DC. p. 229. 

### These modeling results indicate significantly reduced total mercury

28 See “ Revised Development of Baseline Emission Factors for Boilers and Process Heaters at Commercial, Industrial, and Institutional Facilities

, ” which available in the doc ket.
29 See 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 M ACT 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 c hloride 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 als o 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.

30 All health effects language for this section came from: Agency for Toxic Substances and Disease Reg istry (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 (Cl 2 

### ) 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 to xic 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 sy stem 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 pulm onary edema. Chronic (long

term) 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 re ported menstrual irregularities in women occupationally exposed to fluoride. The EPA has not classified hydrogen fluoride for carcinogenicity.

7.5.5.6 Toluene 34



### Toluene is found in evaporative as well as exhaust emissions from motor vehicles. Under
the 2005 Guideli nes for Carcinogen Risk Assessment, there is inadequate information to assess

31 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR). 2007

. Medical Managemen t 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.g ov/MHMI/mmg172.html#bookmark02

. 32 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Medical Management Guidelines for Hydrogen Cyanide (HCN) ( CAS# : 7782

- 50

- 5). Atlanta, GA: U.S. Departme nt of Health and Human Services, Public Health Service. Available on the Internet at http://www.atsdr.cdc.gov/Mhmi/mmg8.html#bookmark02

. 33 All health effects language for this section ca me 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
34 All health effects language for this section came from: U.S. EPA. 2005

. “ Full IRIS Summary for Toluene (CASRN 108

- 88

- 3) ” Environmental Protection Agency, Integrated Risk Information Sy stem (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 bioass ays 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 a nd 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 inhalati on 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, an d 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 Form aldehyde
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

35 U.S. EPA. 1987 . Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to Formaldehyde, Office of Pesticides and Toxic Substances, April 1987. 

### In an analysis of the lymphohematopoietic cancer mortality from an exten ded follow

- up of these workers, NCI

36 Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003 . Mortality from lymphohematopoetic malignancies among workers in formaldehyde industries. Journal of the National Cancer Institute 95: 1615

- 1623.
37 Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004 . Mortality from solid cancers among workers in formaldehyde industries. American Journal of Epidemiology 159: 1 117

- 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 fo cus 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

conser vative (health protective) estimate of human risk. 48

38 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009

. Mortalit y from lymphohematopoietic malignancies among workers in formaldehyde industries: The National Cancer Institute cohort. J. National Cancer Inst. 101: 751

- 761. 

### 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 mod el

- based inference in the published CIIT study that formaldehyde ’ s direct mutagenic action is not relevant

39 

### P inkerton, L. E. 2004 . Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup. Environ. Med. 61: 193

- 200.
40 Coggon, D, EC Harris, J Poole, KT Palmer. 2003 . Extended follow

- up of a cohort of British chemical workers exposed to formaldehyd e. J National Cancer Inst. 95:1608

- 1615.
41 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003 . Biologically motivate d computational modeling of formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432

- 447.
42 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2004 . Human respiratory

tract cancer risks of inhaled formaldehyde: Dose

- response predictions derived from biologically

- motivated computational modeling of a combined rodent and human dataset. Tox Sci 82: 279

- 296.
43 Chemical Indus try Institute of Toxicology (CIIT). 1999 . Formaldehyde: Hazard characterization and dose

- response assessment for carcinogenicity by the route of inhalation. CIIT, September 28, 1999. Research T riangle Park, NC.
44 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2

- Stage Clonal Growth Models for Formaldehyde with Relevance to Other Biologically

- Based Dose Response (BBDR) Models. U.S. Environmental Protection Agency, Washington, D.C., EPA/60 0/R

- 08/103, 2006
45 Subr amaniam, R; Chen, C; Crump, K; et al. (2006 ) Uncertainties in biologically

- based modelin g of formaldehyde

induced cancer risk: identification of key issues. Risk Anal 28(4):907

- 923.
46 Subr amaniam, R; Chen, C; Crump, K; et al. (2007 ). Uncertainties in the CIIT 2

- stage model for formalde hyde

induced nasal cancer in the F344 rat: a limited sensitivity analysis

- I. Risk Anal 27:1237
47 Crump, K; Chen, C; Fox, J; et al. (2006 ) Sensitivity analysis of biologically motivated model for formaldehyd e

induced respiratory cancer in humans. Ann Occup Hyg 52:481

- 495.
48 Crump, K; Chen, C; Fox, J; et al. (2006 ) Sensitivity analysis of biologically motivated model for formaldehyde

induced 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, i n 20 04, 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 r eviewing 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 l eukemia was
characterized as “ strong. ” 50



### 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 ir ritation, 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 s uggest that formaldehyde may increase the risk of asthma

— particularly in the

young. EPA is reviewing the recent work cited above from the NCI and NIOSH, as well as the analysis by the CIIT Centers for Health Research and other studies, as part
of a reassessment of the human hazard and dose

- response associated with formaldehyde.

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)anthrac ene , 53 

### benzo(b)fluoranthene

, 54

49 Subrama niam, R; Chen, C; Crump, K; et al. (2007 ). Uncertainties in the CIIT 2

- stage model for formaldehyde

induced nasal cancer in the F344 rat: a limited sens itivity analysis

- I. Risk Anal 27:1237
50 International Agency for Research on Cancer (2006 ) Formaldehyde, 2

- Butoxyethanol and 1

- tert

- Butoxypropan

- 2

- ol. Monographs Volume 88. World Health Organization, Lyon, France.
51 Agency for Toxic Substances and Disease Registry (ATSDR). 1999 . Toxicological profile for Formaldehyde. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. http://www.atsdr.cdc.gov/toxprofiles/tp111.html
52 WHO (2002 ) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint sponsorship of the United Nations Environment Programme, the Inter national Labour Organization, and the World Health Organization, and produced within the framework of the Inter

- Organization Programme for the Sound Management of Chemicals. Geneva.
53 U.S. EPA (1997) . Integrated Risk Information Syst em 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.
54 U.S. EPA (1997). Integrated Risk Information System File of benzo(b)fluoranthene. Research and Development, National Center f or 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 ad verse birth outcomes, including low birth weight and reduced length at birth, as well as impaired cognitive development at age three. 60 , 61



### 7.5.5.9 Acetaldehyde EPA has not yet evaluated these recent studies.

Acetaldehyde is classified in EPA ’ s IRIS database as a pr obable 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 11 th 

### Report on Carcinogens and is clas sified as possibly carcinogenic to humans (Group 2B) by the
IARC. 63 , 64



### The primary noncancer effects of exposure to acetaldehyde vapors include irritation of the eyes, skin, and respiratory tract. EPA is currently conducting a reassessment of cancer risk from inhalation exposure to acetaldehyde.

65

55 U.S. EPA (1997). Integrated Risk Information System File of benzo(k)fluoranthene. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea/iris/subst/0452.htm. 

### In short

- term (4 week) rat studies, degeneration of olfactory

56 U.S. EPA (1998) . Integrated Risk Information System File of benzo(a)pyrene. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea/iris/subst/0136.htm.
57 U. S. EPA (1997). Integrated Risk Information System File of chrysene . Research and Development, National Center for Environmental Assessment, Washington, DC. This material is availa ble electronically at http://www.epa.gov/ncea/iris/subst/0455.htm
58 U.S. EPA (1997). Integrated Risk Information System File of dibenz(a,h)anthracene . Research and Dev elopment, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea/iris/subst/0456.htm.
59 U.S. EPA (1997). Integrated Risk Information System File of indeno(1,2,3

- cd)pyrene. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea/iris/subst/0457. htm.
60 Perera, F.P.; Rauh, V.; Tsai, W

- Y.; et al. (2002 ) Effect of transplacental exposure to environmental pollutants on birth outcomes in a multiethnic population. Environ Health Perspect. 111: 2 01

- 205.
61 Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D.; Diaz, D.; Hoepner, L.; Barr, D.; Tu, Y.H.; Camann, D.; Kinney, P. (2006 ) Effect of prenatal exposure to airborne polycyclic aromatic hydrocarbons on neurodevelopment in the first 3 years of life among inner

- city children. Environ Health Perspect 114: 1287

- 1292.
62 U.S. EPA (1988) . Integrated Risk Information System File of Acetaldehyde. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0290.htm.
63 U.S. Departmen t of Health and Human Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183

. 64 International Agency for Research on Cancer (IARC). 1999 . Re

- evaluation of some organic chemicals, hydrazine, and hydrogen peroxide. IARC Monographs on t he Evaluation of Carcinogenic Risk of Chemical to Humans, Vol 71. Lyon, France.
65 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde . This material is ava ilable 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 refe rence 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



### 7.5.5.10 Nickel The agency is currently conducting a reassessment of the h ealth hazards from inhalation exposure to acetaldehyde.

68



### 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
fore arms, 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 e ffects 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 com pounds (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 Manganese 69



### 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 exp osure 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

66 Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982 ). Inhalation toxicity of acetaldehyde in rats. I. Acute and subacute studi es. Toxicology. 23: 293

- 297.
67 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993 ) Aerosolized acetaldehyde induces histamine

mediated bronchoconstriction in asthma tics. Am. Rev. Respir.Dis.148(4 Pt 1): 940

- 943.
68 All health effects language for this section came from: U.S. EPA, “ National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional Boilers and Process Heaters; Proposed Rule, ” 68 Federal Register 8 (January 13, 2003 ). pp. 1664

- 1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf
69 All health effects language for this section came from: U.S. EPA, “ National Emission Standards for Hazardous Ai r 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.e pa.gov/ttn/atw/boiler/fr13ja03.pdf

### 7

- 56 manganism attributed to inhalation exposures. The EPA has cla ssified 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 l evels. 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 ex posed 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) hemoglo bin, 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 incre ases 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, suc h 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 1 0 times higher than human background body burdens). In other animal studies, exposure to 2,3,7,8

- TCDD has caused reproductive damage and
birth d efects. Some animal species exposed to CDDs during pregnancy had miscarriages and the offspri ng of animals expos ed 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.

70 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR). 1999

. ToxFAQs for Chlorinated Dibe nzo

- 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 lik ely 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 smal l 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 w ould 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 quantit ative 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

71 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR). 1995

. ToxFAQsTM for Chlorodibenzofurans (CDFs)

. Atlanta, GA: U.S. De partment of Health and Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc.gov/tfacts32.html>.
72 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris

### 7

- 58 comprehe nsive 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 estimatio n, 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 ozone

related mortality. We also provide a qualitative assessme nt 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 mo dels (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 uncert ainties 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 followi ng 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 econom ic 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 confi dence 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 ar ound 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 ov erall uncertainty in the
benefits estimates.

### 7

- 59 7.6.2 LML Assessment for PM 2.5



### PM 2.5 

### 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 PM 2.5 

### , we included the LML assessment. We have more confidence in the mortality impacts among populations exposed to levels of PM 2.5 

### above the lowest LML of the large cohort studies, and our confidence in the r esults 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 morta lity 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

PM 2.5 

### mortality benefits are the largest benefit category that we monetized in this analysis.
To better unde rstand the concentration

- response relationship between PM 2.5 

### exposure and premature mortality, EPA conducted an expert elicitation in 2006 (Roman et al., 2008 ; IEc, 2006 ). In general, the results of the expert e licitation support the conclusion that the benefits of PM 2.5 

### control are very likely to be substantial. In previous RIAs, EPA presented benefits estimates using concentration response functions derived from the PM 2.5 

### 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 PM 2.5 

### 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 der ived 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 concentration

response function provided in those studies.

7.6.4 Altern ate 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 premat ure 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. Ch ief 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... ” Th e NAS also recommended that “ ...the greatest emphasis be placed on the multicity and NMMAPS studies without exclusion of the meta

analyses ” (NRC, 2008 ).

In view of the findings of the NAS panel, we include used estimates of short

- term ozon e 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 incorpora te 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 o f 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 be nefits 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 PM 2.5 

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

### 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 lin ear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5, 

### including both regions that are in attainment with fine particle stan dard and those that do not meet the standard down to the lowest modeled concentrations.

3. To characterize the uncertainty in the relationship between PM 2.5 

### and premature mortality (which typically accounts for 85% to 95% of total monetized benefits), we inc lude 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 transf erability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 

### estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis. For more information on the uncertainties associated with PM 2.5 

### benefits, please consult the PM 2.5 

### NAAQS RIA (Table 5.5).

It is important to note that the monetized benefit

- per

- ton estimate s 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 distribut ion 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 benefit s 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

- to n 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 impact s 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 e stimated 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.
M ethodological and t ime 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 impairme nt. 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, c onfounding, 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 RI A 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 p rovide some evidence of the uncertainty surrounding the benefits results presented in this analysis.

7.7 Section 7 References Abt Associates, Inc. 2008. Environmental Benefits and Mapping Program (Version 3.0)

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### 7

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- 76.

### 8

- 1 SECTION 8
COMPARISON OF MONETI ZED BENEFITS AND COS TS

8.1 Boiler MACT
Because we were unable to monetize the benefits associated with reducing HAPs, all monetized benefits reflect improvements in ambient PM 2.5 

### 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 th e 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 othe r 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 PM 2.5 

### and ozone concentrations. This res ults 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 es timate 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 Monetiz ed Benefits b $22,000 to $54,000 $20,000 to $49,000
Total Social Costs c $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 mercu ry 2,700 tons of other metals 23 grams of dioxins/furans (TEQ)

Health effects from SO 2 exposure Ecosystem effects Visibility impairment
Alternative
Total Monetized Benefits b $18,000 to $43,000 $16,000 to $39,000
Total Social Costs b $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 eff ects from SO 2 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 a ssumption.

b The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through

reductions of directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 , 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 PM 2.5 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 particl e 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 milli on 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 Benefits b $210 to $520 $190 to $470

Total Social Costs c $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 SO 2 exposure Ecosystem effects Visibility impairment Proposed MACT Approach

Total Monetized Benefits b $200 to $490 $180 to $440
Total Social Costs c $850 $850

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

Non

- monetized Benefits 1,100 tons of carbon monoxide
340 tons of HCl
8 ton s of HF 90 pounds of mercury
320 tons of other metals <1 gram of dioxins/furans (TEQ)
Health effects from SO 2 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 PM 2.5 through

reductions of directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 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 differ entiation 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 PM 2.5 

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



### Figure 8

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

a Net benefits are quantified in terms of PM 2.5 benefits for the implementation year (2014). This graph shows 14

benefits est imates 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 combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions



### Figure 8

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

a Net benefits are quantified in terms of PM 2.5 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 c ausing 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. Cal le, 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 MO DEL TO ASSESS T HE ECONOMIC
IMPACTS OF ENVIRONME NTAL
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,
200 0 ). 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 multimar ket 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 pol icy ’ 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 pr ovide 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 diox ide (CO 2 

### ) 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 incre ases 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 adjustm ents 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 y ear, 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 abstrac t 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 NAICS a Energy Industries

COL Coal 2121 CRU Crude Oil Extraction 211111 (exc. nat gas)
ELE Electric Generation 2211 GAS Natural G as 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
FR U 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 31 5 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 Res ins 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 NAICS a

Plastics and Rubber

PLS Plastic 3261 RUB Rubber 3262
Nonmetallic Minerals

CLY Clay 3271 GLS Glass 3272 CEM Cement 3273
LIM Lime and Gypsum 3274 ONM Other Non

- Metallic Minerals 3279 Primary Metals

I_S Iron and Steel 3311 3312 33151 ALU Aluminum 3313 331521 331524 OPM Other Primary Metals 3314 331522 331525 331528
Fabricated Metals

FRG Forging and Stamping 3321 CUT Cutlery 3322
FMP Fabricated Metals 3323 BOI Boilers and Tanks 3324
HRD Hardware 3325
WIR Springs and Wires 3326 MSP Machine Shops 3327 EGV Engraving 3328 OFM Other Fabricated Metals 3329 Machinery and Equipment

CEQ Construction and Agricultural Equipment 3331

IEQ Industrial Equipment 3332 SEQ Service Industry Equipment 3333 HVC HVAC Equipment 3334 MEQ Me talworking 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 Equ ipment 3346 LGT Lighting 3351 APP Appliances 3352 (continued)

### A

- 5 Table A

- 1. Industry Sectors Included in Multimarket Model (continued)

Industry Label Description Representative NAICS a Electronic Equipment (continued)

ELQ Electric Equipment 3353 OEQ Othe r 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 M iscellaneous 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 Serv ices 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 sys tem, 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 Engi nes $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 Gov ernment 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 Instr uments $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 $1 74 $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 $1 1

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 Governmen t 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 tha n $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 $2 4 $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 $1 9 $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 T V 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 em ployment 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 in dustries with finer NAICs detail, estimates were calculated by selecting a primary NAICS supersector estimate (AEO or Global Insights) and distributing total employment from the larger NAICS supersectors across more detailed NAICS sectors within the super

- sector. The distributions were determined using observed 2008 BLS employment data. In the last step, In order to match aggregate U.S. employment numbers reported in the AEO 2009 release (140.1 million), a single adjustment factor was applied to all sectors that use Global Insights ’ supersector data. 1



### A.2.2 Economic Behavior Table A

- 4 reports the baseline employment for each of the 100 sectors.

A.2.2.1 U.S. Supply
In a postpolicy scenario, industry responds to changes in the new market

- clearing “ net ” price f or 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 s ector (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 m aintain 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 A luminum 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 Engra ving 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 Ir on 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 Metalwor king 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 Equip ment 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 3 6 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 We aring 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 i ncentives 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 intern ally consistent estimate of the use ratio and describes the dollar amount of an input that is required to produce a dollar of output. Higher ratios suggest strong links between industries, while lower ratios suggest weaker links. Given the
short time horiz on, 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 coll eagues (2004 ) and Ho and colleagues (2008) also use this assumption when examining near

- term effects of environmental policy.

Price

Quantity Supply

Q Supply′

P

Q′


### Figure A

- 1. Direct Compliance Costs Reduce Production Ra tes at Benchmark Prices

### A

- 15 Price

Quantity Supply

Q Supply′

P

Q′ Supply′′

Q′′


### Figure A

- 2. Indirect Costs Further Reduce Production Rates at Benchmark Prices

Following guidance in the OAQPS economic resource manual (EPA, 1999 ), we use a general form for the U.S. industry suppl y function:

g ε n

i i i gi g g 

### ) P P ( t P b Q ** **  ** **  ** ** 

**− ′** 

**− −**  **′ =**  **′** 

# ∑

**=** 1 

### α (A.5)

where

g Q **′** 

### = with

- policy supply quantity (g)

b 

### = calibrated scale parameter for the supply price relationship

g P **′** 

### = with

- policy price for output (g)

t 

### = direct compliance costs per unit of supply

gi **α** 

### = input use ratio (g using input i)

i P **′** 

### = with

- policy input (i) price

i P 

### = benchmark input (i) price

g **ε** 

### = price e lasticity of supply for output (g)

The key supply parameter that controls the industry production adjustments is the price elasticity of supply ( ε g 

### ). To our knowledge, there is no existing empirical work that estimates
short

- run supply elasticities for all industry 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

curren tly 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 g 

### P c Q **′ =**  **′** (A.6)

where

g Q **′** 

### = with

- policy supply quantity (g)

c = calibrated sc ale parameter for the supply and price relationship

g P **′** 

### = with

- policy U.S. price for output (g)

ROW
g 

### ε 

### = price elasticity of supply of goods from the ROW to the United States (imports) (g)
The key supply parameter that co ntrols the ROW supply adjustments is the price elasticity of supply ( ROW
g 

### ε 

### ). We obtained these estimates for a variety of industry groups from a
recently published article by Broda and colleagues (2008b)

.
A.2.2.3 Price Elasticity of Supply: Rest of World (ROW)
Broda and colleagues (2008a and 2008b) provide an empirical basis for the multimarket model supply elasticities. Broda et al. provide over 1,000 inver se 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 detaile d 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 m odel, 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 restri cted 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 l ikely 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 descr ibe 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 Agricu ltural 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 C ement 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 Instru ments 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 Recordi ng 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 Transportati on 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
W HL 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.br oda/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 fo r 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 th e 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 Purdu e 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 structur e 2

1 See Chapter 14 of the GTAP 7 Database Documentation for the full description of the parameters at 

### ), the parameters provide us with some additional information about the relative trade elasticity differences between industry sectors. To use the GTAP information to develop assumptions about the multimarket model sectors with missing elasticities, we c hose a base industrial sector (iron and steel) for which we had parameter value from Broda et al. Next, we developed industry

- specific ratios for missing industries using the corresponding GTAP sector trade elasticities and the GTAP iron and steel

https://www.gtap.agecon.purdue.edu/resour ces/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 supp ly 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 suppl y would be equal to or less elastic
than other countries ’ supply of imports. When we aggregated and averaged the origina l
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 maintain ing the structure of the original elasticities. For sectors in which no standard deviation was available, 1



### A.2.2.5 Demand we used professional judgment to apply the closest available substitute from a similar industry. Without a comparable way of scaling our foreign elas ticities for the sectors in which we used the GTAP elasticities, we elected to keep the domestic and foreign supply elasticities the same.

Uses for industry output are divided into three groups: investment/government use,
domestic intermedia te 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.

g gi i 

### Q Q **′ α**  **= ′**  

### (A.7)

i Q **′** 

### = with

- policy input demand quantity (i)

gi **α** 

### = input use ratio (g using input i)

g Q **′** 

### = with

- policy output quantity (g)

Other final use does respond to market price changes. Following guidance in the OAQPS

econo mic 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 th e exact 3

- or 4

- digit code).

### A

- 22 

# ( **)**
g

g g 

### P a Q **η** 

### ′ **= ′**  (A.8)

where

g Q **′** 

### = with

- policy demand quantity (g)

a 

### = calibrated sca le parameter for the demand and price relationship

g P **′** 

### = with

- policy price for output (g)

g **η** 

### = price elasticity of demand (g)

The key parameter that controls consumption adjustments is the price elasticity of demand ( η g 

### ). 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 n ot 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 add ress 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 investme nt 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 g oods/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
η g ACM Accommodations −0.7
ADM Administrative Services −0.7
AGR Agricultural −0.8
ALU Aluminum −1.0
ANM Animal Foods −0.6
APP Appliances −2.6
ARC Aircraft −2.5
ART Arts −0.7
ATP Air Transportation −0.8
BAK Baked Goods −0.6
BEV Beverages and Tobacco −0.6
BOI Boilers and Tanks −0.5
CEM Cement −0.8 CEQ Construction and Agricultural Equipment −1.7
CHM Chemicals and Gases −1.0
CLY Clay −0.8 CMQ Communication Equipment −2.6
CNS Construction −0.8
COL Coal −0.1
CPP Converted Pa per 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 Meta ls −0.5
FRG Forging and Stamping −0.5

FRT Fertilizer −1.0
FRU Fruits and Vegetables −0.6
FUR Furniture −0.7 (continued)

### A

- 24 Table A

- 6. U.S. Demand Elasticities (continued)

Industry Label Industry Description Demand Elasticity
η g GAS Natural Gas −0.3 GEQ General Equipment −1.7
GLS Glass −0.8
GRN Grain Milling −0.6

HLT Health Care −0.7

HRD Hardware −0.5
HVC HVAC Equipment −1.7 I_S Iron and Steel −1.0 IEQ Industrial Equipment −1.7

INF Information −0.7
INS Instruments −2.6
LEA Leather −1.1
LGT Lig hting −2.6

LIM Lime and Gypsum −0.8
LUM Other Lumber −0.7 M_V Motor Vehicles −2.5
MEA Meat Products −0.6

MED Drugs and Medicine −1.0 MEQ Metalworking Equipment −1.7
MGT Magnetic Recording Equipment −2.6

MIL Dairy Products −0.6

MIN Mining −0.6
MNG Management −0.7
MSC Miscellaneous Manufacturing −1.7
MSP Machine Shops −0.5

MVP Motor Vehicle Parts −2.5
OCM Other Chemicals −1.0 OEQ Other Electric Equipment −2.6
OFD Other Food Products −0.6
OFM Other Fabricated Metals −0.5
OIL Refined Petroleum −0.1
ONM Other Non

- metallic Minerals −0.8
OPM Other Primary Metals −1.0
OSV Other Services

− 0.7
OTP Other Transportation Services

− 0.8 (continued)

### A

- 25 Table A

- 6. U.S. Demand Elasticities (continued)

Industry Label Industry Description Demand Elasticity
η g OTQ Other Transport Equip −2.5
PAI Paints and Adhesives −1.0
PAP Pulp and Paper Mills −0.7
PFS Professional Services −0.7

PIP Pipeline Transport −0.8
PLS Plastic −1.0

PLY Plywood and Veneer −0.7

PRN Printing −0.7
PUB Public Services −0.7 R_R Ra il 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 Equip ment −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 C arbon 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 t he 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

- 2 065.

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. “ Im pact of Carbon Price Policies on US Industry ” (RFF Discussion Paper 08

- 37).

< http://www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?. Publicationid=20 680 >.

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 Indu stries. ” 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 Anal ysis 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 MO DEL RESULTS BY SECTO R

### B

- 1 Table B

- 1. Prices (Percentage Change from B enchmark): 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 Tab le 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%

El ectronic 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 −0.003 6% −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%

Prim ary 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%

M anufacturing
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%