## April 2010 

# Regulatory Impact Analysis : National Emission Standards for
Hazardous Air Pollutants for

Industrial, Commercial, and
Institutio nal Boilers and Process
Heaters

Draft Report

Prepared for
Tom Walton U.S. Environmental Protection Agency Office of Air Quality Planning and Standards (OAQPS) Air Benefit and Cost Group (MD

- C439

- 02) Research Triangle Park, NC 27711

Prepared by

RTI International 3040 Cornwallis Road Research Triangle Park, NC 27709 and Kajal B. Kapur, Energy, Environment, and Econ omics Consulting 1 Steubin Lane Charlottesville, VA 22911

RTI Project Number 0209897.004.074_________________________________ RTI International is a trade name of Research Triangle Institute

. 

### RTI Project Number 0209897.004.074 

# Regulatory Impact Analysis:
National Emission Standards for
Hazardous Air Pollutants for

Industrial, Commercial, and
Insti tutional Boilers and Process
Heaters

Draft Report

April 2010

Prepared for

Tom Walton U.S. Environmental Protection Agency Office of Air Quality Planning and Standards (OAQPS) Air Benefit and Cost Group (MD

- C439

- 02) Research Triangle Park, NC 27711

P repared by

RTI International 3040 Cornwallis Road Research Triangle Park, NC 27709 and Kajal B. Kapur, Energy, Environment, and Economics Consulting 1 Steubin Lane Charlottesville, VA 22911

### iii CONTENTS

Section Page

1 Introduc tion

................................ ................................ ................................ .............. 1

- 1

1.1 Executive Summary

................................ ................................ ......................... 1

- 1

1.2 Organization of this Report

................................ ................................ .............. 1

- 4

2 Industry Profiles

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

- 1

2.1 Food Manufacturing

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

- 1

2.1.1 Introduction

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

- 1

2.1.2 Supply and Demand Characteristics

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

- 4

2.1.3 Firm and Market Characteristics

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

- 8

2.2 Wood Product Manufacturing

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

- 18

2.2.1 Introduction

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

- 18

2.2.2 Supply and Demand Characteristics

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

- 21

2.2.3 Firm and Market Characteristics

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

- 26

2.3 Paper Manufacturing

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

- 36

2.3.1 Introduction

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

- 36

2.3.2 Supply and Demand Characteristics

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

- 40

2.3.3 Firm and Market Characteristics

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

- 46

2.4 Chemical Manufacturi ng

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

- 55

2.4.1 Introduction

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

- 55

2.4.2 Supply and Demand Characteristics

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

- 57

2.4.3 Firm and Market Characteristics

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

- 62

3 Engineering Co st Analysis

................................ ................................ ........................ 3

- 1

3.1 Major Sources

................................ ................................ ................................ .. 3

- 1

3.2 Area Sources

................................ ................................ ................................ .... 3

- 2

### iv 4 Economic Impact Analysis

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

- 1

4.1 Partial Equilibrium Analysis (Multiple Mar kets)

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

- 1

4.1.1 Overview

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

- 1

4.1.2 Economic Impact Analysis Results

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

- 2

5 Small Entity Analyses

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

- 1

5.1 Small Entity Screening Analysis

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

- 1

5.1.1 Small Businesses

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

- 1

5.1.2 Small Government Analysis

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

- 5

5.2 Initial Regulatory Flexibility Analysis (IFRA)

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

- 6

5.2.1 Reasons Why Action Is Being Considered

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

- 6

5.2.2 Statement of Objectives and Legal Basis of Proposed Rules

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

- 7

5.2.3 Description and Estimate of the Number of Small Entities

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

- 7

5.2.4 Description and Compliance Costs

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

- 7

5.2.5 Description of Federal Rules that May Overlap or Conflict with Proposed Rules

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

- 7

5.2.6 Description of Regulatory Alternatives that Minimize Si gnificant Economic Impacts on Small Entities

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

- 8

6 Human Health Benefits of Emissions Reductions

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

- 1

6.1 Synopsis

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

- 1

6.2 Calculation of PM 2.5 

### Human Health Benefits

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

- 1

6.2 Unquantified Benefits

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

- 1 3

6.2.1 Carbon Monoxide Benefits

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

- 13

6.2.2 Other SO 2 

### Benefits

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

- 13

6.2.3 HAP Benefits

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

- 14

6.3 Characterization of Uncertainty in the Monetized PM 2.5 

### Benefits

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

- 25

6.4 Comparison of Benefits and Costs

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

- 28

7 Supplemental Economic A n al yses for Non

- hazardous Solid Waste Definition

.......... 7

- 1

7.1.1 Economic Impact Analysis Results

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

- 1

7.1.2 Benefits Analysis Results

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

- 4

### v References

................................ ................................ ................................ ......................... R

- 1

Appendixes

A OAQPS Multimarket Model to Assess the Economic Impacts of Environmental Regulation

................................ ................................ ................ A

- 1

B Detailed Economic Model R esults by Sector

................................ .................... B

- 1

### vi LIST OF FIGURES

Number Page

2

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

........ 2

- 3
2

- 2 Distribution of Total Value of Shipments within Food Manuf acturing (NAICS 311): 2006

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

- 4
2

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

– 2005

....... 2

- 8
2

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

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

- 10
2

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

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

- 11
2

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

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

- 12
2

- 7 Capacity Trends in Food Ma nufacturing (NAICS 311)

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

- 13
2

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

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

- 17
2

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

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

- 17
2

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

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

- 18
2

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

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

- 21
2

- 12 Distribution of Employm ent within Wood Product Manufacturing (NAICS 322): 2007

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

- 22
2

- 13 Electrical Power Use Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997

– 2005

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

- 26
2

- 14 Establishment Concentratio n in the Wood Product Manufacturing Industry (NAICS 321): 2002

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

- 28
2

- 15 Capacity Utilization Trends in the Wood Product Manufacturing Industry (NAICS 321)

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

- 29
2

- 16 Employment Concentration in th e Wood Product Manufacturing Industry (NAICS 321): 2002

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

- 30
2

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

........ 2

- 31
2

- 18 Industrial Production Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997

– 2009

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

- 34
2

- 19 International Trade Trends in the Wood Product Manufacturing Industry (NAICS 321)]

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

- 35
2

- 20 Producer Price Trends in the Wood Product Manuf acturing Industry (NAICS 321)

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

- 36
2

- 21 Distribution of Value of Shipments within Paper Manufacturing (NAICS 322): 2007

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

- 39
2

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

...... 2

- 39
2

- 23 Electrical Power Use Trends in the Paper Manufacturing Industry: 1997

– 2005

..... 2

- 44
2

- 24 Establishment Concentration in Paper Manufacturing Industry (NAICS 322): 2002

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

- 46

### vii 2

- 25 Capacity Utilization Trends in the Paper Manufacturing Industry (NAICS 322)

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

- 47
2

- 26 Employment Concentration in the Paper Manufacturing Industry (NAICS 322): 2002

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

- 48
2

- 2 7 Capacity Trends in the Paper Manufacturing Industry (NAICS 322)

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

- 49
2

- 28 Industrial Production Trends in the Paper Manufacturing Industry (NAICS 322): 1997

– 2009

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

- 53
2

- 29 International Trade Tr ends in the Paper Manufacturing Industry (NAICS 322)

..... 2

- 54
2

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

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

- 55
2

- 31 Distribution of Employment within Chemical Manufactur ing (NAICS 325): 2007

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

- 58
2

- 32 Distribution of Total Value of Shipments within Chemical Manufacturing (NAICS 325): 2007

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

- 58
2

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

– 2005

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

- 62
2

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

...... 2

- 64
2

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

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

- 65
2

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

........ 2

- 65
2

- 37 Capacity Trends in Chemical Manufacturing (NAICS 325)

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

- 66
2

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

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

- 71
2

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

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

- 72
2

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

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

- 72

4

- 1 Market

- Level Changes by Source and Option

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

- 3
4

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

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

- 5
4

- 3 Distribution of Total Surplus Changes by Sector: Area Source s

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

- 7
4

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

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

- 9

5

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

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

- 4

6

- 1 Breakdown of Monetized PM 2

. 5 

### Health benefits using Mortality Function from Pope et al. (2002)

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

- 6
6

- 2 Total Monetized PM 2

. 5 

### Benefits for the Proposed Boiler Rules in 2013

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

- 11
6

- 3 B reakdown of Monetized benefits for the Proposed Boiler Rules by PM 2

. 5 

### Precursor Pollutant and Source

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

- 12
6

- 4 Breakdown of Monetized B enefits for the Proposed Boiler MACT (Major Sources) by Fuel Type

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

- 12
6

- 5 Breakdown of Monetized benefits for the Proposed Boiler Area Source Rule by Fuel Type

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

- 12
6

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

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

- 32
6

- 7 Net Benefits for the Proposed Boiler Rules at 7% Discount Rate

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

- 33

### viii LIST OF TABLES

Number Page

1

- 1 Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2013 (millions of 2008$)

................................ ... 1

- 1
1

- 2 Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler Area Source Rule in 2013 (millions of 2008$)

................................ ............. 1

- 2
2

- 1 Key Statistics: Food Manufacturing (Nor th American Industry Classification System [NAICS] 311)

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

- 1
2

- 2 Industry Ratios: Food Manufacturing (NAICS 311)

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

- 2
2

- 3 Costs of Goods and Services Used in Food Manufacturing (NAICS 311) ($2007)

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

- 5
2

- 4 Key Goods and Services Used in Food Manufacturing (NAICS 311) ($2007, 10 6 

### )

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

- 6
2

- 5 Energy Used in Food Manufacturing (NAICS 311)

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

- 7
2

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

### )

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

- 9
2

- 7 Top Publicly Held US Food Companies: 2007

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

- 13
2

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

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

- 14
2

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

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

- 15
2

- 10 Distribution of Economic Data by Enterprise Size: Food Manufacturing (NAICS 311)

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

- 16
2

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

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

- 19
2

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

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

- 19
2

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

........ 2

- 23
2

- 14 Key Goods and Services Used in Wood Product Manufacturing (NAICS 321) ($2007, 10 6 

### )

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

- 24
2

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

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

- 25
2

- 16 Demand by Secto r: Wood Product Manufacturing (NAICS 321) ($2007, 10 6 

### )

...... 2

- 27
2

- 17 Largest US Paper and Forest Products Companies: 2006

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

- 31
2

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

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

- 32
2

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

........ 2

- 33
2

- 20 Key Statistics: Paper Manufacturing (NAICS 322)

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

- 37
2

- 21 Industry Ratios: Paper Manufacturing (NAICS 322)

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

- 37
2

- 22 Costs of Goods and Services Used in the Paper Manufacturing Industry (NAICS 322)

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

- 41
2

- 23 Key Goods and Services Used in the Paper Ma nufacturing Industry (NAICS 322) ($10 6 

### , $2007)

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

- 42
2

- 24 Energy Used in Paper Manufacturing (NAICS 322)

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

- 43

### ix 2

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

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

- 44
2

- 26 Demand by Sector: Paper Manufacturing Industry (NAICS 322) ($10 6 

### , $2007)

.... 2

- 45
2

- 27 Largest US Paper and Forest Products Companies: 2006

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

- 50
2

- 28 Distribution of Economic Data by Enterprise Size: Paper Manufacturing (NAICS 322)

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

- 50
2

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

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

- 52
2

- 30 Key Statistics: Chemical Manufacturing (NAICS 325)

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

- 56
2

- 31 Industry Ratios: Chemical Manufacturing (NAICS 325)

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

- 56
2

- 32 Key Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007, 10 6 

### )

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

- 59
2

- 33 Costs of Goods an d Services Used in Chemical Manufacturing (NAICS 325)

....... 2

- 60
2

- 34 Energy Used in Chemical Manufacturing (NAICS 325)

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

- 61
2

- 35 Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007 10 6 

### )

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

- 63
2

- 36 Top Chemical Producers: 2007

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

- 67
2

- 37 2007 Corporate Income and Profitability (NAICS 325)

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

- 68
2

- 38 Small Business Size Standards: Chemical Manufacturing (NAICS 3 25)

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

- 70
2

- 39 Distribution of Economic Data by Enterprise Size: Chemical Manufacturing (NAICS 325)

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

- 71
2

- 40 Distribution of Economic Data by Enterprise Size: Chemical Manufacturing (NAICS 325)

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

- 71

3

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

......... 3

- 2
3

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

.............................. 3

- 3

4

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

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

- 4
4

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

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

- 6

5

- 1 Affected Sectors and Size Standards

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

- 2
5

- 2 Major Sources: Sales Tests Using Small Companies Identified in the Combustio n Survey

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

- 5

6

- 1 Human Health and Welfare Effects of PM 2

. 5


### ................................ .......................... 6

- 2
6

- 2 Summary of Monetized benefits Estimates for Boiler MACT (for Major Sources) in 2013 (2008$)

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

- 7
6

- 3 Summary of Monetized benefit s Estimates for Boiler Area Source Rule in 2013 (2008$)

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

- 8
6

- 4 Summary of Reductions in Health Incidences from PM 2.5 

### benefits for the Proposed Boiler Rules in 2013

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

- 9
6

- 5 All PM 2.5 

### Benefits Esti mates for the Proposed Boiler Rules at discount rates of 3% and 7% in 2013 (in millions of 2008$)

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

- 10
6

- 6 Top HAPs by Mass from Boilers by Fuel Type

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

- 16

### x 6

- 7 Sensitivity Analyses for Monetized PM 2.5 

### Health benefits (in millions of 2008$)

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

- 26
6

- 8 Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler MACT (Major Sources) in 2013 (millions of 2008$)

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

- 26 9
6

- 9 Summa ry of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler Area Source Rule in 2013 (millions of 2008$)

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

- 30

7

- 1 Ma rket

- Level Price and Quantity Changes: 2015 (Alternative Approach)

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

- 2

7

- 2 Distribution of Social Costs (million, 2008$): 2 015

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

- 3

7

- 3 Employment Changes: 2015

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

- 3

7

- 4 Summary of Monetized Benefits for the Boiler MACT for Existing Major Sources in 2013 (2008$) (Alternate Approach)

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

- 4
7

- 5 Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler MACT (M ajor Sources) in 2013 (Alternative Approach)(millions of 2008$)

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

- 5

### 1

- 1 SECTION 1

I NTRODUCTION

The U.S. Environmental Protection Agency (EPA) is proposing national emission standards for hazardous air pollutants (NESHAP) for new and existing industrial, comme rcial, and institutional boilers and process heaters. 1 

### The proposed rule would require all major sources to meet hazardous air pollutant (HAP) emissions standards reflecting the application of the maximum achievable control technology (MACT). Under a separ ate action, EPA is also proposing a NESHAP for two area source categories: industrial boilers and institutional and

commercial boilers. 2



### 1.1 Executive Summary The proposed emission standards for controlling mercury and polycyclic organic matter (POM) emissions are based on the MACT. The proposed emission standards for controlling other HAPs are based on EPA ’ s proposed determination as to what constitutes the generally available control technology (GACT) or management practices. As part of the regulatory process

, EPA is required to develop a regulatory impact analysis (RIA). The RIA includes an economic impact analysis (EIA) and a small entity impacts analysis and documents

the RIA methods and results.

The key results of the RIA are as follows:

 

### Engineering C ost Analysis: EPA estimates the proposed major source NESHAP’s total annualized costs will be $ 3 .1 billion (2008$). For the area source NESHAP, EPA estimates the total annualized costs will be $ 0

. 5 billion.

 

### Market Analysis: Under the proposed major source NESHAP, the Agency’s economic model suggests t he average national prices for industrial sectors could be 0.01% higher with the NESHAP, while average annual domestic production may fall by about 0.01%. Because of higher domestic prices, imports rise by 0.0 1% per year. Market

- level effects for the proposed area source NESHAP are smaller when compared to the proposed major source rule; avera ge price, production, and import changes are less than 0.01%.

 

### Social Cost Analysis: The estimated social cost of the pr oposed major source rule is just under $ 3 .1 billion (2008$). In the near term, the Agency’s economic model suggests that industries are able to pass approximately $ 0.9 billion of the rule’s costs to consumers (e.g., higher market prices). Domestic industri es’ surplus falls by $ 2.7 billion, while other countries on net benefit from higher prices (a net increase in rest

of

- the world [ROW] surplus of $0. 1 billion). Additional costs and fuel savings for

1 On June 19, 2007

, the U .S. Court of Appeals for the District of Columbia Circuit (DC Circuit) vacated the NESHAP for industrial/commercial/institutiona l boilers and process heaters. This action provid es EPA ’ s proposed rule in response to the court ’ s vacatur.
2 G as

- fired boilers are not part of the area source categories of industrial boilers and institutional/commercial boilers.

### 1

- 2 new and existing major sources that are not included in th e economic model represent a net benefit of $0. 4 billion. The estimated social cost of the proposed area source rule is approximately $ 0.5 billion (2008$). In the near term, the Agency’s economic model suggests that industries are able to pass approximatel y $ 0 .3 billion of the rule’s costs to consumers. Domestic industries’ surplus falls by $ 0 .3 billion and the net increase in ROW surplus is less than $0. 1 billion. Additional costs and fuel savings for unknown, existing, and new area sources not included in the economic model results represent a net benefit of $ 0.1 billion.

 

### Employment Changes: Near

- term employment changes associated with the proposed major source rule are estimated to be less than 8,000 job losses; over a longer time period, net employment e ffects range between 6,000 job losses to 1 2,000 job gains. For the area source rule, near

- term employment changes associated with the proposed major source rule are estimated to be less than 1,000 job losses; over a longer time period, net employment effec ts also range between 1,000 job losses to 3,000 job gains.

 

### Small Entity Analyses: EPA performed a screening analysis for impacts on small entities by comparing compliance costs to sales/revenues (e.g., sales and revenue tests). EPA’s analysis found the tes ts were typically higher than 3% for small entities included in the screening analysis. EPA has prepared an Initial Regulatory Flexibility Analysis (IRFA) that discusses alternative regulatory or policy options that minimize the rule’s small entity impacts . It includes key information about key results from the Small Business Advocacy Review (SBAR) panel.

 

### Benefits Analysis: In the year of full implementation (2013), EPA estimates the PM 2.5 

### co

- benefits of the proposed major source rule are $1 7 billion to $4 2 billion and $1 6 billion to $3 8 billion, at 3% and 7% discount rates respectively. In the year of full implementation (2013), EPA estimates the PM 2.5 

### co

- benefits of this proposed area source rule are $1 .0 billion to $ 2 .4 billion and $ 900 m illion to $ 2.2 bi llion, at 3% and 7% discount rates respectively. All estimates are in 2008 dollars. Using alternate relationships between PM 2.5 

### and premature mortality supplied by experts, higher and lower co

- benefits estimates are plausible, but most of the expert

- based estimates fall between these estimates. The benefits from reducing other air pollutants have not been monetized in this analysis, including reducing 37 0,000 tons of carbon monoxide, 8.7 tons of mercury, 4 3 ,000 tons of HCl , and 1,200 tons of HF each year. I n addition, ecosystem benefits and visibility benefits have not been monetized in this analysis.

 

### Net Benefits: The net benefits for the proposed major source rule only are $1 4 billion to $3 9 billion and $ 13 billion to $3 5 billion, at 3% and 7% discount rat es, respectively in 2013 . The net benefits for the area source rule only are $500 m illion to $ 1.9 billion and $ 400 million to $ 1.7 billion in 2013 , at 3% and 7% discount rates, respectively. All estimates are in 2008 dollars.

### 1

- 3 Table 1

- 1. Summary of th e Monetized Benefits, Social Costs, and Net Benefits for the Boiler MACT (Major Sources) in 201 3 ( m illions of 2008$) 1

Proposed Option

3% Discount Rate 7% Discount Rate

Total Monetized Benefits $ 17,000 to $ 42,000 2 $ 16,000 to $ 38,000 2

Total Social Costs $3,100 3 $3,100

Net Benefits $14,000 to $39,000 $13,000 to $35,000

Option 1N and 1E

3% Discount Rate 7% Discount Rate

Total Monetized Benefits $ 18,000 to $ 43,000 4 $ 16,000 to $ 39,000 4

Total Social Costs $12,000 3 $12,000

Net Benefits $5,400 to $31,000 $3,800 to $27,000

Proposed Option with Alternate Solid Waste Definition

3% Discount Rate 7% Discount Rate

Total Monetized Benefits $ 3,100 to $ 7,700 5 $ 2,800 to $6 ,900 5

Total Social Costs $2,400 3 $2,400

Net Benefits $740 to $5,300 $450 to $4,500

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

2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 29,000 tons of directly emitte d PM 2.5 and PM 2.5 precursors such as 1,700 tons of VOC and 360,000 tons of SO 2 . The monetized benefits from reducing 330,000 tons of carbon monoxide, 42,000 tons of hydrochloric acid, 1,200 tons of HF, and 7.9 tons of mercury, and 730 grams of dioxins/fur ans each year are not included in these estimates. In addition, the monetized benefits from reducing ecosystem effects and visibility impairment are not included.

3 The methodology used to estimate social costs for one year in the multimarket model usin g surplus changes results in the same social costs for both discount rates.

4 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 29,000 tons of directly emitted PM 2.5 and PM 2.5 pr ecursors such as 6,700 tons of VOC and 370,000 tons of SO 2 . The monetized benefits from reducing 390,000 tons of carbon monoxide, 43,000 tons of hydrochloric acid, 8,600 tons of HF, and 8.1 tons of mercury, and 750 grams of dioxins/furans each year are no t included in these estimates. In addition, the monetized benefits from reducing ecosystem effects and visibility impairment are not included.

5 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 th rough reductions of 8,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 4,700 tons of VOC and 44,000 tons of SO 2 . The monetized benefits from reducing 280,000 tons of carbon monoxide, 5,600 tons of hydrochloric acid, 1,100 tons of HF, and 1. 5 tons of mercury, and 290 grams of dioxins/furans each year are not included in these estimates. In addition, the monetized benefits from reducing ecosystem effects and visibility impairment are not included.

### 1

- 4 Table 1

- 2 . Summary of the Monetized Be nefits, Social Costs, and Net Benefits for the Boiler Area Source Rule in 201 3 ( m illions of 2008$) 1

Proposed Option

3% Discount Rate 7% Discount Rate

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

Total Social Costs $500 3 $500

Net Benefits $500 to $1,900 $400 to $1,700

Option 1N and 1E

3% Discount Rate 7% Discount Rate

Total Monetized Benefits $ 8,300 to $ 20,000 4 $ 7,500 to $ 18,000 4

Total Social Costs $35,00 3 $35,000

Net Benefits $

- 27,000 to $

- 16,000 $

- 28,000 to $

- 17,000

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

2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 2,700 tons of directly emitted PM 2.5 an d PM 2.5 precursors such as 1,200 tons of VOC and 1,500 tons of SO 2 . The monetized benefits from reducing 40,000 tons of carbon monoxide, 127 tons of hydrochloric acid, 5 tons of HF, and 0.75 tons of mercury, and 460 grams of dioxins/furans each year are n ot included in these estimates. In addition, the monetized benefits from reducing ecosystem effects and visibility impairment are not included.

3 The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both discount rates.

4 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 23,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 2,100 tons of VOC and 1,700 tons of SO 2 . The monetized benefits from reducing 58,000 tons of carbon monoxide, 140 tons of hydrochloric acid, 6.4 tons of HF, and 1.5 tons of mercury, and 530 grams of dioxins/furans each year are not included in these estim ates. In addition, the monetized benefits from reducing ecosystem effects and visibility impairment are not included.



### 1.2 Organization of this Report

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

EIA:

 

### Section 2 presents the affected industry profiles.

 

### Section 3 describes the engineering cost analysis.

 

### Section 4 describes the economic impact analysis.

 

### Section 5 describes the small entity analyses.

 

### Section 6 presents the benefits estimates.

 

### Section 7 presents suppl emental economic analyses for an alternative non

- hazardous solid waste definition

 

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

### 1

- 5  

### Appendix B provides additional economic model result tables by sector.

### 2

- 1 SECTION 2

INDUSTRY PROFILES

In this section, we provide an introduction selected industries that are affected by the proposed rules . The purpose is to give the reader a general understanding of economic aspects and industry trends to provide additional context for the economic impac t analysis.

2.1 Food Manufacturing

2.1.1 Introduction

Food manufacturing involves the transformation of raw agricultural and livestock products into processed food. Between 1997 and 2002, shipment values stagnated, falling 0. 38 %

, while the number of employ ees and payroll increased 2.7 1 % and 7.76 %

, respectively ( Table

2

- 1). This trend reversed between 2002 and 2006, as shipment values rose 4.77 % and number of employees and payroll fell 5.94 % and 3.28 % respectively ( Table 2

- 1 ). Shipments, payroll, and employ ment continued to increase between 2006 and 2007, but there was a notable drop in the number of establishments between 2002 and 2007 ( Table 2

- 1 ). As Table 2

- 2 shows, p ayroll per employee grew 4.91 % from 1997 to 2002 and continued to increase, albeit at a s lower rate of

2.83 %, from 2002 to 2006 . Between 2006 and 2007, the payroll per employee declined as the growth in employees outpaced the increase in the annual payroll ( Table 2

- 2 ).

Table 2

- 1

. Key Statistics: Food Manufacturing ( North American Industry Clas sification System [ NAICS ] 311)

1997 2002 2006 2007

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

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

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

Establishments 26,302 27,89 9 NA 22,055

NA = Not a vailable

. 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://f actfinder.census.gov>; (July 8, 2008)

. U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS Basis) : 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008)

. U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (2007 NAICS Basis): 2002 and 2007.” <http://factfinder.census.gov>; (January 4, 2010)

.

### 2

- 2 Table 2

- 2

. Industry Data : Food Manufacturing (NAICS 311)

Industry Data 1997 2002 2006 2007

Total s hipments ( $2007, millions ) $528,928 $526,939 $552,075 589,550
Shipments per establishment ($ 2007, thousands ) $20,110 $18,887 NA $26,731

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

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

Average Annual payroll per employee ($ 2007 ) $32,800.97 $34,412.12 $ 3 5,385.46 $34,409.00

Average Employees per establishment 56 54 NA 67

NA = Not available.
Sources: U.S. Census Bureau; generated by RTI International; using A merican 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 A merican FactFinder; “ Sector 00: All sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997. ” <http://factfinder.census.gov>; (July 8, 2008)

. U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 00: All sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (2007 NAICS Basis): 2002 and 2007. ” <http://factfinder.census.g ov>; (January 4, 2010)

. 

### The food manufacturing industry consists of nine different industry groups, each distinguished by the livestock or agricultural products used as raw materials for the processed food products as follow s:

 

### Animal Food Manufacturing ( North American Industry Classification System [ NAICS ] 3111)

 

### Grain and Oilseed Milling (NAICS 3112)

 

### Sugar and Confectionery Product Manufacturing (NAICS 3113)

 

### Fruit and Vegetable Preserving and Specialty Food Manufacturing (NA ICS 3114)

 

### Dairy Product Manufacturing (NAICS 3115)

 

### Animal Slaughtering and Processing (NAICS 3116)

 

### Seafood Product Preparation and Packaging (3117)

 

### Bakeries and Tortilla Manufacturing (NAICS 3118)

In 2006, Animal Slaughtering and Processing made up the lar gest share of both employment (33%) and the value of shipments (27%) in food manufacturing (Figures 2

- 1 and 2

- 2 ).

### 2

- 3 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 effe cts of a regulatory program and the distribution of costs across the U.S. economy.

Animal Food Manufacturing,
3% Grain and Oilseed Milling,

4% Sugar and Confectionery Product Manufacturing,
5% Fruit and Vegetable Preserving and Specialty Food Manufacturing,
12%

Dairy Product Manufacturing,
9%

Animal Slaughtering and Processing, 33% Seafood Product Preparation and

Packaging, 3% Bakeries and Tortilla

Manufacturing,
20% Other Food Manufacturing,
11%



### Figure 2

- 1

. Distribution of Employment within Food Manufacturing (NAICS 311): 2006

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005. ” <http://factfinder.census.gov>; (July 8, 2008)

.

### 2

- 4 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.censu s.gov>; (July 8, 2008)

.



### 2.1.2 Supply and Demand Characteristics

Next, we provide a broad overview of the supply and demand side s of the food manufacturing industry. We emphasize the economic interactions this industry has with other industries and people, including identifying the key goods and services used by the industry and the major uses and consumers of food manufacturing products.

2.1.2.1 Goods and Services Used in Food Manufacturing

In 2006, the cost of materials made up 57% of the value of shipme nts in food production. Total employee compensation accounted for 12% of this value, with half of that coming from production workers ’ wages ( Table 2

- 3 ).

### 2

- 5 Table 2

- 3. Costs of Goods and Services Used in Food Manufacturing (NAICS 311) ($2007)

Industry Ratios 2005 Share 2006 Share

Total shipments ($ millions ) $563,797 100% $552,075 100%
Total compensation ($ millions ) $64,909 12% $64,027 12%

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

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

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

Ave rage 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. Cens us 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

- 6 The top 10 industry groups supplying inputs to food production accounted for 84% of the total intermediate inputs to the industry, with the top three industry groups (food products, animal products, and crop products) accounting for over half of the total intermediat e inputs ( Table 2

- 4 ). Electric power generation, transmission

, and distribution accounted for 2% of the total intermediate inputs, whereas boilers, tanks

, and shipping containers accounted for 1%.

Table 2

- 4

. Key Goods and Services Used in Food Manufacturin g (NAICS 311) ($2007, millions )

Sector BEA Code Food Products
Food products 3110 $91,518
Animal products 1120 $85,785
Crop products 1110 $43,109
Management of companies and enterprises 5500 $34,235

Wholesale trade 4200 $27,849
Converted paper product s 3222 $18,782
Truck transportation 4840 $12,943
Plastics and rubber products 3260 $9,641
Electric power generation, transmission, and distribution 2211 $6,004
Boilers, tanks, and shipping containers 3324 $4,564
Total intermediate inputs T005 $400,067

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

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

.



### 2.1.2.2 Energy

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

energy

- intensive industry to model within its Industrial Demand Module (DOE, 2008a ). In 2002, food manufacturing accounted for 6.86% of the total fuel consumption by all manufacturing industries (NAICS 311

– 339) and 19.24% of the conventional boiler use fuel consumption by all manufacturing industries ( DOE , En ergy Information Administration, 2007).

In both 2005 and 2006, purchased electricity and fuel each accounted for 1% of the total value of shipments in food manufacturing ( Table 2

- 3 ). In 2002, total energy consumption totaled
1,116 TB TU

, a 7% increase over 1998 (Table 2

- 5 ). Of this total fuel consumption, the largest share (41.72%) was consumed for indirect uses including conventional boiler use and combined heat and power (CHP) and/or cogeneration process (MECS Table 5.2). Between 1997 a nd 2005, while the manufacturing sector as a whole used less electricity

, food manufacturing used more

### 2

- 7 electricity (Figure 2

- 3 ). From 2005to 2006, the electricity consumption increased by nearly 9% (Table 2

- 5).

Table 2

- 5

. Energy Used in Food Manufacturing (NAICS 311)

Fuel Type 1998 2002 2006

Net e lectricity 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 short tons) 6 8 7

Coke and breeze (million short tons) * * *

Other e (trillion B TU ) 97 90 107

Total (trillion B TU ) 1,044 1,116 1,186

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

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

- site cogeneration or generation from combustible fuels because that energy has already been included as generating fuel (for example, coal).

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

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

such as independent gas producers, gas broke rs, 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 refin eries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs).

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

that respondents indicated was use d to produce heat and power.

* Estimate less than 0.5.

Sources: U.S. Department of Energy, Energy Information Administration. 2007. “2002 Energy Consumption by Manufacturers

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

< http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html >.
U.S. Department of Energy, Energy Information Administration. 2009 a

. “ 2006 Energy Consumption by Manufacturers

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

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

.

### 2

- 8 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. 2008. “ Industrial Production and Capacity Utilization: Electric Power Use: Manufacturing and Mining. ” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G311.S. <htt p://www.federalreserve.gov/datadownload/>

.



### 2.1.2.3 Uses and Consumers

The majority of food manufacturing ’ s total commodity output (58%) is sold for personal consumption. Of the sales for intermediate use, 42% are sold back into the food manufacturing industry (Table 2

- 6 ).

### 2

- 9 Table 2

- 6

. Demand by Sector: Food Manufacturing (NAICS 311) ($2007, millions )

Sector BEA Code Food Products Food manufacturing 3110 $91,518 Food services and drinking places 7220 $37,291 Animal production 1120 $15,870 General state and local government services S007 $15,170

Retail trade 4A00 $13,985 Beverage manufacturing 3121 $11,703 Hospitals 6220 $9,539

Educational services 6100 $4,485 Nursing and residential care facilities 6230 $4,187

Social assistance 6240 $2,277

Total intermediate use T001 $217,570 Personal consumption expenditures F010 $301,748 Exports of goods and services F040 $28,151 Imports of goods and services F050 −$33,119 Total final uses (GDP) T004 $299,470 Total commodity output T007 $517,040

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

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

.



### 2.1.3 Firm and Market Characteristics

This remaining subsection descr ibes geographic, production, and market data. These data provide the basis for further analysis, including regulatory flexibility analyses

, and give a complete picture of the recent historical trends of production and pricing.

2.1.3.1 Location

In 2002, Cal ifornia 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

- 10 Establishments

Fewer than 250

250 - 499

500 - 999

1,000 - 2,000

More than 2,000



### Figure 2

- 4

. Establishment Concentration in Food Manufacturing (NAICS 311): 2002

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

.



### 2.1.3.2 Production Capacity and Utilization

Capacity utilization of the food manufacturing industr y did not fall off during the recession of 2001 as much as the manufacturing sector as a whole (Figure 2

- 5 ). Food manufacturing ’ s capacity utilization has remained highe r than manufacturing as a whole and went above 85% in the spring of 2008. The effects o f the recent economic downturn have not affected capacity utilization as sharply in the food industry relative to the overall manufacturing sector (Figure 2

- 5).

### 2

- 11 50 60 70 80 90 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 Res erve Board. 2008. “ Industrial Production and Capacity Utilization: Capacity Utilization. ” Series ID: G17/CAPUTL/CAPUTL.GMF.S & G17/CAPUTL/CAPUTL.G311.S. <http://www.federalreserve.gov/datadownload/>

.



### 2.1.3.3 Employment

The geographic distribution of employment in food manufacturing varies subs tantially
from the distribution of establishments. In 2002, Arkansas, ranked thirty

- first in number of

establishments and had the eight h most employees (53,844) because of its nation al high of 199 employees per establishment. New York, ranked seco nd in num ber of establishments, had only

the ten th 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

- 12 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 (N AICS 311): 2002

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Pl aces: 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 su bstantially 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. The se 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

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

### 2

- 13 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 (2002=100)

Manufacturing Food



### Figure 2

- 7

. Capacity Trends in Food Manufacturing (NAICS 311)

Source: Federal Reserve Board. 2008. “ Industrial Production and Capacity Utilization: Industrial Capa city. ” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G311.S. <http://www.federalreserve.gov/datadownload/>

.



### Table 2

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

Sales ($ millions ) % of Sales in United States

Kraft Foods Inc

. 37

, 241 57.8%

Tyson Foods Inc

. 26

, 900 90.0%

General Mills Inc

. 12

, 442 82.9%

Sara Lee Corp. 12

, 278 53.8%

ConAgra Foods Inc. 12

, 028 89.2%

Smithfield Foods Inc. a 11

, 911 86.2%

Dean Foods Co. 11

, 822 >99.0%

Kellogg Co. a 11

, 776 66.1%

H.J. Heinz Co. 9

, 002 42.3%

Campbell Soup Co. 7

, 867 69.0%

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

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

### 2

- 14 For the industry as a whole, the number of corporations as well as the number of corporations with net income in the food manufacturing industry grew between 2004 and 2005

. Although the overall number of companies continued to grow in 2006, the number of those with a positive net income declined along with profit margins and total receipts (Table 2

- 8).

Table 2

- 8

. Corporate Income and Profitability for Food Manufacturing (NA ICS 311)

2004 2005 2006

Number of c orporations 14

, 408 14

, 956 16

, 146 Number of corporations with net income 6

, 541 7

, 503 7

, 333

Total r eceipts ( thousands ) $502,149,944 $504,944,378 $484,193,319

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

Before

- t ax profit margin 5.27% 10.09% 7.43%

After

- tax profit margin 3.74% 7.62% 5.11%

Source: Internal Revenue Service, U.S. Department of Treasury. 2008. “ Corporation Source Book: Data File 2005. ” <http://www.irs.gov/taxstats/article/0,,id=167 415,00.html>; (January, 14 2009)

.



### 2.1.3.6 Size Distribution

The prim ary criterion for categorizing a business as small is number of employees, using definitions by the SBA for regulatory flexibility analyse s. The data describing size standards are provided in Table 2

- 9. Over 80% of the NAICS industries within the food manu facturing industry use a cutoff of 500 employees. In 2002, enterprises with fewer than 500 employees accounted for 32% of employment and 23% of receipts within food manufacturing (Table 2

- 10 ).

2.1.3.7 Domestic Production

Between 1997 and early 2008 , overal l manufacturing production grew faster (34.88%) than the food manufacturing component (26.18%) (Figure 2

- 8 ). The food manufacturing industry has been less volatile, particularly during the recession of 2001 and the current economic

downturn

.

2.1.3.8 Intern ational 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

- 15 Table 2

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

NAICS Description Employees

311111 Dog and Cat Food Manufacturing 500

311119 Other Animal Food Manufacturing 500

311211 Flour Milling 500

311212 Rice Milling 500

311213 Malt Manufacturing 500

311221 Wet Corn Milling 750

311222 Soybean Processing 500

311223 Other Oilseed Processing 1

, 000

311225 Fats and Oils Refining and Blending 1

, 000

311230 Breakfast Cereal Manufacturing 1

, 000

3 11311 Sugarcane Mills 500

311312 Cane Sugar Refining 750

311313 Beet Sugar Manufacturing 750

311320 Chocolate and Confectionery Manufacturing from Cacao Beans 500

311330 Confectionery Manufacturing from Purchased Chocolate 500

311340 Non

- Chocolate Con fectionery Manufacturing 500

311411 Frozen Fruit, Juice and Vegetable Manufacturing 500

311412 Frozen Specialty Food Manufacturing 500

311421 Fruit and Vegetable Canning3 3

, 500

311422 Specialty Canning 1

, 000

311423 Dried and Dehydrated Food Manufactur ing 500

311511 Fluid Milk Manufacturing 500

311512 Creamery Butter Manufacturing 500

311513 Cheese Manufacturing 500

311514 Dry, Condensed, and Evaporated Dairy Product Manufacturing 500

311520 Ice Cream and Frozen Dessert Manufacturing 500

311611 An imal (except Poultry) Slaughtering 500

311612 Meat Processed from Carcasses 500

311613 Rendering and Meat By

- product Processing 500

311615 Poultry Processing 500

311711 Seafood Canning 500

311712 Fresh and Frozen Seafood Processing 500

311811 Retail Bakeries 500

311812 Commercial Bakeries 500

311813 Frozen Cakes, Pies, and Other Pastries Manufacturing 500

311821 Cookie and Cracker Manufacturing 750

311822 Flour Mixes and Dough Manufacturing from Purchased Flour 500
(continued)

### 2

- 16 Table 2

- 9. Small Bu siness Size Standards: Food Manufacturing (NAICS 311) (continued)

NAICS Description Employees

311823 Dry Pasta Manufacturing 500

311830 Tortilla Manufacturing 500

311911 Roasted Nuts and Peanut Butter Manufacturing 500

311919 Other Snack Food Manufactu ring 500

311920 Coffee and Tea Manufacturing 500

311930 Flavoring Syrup and Concentrate Manufacturing 500

311941 Mayonnaise, Dressing and Other Prepared Sauce Manufacturing 500

311942 Spice and Extract Manufacturing 500

311991 Perishable Prepared Food Manufacturing 500

311999 All Other Miscellaneous Food Manufacturing 500

Source: U. S. Small Business Administration (SBA). 2008. “ Table of Small Business Size Standards Matched to North American Industry Classification System Codes. ” Effective August 22 , 200 8

. <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. Businesses (SUSB) employment category for zero employees. These entities only operated for a

fraction of the year

. Source: U.S. Census Bureau. 2008

. “ Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002. ” < http://www.census.gov/csd/susb/download_susb02.htm >.

### 2

- 17 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 Manufacturi ng (NAICS 311)

Source: Federal Reserve Board. 2008. “ Industrial Production and Capacity Utilization: Industrial Production. ” Series ID: G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.N & G17/IP_MAJOR_INDUSTRY_ GROUPS/IP.G311A2.N. <http://www.federalreserve.gov/datado wnload/>

.

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 Commission

. 2009 a

. “ U.S. Domestic Exports ” & “ U.S. Imports for Consumption. ” < http://www.federalreserve.gov/datadownload/ >

.

### 2

- 18 2.1.3.9 Market Prices

Prices of goods in food manufacturing have moved generally in line with prices in overall manufacturing (se e Figure 2

- 10 ). Both indexes increased over 31% since between early 2003 and early 2008 , a CAGR of 5.13%. This rise was followed by a marked decline in recent years along with the downward trend in prices throughout the economy.

0 20 40 60 80 100 120 140 160 180 20019901991199219931994199519961997199819992000200120022003200420052006200720082009Index Values (December 1984=Base)

Manufacturing Food


### Figure 2

- 10

. Producer Pri ce Trends in Food Manufacturing (NAICS 311)

Source: U.S. Department of Labor; Bureau of Labor Statistics. 2010. “Producer Price Indexes.” <http://www.bls.gov/pPI/Series Id: PCU311

— 311

— Food Manufacturing & PCUOMFG

– OMFG

– Total Manufacturing>.



### 2.2 Wood Product Manufacturing

2.2.1 Introduction

According to a report by Standard & Poor’s (2008) , a number of factors are shaping the current economic environment for wood products, including, but not limited to, the housing slump, high i nput costs, low prices for lumber and other building materials, and a weak dollar.

Table 2

- 11 shows that revenues in this industry are not entirely predictable, exhibiting a drop in shipment revenue between 1997 and 2002 but a rise back to within $5 billio n of the 1997 value in 2006 and a decline to within $14 billion of the 2006 value in 2007.

### 2

- 19 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 $17,43 9
Employees 570,034 543,459 536,094 519,651

Establishments 17,367 17,255 NA 14,862

NA = Not available.

Source s : 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; generate d by RTI International; using American FactFinder; “ Sector 00: All S ectors: 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 .” A ccessed on December 27 , 2009.



### While total payroll dropped 3% over from 1997 to 2007 , annual payroll per employee rose 6.5% because of the decline in the number of employees ( Table 2

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

- 12).

Table 2

- 12

. Industry Data : Wood Product Manufacturing (NAICS 321)

Industry Data 1997 2002 2006 2007
Total shipments ( $2007, millions ) $110,956 $102,721 $115,390 $101,879
Shipments per establishment ( $ tho usands ) $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

A verage Employees per establishment 33 31 NA 35

NA = Not available.

Source s : U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and In dustries: 2006 and 2005. ” <http://factfinde r.census.gov>; (July 8, 2008)

. U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 00: All S ectors: Core Busin ess Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997. ” <http://factfinder.census.gov>; (July 8, 2008)

.

### 2

- 20 U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “ Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy

- Wide Key Statistics: 2007 .” Accessed on December 27, 2009. 

### The U.S. Census Bureau categorizes this industry’s facilities into three categories: “sawmills and wood preservation;” “veneer, plywood, and engineered wood product manufacturing;” and “other wood product manufacturing.” These are further divided into the following types of facilities as define d by the Census Bureau :

 

### Sawmills and Wood Preservation

– Sawmills and Wood Preservation (NAICS 32111): This industry comprises establishments primarily engaged in one or more of the following manufacturing activities: (a) sawin g 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 oth er establishments with creosote or other preservatives to prevent decay and to protect against fire and insects. Sawmills may plane the rough lumber that they make with a planing machine to achieve smoothness and uniformity of size.

 

### Veneer, Plywood, and En gineered Wood Product Manufacturing

– Veneer, Plywood, and Engineered Wood Product Manufacturing (NAICS 32121): This industry comprises establishments primarily engaged in one or more of the following manufacturing activities: (a) veneer and/or plywood, (b) engineered wood members, and (c) reconstituted wood products. This industry includes manufacturing plywood from veneer made in the same establishment or from veneer made in other establishments, and manufacturing plywood faced with 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 o ther millwork including wood flooring. Dimension stock or cut stock is defined as lumber and worked wood products cut or shaped to specialized sizes. These establishments generally use woodworking machinery, such as jointers, planers, lathes, and routers t o shape wood.

– Wood Container and Pallet Manufacturing (NAICS 32192): This industry comprises establishments primarily engaged in manufacturing wood pallets, wood box shook, wood boxes, other wood containers, and wood parts for pallets and containers.

– All O ther Wood Product Manufacturing (NAICS 32199): This industry comprises establishments primarily engaged in manufacturing wood products (except establishments operating sawmills and wood preservation facilities; and

### 2

- 21 establishments manufacturing veneer, plyw ood, engineered wood products, millwork, wood containers, or pallets).

Figure 2

- 11 shows that the industry proportion of the value of shipments for other wood product manufacturing (51%) was greater than the value of shipments for sawmills and wood

preserv ation (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; g enerated 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 2 007 numbers]



### 2.2.2 Supply and Demand Characteristics

Next, we provide a broad overview of the supply and demand sides of the wood product manufacturing industry. We emphasize the economic interactions this industry has with other industries and people and identify the key goods and services used by the industry and the major uses and consumers wood products.

### 2

- 22 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 an d Environment; “ Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy

- Wide Key Statistics: 2007 ” Release Date: 12/22/09

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



### 2.2.2.1 Goods and Services Used in Wood Product Manufacturing

In 200 7 , the cost of materials made up 59 % of the total shipment value of goods in the wood product manufacturing industry (Table 2

- 13) . Total compensation of employees represented 22 % of the total value in 2007

. Both the number of total shipments and the number of employees in this industry decreased between 2005 and 2007

— the former by 14% and the latter by 3%.

The top 10 industry groups supplying inputs to the wood product industry account ed for

80 % of the total intermediate inputs according to 2008 Bureau of Economic Analysis data (Table

2

- 14)

. The largest comes from the wood product industry itself. This is quite understandable, since the descriptions of the various industries within wood product manufacturing imply that they supply each other with products in order to add value and distribute their products to the broader market. The top five inputs are rounded out by forestry and logging products, wholesale trade, management of companies and enterprises, and truck transportat ion, which together make up 70% of the total cost of input.

### 2

- 23 Table 2

- 13

. Cost s 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 benefi ts $4,442 4% $4,683 4% $5,069 5%

Total employees 538,890 536,094 524,212

Average compensati on 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 produc tion 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%

Purchase d electricity $1,530 1% $1,625 1% $1,446 1%

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

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

Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Ind ustries: 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: Man ufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007. ” Accessed on December 27, 2009. [Source for 2007 numbers]



### 2.2.2.1.1 Energy. The Department of Energy ( DOE ) categorizes wood product manufacturing (NAICS 321) as a non

- e nergy

- intensive industry

. T he 2008 Annual Energy

Outlook predicts that the wood product industry will be one of five (out of eight) non

- energy

intensive industries experienc ing positive average growth of delivered energy consumption

between 2006 and 2030 (DOE, 2008 )

.

### 2

- 24 Table 2

- 14

. Key Goods and Services Used in Wood Product M anufacturing (NAICS 321) ( $2007 , millions )

Description BEA Commodity Code Wood Products

Wood products 3210 $20,989

Forestry and logging products 1130 $18,914

Wholesal e trade 4200 $5,417

Management of companies and enterprises 5500 $2,853

Truck transportation 4840 $2,542

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

Other fabricated metal products 332B $1,310

Nonmetallic mineral products 327 0 $1,110

Real estate 5310 $799

All other administrative and support services 561A $748

Architectural and structural metal products 3323 $725

Rail transportation 4820 $723

Other i nputs $14,650

Total intermediate inputs T005 $72,169

Source: U.S. Bure au 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 bet ween 1998 and 2002 Figure 2

- 13 shows that electrical power use decreased, since 2000

.

2.2.2.2 Uses and Consumers

Table 2

- 16 shows that three of the top four consumers of wood products are represented by the construction sector of the economy (NAICS 23). N ew residential construction, new nonresidential construction, and maintenance and repair construction consume 35% of the total commodity output in this industry. The top 10 consumers of wood products make up 54% of the demand for wood products. Although ma ny of the top consumers deal with construction, repair, or real estate services, other types of consumers, such as food services and drinking places, rail transportation, plastics and rubber products manufacturing, and other, use these products.

### 2

- 25 Table 2

- 1 5

. 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 genera tion from noncombustible renewable

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

- site cogeneration or generation from combustible fuels because that energy has already been included as generating fuel (for example, coal).

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

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

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

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

ethane

- propane mixtures, propane

- butane mixtures, and isobute ne produced at refineries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs).

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

that responden ts indicated was used to produce heat and power.

* Estimate less than 0.5.

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

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

. “ 2002 Energy Consumption by Manufacturers

— Data Tables. ” Tables 3.2 and N3.2. <http://www.eia.doe.gov/emeu/mecs/ mecs2002/data02/shelltables.html>. Washington, DC : DOE. U.S. Department of Energy, Energy Information Admi nistration. 2007 b

. “ 200 6 Energy Consumption by Manufacturers

— Data Tables. ” Tables 3.1. < http://www.eia.doe.gov/e meu/mecs/mecs2006/ 2006tables.html >. [Source for 2006 numbers]

### 2

- 26 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



### Figure 2

- 13. Electrical Power Use Trends in the Wood Product Manufacturing Industry (NAICS 321): 1997

– 2005

Source: Federal Reserve Board. 2009. “ Industrial Production and Capacity Utilizati on: Electric Power Use: Manufacturing and Mining .” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G321.S. <http://www.federalr eserve.gov/datadownload/>. Accessed on December 15, 2009

.



### 2.2.3 Firm and Market Characteristics

Th is section describes geographic, production, and market data. Th ese data provide the basis for further analysis , including regulatory flexibility analyses , as well as a complete picture of the recent historical trends of productio n and pricing.

2.2.3.1 Location

As Figure 2

- 14 illustrates, the states with the largest number of wood product manufacturing establishments are dispersed throughout the country, with a significant

concentration of establishments in the northeastern states. Other states with many establishments
include California, Texas, and North Carolina.

### 2

- 27 Table 2

- 16

. Demand by Sector: Wood Product Manufacturing (NAICS 321) ($2007, millions )

Sector BEA Code 3210 Wood Products

New residential construction 2302 $19,997

New nonresidential construction 2301 $11,854
Furniture and related product manufacturing 3370 $8,197
Maintenance and repair construction 2303 $4,048
Motor vehicle body, trailer and parts manufacturing 336A $2,516

Real estate 5310 $2,335

Food services and drinking places 7220 $2,307
Other miscellaneous manufacturing 3399 $1,311

Wholesale trade 4200 $1,284
Rail transportation 4820 $1,138

Retail trade 4A00 $1,047
Plastics and rubber products manufacturing 3260 $877
General state and local government us e S007 $3,116
Owner occupied dwelling S008 $11,209

Private fixed investment F020 $7,933
Exports of goods and services F040 $3,978
Total final uses (gross domestic product [GDP]) T004 $3,719
Total commodity output T007 $101,753

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

. “ 2002 Benchmark Input

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



### 2.2.3.2 Production Capacity and Utilization

Capa city utilization of the wood product manufacturing industry has been experienc ing capacity utilization increases and declines with more extreme fluctuations than those of all manufacturing industries combined. The decline in wood product manufacturing is s imilar 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 manufac turing 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

- 28 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): 20 02

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

.



### 2.2.3.3 Employment

California has the largest number of employees in the wood product m anufacturing industry with over 3 9 ,00 0 reported in the 2002 cen sus followed by over 32,000 in Oregon (Figure 2

- 17 )

. The states with the highest number of employees do not directly correlate with the states with the highest number of establishments. States such as Indiana, Georgia, Arkansas, and Oregon had fewer than 6 00 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 s ector has been growing consistently since 1997,

the wood product manufacturing industry has experienced inconsistent growth. After a small amount of growth in capacity between 1997 and 2001, the wood product manufacturing industry’s capacity dipped between 2002 and 2005 but has been growing at a slow rate since then though it started to dip again in 2008 and 2009 (Figure 2

- 17)

.

### 2

- 29 45 50 55 60 65 70 75 80 85 90 95Jan-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 Manufacturi ng Industry (NAICS 321)

Source: Federal Reserve Board. 20 09. “ Industrial Production and Capacity Utilization: Capacity Utilization .” Series ID: G17/CAPUTL/CAPUTL.GMF.S & G17/CAPUTL/CAPUTL.G321.S. <http://www.federalreserve.gov/datadownload/>. Accessed on December 15, 2009.



### 2.2.3.5 Firm Characteristics

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

— International Paper and Weyerhaeuser

— generating nearly $22 billion each (Table 2

- 1 7 . The top two companies ’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor’s ( S&P ’ s ) list ( Benwart, 2006 ). Alt hough these numbers do not exclusively reflect wood products, they do convey the market envir onment in

which firms in this sector compete.

2.2.3.6 Size Distribution

The primary criterion for categorizing a business as small is the number of employees, using definitions by the SBA for regulatory flexibility analyse s. According to SUSB reports for

2 002, small companies were the recipients of the majority of receipts in 2002; 53 % of receipts

were generated by companies with fewer than 500 employees (Table 2

- 18)

. The number of employees in the small business cutoff is 500 employees for all subindustrie s in the wood product manufacturing industry ( Tab le 2

- 19).

### 2

- 30 Employemnt by State

Less than 2,000

2,000 - 4,999

5,000 - 9,999

10,000 - 20,000

More than 20,000



### Figure 2

- 16. Employment Concentration in the Wood Product Manufactu ring Industry (NAICS 321): 2002

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

.



### 2.2.3.7 Domestic Production

Similar to industry capacity rates, industry production rates for wood product manufacturing have decreas ed since 2006 compared to the steady increase in production for the manufacturing sector since 1997 (Figure 2

- 18)

. Similar to capacity utilization trends (Figure

2

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

sector.

2.2.3.8 International Trade

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

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

- 31 100 105 110 115 120 125 130 135 140 145 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. “ Industrial Production and Capacity Utilization: Industrial Capacity. ” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G321.S. <http://www.fede ralreserve.gov/datadownload/>. Accessed on December 15, 2009

.



### Table 2

- 17

. Largest U.S. Paper and Forest Products Companies: 2006

Company Revenues ($ millions ) a

International Paper 21,995

Weyerhaeuser 21,896

Smurfit

- Stone 7,157

MeadWestvaco 6,530

Temple

- Inland 5,558

B owater 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 & Fores t Products. ” Standard and Poor ’ s Industry Surveys . 176(28).2-32 

### 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 t o 99 Employees 100 t o 499 Employe es 500 t o 749 Employees 750 t o 999 Employees 1,000 t o 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 $165,814 $161,363 $159,262 $168,231

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

Source: U.S. Census Bureau. 2008

. “ Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002. ” <http://www.census.gov/csd/susb/download_susb02.htm>.

### 2

- 33 Table 2

- 19

. Small Business Size Standards: Wood Product Manufacturing (NAICS 321)

NA ICS NAICS Description Employees

321113 Sawmills 500

321114 Wood Preservation 500

321211 Hardwood Veneer and Plywood Manufacturing 500

321212 Softwood Veneer and Plywood Manufacturing 500

321213 Engineered Wood Member (except Truss) Manufacturing 500

32121 Truss Manufacturing 500

321219 Reconstituted Wood Product Manufacturing 500

321911 Wood Window and Door Manufacturing 500

321912 Cut Stock, Resawing Lumber, and Planing 500

321918 Other Millwork (including Flooring) 500

321920 Wood Container and Pallet Manufacturing 500

321991 Manufactured Home (Mobile Home) Manufacturing 500

321992 Prefabricated Wood Building Manufacturing 500

321999 All Other Miscellaneous Wood Product Manufacturing 500

Source: U. S. Small Business Administration (SBA). 2008

. “ Table of Small Business Size Standards Matched to North American Industry Classification System Codes. ” Effective August 22, 2008. <http://www.sba.gov/services/contractingopportunities/sizestand ardstopics/size/index.html>.



### 2.2.3.9 Market Prices

Prices of goods in the wood product manufacturing industry have remained roughly the same since 2005. The prices for the entire manufacturing sector increased between 2003 and

2008 but have decreased since August 2008. Producer price indices (PPIs) show that producer prices for wood products increased by 6 % from 2004 to 2007 (Figure 2

- 20)

.

### 2

- 34 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: F ederal Reserve Board. “ Industrial Production and Capacity Utilization: Industrial Production .” Series ID: G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.S & G17/IP_MAJOR_INDUSTRY_GROUPS/ IP.G321.S. <http://www.fede ralreserve.gov/datadownload/>. Accessed on December 1 5, 2009

.

### 2

- 35 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. 2008a. “ U.S. Domestic Exports ” & “ U.S . Imports for Consumption. ” <http://dataweb.usitc.gov/scripts/user_set.asp> ; (July 17, 2008)

.

### 2

- 36 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)

Source: U. S. Bureau of Labor Statistics (BLS). 200 9

. “ Producer Price Index. ” Series ID: PCU32 1

— 32 1

— & 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 200 7 , shipments in the industry declined 7%, and employment declined by

2 7 % (Table 2

- 21 ). Whi le total payroll dropped 2 6 % over this time, annual payroll per employee

rose 2 % from 1997 to 200 7 because of the decline in the number of employees (Table 2

- 20 ). Shipments per employee grew 2 8 % from 1997 to 200 7 , with much of that growth taking place

betw een 2002 and 2006 (Table 2

- 21 ).

### 2

- 37 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 a vailable

. Sources: U.S. Census Bureau ; generated by RTI International; using American FactFinder; “ Sector 31: Annual Survey of Manufac tures: 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 S ectors: Core Busines s 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 Stati stics: 2007.” Accessed on December 28, 2009. [Source for 2007 numbers]



### Table 2

- 21

. Industry Data : Paper Manufacturing (NAICS 322)

Industry Data 1997 2002 2006 2007
Total shipments ($2007, millions ) $188,496 $175,983 $174,887 $175,806

Shipments per establ ishment ($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 $50,1 89 $51,174 $49,904

Average Employees per establishment 98 89 NA 87

NA = Not a vailable

. 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 Am erican 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 Stati stics: 2007 .” <http://factfinder.census.gov>

. Accessed on December 28, 2009. [Source for 2007 numbers]

### 2

- 38 The U.S. Census Bureau categorizes this industry ’ s facilities into two categories: pulp, paper, and paperboard manufacturing and converted paper product manufacturing. These are further divided into the following types of facilities as defined by the Census Bureau ( 2001 ):

 

### Pulp, Paper, and Paperboard:

– Pulp Mills (NAICS 32211): This industry comprises establishments primarily engaged in manufacturing pulp without manufacturing paper or paperboard. The pulp is made by separating the cellulose fibers from the other impurities in wood or other materials, such as used or recycled rags, linters, scrap paper, and straw.

– Paper Mills ( NAICS 32212): This industry comprises establishments primarily engaged in manufacturing paper from pulp. These establishments may manufacture or purchase pulp. In addition, the establishments may convert the paper they make. The activity of making paper cl assifies an establishment into this industry regardless of the output.

– Paperboard Mills (NAICS 32213): This industry comprises establishments primarily engaged in manufacturing paperboard from pulp. These establishments may manufacture or purchase pulp. In addition, the establishments may also convert the paperboard they make.

 

### Converted Paper Products:

– Paperboard Containers Manufacturing (NAICS 32221): This industry comprises establishments primarily engaged in converting paperboard into containers without manufacturing paperboard. These establishments use corrugating, cutting, and shaping machinery to form paperboard into containers. Products made by these establishments include boxes; corrugated sheets, pads, and pallets; paper dishes; and fiber drums and reels.

– Paper Bag and Coated and Treated Paper Manufacturing (NAICS 32222): This industry comprises establishments primarily engaged in one or more of the following manufacturing activities: cutting and coating paper and paperboard; cutting and laminating p aper 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 o ther 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 writ ing, filing, art work, and similar applications.

– Other Converted Paper Products (NAICS 32229): This industry comprises establishments primarily engaged in one of the following manufacturing activities:

**•** converting paper and paperboard into products (except containers, bags, coated and treated paper and paperboard, and stationery products), or

### 2

- 39 **•** 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 c onverted paper products was 54% of the value of all paper products in 2007, while the value of shipments for pulp, paper, and paperboard products was 46%. Figure 2

- 22 indicates that 70% of industry employees worked in the converted paper product category of the industry due to the labor intensive aspects of those

facilities

.

PPP (3221)
46% CPP (3222)
54%



### Figure 2

- 21

. Distribution of Value of Shipments within Paper Manufacturing (NAICS 322): 2007

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American Fac tFinder: “ Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007 .” Accessed on December 28, 2009.

PPP (3221)
30%

CPP (3222)
70%



### Figure 2

- 22

. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “ Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007 .”

< http://factf inder.census.gov >

. Accessed on December 28, 2009.

### 2

- 40 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 200 7 , 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 200 7 , down from 17% in 2005. The total number of employees dropped by

2 %, between 2005 and 200 7 , whil e shipments increased by 3 % in the same period.

The top 10 industry groups supplying inputs to the paper manufacturing subsector accounted for 70% of the total intermediate inputs according to 2008 Bureau of Economic Analysis (BEA) data (Table 2

- 23 ). Inpu ts for pulp, paper, and paperboard products are notably different from inputs for converted paper products because the NAICS 3221 group represents the initial step in the paper manufacturing process; thus, its inputs include more raw resources such

as wood products, forestry and logging products, natural gas, and electricity. This becomes evident when observing inputs for converted paper products: 49% of the cost of inputs comes from pulp, paper, and paperboard products.

### 2

- 41 Table 2

- 22

. Costs of Goods and Servi ces Used in the Paper Manufacturing Industry (NAICS 322)

Variable 2005 Share 2006 Share 2 007 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%

Annua l payroll $21,792 13% $21,188 12% $20,804 12%

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

Total employees 426,748 414,049 417,367

Average compensation per employee $67,596 $67,121 $65,051
Total production workers wages ($2007,

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

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

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

Average production wages per hour $21 $21 $21

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

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

Purchase electricity $3,788 2% $3,841 2% $3,780 2%
Purchased fuel ($2007) $5,537 3% $5,509 3% $5,511 3%

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

Source s : U.S. Census Bureau; ge nerated 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; gene rated by Kapur Energy and Environment; using American FactFinder; “ Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Indus try for the United States: 2007.” <http://factfinder.census.gov>

. A ccessed on December 28 , 20 09

. [Source for 2007 numbers]



### 2.3.2.1.1 Energy

. The Department of Energy (DOE) categorizes paper manufacturing (NAICS 322) as an energy

- intensive subsector. The 2008 Annual Energy Outlook predicts that the paper

- producing subsector will be one of four subsectors experien cing positive average growth of delivered energy consumption between 2006 and 2030 (DOE, 2008 ).

### 2

- 42 Table 2

- 23. Key Goods and Services Used in the Paper Manufacturing Industry (NAICS 322) ( $ millions , $2007)

Description BEA Code NAICS 3221 Pulp, Paper, and Paperboard NAICS 3222 Converted Paper Products Total

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

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

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

Forestry and logg ing 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

Natura l gas distribution 2212 $2,680 $345 $3,026

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

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

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

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

.



### Energy generation from the recovery boiler is often insufficient for total plant needs, so facilities augment recovery boilers with fossil fuel

– fired and wood waste

– fired boilers (hogged fuel) to generate steam and often electricity. Industry wide , the use of pu lp 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 200 6 by 14%. Figure 2

- 24 indic ates that total electrical power use changed sporadically between 2002

and 2004 but decreased consistently and rapidly after 2004.

### 2

- 43 Table 2

- 24

. Energy Used in Paper Manufacturing (NAICS 322)

Fuel Type 1998 2002 2006

Net electricity a (million kWh) 70,364 6 5,503 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 (million bbl) 1 2 1

Coal (million short tons) 12 11 10

Coke and breeze (million short tons)

— *

—

Ot

her 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 d iesel fuels.

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

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

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

- propane mixtures, propane

- butane mixtures, and isobutene produced at refineries or natural gas processing plants, including plants that fractionate raw natu ral 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 o f Energy, Energy Information Administration. 2007

. “ 2002 Energy Consumption by Manufacturers

— Data Tables. ” Tables 3.2 and N3.2. <http://www.eia.doe.gov/emeu/mecs/mecs2002/ data02/shel ltables.html>. Washington, DC: DOE. U.S. Department of Energy, Energy Information Administration. 2007 b

. “ 2006 Energy Consumption by Manufacturers

— Data Tables. ” Table 3.1. < http://www.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html >. A ccessed on December 27 , 20 09

. [Source for 2006 numbers]



### Over the last 25 years, the pulp and paper subsector has changed its energy generation methods from fossil fuels to a greater use of processes such as increases in the use of wood wastes in place of fuel (Table 2

- 25 ). During the 1972

– 1999 period, the proportion of total industry power generated from the combination of woodroom wa stes, spent liquor solids, and

other self

- generated methods increased from about 41% to about 5 6 %, while coal, fuel oil, and natural gas use decreased from about 54% to about 36% (EPA, 2002 ).

### 2

- 44 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. 200 9

. “ Industrial Production and Capacity Utilization: Electric Power Use: Manufacturing and Mining. ” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G322.S. <http://www.federalreserve.go v/datadownload/>

.



### Table 2

- 25

. Estimated Energy Sources for the U.S. Pulp and Paper Industry

Energy Source 1972 1979 1990 1999

Purchased steam 5.4% 6.7% 7.3% 1.5%

Coal 9.8% 9.1% 13.7% 12.5%

Fuel oil 22.3% 19.1% 6.4% 6.3%

Natural gas 21.5% 17.8% 16.4% 17.6%

Other purchased energy

— — — 6.7%

Waste wood and wood chips (hogged fuel)

and bark 6.6% 9.2% 15.4% 13.5%

Spent liquor solids 33.7% 37.3% 39.4% 40.3%

Other self

- generated power 0.6% 0.8% 1.2% 1.6%

Source: U.S. Environmental Protection Agency. 2002

. “ Profile of the Pulp and Paper Industry. ” Sector Notebook Project. <http://www.epa.gov/Compliance/res ources/publications/assistance/ sectors/notebooks/ index.html>.

### 2

- 45 2.3.2.2 Uses and Consumers

Products manufactured in the NAICS groups 3221 and 3222 have different, but complementary, consumer profiles. NAICS 3221 supplies a significant portion of NAICS 3222 demand (37% of total commodity output). Both industries specialize in products with intermediate uses, with an average of 92% of sal es 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 o f converted paper products ( Table 2

- 26 ). Pulp, paper, and paperboard products have a large trade deficit, while converted paper products have a very small trade surplus.

Table 2

- 26

. Demand by Sector: Paper Manufacturing Industry (NAICS 322) ( $ millions , $2007)

Sector BEA Co de 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

G eneral 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 32 60 $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 g oods 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 Inp ut

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

### 2

- 46 2.3.3 Firm and Market Characteristics

This section describes geographic, production, and market data. These data provide the basis for further analys is, including regulatory flexibility analyse s, 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 Unit ed States, followed by Illinois and some bordering northeastern states. The location of establishments in the paper manufacturing industry varies a great deal by subsector. Wisconsin and New York have the most pulp, paper, and paperboard establishments,

wh ile California dominates with over 500 converted paper product establishments. Overall, the United States has 561 pulp, paper, and paperboard establishments and 4,956 converted paper product establishments.

Establishments

Fewer than 50

50 - 99

100 - 199

200 - 300

More than 300



### Figure 2

- 24

. Establishment Concentration in Pap er Manufacturing Industry (NAICS 322): 2002

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

### 2

- 47 2.3.3.2 Production Capacity and Utilization

Capacity utilization of the paper manufacturing subsector has been expe riencing 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 ).

65 70 75 80 85 90Jan-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

. Capa city Utilization Trends in the Paper Manufacturing Industry (NAICS 322)

Source: Source: Federal Reserve Board. 200 9

. “ Industrial Production and Capacity Utilization: Capacity Utilization. ” Series ID: G17/CAPUTL/CAPUTL.GMF.S & G17/CAPUTL/CAPUTL.G322.S. <htt p://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 pe r establishment, 283, than the pulp, paper, and paperboard group, 67.

2.3.3.4 Plants and Capacity

While the manufacturing sector has been growing consistently since 1997, the paper manufacturing sector has not experienced the same amount of success in the same period.

### 2

- 48 Despite a small amount of growth in capacity between 1997 and 2001, the paper manufacturing

subsector ’ s capacity has declined to as much as 7% below 1997 capacity levels (Figure 2

- 27 ).

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 Manuf acturing 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 Area s, Counties, and Places: 2002. ” <http://factfinder.census.gov>; (July 23, 2008 ).

### 2

- 49 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. 200 9

. “ Industrial Produc tion and Capacity Utilization: Industrial Capacity. ” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G322.S. <http://www.federalreserve.gov/datadownload/>

.



### 2.3.3.5 Firm Characteristics

In 2006, the top 10 paper and forest product companies produced over $ 75 b illion in sales, with the top two companies

— Internat ional 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

number s do not exclusively reflect paper products, they do convey the market environment in which firms in this sector compete.

2.3.3.6 Size Distribution

The primary criterion for categorizing a business as small is the number of employees, using definitions by the SBA for regulatory flexibility analyse s. According to SUSB reports for 2002, large companies dominated revenue

- generating transactions in the paper manufacturing
subsector; 80 % of receipts were generated by companies with 500 employees or more (Table 2

- 28 ). This was especially true in the pulp, paper, and paperboard group, in which large companies generated 92% of receipts. The number of employees in the small business cutoff varies according to six

- digit NAICS codes (Table 2

- 29 ). The cutoff for all sub sectors in the pulp,

paper , and paperboard group is 750 employees, while the cutoff for most converted paper product groups is 500 employees.

### 2

- 50 Table 2

- 27

. Largest U.S. Paper and Forest Products Companies: 2006

Company Revenues ($ millions ) a

International Pa per 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 fores t products in certain cases.

Source s : Benwart, S.J. 2006

. “ Paper & Forest Products. Standard and Poor ’ s Industry Surveys. ” 176(28). U.S. and international sales data from company reports.



### Table 2

- 28

. Distribution of Economic Data 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

Est ablishments 5,546 1,488 1,271 755 83 69 138
Employment 495,990 11,325 52,334 78,402 13,293 12,496 23,283
Receipts ($ millions ) $154,746 $2,218 $9,483 $17,620 $3,034 $3,951 $6,798

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

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

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

a Excludes SUSB employment category for zero employees. These entit ies 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

- 51 2.3.3.7 Domestic Production

Similar to industry capacity rates, subsector productio n rates for paper manufacturing have witnessed a decreasing rate of production compared to the steady increase in production for the manufacturing sector since 1997 (Figu re 2

- 28 ). It seems that the paper manufacturing sector

was not able to return to its f ormer levels of growth following the 2001 recession; it has experienced a downward production trend since then.

2.3.3.8 International Trade

Since 1997, paper manufacturing products, both pulp, paper, and paperboard products and converted paper products, ha ve contributed to an increasing trade surplus in this sector (Figure 2

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

### 2

- 52 Table 2

- 29

. Small Business Size Standards: Paper Manufacturing (NAICS 322)

NAICS NAICS Description Employees

32 2110 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 M anufacturing 500

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

322215 Non

- Folding Sanitary Food Container Manufacturing 750

322221 Coated and Laminated Packaging Paper Manufacturing 500

322222 Coated and Laminated Paper Manufactu ring 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

322 231 Die

- Cut Paper and Paperboard Office Supplies, Manufacturing 500

322232 Envelope Manufacturing 500

322233 Stationery, Tablet, and Related Product Manufacturing 500

322291 Sanitary Paper Product Manufacturing 500

322299 All Other Converted Paper Prod uct 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/contractingoppor tunities/sizestandardstopics/size/index.html >.

### 2

- 53 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

- 28

. Industrial Production Trends in the Paper Manufacturing Industry (NAICS 322): 1997

– 200 9

Source: Federal Reserve Board. 200 9

. “ Industrial Production and Capacity Utilization: Industrial Production. ” Series ID: G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.S & G17/IP_MAJOR_INDUSTRY_ GROUPS/IP.G322.S. <http://www.federalreserve.gov/datadownload/>

.

### 2

- 54 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 b

. “ U.S. Total Exports ” & “ U.S. Imports for Consumption. ” <http://dataweb.usitc.gov/scripts/user_set.asp>

.



### 2.3.3.9 Market Prices

Pri ces of goods in paper manufacturing have been increasing at a rate consistent with all manufacturing products (Figure 2

- 30 ). Producer price indices (PPIs) show that producer prices for paper in 2007 increased by 20% since 1997, while producer prices for al l manufacturing goods increased by roughly 27%.

### 2

- 55 90 95 100 105 110 115 120 125 130 135 140Dec-03Jun-04Dec-04Jun-05Dec-05Jun-06Dec-06Jun-07Dec-07Jun-08Dec-08Jun-09PPI (Dec-03=100)

Manufacturing PPI Paper PPI



### Figure 2

- 30

. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222 )

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

. “ Producer Price Index. ” Series ID: PCU322

– 322

– & PCUOMFG

– OMFG

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

.



### 2.4 Chemical Manufacturing

2.4.1 Introduction

The chemical manufacturing indus try 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 me dicine, soap, and printer ink. According to the American Chemistry Council, 29.7% of jobs in the U.S. economy are dependent on chemical manufacturing (American Chemistry Council, 2007 ). From 1997 to2007, shipments in the industry grew 42%, while employment declined by 8% (Table 2

- 30). While total payroll dropped 0.6% over this time, annual payroll per employee rose 7.8% from 1997 to 2007 because of the decline in the number of employees (Table 2

- 31). Shipments per empl oyee grew 54% from 1997 to 2007, with much of that growth taking place between 2002 and 2006 (Table 2

- 31).

### 2

- 56 Table 2

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

1997 2002 2006 2007

Shipments ( $2007, millions ) $521,251 $531,173 $675,223 $738,30 3

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

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

Establishments 13474 13,475 NA 12,937

NA = Not available.

Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sec tor 31 : Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005. ” <http://factfinder.census.gov>; (July 8 , 2 008). U.S. Census Bureau; generated by RTI International; using American FactFinder; “ Sector 00 : All S ectors: Core Business Statistics Series: Comparative Statisti cs 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: Geogra phic Area Series: Economy

- Wide Key Statistics: 2007. ” <http://factfinder.census.gov>

. A ccessed on December 27 , 20 09

. [Source for 2007 numbers]



### Table 2

- 31

. Industry Data : Chemical Manufacturing (NAICS 325)

Industry Data 1997 2002 2006 2007

Total shipments ( $2007, millions ) $521,251 $531,173 $675,223 $738,303
Shipments per establishment ($ thousands ) $38,686 $39,419 NA $57,069
Shipments per employee ($ 2007 ) $590,556 $622,548 $903,750 $906,979
Shipments per $ of payroll ($ 2007 ) $10.43 $10.35 $14.37 $14.87
Annual payroll per employee ($ 2007 ) $56,603 $60,145 $62,882 $60,991

Employees per establishment 66 63 NA 63

NA = Not a vailable

. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufact ures: 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 S ectors: 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>

. A ccessed on December, 27 , 20 09

. [Source for 2007 numbers]

### 2

- 57 Chemical manufacturing (NAICS 325) covers a diverse set of industry gro ups, which we have aggregated into the following three groups:

 

### Bulk Chemicals

— Includes the most energy

- intensive industry groups as aggregated by the Department of Energy (DOE) (DOE/EIA

- 0554, 2008 ): Basic Chemical Manufacturing (NA ICS 3251); Resin, Rubber, and Artificial Fibers Manufacturing (NAICS 3252); and Agricultural Chemical Manufacturing (NAICS 3253).

 

### Pharmaceutical and Medicine Manufacturing (NAICS 3254)

— Consists primarily of pharmaceutical preparation manufacturing. This i ndustry 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 Che mical Product and Preparation Manufacturing (NAICS 3259).

In 2007, each of these groups generated approximately one

- third of the total employment in chemical manufacturing (Figure 2

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

of chemical manufacturing’s total value of shipments (Figure 2

- 32).

2.4.2 Supply and Demand Characteristics

Next, we provide a broad overview of the supply and demand side of the chemical manufacturing industry. We emphasize the economic interactions this industry h as with other industries and people, including identifying the key goods and services used by the industry and the major uses and consumers of chemical manufacturing products.

### 2

- 58 Bulk Chemicals,
35%

Pharmaceuticals,
31% Other Chemicals,
34%



### Figure 2

- 31

. Distribution of Employment within Chemical Manufacturing (NAICS 325): 2007

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “ Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the U.S.: 2007 .” Release date: October 30, 2009

. A ccessed on December 27, 2009

.

Bulk Chemicals,
50%

Pharmaceuticals,
26% Other Chemicals,
24%



### Figure 2

- 32

. Distribution of Total Value of Shipments within Chemical Manufacturing (NAICS 325): 2007

Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “ Sector 31:EC0731I1: Manufactur ing Industry Series: Detailed Statistics by Industry for U

. S

. : 2007 .” <http://factfinder.census.gov>

. A ccessed on December 27, 2009

.

### 2

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

2002 accounted for 71% of the total i ntermediate inputs (Table 2

- 32). Bulk chemicals’ production was the most energy intensive, using 79% of the chemical manufacturing inputs from petroleum and coal products, electric power generation, transmission and distribution, and natural gas distributi on.

2.4.2.1 Goods and Services Used in Chemical Manufacturing

In2007, the cost of materials made up 49% of chemical manufacturing’s total shipment value (Table 2

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

down from 10% in 2005.

2.4.2.1.1 Energy. The Department of Energy (DOE) classifies bulk chemical manufacturing as an energy

- intensive industry. Pharmaceuticals and other chemical manufacturing are categorized as non

- energy

- intensive industries, grouped together with other industry groups under the “Balance of Manufacturing” category (DOE, 2008 ).

Table 2

- 32. Key Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007, millions )

Good or Service BEA Code Bulk Chemicals Pharmaceuticals Other Ch emicals 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

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

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

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

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

Electric power generation, transmission, and

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

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

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

Source: U.S. Bureau of Economic An alysis (BEA). 2008. “2002 Benchmark Input

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

.

### 2

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

feedstock to derive value

- added chemicals. In 2007, 70% of chemical manufacturing’s energy

bill was spent on fuel used as feedstocks (O’Reilly, 2008 ). These fuel costs represented 2% of chemical manufacturing’s total value of shipments (Table 2

- 33).

As a whole, chemical manufacturing use less energy over the last 10 years. According to DOE, natural gas use by the chemical manufacturing industry dropped 30% from 1998 to 2006, and electricity use fell 10% (Table 2

- 34). From 1997 to 2005, when data ceased to be available, chemical manufacturing used less electricity relative to the man ufacturing sector as a whole (Figure 2

- 33).

Table 2

- 33

. Costs of Goods and Services Used in Chemical Manufacturing (NAICS 325) ($2007)

Variable 2005 Share 2006 Share 2007 Share
Total shipments $646,895 100% $675,223 100% $722,494 100%
Total compensation ( millions ) $62,669 10% $61,683 9% $63,591 9%
Annual payroll $48,159 7% $46,981 7% $48,780 7%
Fringe benefits $14,510 2% $14,702 2% $14,811 2%
Total employees 756,078 747,134 801,567
Average compensation per

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

Tot al production workers ’ wages ( millions ) $22,643 4% $22,231 3% $23,157 3%

Total production workers 431,502 430,880 463,802
Total production hours ( thousands ) 899,499 885,993 948,244

Average production wages per

hour $25 $25 $24

Total cost of ma terials ($ 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.go v>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “ Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007 .” Accessed on December, 27, 200 9.

### 2

- 61 Table 2

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

Fuel Type 1998 2002 2006

Total (trillion B TU ) 3,704 3,769 3,159

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

Residual fuel oil (million bbl) 8 7 4

Distillate fuel o il 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 B TU ) 748 1,158 1,045

Total (trillion B TU ) 3,704 3,769 3,159

a N et 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 combustibl e 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 di stribution companies, and any other supplier(s),

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

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

ethane

- propane mixtures, propane

- butane mixtures, and isobutene produced at refineries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs).

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

that respondents indicated was used to produce heat and power.

* Estimate less than 0.5.

Source s : U.S. Department of Energy, Energy Information Administration. 2007 b

. “ 2006 Energy Consumption by M anufacturers

— Data Tables. ” Table 3.1. Washington, DC: DOE. <http://www.eia.doe.gov/eme u/mecs/ mecs2006/2006tables.html >. [Source for 2006 numbers] U.S. Department of Energy, Energy Information Administration. 2007. “ 2002 Energy Consumption by Manufacturer s

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

### 2

- 62 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 Production and Capacity Utilization: Electric Power Use: Manufacturing and Mining .” Series ID: G17/KW/KW.GMF.S & G17/KW/KW.G325.S. <http://www.federalreserve.gov/datadownload/> ; (November 17, 2009).



### 2.4.2.2 Use s and Consumers

Products manufactured in the groups bulk chemicals, pharmaceuticals, and other chemicals have very different consumer profiles. Bulk chemicals is dominated by intermediate use, representing 93% of its total commodity output and 56% of the total intermediate use of chemical manufacturing products. Pharmaceuticals has both a high level of demand from personal consumption, accounting for 67% of the total personal consumption of chemical manufacturing products, and a large trade deficit (Table 2

- 35).

2.4.3 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 recen t historical trends of production and pricing.

2.4.3.1 Location

In 2002, California had the most chemical manufacturing establishments in the United States, followed by Texas and New Jersey (Figure 2

- 34). The composition of establishments in

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

### 2

- 63 employed an approximately equal share of people in 2002, 54% of the total establishments were other chemicals establishments, and only 13% were pharmaceutical establishments.

2.4. 3.2 Production Capacity and Utilization

Capacity utilization of the chemical manufacturing industry has been broadly in line with the manufacturing sector (Figure 2

- 35). In the second half of 2005, the chemical manufacturing industry’s capacity utilization fell dramatically because of the multiple hurricanes affecting the Gulf Coast states. The impact of the economic downturn in 2001 can be seen in the capacity utilization of both manufacturing and chemical manufacturing.

Table 2

- 35

. Demand by Sector: Chem ical Manufacturing (NAICS 325) ($2007 millions )

Sector BEA Code Bulk Chemicals Pharmaceuticals Other Chemicals Total
Plastics and rubber products manufacturing 3260 $39,353 $0 $3,057 $42,410
Basic chemical manufacturing 3251 $33,972 $0 $1,675 $35,647

Ph armaceutical and medicine manufacturing 3254 $4,778 $25,125 $462 $30,365

Resin, rubber, and artificial fibers manufacturing 3252 $28,249 $0 $1,076 $29,325

Ambulatory health care services 6210 $2,716 $22,900 $934 $26,550
General state and local governmen t services S007 $7,150 $10,586 $8,807 $26,543

Hospitals 6220 $2,936 $15,390 $394 $18,720
Other chemical product and preparation manufacturing 3259 $8,021 $0 $2,680 $10,701

Textile mills 3130 $9,568 $0 $930 $10,498
Soap, cleaning compound, and toiletry manufacturing 3256 $3,886 $0 $6,289 $10,176

Total intermediate use T001 $212,996 $83,279 $82,107 $378,382
Personal consumption expenditures F010 $4,449 $123,746 $55,882 $184,077
Exports of goods and services F040 $47,121 $15,683 $13,136 $75,940
Imports of goods and services F050

− $38,732

− $67,950

− $10,906

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

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

. “ 2002 Benchmark Input

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

### 2

- 64 2.4.3.3 Employment

The geographic distribution of employment in chemical manufacturing d iffers largely among the different groups. In California, 52% of the chemical manufacturing employment comes from the pharmaceutical industry, while 60% of the chemical manufacturing employment

in the Gulf Coast states comes from bulk chemicals manufacturi ng (Figure 2

- 36).

2.4.3.4 Plants and Capacity

Production capacity in chemical manufacturing has grown 33% since 1997. This growth, however, is 9% less than the growth rate for the manufacturing industry as a whole (Figure 2

- 37).

Establishments

Fewer than 100

100 - 249

250 - 474

475 - 750

More than 750



### Figure 2

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

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

.

### 2

- 65 65 70 75 80 85Jan-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

. Capacity Utilization Trends in Chemical Manufacturing (NAICS 325)

Source: Federal Reserve Board. 200 9

. “ Industrial Production and Capacity Utilization: Capacity Utilization. ” Series ID: G17/CAPUTL/CAPUTL.GMF.S & G17/CAPUTL/CAPUTL.G325.S. <http://www.federalreserve.gov/datadownload/>.

Employment

Fewer than 5,000

5,000 - 13,999

14,000 - 29,999

30,000 - 40,000

More than 40,000



### Figure 2

- 36

. Employment Concentration in Chemical Manufacturing (NAIC S 325): 2002

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

.

### 2

- 66 100 110 120 130 140 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)

Source: Federal Reserve Board. 2009 . “ Industrial Production and Capacity Utilization: Indust rial Capacity .” Series ID: G17/CAP/CAP.GMF.S & G17/CAP/CAP.G325.S. <http://www.federalreserve.gov/datadownload/>.



### 2.4.3.5 Firm Characteristics

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

com pany, Dow Chemicals, has 108 out of 150 manufacturing sites located outside of the United States (Dow Chemical Company, 2008 ).

In 2007, 58%of U.S. chemical manufacturing corporations generated net income. Including those with and without net income, chemical manufacturers had an average before

- tax profit margin of 10.24%. Profitability is highest for pharmaceutical and medicine corporations (Table 2

- 37).

### 2

- 67 Table 2

- 36

. Top Chemical Producers : 2007

Chemical Sales

($ millions ) % of Total Sales % of Sales in U nited 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 %

Praxai r 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 calculate d from total sales, not chemical sales

. b Percentage of s ales in the United States is actually percentage of s ales in North Ame rica.

Source s : O ’ Reilly , R. 2008. “ Chemicals. ” Standard and Poor ’ s Industry Surveys

. 176(28)

.

### 2

- 68 Table 2

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

Industry N umber 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, synthetic ru bber, and artificial synthetic fibers and filaments 1,067 648 $44,692,366 $40,078,009 8.06% 6.33%

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

Paint, coating, and adhesive 1,411 1,260 $51,778,868 $49,486,744 5.39% 4.02%

Soap, cleaning compound, and toilet preparation 1,862 463 $150,506,485 $139,836,602 9.07% 7.51%

Other chemical product and preparation 2,946 1,773 $89,014,032 $84,062,534 6.71% 5.27%

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

Source: Internal Revenue Service, U.S. Department of Treasury. 2008. “ Corporation Source Book: Data File 2007

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



### 2.4.3.6 Size Distribution

The primary criterion for ca tegorizing a business as small is number of employees, using definitions by the SBA for regulatory flexibility analyses. The data describing size standards are provided in Table 2

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

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

2.4.3.7 Domestic Production

In the late 1990s, overall manufacturing production was growing much faster than the chemical manufacturing component (Figure 2

- 37). Following the reces sion of 2001, however, the components have moved broadly in line with one another, except for the drop in chemical manufacturing production caused by the hurricane season of 2005.

2.4.3.8 International Trade

In the year 2000, the United States moved from h aving a trade surplus to a trade deficit in chemical manufacturing products (Figure 2

- 38). This change occurred because the trade deficit

### 2

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

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

2.4.3.9 Market Prices

Prices of goods in chemical manufacturing have accelerated rapidly in the last 2 years, having outpaced overall manufacturing since 2002 (Figure 2

- 39). Much of this recent

acce leration seen in the industry PPI is due to the bulk chemicals segment, largely reflecting the rapid increase in fertilizer prices.

### 2

- 70 Table 2

- 38

. Small Business Size Standards: Chemical Manufacturing (NAICS 325)

NAICS Description Employees

325110 Petrochemi cal 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 Manu facturing 500

325188 All Other Basic Inorganic Chemical Manufacturing 1,000

325191 Gum and Wood Chemical Manufacturing 500

325192 Cyclic Crude and Intermediate Manufacturing 750

325193 Ethyl Alcohol Manufacturing 1,000

325199 All Other Basic Organic C hemical Manufacturing 1,000

325211 Plastics Material and Resin Manufacturing 750

325212 Synthetic Rubber Manufacturing 1,000

325221 Cellulosic Organic Fiber Manufacturing 1,000

325222 Noncellulosic Organic Fiber Manufacturing 1,000

325311 Nitrogenous Fertilizer Manufacturing 1,000

325312 Phosphatic Fertilizer Manufacturing 500

325314 Fertilizer (Mixing Only) Manufacturing 500

325320 Pesticide and Other Agricultural Chemical Manufacturing 500

325411 Medicinal and Botanical Manufacturing 750

325412 Pharmaceutical Preparation Manufacturing 750

325413 In

- Vitro Diagnostic Substance Manufacturing 500

325414 Biological Product (except Diagnostic) Manufacturing 500

325510 Paint and Coating Manufacturing 500

325520 Adhesive Manufacturing 500

325611 Soa p and Other Detergent Manufacturing 750

325612 Polish and Other Sanitation Good Manufacturing 500

325613 Surface Active Agent Manufacturing 500

325620 Toilet Preparation Manufacturing 500

325910 Printing Ink Manufacturing 500

325920 Explosives Manufac turing 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 Administra tion (SBA). 2008. “ Table of Small Business Size Standards Matched to North American Industry Classification S ystem Codes.” Effective August 22 , 2008. <http://www.sba.gov/services/contractingopportunities/sizestandardstopics/size/index.html>.

### 2

- 71 Table 2

- 39. Di stribution 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 9,34 1 5,413 1,974 790 95 56 71

Establishments 13,096 5,433 2,208 1,352 250 185 276

Employment 827,430 34,838 78,090 113,326 28,025 18,119 28,338
Receipts ($ millions ) $468,211 $9,631 $21,394 $39,111 $12,217 $7,324 $14,762

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

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

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

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

Source: U.S. Census Bureau. 2008

. “ Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Si zes: 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

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

Source: Federal Reserve Board. 2009

. “ Industrial Production and Capacity Utilization: Industrial Production .” Series ID: G17/IP_MAJOR_INDUSTRY_GROUPS/IP.GMF.S & G17/IP_MAJOR_INDUSTRY_ GROUPS/IP.G325.S. <http://www.federalreserve.gov/datadownload/>.

### 2

- 72 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007$1,000,000 (2007)

Imports Exports



### Figure 2

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

Source: U.S. International Trade Commission. 2008a

. “ U.S. Domestic Exports ” & “ U.S. Imports for Consumption. ” <http://dataweb.usitc.gov/scripts/user_set.asp>

.

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

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

Source: U.S. Bureau of Labor Statistics (BLS). 200 9 . Producer Price Index. Series ID: PCU325

— 325

— &PCUOMFG

— OMFG

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

### 3

- 1 SECTION 3

ENGINEERING COST ANA LYSIS

We provide an overview of the engineering cost analysis used to estimate the additional private expenditures industry may make in order to comply with the rule. A detailed discussion of the methodology used to estimate cost impacts is presented in “ M ethodology for Estimating Control Costs for Industrial, Commercial, Institutional Boilers and Process Heaters National
Emission Standards for Hazardous Air Pollutants

– Major Source (2010)” and “ Methodology for Estimating Cost and Emission Impacts from Ind ustrial, Commercial, Institutional Boilers and
Process Heaters at Major Sources of Hazardous Air Pollutant Emissions (2010)” in the Docket.

3.1 Major Sources

To estimate the national cost impacts of the proposed rule for existing sources, EPA developed ave rage baseline emission factors for each fuel type/control device combination based

on the emission data obtained and contained in the Boiler MACT emission database. If a unit reported emission data, we assigned its unit

- specific emission data as its baseli ne emissions. For units that did not report emission data, we assigned the appropriate emission factors to each existing unit in the inventory database, based on the average emission factors for boilers with similar fuel, design, control devices. We then c ompared each unit’s baseline emission factors to the proposed MACT floor emission limit to determine if control devices were needed to meet the emission limits. The control analysis considered fabric filters, carbon bed adsorbers, and activated carbon inje ction to be the primary control devices for mercury control, electrostatic precipitators for units meeting mercury limits but requiring additional control to meet the PM
limits, wet scrubbers to meet the HCl limits, tune

- ups, replacement burners, and combu stion controls for CO and organic HAP control, and carbon injection for dioxin/furan control. We identified where one control device could achieve reductions in multiple pollutants, for example a fabric filter was expected to achieve both PM and mercury co ntrol in order to avoid overestimating the costs. We also included costs for testing and monitoring requirements contained in the proposed rule. The resulting total national cost impact of the proposed rule is

10.0 billion dollars in capital expenditures a nd 3.5 billion dollars per year in total annual costs. Considering estimated fuel savings resulting from work practice standards and combustion
controls, the total annualized costs are reduced to 3.1 billion. The total capital and annual costs

include cost s for control devices, work practices, testing and monitoring . Table 3

- 1 of this shows the capital and annual cost impacts for each subcategory. Costs include testing and monitoring costs, but not recordkeeping and reporting costs.

### 3

- 2 Table 3

- 1. Summary o f Ca pital a nd Annual Costs f or New a nd Existing Major Sources

Source Subcategory Estimated/ Projected No. of Affected Units Capital Costs (10 6 $) Annualized Cost (10 6 $/yr)

Existing Units Coal units 578 4, 880 1,854

Biomass units 420 2,003 613

Liquid units 826 1,487 503

Gas (NG/RG) units 11,532 75 33

Gas (other) units 199 1,554 481

Energy Audit ALL 26

New Units Coal units 0 0 0

Biomass units 0 0 0

Liquid units 11 12 6.1

Gas (NG/RG) units 33 0.2 0.01

Gas (other) units 2 5.5 1.7



### Using Department of Energy projections on fuel expenditures, the number of additional boilers that could be potentially constructed was estimated. The resulting total national cost impact of the proposed rule in th e 3rd year is 17 million dollars in capital expenditures and 6.2 million dollars per year in total annual costs, when considering a 1 percent fuel savings.

A discussion of the methodology used to estimate cost impacts is presented in “Methodology and Resu lts of Estimating the Cost of Complying with the Industrial, Commercial, and Institutional Boiler and Process Heater NESHAP (2010)” in the Docket.

3.2 Area Sources

To estimate the national cost impacts of the proposed rule for existing sources, EPA develop ed several model boilers and determined the cost of control for these model boilers. The EPA assigned a model boiler to each existing unit based on the fuel, size, and current controls. The analysis considered all air pollution control equipment currently in operation at existing

boilers. Model costs were then assigned to all existing units that could not otherwise meet the proposed standards. The resulting total national cost impact of the proposed rule for existing units is $ 279 million dollars in total a nnualized costs after considering fuel savings from efficiency improvements. The total annualized costs for installing controls, conducting an annual

### 3

- 3 tune

- up and an energy assessment, and implementing testing and monitoring requirements, is $ 696 million . T able 3 of this preamble shows the total annualized cost impacts for each subcategory.

Table 3

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

Source Subcategory Estimated/ Projected No. of Affected Units Total Annualized Cost (10 6 $/yr) a

Existing Units Coal 3,710 160

Biomass 10,958 47

Oil 168,003 436

Facility Energy Audit All 52

New Units b Coal 155 54

Biomass 200 13

Oil 6,424 244

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

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



### Using DOE projections on fuel expenditures, as well as the history of installation dates of
area source boilers in the dataset, the number of additi onal boilers that could be potentially

constructed was estimated. The resulting total national cost impact of the proposed rule on new sources by the 3rd year, 2013, is $ 311 million dollars in total annualized costs. When accounting for a 1 percent fuel sa vings resulting from improvements to combustion efficiency, the total national cost impact on new sources is $ 260 million

.

### 4

- 1 SECTION 4

ECONOMIC IMPACT ANAL YSIS

EPA prepares an RIA to provide decision makers with a measure of the social costs of using reso urces to comply with a program (EPA, 2000 ). The social costs can then be compared with estimated social benefits (as presented in Section 6 ). As noted in EPA ’ s (2000) Guidelines for Preparing Economic Analyses , several tools are available t o estimate social costs and range from simple direct compliance cost methods to the development of a more complex market analysis that estimates market changes (e.g., price and consumption) and economic welfare changes (e.g., changes in consumer and produc er surplus).

The Office of Air Quality Planning and Standards (OAQPS) adopted a standard market analysis as described in the Offi ce’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 intermediate run; industries have limited flexibility to adjust production technologies (e.g., alter capital stock). The

intermediate time hori zon allows us to capture important transitory stakeholder outcomes. Key measures in this analysis include industry

- level changes in price levels, production and consumption, jobs, international trade

, and social costs (changes in producer and consumer

surp lus).

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. I n this section, we provide an overview of the economic model. Appendix A provides additional details on the behavioral assumptions, data, parameters, and model equations.

4.1.1 Overview

Although several tools are available to estimate social costs, current EPA guidelines suggest that multimarket models “ ...are best used when potential economic impacts and equity effects on related markets might be considerable ” and modeling using a computable general equilibrium model is not available or practical (EPA, 2000 , p. 146). Other guides for environmental economists offer similar advice (Berck and Hoffmann, 2002 ; Just, Hueth, and
Schmitz, 2004 ). Multimarket models focus on “ short

- run ” time horizons and measure a policy ’ s

near

- term or transition costs (EPA, 1999 )

. The multimarket model contains the following

features:

### 4

- 2  

### Industry sectors and benchmark data set

– 100 industry sectors

– a single benchmark year (2010)

– estimates of industry employment

 

### Economic behavior

– industries respond to regulatory costs by changing production rates

– market prices rise and fall to reflect higher energy and other non

- energy material

costs and changes in demand

– customers respond to these price inc reases 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 industr ies and is influenced by price changes that occur in each sector. Th e links allow EPA to account for indirect effects the regulation has on related markets.

– production adjustments influence employment levels

– international trade (imports/exports) responds to domestic price ch anges

 

### Model time horizon ( “ short run ” ) for a single period (2013)

– fixed production resources (e.g., capital) lead to an upward

- sloping industry supply function

– firms cannot alter input mixes; there is no substitution among production inputs (capital, labor , energy intermediates, and other intermediate goods and services)

– price of labor (i.e., wage) is fixed

– investment and government expenditures are fixed

4.1.2 Economic Impact Analysis Results

4.1.2.1 Market

- Level Results

Market

- level impacts include price and quantity adjustments including the changes in

international trade (Figure 4

- 1) . Under the proposed major source NESHAP, the A gency’s economic model suggests the average national prices for industrial sectors could be 0.0 1 % higher

### 4

- 3 - 0.10% - 0.09% - 0.08% - 0.07% - 0.06% - 0.05% - 0.04% - 0.03% - 0.02% - 0.01% 0.00% 0.01% 0.02% 0.03% 0.04% 0.05% 0.06% 0.07% 0.08% 0.09% 0.10% Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N Major Sources Area Sources Major & Area SourcesPercentage Change from Baseline

Prices Production Imports Consumption Exports



### Figure 4

- 1. Market

- Level Changes by Source and Option

with the NESHAP, while average annual domestic production may fall by about 0.0 1 %. Because of higher domes tic prices, imports rise by 0.01 % per year. Market

- level effects for the proposed

area source NESHAP are smaller w hen compared to the proposed major source rule; average price, production, and i mpo rt changes are less than 0.01 %. Industrial sector details are provided in Appendix B.

4.1.2.2 Social Cost Estimates Major Source Rule

In the near term, the Agency ’ s economic model suggests that i ndustries are able to pass on $ 0.9 billion (2008$) of the proposed area 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 $2.7 billion and the net loss in aggregate is $3.6 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 cert ain degree by imports ; the mo del estimates a net gain of $0.1 billion to foreign companies

. After accounting for international trade effects, the

### 4

- 4 Agency ’ s economic model projects the net surplus loss associa ted with the proposed rule is $3.5

billion. As sh own in Figure 4

- 2 , the surplus losses are concentrated in other services ( 21 %) ;
lumber , paper, and printing (1 9 %) ; and chemicals (16 percent)

T he Agency also considered other elements of the engineering cost analysis that could not

be modeled within the mu ltimarket model (e.g., fuel savings benefits [existing and new major sources] and total annualized compliance costs [new major sources] ) . The net effect of the adjustments is a total surplus loss estimate of $ 3 .1 billion.

Table 4

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

Approach Primary Option Option 1E and Option 1N

Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus −$ 0.9 −$ 3.9

Change in U.S. producer surplus −$ 2.7 −$ 9.0

Change in U.S. surplus

− $ 3.6 −$1 2

. 9

Net change in rest of world surplus $0. 1 $0. 5

Net change in total surplus −$ 3

. 5 −$ 12

. 4

Direct Compliance Costs Method

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

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

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

Change in Total Surplus −$ 3 .1 −$ 12

. 1

### 4

- 5 20.6%

19.1%

15.7%

12.1%

9.9%

5.4%

4.0%

3.2%

2.4%

1.4%

1.4%

1.2%

1.2%

1.1%

0.7%

0.5% 24.7%

10.1%

13.4%

17.8%

6.1%

4.5%

3.0%

7.1%

1.6%

1.8%

1.3%

4.6%

0.8%

1.6%

0.9%

0.6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Other Services

Lumber, paper, and printing

Chemicals

Energy Industries

Food, beverages, and textiles

Primary Metals

Nonmanufacturing

Transportation Equipment

Machinery and Equipment

Plastics and Rubber

Other

Transportation Services

Wholesale and Retail Trade

Fabricated Metals

Electronic Equipment

Nonmetallic Minerals Primary Option: $3.5 Billion Surplus Change Option 1E and Option 1N: $12.4 billion Surplus Change



### Figure 4

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

4.1.2.3 S ocial Cost Estimates Area Source Rule

In the near term, the Agency ’ s economic model suggests that industri es are able to pass on $ 0.3 billion (2008$) of the proposed area source rule ’ s costs to U.S. househol ds in the form of higher prices (Table 4

- 2). Exist ing U.S. industries’ surplus falls by $0.3 billion and the net loss

for U.S. stake holders is $0.6 billion. As U.S. prices rise, other countries are affected through international trade relationships. Households that buy U.S. exports pay higher prices and p urchase fewer U.S. produced goods. Other countries that that sell goods to the United States benefit; the

model estima tes a net rest of the world gain of less than $0. 1 billion. After accounting for international trade effects, the Agency ’ s economic model projects the net surplus (consumer and producer) loss associated with the proposed rule is $ 0.6 billion

. As shown in Figure 4

- 3

, the surplus losses are concentrated in the other services ( 86 %)

.

T he 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

### 4

- 6 sources] and total annualized compliance costs [ unknown existing and new area sources] ) . The net effect of the adjustments is a t otal s urplus loss estimate of $ 0.5 billion.

Table 4

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

Approach Pr imary Option Option 1E and Option 1N

Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus −$ 0.3 −$16.5

Change in U.S. producer surplus −$ 0.3 −$16.5

Change in U.S. surplus −$ 0.6 −$3 3.1

Net change in rest of world surplus Less than $0.1 −$0.1

Net change in total surplus −$ 0.6 −$33. 2

Direct Compliance Costs Method

Total annualized costs, u nknown existing area sources (not modeled) Less than $0.1 −$0.3

Total annualized costs, new area sources (not modeled) −$ 0.3 −$2. 3

Fuel savings, existing area sources (not modeled) $ 0.4 $0. 4

Fuel savings, new area sources (not modeled) Less than $0.1 Le ss than $0.1

Change in Total Surplus −$ 0.5 −$3 5

. 3

### 4

- 7 86.2%

5.2%

2.8%

1.9%

2.6%

0.2%

0.4%

0.2%

0.1%

0.1%

0.1%

0.1%

0.1%

0.0%

0.0%

0.0% 85.0%

6.1%

2.9%

1.7%

2.8%

0.2%

0.5%

0.2%

0.1%

0.1%

0.1%

0.1%

0.1%

0.0%

0.0%

0.0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Other Services

Food, beverages, and textiles

Nonmanufacturing

Lumber, paper, and printing

Wholesale and Retail Trade

Other

Transportation Services

Chemicals

Energy Industries

Primary Metals

Plastics and Rubber

Nonmetallic Minerals

Fabricated Metals

Electronic Equipment

Machinery and Equipment

Transportation Equipment Proposed option: $0.6 billion Surplus Change Option 1E and Option 1N: $33.2 Billion Surplus Change



### Figure 4

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

4.1.2.4 Job Effects

Precise job effect estimates cannot be estimated with certainty. Morgenstern et al. (2002) identify three economic mechanisms by which pollution abatement activities can indirectly influence jobs:

 

### higher production costs raise market prices, higher prices reduce consumption, and employment within an industry falls (“demand effect”);

 

### pol lution abatement activities require additional labor services to produce the same level of output (“cost effect”); and

 

### post regulation production technologies may be more or less labor intensive (i.e., more/less labor is required per dollar of output) (“f actor

- shift effect”).

Several empirical studies, including Morgenstern et al. (2002), suggest the net employment decline is zero or economically small (e.g., Cole and Elliot, 2007; Berman and Bui, 2001). However, others show the question has not been resol ved in the literature (Henderson,

### 4

- 8 1996; Greenstone, 2002). Morgenstern et al. use a six

- year panel ( U.S. Census data for plant

- level prices, inputs (including labor), outputs, and environmental expenditures) to econometrically estimate the production techn ologies and industry

- level demand elasticities. Their identification strategy leverages repeat plant

- level observations over time and uses plant

- le vel and year fixed effects ( e.g. , plant and time dummy variables). After estimating their model, Morgenstern show and compute the change in employment associated with an additional $1 million ($1987) in environmental spending. The ir estimate s covers four manufacturing industries (pulp and paper, plasti cs, petroleum, and steel) and Morgenstern, et al. present r e sults separately for the cost, factor
shift, and demand effects, as well as the net effect. The y also estimate and report an industry

- wide average parameter that combines the four industry

- wide estimates and weighting them by each industry’s share of envir onmental expenditures.

EPA has most often estimated employment changes associated with plant closures due to environmental regulation or changes in output for the regulated industry (EPA

, 1999a ; EPA

, 2000). This analysis goes beyond what EPA has typically done in two ways. First, because the multimarket model provides estimates for changes in output for sectors not directly regulated, we

were able to estimate a more comprehensive “ demand effect .” Secondly, parameters estimated in the Morgenstern paper were used to estimate all three effects ( “ demand ,” “cost,” and “ factor

shift ” ). This transfer of results from the Morgenstern study is uncertain but avoids ignoring th e
“cost effect” and the “factor

- shift effect .”

We calculated “demand effect” employment ch anges by assuming that the number of jobs declines proportionally with multi

- market model’s simulated output changes. These results were calculated for all sectors in the EPA model that show a change in output.

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

. To do this, we multiplied the point estimate for the total demand effect ( −3.56 jobs per million ($1987) of environmental compliance expenditure) by the total environmental compliance expenditures used in the partial equilibrium model. For example, the jo bs effect estimate for the Major Source Rule is estimated to be 7,000 jobs ( −3.56× $3.5 billion × 0.59) 1



### We also present the results of using the Morgenstern paper to estimate employment “cost”

and “facto r

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

and “factor

- shift” employment changes is uncertain, it is helpful to compare the potential job . Demand effect results are provided in Figure 4

- 4.

1 Since Morgenstern’s analysis reports environmental expenditures in $1987 , we make an inflation adjustment the engineering cost analysis using GDP implicit price deflator (64.76/108.48) = 0.59)

### 4

- 9 - 6

- 19 - 1

- 62 - 7

- 81 - 7

- 26 - 1

- 71 - 9

- 97 5 18 1 48

6 66

6 20
1 53

7 73

3 12 1 31

4 42

- 250 - 225 - 200 - 175 - 150 - 125 - 100 - 75 - 50 - 25

0 25 50 75 100 125 150 175 200 225 250 Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N Major Sources Area Sources Major & Area Sources1,000 JobsBase =144,100

Demand Effect: Partial Equilbrium (multiple markets) Demand Effect: Literature

- based estimate Cost Effect: Literature

- based estimate Factor Shift Effect: Literature

- based estimate Net Effect: Literature

- based estimate



### Figure 4

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

g ains from these effects to the job losses associated with the “demand” effect. Figure 4

- 4 shows that using the Morgenstern point estimates of parameters to estimate the “cost” and “factor shift” employment gains may be greater than the employment losses us ing either of the two ways of estimating “demand” employment losses. The 95% confidence intervals are shown for all of the estimates based on the Morgenstern parameters. As shown, at the 95% confidence level, we cannot be certain if net employment changes are positive or negative.

Although the Morgenstern paper provides additional information about the potential job effects of environmental protection programs, there are several qualifications EPA considered as part of the analysis. First, EPA has used th e weight ed average parameter estimates for a narrow set of manufacturing industries (pulp and paper, plastics, petroleum, and steel). Absent other data and estimates, this approach seems reasonable and the estimates come from a respected peer

reviewed sou rce. However, EPA acknowledges the proposed rule covers a broader set of industries not considered in original empirical study. By transferring the estimates to other

### 4

- 10 industrial sectors, we make the assumption that estimates are similar in size. In addi tion, EPA assumes also that Morgenstern et al.’s estimates derived from the 1979

- 1991 still applicable for policy taking place in 2013, almost 20 years later. Second, the multi

- market model only considers

ne ar term employment effects in a U.S. economy wher e production technologies are fixed. As a result, the modeling system places more emphasis on the short term “demand effect” whereas the Morgenstern paper emphasizes other important long term responses. For example, positive job gains associated with “f actor shift effects” are more plausible when production choices become more flexible over time and industries can substitute labor for other production inputs. Third, the Morgenste rn paper estimates rely on sector demand elasticities that are different fr om the demand elasticity parameters used in the multi

- market model. As a result, the demand effects are not directly comparable with the demand effects estimated by the multi

- market model. Fourth, Morgenstern identifies the industry average as economi cally and statistically insignificant effect (i.e., the point estimates are small, measured imprecisely, and not distinguishable from zero.) EPA acknowledges this fact and has reported the 95 percent confidence intervals in Figure 4

- 4.
Fifth, Morgenstern ’s methodology assumes large plants bear most of the regulatory costs. By transferring the estimates, EPA assum es a similar distribution of regulatory costs by plant size and that the regulatory burden does not disproportionately fall on smaller plants

.

### 5

- 1 SECTION 5

SMALL ENTITY ANALYSE S

The RFA as amended by SBREFA generally requires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or any other st atute, unless the agency certifies that the rule will not

have a significant economic impact on a substantial number of small entities ( SISNOSE ) . Small entities include small businesses, small governmental jurisdictions, and small not

- for

- profit enterprise s. EPA assessed the potential small entity economic impacts using a screening analysis.

A fter reviewing screening analysi s results, EPA has determined it cannot certify that the proposed rules will not have a SISNOSE and presumes that both proposed rules a re not eligible for certification under the RFA as amended by SBREFA. As a result, EPA has prepared and included an IRFA that discusses alternative regulatory or policy options that minimize the rules ’ small entity impacts. It includes information about ke y results from the SBAR panel.

5.1 Small Entity Screening Analysis

5.1.1 Small Businesses

The sectors covered by the rule were identified through lists of small entities at major and

area sources provided by the engineering analysis . Table 5

- 1 provides a li st of the sectors affected (3

- digit NAICS) and the range of SBA size definitions

.

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

For each 3

- digit NAICS code, the SUSB provides national information on the distribu tion of economic variables by industry and enterprise size (U.S. Census, 2008 ). The Census Bureau and the Office of Advocacy of the SBA supported and developed these files for use in a broad range of economic analyses. 1



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

 

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

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

### 5

- 2 Table 5

- 1. Affected Sectors and Size Standards

2007 NAICS D escription Size Standard (Effective August 22, 2008)

211 Oil and Gas Extraction 500 employees

212 Mining (except Oil and Gas) 500 employees

221 Utilities a

311 Food Manufacturing 500 to 1,000 employees

312 Beverage and Tobacco Product Manufacturing 50 0 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 Manufactu ring Typically 500 to 1,500 employees

325 Chemical Manufacturing 500 to 1,000 employees

326 Plastics and Rubber Products Manufacturing Typically 500 to 1,000 employees

327 Nonmetallic Mineral Product Manufacturing 500 to 1,000 employees

331 Primary Met al Manufacturing 500 to 1,000 employees

332 Fabricated Metal Product Manufacturing 500 to 1,500 employees

335 Electrical Equipment Manufacturing 500 to 1,000 employees

336 Transportation Equipment Manufacturing 500 to 1,000 employees

337 Furniture and Related Product Manufacturing 500 employees

339 Miscellaneous Manufacturing 500 employees

423 Merchant Wholesalers, Durable Goods 100 employees

493 Warehousing and Storage $25.5 million in annual receipts

562 Waste Management and Remediation Services T ypically $7 to $14 million in annual receipts

611 Educational Services Typically $7 to $35.5 million in annual receipts

a NAICS codes 221111, 221112, 221113, 221119, 221121, 221122: A firm is small if, including its affiliates, it is

primarily engaged in the generation, transmission, and/or distribution of electric energy for sale and its total electric output for the preceding fiscal year did not exceed 4 million megawatt hours.

 

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

### 5

- 3  

### Enterprise : An enterprise is a bus iness organization consisting of one or more domestic establishments that were specified under common ownership or control. The enterprise and the establishment are the same for single

- establishment firms. Each multi

- establishment company forms one enterpr ise

— the enterprise employment and annual payroll are summed from the associated establishments. Enterprise size designations are determined by the total employment of all associated establishments.

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

“ ultimate parent company, ” we assumed in this analysis that the “ enterprise ” definition above is consistent with the concept of ultimate parent company that is typically used for SBREFA screening analyses, and the terms are used interchangeably.

The analysis generated a set of establishment sales tests (represented as cost

- to

- receipt ratios) for NAICS codes associat ed with sectors listed in Table 5

- 2. Although the appropriate SBA size definition should be applied a t the parent company (enterprise) level, we can only compute and compare ratios for a model establishment owned by an enterprise within an SUSB size range (employment or receipts). Using the SUSB size range helps us account for receipt

differences between establishments owned by large and small enterprises and also allows us to consider the variation in small business definitions across affected industries. Using establishment receipts is also a conservative approach, because an establishment ’ s parent compa ny (the

“ enterprise ” ) may have other economic resources that could be used to cover the costs of the regulatory program.

For each representative establishment in the SUSB data, we developed a range of 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 code. For area sources, we were limited to two representative small entity facility

- level costs (approximately $ 26,000 ,0 00 to $1. 4 million) 1

1 Prior to computing the cost

- to

- receipt ratios, we adjusted the engineering compliance costs to reflect 2002 dollars using the implicit price deflators for gross domestic produ ct (GDP). The values used are 2002 = 92.118 and 2008 = 108.483 (U.S. BEA, 2010 ). 

### . 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).

### 5

- 4 63% 98%

25% 100%

17% 78%

8% 65%

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

Primary Option Option 1E and Option 1N Primary Option Option 1E and Option 1N

Major Area Upper Bound

Lower Bound



### Figure 5

- 1. Share of NAICS/Enterprise Employment Categori es (<500 employees) with Sales Tests Exceeding 3%

5.1.1.2 Additional Small Business Analysis Using Sample of Small Businesses Identified in Combustion Facility Survey

Next, we performed a more detailed analysis that compares the Census SUSB representative small entity results with a firm

- specific sample of major small private enterprises. In this approach, we identified a sample of survey facility names listed as small, traced the ultimate parent company name to verify the facility was owned by a small bus iness, and collected the most recent parent company sales and employment figures. As Table 5

- 2 shows, the average

cost

- to

- sales ratios for small major source companies are above 5%. The median ratios are below

one percent for the primary option and above 5 percent for the Option 1E option

.

### 5

- 5 Table 5

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

Sample Statistic Primary Option Option 1E

Mean 5.0 % 15.7 %

Median 0.4 % 5.3 %

Maximum 75.8 % 100.0%

Minimum < 0.1% <0.1%

Ultim ate parent company observations 50 50

Ultimate parent companies with sale tests exceeding 3% 14 30



### 5.1.2 Small Government and Non

- Profit Analysis

In addition to the private sector, this rule also covers sectors that include entities owned by

sma ll and large governments. Given the uncertainty and data limitations associated with identifying and appropriately classifying these entities, we computed a “ revenue ” test for a model small government, where the annualized compliance cost is a percentage o f annual government revenues (U.S. Census, 2005a and b ).

Compliance costs were estimated for model facilities for major and area sources for multiple options. A summary of the compliance costs used for the small entity analy sis follows:

Major Sources:

 

### P rimary option : $4.0 million (median cost small public facility)

 

### Option 1E and 1N : $5. 3 million (median cost small public facility)

Area Sources :

 

### P rimary option :

– Other public: $ 3.0 million

– Hospital: $1 1 ,000

– Schools: $4,5 00

### 5

- 6 – C hurches: $2,2 00

 

### Option 1E and Option 1N :

– Other public: $ 2.9 million

– Hospital: $142,6 00

– Schools : $ 346,000

– Churches : $ 45,000

From the 2002 Census (in 2008 dollars), the average revenue for small governments (counties and municipalities with populations fewe r than 10,000) are $3 million per entity, and the average revenue for local governments with populations fewer than 50,000 is $7 million per entity. Churches are assumed to have an operation budget of $150,000.

The analysis shows that small major source pu blic facilities would have cost

- to

- revenue ratios that exceed 10% under the both regulatory options. The following s mall area source

facilities would have cost

- to

- revenue ratios exceeding 1 percent:

**•** Primary option: other public (ratio > 10 percent) and c h urches (ratio = 1.5 percent)

**•** Option 1E and Option 1N : other public (ratio > 10 percent), hospitals ( ratio > 3 percent), schools (ratio > 10 percent), and churches (ratio >10 percent).

5.2 Initial Regulatory Flexibility Analysis (IFRA)

An IRFA illustr ates how EPA considered the proposed rule ’ s small entity effects before a rule is finalized and provides information about how the objectives of the rule were achieved while minimizing significant econo mic impacts on small entities. We provide a summary of IRFA elements; the preambles for each rule provide additional details.

5.2.1 Reasons Why Action Is Being Considered

In 2004, EPA promulgated national emission NESHAP for new and existing industrial/commercial/institutional boilers and proc ess heaters. How ever, in 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. The proposed action provides

EPA ’ s proposed rule in response to the court ’ s v acatur

. Under authority of section 112 of the

### 5

- 7 Clean Air Act (CAA) , EPA is also proposing a NESHAP for two area source categories:

industrial boilers and institutional/commercial boilers.

5.2.2 Statement of O bjectives and Legal Basis of Proposed Rules

The p roposed rules would protect air quality and promote public health by reducing

emissions of the HAP s. Section 112(d) of the CAA requires EPA to establish NESHAPs for both major and area sources of HAP s that are listed for regulation under CAA section 112(c)

. A major source emits or has the potential to emit 10 tons per year (tpy) or more of any single HAP or 25 tpy or more of any combination of HAP

. An area source is a stationary source that is not a major

source.

5.2.3 Description and E stimate of the N umber of Small Entities

The sectors covered by the rule were identified through lists of small entities at major and area sources included in the survey. A listing of the sectors affected (3

- digit NAICS) and the range of SBA size definitions are provided in Ta ble 5

- 1 . EPA believes a substantial number of small entities will be affected by the proposed rules, but data limitations preclude us from providing precise estimates of the number of small entities affected.

5.2.4 Description and Compliance Costs

5.2.4.1 Major Sources

A discussion of the methodology used to estimate cost impacts is presented in “Methodology and Results of Estimating the Cost of Complying with the Industrial, Commercial,

and Institutional Boiler and Process Heater NESHAP” in the Docket and Section 4 of the RIA.

5.2.4.2 Area Sources

A detailed discussion of the methodology used to estimate cost impacts is presented in the memorandum “Estimation of Impacts for Industrial, Commercial, and Institutional Boilers Area

Source NESHAP” in the Docket and Section 4 of the RIA.

5.2.5 Description of Federal Rules that May Overlap or C onflict with Proposed Rules

The proposed major source rule regulates source categories covering industrial boilers, institutional boilers, commercial boilers, and process he aters that may include combustion units that are already regulated by other MACT standards

. Therefore, EPA proposes to exclude any boiler or process heater that is already or will be subject to regulation under another MACT

standard

. Boilers located at are a source facilities may be regulated pursuant to CAA section 129

. Section 129(h) states that no unit subject to standards under section 129 shall be subject to

### 5

- 8 standards under section 112(d) of the CAA

. As a result, EPA proposes to exclude any boiler that is subject to regulation under section 129

. EPA has codified new source performance standards (NSPS) for industrial boilers (40 CFR 60, subparts Db and Dc)

, but the NSPS does not regulate

sources of HAP s. As a result, sources subject to the NSPS will still be subject to the proposed

rules

. However, EPA has minimized the monitoring requirements, testing requirements, and recordkeeping requirements to avoid duplicating NSPS requirements.

5.2.6 Description of Regulatory Alternatives that Minimize Significant E conomic Impacts on Small Entities

As required by section 609(b) of the RFA, as amended by SBREFA, EPA has conducted

outreach to small entities and convened a SBAR Panel to obtain advice and recommendation of representatives of the small entities that poten tially would be subject to the requirements of this rule. On January 22, 2009, EPA’s Small Business Advocacy Chairperson convened a Panel under section 609(b) of the RFA. In addition to the Chair, the Panel consisted of the Director of the

Sector Policies and Programs Division within EPA’s Office of Air and Radiation, the Chief Counsel for Advocacy of the SBA, and the Administrator of the Office of Information and Regulatory Affairs within the Office of Management and Budget.

As part of the SBAR Panel proce ss, we conducted outreach with representatives from 14 various small entities that would be affected by this rule. The small entity representatives (SERs) included associations representing schools, churches, hotels/motels, wood product facilities, and

man ufacturers of home furnishings. We met with these SERs to discuss the potential rulemaking approaches and potential options to decrease the impact of the rulemaking on their
industries/sectors. We distributed outreach materials to the SERs; these materials included background on the rulemaking, possible regulatory approaches, preliminary cost and economic impacts, and possible rulemaking alternatives. The Panel met with SERs from the industries that will be directly affected by this rule on February 10, 200 9, to discuss the outreach materials and receive feedback on the approaches and alternatives detailed in the outreach packet. (EPA also met with SERs on November 13, 2008, for an initial outreach meeting.) The Panel received

written comments from the SERs following the meeting in response to discussions at the meeting and the questions posed to the SERs by the Agency. The SERs were specifically asked to provide comment on regulatory alternatives that could help minimize the rules’ impact on small

businesses

.

5.2.6.1 Panel Recommendations for Small Business Flexibilities

The Panel recommended that EPA consider and seek comment on a wide range of regulatory alternatives to mitigate the impacts of the rulemaking on small businesses, including

### 5

- 9 those flexibility options described below. The following section summarizes the SBAR Panel recommendations. EPA has proposed provisions consistent with four of the Panel’s

recommendations.

Consistent with the RFA/SBREFA requirements, the Panel evaluated the assembled

materi als and small

- entity comments on issues related to elements of the IRFA

. A copy of the

Final Panel Report (including all comments received from SERs in response to the Panel ’ s outreach meeting ), as well as summaries of both outreach meetings that were held with the SERs

, is included in the docket for the proposed rule s. A summary of the Panel recommendations is

detailed below . The proposal s include proposed provisions for all but one of the Panel

recommendations.

5.2.6.1.1 Work Practice Standards. The panel recommended that EPA consider requiring

annual tune

- ups, including standardized criteria outlining proper tune

- up methods targeted at smaller boiler operators. The panel further recommended that EPA take comment on the efficacy of energy assessments/audit s at improving combustion efficiency and the cost of performing the audits, especially to smaller boiler operators.

A work practice standard, instead of MACT emission limits, may be proposed if it can be justified under section 112(h) of the CAA, that is, it is impracticable to enforce the emission standards due to technical or economic limitations. Work practice standards could reduce fuel use and improve combustion efficiency, which would result in reduced emissions.

In general, SERs commented that a regu latory approach to improve combustion efficiency, such as work practice standards, would have positive impacts with respect to the environment and energy use and save on compliance costs. The SERs were concerned with work practice standards that would requ ire energy audits and implementation of audit findings. The basis of these concerns rested on the uncertainty that there is no guarantee that there are available funds to implement a particular audit’s findings.

5.2.6.1.2 Subcategorization. The Panel recom mended that EPA allow subcategorizations suggested by the SERs, unless EPA finds that a subcategorization is inconsistent with the CAA.

SERs commented that subcategorization is a key concept that could ensure that like boilers are compared with similar bo ilers so that MACT floors are more reasonable and could be achieved by all units within a subcategory using appropriate emission reduction strategies. SERs commented that EPA should subcategorize based on fuel type, boiler type, duty cycle, and

location.

### 5

- 10 5 .2.6.1.3 Health Based Compliance Alternatives (HBCA). The Panel recommended that EPA adopt the HBCA as a regulatory flexibility option for the boiler MACT rulemaking. The panel recognized, however, that EPA has concerns about its legal authority to provide an HBCA under the CAA, and EPA may ultimately determine that this flexibility is inconsistent with the

CAA.

SERs commented that adopting an HBCA would perhaps be the most important step EPA could take to mitigate the serious financial harm the boiler MACT would otherwise inflict on small entities using solid fuels nationwide; therefore, HBCA should be a critical component of any future rule to lessen the impact on small entities.

5.2.6.1.4 Emissions Averaging. The Panel recommended that EPA consider a pro vision for emission averaging and long averaging times for the proposed emission limits.

SERs commented that a measure EPA should consider to lessen the regulatory burden of complying with the boiler MACT is to allow emissions averaging at sources with mul tiple regulated units. SERs commented that another approach that can aide small entity compliance is to set longer averaging times (i.e., 30 days or more) rather than looking at a mere 3

- run (hour) average for performance. Given the inherent variability in boiler performance, an annual or quarterly averaging period for all HAP would prevent a single spike in emissions from throwing a unit into noncompliance.

5.2.6.1.5 Compliance Costs. The Panel recommended that EPA carefully weigh the potential burden of c ompliance requirements and consider for small entities options, such as emission averaging within the facility, reduced monitoring/testing requirements, or allowing more time for compliance.

SERs noted that recordkeeping activities, as written in the vaca ted boiler MACT, would be especially challenging for small entities that do not have a dedicated environmental affairs department.

### 6

- 1 SECTION 6

HUMAN HEALTH BENEFIT S OF EMISSIONS REDUC TIONS

6.1 Synopsis

In this section, we provide an estimate of the monetiz ed benefits associated with reducing particulate matter (PM) for the proposed Boiler MACT and Boiler Area Source Rule. For these rules, the PM reductions are the result of emission limits on PM (as a surrogate for metals) as well

as emission limits on oth er HAPs. The total PM 2.5 

### reductions are the consequence of the technologies installed to meet these multiple limits. These estimates reflect the monetized human health benefits of reducing cases of morbidity and premature mortality among populations

expo sed to the PM 2.5 

### precursors reduced by this rulemaking. Using a 3% discount rate, we estimate the total monetized benefits of both the proposed Boiler rules to be $ 18 billion to $ 45 billion in the implementation year (2013). Using a 7% discount rate, we estimate the total monetized benefits of the proposed Boiler rules to be $ 17 billion to $ 40 billion in the implementation year. All estimates are in 2008$.

These estimates reflect EPA’s most current interpretation of the scientific literature

. Higher or lower estimates of benefits are possible using other assumptions; examples of this are provided in Figure 6

- 2. Data, resource, and methodological limitations prevented EPA from monetizing the benefits from several important benefit categories, including benefits from reducing hazardous air pollutants, ecosystem effects, and visibility impairment. The benefits from reducing hazardous air pollutants have not been monetized in this analysis, including reducing 370,000 tons of carbon monoxide, 8.7 tons of me rcury, 43,000 tons of HCl, and 1,200

tons of HF each year

.

6. 2 Calculation of PM 2.5 

### Human Health B enefits

This rulemaking would reduce emissions of PM 2.5 

### , SO 2, 

### and VOCs. Because SOx and VOCs are also precursors to PM 2.5, 

### reducing these emissions would also reduce PM 2.5 

### formation, human exposure, and the incidence of PM 2.5 

### - related health effects. For these rules, the PM reductions are the result of emission limits on PM (as a surrogate for metals) as well as emission

limits on other HAPs. The total PM 2 .5 

### reductions are the consequence of the technologies installed to meet these multiple limits. Due to analytical limitations, it was not possible to provide a comprehensive estimate of PM 2.5 

### - related benefits. Instead, we used the “benefit

- per

ton” approa ch to estimate these benefits based on the methodology described in Fann et al. (2009). The key assumptions are described in detail below. These PM 2.5 

### benefit

- per

- ton estimates provide the total monetized human health benefits (the sum of premature morta lity and

### 6

- 2 premature morbidity) of reducing one ton of PM 2.5 

### from a specified source. EPA has used the benefit per

- ton technique in several previous RIAs, including the recent NO 2 

### NAAQS RIA (U.S. EPA, 2010b). Table 6

- 1 shows the quantified and unquantified benefits captured in those benefit

per

- ton estimates.

Table 6

- 1

. Human Health and Welfare Effects of PM 2.5

Pollutant /

Effect Quantified and Monetized in Primary Estimates Unquantified Effects Changes in:

PM 2.5 Adult premature mortality

Bronchitis: chronic and acute Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lower and upper respiratory illness

Minor restricted

- activity days Work loss days
Asthma exacerbations (as thmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis

Non

- asthma respiratory emergency room visits Visibility Household soiling



### Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the 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 sensitivity analysis

.

 

### One estimate is based on the concentration

- response (C

- R) function developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002), a study that EPA has previously used to generate its primary PM benefits estimate.

 

### One estimate is based on the C

- R function developed from the extended analysis of the Harvard Six Cities cohort, as reported by Laden et al

. (2006). This study, published after the completion of the Staff Paper for the 2006 P M 2.5 

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

### NAAQS RIA and P M 2.5 

### benefits estimates in RIAs completed since the P M 2.5 

### NAAQS.

 

### Twelve estimates are based on the C

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

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

### NAAQS. For that study, twelve experts (labeled A through L) provided independent estimates of the P M 2.5 

### - mortality concentration

- response function. EPA practice has been to develop independent

### 6

- 3 estimates of P M 2.5 

### - mortality estimates corresponding to the concentration

- response function provided by each of the twelve experts, to better characterize the degree of variability in the expert responses.

The effect coefficients a re drawn from epidemiology studies examining two large population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six Cities cohort (Laden et al., 2006). 1 

### These are logical choices for anchor points in our presentation

becau se, while both studies are well designed and peer reviewed, there are strengths and weaknesses inherent in each, which we believe argues for using both studies to generate benefits

estimates. Previously, EPA had calculated benefits based on these two empir ical studies, but derived the range of benefits, including the minimum and maximum results, from an expert elicitation of the relationship between exposure to PM 2.5 

### and premature mortality (Roman et al., 2008). 2



### Readers interested in reviewing the methodology for creating the benefit

- per

- ton estimates used in this analysis should consult Fann et al. (2009). As described in the documentation for the benefit per

- ton estimates cited above, national per

- ton estimates are developed for selected pollutant/source category combinations. The per

- ton values calculated therefore apply only to tons reduced from those specific pollutant/ source combinations (e.g., SO 2 

### emitted from electric generating units; NO 2 

### emitted from mobile sources). Our estimate of PM 2.5 

### control benefits is

therefore based on the total PM 2.5 

### emissions controlled by sector and multiplied by this per

- ton
value. Within this assessment, we include the benef its estimates derived from the concentration

response function provided by each of the twelve experts to better characterize the uncertainty in

the concentration

- response function for mortality and the degree of variability in the expert

responses. Because the experts used these cohort studies to inform their concentration

- response functions, benefits estimates using these functions generally fall between results using these epidemiology studies (see Figure 6

- 2). In general, the expert elicitation results s upport the

conclusion that the benefits of PM 2.5 

### control are very likely to be substantial.

The benefit

- per

- ton coefficients in this analysis were derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically, this analysis uses the benefit

- per

- ton method first applied in the Portland Cem ent NESHAP RIA (U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an expanded geographic scope of the benefit

- per

- ton calculation, and the functions directly from the

1 These two studies specify multi

- pollutant models that control for SO 2 , among other pollutants.

2 Please see the Section 5.2 of the Portland Cement RIA in Appendix 5A for more information regarding the change in the presentation of benefits estimates.

### 6

- 4 epidemiology studies without an adjustment for an assumed thr eshold. 3



### EPA strives to use the best available science to support our benefits analyses, and we recognize that interpretation of the science regarding air pollution and health is dynamic and evolving. Based on our review of the body of scientific lite rature, EPA applied the no

- threshold model in this analysis. EPA ’ s Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by EPA ’ s Clean Air Scientific Advisory Committee (U.S.

EPA

- SAB, 2009a; U.S. EPA

- SAB, 2009 b) , concluded that the scientific literature consistently

finds that a no

- threshold log

- linear model most adequately portrays the PM

- mortality

concentration

- response relationship while recognizing potential uncertainty about the exact shape

of the concentr ation

- response function. Removing the threshold assumption is a key difference between the method used in this analysis of PM benefits and the methods used in RIAs prior to Portland Cement, and we now calculate incremental benefits down

to the lowest modeled PM 2.5 

### air qua lity levels.

4 

### In conjunction 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. 5



### As is the nature of Regulato ry Impact Analyses (RIAs), the assumptions and methods used to estimate air quality benefits evolve over time to reflect the Agency ’ s most current interpretation of the scientific and economic literature. For a period of time (2004

- 2008), the

Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta

- analyses of the

wage

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

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

analysis of 43 studies. The mean estimate of $5.5 million (2000$) 6

3 The benefit

- per

- ton estimates have also been updated since the Cement RIA to incorporate a revised VSL, as discussed on the next page. 

### was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta

- analysis. However, the

4 It is important to note that uncertainty regarding the shape of the concentration

- response function is conceptually distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is a discontinuity, which is a specific example of non

- l inearity.

5 In the Portland Cement RIA (U.S. EPA, 2009a), EPA solicited comment on the use of the no

- threshold model for benefits analysis within the preamble of that proposed rule. The comment period for the Portland Cement proposed NESHAP closed on Sept ember 4, 2009 (Docket ID No. EPA

– HQ

– OAR

– 2002

– 0051 available at http://www.regulations.gov ). EPA is currently reviewing those comments.

6 In this analysis, we adjust the VSL to account for a different currency year ( 2008$) and to account for income growth to 2015. After applying these adjustments to the $5.5 million value, the VSL is $7.9m.

### 6

- 5 Agency neither changed its o fficial guidance on the use of VSL in rule

- makings nor subjected the interim estimate to a scientific peer

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

- review group.

During this time, the Agency continued work to update its guidan ce on valuing mortality risk reductions, including commissioning a report from meta

- analytic experts to evaluate methodological questions raised by EPA and the SAB on combining estimates from the various

data sources. In addition, the Agency consulted seve ral times with the Science Advisory Board Environmental Economics Advisory Committee (SAB

- EEAC) on the issue. With input from the

meta

- analytic experts, the SAB

- EEAC advised the Agency to update its guidance using specific, appropriate meta

- analytic techni ques to combine estimates from unique data sources and different studies, including those using different methodologies (i.e., wage

- risk and stated preference) (U.S.

EPA

- SAB, 2007).

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

- reviewed estimate applied consistently best reflects the SAB

- EEAC advice it has received. Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 7 

### w hile the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991.

The mean VSL across these studies is $6.3 million (2000$). 8



### Figure 6

- 1 illustrates the relative breakdown of the monetized PM 2.5 

### health benefits. The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB

- EEAC ’ s specific recommendations. The Ag ency anticipates presenting results from this effort to the SAB

- EEAC in Spring 2010 and that draft guidance will be available shortly thereafter.

Tables 6

- 2 and 6

- 3 prov ide a general summary of the results by pollutant, including the

emission reductions and monetized benefits

- per

- ton at discount rates of 3% and 7%. 9

7 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008), EPA retained the VSL endorsed by the SAB with the understan ding that further updates to the mortality risk valuation guidance would be forthcoming in the near future. Therefore, this report does not represent final agency policy. 

### Table 6

- 4

8 In this analysis, we adjust the VSL to account for a different currency year (2008 $) and to account for income growth to 2015. After applying these adjustments to the $6.3 million value, the VSL is $9.1m.

9 To comply with Circular A

- 4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). These benefits are esti mated for a specific analysis year (i.e., 2013), and most of the PM benefits occur within that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 years of follow

- up medical costs and lost wages. For premature mortality, we assume that there is a “ cessation ”

### 6

- 6 Adult Mortality

- Pope et al. 93% Chronic Bronchitis 4%

AMI 2% Acute Respiratory Symptoms 0.5%

Infant Mortality 0.4%

Work Loss Days 0.2%

Hospital Admissions, Cardio
0.2% Hospital Admissions, Resp
0.04% Asthma Exacerbation 0.01%
Acute Bronchitis 0.01% Upper Resp Symp 0.00% Lower Resp Symp 0.00% ER Visits, Resp 0.00% Other 1%



### Figure 6

- 1

. Breakdown of Monetized PM 2.5 

### Health B enefits using Mortality Function from Pope e t al. (2002) a

a This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example. Using the

Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult mortality would b e 97%. This chart shows the breakdown using a 3% discount rate, and the results would be similar if a 7% discount rate was used.



### provides a summary of the reductions in health incidences as a result of the pollution reductions.

In Table 6

- 5, we provide th e benefits using our anchor points of Pope et al. and Laden et al. as well as the results from the expert elicitation on PM mortality. Figures 6

- 2 and 6

- 3 provide a visual representation of the range of benefits estimates and the pollutant breakdown of the

monetized benefits of the proposed option

.

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 str ucture 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 death s but rather the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis year and the valuation of premature mortalities that occur after the analysis year. As such, the monetized benefits using a 7% discount rate are o nly approximately 10% less than the monetized benefits using a 3% discount rate.

### 6

- 7 Table 6

- 2

. Summary of Monetized B enefits Estimates for Boiler MACT (for Major Sources) in 2013 (2008$) a

Pollutant Emissions Reductions (tons) Benefit per ton (Pope,

3%) Benefit per ton (Laden,

3%) Benefit
pe r ton (Pope,

7%) Benefit per ton (Laden,

7%) Total Monetized Benefits (millions

2008$ at 3%) Total Monetized Benefits (millions

2008$ at 7%)Option 1E and 1N Direct PM 2.5 Major 29,336 $230,000 $560,000 $210,000 $500,000 $6,700 to $16,000 $6,100 to $15,00 0

PM 2.5 Precursors

SO 2 Major 367,898 $29,000 $72,000 $27,000 $65,000 $11,000 to $27,000 $9,800 to $24,000

VOC 6,664 $1,200 $3,000 $1,100 $2,700 $8.1 to $20.0 $7.4 to $18.0

Total $18,000 to $43,000 $16,000 to $39,000PrimaryOptions Dire ct PM 2.5 Major 29,020 $230,000 $560,000 $210,000 $500,000 $6,600 to $16,000 $6,000 to $15,000

PM 2.5 Precursors

SO 2 Major 360,779 $29,000 $72,000 $27,000 $65,000 $11,000 to $26,000 $9,600 to $23,000

VOC 1,771 $1,200 $3,000 $1,100 $2,700 $2.2 to $5.3 $2.0 to $4.8

Total $17,000 to $42,000 $16,000 to $38,000

a All estimates are for the implementation year (2013), and are rounded to two significant figures so numbers may

not sum across columns. All fine particles are assumed to have equivalen t health effects, but 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 include 45 new major sources expected to be affected by 2013.

### 6

- 8 Table 6

- 3. Summary of Monetized B enefits Estimates for Boiler Area Source Rule in 2013 (2008$) a

Pollutant Emissions Reductions (tons) Benefit per ton (Pope,

3%) Benefit per ton (Laden,

3%) Benefit per ton (Pope,

7%) Benefit per ton (Laden,

7%) Total Monetized Benefits (millions

2008$ at 3%) Total Monetized Benefits (millions

2008$ at 7%)Option 1Eand 1N Direct PM 2.5 Area 22,920 $360,000 $880,000 $330,000 $790,000 $8,200 to $20,000 $7,500 to $18,000

PM 2.5 Precursors

SO 2 Area 1,745 $20,000 $49,000 $18,000 $44,000 $35 to $86 $32 to $77

VOC 2,119 $1,200 $3,000 $1,100 $2,700 $2.6 to $6.3 $2.30 to $5.7

Total $8,300 to $20,000 $7,500 to $18,000Primary Options Direct PM 2.5 Ar ea 2,682 $360,000 $880,000 $330,000 $790,000 $960 to $2,400 $880 to $2,100

PM 2.5 Precursors

SO 2 Area 1,539 $20,000 $49,000 $18,000 $44,000 $31 to $76 $28 to $68

VOC 1,179 $1,200 $3,000 $1,100 $2,700 $1.4 to $3.5 $1.30 to $3.2

Total $1,00 0 to $2,400 $910 to $2,200

a A ll estimates are for the implementation year (2013), and are rounded to two significant figures so numbers may

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. These estimates include 6,779 new area sources expected t o be affected by 2013.

### 6

- 9 Table 6

- 4. Summary of Reductions in Health Incidences from PM 2.5 

### B enefits for the Proposed Boiler Rules in 2013 a

Boiler MACT (Major Sources) Boiler Area Source Rule
Option 1E and 1N Primary Options Option 1E and 1N Primary Option s Avoided Premature Mortality
Pope et al. 2,000 1,900 930 110

Laden et al. 5,000 4,900 2,400 300
Avoided Morbidity

Chronic Bronchitis 1,300 1,300 670 81
Acute Myocardial Infarction 3,100 3,100 1,500 190
Hospital Admissions, Resp 470 470 220 27
Hospital Admissions, Cardio 1,000 990 460 57
Emergency Room Visits, Resp 1,900 1,800 690 85

Acute Bronchitis 3,200 3,100 1,600 190
Work Loss Days 260,000 250,000 130,000 16,000

Asthma Exacerbation 35,000 34,000 17,000 2,100
Acute Respiratory Sympt oms 1,500,000 1,500,000 780,000 95,000
Lower Respiratory Symptoms 38,000 37,000 19,000 2,300
Upper Respiratory Symptoms 28,000 28,000 14,000 1,700

a All estimates are for the analysis year (2013) and are rounded to whole numbers with two significant fig ures. All

fine particles are assumed to have equivalent health effects, but each PM 2.5 precursor pollutant has a different propensity to form PM 2.5 . Confidence intervals are unavailable for this analysis because of the benefit

- per

- ton methodology. These es timates include 45 new major sources and 6,779 new area sources expected to be affected by 2013.

### 6

- 10 Table 6

- 5

. All PM 2.5 

### B enefits Estimates for the Proposed Boiler Rules at discount rates of 3% and 7% in 2013 (in millions of 2008$) a

Option 1E and 1N Pro posal Option

3% 7% 3% 7%

Benefit

- per - ton Coefficients Derived from Epidemiology Literature
Pope et al. $26,000 $23,000 $18,000 $17,000

Laden et al. $63,000 $57,000 $45,000 $40,000

Benefit

- per - ton Coefficients Derived from Expert Elicitation
Expert A $8,100 $7,400 $47,000 $43,000
Expert B $6,200 $5,600 $36,000 $33,000
Expert C $6,200 $5,600 $36,000 $33,000
Expert D $4,400 $4,000 $26,000 $23,000
Expert E $10,000 $9,100 $58,000 $53,000
Expert F $5,700 $5,100 $33,000 $30,000
Expert G $3,700 $3,400 $22,000 $20,000
Expert H $4,700 $4,200 $27,000 $25,000
Expert I $6,100 $5,600 $36,000 $32,000
Expert J $5,000 $4,500 $29,000 $26,000
Expert K $1,200 $1,100 $7,100 $6,500
Expert L $4,500 $4,100 $26,000 $24,000

a All estimates are rounded to two signi ficant figures. Estimates do not include confidence intervals because they

were derived through the benefit

- per

- ton technique described above. The benefits estimates from the Expert Elicitation are provided as a reasonable characterization of the uncertain ty in the mortality estimates associated with the concentration

- response function. Confidence intervals are unavailable for this analysis because of the benefit

- per

- ton methodology. These estimates include 45 new major sources and 6,779 new area sources ex pected to be affected by 2013.

### 6

- 11 $0 $10 $20 $30 $40 $50 $60

Pope et al. Laden et al.Billions (2008$)

Benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% DR

7% DR



### Figure 6

- 2

. Total Monetized PM 2.5 

### Benefits for the Proposed Boiler Rules in 2013

a This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the

Pope et al. study and th e Laden et al

. study, as well as 12 effect coefficients derived from EPA ’ s expert elicitation on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration

- response function provided in those studies.

### 6

- 12 Figure 6

- 3

. Breakdown of Monetized B enefits for the Proposed Boiler Rules by PM 2.5 

### Precursor Pollutant and Source

Figure 6

- 4. Breakdown of Monetized B enefits for the Proposed Boiler MACT (Major Sources) by Fuel Type

### 6

- 13 Figure 6

- 5. Breakdown of Monetized B enefits for the Proposed Boiler Area Source Rule by Fuel Type

6.2 Unquantified Benefits

The monetized benefits estimated in this RIA only reflect the portion of benefits

attributable to the health effect reductions associated with ambient fine particles. Data, resource, and methodological limitations prevented EPA from quantifying or monetizing the benefits from several important benefit categories, including benefits from reducing toxic emissions, ecosyst em effects, and visibility impairment. The health benefits from reducing thousands of tons of hazardous air pollutants (HAPs) and millions of tons of carbon monoxide each year have not been monetized in this analysis. In addition to being a PM 2.5 

### precursor , SO 2 

### emissions also contribute to adverse effects from acidic deposition in aquatic and terrestrial ecosystems, increase mercury methylation, as well as visibility impairment.

6.2.1 Carbon Monoxide Benefits

Carbon monoxide (CO) exposure is associated wit h a variety of health effects. Without knowing the location of the emission reductions and the resulting ambient concentrations using

fine

- scale air quality modeling, we were unable to estimate the exposure to CO for nearby populations. Due to data, reso urce, and methodological limitations, we were unable to estimate

### 6

- 14 the benefits associated with the 37 0,000 tons reductions in CO emissions that would occur as a

result of this rule.

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

carbon

- containing fuels and photochemical reactions in the atmosphere. The amount of CO emitted from these reactions, relative to carbon dioxide (CO 2 

### ), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperatu re, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that are depend ent upon concentration and duration of exposure.

The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded

that short

- term exposure to CO is “likely to have a causal relationship” with cardiovascular morbidity, particularly in indi viduals with coronary heart disease. Epidemiologic studies associate

short

- term CO exposure with increased risk of emergency department visits and hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac chest pain),

as well as those who have experienced a heart attack. Other subpopulations potentially at risk include individuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, suc h as older adults or the developing young. The evidence is suggestive of a causal relationship between short

- term exposure to CO and respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship

for birth outcomes and develo pmental effects following long

- term exposure to CO, and for central nervous system effects linked to short

- and long

- term exposure to CO.

6.2.2 Other SO 2 

### Benefits

In addition to being a precursor to PM 2.5 

### , SO 2 

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

associated with reduced SO 2 

### exposure in this analysis because we do not have air quality modeling data available. Without knowing the location of the emission re ductions 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 reductions in SO 2 

### emissions.

Followin g an extensive evaluation of health evidence from epidemiologic and laboratory
studies, the Integrated Science Assessment for Sulfur Dioxide concluded that there is a causal relationship between respiratory health effects and short

- term exposure to SO 2 

### (U. S. EPA, 2008).

The immediate effect of SO 2 

### on the respiratory system in humans is bronchoconstriction.

### 6

- 1 5 Asthmatics are more sensitive to the effects of SO 2 

### likely resulting from preexisting inflammation

associated with this disease. A clear concentration

- response 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. Based on

our review of t his 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 hospitalizations. The differing eviden ce 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

observe d associations to adjustment for pollutants.

SO 2 

### emissions also contribute to adverse welfare effects from acidic deposition, visibility impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which

can cause a loss of bi odiversity 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 Stat es, the surface waters affected by acidification are a source of food for some recreational and subsistence fishermen and for other consumers and support several cultural services, including aesthetic and educational services and recreational fishing. B iol ogical effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root growth, which restricts the ability of the plant to take up water and nutrients. These direct effects can, in
turn, increase the sensitivity of these plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial acidification affects several important ecological services, including declines in habi tat for threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil erosion and reductions in water retention (cultural and regulating). (U.S. EPA, 20 08d)

Reducing SO 2 

### emissions and the secondary formation of PM 2.5 

### would improve the level of visibility throughout the United States. Fine particles with significant light

- extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon , and soil (Sisler, 1996). These suspended particles and gases degrade visibility by scattering and absorbing light. Higher visibility impairment levels in the East are due to generally higher concentrations of fine particles,

### 6

- 16 particularly sulfates, and higher average relative humidity levels. In fact, particulate sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually and 75% during summer). In the western U.S., particulate sulfate contributes to 20

- 50% of re gional haze (U.S. EPA, 2009c). Visibility has direct significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in recr eational activities.

6.2.3 HAP B enefits

Due to data, resource, and methodology limitations, we were unable to estimate the benefits associated with the thousands tons of hazardous air pollutants that would be reduced as a

result of this rule. Available emissions data show that several different HAPs are emitted from boilers, either contained within the fuel burned or formed during the combustion process.

Although numerous HAPs may be emitted from boilers, a few HAPs account for the majority of the tota l mass of HAPs emissions. See Table 6

- 6 for the list of the major HAPs for each fuel type. This rule is anticipated to reduce 370,000 tons of carbon monoxide, 8.7 tons of mercury, 43,000 tons of HCl, and 1,200 tons of HF each year . We discuss the health effects associated with the top HAPs as well as the HAPs for which we have emission reduction

estimates

.

Table 6

- 6

. Top HAPs by Mass from Boilers by Fuel Type

Coal Gas Biomass Oil

68% HCl 44% Formaldehyde 32% Acetaldehyde 28% Nickel

5% HF 25% PAH 28% H Cl 19% Manganese

3% Toluene 25% Formaldehyde



### 6.2.3.1 Mercury

Mercury is a highly neurotoxic contaminant that enters the food web as a methylated compound, methylmercury (U.S. EPA, 2008d) . The contaminant is concentrated in higher trophic
levels, in cluding fish eaten by humans. Experimental evidence has established that only inconsequential amounts of methylmercury can be produced in the absence of sulfate. Current evidence indicates that in watersheds where mercury is present, increased SO x 

### deposi tion very likely results in methylmercury accumulation in fish (Drevnick et al., 2007; Munthe et al, 2007).

The SO 2 

### ISA concluded that evidence is sufficient to infer a casual relationship between sulfur

### 6

- 17 deposition and increased mercury methylation in wet lands and aquatic environments (U.S. EPA, 2008d).

In addition to the role of sulfate deposition on methylation, these proposed rules would

also reduce 8.7 tons of mercury emissions per year. Due to time and resource limitations, we

were unable to model m ercury dispersion, deposition, methylation, bioaccumulation in fish tissue, and human consumption of mercury

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

Mercury exposure is associ ated with a variety of health effects. 10



### 6.2.3.2 Hydrogen Chloride (HCl) Mercury exists in three forms: elemental mercury, inorganic mercury compounds (primarily mercuric chloride), and organic mercury compounds (primarily methyl mercury). Each form exhibits different health

effects. Va rious major sources may release elemental or inorganic mercury; environmental methylmercury is typically formed by biological processes after mercury has precipitated from the air. Acute exposure to high levels of elemental mercury in humans results in cen tral nervous system (CNS) effects such as tremors, mood changes, and slowed sensory and motor nerve function. High inhalation exposures can also cause kidney damage and effects on the gastrointestinal tract and respiratory system. Chronic exposure to elem ental mercury in humans also affects the CNS, with effects such as increased excitability, irritability, excessive shyness, and tremors. EPA has not classified elemental mercury with respect to cancer. Acute exposure to inorganic mercury by the oral route may result in effects such as nausea, vomiting, and severe abdominal pain. The major effect from chronic exposure to inorganic mercury is kidney damage. Reproductive and developmental animal studies have reported effects such as alterations in

testicula r tissue, increased embryo resorption rates, and abnormalities of development. Mercuric chloride (an inorganic mercury compound) exposure has been shown to result in forestomach, thyroid, and renal tumors in experimental animals. EPA has classified mercu ric chloride as a Group C, possible human carcinogen.

Hydrogen chloride, also called hydrochloric acid, is corrosive to the eyes, skin, and mucous membranes. Acute inhalation exposure may cause eye, nose, and respiratory t ract irritation and inflammation and pulmonary edema in humans. Chronic occupational exposure to hydrochloric acid has been reported to cause gastritis, bronchitis, and dermatitis in workers.

10 U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional

Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664

- 1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf

### 6

- 18 Prolonged exposure to low concentrations may also cause dental discoloration and erosion. No information is available on the reproductive or developmental effects of hydrochloric acid in humans. In rats exposed to hydrochloric acid by inhalation, altered estrus cycles have been reported in females and increased fet al mortality and decreased fetal weight have been reported in offspring. EPA has not classified hydrochloric acid for carcinogenicity. 11



### 6.2.3.3 Chlorine gas (Cl2)

Chlorine gas is irritating and corrosive to the eyes, skin, and respiratory tract. Exposur e 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. 12



### 6.2.3.4 Hydrogen cyanide (HCN)

Hydrogen cyanide is highly toxic by all routes of exposure and may cause abrupt onse t 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 cas es by coma and death. Hydrogen cyanide acts as a cellular asphyxiant. By binding to mitochondrial cytochrome oxidase, it prevents the utilization of oxygen in cellular metabolism. The central nervous system and myocardium are particularly sensitive to the toxic effects of cyanide. 13



### 6.2.3. 5 Hydrogen Fluoride (HF)

Acute (short

- term) inhalation exposure to gaseous hydrogen fluoride can cause severe respiratory damage in humans, including severe irritation and pulmonary edema. Chronic (long

term) exposure to f luoride 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

11 U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Indus trial/Commercial/Institutional

Boilers and Process Heaters; Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp. 1664

- 1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler /fr13ja03.pdf

12 Agency for Toxic Substances and Disease Registry (ATSDR). 

### 2007. Medical Management Guidelines for Chlorine (CAS 7782

- 50

- 5; UN 1017). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Interne t at http://www.atsdr.cdc.gov/MHMI/mmg172.html#bookmark02

. 13 Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Medical Management Guidelines for Hydrogen Cyanide (HCN) ( C AS# : 7782

- 50

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

.

### 6

- 19 cause dental fluorosis. One study reported menstrual irregularities in women occup ationally exposed to fluoride. The EPA has not classified hydrogen fluoride for carcinogenicity. 14



### 6.2.3. 6 Toluene

Toluene is found in evaporative as well as exhaust emissions from motor vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessment, th ere is inadequate information to assess the carcinogenic potential of toluene because studies of humans chronically exposed to toluene are inconclusive, toluene was not carcinogenic in adequate inhalation cancer bioassays of rats and mice exposed for life, and increased incidences of mammary cancer and leukemia were reported in a lifetime rat oral bioassay. 15



### The central nervous system (CNS) is the primary target for toluene toxicity in both humans and animals for acute and chronic exposures. CNS dysfuncti on (which is often reversible) and narcosis have been frequently observed in humans acutely exposed to low or moderate levels of toluene by inhalation; symptoms include fatigue, sleepiness, headaches, and nausea. Central nervous system depression has been reported to occur in chronic abusers exposed to high levels of toluene. Symptoms include ataxia, tremors, cerebral atrophy, nystagmus (involuntary eye movements), and impaired speech, hearing, and vision. Chronic inhalation exposure of humans to toluene a lso causes irritation of the upper respiratory tract, eye irritation, dizziness, headaches, and difficulty with sleep. 16



### Human studies have also reported developmental effects, such as CNS dysfunction,
attention deficits, and minor craniofacial and limb an omalies, 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 neurologica l effects (i.e., impaired color vision, impaired hearing,

14 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

15 U.S. EPA

. 2005

. “Full IRIS Summary for Toluene (CASRN 108

- 88

- 3)” Environmental Protection Agency,

Integrated Risk Information System (IRIS), Office of Health and En vironmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH

. Available on the Internet at

< http://www.epa.gov/iris/subst/0118.htm >

. 16 U.S. EPA

. 2005

. “Full IRIS Summary for Tolue ne (CASRN 108

- 88

- 3)” Environmental Protection Agency, Integrated Risk Information System (IRIS), Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH . Available on the Internet at

< http://www.epa.gov/iris/subst/0118.htm >

. 

### C: \ Documents and Settings \ bmd \ Local Settings \ Temporary Internet Files \ Content.Outlook \ NHURJV2L \ www.epa.gov \ ncea\ iris

### 6

- 20 decreased performance in neurobehavioral analysis, changes in motor and sensory nerve conduction velocity, headache, dizziness) as the most sensitive endpoint. 17



### 6.2.3. 7 Formaldehyde

Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys. 18 

### 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. 19

, 20 

### In an analysis of the lymphohematopoietic cancer mortality from an extended follow

- up of thes e workers, NCI confirmed an association between lymphohematopoietic cancer risk and peak exposures. 21 

### A recent National Institute of Occupational Safety and Health (NIOSH) study of garment workers also found increased risk of death due to leukemia among wor kers exposed to formaldehyde. 22



### 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

. 23



### In the past 15 years there has been substantial research on the inhalation dosimetry for formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the Chemical Industry Institute of Toxicology), with a focus on use of roden t data for refinement of the quantitative cancer dose

- response assessment. 24 , 25 , 26

17 U.S. EPA

. 2005

. “Full IRIS Summary for Toluene (CASRN 108

- 88

- 3)” Environmental Protection Agency,

Integrated Risk Information System (IRIS), Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH . Available on the Internet at

< 

### CIIT’s risk assessment of formaldehyde

http://www.epa.gov/iris/subst/0118.htm >. 

### C: \ Documents and Settings \ bmd \ Local Settings \ Temporary Internet Files \ Content.Outlook \ NHURJV2L \ www.epa.gov \ ncea\ iris

18 U.S. EPA. 1987. Asses sment of Health Risks to Garment Workers and Certain Home Residents from Exposure to

Formaldehyde, Office of Pesticides and Toxic Substances, April 1987.

19 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Mortality from lymphoh ematopoetic

malignancies among workers in formaldehyde industries. Journal of the National Cancer Institute 95: 1615

- 1623.

20 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from solid cancers among

workers in formal dehyde industries. American Journal of Epidemiology 159: 1117

- 1130.

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

Mortality from lymphohematopoietic malignancies among workers in formalde hyde industries: The National Cancer Institute cohort. J. National Cancer Inst. 101: 751

- 761.

22 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup.

Environ. Med. 61: 193

- 200.

23 Coggon, D, EC Harr is, J Poole, KT Palmer. 2003. Extended follow

- up of a cohort of British chemical workers

exposed to formaldehyde. J National Cancer Inst. 95:1608

- 1615.

24 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologic ally

motivated computational modeling of formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432

- 447.

### 6

- 21 incorporated mechanistic and dosimetric information on formaldehyde. However, it should be noted that recent research published by EPA i ndicates that when two

- stage modeling assumptions are varied, resulting dose

- response estimates can vary by several orders of magnitude. 27 , 28 , 29 , 30 

### These findings are not supportive of interpreting the CIIT model results as providing a conservative (health pr otective) estimate of human risk. 31 

### EPA research also examined the

contribution of the two

- stage modeling for formaldehyde towards characterizing the relative weights of key events in the mode

- of

- action of a carcinogen. For example, the model

- based

infere nce in the published CIIT study that formaldehyde’s direct mutagenic action is not relevant to the compound’s tumorigenicity was found not to hold under variations of modeling assumptions. 32



### Based on the developments of the last decade, i n 2004, the working group of the IARC concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient evidence in humans and sufficient evidence in experimental animals

- a higher classification than previous IARC evaluations. After reviewing the currently available epidemiological evidence, the IARC (2006) characterized the human evidence for formaldehyde carcinogenicity as “sufficient,” based upon the data on nasopharyngeal cancers; the epidemiologic evidence on

leukemia was characterized as “st rong.” 33

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

tract cancer risks of inhaled form aldehyde: Dose

- response predictions derived from biologically

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

- 296. 

### 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.

26 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard characterization and dose

- response

assessment for carcinogenicity by the route of inhalation. CIIT, September 28, 1999. Research Triangle Park, NC.

27 U.S. EPA. Analysis of the Sen sitivity and Uncertainty in 2

- Stage Clonal Growth Models for Formaldehyde with

Relevance to Other Biologically

- Based Dose Response (BBDR) Models. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R

- 08/103, 2008

28 Subramaniam, R; Chen, C; Crum p, K; .et .al. (2008) Uncertainties in biologically

- based modeling of

formaldehyde

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

- 923.
29 Subramaniam, 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 sensitivity analysis

- I. Risk Anal 27:1237

30 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehyde

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

- 495.

31 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehyde

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

- 495.

32 Subramaniam, R; Chen, C; Crump, K; .et .al. (200 7). Uncertainties in the CIIT 2

- stage model for formaldehyde

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

- I. Risk Anal 27:1237

33 International Agency for Research on Cancer (2006) Formaldehyde, 2

- Butoxyethanol and 1

- tert

- Butoxypropa n

- 2

-

ol. Monographs Volume 88. World Health Organization, Lyon, France.

### 6

- 22 F ormaldehyde exposure also causes a range of noncancer health effects, including irritation of the eyes (burning and watering of the eyes), nose and throat. Effects from repeated exposure in humans include respiratory tract irritation, chronic bronchitis a nd nasal epithelial lesions such as metaplasia and loss of cilia. Animal studies suggest that formaldehyde may also cause airway inflammation

– including eosinophil infiltration into the airways. There are several studies that suggest that formaldehyde ma y increase the risk of asthma

– particularly in the

young. 34

, 35



### 6.2.3.8 Polycyclic Aromatic Hydrocarbons (PAHs)

At least seven PAH compounds are classified by EPA as probable human carcinogens based on animal data, including benzo(a)anthracene 36 

### , benzo(b)flu oranthene 37



### , benzo(k)fluoranthene 38 

### , benzo(a)pyrene 39 

### , chrysene 40 

### , dibenz(a,h)anthracene 41 

### , and indeno(1,2,3

- cd)pyrene 42

34 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 Servi ce. http://www.atsdr.cdc.gov/toxprofiles/tp111.html 

### . Recent studies have found that maternal exposures to PAHs in a population of pregnant women were associated with several adverse birth out comes, including low birth weight

35 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint

sponsorship of the United Nations Environment Programme, the International Labour Organization, and the World Health Organization, and produced within the framework of the Inter

- Organization Programme for the Sound Management of Chemicals. Geneva.

36 U.S. EPA (1997). Integrated Risk Information System File of benzo(a)anthracene. Research and Devel opment,

National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea/iris/subst/0454.htm.

37 U.S. EPA (1997). Integrated Risk Information System File of benzo(b)fluoranthene. Research a nd Development,

National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea/iris/subst/0453.htm.

38 U.S. EPA (1997). Integrated Risk Information System File of benzo(k)fluoranthene. Re search and Development,

National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea/iris/subst/0452.htm.

39 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.

40 U.S. EPA (1997). Integrated Risk Information System File of chrysene. Resea rch and Development, National

Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea/iris/subst/0455.htm

41 U.S. EPA (1997). Integrated Risk Information System File of dibenz(a,h)anthracene . Research and Development,

National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/ncea/iris/subst/0456.htm.

42 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.

### 6

- 23 and reduced length at birth, as well as impaired cognitive development at age three. 43 , 44



### 6.2.3. 9 Acetaldehyde EPA has not yet evaluated these recent studies.

Acetaldehyde is classified in EPA’s IRIS database as a probable human carcinogen, based on nasal tumors in rats, and is considered toxic by the inhalation, oral, and intravenous routes. 45


### Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS in the 11 th


### Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the

IARC. 46 , 47



### The primary noncancer effects of exposure to acetaldehyde vapors include irritation of the

e yes, skin, and respiratory tract. EPA is currently conducting a reassessment of cancer risk from inhalation exposure to acetaldehyde.

48 

### In short

- term (4 week) rat studies, degeneration of olfactory epithelium was observed at various concentration levels of acetaldehyde exposure. 49 

### Data from these studies were used by EPA to develop an inhalation referenc e concentration. Some asthmatics have been shown to be a sensitive subpopulation to decrements in functional expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde inhalation. 50



### 6.2.3. 10 Nickel The agency is currently conducting a reassessment of the hea lth hazards from inhalation exposure to acetaldehyde.

Nickel is an essential element in some animal species, and it has been suggested it may be essential for human nutrition. Nickel dermatitis, consisting of itching of the fingers, hand an d

43 Perera, F.P.; Rauh, V.; Tsai, W

- Y.; et al. (2002) Effect of tra nsplacental exposure to environmental pollutants on

birth outcomes in a multiethnic population. Environ Health Perspect. 111: 201

- 205.

44 Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D.; Diaz, D.; Hoepner, L.; Barr, D.; Tu, Y.H.; Camann,

D.; Kin ney, 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.

45 U.S. EPA (1988). Integrated Risk Information Sys tem 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.

46 U.S. Department of Health and Human Services National T oxicology Program 11th Report on Carcinogens

available at: http://ntp.niehs.nih.gov/go/16183.

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

- evaluation of some organic chemicals, hydrazine,

and hydrogen peroxide. IARC Monographs on the Eva luation of Carcinogenic Risk of Chemical to Humans, Vol 71. Lyon, France.

48 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material is available

electronically at http://www.epa.gov/iris/subst/0290.htm.

49 Appleman, L.M., R .A. Woutersen, and V.J. Feron. (1982). Inhalation toxicity of acetaldehyde in rats. I. Acute and

subacute studies. Toxicology. 23: 293

- 297.

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

med iated bronchoconstriction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1): 940

- 943.

### 6

- 24 forearms, is the most common effect in humans from chronic (long

- term) skin contact with nickel. Respiratory effects have also been reported in humans from inhalation exposure to nickel. No information is available regarding the reproductive or developm ental effects of nickel in humans, but animal studies have reported such effects. Human and animal studies have reported an increased risk of lung and nasal cancers from exposure to nickel refinery dusts and nickel

subsulfide. Animal studies of soluble nic kel compounds (i.e., nickel carbonyl) have reported lung tumors. The EPA has classified nickel refinery subsulfide as Group A, human carcinogens and nickel carbonyl as a Group B2, probable human carcinogen. 51



### 6.2.3. 11 Manganese

Health effects in humans have been associated with both deficiencies and excess intakes of manganese. Chronic (long

- term) exposure to low levels of manganese in the diet is considered to be nutritionally essential in humans, with a recommended daily allowance of 2 to 5 milligrams

per day. Chronic exposure to high levels of manganese by inhalation in humans results primarily in CNS effects. Visual reaction time, hand steadiness, and eye

- hand coordination were affected in chronically

- exposed workers. Manganism, characterized by feelings of weakness and lethargy, tremors, a masklike face, and psychological disturbances, may result from chronic exposure to higher levels. Impotence and loss of libido have been noted in male workers afflicted with manganism attributed to inhalation exposures. The EPA has classified manganese in Group D, not classifiable as to carcinogenicity in humans. 52



### 6.2.3.12 Dioxins (Chlorinated dibenzodioxins (CDDs)

The most noted health effect in people exposed to large amounts of 2,3,7,8

- TCDD is

chloracne. Chloracne is a severe skin disease with acne

- like lesions that occur mainly on the face and upper body. Other skin effects noted in people exposed to high doses of 2,3,7,8

- TCDD include skin rashes, discoloration, and excessive body hair. Changes in blood and urine tha t may indicate liver damage also are seen in people. Exposure to high concentrations of CDDs may induce long

- term alterations in glucose metabolism and subtle changes in hormonal levels.

In certain animal species, 2,3,7,8

- TCDD is especially harmful and ca n cause death after a single exposure. Exposure to lower levels can cause a variety of effects in animals, such as weight

51 U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Industrial/Commercial/Institutional

Boilers and Process Heaters; Proposed Rule,” 68 Federal Regist er 8 (January 13, 2003). pp. 1664

- 1665. Available on the internet at http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf

52 U.S. EPA, “National Emission Standards for Hazardous Air Pollutants for Indu strial/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/boile r/fr13ja03.pdf

### 6

- 25 loss, liver damage, and disruption of the endocrine system. In many species of animals, 2,3,7,8

- TCDD weakens the immune system and cau ses a decrease in the system's ability to fight bacteria and viruses. In other animal studies, exposure to 2,3,7,8

- TCDD has caused reproductive damage and birth defects. Some animal species exposed to CDDs during pregnancy had miscarriages and the offsprin g of animals exposed to 2,3,7,8

- TCDD during pregnancy often had severe birth defects including skeletal deformities, kidney defects, and weakened immune responses. 53



### 6.2.3.13 Furans (Chlorinated dibenzofurans (CDFs))

Most of the information on the adverse health effects comes from studies in people who were accidentally exposed to food contaminated with CDFs. The amounts that these people were exposed to were much higher than are likely from environmental exposures or from a normal diet.

CDFs caused skin a nd eye irritations, including severe acne, darkened skin color, and swollen eyelids with discharge from the eyes. CDF poisoning also caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the nervous system, and mild chan ges in the liver. Children born to exposed mothers had skin irritation and more difficulty learning.

Many of the same effects that occurred in people also occurred in laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomach s, 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 s mall amounts over several weeks or months. 54



### 6.2.3.1 4 Other Air Toxics

In addition to the compounds described above, other compounds in gaseous hydrocarbon and PM emissions from boilers would be affected by this rule. Information regarding the health

effec ts of these compounds can be found in EPA ’ s IRIS database. 55



### 6.3 Characterization of Uncertainty in the Monetized PM 2.5 

### Be nefits

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

are used to derive the final estimate of economic benefits, including emission inventories, air quality models (with their associated parameters and inputs), epidemiological estimates of

53 Agency for Toxic Substances and Disease Registry (ATSDR). 1999

. ToxFAQs for Chlorinated Dibenzo

- p

- dioxins

(CDDs) (CAS# : 2,3,7,8

- TCDD 1746

- 01

- 6 ). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available o n the Internet at http://www.atsdr.cdc.gov/tfacts104.html

. 54 Agency for Toxic Substances and Disease Registry (ATSDR). 1995. ToxFAQsTM for Chlorodibenzofurans (CDFs)

.

Atlanta, GA: U.S. Department of Heal th and Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc.gov/tfacts32.html>.

55 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris

### 6

- 26 concentration

- response (C

- R) functions, estimates of values, population estimates, income
estimates, and es timates of the future state of the world (i.e., regulations, technology, and human behavior). For some parameters or inputs it may be possible to provide a statistical representation of the underlying uncertainty distribution. For other parameters or inp uts, the necessary

information is not available.

The annual benefit estimates presented in this analysis are also inherently variable due to the processes that govern pollutant emissions and ambient air quality in a given year. Factors

such as hours of e quipment use and weather are constantly variable, regardless of our ability to measure them accurately. As discussed in the PM 2.5 

### NAAQS RIA (Table 5.5) (U.S. EPA, 2006), there are a variety of uncertainties associated with these PM benefits. Therefore, t he estimates of annual benefits should be viewed as representative of the magnitude of benefits expected, rather than the actual benefits that would occur every year.

We performed a couple of sensitivity analyses on the benefits results to assess the

sens itivity of the primary results to various data inputs and assumptions. We then changed each default input one at a time and recalculated the total monetized benefits to assess the percent change from the default. We present the results of this sensitivit y analysis in Table 6

- 7. We indicated each input parameter, the value used as the default, and the values for the sensitivity analyses, and then we provide the total monetary benefits for each input and the percent change

from the default value for the pr oposed option

.

Table 6

- 7. Sensitivity Analyses for Monetized PM 2.5 

### Health B enefits (in millions of 2008$)

Total PM 2.5

Benefits % Change from Default

Threshold Assumption (with Epidemiology Study) No Threshold (Pope) $18,000 N/A No Threshold (L aden) $45,000 N/A Threshold (Pope) $14,810

- 18% Threshold (Laden) $32,000

- 29%

Discount Rate (with Epidemiology Study) 3% (Pope) $18,000 N/A 3% (Laden) $45,000 N/A 7% (Pope) $17,000

- 5.6% 7% (Laden) $40,000

- 11%



### Above we present the e stimates of the total monetized benefits, based on our interpretation of the best available scientific literature and methods and supported by the SAB

- HES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions and

### 6

- 27 uncertaint ies. For example, for key assumptions underlying the estimates for premature mortality, which typically account for at least 90% of the total monetized benefits, we were able to quantify include the following:

1. PM 2.5 

### benefits were derived through benefit per

- ton estimates, which do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an over

- estimate or under

estimate of the actual benefits of controlling di rectly emitted fine particulates.

2. We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

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

### released from diesel engines and other industrial sources, but no clear scientific grounds exist for supporting differential effects estimates by particle type.

3. We assume that the health impact func tion for fine particles is linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5, 

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

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

### and premature mortality (which typically accounts for 85% to 95% of total monetized benefits), we include a set of twelve estimates based on results of the expert elicitation study in addition to our core estimates. Even these multiple characterizations omit the uncertainty in air quality estimates, baseline incidence rate s, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the 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).

This RIA does not include the type of detailed uncertainty assessment found in the PM NAAQS RIA because we lack the necessary air quality input and monitoring data to run the

benefits model. Moreover, it was not possible to develop benefit

- per

- ton metrics and associated

estimates of uncert ainty using the benefits estimates from the PM RIA because of the significant differences between the sources affected in that rule and those regulated here. However, the results of the Monte Carlo analyses of the health and welfare benefits presented in C hapter 5 of

the PM NAAQS RIA can provide some evidence of the uncertainty surrounding the benefits results presented in this analysis.

### 6

- 28 It is important to note that the monetized benefit

- per

- ton estimates used here reflect specific geographic patterns of em issions reductions and specific air quality and benefits modeling assumptions. For example, these estimates do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of

these $/ton values to estimate benefits associated with different emission control programs (e.g., for reducing emissions from large stationary sources like EGUs) may lead to higher or lower

benefit estimates than if benefits were calculated based on direct air quality modeling. Great care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are all based on national or broad regional emission reduction programs and therefore represent average benefits

- p er

- ton over the entire United States. The benefits

- per

- ton for emission reductions in specific locations may be very different than the estimates presented

here.

6.4 Comparison of Benefits and Costs

Using a 3% discount rate, we estimate the total combine d monetized benefits of the proposed Boiler MACT and Area Source Rule to be $ 18 billion to $ 45 billion in the implementation year (2013). Using a 7% discount rate, we estimate the total monetized benefits of the proposed Boiler MACT and Area Source Rule t o be $ 17 billion to $4 0 billion. The combined annualized social costs of the proposed Boiler MACT and Area Source Rule are $ 3.6

billion at a 7% interest rate. 56



### The annualized social costs of the proposed Boiler MACT only are $ 3.1 billion, and the annualized social costs of the proposed Area Source Rule are $ 500 m illion a t a 7% discount rate. The net benefits for the proposed Boiler MACT for major sources only are $1 4 billion to $3 9 billion and $ 13 billion to $3 5 billion, at 3% and 7% discount rates, respectively. The net benefits for the Boiler Area Source rule only are $ 500 m illion to $ 1.9 billion and $ 400 million to $ 1.7 billion, at 3% and 7% discount rates, respectively. All estimates are in 2008$ for the year 2013

. Thus, the combined net benefits are $ 1 5 billion to $41 billion at a
3% discount rate and $ 13 b illion to $3 7 billion at a 7% discount rate. All estimates are in 2008$ for the year 2013

.

Tables 6

- 8 and 6

- 9 show a summary of the monetized benefits, social costs, and net benefits for the pr oposed options and the alternative options for the Boiler MACT for Major Sources and for the Boiler Area Source Rule, respectively. Figures 6

- 4 and 6

- 5 show the full range of net benefits estimates (i.e., annual benefits minus annualized costs) utilizing t he 14

different PM 2.5 

### mortality functions at discount rates of 3% and 7%. In addition, the benefits from

56 For more information on the annualized so cial costs, please refer to Section 4 of this RIA.

### 6

- 29 reducing 420,000 tons of carbon monoxide, 21 tons of mercury, 48,000 tons of HCl, and 1,200

tons of H F each year have not been included in these estim ates. EPA believes that the benefits are likely to exceed the costs under this rulemaking even when taking into account uncertainties

in the cost and benefit estimates.

### 6

- 30 Table 6

- 8 . Summary of the Monetized Benefits, Social Costs, and Net Benefits for th e Boiler MACT (Major Sources) in 201 3 ( m illions of 2008$) 1

Proposed Option

3% Discount Rate 7% Discount Rate

Total Monetized Benefits $ 17,000 to $ 42,000 2 $ 16,000 to $ 38,000 2

Total Social Costs $3,100 3 $3,100

Net Benefits $14,000 to $39,000 $13,000 to $35,000

Option 1N and 1E

3% Discount Rate 7% Discount Rate

Total Monetized Benefits $ 18,000 to $ 43,000 4 $ 16,000 to $ 39,000 4

Total Social Costs $12,000 3 $12,000

Net Benefits $5,400 to $31,000 $3,800 to $27,000

1 All estimates are for the implementatio n year (2013), and are rounded to two significant figures.

2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 29,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such a s 1,700 tons of VOC and 360,000 tons of SO 2 . The monetized benefits from reducing 330,000 tons of carbon monoxide, 42,000 tons of hydrochloric acid, 1,200 tons of HF, and 7.9 tons of mercury, and 730 grams of dioxins/furans each year are not included in t hese estimates. In addition, the monetized benefits from reducing ecosystem effects and visibility impairment are not included.

3 The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the sa me social costs for both discount rates.

4 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 29,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 6,700 tons of VOC and 370,000 tons of SO 2 . The monetized benefits from reducing 390,000 tons of carbon monoxide, 43,000 tons of hydrochloric acid, 8,600 tons of HF, and 8.1 tons of mercury, and 750 grams of dioxins/furans each year are not included in these estimates. In addition, the monetized benefits from reducing ecosystem effects and visibility impairment are not included.

### 6

- 31 Table 6

- 9 . Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler Area Source Rule in 201 3 ( m illions of 2008$) 1

Pr oposed Option

3% Discount Rate 7% Discount Rate

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

Total Social Costs $500 3 $500

Net Benefits $500 to $1,900 $400 to $1,700

Option 1N and 1E

3% Discount Rate 7% Discount Rate

Total Monetized Benefits $ 8,300 to $ 20,000 4 $ 7,500 to $ 18,000 4

Total Social Costs $35,00 3 $35,000

Net Benefits $

- 27,000 to $

- 16,000 $

- 28,000 to $

- 17,000

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

2 The total mon etized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 2,700 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 1,200 tons of VOC and 1,500 tons of SO 2 . The monetized benefits from reduc ing 40,000 tons of carbon monoxide, 127 tons of hydrochloric acid, 5 tons of HF, and 0.75 tons of mercury, and 460 grams of dioxins/furans each year are not included in these estimates. In addition, the monetized benefits from reducing ecosystem effects a nd visibility impairment are not included.

3 The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both discount rates.

4 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 23,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 2,100 tons of VOC and 1,700 tons of SO 2 . The monetized benefits from reducing 58,000 tons of carbon m onoxide, 140 tons of hydrochloric acid, 6.4 tons of HF, and 1.5 tons of mercury, and 530 grams of dioxins/furans each year are not included in these estimates. In addition, the monetized benefits from reducing ecosystem effects and visibility impairment a re not included.

### 6

- 32 $0 $10 $20 $30 $40 $50

Pope et al. Laden et al.Billions (2008$)

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



### Figure 6

- 4

. Net Benefits for the Proposed Boiler Rules at 3% Discount Rat e a

a Net Benefits are quantified in terms of PM 2.5 benefits for implementation year (2013). This graph shows 14 benefits

estimates combined with the cost estimate. All combinations are treated as independent and equally probable. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . Th e monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.

### 6

- 33 $0 $10 $20 $30 $40 $50

Pope et al. Laden et al.Billions (2008$)

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



### Figure 6

- 5

. Net Benefits for the Proposed Boiler Rules at 7% Discount Rate a

a Net Benefits are quantified in terms of PM 2.5 benefits for implementation year (2013). This graph shows 14 benefits

estimates combined with the cost estimate. All combinations are treated as independent and equally probable. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.

### 7

- 1 SECTION 7

SUPPLEMENTAL ECONOMI C ANALYSES F OR NON

- HAZARDOUS SOLID WAST E

DEFINITION

EPA also considered an economic analysis for an alternate approach for defining non

hazardous solid waste

. Under the alternative approach, the universe of sources in the energy recovery and waste burning cement kiln subcategories would chang e while the number of sources in the remaining three subcategories (i.e., incinerators, burn

- off ovens, and small, remote incinerators) does not change

. This section provides an overview of the results.

7.1.1 Economic Impact Analysis Results

7.1.1.1 Mark et

- Level Results

Market

- level impacts include price and quantity adjustments including the changes in

i nternational trade (Table 7

- 1)

. T he Agency’s economic model suggests t he average national

market

- level variables (prices, production

- levels, consumption, and international trade) will not significantly change (e.g., are less than 0.5 %).

7.1.1.2 Social Cost Estimates

In the near term, the Agency’s economic model suggests that i ndustries are able to pass on $ 0.8 b illion (2008$) of th e costs to U.S. househol ds in the form of higher prices (Table 7

- 2). Existing U.S. industries’ surplus falls by $ 2.1 b illion, and the net loss for U.S. stakeholders is $ 2.9 b illion

. 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

estima tes a net rest of the world gain of less than $0.1 billion . After accounting for international trade effects, the Agency’s economic model projects the net s urplus loss associa ted with the proposed rule is $ 2.8 b illion

. The Agency also considered other elements of the engineering cost analysis that could not be modeled within the multimarket model (e.g., fuel savings ) . The net effect of the adjustments is app roximately than $0.4 b illion.

7.1.1.3 Job Effects

Near

- term employment changes associated with the pro posed rule are estimated to be less

than 5 ,000 job l osses; over a longer time period, net em ployment effects could range between
5, 000 job losses to 10,000 job gains. Additional details and caveats associated with these estimates are present in section 4.

### 7

- 2 Table 7

- 1. Market

- Level Price and Quantity Changes : 2013 (Alternate Definition )

Industry Sector Prices Production Imports Consu mption Exports
Energy 0.04%

- 0.01% 0.08% less than 0.01% less than 0.01% Nonmanufacturing less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Manufacturing

Food, beverages, and textiles 0.02%

- 0.02% 0.03%

- 0.01%

- 0.01% Lumber, paper, and printing 0.14%

- 0.06% 0.15%

- 0.04%

- 0.10%
Chemicals 0.02%

- 0.03% 0.02%

- 0.02%

- 0.02%
Plastics and Rubber less than 0.01%

- 0.01% 0.01%

- 0.01% less than 0.01%
Nonmetallic Minerals less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Primary Metals 0.04%

- 0.04% 0.04%

- 0.02%

- 0.04%
Fabricated Metals less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Machinery and Equipment 0.01%

- 0.02% 0.01% less than 0. 01%

- 0.02% Electronic Equipment less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Transportation Equipment less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01%
Other less than 0.01%

- 0. 01% less than 0.01% less than 0.01% less than 0.01%
Wholesale and Retail Trade less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01% Transportation Services less than 0.01% less than 0.01% less than 0.01% less than 0 .01% less than 0.01%
Other Services less than 0.01% less than 0.01% less than 0.01% less than 0.01% less than 0.01%

### 7

- 3 Table 7

- 2

. Distribution of Social Costs (m illion, 2008$): 201 3

Method Alternate Definition

Partial Equilibrium Model (Multiple Markets)

Change in U.S. consumer surplus −$0.8

Change in U.S. producer surplus −$2.1

Change in U.S. surplus −$2.9

Net change in rest of world surplus $0.1

Net change in total surplus −$2.8

Direct Compliance Costs Method

Fuel savings (not modeled) $ 0.4

Change in Total Surplus −$ 2.4



### Table 7

- 3

. Employment Changes : 2013

Method 1,000 Jobs

Partial equilibrium model (multiple m arkets)

− 4.5

Literature

- based estimate (net effect) + 2.6
( −4.7 to + 9.9 )

Literature

- based estimate: Demand Effect − 5.9
( −12.5 to 0.7 )

Literature

- based estimate: Cost Effect + 4.0
(+1.3 to +6.7 )

Literature

- based estimate: Factor Shift Effect +4.4
(+0.1 to +8.8 )
Note: 95 percent confidence intervals for literature

- based estimates are shown in parenthesis.

### 7

- 4 7.1.2 Benefits Analysis Results

Tabl e 7

- 4 provides a general summary of the alternate approach results by pollutant, including the emission reductions and monetized benefits

- per

- ton at discount rates of 3% and 7%. These estimates reflect EPA’s most current interpretation of the scientific l iterature. Higher or lower estimates of benefits are possible using other assumptions, but most expert

- derived estimates fall within these estimates. Data, resource, and methodological limitations prevented EPA from monetizing the benefits from several i mportant benefit categories, including benefits from reducing hazardous air pollutants, ecosystem effects, and visibility impairment. The

benefits from reducing hazardous air pollutants have not been monetized in this analysis, including reducing 280,000 tons of carbon monoxide, 5,600 tons of hydrochloric acid, 1,100 tons of HF, and 1.5 tons of mercury, and 290 grams of dioxins/furans each year

. Tables 7

- 5 shows a summary of the monetized benefits, social costs, and net benefits for the proposed options and the alternative options for the Boiler MACT for existing major sources

.

Table 7

- 4. Summary of Monetized Benefits for the Boiler MACT for Existing Major Sources in 2013 (2008$) (Alternate Approach)*

Pollutant Emissions Reductions (tons) Benefit per ton (Pope,

3%) Benefit per ton (Laden,

3%) Benefit per ton (Pope,

7%) Benefit per ton (Laden,

7%) Total Monetized Benefits (millions

2008$ at 3%) Total Monetized Benefits (millions 2008$ at 7%)

Direct PM 2.5 Major 8,040 $230,000 $560,000 $210,000 $500,000 $1, 800 to $4,500 $1,700 to $4,100

PM 2.5 Precursors

SO 2 Major 44,157 $29,000 $72,000 $27,000 $65,000 $1,300 to $3,200 $1,200 to $2,900

VOC 4,703 $1,200 $3,000 $1,100 $2,700 $5.7 to $14.0 $5.2 to $13.0

Total $3,100 to $7,700 $2,800 to $6,900
*All estimates are for the analysis year (fifth year after proposal 201 3 ), and are rounded to two significant figures so numbers may not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to form PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.

### 7

- 5 Table 7

- 5 . Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler MACT (Major Sources) in 201 3 ( m illions of 2008$) 1

Proposed Option with Alternate Solid Waste Definition

3% Discount Rate 7% Discount Rate

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

Total Social C osts $2,400 3 $2,400

Net Benefits $740 to $5,300 $450 to $4,500

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

2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of 8,000 tons of directly emitted PM 2.5 and PM 2.5 precursors such as 4,700 tons of VOC and 44,000 tons of SO 2 . The monetized benefits from reducing 280,000 tons of carbon monoxide, 5,600 tons of hydrochloric acid, 1,10 0 tons of HF, and 1.5 tons of mercury, and 290 grams of dioxins/furans each year are not included in these estimates. In addition, the monetized benefits from reducing ecosystem effects and visibility impairment are not included.

3 The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both discount rates.

### R

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U.S. Environmental Protection Agency. 2006. Regulatory Impact Analysis , 2006 National Ambient Air Quality Standards for Particulate Matter , Chapter 5. Available at

< http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205

-- Benefits.pdf >.

U.S. Envir onmental Protection Agency (U.S. EPA). 2008. Guidelines for Preparing Economic Analyses: External Review Draft. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. September. Available at <http://yosemite .epa.gov/ee/epa/eermfile.nsf/vwAN/EE

- 0516

- 01.pdf/$File/EE

- 0516

- 01.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for Sulfur Oxides

— Health Criteria (Final Report). National Center for Environmental Assessm ent, Re search Triangle Park, NC

. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>.

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Guidelines for Preparing Economic Analyses: External Review Draft. National Center for Environment al Economics, Office of Policy Economics and Innovation. Washington, DC. Available at <http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE

- 0516

- 01.pdf/$File/EE

- 0516

- 01.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008d. Integrated Science Assessm ent for Ox ides of Nitrogen and Sulfur

–

Ecological Criteria National (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R

- 08/139. December. Available a t <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=20148 5>.

U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry

. Office of Air Quality Planning and Standards, Research Triangl e Park, NC. April. Available at

< http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4

- 20

- 09.pdf >.

U.S. Environmental Protection Agency (U.S. EPA). 2009b. Propo sed SO 2 

### NAAQS Regulatory Impact Analysis (RIA) . Office of Air Quality Planning and Standards, Research Triangle Park, NC. November. Available at

< http://www.epa.gov/ttn/ecas/regd ata/RIAs/pso2full11

- 16

- 09.pdf >.

### R

- 11 U.S. Environmental Protection Agency (U.S. EPA). 2009c. Integrated Science Assessment for Particulate Matter (Final Report). EPA

- 600

- R

- 08

- 139F. National Center for Environmental Assessment

— RTP Division. December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546 >.

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Integrated Science Assessment for Carbon Monoxide

. National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R

- 09/019F. January. Available at

< http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=218686 >.

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Final Regulatory Impact Analysis (RIA) for the NO 2 

### National Ambient Air Quality Standards (NAAQS)

. Office of Air Quality Planning and Standards, Research Triangle Park, NC. January. Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf >.

U.S. Enviro nmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2007. SAB Advisory on EPA’s Issues in Valuing Mortality Risk Reductio n. EPA

- SAB

- 08

- 001. October. Availab le at

< http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$F ile/sab

- 08

- 001.pdf >.

U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2009a. Review of EPA’s Integrated Science Asse ssment for Particulate Matter (First External Review Draft, December 2008). E PA

- COUNCIL

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< http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86 e/73ACCA834AB44A10852575BD0064346B/$File/EPA

- CASAC

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- unsigned.pdf >.

U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

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. EPA

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- CASAC

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- unsigned.pdf >.

U.S. Internati onal Trade Commission. 2008a. “ U.S. Domestic Exports ” & “ U.S. Imports for Consumption. ” <http://dataweb.usitc.gov/scripts/user_set.asp>.

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U.S. Internationa l Trade Commission. 2009. “ U.S. Domestic Exports ” & “ U.S. Imports for Consumption. ” < http://www.federalreserve.gov/datadownload/ >.

U.S. Small Business Administration (SBA). 2008. “ Table of Small Business Size Standards Matched to North American Industry Classification System Codes. ” Effective August 22, 2008. <http://www.sba.gov/services/contractingopportunities/sizestandardstopics/ size/index.html>.

### R

- 12 Viscusi, V.K., and J.E. Aldy. 2003. “ The Value of a Statistic al Life: A Critical Review of Market Estimates throughout the World. ” Journal of Risk and Uncertainty 27(1):5

- 76.

### APPENDIX A OAQPS MULTI M ARKET MODEL TO ASSES S THE ECONOMIC

IMPACTS OF ENVIRONME NTAL

REGULATION

### A

- 1 A.1 Introduction

A n economic impact analys is (EIA) provides information about a policy ’ s effects (i.e., social costs); emphasis is also place d on how the costs are distributed among stakeholders (EPA,

2000 ). In addition, large

- scale policies require additional analysis to bett er understand how costs are passed across the economy. Although several tools are available to estimate social costs,

current EPA guidelines suggest that multi market models “ ...are best used when potential economic impacts and equity effects on related marke ts 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 ).

Multi market models focus on “ short

- run ” time horizons and measure a policy ’ s near term or transition costs ( EPA , 1999 ). Recent studies suggest sho rt

- run analyses can complement full dynamic general equilibrium analysis.

The multi market model described in this appendix is a new addition to the Office of Air Quality Planning and Standards’ ( OAQPS ’ s ) economic model tool kit; it is designed to be used

as a transparent tool that can respond quickly to requests about how stakeholders in 100 U.S. industries might respond to new environmental policy. Next, we provide an overview of the model, data, and parameters.

A.2 Multi m arket Model

T he multimarket mode l contains the following features:

 

### Industry sectors and benchmark data s et

– 100 industry sectors

– a single benchmark year (2010)

– estimates of industry carbon dioxide ( CO 2 

### ) emissions

– e stimates of industry employment

 

### Economic behavior

– industries respond to re gulatory cos ts by changing production rates

– market prices rise to reflect higher energy and othe r nonenergy material costs

– customers respond to these price in creases and consumption falls

 

### Model scope

### A

- 2  

### 100 sectors are linked with each other based on their u se of energy and other nonenergy materials. For example, the construction industry is linked with the petroleum, cement, and steel industr ies and is influenced by price changes that occur in each sector. Th e links allow EPA to account for indirect effects the regulation has on related markets.

– Links come from input

- output information used from OAQPS’s computable general equilibrium model (EMPAX)

– production adjustments influence employment levels

– international trade (impor ts/exports) behavior considered

 

### Mo del time horizon ( “ short

- run ”)

– fixed production resources (e. g., capital) leads to an upward

- sl oping industry supply function

– firms cannot alter input mixes; there is no substitution among production inputs (capital, labor, energy intermed iates, and other intermediates)

– p rice of labor (i.e., wage) is fixed

– investment and government expendi tures are fixed

A.2.1 Industry Sectors and Benchmark Data Set

The multi market model includes 100 industries. For the benchmark year, the model uses information from OAQPS ’ s computable general equilibrium model ’ s balanced social accounting matrix (SAM) and the following accounting identity holds (EPA, 2008 ):

Output + Imports = Consumption + Investment + Government + Exports (A .1 )

If we abstract and trea t each industry as a national market, the identity r epresents the prepolicy market

- clearing condition, or benchmark “ equilibrium ” ; supply equals demand in each

market. In Table A

- 1, we identify the 100 indus tries for the multi market model; Table A

- 2 provid es 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. Indu stry Sectors Included in Multim arket 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 Gas 211112 2212 4862
OIL Refined Petroleum 324 Nonmanufacturing

AGR Agricultural 11
MIN Mining 21 less others
CNS Construction 23

Manufactured Goods Food, beverages, and textiles

ANM Animal Foods 3111
GRN Grain Milling 3112
SGR Sugar 3113
FRU Fruits a nd Vegetables 3114
MIL Dairy Products 3115
MEA Meat Products 3116
SEA Seafood 3117
BAK Baked Goods 3118
OFD Other Food Products 3119
BEV Beverages and Tobacco 312
TEX Textile Mills 313
TPM Textile Product Mills 314
WAP Wearing Apparel 315
LEA Lea ther 316 Lumber, paper, and printing

SAW Sawmills 3211
PLY Plywood and Veneer 3212
LUM Other Lumber 3219
PAP Pulp and Paper Mills 3221
CPP Converted Paper Products 3222
PRN Printing 323
Chemicals

CHM Chemicals and Gases 3251
RSN Resins 3252
FRT Fertilizer 3253
MED Drugs and Medicine 3254
PAI Paints and Adhesives 3255
SOP Soap 3256
OCM Other Chemicals 3259
(continued)

### A

- 4 Table A

- 1. Indu stry Sectors Included in Multim arket Model (continued)

Industry Label Description Representative NAICS a

Pl

astics 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 Alumi num 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 M achine 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 Metalwork ing Equipment 3335 EEQ Engines 3336 GEQ General Equipment 3339 Electronic Equipment

CPU Computers 3341 CMQ Communication Equipment 3342 TVQ TV Equipment 3343
SMI Semiconductor Equipment 3344
INS Instruments 3345
MGT Magnetic Recording Equipment 3346
LGT Lighting 3351
APP Appliances 3352
(continued)

### A

- 5 Table A

- 1. Indu stry Sectors Included in Multim arket Model (continued)

Industry Label Description Representative NAICS a
Electronic Equipment (continued)

ELQ Electric Equipment 3353 OEQ Other Elect ric 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 Equip ment 3369
Other
FUR Furniture 337
MSC Miscella neous 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 Transp ortation 484
PIP Pipeline Transport 486
OTP Other Transportation Services 485 487 488
Other Services

INF Information 51
FIN Finance and Insurance 52
REL Real Estate 53
PFS Professional Services 54
MNG Management 55
ADM Administrative Services 56
EDU Education 61
HLT Health Care 62
ART Arts 71
ACM Accommodations 72
OSV Other Services 81
PUB Public Services 92

a NAICS = North American Industr y Classification System . Industry assignments are based on data used in the

EMPAX

- modeling system, which relies on the commodity code system used in IMPLAN.

### A

- 6 Table A

- 2. 2010 Benchmark Data Set ( billion 2006$)

Industry

Label Industry Description Output Imports Consumption Investment and Government Exports

ACM Accommodations $828 $6 $816 $17 $1

ADM Admin istrative 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

C EQ Construction and Agricultural Equipment $70 $24 $47 $33 $14

CHM Chemicals and Gases $284 $75 $300 $10 $49

CLY Clay $8 $4 $10 $1 $2
CMQ Communication Equipment $73 $40 $47 $56 $11

CNS Construction $983 $77 $594 $465 Less than $1

COL Coal $44 $2 $42 Less than $1 $4

CPP Converted Paper Products $52 $2 $43 $6 $6

CPU Computers $145 $76 $132 $52 $37

CRU Crude Oil Extraction $67 $189 $255 Less than $1 Less than $1

CUT Cutlery $11 $5 $9 $5 $2

EDU Education $970 Less than $1 $257 $701 $13
EEQ Engines $ 36 $14 $30 $6 $13

EGV Engraving $21 Less than $1 $9 $5 $7

ELE Electric Generation $317 Less than $1 $287 $31 Less than $1
ELQ Electric E quipment $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 Governme nt 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 Healt h 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 Instrument s $112 $44 $71 $65 $20

LEA Leather $4 $26 $29 Less than $1 $1

LGT Lighting $12 $11 $16 $5 $1

LIM Lime and Gypsum $7 Less than $1 $1 Less than $1 $5

LUM Other Lumber $41 $2 $32 $9 $2
M_V Motor Vehicles $272 $190 $313 $106 $43

MEA Meat Products $174 $9 $169 $5 $10

MED Drugs and Medicine $258 $102 $316 $18 $27
MEQ Metalworking Equipment $24 $11 $16 $14 $4

MGT Magnetic Recording Equipment $15 $2 $13 $2 $2

MIL Dairy Products $87 $3 $84 $4 $2

MIN Mining $53 $2 $30 $15 $11

MNG Management $469 Less than $1 $378 Less than $1 $92

MSC Miscellaneous Manufacturing $178 $129 $189 $73 $46

MSP Machine Shops $38 $2 $32 $5 $2

MVP Motor Vehicle Parts $220 $75 $226 $17 $52

OCM Other Chemicals $45 $2 $23 $9 $15
OEQ Other Electric Equipment $31 $16 $28 $7 $11

OF D Other Food Products $92 $7 $90 $2 $7

OFM Other Fabricated Metals $56 $28 $50 $22 $12

(continued)

### A

- 8 Table A

- 2. 2010 Benchmark Data Set ( billion 2006$) (continued)

Industry

Label Industry Description Output Imports Consumption Investment and Government Exp orts

OIL Refined Petroleum $415 $106 $462 $12 $47

ONM Other Non

- Metallic Minerals $13 $5 $15 $1 $2

OPM Other Primary Metals $40 $27 $52 $2 $12

OSV Other Services $2,321 Less than $1 $1,479 $598 $244

OTP Other Transportation Services $245 Less than $1 $202 $22 $22

OTQ Other Transport Equip $23 $10 $14 $13 $5

PAI Paints and Adhesives $36 $1 $28 $3 $6

PAP Pulp and Paper Mills $131 $21 $133 $5 $14

PFS Professional Services $2,103 $84 $1,715 $461 $10

PIP Pipeline Transport $37 $83 $20 $98 $1

PLS Plast ic $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 $2 6

RTL Retail Trade $1,440 $53 $1,412 $82 Less than $1

RTP Railroad Transportation $70 Less than $1 $42 $18 $11

RUB Rubber $38 $20 $36 $15 $6

SAW Sawmills $29 $9 $36 $1 $1

SEA Seafood $13 $3 $14 $1 $1
SEQ Service Industry Equipment $29 $23 $22 $24 $6

SGR Sugar $34 $6 $36 $2 $3

SHP Ships $36 $6 $13 $29 Less than $1

SMI Semiconductor Equipment $141 $69 $157 $12 $41

SOP Soap $82 $5 $74 $3 $9

TEX Textile Mills $29 $9 $31 $1 $6

TKB Truck Bodies $58 $12 $34 $32 $5

TPM Textile Product Mills $27 $19 $37 $7 $2

TTP Freight Truck Transportation $301 Less than $1 $211 $39 $51

(continued)

### A

- 9 Table A

- 2. 2010 B enchmark Data Set ( billion 2006$) (continued)

Industry

Label Industry Description Output Imports Consumption Investment and Government Exports
TVQ TV Equ ipment $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 employ ment 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 Sta tistics (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 releas e or Global Insights For multimarket indust ries 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

su per sectors across more detail ed NAICS sectors within the super

- sect or

. The d istributions 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 tha t 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 post policy scenario, industry respon ds to changes in the new market

- clearing “ net ” price for t he 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 se ctor (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 ri se to ma intain the original

pre policy production levels (Q).

1 This step is required because Global Insight’s data used by EPA are an older vintage than the forecasts used in the AEO.

### A

- 11 Table A

- 4

. 2010 U.S. Employment Projections

Industry Label Industry Description Projected Employment (1,000)

ACM Accommodations 11,239

ADM Administrative Services 9,274

AGR Agricultural 1,607

ALU Aluminum 87

ANM Animal Foods 45

APP Appliances 33

ARC Aircraft 449

ART Arts 1,939

ATP Air Transportation 506

BAK Baked Goods 247

BEV Beverages and Tobacco 92

BOI Boilers and Tanks 67

CEM Cement 164
CEQ Construction and Agricultural Equipment 176

CHM Chemicals and Gases 147

CLY Clay 38 CMQ Communication Equipment 73

CNS Construction 7,446

COL Coal 79

CPP Converted Paper Products 306

CPU Computers 104

CRU Crude Oil Extraction 384

CUT Cutlery 34

EDU Education 2,892
EEQ Engines 75

EGV Engr aving 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 I ron 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 Metalwo rking 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 Equi pment 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

. 201 0 U.S. Employment Projections (continued)

Industry Label Industry Description Projected Employment (1,000)
OTQ Other Transport Equip ment 36

PAI Paints and Adhesives 60

PAP Pulp and Paper Mills 121

PFS Professional Services 18,989

PIP Pipeline Transpor t 43

PLS Plastic 473

PLY Plywood and Veneer 74

PRN Printing 248

PUB Public Services 21,787
R_R Rail Cars 25

REL Real Estate 2,158

RSN Resins 102

RTL Retail Trade 15,283

RTP Railroad Transportation 236

RUB Rubber 117

SAW Sawmills 84

SEA Seafood 36 SEQ Service Industry Equipment 77

SGR Sugar 62

SHP Ships 140

SMI Semiconductor Equipment 245

SOP Soap 104

TEX Textile Mills 110

TKB Truck Bodies 126

TPM Textile Product Mills 32

TTP Freight T ruck Transportation 1,429
TVQ TV Equipment 15

WAP W earing Apparel 153

WHL Wholesale Trade 5,869

WIR Springs and Wires 36

WTP Water Transportation 67

Total 144,100

### A

- 14 The multi market model also simultaneously considers how the policy influences other important production costs (via changes in energy a nd other intermediate material prices). As a
result, the m ulti market model can provide additional information about how policy costs are transmitted through the economy. As shown in Figure A

- 2, the indirect cost change provides the industry with additional incentives to alter production rates at current market prices.

The %Δ indirect effects associated with each input are approximated using an input “ use ” ratio and the price change that occurs in the input market.

%Δ indirect costs = input use ratio x %Δ input price (A .4 )

The social accounting matrix provides an 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 ex amining near

- term effects of environmental policy.

Price

Quantity Supply

Q Supply′

P

Q′



### Figure A

- 1. Direct Compliance Costs Reduce Production Rates 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 (OAQPS, 1999 ), we use a general form for the U.S. industry supply 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 fo r the supply price relationship

g P **′** 

### = w ith

- policy price for output (g)

t 

### = direct compliance costs per unit of s upply

gi **α** 

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

i P **′** 

### = with

- policy input (i) price

i P 

### = benchmark input (i) price

g **ε** 

### = price elas ticity 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 m odel. As a result, we assume the U.S. supply elasticities are a function of econometrically estimated rest

- of

- world ( ROW) export supply elasticities (see discussion in the next section) . We report the values currently available in the model in Table A

- 5.

### A

- 16 A .2.2.2 International Competition

International competition is captured by a single ROW supply function: 

# ( **)** ROW
g ε
g g 

### P c Q **′ =**  **′** (A .6 )

where

g Q **′** 

### = with

- policy supply quantity (g)

c = calibrated scale parameter for t he supply and price rel ationship

g P **′** 

### = with

- p olicy U.S. price for output (g)

ROW

g 

### ε 

### = price elasticity of supply of goods from the ROW to the United States (imports) (g)

The key supply parameter that controls the ROW supply adjustments is the p rice 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 (2008 b ).

A.2.2.3 Price Elasticity of Supply: Rest of World (ROW)

Broda and colleagues (2008) provide an empirical basis for the multimarket model supply elasticities. Broda et al. provide over 1,000 inverse elasticities that RTI organized to be comparable with the 100

- sector model. The first step was to m atch the Harmonized Trade System (HS) elasticities estimated in the article to the appropriate NAICS codes. Many of the HS codes correspond with a detailed NAICS codes (5

- and 6

- digit level), while the multimarket sector industries typically correspond wit h more aggregated sectors (NAICS 2

- , 3

- , or 4

- digit

levels ). To adapt these labels to our model, we combined the 5

- and 6

- digit NAICS under their

3

- and 4

- digit codes and calculated an average inverse elasticity value for codes that fell within

the multim arket model ’ s aggregate industrial sectors. This gives a crude way to account for the variety of products detailed in the original data set. We also restricted the elasticity sample to those that Broda et al. classify as “medium” and “low” categories; thes e categories tend to have lower elasticity values that are consistent with the multimarket model’s modeling horizon (i.e., in the short run importers are likely to have less flexibility to respond to price changes and elasticities are low). 1

1 Broda et al.’s intent was to use these categories to describe or proxy for domes tic market power.

### A

- 17 Table A

- 5

. Su pply Elasticities

Industry Label Industry Description Rest of World (ROW) U.S.

ACM Accommodations 0.7 0.7

ADM Administrative Services 0.7 0.7

AGR Agricultural 1.0 1.0

ALU Aluminum 0.8 0.5

ANM Animal Foods 1.1 0.8

APP Appliances 0.9 0.8

ARC Aircraft 0.9 0.6

ART Arts 0.7 0.7

ATP Air Transportation 0.7 0.7

BAK Baked Goods 0.8 0.7

BEV Beverages and Tobacco 2.9 2.9

BOI Boilers and Tanks 1.1 0.8

CEM Cement 0.9 0.7
CEQ Construction and Agricultural Equipment 0.8 0.6

CHM Chemicals and Gases 1.1 0.8

CLY Clay 0.8 0.6
CMQ Communication Equipment 2.5 1.0

CNS Construction 0.7 0.7

COL Coal 2.2 2.2

CPP Converted Paper Products 0.9 0.7

CPU Computers 1.0 0.7

CRU Crude Oil Extraction 3.7 3.7

CUT Cutlery 1.4 1.1

EDU Education 0.7 0.7 EEQ Engines 1.2 1. 0

EGV Engraving 1.1 0.8

ELE Electric Generation 2.0 2.0 ELQ Electric Equipment 0.8 0.6

FIN Finance and Insurance 0.7 0.7

FMP Fabricated Metals 1.2 1.1

FRG Forging and Stamping 1.6 1.5
(continued)

### A

- 18 Table A

- 5

. Supply Elasticities (continued)

Industry Label Industry Description Rest of World (ROW) U.S.

FRT Fertilizer 1.0 0.7

FRU Fruits and Vegetables 1.0 0.7

FUR Furniture 1.9 1.9

GAS Natural Gas 12.2 12.2
GEQ General Equipment 1.0 0.7

GLS Glass 0.8 0.6

GRN Grain Milling 1.7 1.5

HLT Health Care 0 .7 0.7

HRD Hardware 1.1 0.8

HVC HVAC Equipment 0.9 0.6
I_S Iron and Steel 1.0 0.6
IEQ Industrial Equipment 0.9 0.6

INF Information 0.7 0.7

INS Instruments 0.9 0.6

LEA Leather 0.9 0.7

LGT Lighting 1.1 0.7

LIM Lime and Gypsum 0.9 0.7

LUM Other Lumb er 0.9 0.7
M_V Motor Vehicles 1.3 0.7

MEA Meat Products 1.2 3.9

MED Drugs and Medicine 1.3 1.0
MEQ Metalworking Equipment 0.7 0.5

MGT Magnetic Recording Equipment 1.0 0.7

MIL Dairy Products 1.1 0.9

MIN Mining 2.2 2.2

MNG Management 0.7 0.7

MSC Mis cellaneous 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)

Indust ry 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 Serv ices 0.7 0.7
OTQ Other Transport Equip ment 1.0 0.7

PAI Paints and Adhesives 1.0 0.7

PAP Pulp and Paper Mills 1.1 0.7

PFS Professional Services 0.7 0.7

PIP Pipeline Transport 2.0 2.0

PLS Plastic 1.0 0.7

PLY Plywood and Veneer 1.3 1.3

PRN Printing 1. 0 0.7

PUB Public Services 0.7 0.7
R_R Rail Cars 1.8 0.7

REL Real Estate 0.7 0.7

RSN Resins 1.0 0.7

RTL Retail Trade 0.7 0.7

RTP Railroad Transportation 0.7 0.7

RUB Rubber 1.3 1.1

SAW Sawmills 0.8 0.6

SEA Seafood 1.1 0.8
SEQ Service Industry Equip ment 0.8 0.6

SGR Sugar 1.1 0.8

SHP Ships 1.0 0.7

SMI Semiconductor Equipment 1.2 1.0

SOP Soap 0.8 0.6

TEX Textile Mills 1.0 0.7

TKB Truck Bodies 3.2 3.1

TPM Textile Product Mills 0.8 0.6

TTP Freight Truck Transportation 0.7 0.7
TVQ TV Equipment 5. 8 5.4
(continued)

### A

- 20 Table A

- 5

. Supply Elasticities (continued)

Industry Label Industry Description Rest of World (ROW) U.S.

WAP Wearing Apparel 1.2 0.8

WHL Wholesale Trade 0.7 0.7

WIR Springs and Wires 1.9 0.8

WTP Water Transportation 0.7 0.7

Note: RT I mapped Broda et al. data for their in dustry aggregation to the multi market 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 c ases 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. 2008 a

. “ Export Supply Elasticities. ” http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/TradeElastic ities.html . Accessed September 2009

.


### Our ideal preference was to use an exact 3

- or 4

- digit match from the medium categ ory if

one was available. If the multimarket model had a 4

- digit code for which there was no direct match, we aggregated up a level and applied the relevant 3

- digit elasticity. If a multimarket code
was not covered in the medium set of elasticities, we use d the low elasticity category. This method was sufficient for mapping the majority of the sectors in the model. After applying our inverse elasticity values to the multimarket sectors, we calculated the inverse of the value to arrive at the actual supply e lasticity. Since Broda et al.’s article focused on industrial production goods, some of the multimarket sectors were not covered in the elasticity data. These sectors included mainly service industries, transportation, and energy sources.

In order to fill these gaps, we turned to the source substitution elasticities from Purdue University’s Global Trade Analysis Project (GTAP) 1 

### . Although the elasticities in the GTAP model are a different type of international trade elasticity and cannot be directly applied in the multimarket model (e.g., they are based on the Armington structure 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 assumpt ions about the multimarket model sectors with missing elasticities, we chose a base industrial sector (iron and steel) for which we had parameter value from Broda et al. Next, we developed industry

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

https://www.gtap.agecon.purdue.edu/resources/download/4184.pdf ; see Table 14.2 for elasticities. 2 Detailed documentation of the entire GTAP 7 Database is available at https://www.gtap.agecon.purdue.edu/databases/v7/v7_doco.asp . The GTAP also uses a unique system of categorizing commodities that does not match the NAICS or HS system exactly.

### A

- 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 elasti city for iron and steel (2.95). As a result, the multimarket import supply elasticity for coal is computed as 2.2 (2.2 x 1.0).

A.2.2.4 Price Elasticity of Supply: United States

We also used Broda et al.’s elasticities to derive a set of domestic supply ela sticities for the model. We have assumed that a product’s domestic supply would be equal to or less elastic than other countries’ supply of imports. When we aggregated and averaged the original

elasticities to the 3

- and 4

- digit NAICS level for our foreig n 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 f or each sector that was lower than its foreign counterpart while maintaining the structure of the original elasticities. For sectors in which no standard
deviation was available, 1



### A.2.2.5 Demand we used professional judgment to apply the closest available substitute

from a similar industry. Without a comparable way of scaling our foreign elasticities for the sectors in which we used the GTAP elasticities, we elected to keep the domestic and foreign supply elasticities the same.

Uses for industry output are divided into three groups: investment/government use, domestic intermediate uses, and other final use (domestic and exports). Given the short time horizon, investment/government does not change. Intermediate use is determined by the input

use ratios and t he industry output decisions.

g gi i 

### Q Q **′ α**  **= ′**  

### (A .7 )

i Q **′** 

### = with

- p olicy input demand quantity (i)

gi **α** 

### = in put 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 economic resource manual (OAQPS, 1999 ), we use a general form for the U.S. industry demand

function:

1 No standard deviations were calculated for the 3

- and 4

- digit codes that had only one observation (i.e., Broda et al.’s model used the exact 3

- or 4

- dig it code).

### A

- 22 

# ( **)**

g

g g 

### P a Q **η** 

### ′ **= ′**  (A .8 )

where

g Q **′** 

### = with

- p olicy demand quantity (g)

a 

### = calibrated scale parameter fo r the demand and price relationship

g P **′** 

### = w ith

- policy price for output (g)

g **η** 

### = price elasticity of demand (g)

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

### ). To approximate the response, we use demand elasticities that were simu lated with a general equilibrium model (Ho, Morgenstern, and Shih, 2008 ). Tabl e 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 othe r 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 equit y effects on related markets might be important to stakeholders not directly affected by an environmental policy. However, the model can also be run in single

- market partial equilibrium mode to support and provide insights for other types of environmental policies.

A.2.2.7 Model Time Horizon

The model is designed to address short

- run and transitional effects associated with environmental policy. Production technologies are fixed; the model does not assess substitution among production inputs (labor, energy intermediates, and other intermediates) and assumes each investment cannot be changed during the time frame of the analysis. These issues are better addressed using other frameworks such as computable general equilibrium modeling. Similarly,
government pur chases from each sector do not adjust in response to changes in goods/service prices. Although, employment levels (number of jobs) adjust as production levels change, wages

are assumed to be fixed.

### A

- 23 Table A

- 6

. U.S. Demand Elasticities

Industry Label Industr y 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 Go ods −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

CP P Converted Paper Products −0.7

CPU Computers −2.6

CRU Crude Oil Extraction −0.3

CUT Cutlery −0.5

EDU Education −0.7 EEQ Engines −1.7

EGV Engraving −0.5

ELE Electric Generation −0.2 ELQ Electric Equipment −2.6

FIN Finance and Insurance −0.7

FMP F abricated Metals −0.5

FRG Forging and Stamping −0.5

FRT Fertilizer −1.0

FRU Fruits and Vegetables −0.6

FUR Furniture −0.7
(continued)

### A

- 24 Table A

- 6

. U.S. Demand Elasticities (continued)

Industry Label Industry Description Demand Elasticity
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 Lighting −2.6

LIM Lime and Gypsum −0.8

LUM Other Lumber −0.7
M_V Motor Vehicles −2.5

MEA Meat Products −0.6

MED Drugs and Medicine −1.0
MEQ Metalworking Equipment −1.7

MGT Magnetic Recording Equipment −2.6

MIL Dairy Products −0.6

MIN Mi ning −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 Ref ined 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 Deman d 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 Service s −0.7
R_R Rail Cars −2.5

REL Real Estate −0.7

RSN Resins −1.0

RTL Retail Trade −0.7

RTP Railroad Transportation −0.8

RUB Rubber −1.0

SAW Sawmills −0.7

SEA Seafood −0.6 SEQ Service Industry Equipment −1.7

SGR Sugar −0.6

SHP Ships −2.5

SMI Semic onductor Equipment −2.6

SOP Soap −1.0

TEX Textile Mills −1.1

TKB Truck Bodies −2.5

TPM Textile Product Mills −1.1

TTP Freight Truck Transportation −0.8
TVQ TV Equipment −2.6

WAP Wearing Apparel −2.4

WHL Wholesale Trade −0.7

WIR Springs and Wires

− 0.5

WTP Water Transportation −0.8

Note: RTI assigned an elasticity using the most similar industry from Ho and colleagues ’ in dustry aggregation

. Source: Ho, M. S, R. Morgenstern, and J. S. Shih. 2008. “ Impact of Carbon Price Policies on US In dustry. ” RF F Discussion Paper 08

- 37. Http://Www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?. Publicatio nid=20680. Accessed August 2009

. Table B.6.

### APPENDIX B

DETAILED ECONOMIC MO DEL RESULTS BY SECTO R

### B

- 1 Table B

- 1. Prices (Percent age Change from Benchmark): I ndustry Detail

Major Area

Primary Option Option 1E Primary Option Option 1N

Energy 0.04% 0.19% <0.01%

— 0.01%
Nonmanufacturing <0.01% 0.01% <0.01% 0.02%
Manufacturing

Food, beverages, and textiles 0.02% 0.05% <0.01% 0.14%
Lumber, p aper, and printing 0.24% 0.43% 0.01% 0.31%

Chemicals 0.02% 0.07% <0.01%

— 0.02%

Plastics and rubber 0.01% 0.08% <0.01%

— 0.04%

Nonmetallic minerals 0.01% 0.06% <0.01%

— 0.02%
Primary metals 0.04% 0.10% <0.01%

— 0.01%

Fabricated metals 0.01% 0.07% <0 .01%

— 0.02%
Machinery and equipment 0.01% 0.02% <0.01%

— 0.01%
Electronic equipment <0.01% <0.01% <0.01%

— 0.01%
Transportation equipment <0.01% 0.02% <0.01% <0.01%

Other 0.01% 0.03% <0.01% 0.01%

Wholesale and retail trade <0.01%

— 0.01% <0.01% 0.01 %
Transportation services

— 0.01% 0.02% <0.01% <0.01%

Other services <0.01% <0.01% <0.01% 0.08%

### B

- 2 Table B

- 2. Production (Percent age Change from Benchmark): Industry Detail

Major Area

Primary Option Option 1E Primary Option Option 1N

Energy

— 0 .02%

— 0.13% <0.01%

— 0.01%
Nonmanufacturing

— 0.01%

— 0.02% <0.01%

— 0.04%
Manufacturing

Food, beverages, and textiles

— 0.02%

— 0.05% <0.01%

— 0.11%
Lumber, paper, and printing

— 0.10%

— 0.19% <0.01%

— 0.16%

Chemicals

— 0.04%

— 0.12% <0.01%

— 0.01%

Plastics and rubber

— 0.02%

— 0.08% <0.01%

— 0.02%

Nonmetallic minerals

— 0.01%

— 0.03% <0.01%

— 0.01%
Primary metals

— 0.05%

— 0.13% <0.01% <0.01%

Fabricated metals

— 0.01%

— 0.04% <0.01%

— 0.01%
Machinery and equipment

— 0.02%

— 0.04% <0.01% <0.01%

Electro nic equipment <0.01%

— 0.02% <0.01% <0.01%
Transportation equipment

— 0.01%

— 0.06% <0.01% <0.01%

Other

— 0.02%

— 0.06% <0.01%

— 0.04%

Wholesale and retail trade <0.01%

— 0.01% <0.01%

— 0.02%
Transportation services <0.01%

— 0.07% <0.01%

— 0.01%

Other serv ices <0.01%

— 0.01% <0.01%

— 0.04%

### B

- 3 Table B

- 3. Consumption (Percentage Change from Benchmark): Industry Detail

Major Area

Primary Option Option 1E Primary Option Option 1N

Energy

— 0.01%

— 0.05% <0.01%

— 0.01%
Nonmanufacturing

— 0.01%

— 0.01% <0.0 1%

— 0.04%
Manufacturing

Food, beverages, and textiles

— 0.01%

— 0.03% <0.01%

— 0.08%
Lumber, paper, and printing

— 0.06%

— 0.12% <0.01%

— 0.06%

Chemicals

— 0.03%

— 0.08% <0.01%

— 0.01%

Plastics and rubber

— 0.01%

— 0.05% <0.01%

— 0.02%

Nonmetallic m inerals

— 0.01%

— 0.02% <0.01%

— 0.02%
Primary metals

— 0.02%

— 0.07% <0.01%

— 0.01%

Fabricated metals

— 0.01%

— 0.03% <0.01%

— 0.01%
Machinery and equipment

— 0.01%

— 0.02% <0.01%

— 0.01%
Electronic equipment <0.01%

— 0.01% <0.01% <0.01%
Transportation equip ment <0.01%

— 0.03% <0.01% <0.01%

Other

— 0.01%

— 0.02% <0.01%

— 0.02%

Wholesale and retail trade <0.01%

— 0.01% <0.01%

— 0.02%
Transportation services <0.01%

— 0.02% <0.01%

— 0.01%

Other Services <0.01%

— 0.01% <0.01%

— 0.04%

### B

- 4 Table B

- 4. Imports (Percent age Change from Benchmark): Industry Detail

Major Area

Primary Option Option 1E Primary Option Option 1N

Energy 0.09% 0.29% <0.01%

— 0.01%
Nonmanufacturing <0.01% 0.01% <0.01% 0.01%
Manufacturing

Food, beverages, and textiles 0.03% 0 .07% <0.01% 0.16%
Lumber, paper, and printing 0.24% 0.44% 0.01% 0.32%

Chemicals 0.03% 0.08% <0.01%

— 0.02%

Plastics and rubber 0.01% 0.09% <0.01%

— 0.04%

Nonmetallic minerals <0.01% 0.02% <0.01%

— 0.01%
Primary metals 0.04% 0.10% <0.01%

— 0.01%

Fab ricated metals 0.01% 0.07% <0.01%

— 0.02%
Machinery and equipment 0.01% 0.02% <0.01%

— 0.01%
Electronic equipment <0.01% <0.01% <0.01%

— 0.01%
Transportation equipment <0.01% 0.03% <0.01% <0.01%

Other 0.02% 0.05% <0.01% 0.02%

Wholesale and retail tr ade <0.01%

— 0.01% <0.01% <0.01%
Transportation services <0.01% 0.02% <0.01% <0.01%

Other services <0.01% <0.01% <0.01% 0.01%

### B

- 5 Table B

- 5. Exports (Percent age Change from Benchmark): Industry Detail

Major Area

Primary Option Option 1E Primary Option Option 1N

Energy

— 0.01%

— 0.02% <0.01% <0.01%
Nonmanufacturing <0.01%

— 0.01% <0.01%

— 0.01%
Manufacturing

Food, beverages, and textiles

— 0.02%

— 0.03% <0.01%

— 0.09%
Lumber, paper, and printing

— 0.17%

— 0.30% <0.01%

— 0.22%

Chemicals

— 0.02%

— 0.07% <0.01% 0.02%

Plastics and rubber

— 0.01%

— 0.08% <0.01% 0.04%

Nonmetallic minerals <0.01%

— 0.05% <0.01% 0.02%
Primary metals

— 0.04%

— 0.10% <0.01% 0.01%

Fabricated metals <0.01%

— 0.03% <0.01% 0.01%
Machinery and equipment

— 0.02%

— 0.03% <0.01% 0.02%
Electronic equipment <0.01% 0.01% <0.01% 0.02%
Transportation equipment

— 0.01%

— 0.06% <0.01% 0.01%

Other

— 0.01%

— 0.03% <0.01% 0.01%

Wholesale and retail trade <0.01% 0.01% <0.01% <0.01%
Transportation services <0.01%

— 0.02% <0.01% < 0.01%

Other services <0.01% <0.01% <0.01%

— 0.06%