1

December 20, 2012 

MEMORANDUM

SUBJECT: Regulatory Impact Results for the Reconsideration Final Rule for Standards of 

Performance for New Stationary Sources and Emission Guidelines for Existing 

Sources: Commercial and Industrial Solid Waste Incineration Units 

FROM: Tom Walton 

Economist 

AEG (C439-02) 

TO: Toni Jones 

 Environmental Engineer 

FIG ( E143-03) 

The EPA analyzed the economic impacts and benefits of this reconsideration final rule 

using the methodology that was discussed in the original final rule RIA and in the preamble to 

the original final rule. See FR 76 15704. 

Changes Since 2010 Final Rule to Emission Reductions and Engineering Costs 

The changes in emission reductions and annual engineering costs in the final CISWI 

reconsideration are the result of revisions made to the CISWI unit inventory since promulgation 

of the March 2011 final rule. Since the March 2011 final rule, some units were identified that 

were not previously in the CISWI inventory database, some units were removed from the 

inventory, and one unit was moved from one subcategory to another . Making these changes 

resulted in 1 additional incinerator, 14 additional small remote incinerators, 8 fewer energy 

recovery units burning solid waste, no change in the number of energy recovery units burning 

liquid waste, and 11 additional waste-burning kilns. Altogether, the current CISWI inventory 

comprises 18 more units than the inventory at the time the March 2011 final rule was 

promulgated. If all units choose to comply with the rule, the resulting incremental cost impact for 

the revised inventory of CISWI units to comply with the final amended rule is approximately 

184 million dollars in capital expenditures and 42 million dollars per year in total annual costs. 

 The changes in emission reductions and annual engineering costs in the final CISWI 

reconsideration are mainly the result of revisions made to the emission limits due to receiving 

new data, subcategory inventory changes, and changes to the emissions monitoring provisions. 

Incremental annual engineering costs for liquid/gas burning energy recover units decreased by 

approximately $219,000 because a lower baseline CO emission concentration was determined 
2

for one of the units, thus eliminating the need for an oxidation catalyst to meet the new CO limit. 

Incremental annual engineering costs for energy recover units burning solids decreased by about 

$70 million because eight units were cut from the inventory and a revised activated carbon 

injection cost algorithm was used in estimating costs. Incremental annual engineering costs for 

incinerators increased by about $340,000 because of a more stringent CO limit, which prompted 

the need for afterburner retrofits on units that can’t meet the revised limit. Incremental annual 

engineering costs for small remote units increased by approximately $3.2 million; although 

limits became less stringent and fewer controls were required per unit, the additional annual 

costs required for an additional 14 units to comply ($3.7 million) outweighed the cost reduction 

from decreased control requirements ($498,000). Incremental annual engineering costs for 

waste-burning kilns increased by about $109 million because additional wet scrubbers, activated 

carbon injection, regenerative thermal oxidizers, and fabric filter improvements were required 

for the original 12 units to meet revised limits, and an additional 11 units were added to the 

inventory, many of which require similar controls to meet the revised limits. 

Table 1 shows the changes in emission reductions of directly emitted PM2.5, SO2, and 

NOx. Table 2 shows an estimate of the changes in monetized benefits associated with the 

emission reductions and engineering costs in the final CISWI reconsideration. 

Table 1. Changes in Emission Reductions for the Final CISWI Reconsiderationa

Direct PM2.5 

(tons per year)

SO2 

(tons per year) 

NOx 

(tons per year)

Final CISWI Rule (March 2011) 759 5,259 5,734 

Changes due to increase in scope 

(addition of 18 units) 

+98 +970 -8 

Changes due to provision changes in this final reconsideration +60 +33 -327 

Net changes since final rule +158 +1,003 -335 

Final CISWI Reconsideration 917 6,262 5,399 

a

 We provide only the emission changes associated with these 2 pollutants in this table because the other pollutants 

(e.g., Cd, CO, HCl, Pb, Hg, D/F) were not monetized in the RIA. 
3

Table 2. Changes in Benefits and Costs for the Final CISWI Reconsideration

Monetized Benefits in 2015a

 Annual Engineering 

Costsb 

3% discount rate 7% discount rate (considering fuel savings)

Final CISWI Rule (March 2011) $360 to $870 million $320 to $790 million $218 million 

Changes due to increase in scope 

(addition of 18 units) +$51 to $120 million +$46 to $110 million +$30 million 

Changes due to provision changes in this 

final reconsideration +$13 to $32 million +12 to $29 million +$10 million 

Net changes since final rule +$64 to $160 million +58 to $140 million $40 million 

Final CISWI Reconsideration $420 to $1,000 

million $380 to $930 million $258 million 

a

 These benefits do not include benefits associated with reduced exposure to HAP, direct exposure to SO2, visibility 

impairment, or ecosystem effects. These benefits reflect the final rule, which were 4% higher than shown in the 

RIA. 

b Minimum and maximum fuel savings reflect a range of fuel prices for the final reconsideration. These costs reflect 

the final rule, which were 22% lower than shown in the RIA. 

We estimated the total monetized benefits for the final CISWI RIA (March 2011) to be 

$340 million to $830 million at 3 percent discount rate and $310 million to $750 million at 7 

percent discount rate. However, EPA noted that the RIA did not incorporate the final engineering 

costs and emission reductions, which would decrease the engineering costs by approximately 

22% and increase the monetized benefits by approximately 4%. For this final reconsideration, we 

estimate the total monetized benefits to be $420 million to $1 billion at 3 percent discount rate 

and $380 million to $930 million at 7 percent discount rate. All estimates are in 2008$. 

Revised Economic Impacts

The market impact results are very similar to the results in the final rule RIA. The 

Agency’s economic model suggests average national price increases for industrial sectors are 

less than 0.001 percent, while average annual domestic production may fall by less than 0.001 

percent. Because of higher domestic prices, imports slightly rise by 0.001 percent and exports 

fall by 0.001 percent. The change in US surplus is now -258 million dollars (2006$). For the 

final rule RIA, the change in surplus was -283 million dollars (2006$). Table 3 provides the 

price, production, import, and export changes for this final reconsideration rule, which are very 

close to the estimated changes for the final rule RIA. 

 
4

Table 3. Price, Production, Import, and Export Changes Resulting from the Final CISWI 

Reconsideration 

Industry Sector U.S. Prices U.S. Production Imports U.S. Consumption Exports

Energy 0.001% -0.001% 0.002% 0.000% 0.000% 

Nonmanufacturing 0.004% -0.001% 0.003% 0.000% -0.003% 

Manufacturing 

 Food, beverages, and textiles 0.001% -0.001% 0.001% 0.000% 0.000% 

 Lumber, paper, and printing 0.020% -0.009% 0.021% -0.005% -0.014% 

 Chemicals 0.000% -0.001% 0.000% -0.001% 0.000% 

 Plastics and Rubber 0.000% -0.001% 0.000% -0.001% 0.000% 

 Nonmetallic Minerals 0.087% -0.020% 0.032% -0.012% -0.072% 

 Primary Metals 0.001% -0.001% 0.001% -0.001% -0.001% 

 Fabricated Metals 0.000% 0.000% 0.000% 0.000% 0.000% 

 Machinery and Equipment 0.000% 0.000% 0.000% 0.000% 0.000% 

 Electronic Equipment 0.000% 0.000% 0.000% 0.000% 0.000% 

 Transportation Equipment 0.000% 0.000% 0.000% 0.000% 0.000% 

Other 0.000% -0.001% 0.001% 0.000% 0.000% 

Wholesale and Retail Trade 0.000% 0.000% 0.000% 0.000% 0.000% 

Transportation Services 0.000% -0.001% 0.000% 0.000% 0.000% 

Other Services 0.000% 0.000% 0.000% 0.000% 0.000% 

The results for sales tests for small businesses are lower for the reconsideration final than 

those calculated for the final rule. The number of small entities affected by the rule dropped from 

nine to five. For the final rule, four of the nine had cost-to-sales percentages of more than 3 

percent. For the reconsideration final only one of the five had a cost-to-sales percentage of more 

than 3 percent and the other four had small savings. This is not a significant impact on a 

substantial number of small entities. 

The change in employment estimates between the final rule RIA and the reconsideration 

final is small. The estimated employment changes range between −500 to +1000 employees, 

with a central estimate of +300 employees for the final rule RIA. For the reconsideration final, 

the estimated employment changes range between −400 to +800 employees, with a central 

estimate of +200. 

Revised Benefits 

The health benefits were calculated using the methodology described in the final CISWI 

RIA (U.S. EPA, 2011) 1 using the revised emission reductions estimated for the final 

reconsideration. We were unable to estimate the benefits from reducing exposure to HAPs and 

ozone, ecosystem impairment, and visibility impairment, including reducing 20,000 tons of 

carbon monoxide, 780 tons of HCl, 2.5 tons of lead, 1.8 tons of cadmium, 680 pounds of 

                                                           

1

 U.S. Environmental Protection Agency (U.S. EPA). 2011. Regulatory Impact Analysis: Standards of Performance 

for New Stationary Sources and Emission Guidelines for Existing Sources: Commercial and Industrial Solid 

Waste Incineration Units. February. Available at 

http://www.epa.gov/ttnecas1/regdata/RIAs/CISWIRIAfinal110221_psg2.pdf 
5

mercury, and 58 grams of dioxins/furans. Please refer to the full description in the final CISWI 

RIA of the unquantified benefits as well as analysis limitations and uncertainties. These 

monetized benefits are approximately 18% higher than the final CISWI NSPS due to the 

increased emission reductions of PM2.5 and SO2. Since the reconsideration proposal, we have 

made several updates to the approach we use to estimate mortality and morbidity benefits in the 

PM NAAQS RIAs (U.S. EPA, 2012a,b)2,3, including updated epidemiology studies, health 

endpoints, and population data. Although we have not re-estimated the benefits for this rule to 

apply this new approach, these updates generally offset each other, and we anticipate that the 

rounded benefits estimated for this rule are unlikely to be different than those provided below. 

More information on these updates can be found in the PM NAAQS proposal RIA. We provide 

the benefits results in Tables 4 and 5 and Figure 2. We also provide the breakdown of monetized 

benefits by subcategory in Figure 1. 

Table 4: Summary of Monetized Benefits Estimates for the Final CISWI Reconsideration 

in 2015 (2008$) 

Pollutant 

Emissions 

Reductions 

(tons) 

Benefit 

per ton 

(Pope, 

3%) 

Benefit 

per ton 

(Laden, 

3%) 

Benefit 

per ton 

(Pope, 

7%) 

Benefit 

per ton 

(Laden, 

7%) 

Total Monetized 

Benefits (millions 

2008$ at 3%) 

Total Monetized 

Benefits (millions 

2008$ at 7%) 

Direct 

PM2.5 

917 $230,000 $560,

000 

$210,

000 

$500,

000 

$

210 o 

$5

10 

$

190 

t

o 

$

460 

PM2.5 Precursors 

SO2 6,262 $29,000 $72,000 $27,000 $65,000 $180 to $450 $170 to $410 

NO2 5,399 $4,900 $12,000 $4,400 $11,000 $26 to $64 $24 to $58 

 Total $420 to $1,000 $380 to $930 

*All estimates are for the implementation year (2015), and are rounded to two significant figures so numbers may 

not sum across columns. These models assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow 

differentiation of effect estimates by particle type. The benefit per ton estimates vary because each ton of precursor 

reduced has a different propensity to become PM2.5. The monetized benefits incorporate the conversion from 

precursor emissions to ambient fine particles. These estimates do not include benefits from reducing HAP emissions 

or ozone benefits. 

                                                           

2

 U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the Proposed Revisions 

to the National Ambient Air Quality Standards for Particulate Matter. EPA-452/R-12-003. Office of Air Quality 

Planning and Standards, Health and Environmental Impacts Division. June. Available at 

http://www.epa.gov/ttnecas1/regdata/RIAs/PMRIACombinedFile_Bookmarked.pdf. 3

 U.S. Environmental Protection Agency (U.S. EPA). 2012b. Regulatory Impact Analysis for the Final Revisions to 

the National Ambient Air Quality Standards for Particulate Matter. EPA-452/R-12-003. Office of Air Quality 

Planning and Standards, Health and Environmental Impacts Division. December. Available at 

http://www.epa.gov/pm/2012/finalria.pdf. 
6

Table 5: Summary of Estimated Reductions in Health Incidences from PM2.5 for the Final 

CISWI Reconsideration in 2015* 

Avoided Premature Mortality 

Pope et al. 47 

Laden et al. 120 

Avoided Morbidity 

Chronic Bronchitis 32 

Acute Myocardial Infarction 75 

Hospital Admissions, Respiratory 11 

Hospital Admissions, Cardiovascular 24 

Emergency Room Visits, Respiratory 45 

Acute Bronchitis 76 

Work Loss Days 6,200 

Asthma Exacerbation 830 

MRAD 37,000 

Lower Respiratory Symptoms 910 

Upper Respiratory Symptoms 680 

*All estimates are for the analysis year (2015) and are rounded to whole numbers with two significant figures. These 

models assume that all fine particles, regardless of their chemical composition, are equally potent in causing 

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

by particle type. These estimates do not include benefits from reducing HAP emissions and ozone exposure, nor 

energy disbenefits associated with the increased emissions from additional energy usage. 
7

Figure 1: Breakdown of Monetized Benefits by Subcategory 

Energy recover

unit ‐ solids

55%

Energy recovery

unit ‐ liquid/gas

11%

Incinerator

1%

Waste‐burning

kiln

33%
8

Figure 2: Total Monetized PM2.5 Benefits Estimates for the Final CISWI Reconsideration 

in 2015 

Revised Net Benefits 

Table 6 shows the estimated costs and benefits for the reconsideration final. The 

estimated net benefits are higher than the final rule RIA, which was $30 million to $470 million 

at 7 percent and was $60 million to $550 million at 3 percent. 

Table 6. Summary of Estimated Social Costs and Benefits 

Category Primary 

Estimate 

Low 

Estimate 

High 

Estimate 

Year 

Dollar 

Discount 

Rate 

Period 

Covered 

Benefits 

Annualized Monetized 

($millions/year) 

$380 $930 2008 7% 2015 

$420 $1,000 2008 3% 2015 

Costs 

Annualized Monetized 

($millions/year) 

$258 2008 7% 2015 

$258 2008 3% 2015 

Net Benefits 

Annualized Monetized 

($millions/year) 

$120 $670 2008 7% 2015 

$160 $770 2008 3% 2015 

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

Pope et al.

Laden et al.

Millions (2008$)

Benefits estimates derived from 2 epidemiology functions and 12 expert

functions

3% DR

7% DR
February 2011 

Regulatory Impact Analysis: 

Standards of Performance for New 

Stationary Sources and Emission 

Guidelines for Existing Sources: 

Commercial and Industrial Solid 

Waste Incineration Units 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Economics Group 

Risk and Benefits Group 

(MD-C439-02) 

Research Triangle Park, NC 27711 
iii 

CONTENTS 

Section Page

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

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

1.2 Organization of this Report .................................................................................. 1-2

2 Overview of the Engineering Cost Analysis ................................................................. 2-1

3 Economic Impact Analysis ........................................................................................... 3-1

3.1 Partial Equilibrium Analysis (Multiple Markets) ................................................ 3-1

3.1.1 Overview .................................................................................................. 3-1

3.1.2 Economic Impact Analysis Results ......................................................... 3-3

4 Small Entity Analyses ................................................................................................... 4-1

4.1 Small Entity Screening Analysis ......................................................................... 4-1

4.1.1 Small Businesses ...................................................................................... 4-1

4.1.2 Small Entity Analysis ............................................................................. 4-2

5 Human Health Benefits of Emissions Reductions ........................................................ 5-1

5.1 Synopsis ............................................................................................................... 5-1

5.2 Calculation of PM2.5 Human Health Benefits ...................................................... 5-2

5.3 Energy Disbenefits ............................................................................................. 5-13

5.3.1 Social Cost of Carbon and Greenhouse Gas Disbenefits ....................... 5-13

5.4 Unquantified Benefits ........................................................................................ 5-16

5.4.1 Carbon Monoxide Benefits .................................................................... 5-17

5.4.2 Other SO2 Benefits ................................................................................. 5-17

5.4.3 HAP Benefits ......................................................................................... 5-19
iv 

5.5 Characterization of Uncertainty in the Monetized PM2.5 Benefits ..................... 5-29

6 Comparison of Benefits and Costs ................................................................................ 6-1

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

Appendixes 

A: OAQPS Multimarket Model to Assess the Economic Impacts of 

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

B: Industry Profiles .......................................................................................................B-1
v 

LIST OF FIGURES 

Number Page

3-1. Distribution of Total Surplus Changes by Industry (Total Surplus 

Change = $224 million, 2008$) (MACT Floor) ........................................................ 3-6

3-2. Employment Impacts Using Morgenstern, Pizer, Shih (2002) (1,000 FTEs) .......... 3-10

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

Function from Pope et al. (2002) ............................................................................... 5-8

5-2. Total Monetized PM2.5 Benefits Estimates for the CISWI NSPS and EG in 

2016.......................................................................................................................... 5-12

5-3. Breakdown of Monetized Benefits Estimates for the CISWI NSPS and EG by 

PM2.5 Precursor Pollutant and Source (MACT floor option) ................................... 5-12

5-4. Breakdown of Monetized Benefits Estimates for the CISWI NSPS and EG by 

Subcategory (MACT floor option) .......................................................................... 5-13

5-5. Estimated County Level Carcinogenic Risk (from 2002 NATA) ........................... 5-21

5-6. Estimated County Level Noncancer (Respiratory) Risk (from 2002 NATA) ......... 5-21

5-7. Percentage of Adult Population by Annual Mean PM2.5 Exposure (pre- and 

post-policy policy) ................................................................................................... 5-31

5-8. Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels 

(pre- and post-policy policy) .................................................................................... 5-32

6-1. Net Benefits for the CISWI NSPS and Emissions Guidelines at 3% Discount 

Rate ............................................................................................................................ 6-3

6-2. Net Benefits for the CISWI NSPS and Emissions Guidelines at 7% Discount 

Rate ............................................................................................................................ 6-4
vi 

LIST OF TABLES 

Number Page

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

CISWI NSPS and Emissions Guidelines in 2016 (millions of 2008$) ...................... 1-4

2-1. Summary of Capital and Annual Costs for Existing CISWI Sources ........................ 2-2

3-1. Market-Level Price and Quantity Changes: 2016 (MACT Floor) ............................. 3-3

3-2. Market-Level Price and Quantity Changes: 2016 (Beyond the MACT Floor) ......... 3-4

3-3. Distribution of Social Costs (million, 2008$): 2016 .................................................. 3-5

3-4. Distribution of Social Costs (million, 2008$): 2016 .................................................. 3-5 

3-5. Employment Impacts Using Morgenstern, Pizer, Shih (2002) (FTE) ....................... 3-9 

4-1. Affected Sectors and Size Standards ......................................................................... 4-1

4-2. Sales Tests Using Small Companies Identified in the Combustion Survey .............. 4-2

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

5-2. Summary of Monetized Benefits Estimates for the CISWI NSPS and EG in 

2016 (2008$) .............................................................................................................. 5-9

5-3. Summary of Reductions in Health Incidences from PM2.5 Benefits for the 

CISWI NSPS and EG in 2016 ................................................................................. 5-10

5-4. All PM2.5 Benefits Estimates for the CISWI NSPS and EG at Discount Rates 

of 3% and 7% in 2016 (in millions of 2008$) ......................................................... 5-11

5-5. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2016 ..................... 5-15

5-6. Monetized SCC-derived Disbenefits of CO2 Emission Increases in 2016 

(millions of 2008$) .................................................................................................. 5-16

6-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

CISWI NSPS and EG in 2016 (millions of 2008$) ................................................... 6-2
1-1 

SECTION 1 

INTRODUCTION 

The U.S. Environmental Protection Agency (EPA) is promulgating new standards of 

performance and emission guidelines based on a review of the standards and guidelines as part of 

the Clean Air Act Section 129(a)(5) requirement to review the new source performance 

standards and emission guidelines every 5 years. Additionally, when revising the standards of 

performance and emission guidelines we considered the District of Columbia Circuit Court 

rulings on maximum achievable control technology standards that were issued after 

promulgation of the new source performance standards and emission guidelines for commercial 

and industrial solid waste incineration units in 2000 and a concurrently promulgated definition of 

nonhazardous secondary materials as solid waste under the Resource Conservation and Recovery 

Act. EPA is also promulgating other amendments that EPA believes are necessary to adequately 

address air emissions from commercial and industrial solid waste incineration units and to clarify 

certain portions of the rules. 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 RIA does not include the final engineering costs and emission reductions into this 

RIA (see Chapter 2 for more detail on the engineering costs that were not accounted for). We 

estimate that incorporating these final estimates would decrease the engineering costs by 

approximately 22% and increase the monetized benefits by approximately 4% from those shown 

in this RIA. 

1.1 Executive Summary 

The key results of the RIA are as follows: 

 Engineering Cost Analysis: EPA estimates the promulgated rule’s total annualized 

costs will be $280 million (2008$). 

 Market Analysis: Under the promulgated rule, the Agency’s economic model 

suggests the average national market-level variables (prices, production-levels, 

consumption, international trade) will not change significantly (e.g., are less than 

0.1%).

 Social Cost Analysis: The estimated social cost is approximately $280 million 

(2008$). In the near term, the Agency’s economic model suggests that industries are 

able to pass approximately $76 million of the rule’s costs to consumers (e.g., 

marginally higher market prices). Domestic industries’ surplus falls by $207 million, 

while other countries on net benefit from higher prices (a net increase in rest-of-the 
1-2 

world [ROW] surplus of $3 million). Additional new source costs not included in the 

economic model represent a net cost of less than $1 million.

 Employment Changes: The estimated employment changes range between −500 to 

1,000 employees, with a central estimate of +300 employees.

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

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

tests). EPA’s analysis found the tests exceeded 3% for four of nine small entities 

included in the screening analysis. After reviewing screening analysis results, EPA 

has determined the promulgated rule will not have a SISNOSE and presumes that rule 

is eligible for certification under the RFA as amended by SBREFA. We provide the 

factual basis for certification in Section 4.

 Benefits Analysis: The benefits from reducing some air pollutants have not been 

monetized in this analysis, including reducing a 25,000 tons of carbon monoxide, 470 

tons of HCl, 4.1 tons of lead, 0.95 tons of cadmium, 260 pounds of mercury, and 92 

grams of total dioxins/furans each year. We assess the benefits of these emission 

reductions qualitatively in this analysis. Thus, all monetized benefits reported reflect 

improvements in ambient PM2.5 concentrations. As such, although the monetized 

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

unclear. In the year of full implementation (2016), EPA estimates the monetized 

PM2.5 benefits of the promulgated NSPS and Emission Guidelines are $340 million to 

$830 million and $310 million to $750 million, at 3% and 7% discount rates 

respectively. All estimates are in 2008$. Using alternate relationships between PM2.5 

and premature mortality supplied by experts, higher and lower benefits estimates are 

plausible, but most of the expert-based estimates fall between these estimates. In 

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

analysis. 

 Net Benefits: The net benefits for the NSPS and Emission Guidelines are $60 million 

to $550 million and $30 million to $470 million, at 3% and 7% discount rates 

respectively (Table 1-1). All estimates are in 2008$ for the year 2016. These results 

are shown in Tables 1-1 

1.2 Organization of this Report 

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

EIA: 

 Section 2 describes the engineering cost analysis. 

 Section 3 describes the economic impact analysis. 

 Section 4 describes the small entity analyses. 

 Section 5 presents the benefits estimates. 

 Section 6 presents the net benefits. 
1-3 

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

 Appendix B describes the affected Industry profiles. 
1-4 

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

CISWI NSPS and Emissions Guidelines in 2016 (millions of 2008$)a

 3% Discount Rate 7% Discount Rate 

Option 1: MACT Floor 

Total Monetized Benefitsb $340 to $830 $310 To $750 

Total Social Costsc $280 $280 

Net Benefits $60 to $550 $30 To $470 

Non-monetized Benefits 25,000 tons of carbon monoxide 

470 tons of HCl 

260 pounds of mercury 

0.95 tons of cadmium 

4.1 tons of lead 

92 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

Visibility impairment 

Option 2: Beyond the Floor 

Total Monetized Benefitsb $430 to $1,100 $390 To $960 

Total Social Costsc $300 $300 

Net Benefits $130 to $770 $90 To $660 

Non-monetized Benefits 25,000 tons of carbon monoxide 

470 tons of HCl 

260 pounds of mercury 

0.95 tons of cadmium 

4.1 tons of lead 

92 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

 Visibility impairment 

a

 All estimates are for the implementation year (2016), and are rounded to two significant figures. These results 

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

b

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

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

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

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

their chemical composition, are equally potent in causing premature mortality because the scientific evidence is 

not yet sufficient to allow differentiation of effect estimates by particle type. These estimates include energy 

disbenefits valued at $3.8 million. 

c

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

results in the same social costs for both discount rates. 
2-1 

SECTION 2 

OVERVIEW OF THE ENGINEERING COST ANALYSIS 

This RIA does not incorporate the final engineering costs and emission reductions. We 

estimate that incorporating these final estimates would decrease the engineering costs by 

approximately 22% and increase the monetized benefits by approximately 4% from those shown 

in this RIA. The changes made that were not included in the RIA are the following: 

 The cost estimates for existing units were revised to reflect removal of several units from the 

CISWI population, including one energy recovery unit, several cyclonic burn barrels, and a few 

cement kilns. 

 -For the remaining kilns, we identified ones that will likely install regenerative thermal oxidizers to 

meet the NESHAP limits, and therefore should not have these costs repeated in the CISWI cost 

estimates. 

This section provides an overview of the engineering cost analysis used to estimate the 

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 “Revised Compliance Cost 

Analysis for Existing and New CISWI Units” in the EPA-HQ-OAR-2003-0119 Docket. Note 

that there were some very late adjustments made to the CISWI inventory and carbon monoxide 

test data that were performed shortly before signature of the final package. These adjustments 

affected the bottom-line cost estimate of the final rules. The cost memorandum in the docket 

reflects the results of the late inventory and data revisions and the resulting costs. However, there 

was insufficient time to revise the RIA to reflect these late revisions, so the cost totals presented 

below differ from those presented in “Revised Compliance Cost Analysis for Existing and New 

CISWI Units” in the EPA-HQ-OAR-2003-0119 Docket. 

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

maximum three-run average value for each pollutant measured in tests (i.e., the highest result of 

an emission test performed) of each unit in the inventory to its corresponding emission limit to 

determine which, if any, control devices would be needed in order to comply with the standards. 

In order to fill data gaps for units having no emissions data, these maximum test averages were 

averaged over each subcategory to develop an emission factor for each pollutant and each 

subcategory. If identical units were operated at the facility but emissions data were available for 

only one unit, the emissions data from the unit with data were applied to the identical units. For 

the remaining units that did not report emissions data, we assigned the appropriate emission 

factor for the units in that subcategory1

. The control analysis considered fabric filters to be the 

 

1

 Data gaps varied by pollutant, but overall over all units and all nine pollutants, about 28% of the baseline emission 

concentration data gaps were filled using average emission factors. 
2-2 

primary control device for particulate matter, cadmium, and lead control; packed bed scrubbers 

and dry sorbent injection for hydrogen chloride and sulfur dioxide control; activated carbon 

injection for mercury and dioxins/furans control; selective non-catalytic reduction (SNCR) for 

oxides of nitrogen; and afterburner retrofits, regenerative thermal oxidizers, tune-ups, advanced 

combustion controls, and oxidation catalysts for carbon monoxide controls. We also considered 

whether existing control devices could be improved to achieve the limits, such as adding more 

lime to duct sorbent injection systems to meet the sulfur dioxide limits. We included costs for 

testing and monitoring requirements contained in the rule. Finally, we analyzed the costs of 

waste segregation practices and alternative disposal options, such as diverting waste to a landfill 

to see if less expensive options to incineration were available. In certain cases, such as 

incinerators, our data suggest that sending waste to a landfill may likely be less costly than 

operating and maintaining an incineration unit. The resulting total national cost impact of the 

rule is 706 million dollars in capital expenditures and 280 million dollars per year in total annual 

costs. The total capital and annual costs include costs for control devices, work practices, testing 

and monitoring. Costs include testing and monitoring costs, as well as estimated recordkeeping 

and reporting costs. Based on the cost to comply with the rule, availability of alternatives to 

incineration, and historic negative growth in this source category, we anticipate only one new 

incineration unit within the next five years, with an annual compliance cost of $829,500, and up 

to five new small remote incinerators at an annual cost of $351,000 per unit2

. 

Table 2-1. Summary of Capital and Annual Costs for Existing CISWI Sources 

Subcategory 

Number of 

Affected Units 

Capital Costs 

(millions of 2008$) 

Annualized Costs 

(millions of 2008$) 

Energy Recovery Units – Solids 31 507.6 194.3 

Energy Recovery Units – 

Liquid/Gas 

6 32.3 7.6 

Cement Kilns 16 157.7 73.7 

Incinerators 28 5.7 3.3 

Small remote incinerators 19 2.8 1.2 

Based on this analysis, EPA anticipates an overall total capital investment of $706 

million plus the cost of recordkeeping and reporting required by this rule, with an associated total 

 

2

 For new units, EPA fully anticipates new kilns and boilers would choose fuels in such a way that would not subject 

to CISWI regulation. The only new sources are facilities that dispose of waste and only have incineration options 

available. 
2-3 

annual cost of $280 million plus recordkeeping and reporting costs. The requirements result in 

industry recordkeeping and reporting burden associated with reviewing the amendments for all 

CISWI and inspections of scrubbers, fabric filters, and other air pollution control devices that 

may be used to meet the emission limits for all CISWI. Ongoing parametric monitoring 

requirements for ESPs, SNCR, dry sorbent injection and activated carbon injection are also 

required of all CISWI units. Stack testing and development of new parameter limits would be 

necessary for CISWI that need to make performance improvements to meet the emission limits 

and for CISWI that, prior to this action, have not been required to demonstrate compliance with 

certain pollutants. Visual emissions tests of ash handling would be required for all subcategories 

except kilns on an annual basis. Energy recovery units would be required to continuously 

monitor opacity, and units larger than 250 MMBtu/hr would be required to monitor PM 

emissions using a PM CEMS. Kilns would be required to continuously monitor Hg emissions 

using an Hg CEMS. Any new CISWI would also be required to continuously monitor CO 

emissions. Annualized capital/startup costs and operation and maintenance (O&M) costs are 

associated with the EG monitoring requirements, EPA Method 22 of Appendix A-7 testing, 

initial stack testing, storage of data and reports, and photocopying and postage. 
3-1 

SECTION 3 

ECONOMIC IMPACT ANALYSIS 

EPA prepares an EIA to provide a measure of the social costs of using resources to 

comply with a program (U.S. EPA, 2000). The social costs can then be compared with estimated 

social benefits (as presented in Section 5). As noted in EPA’s (2000) Guidelines for Preparing 

Economic Analyses, several tools are available to estimate social costs and range from simple 

direct compliance cost methods to the development of a more complex market analysis that 

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

changes in consumer and producer surplus). 

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

analysis as described in the Office’s resource manual (U.S. EPA, 1999). The approach uses a 

single-period multimarket partial equilibrium model to compare pre-policy market baselines with 

expected post-policy market outcomes. The analysis’ time horizon is the short run; for this 

analysis, we use the model to approximate baseline conditions for 2016. In this analysis, some 

production factors are fixed and some are variable and is distinguished from the very short run 

where all factors are fixed and producers cannot adjust inputs or outputs (U.S. EPA, 1999, 5-6). 

The intermediate time horizon allows us to capture important transitory stakeholder outcomes. 

Key measures in this analysis include industry-level changes in price levels, production and 

consumption, jobs, international trade, and social costs (changes in producer and consumer 

surplus). 

3.1 Partial Equilibrium Analysis (Multiple Markets) 

The partial equilibrium analysis develops a market model that simulates how 

stakeholders (consumers and industries) might respond to the additional regulatory program 

costs. In this section, we provide an overview of the economic model used in the analysis. 

3.1.1 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 (U.S. 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 (U.S. EPA, 1999). Recent studies suggest short-run analyses can 

complement full dynamic general equilibrium analysis. For example, Morgenstern and 
3-2 

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

and colleagues, 2004; Ho, Morgenstern, and Shih, 2008). Aldy and Pizer (2009) assess near-term 

competitiveness effects of a domestic cap-and-trade program to address stakeholder concerns 

about shifts in economic activity and jobs to other countries. A single-period multimarket partial 

equilibrium model contains the following features: 

 Industry sectors and benchmark data set 

– All industries aggregated to 100 industry sectors 

– a single benchmark year (2010)1

– estimates of industry employment 

 Economic behavior 

– industries respond to regulatory costs by changing production rates 

– market prices rise to reflect higher energy and other non-energy material costs 

– customers respond to these price increases and consumption falls 

 Model scope 

– 100 sectors are linked with each other based on their use of energy and other nonenergy materials. For example, the construction industry is linked with the 

petroleum, cement, and steel industries and is influenced by price changes that 

occur in each sector. The links allow EPA to account for indirect effects the 

regulation has on related markets. 

– production adjustments influence employment levels 

– international trade (imports/exports) behavior considered 

 Model time horizon (“short run”) 

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

supply function 

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

production inputs 

– price of labor (i.e., wage) is fixed 

– investment and government expenditures are fixed 

– Appendix A provides additional details on the behavioral assumptions, data, 

parameters, and model equations. 

 

1

 For this analysis, we use the model to approximate baseline conditions for 2016. 
3-3 

3.1.2 Economic Impact Analysis Results 

3.1.2.1 Market-Level Results 

Market-level impacts include price and quantity adjustments including the changes in 

international trade (Table 3-1). The Agency’s economic model suggests the average national 

market-level variables (prices, production-levels, consumption, international trade) will not 

significantly change (e.g., are less than 0.1%). Similar results are present for the Beyond the 

MACT floor option and are presented in Table 3-2. 

Table 3-1. Market-Level Price and Quantity Changes: 2016 (MACT Floor) 

Industry Sector Prices Production Imports Consumption Exports 

Energy 0.00243% −0.00086% 0.00508% −0.00064% −0.00040%

Nonmanufacturing 0.00239% −0.00056% 0.00151% −0.00041% −0.00184%

Manufacturing 

Food, beverages, and textiles 0.00068% −0.00093% 0.00114% −0.00051% −0.00042%

Lumber, paper, and printing 0.03014% −0.01259% 0.03070% −0.00727% −0.02103%

Chemicals 0.00010% −0.00136% 0.00014% −0.00105% −0.00010%

Plastics and Rubber 0.00046% −0.00110% 0.00049% −0.00088% −0.00045%

Nonmetallic Minerals 0.04871% −0.01153% 0.01782% −0.00720% −0.04026%

Primary Metals 0.00059% −0.00099% 0.00057% −0.00058% −0.00056%

Fabricated Metals −0.00015% −0.00041% −0.00015% −0.00035% 0.00008%

Machinery and Equipment −0.00007% −0.00028% −0.00006% −0.00019% 0.00012%

Electronic Equipment −0.00004% −0.00043% −0.00001% −0.00020% 0.00010%

Transportation Equipment 0.00002% −0.00019% 0.00004% −0.00011% −0.00004%

Other 0.00058% −0.00130% 0.00098% −0.00044% −0.00054%

Wholesale and Retail Trade −0.00027% −0.00018% −0.00018% −0.00018% 0.00020%

Transportation Services −0.00052% −0.00045% −0.00026% −0.00031% 0.00043%

Other Services 0.00001% −0.00018% −0.00006% −0.00018% −0.00002%
3-4 

Table 3-2. Market-Level Price and Quantity Changes: 2016 (Beyond the MACT Floor) 

Industry Sector Prices Production Imports Consumption Exports 

Energy 0.00254% −0.00090% 0.00531% −0.00068% −0.00042%

Nonmanufacturing 0.00243% −0.00058% 0.00153% −0.00043% −0.00187%

Manufacturing 

Food, beverages, and textiles 0.00071% −0.00096% 0.00118% −0.00053% −0.00044%

Lumber, paper, and printing 0.03158% −0.01319% 0.03217% −0.00761% −0.02204%

Chemicals 0.00022% −0.00156% 0.00027% −0.00118% −0.00021%

Plastics and Rubber 0.00064% −0.00124% 0.00069% −0.00097% −0.00063%

Nonmetallic Minerals 0.04872% −0.01157% 0.01783% −0.00723% −0.04027%

Primary Metals 0.00093% −0.00128% 0.00089% −0.00071% −0.00088%

Fabricated Metals −0.00010% −0.00045% −0.00008% −0.00038% 0.00005%

Machinery and Equipment −0.00006% −0.00031% −0.00004% −0.00021% 0.00009%

Electronic Equipment −0.00004% −0.00046% −0.00001% −0.00022% 0.00010%

Transportation Equipment 0.00003% −0.00020% 0.00006% −0.00012% −0.00006%

Other 0.00063% −0.00140% 0.00107% −0.00047% −0.00059%

Wholesale and Retail Trade −0.00029% −0.00019% −0.00020% −0.00019% 0.00022%

Transportation Services −0.00055% −0.00047% −0.00028% −0.00033% 0.00046%

Other Services 0.00001% −0.00018% −0.00006% −0.00018% −0.00002%

3.1.2.2 Social Cost Estimates 

In the short run, 2016, industries are able to pass on $76 million (2008$) the costs to U.S. 

households in the form of higher prices (Table 3-3). In 2016, existing U.S. industries’ surplus 

falls by $207 million, and the net loss for U.S. stakeholders is $283 million. As U.S. prices rise, 

other countries are affected through international trade relationships. Households that buy goods 

from the United States experience losses, while industries that sell goods to the United States 

benefit; the model estimates a net gain of $3 million. After accounting for international trade 

effects, the Agency’s economic model projects the net total surplus loss associated with the rule 

is $280 million. Similar results are present for the Beyond the MACT floor option (Table 3-4). 

As shown in Figure 3-1, the surplus losses are concentrated in lumber, paper, and printing 

(29.0%) and other services (21.8%). The Agency also considered other elements of the 

engineering cost analysis that could not be modeled within the multimarket model (e.g., total 

annualized cost for new sources). The net effect of the adjustments is approximately $1 million. 
3-5 

Table 3-3. Distribution of Social Costs (million, 2008$): 2016 

Method MACT Floor 

Partial Equilibrium Model (Multiple Markets) 

Change in U.S. consumer surplus −$76 

Change in U.S. producer surplus −$207

Change in U.S. surplus −$283 

Direct Compliance Costs Method 

Total annualized costs, new sources (not modeled) $1 

Change in U.S. Surplus −$284 

Net change in rest of world surplus $3

Net change in total surplus −$281 

Table 3-4. Distribution of Social Costs (million, 2008$): 2016 

Method Beyond the MACT Floor 

Partial Equilibrium Model (Multiple Markets) 

Change in U.S. consumer surplus −$78 

Change in U.S. producer surplus −$220

Change in U.S. surplus −$298 

Direct Compliance Costs Method 

Total annualized costs, new sources (not modeled) $1 

Total annualized cost savings, unknown sources (not modeled) Less than $1 million 

Change in Total Surplus −$299 

Net change in rest of world surplus $4

Net change in total surplus −$295 
3-6 

29.0%

21.8%

11.2%

9.4%

8.7%

6.8%

4.4%

1.5%

1.3%

1.3%

1.2%

0.9%

0.8%

0.8%

0.5%

0.5%

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

Lumber,paper, and printing

Other Services

Nonmetallic Minerals

Nonmanufacturing

Energy Industries

Chemicals

Food, beverages, and textiles

Primary Metals

Wholesale and Retail Trade

Other

Plastics and Rubber

Transportation Equipment

Electronic Equipment

Transportation Services

Fabricated Metals

Machinery and Equipment

Figure 3-1. Distribution of Total Surplus Changes by Industry (Total Surplus 

Change = $224 million, 2008$) (MACT Floor) 

3.1.2.3 Job Effects 

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

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

13563, however, states, “Our regulatory system must protect public health, welfare, safety, and 

our environment while promoting economic growth, innovation, competitiveness, and job 

creation” (emphasis added). Therefore, we have provided this analysis to inform the discussion 

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

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

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

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

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

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

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

to regulation may have a stimulative effect that results in a net increase in overall employment. 
3-7 

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

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

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

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

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

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

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

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

prices. 

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

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

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

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

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

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

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

 Direct Effects:

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

Environmental regulations increase production costs causing firms to increase 

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

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

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

– Regulated firms demand labor workers to operate and maintain pollution 

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

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

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

on whether the operation and maintenance of pollution controls are labor 

intensive 

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

regulation requiring emission reductions is promulgated, affected sources must 

immediately place orders for pollution control equipment and services. Filling 

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

equipment, performance of engineering analyses and significant expenditures for 

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

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

pollution control equipment must be installed and operating. 
3-8 

. 

Indirect and Induced Effects: 

– Environmental regulations create employment in many basic industries. In 

addition to the increase in employment in the environmental protection industry 

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

create employment in industries that provide intermediate goods to the environmental 

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

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

order steel, tanks, vessels, blowers, pumps, and chemicals to manufacture and 

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

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

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

employment impacts “induced” by increased output of the environmental 

protection sector, or decreased output of the regulated sectors. 

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

empirically derived relationship reported in Morgenstern, Pizer and Shih (2002), a peerreviewed, published study. Estimates of the employment impacts of the capital investments and 

other non-recurring requirements of the rule are derived from the cost analysis developed for the 

regulation. 2

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

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

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

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

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

products. Satisfying this new demand requires additional inputs, including labor, and may alter 

the relative proportions of labor and capital used by regulated firms in their production 

processes. 

Thus, Morgenstern et al., decompose the overall effect of a regulation on employment 

into the following three subcomponents: 

 The “Demand Effect”: higher production costs raise market prices, reducing 

consumption (and production), thereby reducing demand for labor within the 

regulated industry 3;

 

2

 Richard D. Morgenstern, William A. Pizer, and Jhih-Shyang Shih, Journal of Environmental Economics and 

Management | May 2002 | Vol. 43, no. 3 | pp. 412-436. 3

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

reductions in output.. 
3-9 

 The “Cost Effect”: As production costs increase, plants use more of all inputs, 

including labor, to maintain a given level of output. For example, in order to reduce 

pollutant emissions while holding output levels constant, regulated firms may require 

additional labor; 

 The “Factor-Shift Effect”: Regulated firms’ production technologies may be more or 

less labor intensive after complying with a regulation (i.e., more/less labor is required 

per dollar of output). 

Decomposing the overall employment impact of environmental regulation into three 

subcomponents clarifies the conceptual relationship between environmental regulation and 

employment in regulated sectors, and permitted Morgenstern, et al. to provide an empirical 

estimate of the net impact. For present purposes, the net effect is of particular interest, and is the 

focus of our analysis. 

Morgenstern, Pizer and Shih (2002): Empirical Results 

Morgenstern et al. empirically estimate a model for four highly polluting, regulated industries 

(pulp and paper, plastics, petroleum refining and steel) to examine the effect of higher abatement 

costs from regulation on employment. They conclude that increased abatement expenditures 

generally do not cause a significant change in employment. More specifically, their results show 

that, on average across the four industries, each additional $1 million spending on pollution 

abatement results in a (statistically insignificant) net increase of 1.55 (+/- 2.24) jobs.4

 “In plastics 

and petroleum, [Morgenstern et al] find that increased regulation raises employment by a small 

but statistically significant amount: 6.9 and 2.2 jobs per million dollars of regulatory expense, 

respectively. In pulp and paper and steel, the estimates are even smaller and insignificantly 

different from zero.”5 By applying these estimates to pollution abatement costs, we estimated the 

net employment effect for major and areas sources to range from -4,100 to +8,500 jobs in the 

directly affected sectors with a central estimate of +2,200 (Table 4-3). 6, 7 

 

4

 These results are similar to Berman and Bui, who find that while sharply increased air quality regulation in Los 

Angeles to reduce NOx emissions resulted in large abatement costs they did not result in substantially reduced 

employment. "Environmental regulation and labor demand: evidence from the South Coast Air Basin." Journal 

of Public Economics 79(2): 265-295. 5

 Morgenstern, Pizer and Shih, p. 413. 6

 Since Morgenstern’s analysis reports environmental expenditures in $1987, we make an inflation adjustment the 

engineering cost analysis using GDP implicit price deflator (64.76/108.48) = 0.60) 7

 Net employment effect = 1.55× $2,400 million × 0.60 
3-10 

Table 3-5. Employment Impacts Using Morgenstern, Pizer, Shih (2002) (FTE) 

 Demand Effect Cost Effect Factor Shift Effect Net Effect 

Change in Full-Time Jobs 

per Million Dollars of 

Environmental Expenditurea

−3.56 2.42 2.68 1.55 

Standard Error 2.03 1.35 0.83 2.24 

EPA estimate −600 

−1,300 to +100 

400 

+100 to +700 

500 

Less than 100 to 

+900 

300 

−500 to +1,000 

a Expressed in 1987 dollars. See footnote 7 for inflation adjustment factor used in the analysis. 

‐0.59

0.40 0.45

0.26

‐5.0

‐4.5

‐4.0

‐3.5

‐3.0

‐2.5

‐2.0

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1,000 Jobs

Base=144,100

DemandEffect: Estimate derivedfromMorgenstern, et al. CostEffect:  Estimate derivedfrom Morgenstern, etal.

Factor Shift Effect: Estimate derivedfrom Morgenstern, etal. NetEffect: Estimate derivedfromMorgenstern, et al.

Figure 3-2. Employment Impacts Using Morgenstern, Pizer, Shih (2002) (1,000 FTEs) 

Limitations of the Analysis 

Although the Morgenstern et al. paper provides information about the potential job effects of 

environmental protection programs, there are several caveats associated with using those 

estimates to analyze the final rule. First, the Morgenstern et al. estimates presented in Table 4-3 
3-11 

and used in EPA’s analysis represent the weighted average parameter estimates for a set of 

manufacturing industries (pulp and paper, plastics, petroleum, and steel). This set of industries 

only partially overlaps with the sectors affected by this rule. Second, relying on Morgenstern et 

al. implicitly assumes that estimates derived from 1979–1991 data are still applicable. Third, the 

methodology used in Morgenstern et al. assumes that regulations affect plants in proportion to 

their total costs. In other words, each additional dollar of regulatory burden affects a plant by an 

amount equal to that plant's total costs relative to the aggregate industry costs. By transferring 

the estimates, EPA assumes a similar distribution of regulatory costs by plant size and that the 

regulatory burden does not disproportionately fall on smaller or larger plants. Further, 

Morgenstern et al. does not include most indirect effects and all induced effects. 
4-1 

SECTION 4 

SMALL ENTITY ANALYSES 

The RFA as amended by SBREFA generally requires an agency to prepare a regulatory 

flexibility analysis of any rule subject to notice and comment rulemaking requirements under the 

Administrative Procedure Act or any other statute, unless the agency certifies that the rule will 

not have a significant economic impact on a substantial number of small entities (SISNOSE). 

Small entities include small businesses, small governmental jurisdictions, and small not-forprofit enterprises. EPA assessed the potential small entity economic impacts using a screening 

analysis. After reviewing screening analysis results, EPA has determined the promulgated rule 

will not have a SISNOSE and presumes that rule is eligible for certification under the RFA as 

amended by SBREFA. We provide the factual basis for certification below. 

4.1 Small Entity Screening Analysis 

4.1.1 Small Businesses 

The sectors covered by the rule were identified through lists of small entities provided by 

the engineering analysis. Table 4-1 provides a list of the sectors affected (3-digit NAICS) and the 

range of SBA size definitions. 

Table 4-1. Affected Sectors and Size Standards 

2007 

NAICS Description 

Size Standard 

(Effective August 22, 2008) 

221 Utilities a

311 Food Manufacturing 500 to 1,000 employees 

321 Wood Product Manufacturing 500 employees 

322 Paper Manufacturing 500 to 750 employees 

324 Petroleum and Coal Products Manufacturing Typically 500 to 1,500 employees 

325 Chemical Manufacturing 500 to 1,000 employees 

326 Plastics and Rubber Products Manufacturing Typically 500 to 1,000 employees 

337 Furniture and Related Product Manufacturing 500 employees 

562 Waste Management and Remediation Services Typically $7 to $14 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. 
4-2 

4.1.2 Small Entity Analysis 

Using the information collection effort for facilities with combustion units in the major 

source Boiler rule, we identified affected facility names listed as small, traced the ultimate parent 

company name to verify the facility was owned by a small business, and collected the most 

recent parent company sales and employment figures. As Table 4-2 shows, the average cost-tosales ratios for small companies are approximately 3.4% and 3.5% for the MACT Floor and 

Beyond the MACT Floor options. The median ratios are 2.2% for the MACT Floor option and 

2.3% for the Beyond the MACT Floor option. Each option has 4 entities that have sales test that 

exceeds 3%. 

Table 4-2. Sales Tests Using Small Companies Identified in the Combustion Survey 

Sample Statistic MACT Floor 

Beyond the MACT 

Floor 

Mean 3.4% 3.5% 

Median 2.2% 2.3% 

Maximum 17.3% 17.3% 

Minimum −2.4% −2.5% 

Ultimate parent company observations 10 10 

Ultimate parent company observations with sales 

data 

9 9 

Ultimate parent companies with Sale Tests 

Exceeding 3% 

4 4 

We estimate that there are 88 entities subject to this regulation, of which 10 of them are 

considered to be small companies. The small entities directly regulated by the rule are facilities 

engaged in industrial or commercial operations, such as paper and paperboard manufacturing and 

utility providers. The average cost-to-sales ratios for small companies are below 3.5 percent. 

The median ratio is 2.2 percent. Only four entities, which are in 3 different industries, have a 

sales test that exceeds 3 percent. For the purposes of this rulemaking, four is not considered a 

"substantial number" of small entities. 

Although this rule will not have a significant economic impact on a substantial number of 

small entities, EPA nonetheless has tried to reduce the impact of this rule on small entities. 
5-1 

SECTION 5 

HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS 

The RIA does not include the final engineering costs and emission reductions into this 

RIA (see Chapter 2 for more detail on the engineering costs that were not accounted for). We 

estimate that incorporating these final estimates would decrease the engineering costs by 

approximately 22% and increase the monetized benefits by approximately 4% from those shown 

in this RIA. 

5.1 Synopsis 

In this section, we provide an estimate of the monetized benefits associated with reducing 

particulate matter (PM) for the CISWI NSPS and EG. For this rule, the PM reductions are the 

result of emission limits on PM, emission limits on PM2.5 precursors such as NOx and SO2, as 

well as ancillary reductions from emission limits on other pollutants. The latter are often referred 

to as “co-benefits.” The total PM2.5 reductions are the consequence of the technologies installed 

or waste diversion to meet these multiple limits. These estimates reflect the monetized human 

health benefits of reducing cases of morbidity and premature mortality among populations 

exposed to the PM2.5 precursors reduced by this rulemaking. Because we were unable to 

monetize the direct benefits associated with reducing HAPs, the monetized benefits estimate is 

an underestimate of the total benefits. The extent of this underestimate, whether small or large, is 

unknown. Using a 3% discount rate, we estimate the total monetized benefits of the CISWI 

NSPS and EG (MACT floor option) to be $340 million to $830 million in the implementation 

year (2016). Using a 7% discount rate, we estimate the total monetized benefits of the CISWI 

NSPS and EG (MACT floor option) to be $310 million to $750 million in the implementation 

year. All estimates are in 2008$ and include any energy disbenefits from additional energy 

usage. Due to last minute changes, we were unable to incorporate the final emission reductions 

into this RIA. We estimate that incorporating these final estimates would increase the monetized 

benefits by approximately 4% from those shown in this RIA. 

These estimates reflect EPA’s most current interpretation of the scientific literature (U.S. 

EPA, 2009b). Higher or lower estimates of benefits are possible using other assumptions; 

examples of this are provided in Figure 5-2. Methodological and time limitations under the 

court-ordered schedule 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 other air pollutants have not been 

monetized in this analysis, including reducing 25,000 tons of carbon monoxide, 470 tons of HCl, 
5-2 

4.1 tons of lead, 0.95 tons of cadmium, 260 pounds of mercury, and 92 grams of total 

dioxins/furans each year. We assess the benefits of these emission reductions qualitatively in this 

analysis. 

5.2 Calculation of PM2.5 Human Health Benefits 

This rulemaking would reduce emissions of PM2.5, SO2, and NO2. Because SOx and NO2

are also precursors to PM2.5, reducing these emissions would also reduce PM2.5 formation, human 

exposure, and the incidence of PM2.5-related health effects. For this rule, the PM reductions are 

the result of emission limits on PM, emission limits on PM2.5 precursors such as NOx and SO2, as 

well as ancillary reductions from emission limits on other pollutants. The total PM2.5 reductions 

are the consequence of the technologies installed or waste diversion to meet these multiple 

limits. Due to analytical limitations, it was not possible to provide a comprehensive estimate of 

PM2.5-related benefits. Instead, we used the “benefit-per-ton” approach to estimate these benefits 

based on the methodology described in Fann, Fulcher, and Hubbell (2009). The key assumptions 

are described in detail below. These PM2.5 benefit-per-ton estimates provide the total monetized 

human health benefits (the sum of premature mortality and premature morbidity) of reducing one 

ton of PM2.5 (or precursor) from a specified source. EPA has used the benefit per-ton technique 

in several previous RIAs, including the recent SO2 NAAQS RIA (U.S. EPA, 2010b). Table 5-1 

shows the quantified and unquantified benefits captured in those benefit-per-ton estimates. 

Table 5-1. Human Health and Welfare Effects of PM2.5 

Pollutant / 

Effect 

Quantified and Monetized 

in Primary Estimates 

Unquantified Effects 

Changes in: 

PM2.5 Adult premature mortality 

Bronchitis: chronic and acute 

Hospital admissions: respiratory and 

cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial infarction) 

Lower and upper respiratory illness 

Minor restricted-activity days 

Work loss days 

Asthma exacerbations (asthmatic population) 

Infant mortality 

Subchronic bronchitis cases 

Low birth weight 

Pre-term births 

Pulmonary function 

Nonfatal cardiovascular outcomes other than 

myocardial infarctions 

Chronic respiratory diseases other than chronic 

bronchitis 

Non-asthma respiratory emergency room visits 

Visibility 

Household soiling 

Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the PM2.5 benefits 

estimates utilize the concentration-response functions as reported in the epidemiology literature, 

as well as the 12 functions obtained in EPA’s expert elicitation study as a sensitivity analysis. 
5-3 

 One estimate is based on the concentration-response (C-R) function developed from 

the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope 

et al. (2002), a study that EPA has previously used to generate its primary benefits 

estimate. When calculating the estimate, EPA applied the effect coefficient as 

reported in the study without an adjustment for assumed concentration threshold of 

10 μg/m3 as was done in recent (2006–2009) Office of Air and Radiation RIAs. 

 One estimate is based on the C-R function developed from the extended analysis of 

the Harvard Six Cities cohort, as reported by Laden et al. (2006). This study, 

published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS, has 

been used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 benefits 

estimates in RIAs completed since the PM2.5 NAAQS. When calculating the estimate, 

EPA applied the effect coefficient as reported in the study without an adjustment for 

assumed concentration threshold of 10 μg/m3

 as was done in recent (2006–2009) 

RIAs. 

 Twelve estimates are based on the C-R functions from EPA’s expert elicitation study 

(IEc, 2006; Roman et al., 2008) on the PM2.5 -mortality relationship and interpreted 

for benefits analysis in EPA’s final RIA for the PM2.5 NAAQS. For that study, twelve 

experts (labeled A through L) provided independent estimates of the PM2.5 -mortality 

concentration-response function. EPA practice has been to develop independent 

estimates of PM2.5 -mortality estimates corresponding to the concentration-response 

function provided by each of the twelve experts, to better characterize the degree of 

variability in the expert responses. 

Readers interested in reviewing the general methodology for creating the benefit-per-ton 

estimates used in this analysis should consult Fann, Fulcher, and Hubbell (2009). As described in 

Fann, Fulcher, and Hubbell (2009), benefit-per-ton estimates are developed for selected 

pollutant/source category combinations. The per-ton values calculated therefore apply only to 

tons reduced from those specific pollutant/source combinations (e.g., SO2 emitted from electric 

generating units; NO2 emitted from mobile sources). In this analysis, we apply the national 

average benefit-per-ton estimate for a 2016 analysis year and multiply it by the corresponding 

emission reductions of directly emitted PM2.5, SO2, and NOx to quantify the benefits of this rule. 

The benefit-per-ton estimates found in Fann, Fulcher, and Hubbell (2009) reflect a specific set of 

key assumptions and input data. As we update these underlying assumptions to reflect the 

scientific literature, we re-estimate the benefit-per-ton estimates and post the updated estimates 

at http://www.epa.gov/air/benmap/bpt.html. In addition, we adjust these estimates to match the 

currency year for the costs in this analysis. 

These models assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type (U.S. EPA, 2009b). Directly 
5-4 

emitted PM, SO2, and NOx are the dominant PM2.5 precursors affected by this rule. Even though 

we assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary 

between precursors because each ton of precursor reduced has a different propensity to form 

PM2.5 and a different pattern of transport, resulting geographic distribution of exposure. When 

more people are exposed, the benefits per ton are greater. For example, SO2 tends to have a 

lower benefit-per-ton estimate than direct PM2.5 because sulfate particles formed from SO2

emissions can transport many miles, meaning that higher exposures may occur over areas with 

low populations. On the other hand, to the extent that direct PM2.5 emissions occur in high 

density population areas, exposures will tend to be higher there, leading to higher monetized 

health benefits for direct PM2.5 than for SO2 emissions. The benefit-per-ton coefficients in this 

analysis were derived using modified versions of the health impact functions used in the PM 

NAAQS Regulatory Impact Analysis. Specifically, this analysis uses the benefit-per-ton method 

first applied in the Portland Cement NESHAP RIA (U.S. EPA, 2009a), which incorporated three 

updates: a new population dataset, an expanded geographic scope of the benefit-per-ton 

calculation, and the functions directly from the epidemiology studies without an adjustment for 

an assumed threshold.1

 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 the Portland 

Cement proposal, and we now calculate incremental benefits down to the lowest modeled PM2.5 

air quality levels. 

EPA strives to use the best available science to support our benefits analyses, and we 

recognize that interpretation of the science regarding air pollution and health is dynamic and 

evolving. Based on our review of the current body of scientific literature, EPA now estimates 

PM-related mortality without applying an assumed concentration threshold. EPA’s Integrated 

Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by 

EPA’s Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 

2009b), concluded that the scientific literature consistently finds that a no-threshold log-linear 

model most adequately portrays the PM-mortality concentration-response relationship while 

recognizing potential uncertainty about the exact shape of the concentration-response function. 

Since then, the Health Effects Subcommittee (U.S. EPA-SAB, 2010) of EPA’s Council 

concluded, “The HES fully supports EPA’s decision to use a no-threshold model to estimate 

mortality reductions. This decision is supported by the data, which are quite consistent in 

showing effects down to the lowest measured levels. Analyses of cohorts using data from more 

 

1

These updates were already included in Fann et al. (2009). An example of the effect of these updates is available in 

the Portland Cement proposal RIA (U.S. EPA, 2009a). The benefit-per-ton estimates have also been updated since 

the Portland Cement proposal RIA (U.S. EPA, 2009a) to incorporate a revised VSL, as discussed on the next 

page. 
5-5 

recent years, during which time PM concentrations have fallen, continue to report strong 

associations with mortality. Therefore, there is no evidence to support a truncation of the CRF.” 

In conjunction with the underlying scientific literature, this document provided a basis for 

reconsidering the application of thresholds in PM2.5 concentration-response functions used in 

EPA’s RIAs. For a summary of these scientific review statements and the panel members 

commenting on thresholds since 2002, please consult the Technical Support Document (TSD) 

Summary of Expert Opinions on the Existence of a Threshold (U.S. EPA, 2010c). 

Consistent with this recent scientific advice, we are replacing the previous threshold 

sensitivity analysis with a new “Lowest Measured Level” (LML) assessment. This information 

allows readers to determine the portion of population exposed to annual mean PM2.5 levels at or 

above the LML of each study; in general, our confidence in the estimated PM mortality 

decreases as we consider air quality levels further below the LML in major cohort studies that 

estimate PM-related mortality. While an LML assessment provides some insight into the level of 

uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold 

and continues to quantify PM-related mortality impacts using a full range of modeled air quality 

concentrations. It is important to emphasize that we have high confidence in PM2.5-related effects 

down to the lowest LML of the major cohort studies. Just because we have greater confidence in 

the benefits above the LML, this does not mean that we have no confidence that benefits occur 

below the LML. 

For this analysis, policy-specific air quality data is not available due to time or resource 

limitations. For these rules, we are unable to estimate the percentage of premature mortality 

associated with this specific rule’s emission reductions at each PM2.5 level. However, we believe 

that it is still important to characterize the distribution of exposure to baseline air quality levels. 

As a surrogate measure of mortality impacts, we provide the percentage of the population 

exposed at each PM2.5 level using the most recent modeling available from the recently proposed 

Transport Rule (U.S. EPA, 2010e). It is important to note that baseline exposure is only one 

parameter in the health impact function, along with baseline incidence rates population, and 

change in air quality. In other words, the percentage of the population exposed to air pollution 

below the LML is not the same as the percentage of the population experiencing health impacts 

as a result of a specific emission reduction policy. The most important aspect, which we are 

unable to quantify for rules without air quality modeling, is the shift in exposure associated with 

this specific rule. Therefore, caution is warranted when interpreting the LML assessment. For 

more information on the data and conclusions in the LML assessment for rules without policy-
5-6 

specific air quality modeling, please consult the LML TSD (U.S. EPA, 2010d). The results of 

this analysis are provided in Section 5.5. 

As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods 

used to estimate air quality benefits evolve over time to reflect the Agency’s most current 

interpretation of the scientific and economic literature. For a period of time (2004–2008), the 

Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical 

life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived 

at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of 

the wage-risk literature. The $1 million value represented the lower end of the interquartile range 

from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million value 

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$)2

 was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the 

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected 

the interim estimate to a scientific peer-review process through the Science Advisory Board 

(SAB) or other peer-review group. 

During this time, the Agency continued work to update its guidance on valuing mortality 

risk reductions, including commissioning a report from meta-analytic experts to evaluate 

methodological questions raised by EPA and the SAB on combining estimates from the various 

data sources. In addition, the Agency consulted several times with the Science Advisory Board 

Environmental Economics Advisory Committee (SAB-EEAC) on the issue. With input from the 

meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, 

appropriate meta-analytic techniques to combine estimates from unique data sources and 

different studies, including those using different methodologies (i.e., wage-risk and stated 

preference) (U.S. EPA-SAB, 2007). 

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

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, the 

Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines 

for Preparing Economic Analyses (U.S. EPA, 2000)3

 while the Agency continues its efforts to 

update its guidance on this issue. This approach calculates a mean value across VSL estimates 

 

2

 After adjusting the VSL to account for a different currency year (2008$) and to account for income growth to 

2016, the $5.5 million VSL is $7.9 million. 3

In the (draft) update of the Economic Guidelines (U.S. EPA, 2008b), EPA retained the VSL endorsed by the SAB 

with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the 

near future. Therefore, this report does not represent final agency policy.
5-7 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $6.3 million (2000$).4

 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. 

In implementing these rules, emission controls may lead to reductions in ambient PM2.5 

below the National Ambient Air Quality Standards (NAAQS) for PM in some areas and assist 

other areas with attaining the PM NAAQS. Because the PM NAAQS RIAs also calculate PM 

benefits, there are important differences worth noting in the design and analytical objectives of 

each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a new air 

quality standard nationwide based on an array of emission control strategies for different sources. 

In short, NAAQS RIAs hypothesize, but do not predict, the control strategies that States may 

choose to enact when implementing a NAAQS. The setting of a NAAQS does not directly result 

in costs or benefits, and as such, the NAAQS RIAs are merely illustrative and are not intended to 

be added to the costs and benefits of other regulations that result in specific costs of control and 

emission reductions. However, some costs and benefits estimated in this RIA account for the 

same air quality improvements as estimated in the illustrative PM2.5 NAAQS RIA. 

By contrast, the emission reductions for this rule are from a specific class of wellcharacterized sources. In general, EPA is more confident in the magnitude and location of the 

emission reductions for these rules. It is important to note that emission reductions anticipated 

from these rules do not result in emission increases elsewhere (other than potential energy 

disbenefits). Emission reductions achieved under these and other promulgated rules will 

ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the 

incremental costs and benefits associated with attaining the NAAQS. EPA remains forward 

looking towards the next iteration of the 5-year review cycle for the NAAQS, and as a result 

does not issue updated RIAs for existing NAAQS that retroactively update the baseline for 

NAAQS implementation. For more information on the relationship between the NAAQS and 

rules such as analyzed here, please see Section 1.2.4 of the SO2 NAAQS RIA (U.S. EPA, 2010f). 

Figure 5-1 illustrates the relative breakdown of the monetized PM2.5 health benefits. 

 

4

In this analysis, we adjust the VSL to account for a different currency year (2008$) and to account for income 

growth to 2016. After applying these adjustments to the $6.3 million value, the VSL is $9.1 million.
5-8 

Adult Mortality ‐ Pope et

al. 93%

ChronicBronchitis 4%

AMI 2%

Acute Respiratory Symptoms

0.5%

InfantMortality 0.4%

Work Loss Days 0.2%

HospitalAdmissions, Cardio

0.2%

HospitalAdmissions, Resp

0.04%

AsthmaExacerbation 0.01%

Acute Bronchitis 0.01%

UpperResp Symp 0.00%

LowerResp Symp 0.00%

ERVisits, Resp 0.00%

Other1%

Figure 5-1. Breakdown of Monetized PM2.5 Health Benefits Estimates using Mortality 

Function from Pope et al. (2002)a

a

 This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example. Using the 

Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult 

mortality would be 97%. This chart shows the breakdown using a 3% discount rate, and the results would be 

similar if a 7% discount rate was used. 

Table 5-2 provides a general summary of the primary approach results by pollutant, 

including the emission reductions and monetized benefits-per-ton at discount rates of 3% and 

7%.5 Table 5-3 provides a summary of the reductions in health incidences as a result of the 

pollution reductions. In Table 5-4, we provide the benefits using our anchor points of Pope et al. 

and Laden et al. as well as the results from the expert elicitation on PM mortality. Figures 6-2 

through 6-4 provide a visual representation of the range of benefits estimates and the pollutant 

breakdown of the monetized benefits. 

 

5

To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). 

These benefits are estimated for a specific analysis year (i.e., 2016), and most of the PM benefits occur within 

that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we 

assume 5 years of follow-up medical costs and lost wages. For premature mortality, we assume that there is a 

“cessation” lag between PM exposures and the total realization of changes in health effects. Although the 

structure of the lag is uncertain, EPA follows the advice of the SAB-HES to assume a segmented lag structure 

characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 

20 after the reduction in PM2.5 (U.S. EPA-SAB, 2004). Changes in the lag assumptions do not change the total 

number of estimated deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI 

costs after the analysis year and the valuation of premature mortalities that occur after the analysis year. As such, 

the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized benefits 

using a 3% discount rate. 
5-9 

Table 5-2. Summary of Monetized Benefits Estimates for the CISWI NSPS and EG in 

2016 (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%) 

MACT Floor 

Direct PM2.5 710 $230,000 $560,000 $210,000 $500,000 $160 to $400 $150 to $360 

PM2.5 Precursors 

SO2 5,170 $29,000 $72,000 $27,000 $65,000 $150 to $370 $140 to $340 

NO2 5,544 $4,900 $12,000 $4,400 $11,000 $27 to $66 $24 to $59 

 Total $340 to $830 $310 to $750 

Beyond-the-Floor Direct PM2.5 713 $230,000 $560,000 $210,000 $500,000 $160 to $400 $150 to $360 

PM2.5 Precursors 

SO2 7,496 $29,000 $72,000 $27,000 $65,000 $220 to $540 $200 to $490 

NO2 11,024 $4,900 $12,000 $4,400 $11,000 $53 to $130 $49 to $120 

 Total $440 to $1,100 $400 to $970 

a

 All estimates are for the implementation year (2016), and are rounded to two significant figures so numbers may 

not sum across columns. These models assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality the scientific evidence is not yet sufficient to allow differentiation 

of effect estimates by particle type. The benefit per ton estimates vary because each ton of precursor reduced has a 

different propensity to form PM2.5. The monetized benefits incorporate the conversion from precursor emissions 

to ambient fine particles. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. These estimates do not include the energy disbenefits valued at $3.8 million, but the rounded totals 

do not change. 
5-10 

Table 5-3. Summary of Estimated Reductions in Health Incidences from PM2.5 Benefits 

for the CISWI NSPS and EG in 2016a

 MACT Floor Beyond-the-Floor

Avoided Premature Mortality 

Pope et al. 38 49 

Laden et al. 98 130 

Avoided Morbidity 

Chronic Bronchitis 26 33 

Acute Myocardial Infarction 61 78 

Hospital Admissions, Respiratory 9 12 

Hospital Admissions, Cardiovascular 19 25 

Emergency Room Visits, Respiratory 36 46 

Acute Bronchitis 62 80 

Work Loss Days 5,100 6,500 

Asthma Exacerbation 680 870 

Minor Restricted Activity Days 30,000 39,000 

Lower Respiratory Symptoms 740 950 

Upper Respiratory Symptoms 560 720 

a

 All estimates are for the analysis year (2016) and are rounded to whole numbers with two significant figures. 

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

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

estimates by particle type. The monetized benefits incorporate the conversion from precursor emissions to 

ambient fine particles. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. 
5-11 

Table 5-4. All PM2.5 Benefits Estimates for the CISWI NSPS and EG at Discount Rates of 

3% and 7% in 2016 (in millions of 2008$)a

MACT Floor Beyond-the-Floor 

3% 7% 3% 7% 

Benefit-per-ton Coefficients Derived from Epidemiology Literature 

Pope et al. $340 $310 $440 $400 

Laden et al. $830 $750 $1,100 $970 

Benefit-per-ton Coefficients Derived from Expert Elicitation 

Expert A $880 $800 $1,130 $1,020 

Expert B $680 $610 $870 $790 

Expert C $670 $610 $860 $780 

Expert D $480 $430 $610 $550 

Expert E $1,090 $990 $1,400 $1,260 

Expert F $610 $560 $790 $710 

Expert G $400 $370 $520 $470 

Expert H $510 $460 $650 $590 

Expert I $670 $600 $850 $770 

Expert J $540 $490 $700 $630 

Expert K $130 $120 $170 $160 

Expert L $490 $450 $630 $570 

a All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they 

were derived through the benefit-per-ton technique described above. The benefits estimates from the expert 

elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated 

with the concentration-response function. Confidence intervals are unavailable for this analysis because of the 

benefit-per-ton methodology. These estimates do not include the energy disbenefits valued at $3.8 million. 
5-12 

$0

$200

$400

$600

$800

$1,000

$1,200

Pope et al.

Laden et al.

Millions(2008$)

Benefits estimates derived from2 epidemiology functions and 12 expert functions

3% DR

7% DR

Figure 5-2. Total Monetized PM2.5 Benefits Estimates for the CISWI NSPS and EG in 2016 

a This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the 

Pope et al. (2002) study and the Laden et al. (2006) study, as well as 12 effect coefficients derived from EPA’s 

expert elicitation on PM mortality. The results shown are not the direct results from the studies or expert 

elicitation; rather, the estimates are based in part on the concentration-response function provided in those studies. 

These estimates do not include the energy disbenefits valued at $3.8 million. 

Figure 5-3. Breakdown of Monetized Benefits Estimates for the CISWI NSPS and EG by 

PM2.5 Precursor Pollutant and Source (MACT floor option) 
5-13 

Figure 5-4. Breakdown of Monetized Benefits Estimates for the CISWI NSPS and EG by 

Subcategory (MACT floor option) 

5.3 Energy Disbenefits 

In this section, we provide an estimate of the energy disbenefits associated with the 

increased emissions from additional energy usage.6

 Electricity usage associated with the 

operation of control devices is anticipated to increase emissions of pollutants from utility boilers 

that supply electricity to the incinerators. We estimate that the increased electricity consumption 

associated with the floor option would be 236 million kWh. Using national emission factors from 

eGRID for electrical generating units (EGUs), we estimate the increased emissions to be 155,000 

tpy of CO2 for the floor option. Since NOx and SO2 are covered by capped emissions trading 

programs, we are only estimating the CO2 emission increases from the increased electricity 

demand. The methodology used to calculate these emission increases is described “Secondary 

Impacts of Control Options for the CISWI Standards,” which is available in the docket. 

5.3.1 Social Cost of Carbon and Greenhouse Gas Disbenefits 

EPA has assigned a dollar value to reductions in carbon dioxide (CO2) emissions using 

recent estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized 

damages associated with an incremental increase in carbon emissions in a given year. It is 

intended to include (but is not limited to) changes in net agricultural productivity, human health, 

property damages from increased flood risk, and the value of ecosystem services due to climate 

change. The SCC estimates used in this analysis were developed through an interagency process 

that included EPA and other executive branch entities, and concluded in February 2010. EPA 

first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking 

 

6

 As we use the term “energy disbenefits” in this analysis, we are not referring to the cost of purchasing additional 

electricity or fuel. 
5-14 

to establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average 

Fuel Economy Standards; see the rule’s preamble for discussion about application of SCC (75 

FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a complete 

discussion of the methods used to develop these SCC estimates.7

 

The interagency group selected four SCC values for use in regulatory analyses, which we 

have applied in this analysis: $5, $21, $35, and $65 per metric ton of CO2 emissions8

 in 2010, in 

2007 dollars. The first three values are based on the average SCC from three integrated 

assessment models, at discount rates of 2.5, 3, and 5 percent, respectively. SCCs at several 

discount rates are included because the literature shows that the SCC is quite sensitive to 

assumptions about the discount rate, and because no consensus exists on the appropriate rate to 

use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all 

three models at a 3 percent discount rate. It is included to represent higher-than-expected impacts 

from temperature change further out in the tails of the SCC distribution. Low probability, high 

impact events are incorporated into all of the SCC values through explicit consideration of their 

effects in two of the three models as well as the use of a probability density function for 

equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high 

temperature outcomes, which in turn lead to higher projections of damages. 

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that the interagency group estimated the growth rate of the SCC 

directly using the three integrated assessment models rather than assuming a constant annual 

growth rate. This helps to ensure that the estimates are internally consistent with other modeling 

assumptions. The SCC estimates for the analysis years of 2016, in 2008$ are provided in 

Table 5-5. 

 

7

 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of 

Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Also 

available at http://www.epa.gov/otaq/climate/regulations.htm 8

 The interagency group decided that these estimates apply only to CO2 emissions. Given that warming profiles and 

impacts other than temperature change (e.g., ocean acidification) vary across GHGs, the group concluded 

“transforming gases into CO2-equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, 

would not result in accurate estimates of the social costs of non-CO2 gases” (SCC TSD, pg 13). 
5-15 

Table 5-5. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2016a

Discount Rate and Statistic SCC Estimate (2008$) 

5% Average $5.9 

3% Average $24.7 

2.5% Average $39.9 

3% 95%ile $75.6 

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC, 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional. 

The interagency group noted a number of limitations to the SCC analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. The limited amount of research linking climate impacts to economic damages makes 

the interagency modeling exercise even more difficult. The interagency group hopes that over 

time researchers and modelers will work to fill these gaps and that the SCC estimates used for 

regulatory analysis by the Federal government will continue to evolve with improvements in 

modeling. Additional details on these limitations are discussed in the SCC TSD. 

In light of these limitations, the interagency group has committed to updating the current 

estimates as the science and economic understanding of climate change and its impacts on 

society improves over time. Specifically, the interagency group has set a preliminary goal of 

revisiting the SCC values within two years or at such time as substantially updated models 

become available, and to continue to support research in this area. 
5-16 

Applying the global SCC estimates to the estimated increases in CO2 emissions for the 

range of policy scenarios, we estimate the dollar value of the climate-related disbenefits captured 

by the models for each analysis year. For internal consistency, the annual disbenefits are 

discounted back to NPV terms using the same discount rate as each SCC estimate (i.e., 5%, 3%, 

and 2.5%) rather than 3% and 7%.9 These estimates are provided in Table 5-6. 

Table 5-6. Monetized SCC-derived Disbenefits of CO2 Emission Increases in 2016 

(millions of 2008$)a

Discount Rate and Statistic MACT Floor Beyond-the-Floor 

CO2 (tpy) 154,881 157,071 

5% Average $0.9 $0.9 

3% Average $3.8 $3.9 

2.5% Average $6.2 $6.3 

3% 95%ile $12 $12 

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

5.4 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. Methodological 

and time limitations under the court-ordered schedule prevented EPA from quantifying or 

monetizing the benefits from several important benefit categories, including benefits from 

reducing toxic emissions, ecosystem effects, and visibility impairment. The health benefits from 

reducing hazardous air pollutants (HAPs) and carbon monoxide have not been monetized in this 

analysis. In addition to being a PM2.5 precursor, SO2 emissions also contribute to adverse effects 

from acidic deposition in aquatic and terrestrial ecosystems, increased mercury methylation, as 

well as visibility impairment. The benefits from reducing other air pollutants have not been 

monetized in this analysis, including reducing 25,000 tons of carbon monoxide, 470 tons of HCl, 

4.1 tons of lead, 0.95 tons of cadmium, 260 pounds of mercury, and 92 grams of total 

dioxins/furans each year. Because we were unable to monetize the direct benefits associated with 

reducing HAPs, the monetized benefits estimate is an underestimate of the total benefits. The 

extent of this underestimate, whether small or large, is unknown. 

 

9 It is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be discounted at 

rates that differ from those used to develop the SCC estimates. 
5-17 

5.4.1 Carbon Monoxide Benefits 

Carbon monoxide (CO) exposure is associated with a variety of health effects. Without 

knowing the location of the emission reductions and the resulting ambient concentrations using 

fine-scale air quality modeling, we were unable to estimate the exposure to CO for nearby 

populations. Due to methodological and time limitations under the court-ordered schedule, we 

were unable to estimate the benefits associated with the reductions in CO emissions that would 

occur as a result of this rule. 

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 

carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO 

emitted from these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the 

combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon 

inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia 

(reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple 

tissues and organ systems that are dependent upon concentration and duration of exposure. 

The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded 

that short-term exposure to CO is “likely to have a causal relationship” with cardiovascular 

morbidity, particularly in individuals with coronary heart disease. Epidemiologic studies 

associate short-term CO exposure with increased risk of emergency department visits and 

hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac 

chest pain), as well as those who have experienced a heart attack. Other subpopulations 

potentially at risk include individuals with diseases such as chronic obstructive pulmonary 

disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as 

older adults or the developing young. The evidence is suggestive of a causal relationship 

between short-term exposure to CO and respiratory morbidity and mortality. The evidence is also 

suggestive of a causal relationship for birth outcomes and developmental effects following longterm exposure to CO, and for central nervous system effects linked to short- and long-term 

exposure to CO. 

5.4.2 Other SO2 Benefits 

In addition to being a precursor to PM2.5, SO2 emissions are also associated with a variety 

of respiratory health effects. Unfortunately, we were unable to estimate the health benefits 

associated with reduced SO2 exposure in this analysis because we do not have air quality 

modeling data available. Without knowing the location of the emission reductions and the 

resulting ambient concentrations, we were unable to estimate the exposure to SO2 for nearby 
5-18 

populations. Therefore, this analysis only quantifies and monetizes the PM2.5 benefits associated 

with the reductions in SO2 emissions. 

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the U.S. EPA has concluded that there is a causal relationship between respiratory health 

effects and short-term exposure to SO2 (U.S. EPA, 2008a). According to summary of the ISA in 

EPA’s risk and exposure assessment (REA) for the SO2 NAAQS, “the immediate effect of SO2 

on the respiratory system in humans is bronchoconstriction” (U.S. EPA, 2009c). In addition, the 

REA summarized from the ISA that “asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease.” A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2 at 

concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected (U.S. EPA, 2009c). Based on our review of this 

information, we identified four short-term morbidity endpoints that the SO2 ISA identified as a 

“causal relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to 

SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 

associations to adjustment for pollutants. 

SO2 emissions also contribute to adverse welfare effects from acidic deposition, visibility 

impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which 

can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic 

ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and 

sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern United States, the 

surface waters affected by acidification are a source of food for some recreational and 

subsistence fishermen and for other consumers and support several cultural services, including 

aesthetic and educational services and recreational fishing. Biological effects of acidification in 

terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root 

growth, which restricts the ability of the plant to take up water and nutrients. These direct effects 

can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold 

temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial 

acidification affects several important ecological services, including declines in habitat for 
5-19 

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, 2008c). 

Reducing SO2 emissions and the secondary formation of PM2.5 would improve the level 

of visibility throughout the United States. Fine particles with significant light-extinction 

efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). 

These suspended particles and gases degrade visibility by scattering and absorbing light. Higher 

visibility impairment levels in the East are due to generally higher concentrations of fine 

particles, particularly sulfates, and higher average relative humidity levels. In fact, particulate 

sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually 

and 75% during summer). In the western U.S., particulate sulfate contributes to 20–50% of 

regional haze (U.S. EPA, 2009b). Visibility has direct significance to people’s enjoyment of 

daily activities and their overall sense of wellbeing. Good visibility increases the quality of life 

where individuals live and work, and where they engage in recreational activities. 

5.4.3 HAP Benefits 

Americans are exposed to ambient concentrations of air toxics at levels which have the 

potential to cause adverse health effects.10 The levels of air toxics to which people are exposed 

vary depending on where people live and work and the kinds of activities in which they engage. 

In order to identify and prioritize air toxics, emission source types and locations which are of 

greatest potential concern, U.S. EPA conducts the National-Scale Air Toxics Assessment 

(NATA). The most recent NATA was conducted for calendar year 2002, and was released in 

June 2009.11 NATA for 2002 includes four steps: 

1. Compiling a national emissions inventory of air toxics emissions from outdoor 

sources 

2. Estimating ambient concentrations of air toxics across the United States 

3. Estimating population exposures across the United States 

4. Characterizing potential public health risk due to inhalation of air toxics including 

both cancer and noncancer effects 

 

10 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 11 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 
5-20 

Noncancer health effects can result from chronic,12 subchronic,13 or acute14 inhalation 

exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory 

effects as well as effects on the immune and reproductive systems. According to the 2002 

NATA, nearly the entire U.S. population was exposed to an average concentration of air toxics 

that has the potential for adverse noncancer respiratory health effects. 15 Figures 6-5 and 6-6 

depict estimated county-level carcinogenic risk and noncancer respiratory hazard from the 

assessment. Results from the 2002 NATA suggest that acrolein is the primary driver for 

noncancer respiratory risk.16 Large reductions in HAP emissions may not necessarily translate 

into significant reductions in health risk because toxicity varies by pollutant and whether or not 

there are exposures at or above levels of concern is not known. For example, acetaldehyde mass 

emissions are more than double acrolein emissions on a national basis, according to EPA’s 2005 

National Emissions Inventory (NEI). However, the Integrated Risk Information System (IRIS) 

reference concentration (RfC) for acrolein is considerably lower than that for acetaldehyde, 

suggesting that acrolein could be potentially more toxic than acetaldehyde. Thus, it is important 

to account for the toxicity and exposure, as well as the mass of the targeted emissions when 

designing reduction strategies to maximize health benefits. 

 

12 Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database 

(http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than 

approximately 10% of the life span in humans (more than approximately 90 days to 2 years in typically used 

laboratory animal species). 13 Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an 

organism. 14 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 

15 The NATA modeling framework has a number of limitations which prevent its use as the sole basis for setting 

regulatory standards. These limitations and uncertainties are discussed on the 2002 NATA website. Even so, this 

modeling framework is very useful in identifying air toxic pollutants and sources of greatest concern, setting 

regulatory priorities, and informing the decision making process. U.S. EPA. (2009) 2002 National-Scale Air 

Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 16 Details about the overall confidence of certainty ranking of the individual pieces of NATA assessments including 

both quantitative (e.g., model-to-monitor ratios) and qualitative (e.g., quality of data, review of emission 

inventories) judgments can be found at http://www.epa.gov/ttn/atw/nata/roy/page16.html. 
5-21 

Figure 5-5. Estimated County Level Carcinogenic Risk (from 2002 NATA) 

Figure 5-6. Estimated County Level Noncancer (Respiratory) Risk (from 2002 NATA) 
5-22 

Due to methodology and time limitations under the court-ordered schedule, we did not 

estimate the benefits associated with the hazardous air pollutants that would be reduced as a 

result of these rules. In a few previous analyses of the benefits of reductions in HAPs, EPA has 

quantified the benefits of potential reductions in the incidences of cancer and non-cancer risk 

(e.g., U.S. EPA, 1995). In those analyses, EPA relied on unit risk factors (URF) developed 

through risk assessment procedures.17 These URFs are designed to be conservative, and as such, 

are more likely to represent the high end of the distribution of risk rather than a best or most 

likely estimate of risk. As the purpose of a benefit analysis is to describe the benefits most likely 

to occur from a reduction in pollution, use of high-end, conservative risk estimates would 

overestimate the benefits of the regulation. While we used high-end risk estimates in past 

analyses, advice from the EPA’s Science Advisory Board (SAB) recommended that we avoid 

using high-end estimates in benefit analyses (U.S. EPA, 2002). Since this time, EPA has 

continued to develop better methods for analyzing the benefits of reductions in HAPs. 

In the Second Prospective 812 Analysis, EPA conducted a case study analysis of the 

health effects associated with reducing exposure to benzene in Houston from implementation of 

the Clean Air Act (IEc, 2009). While reviewing the draft report, EPA’s Advisory Council on 

Clean Air Compliance Analysis concluded that “the challenges for assessing progress in health 

improvement as a result of reductions in emissions of hazardous air pollutants (HAPs) are 

daunting...due to a lack of exposure-response functions, uncertainties in emissions inventories 

and background levels, the difficulty of extrapolating risk estimates to low doses and the 

challenges of tracking health progress for diseases, such as cancer, that have long latency 

periods” (U.S. EPA-SAB, 2008). 

In 2009, EPA convened a workshop to address the inherent complexities, limitations, and 

uncertainties in current methods to quantify the benefits of reducing HAPs. Recommendations 

from this workshop included identifying research priorities, focusing on susceptible and 

vulnerable populations, and improving dose-response relationships (Gwinn et al., 2011). 

In summary, monetization of the benefits of reductions in cancer incidences requires 

several important inputs, including central estimates of cancer risks, estimates of exposure to 

carcinogenic HAPs, and estimates of the value of an avoided case of cancer (fatal and non-fatal). 

Due to methodology and time limitations under the court-ordered schedule, we do not have 

sufficient information on emissions from specific sources and thus are unable to model changes 

 

17The unit risk factor is a quantitative estimate of the carcinogenic potency of a pollutant, often expressed as the 

probability of contracting cancer from a 70-year lifetime continuous exposure to a concentration of one μg/m3 of 

a pollutant. 
5-23 

in population exposures to ambient concentrations of HAPs. For this reason, we did not attempt 

to quantify or monetized the health benefits of reductions in HAPs in this analysis.18 Instead, we 

provide a qualitative analysis of the health effects associated with the HAPs anticipated to be 

reduced by these rules. EPA remains committed to improving methods for estimating HAP 

benefits by continuing to explore additional concepts of benefits, including changes in the 

distribution of risk. 

Available emissions data show that several different HAPs are emitted from CISWI. This 

rule is anticipated to reduce, 470 tons of HCl, 4.1 tons of lead, 0.95 tons of cadmium, 260 pounds 

of mercury, and 92 grams of total dioxins/furans each year in the primary approach. 

5.4.3.1 Mercury 

Mercury is a highly neurotoxic contaminant that enters the food web as a methylated 

compound, methylmercury (U.S. EPA, 2008c). The contaminant is concentrated in higher 

trophic levels, including fish eaten by humans. Experimental evidence has established that only 

inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA, 

2008c). Current evidence indicates that in watersheds where mercury is present, increased sulfate 

deposition very likely results in methylmercury accumulation in fish (Drevnick et al., 2007; 

Munthe et al, 2007). The SO2 ISA concluded that evidence is sufficient to infer a casual 

relationship between sulfur deposition and increased mercury methylation in wetlands and 

aquatic environments (U.S. EPA, 2008c). 

In addition to the role of sulfate deposition on methylation, this rule would also reduce 

mercury emissions. Mercury is emitted to the air from various man-made and natural sources. 

These emissions transport through the atmosphere and eventually deposit to land or water bodies. 

This deposition can occur locally, regionally, or globally, depending on the form of mercury 

emitted and other factors such as the weather. The form of mercury emitted varies depending on 

the source type and other factors. Available data indicate that the mercury emissions from these 

sources are a mixture of gaseous elemental mercury, inorganic ionic mercury, and particulate 

bound mercury. Gaseous elemental mercury can be transported very long distances, even 

globally, to regions far from the emissions source (becoming part of the global “pool”) before 

deposition occurs. Inorganic ionic and particulate bound mercury have a shorter atmospheric 

 

18Due to time limitations under the court-ordered schedule for this rule, we were unable to model mercury 

methylation, bioaccumulation in fish tissue, and human consumption of mercury-contaminated fish that would be 

needed in order to estimate the human health benefits from reducing mercury emissions. However, it is important 

to emphasize, that we generally have more data and accepted methods to estimate mercury benefits than we have 

for other HAPs. 
5-24 

lifetime and can deposit to land or water bodies closer to the emissions source. Furthermore, 

elemental mercury in the atmosphere can undergo transformation into ionic mercury, providing a 

significant pathway for deposition of emitted elemental mercury. 

This source category emitted about 0.5 tons of mercury in the air in 2008 in the U.S. 

Based on the EPA’s National Emission Inventory, about 103 tons of mercury were emitted from 

all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that 

about 2,100 tons of mercury were emitted worldwide by anthropogenic sources in 2005. We 

believe that total mercury emissions in the U.S. and globally in 2008 were about the same 

magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 0.5% of the 

total anthropogenic mercury emissions in the U.S. and about 0.03% of the global emissions. 

Overall, this rule would reduce mercury emissions by about 260 pounds per year from current 

levels, and therefore, contribute to reductions in mercury exposures and health effects. Due to 

time limitations under the court-ordered schedule, we were unable to model mercury dispersion, 

deposition, methylation, bioaccumulation in fish tissue, and human consumption of mercurycontaminated fish that would be needed in order to estimate the human health benefits from 

reducing mercury emissions. 

Potential exposure routes to mercury emissions include both direct inhalation and 

consumption of fish containing methylmercury. In the U.S., the primary route of human 

exposure to mercury emissions from industrial sources is generally indirectly through the 

consumption of fish containing methylmercury. As described above, mercury that has been 

emitted to the air eventually settles into water bodies or onto land where it can either move 

directly or be leached into waterbodies. Once deposited, certain microorganisms can change it 

into methylmercury, a highly toxic form that builds up in fish, shellfish and animals that eat fish. 

Consumption of fish and shellfish are the main sources of methylmercury exposure to humans. 

Methylmercury builds up more in some types of fish and shellfish than in others. The levels of 

methylmercury in fish and shellfish vary widely depending on what they eat, how long they live, 

and how high they are in the food chain. Most fish, including ocean species and local freshwater 

fish, contain some methylmercury For example, in recent studies by EPA and the U.S. 

Geological Survey (USGS) of fish tissues, every fish sampled from 291 streams across the 

country contained some methylmercury (Scudder, 2009). 

The majority of fish consumed in the U.S. are ocean species. The methylmercury 

concentrations in ocean fish species are primarily influenced by the global mercury pool. 

However, the methylmercury found in local fish can be due, at least partly, to mercury emissions 

from local sources. Research shows that most people’s fish consumption does not cause a 
5-25 

mercury-related health concern. However, certain people may be at higher risk because of their 

routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families 

who rely heavily on fish for a substantial part of their diet). It has been demonstrated that high 

levels of methylmercury in the bloodstream of unborn babies and young children may harm the 

developing nervous system, making the child less able to think and learn. Moreover, mercury 

exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people 

of all ages. 

Several studies suggest that the methylmercury content of fish may reduce cardioprotective effects of fish consumption. Some of these studies also suggest that methylmercury 

may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of 

the literature concerning methylmercury health effects took note of two epidemiological studies 

that found an association between dietary exposure to methylmercury and cardiovascular 

effects.19 In a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al. (1995) observed 

a relationship between methylmercury exposure via fish consumption and acute myocardial 

infarction (AMI or heart attacks), death from coronary heart disease or cardiovascular disease.20

The NRC also noted a study of 917 seven year old children in the Faroe Islands, whose initial 

exposure to methylmercury was in utero although post natal exposures may have occurred as 

well. At seven years of age, these children exhibited an increase in blood pressure and a decrease 

in heart rate variability.21 Based on these and other studies, NRC concluded in 2000 that, 

“Although the data base is not as extensive as it is for other end points (i.e. neurologic effects) 

the cardiovascular system appears to be a target for methylmercury toxicity in humans and 

animals.”22 NAS also stated that “Additional studies are needed to better characterize the effect 

of methylmercury exposure on blood pressure and cardiovascular function at various stages of 

life.” 

Since publication of the NRC report there have been some 30 published papers 

presenting the findings of studies that have examined the possible cardiovascular effects of 

 

19 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the 

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies 

Press. Washington, DC. pp.168-173. 20Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid peroxidation, and the risk of 

myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men.” Circulation, 91 

(3):645-655. 21Sorensen, N., K. Murata, E. Budtz-Jorgensen, P. Weihe, and Grandjean, P. 1999. “Prenatal Methylmercury 

Exposure As A Cardiovascular Risk Factor At Seven Years of Age,” Epidemiology, pp 370-375. 22National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the 

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies 

Press. Washington, DC. p. 229.
5-26 

methylmercury exposure. These studies include epidemiological, toxicological, and toxicokinetic 

investigations. Over a dozen review papers have also been published. If there is a causal 

relationship between methylmercury exposure and adverse cardiovascular effects, then reducing 

exposure to methylmercury would result in public health benefits from reduced cardiovascular 

effects. 

In early 2010, EPA sponsored a workshop in which a group of experts were asked to 

assess the plausibility of a causal relationship between methylmercury exposure and 

cardiovascular health effects and to advise EPA on methodologies for estimating population 

level cardiovascular health impacts of reduced methylmercury exposure. The report from that 

workshop is in preparation. 

5.4.3.2 Cadmium23

Breathing air with very high levels of cadmium can severely damage the lungs and may 

cause death. In the United States, where proper industrial hygiene is generally practiced, inhaling 

very high levels of cadmium at work is expected to be rare and accidental. Breathing air with 

lower levels of cadmium over long periods of time (for years) results in a build-up of cadmium 

in the kidney, and if sufficiently high, may result in kidney disease. Lung cancer has been found 

in some studies of workers exposed to cadmium in the air and studies of rats that breathed in 

cadmium. The U.S. Department of Health and Human Services (DHHS) has determined that 

cadmium and cadmium compounds are known human carcinogens. The International Agency for 

Research on Cancer (IARC) has determined that cadmium is carcinogenic to humans. The EPA 

has determined that cadmium is a probable human carcinogen.

5.4.3.3 Lead24

The main target for lead toxicity is the nervous system, both in adults and children. Longterm exposure of adults to lead at work has resulted in decreased performance in some tests that 

measure functions of the nervous system. Lead exposure may also cause weakness in fingers, 

wrists, or ankles. Lead exposure also causes small increases in blood pressure, particularly in 

middle-aged and older people. Lead exposure may also cause anemia. At high levels of 

 

23All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). 2008. Public Health Statement for Cadmium. CAS# 1306-19-0. Atlanta, GA: U.S. Department of 

Health and Human Services, Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/PHS/PHS.asp?id=46&tid=15>. 24 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). 2007. Public Health Statement for Lead. CAS#: 7439-92-1. Atlanta, GA: U.S. Department of Health 

and Human Services, Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/ToxProfiles/phs13.html>. 
5-27 

exposure, lead can severely damage the brain and kidneys in adults or children and ultimately 

cause death. In pregnant women, high levels of exposure to lead may cause miscarriage. Highlevel exposure in men can damage the organs responsible for sperm production. 

We have no conclusive proof that lead causes cancer (is carcinogenic) in humans. Kidney 

tumors have developed in rats and mice that had been given large doses of some kind of lead 

compounds. The Department of Health and Human Services (DHHS) has determined that lead 

and lead compounds are reasonably anticipated to be human carcinogens based on limited 

evidence from studies in humans and sufficient evidence from animal studies, and the EPA has 

determined that lead is a probable human carcinogen. The International Agency for Research on 

Cancer (IARC) has determined that inorganic lead is probably carcinogenic to humans. IARC 

determined that organic lead compounds are not classifiable as to their carcinogenicity in 

humans based on inadequate evidence from studies in humans and in animals. 

Children are more sensitive to the health effects of lead than adults. No safe blood lead 

level in children has been determined. Lead affects children in different ways depending on how 

much lead a child swallows. A child who swallows large amounts of lead may develop anemia, 

kidney damage, colic (severe “stomach ache”), muscle weakness, and brain damage, which 

ultimately can kill the child. In some cases, the amount of lead in the child’s body can be 

lowered by giving the child certain drugs that help eliminate lead from the body. If a child 

swallows smaller amounts of lead, such as dust containing lead from paint, much less severe but 

still important effects on blood, development, and behavior may occur. In this case, recovery is 

likely once the child is removed from the source of lead exposure, but there is no guarantee that 

the child will completely avoid all long-term consequences of lead exposure. At still lower levels 

of exposure, lead can affect a child’s mental and physical growth. Fetuses exposed to lead in the 

womb, because their mothers had a lot of lead in their bodies, may be born prematurely and have 

lower weights at birth. Exposure in the womb, in infancy, or in early childhood also may slow 

mental development and cause lower intelligence later in childhood. There is evidence that these 

effects may persist beyond childhood. 

5.4.3.4 Hydrogen Chloride (HCl) 25

Hydrogen chloride gas is intensely irritating to the mucous membranes of the nose, 

throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 

 

25 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). Medical Management Guidelines for Hydrogen Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S. 

Department of Health and Human Services, Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/Mhmi/mmg173.html>. 
5-28 

100 ppm are barely tolerable for 1 hour. The greatest impact is on the upper respiratory tract; 

exposure to high concentrations can rapidly lead to swelling and spasm of the throat and 

suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue 

coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures 

may develop an accumulation of fluid in the lungs. Exposure to hydrogen chloride can lead to 

Reactive Airway Dysfunction Syndrome (RADS), a chemically- or irritant-induced type of 

asthma. Children may be more vulnerable to corrosive agents than adults because of the 

relatively smaller diameter of their airways. Children may also be more vulnerable to gas 

exposure because of increased minute ventilation per kg and failure to evacuate an area promptly 

when exposed. Hydrogen chloride has not been classified for carcinogenic effects. 

5.4.3.5 Dioxins (Chlorinated dibenzodioxins (CDDs) 26

A number of effects have been observed in people exposed to 2,3,7,8-TCDD levels that 

are at least 10 times higher than background levels. The most obvious health effect in people 

exposure to relatively large amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin 

disease with acne-like lesions that occur mainly on the face and upper body. Other skin effects 

noted in people exposed to high doses of 2,3,7,8-TCDD include skin rashes, discoloration, and 

excessive body hair. Changes in blood and urine that may indicate liver damage also are seen in 

people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglobin, lipids, 

sugar, and protein have been reported in people exposed to relatively high concentrations of 

2,3,7,8-TCDD. Most of the effects are considered mild and were reversible. However, in some 

people these effects may last for many years. Slight increases in the risk of diabetes and 

abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-

TCDD. We do not have enough information to know if exposure to 2,3,7,8-TCDD would result 

in reproductive or developmental effects in people, but animal studies suggest that this is a 

potential health concern. 

In certain animal species, 2,3,7,8-TCDD is especially harmful and can cause death after a 

single exposure. Exposure to lower levels can cause a variety of effects in animals, such as 

weight loss, liver damage, and disruption of the endocrine system. In many species of animals, 

2,3,7,8-TCDD weakens the immune system and causes a decrease in the system's ability to fight 

bacteria and viruses at relatively low levels (approximately 10 times higher than human 

background body burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused 

 

26 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). 1999. ToxFAQs for Chlorinated Dibenzo-p-dioxins (CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6). 

Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/tfacts104.html>. 
5-29 

reproductive damage and birth defects. Some animal species exposed to CDDs during pregnancy 

had miscarriages and the offspring of animals exposed to 2,3,7,8-TCDD during pregnancy often 

had severe birth defects including skeletal deformities, kidney defects, and weakened immune 

responses. In some studies, effects were observed at body burdens 10 times higher than human 

background levels. 

5.4.3.6 Furans (Chlorinated dibenzofurans (CDFs)) 27

Most of the information on the adverse health effects comes from studies in people who 

were accidentally exposed to food contaminated with CDFs. The amounts that these people were 

exposed to were much higher than are likely from environmental exposures or from a normal 

diet. Skin and eye irritations, especially severe acne, darkened skin color, and swollen eyelids 

with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also 

caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the 

nervous system, and mild changes in the liver. Children born to exposed mothers had skin 

irritation and more difficulty learning, but it is unknown if this effect was permanent or caused 

by CDFs alone or CDFs and polychlorinated biphenyls in combination. 

Many of the same effects that occurred in people accidentally exposed also occurred in 

laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomachs, 

livers, kidneys, and immune systems were seriously injured. Some animals had birth defects and 

testicular damage, and in severe cases, some animals died. These effects in animals were seen 

when they were fed large amounts of CDFs over a short time, or small amounts over several 

weeks or months. Nothing is known about the possible health effects in animals from eating 

CDFs over a lifetime. 

5.4.3.7 Other Air Toxics 

In addition to the compounds described above, other compounds from SI RICE would be 

affected by this rule. Information regarding the health effects of these compounds can be found 

in EPA’s IRIS database.28

5.5 Characterization of Uncertainty in the Monetized PM2.5 Benefits 

In any complex analysis, there are likely to be many sources of uncertainty. Many inputs 

are used to derive the final estimate of economic benefits, including emission inventories, air 

 

27 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). 1995. ToxFAQsTM for Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of Health and 

Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc.gov/tfacts32.html>. 28 U.S. EPA Integrated Risk Information System (IRIS) database is available at: http://www.epa.gov/iris 
5-30 

quality models (with their associated parameters and inputs), epidemiological estimates of 

concentration-response (C-R) functions, estimates of values, population estimates, income 

estimates, and estimates of the future state of the world (i.e., regulations, technology, and human 

behavior). For some parameters or inputs it may be possible to provide a statistical representation 

of the underlying uncertainty distribution. For other parameters or inputs, the necessary 

information is not available. 

The annual benefit estimates presented in this analysis are also inherently variable due to 

the processes that govern pollutant emissions and ambient air quality in a given year. Factors 

such as hours of equipment use and weather are constantly variable, regardless of our ability to 

measure them accurately. As discussed in the PM2.5 NAAQS RIA (Table 4-5) (U.S. EPA, 2006), 

there are a variety of uncertainties associated with these PM benefits. Therefore, the estimates of 

annual benefits should be viewed as representative of the magnitude of benefits expected, rather 

than the actual benefits that would occur every year. 

It is important to note that the monetized benefit-per-ton estimates used here reflect 

specific geographic patterns of emissions reductions and specific air quality and benefits 

modeling assumptions. For example, these estimates do not reflect local variability in population 

density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of 

these $/ton values to estimate benefits associated with different emission control programs (e.g., 

for reducing emissions from large stationary sources like EGUs) may lead to higher or lower 

benefit estimates than if benefits were calculated based on direct air quality modeling. Great care 

should be taken in applying these estimates to emission reductions occurring in any specific 

location, as these are all based on national or broad regional emission reduction programs and 

therefore represent average benefits-per-ton over the entire United States. The benefits-per-ton 

for emission reductions in specific locations may be very different than the estimates presented 

here. 

PM2.5 mortality benefits are the largest benefit category that we monetized in this 

analysis. To better characterize the uncertainty associated with mortality impacts that are 

estimated to occur in areas with low baseline levels of PM2.5, we included the LML assessment. 

Without policy-specific air quality modeling, we are unable to quantify the shift in exposure 

associated with this specific rule. For this rule, as a surrogate measure of mortality impacts, we 

provide the percentage of the population exposed at each PM2.5 level using the most recent 

modeling available from the recently proposed Transport Rule (U.S. EPA, 2010e). A very large 

proportion of the population is exposed at or above the lowest LML of the cohort studies 

(Figures 6-7 and 6-8), increasing our confidence in the PM mortality analysis. Figure 5-7 shows 
5-31 

a bar chart of the percentage of the population exposed to various air quality levels in the preand post-policy policy. Figure 5-8 shows a cumulative distribution function of the same data. 

Both figures identify the LML for each of the major cohort studies. As the policy shifts the 

distribution of air quality levels, fewer people are exposed to PM2.5 levels at or above the LML. 

Using the Pope et al. (2002) study, the 85% of the population is exposed to annual mean PM2.5

levels at or above the LML of 7.5 μg/m3. Using the Laden et al. (2006) study, 40% of the 

population is exposed above the LML of 10 μg/m3. As we model mortality impacts among 

populations exposed to levels of PM2.5 that are successively lower than the LML of the lowest 

cohort study, our confidence in the results diminishes. However, the analysis above confirms that 

the great majority of the impacts occur at or above the lowest cohort study’s LML. It is important 

to emphasize that we have high confidence in PM2.5-related effects down to the lowest LML of 

the major cohort studies. Just because we have greater confidence in the benefits above the LML, 

this does not mean that we have no confidence that benefits occur below the LML. 

0%

5%

10%

15%

20%

25%

1 2 3 4 5 5.8 6 7 7.5 8 9 10 11 12 13 14 15 16 17 18 19 20

Post‐control Baseline

The control strategy lowers PM2.5

levels substantially, particularly 

among highly exposed 

populations. In the baseline, 85% 

of the population lived in areas 

where PM2.5 levels above the 

lowest measured levels of the 

Pope study, increasing our 

confidence in the estimated 

mortality reductions for this rule.

Pope et al. 2002 Laden et al. 2006

Figure 5-7. Percentage of Adult Population by Annual Mean PM2.5 Exposure 

(pre- and post-policy policy) 
5-32 

Figure 5-8. Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels 

(pre- and post-policy policy) 

Above we present the estimates of the total monetized benefits, based on our 

interpretation of the best available scientific literature and methods and supported by the SABHES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions 

and uncertainties. For example, for key assumptions underlying the estimates for premature 

mortality, which typically account for at least 90% of the total monetized benefits, we were able 

to quantify include the following: 

1. PM2.5 benefits were derived through benefit per-ton estimates, which do not reflect 

local variability in population density, meteorology, exposure, baseline health 

incidence rates, or other local factors that might lead to an over-estimate or underestimate of the actual benefits of controlling directly emitted fine particulates. 

2. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from EGUs may differ 

significantly from direct PM2.5 released from diesel engines and other industrial 

sources, but the scientific evidence is not yet sufficient to allow differentiation of 

effect estimates by particle type. 

3. We assume that the health impact function for fine particles is linear down to the 

lowest air quality levels modeled in this analysis. Thus, the estimates include health 
5-33 

benefits from reducing fine particles in areas with varied concentrations of PM2.5,

including both regions that are in attainment with fine particle standard and those that 

do not meet the standard down to the lowest modeled concentrations. 

4. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality (which typically accounts for 85% to 95% of total monetized benefits), we 

include a set of twelve estimates based on results of the expert elicitation study in 

addition to our core estimates. Even these multiple characterizations omit the 

uncertainty in air quality estimates, baseline incidence rates, populations exposed and 

transferability of the effect estimate to diverse locations. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the 

overall uncertainty in the PM2.5 estimates. This information should be interpreted 

within the context of the larger uncertainty surrounding the entire analysis. For more 

information on the uncertainties associated with PM2.5 benefits, please consult the 

PM2.5 NAAQS RIA (Table 4-5). 

This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA (U.S. EPA, 2006) because we lack the necessary air quality input and monitoring 

data to run the benefits model. In addition, we have not conducted any air quality modeling for 

this rule. Moreover, it was not possible to develop benefit-per-ton metrics and associated 

estimates of uncertainty using the benefits estimates from the PM RIA because of the significant 

differences between the sources affected in that rule and those regulated here. However, the 

results of the Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of 

the PM RIA can provide some evidence of the uncertainty surrounding the benefits results 

presented in this analysis. 
6-1 

SECTION 6 

COMPARISON OF BENEFITS AND COSTS 

This RIA does not include the final engineering costs and emission reductions (see 

Chapter 2 for a description of the changes that have not been accounted for). We estimate that 

incorporating these final estimates would decrease the engineering costs by approximately 22% 

and increase the monetized benefits by approximately 4% from those shown in this RIA. 

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

and Emissions Guidelines to be $340 million to $830 million in the implementation year (2016) 

(Table 6-1). Using a 7% discount rate, we estimate the total monetized benefits of the CISWI 

NSPS and Emissions Guidelines to be $310 million to $750 million. The annualized costs are 

$280 million at a 7% interest rate.1

 Thus, net benefits are $60 million to $550 million at a 3% 

discount rate for the benefits and $30 million to $470 billion at a 7% discount rate. All estimates 

are in 2008$. Figures 6-1 and 6-2 show the full range of net benefits estimates (i.e., annual 

benefits minus annualized costs) utilizing the 14 different PM2.5 mortality functions at discount 

rates of 3% and 7%. The benefits from reducing other air pollutants have not been monetized in 

this analysis, including reducing 25,000 tons of carbon monoxide, 470 tons of HCl, 4.1 tons of 

lead, 0.95 tons of cadmium, 260 pounds of mercury, and 92 grams of total dioxins/furans each 

year. 

 

1

 For more information on the annualized costs, please refer to Section 3 of this RIA. There are no estimates of costs 

available at a 3% discount rate. 
6-2 

Table 6-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

CISWI NSPS and EG in 2016 (millions of 2008$)a 

 3% Discount Rate 7% Discount Rate 

Option 1: MACT Floor 

Total Monetized Benefitsb $340 to $830 $310 to $750 

Total Social Costsc $280 $280 

Net Benefits $60 to $550 $30 to $470 

Non-monetized Benefits 

25,000 tons of carbon monoxide 

470 tons of HCl 

260 pounds of mercury 

0.95 tons of cadmium 

4.1 tons of lead 

92 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

Visibility impairment 

Option 2: Beyond the Floor 

Total Monetized Benefitsb $430 to $1,100 $390 To $960 

Total Social Costsc $300 $300 

Net Benefits $130 to $770 $90 to $660 

Non-monetized Benefits 25,000 tons of carbon monoxide 

470 tons of HCl 

260 pounds of mercury 

0.95 tons of cadmium 

4.1 tons of lead 

92 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

 Visibility impairment 

a

 All estimates are for the implementation year (2016), and are rounded to two significant figures. These results 

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

b

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

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

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

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

their chemical composition, are equally potent in causing premature mortality because the scientific evidence is 

not yet sufficient to allow differentiation of effect estimates by particle type. These estimates include energy 

disbenefits valued at $3.8 million. 

c

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

results in the same social costs for both discount rates. 
6-3 

‐$200

‐$100

$0

$100

$200

$300

$400

$500

$600

$700

$800

$900

Pope et al.

Laden et al.

Millions (2008$)

Cost estimate combinedwith total monetized benefits estimates derived from2epidemiology functions and 12 expert

functions

Figure 6-1. Net Benefits for the CISWI NSPS and Emissions Guidelines at 3% Discount 

Ratea

a

 Net benefits are quantified in terms of PM2.5 benefits for the implementation year (2016). This graph shows 14 

benefits estimates combined with the cost estimate. These models assume that all fine particles, regardless of their 

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

sufficient to allow differentiation of effect estimates by particle type. The monetized benefits incorporate the 

conversion from precursor emissions to ambient fine particles. The monetized benefits incorporate the conversion 

from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at $3.8 

million. 
6-4 

‐$200

‐$100

$0

$100

$200

$300

$400

$500

$600

$700

$800

Pope et al.

Laden et al.

Millions (2008$)

Cost estimate combinedwith total monetized benefits estimates derived from2epidemiology functions and 12 expert

functions

Figure 6-2. Net Benefits for the CISWI NSPS and Emissions Guidelines at 7% Discount 

Ratea

a

 Net benefits are quantified in terms of PM2.5 benefits for the implementation year (2016). This graph shows 14 

benefits estimates combined with the cost estimate. These models assume that all fine particles, regardless of their 

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

sufficient to allow differentiation of effect estimates by particle type. The monetized benefits incorporate the 

conversion from precursor emissions to ambient fine particles. The monetized benefits incorporate the conversion 

from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at $3.8 

million. 
R-1 

REFERENCES 

Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines 

for Hydrogen Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S. Department of 

Health and Human Services, Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/Mhmi/mmg173.html>. 

Agency for Toxic Substances and Disease Registry (ATSDR). 1995. ToxFAQsTM for 

Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of Health and Human 

Services, Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/tfacts32.html>. 

Agency for Toxic Substances and Disease Registry (ATSDR). 1999. ToxFAQs for Chlorinated 

Dibenzo-p-dioxins (CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6). Atlanta, GA: U.S. 

Department of Health and Human Services, Public Health Service. Available on the 

Internet at <http://www.atsdr.cdc.gov/tfacts104.html>. 

Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Public Health Statement for 

Lead. CAS#: 7439-92-1. Atlanta, GA: U.S. Department of Health and Human Services, 

Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/ToxProfiles/phs13.html>. 

Agency for Toxic Substances and Disease Registry (ATSDR). 2008. Public Health Statement for 

Cadmium. CAS# 1306-19-0. Atlanta, GA: U.S. Department of Health and Human 

Services, Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/PHS/PHS.asp?id=46&tid=15>. 

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

Berck, P., and S. Hoffmann. 2002. “Assessing the Employment Impacts of Environmental and 

Natural Resource Policy.” Environmental and Resource Economics 22(1): 133-156. 

Berman, E., and L. T. M. Bui. 2001. Environmental regulation and labor demand: Evidence from 

the south coast air basin. Journal of Public Economics 79, no. 2: 265-295. 

Broda, C., N. Limao, and D. Weinstein. 2008a. “Export Supply Elasticities.” http://faculty. 

chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/ 

TradeElasticities.html. Accessed September 2009. 

Broda, C., N. Limao, and D.E. Weinstein. 2008b. “Optimal Tariffs and Market Power: The 

Evidence.” American Economic Review 98(5): 2032-2065. 

Cole, M. A. and R. J. Elliott. 2007. Do environmental regulations cost jobs? An industry-level 

analysis of the UK. B E Journal of Economic Analysis & Policy 7, no. 1. 

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

Stockholder Summary.” <http://www.dow.com/financial/2007ann/>. 
R-2 

Drevnick, P.E., D.E. Canfield, P.R. Gorski, A.L.C. Shinneman, D.R. Engstrom, D.C.G. Muir, 

G.R. Smith, P.J. Garrison, L.B. Cleckner, J.P. Hurley, R.B. Noble, R.R. Otter, and J.T. 

Oris. 2007. Deposition and cycling of sulfur controls mercury accumulation in Isle 

Royale fish. Environmental Science and Technology 41(21):7266–7272. 

Fann, N., C. M. Fulcher, and B. J. Hubbell. 2009. The influence of location, source, and emission 

type in estimates of the human health benefits of reducing a ton of air pollution. Air Qual 

Atmos Health 2:169–176. 

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

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

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

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

(Paper). 

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

Capacity.” <http://www.federalreserve.gov/datadownload/>. (Paper). 

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

Production.”<http://www.federalreserve.gov/datadownload/>. (Paper). 

Greenstone, M. 2002. The impacts of environmental regulations on industrial activity: Evidence 

from the 1970 and 1977 clean air act amendments and the census of manufactures. 

Journal of Political Economy 110, no. 6: 1175-1219. 

Henderson, J. V. 1996. Effects of air quality regulation. American Economic Review 86, no. 4: 

789-813. 

Ho, M.S., R. Morgenstern, and J.S. Shih. 2008. “Impact of Carbon Price Policies on US 

Industry.” RFF Discussion Paper 08-37. http://www.Rff.Org/Publications/Pages/ 

Publicationdetails.Aspx?. Publicationid=20680. Accessed August 2009. 

Industrial Economics, Inc (IEc). 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared 

for the U.S. EPA, Office of Air Quality Planning and Standards, September. Available on 

the Internet at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf>. 

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

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

2010). 

Just, R.E., D.L. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy. 

Northampton, MA: Edward Elgar. 

Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining 

Estimates of the Value of Statistical Life for Environmental Policy Analysis. 

Environmental and Resource Economics 34:385–406. 
R-3 

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

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

Medicine 173:667-672. 

Morgenstern, R. D., W. A. Pizer, and J. S. Shih. 2002. Jobs versus the environment: An industrylevel perspective. Journal of Environmental Economics and Management 43(3):412-436. 

Morgenstern, R.D., M. Ho, J.S. Shih, and X. Zhang. 2004. “The Near-Term Impacts of Carbon 

Mitigation Policies on Manufacturing Industries.” Energy Policy 32(16): 1825-1841. 

Mrozek, J. R., and L. O. Taylor. 2002. What Determines the Value of Life? A Meta-Analysis. 

Journal of Policy Analysis and Management 21(2):253-270. 

Munthe, J., R. A. Bodaly, B. A. Branfireun, C. T. Driscoll, C. C. Gilmour, R. Harris, M. Horvat, 

M. Lucotte, and O. Malm. 2007. Recovery of mercury-contaminated fisheries. AMBIO: A 

Journal of the Human Environment 36:33–44. 

National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee 

on the Toxicological Effects of Methylmercury, Board on Environmental Studies and 

Toxicology. National Academies Press. Washington, DC. pp.168-173. 

National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee 

on the Toxicological Effects of Methylmercury, Board on Environmental Studies and 

Toxicology. National Academies Press. Washington, DC. p. 229. 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: The National Academies Press. 

National Research Council (NRC). 2009. Hidden Costs of Energy: Unpriced Consequences of 

Energy Production and Use. National Academies Press. Washington, DC. 

Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis. 

Washington, DC. Available on the internet at 

http://www.whitehouse.gov/omb/circulars/a004/a-4.html. 

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

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

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

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

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, 

Bryan J. Hubbell, and Patrick L. Kinney. 2008. Expert Judgment Assessment of the 

Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S. Environ. Sci. 

Technol. 42(7):2268-2274. 
R-4 

Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid 

peroxidation, and the risk of myocardial infarction and coronary, cardiovascular and any 

death in Eastern Finnish men.” Circulation, 91 (3):645-655. 

Scudder, B.C., Chasar, L.C., Wentz, D.A., Bauch, N.J., Brigham, M.E., Moran, P.W., and 

Krabbenhoft, D.P. 2009. Mercury in fish, bed sediment, and water from streams across 

the United States, 1998–2005: U.S. Geological Survey Scientific Investigations Report 

2009–5109, 74 p. 

Shoven, J.B., and J. Whalley. 1995. Applying General Equilibrium. New York: Cambridge 

University Press. 

Sisler, J.F. 1996. Spatial and seasonal patterns and long-term variability of the composition of 

the haze in the United States: an analysis of data from the IMPROVE network. CIRA 

Report, ISSN 0737-5352-32, Colorado State University. 

Sorensen, N., K. Murata, E. Budtz-Jorgensen, P. Weihe, and Grandjean, P. 1999. “Prenatal 

Methylmercury Exposure As A Cardiovascular Risk Factor At Seven Years of Age,” 

Epidemiology, pp 370-375. 

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

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

BEA. 

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

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

January 8, 2010). 

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

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

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

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

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

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

U.S. Census Bureau. 2009. 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>. 

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

“Sector 00: EC0700A1: All sectors: Geographic Area Series: Economy-Wide Key 

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

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

“Sector 00: EC0700A1: All sectors: Geographic Area Series: Economy-Wide Key 

Statistics: 2007.” Accessed on December 27, 2009. 
R-5 

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

“Sector 00: EC0700A1: All sectors: Geographic Area Series: Economy-Wide Key 

Statistics: 2007.” <http://factfinder.census.gov>. Accessed on December 28, 2009. 

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

“Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for 

the United States: 2007.” <http://factfinder.census.gov>. Accessed on December 27, 

2009. 

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

“Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for 

the United States: 2007.” <http://factfinder.census.gov>. Accessed on December 28, 

2009. 

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

sectors: Core Business Statistics Series: Comparative Statistics for the United States and 

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

2008. 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 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). 

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

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

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

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

Consumption by Manufacturers—Data Tables.” Table 3.1. 

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

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

Annual Energy Outlook 2008: With Projections for 2030.” 

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

U.S. Environmental Protection Agency (U.S. EPA). 1999. OAQPS Economic Analysis Resource 

Document. Office of Air Quality Planning and Standards. 

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

U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic 

Analyses. EPA 240-R-00-003. National Center for Environmental Economics, Office of 

Policy Economics and Innovation. Washington, DC. September. Available on the Internet 

at <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>. 
R-6 

U.S. Environmental Protection Agency (U.S. EPA). September 2000. Guidelines for Preparing 

Economic Analyses. EPA 240-R-00-003. Washington, DC: EPA. 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html. Accessed August 

2009. 

U.S. Environmental Protection Agency (U.S. EPA). 2002. “Profile of the Pulp and Paper 

Industry.” Sector Notebook Project. 

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

sectors/notebooks/index.html>. 

U.S. Environmental Protection Agency(U.S. EPA). 2006. Regulatory Impact Analysis, 2006 

National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for 

Sulfur Oxides—Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Guidelines for Preparing Economic 

Analyses: External Review Draft. National Center for Environmental Economics, Office 

of Policy Economics and Innovation. Washington, DC. Available at 

<http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE-0516-01.pdf/$File/EE-0516-

01.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for 

Oxides of Nitrogen and Sulfur–Ecological Criteria National (Final Report). National 

Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139. 

December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>. 

U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: 

National Emission Standards for Hazardous Air Pollutants from the Portland Cement 

Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. April. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf >. 

U.S. Environmental Protection Agency (U.S. EPA). 2009b. 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). 2009c. Risk and Exposure Assessment to 

Support the Review of the SO2 Primary National Ambient Air Quality Standards: Final 

Report. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

August. Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/so2/data/Risk%20and%20Exposure%20Assess

ment%20to%20Support%20the%20Review%20of%20the%20SO2%20Primary%20Natio

nal%20Ambient%20Air%20Quality%20Standards-%20Final%20Report.pdf>. 
R-7 

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 on the Internet 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 SO2 National Ambient Air Quality Standards (NAAQS). Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. June. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010c. Summary of Expert Opinions on the 

Existence of a Threshold in the Concentration-Response Function for PM2.5-related 

Mortality: Technical Support Document. Compiled by Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. July. Available at 

<http://www.epa.gov/ttn/ecas/benefits.html>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010d. Lowest Measured Level (LML) 

Assessment for Rules without Policy-Specific Air Quality Data Available: Technical 

Support Document. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. July. Available at < 

http://www.epa.gov/ttn/ecas/regdata/Benefits/thresholdstsd.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010e. Regulatory Impact Analysis for the 

Proposed Federal Transport Rule. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. July. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/proposaltrria_final.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010f. Regulatory Impact Analysis for the 

SO2 NAAQS. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

June. Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf>. 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2004. 

Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second 

Prospective Analysis – Benefits and Costs of the Clean Air Act, 1990-2020. Advisory by 

the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance 

Analysis. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E540

0433D5D/$File/council_adv_04002.pdf>. 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2007. SAB 

Advisory on EPA’s Issues in Valuing Mortality Risk Reduction. EPA-SAB-08-001. 

October. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$F

ile/sab-08-001.pdf >. 
R-8 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009a. 

Review of EPA’s Integrated Science Assessment for Particulate Matter (First External 

Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available on the Internet 

at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb8525 

6ead006be86e/73ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-

unsigned.pdf>. 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009b. 

Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: 

Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-

009. May. Available on the Internet at <http://yosemite.epa.gov/sab/ 

SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/723FE644C5D758DF85257

5BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>. 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2010. 

Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of 

the Clean Air Act. EPA-COUNCIL-10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB2852577450073877

6/$File/EPA-COUNCIL-10-001-unsigned.pdf>. 

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

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

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>. 

Viscusi, V. K., and J. E. Aldy. 2003. The Value of a Statistical Life: A Critical Review of Market 

Estimates throughout the World. Journal of Risk and Uncertainty 27(1):5-76. 
A-1 

APPENDIX A 

OAQPS MULTIMARKET MODEL TO ASSESS THE ECONOMIC 

IMPACTS OF ENVIRONMENTAL 

REGULATION 

A.1 Introduction 

An economic impact analysis (EIA) provides information about a policy’s effects (i.e., 

social costs); emphasis is also placed on how the costs are distributed among stakeholders (U.S. 

EPA, 2000). In addition, large-scale policies require additional analysis to better understand how 

costs are passed across the economy. Although several tools are available to estimate social 

costs, current EPA guidelines suggest that multimarket models “...are best used when potential 

economic impacts and equity effects on related markets might be considerable” and modeling 

using a computable general equilibrium model is not available or practical (U.S. 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 (U.S. EPA, 1999). Recent studies suggest short-run analyses can complement 

full dynamic general equilibrium analysis. 

The multimarket model described in this appendix is a new addition to the Office of Air 

quality Planning and Standards’ (OAQPS’s) economic model tool kit; it is designed to be used as 

a transparent tool that can respond quickly to requests about how stakeholders in 100 U.S. 

industries might respond to new environmental policy. Next, we provide an overview of the 

model, data, and parameters. 

A.2 Multimarket Model 

The multimarket model contains the following features: 

 Industry sectors and benchmark data set 

– All industries aggregated to 100 industry sectors 

– a single benchmark year (2010) 

– estimates of industry carbon dioxide (CO2) emissions 

– estimates of industry employment 
A-2 

 Economic behavior 

– industries respond to regulatory costs by changing production rates 

– market prices rise to reflect higher energy and other non-energy material costs 

– customers respond to these price increases and consumption falls 

 Model scope 

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

nonenergy materials. For example, the construction industry is linked with the 

petroleum, cement, and steel industries and is influenced by price changes that 

occur in each sector. The links allow EPA to account for indirect effects the 

regulation has on related markets. 

– Links come from input-output information used from OAQPS’s computable 

general equilibrium model (EMPAX) 

– production adjustments influence employment levels 

– international trade (imports/exports) behavior considered 

 Model time horizon (“short-run”) 

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

supply function 

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

production inputs 

– price of labor (i.e., wage) is fixed 

– investment and government expenditures are fixed 

A.2.1 Industry Sectors and Benchmark Data Set 

The multimarket model includes 100 industries. For the benchmark year, the model uses 

information from OAQPS’s computable general equilibrium model’s balanced social accounting 

matrix (SAM) and the following accounting identity holds (U.S. EPA, 2008): 

 Output + Imports = Consumption + Investment + Government + Exports (A.1) 

If we abstract and treat each industry as a national market, the identity represents the prepolicy market-clearing condition, or benchmark “equilibrium”; supply equals demand in each 

market. In Table A-1, we identify the 100 industries for the multimarket model; Table A-2 

provides the 2010 benchmark data set. Since the benchmark data are reported in value terms, we 

also use the common “Harberger convention” and choose units where are all prices are one in the 

benchmark equilibrium (Shoven and Whalley, 1995). 
A-3 

Table A-1. Industry Sectors Included in Multimarket Model 

Industry Label Description Representative NAICSa

Energy Industries 

COL Coal 2121 

CRU Crude Oil Extraction 211111 (exc. nat gas) 

ELE Electric Generation 2211 

GAS Natural Gas 211112 2212 4862 

OIL Refined Petroleum 324 

Nonmanufacturing 

AGR Agricultural 11 

MIN Mining 21 less others 

CNS Construction 23 

Manufactured Goods 

Food, beverages, and textiles 

ANM Animal Foods 3111 

GRN Grain Milling 3112 

SGR Sugar 3113 

FRU Fruits and Vegetables 3114 

MIL Dairy Products 3115 

MEA Meat Products 3116 

SEA Seafood 3117 

BAK Baked Goods 3118 

OFD Other Food Products 3119 

BEV Beverages and Tobacco 312 

TEX Textile Mills 313 

TPM Textile Product Mills 314 

WAP Wearing Apparel 315 

LEA Leather 316 

Lumber, paper, and printing 

SAW Sawmills 3211 

PLY Plywood and Veneer 3212 

LUM Other Lumber 3219 

PAP Pulp and Paper Mills 3221 

CPP Converted Paper Products 3222 

PRN Printing 323 

Chemicals 

CHM Chemicals and Gases 3251 

RSN Resins 3252 

FRT Fertilizer 3253 

MED Drugs and Medicine 3254 

PAI Paints and Adhesives 3255 

SOP Soap 3256 

OCM Other Chemicals 3259 

(continued) 
A-4 

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

Industry Label Description Representative NAICSa

Plastics and Rubber 

PLS Plastic 3261 

RUB Rubber 3262 

Nonmetallic Minerals 

CLY Clay 3271 

GLS Glass 3272 

CEM Cement 3273 

LIM Lime and Gypsum 3274 

ONM Other Non-Metallic Minerals 3279 

Primary Metals 

I_S Iron and Steel 3311 3312 33151 

ALU Aluminum 3313 331521 331524 

OPM Other Primary Metals 3314 331522 331525 331528 

Fabricated Metals 

FRG Forging and Stamping 3321 

CUT Cutlery 3322 

FMP Fabricated Metals 3323 

BOI Boilers and Tanks 3324 

HRD Hardware 3325 

WIR Springs and Wires 3326 

MSP Machine Shops 3327 

EGV Engraving 3328 

OFM Other Fabricated Metals 3329 

Machinery and Equipment 

CEQ Construction and Agricultural 

Equipment 

3331 

IEQ Industrial Equipment 3332 

SEQ Service Industry Equipment 3333 

HVC HVAC Equipment 3334 

MEQ Metalworking Equipment 3335 

EEQ Engines 3336 

GEQ General Equipment 3339 

Electronic Equipment 

CPU Computers 3341 

CMQ Communication Equipment 3342 

TVQ TV Equipment 3343 

SMI Semiconductor Equipment 3344 

INS Instruments 3345 

MGT Magnetic Recording Equipment 3346 

LGT Lighting 3351 

APP Appliances 3352 

(continued) 
A-5 

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

Industry Label Description Representative NAICSa

Electronic Equipment (continued)

ELQ Electric Equipment 3353 

OEQ Other Electric Equipment 3359 

Transportation Equipment 

M_V Motor Vehicles 3361 

TKB Truck Bodies 3362 

MVP Motor Vehicle Parts 3363 

ARC Aircraft 3364 

R_R Rail Cars 3365 

SHP Ships 3366 

OTQ Other Transport Equipment 3369 

Other 

FUR Furniture 337 

MSC Miscellaneous Manufacturing 339 

Services 

Wholesale and Retail Trade 

WHL Wholesale Trade 42 

RTL Retail Trade 44–45 

Transportation Services 

ATP Air Transportation 481 

RTP Railroad Transportation 482 

WTP Water Transportation 483 

TTP Freight Truck Transportation 484 

PIP Pipeline Transport 486 

OTP Other Transportation Services 485 487 488 

Other Services 

INF Information 51 

FIN Finance and Insurance 52 

REL Real Estate 53 

PFS Professional Services 54 

MNG Management 55 

ADM Administrative Services 56 

EDU Education 61 

HLT Health Care 62 

ART Arts 71 

ACM Accommodations 72 

OSV Other Services 81 

PUB Public Services 92 

a NAICS = North American Industry Classification System. Industry assignments are based on data used in the 

EMPAX-modeling system, which relies on the commodity code system used in IMPLAN. 
A-6 

Table A-2. 2010 Benchmark Data Set (billion 2006$) 

Industry 

Label Industry Description Output Imports Consumption

Investment 

and 

Government Exports 

ACM Accommodations $828 $6 $816 $17 $1 

ADM Administrative Services $795 $37 $771 $61 Less than $1 

AGR Agricultural $314 $53 $333 $5 $29 

ALU Aluminum $65 $17 $70 $4 $8 

ANM Animal Foods $45 Less than $1 $36 Less than $1 $9 

APP Appliances $25 $19 $35 $6 $3 

ARC Aircraft $217 $60 $58 $120 $98 

ART Arts $252 Less than $1 $246 $3 $3 

ATP Air Transportation $154 $28 $91 $32 $59 

BAK Baked Goods $61 $3 $61 $2 Less than $1 

BEV Beverages and Tobacco $133 $54 $186 Less than $1 $1 

BOI Boilers and Tanks $27 $2 $19 $9 $2 

CEM Cement $52 Less than $1 $47 $3 $2 

CEQ Construction and 

Agricultural 

Equipment 

$70 $24 $47 $33 $14 

CHM Chemicals and Gases $284 $75 $300 $10 $49 

CLY Clay $8 $4 $10 $1 $2 

CMQ Communication 

Equipment 

$73 $40 $47 $56 $11 

CNS Construction $983 $77 $594 $465 Less than $1 

COL Coal $44 $2 $42 Less than $1 $4 

CPP Converted Paper 

Products 

$52 $2 $43 $6 $6 

CPU Computers $145 $76 $132 $52 $37 

CRU Crude Oil Extraction $67 $189 $255 Less than $1 Less than $1 

CUT Cutlery $11 $5 $9 $5 $2 

EDU Education $970 Less than $1 $257 $701 $13 

EEQ Engines $36 $14 $30 $6 $13 

EGV Engraving $21 Less than $1 $9 $5 $7 

ELE Electric Generation $317 Less than $1 $287 $31 Less than $1 

ELQ Electric Equipment $33 $16 $23 $17 $10 

FIN Finance and Insurance $2,015 $106 $1,972 $43 $106 

FMP Fabricated Metals $66 $1 $58 $7 $2 

FRG Forging and Stamping $20 Less than $1 $17 $1 $2 

FRT Fertilizer $42 $5 $33 $4 $10 

(continued) 
A-7 

Table A-2. 2010 Benchmark Data Set (billion 2006$) (continued) 

Industry 

Label Industry Description Output Imports Consumption

Investment 

and 

Government Exports 

FRU Fruits and Vegetables $74 $12 $76 $4 $5 

FUR Furniture $66 $38 $84 $17 $2 

GAS Natural Gas $139 $32 $160 $6 $6 

GEQ General Equipment $54 $32 $47 $25 $14 

GLS Glass $30 Less than $1 $18 $2 $10 

GRN Grain Milling $77 $9 $74 $2 $10 

HLT Health Care $1,863 Less than $1 $1,823 $20 $20 

HRD Hardware $8 $4 $5 $4 $3 

HVC HVAC Equipment $34 $9 $26 $10 $6 

I_S Iron and Steel $125 $42 $143 $10 $13 

IEQ Industrial Equipment $26 $14 $16 $14 $11 

INF Information $1,305 $77 $1,217 $154 $11 

INS Instruments $112 $44 $71 $65 $20 

LEA Leather $4 $26 $29 Less than $1 $1 

LGT Lighting $12 $11 $16 $5 $1 

LIM Lime and Gypsum $7 Less than $1 $1 Less than $1 $5 

LUM Other Lumber $41 $2 $32 $9 $2 

M_V Motor Vehicles $272 $190 $313 $106 $43 

MEA Meat Products $174 $9 $169 $5 $10 

MED Drugs and Medicine $258 $102 $316 $18 $27 

MEQ Metalworking 

Equipment 

$24 $11 $16 $14 $4 

MGT Magnetic Recording 

Equipment 

$15 $2 $13 $2 $2 

MIL Dairy Products $87 $3 $84 $4 $2 

MIN Mining $53 $2 $30 $15 $11 

MNG Management $469 Less than $1 $378 Less than $1 $92 

MSC Miscellaneous 

Manufacturing 

$178 $129 $189 $73 $46 

MSP Machine Shops $38 $2 $32 $5 $2 

MVP Motor Vehicle Parts $220 $75 $226 $17 $52 

OCM Other Chemicals $45 $2 $23 $9 $15 

OEQ Other Electric 

Equipment 

$31 $16 $28 $7 $11 

OFD Other Food Products $92 $7 $90 $2 $7 

OFM Other Fabricated 

Metals 

$56 $28 $50 $22 $12 

(continued) 
A-8 

Table A-2. 2010 Benchmark Data Set (billion 2006$) (continued) 

Industry 

Label Industry Description Output Imports Consumption

Investment 

and 

Government Exports 

OIL Refined Petroleum $415 $106 $462 $12 $47 

ONM Other Non-Metallic 

Minerals 

$13 $5 $15 $1 $2 

OPM Other Primary Metals $40 $27 $52 $2 $12 

OSV Other Services $2,321 Less than $1 $1,479 $598 $244 

OTP Other Transportation 

Services 

$245 Less than $1 $202 $22 $22 

OTQ Other Transport Equip $23 $10 $14 $13 $5 

PAI Paints and Adhesives $36 $1 $28 $3 $6 

PAP Pulp and Paper Mills $131 $21 $133 $5 $14 

PFS Professional Services $2,103 $84 $1,715 $461 $10 

PIP Pipeline Transport $37 $83 $20 $98 $1 

PLS Plastic $145 $14 $139 $4 $15 

PLY Plywood and Veneer $19 $8 $25 $1 $1 

PRN Printing $51 $1 $34 $11 $6 

PUB Public Services $1,099 $22 $355 $766 Less than $1 

R_R Rail Cars $11 $2 $6 $6 $2 

REL Real Estate $2,719 $2 $2,559 $131 $31 

RSN Resins $107 $23 $98 $6 $26 

RTL Retail Trade $1,440 $53 $1,412 $82 Less than $1 

RTP Railroad Transportation $70 Less than $1 $42 $18 $11 

RUB Rubber $38 $20 $36 $15 $6 

SAW Sawmills $29 $9 $36 $1 $1 

SEA Seafood $13 $3 $14 $1 $1 

SEQ Service Industry 

Equipment 

$29 $23 $22 $24 $6 

SGR Sugar $34 $6 $36 $2 $3 

SHP Ships $36 $6 $13 $29 Less than $1 

SMI Semiconductor 

Equipment 

$141 $69 $157 $12 $41 

SOP Soap $82 $5 $74 $3 $9 

TEX Textile Mills $29 $9 $31 $1 $6 

TKB Truck Bodies $58 $12 $34 $32 $5 

TPM Textile Product Mills $27 $19 $37 $7 $2 

TTP Freight Truck 

Transportation 

$301 Less than $1 $211 $39 $51 

(continued) 
A-9 

Table A-2. 2010 Benchmark Data Set (billion 2006$) (continued) 

Industry 

Label Industry Description Output Imports Consumption 

Investment 

and 

Government Exports 

TVQ TV Equipment $19 $37 $50 $3 $3 

WAP Wearing Apparel $25 $94 $117 $1 Less than $1

WHL Wholesale Trade $1,309 $22 $1,021 $172 $138 

WIR Springs and Wires $5 $2 $1 $3 

WTP Water Transportation $45 $14 $12 $19 

A.2.1.2 Employment Data 

The model includes employment forecasts for each of the 100 sectors. Employment 

estimates are based on data from three sources: the AEO 2009 estimates of employment (AEO 

supplemental Table 126 and indicators of Macroeconomic Activity), and Global Insights, Inc., 

and the Bureau of Labor Statistics (BLS) 2008 end-of year-employment (Current Employment 

Statistics—CES [National]). Typically, 3-digit NAICS sectors’ employment estimates are either 

directly reported in the updated AEO 2009 release or Global Insights For multimarket industries 

with finer NAICs detail, estimates were calculated by selecting a primary NAICS supersector 

estimate (AEO or Global Insights) and distributing total employment from the larger NAICS 

supersectors across more detailed NAICS sectors within the super-sector. The distributions were 

determined using observed 2008 BLS employment data. In the last step, In order to match 

aggregate U.S. employment numbers reported in the AEO 2009 release (140.1 million), a single 

adjustment factor was applied to all sectors that use Global Insights’ supersector data.1

 Table A-4 

reports the baseline employment for each of the 100 sectors. 

 

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-10 

Table A-4. 2010 U.S. Employment Projections 

Industry Label Industry Description 

Projected Employment 

(1,000) 

ACM Accommodations 11,239 

ADM Administrative Services 9,274 

AGR Agricultural 1,607 

ALU Aluminum 87 

ANM Animal Foods 45 

APP Appliances 33 

ARC Aircraft 449 

ART Arts 1,939 

ATP Air Transportation 506 

BAK Baked Goods 247 

BEV Beverages and Tobacco 92 

BOI Boilers and Tanks 67 

CEM Cement 164 

CEQ Construction and Agricultural Equipment 176 

CHM Chemicals and Gases 147 

CLY Clay 38 

CMQ Communication Equipment 73 

CNS Construction 7,446 

COL Coal 79 

CPP Converted Paper Products 306 

CPU Computers 104 

CRU Crude Oil Extraction 384 

CUT Cutlery 34 

EDU Education 2,892 

EEQ Engines 75 

EGV Engraving 100 

ELE Electric Generation 219 

ELQ Electric Equipment 72 

FIN Finance and Insurance 6,051 

FMP Fabricated Metals 285 

FRG Forging and Stamping 75 

FRT Fertilizer 35 

FRU Fruits and Vegetables 153 

FUR Furniture 327 

(continued) 
A-11 

Table A-4. 2010 U.S. Employment Projections (continued) 

Industry Label Industry Description 

Projected Employment 

(1,000) 

GAS Natural Gas 98 

GEQ General Equipment 198 

GLS Glass 71 

GRN Grain Milling 55 

HLT Health Care 15,190 

HRD Hardware 20 

HVC HVAC Equipment 109 

I_S Iron and Steel 205 

IEQ Industrial Equipment 88 

INF Information 2,939 

INS Instruments 250 

LEA Leather 3 

LGT Lighting 26 

LIM Lime and Gypsum 10 

LUM Other Lumber 216 

M_V Motor Vehicles 170 

MEA Meat Products 450 

MED Drugs and Medicine 279 

MEQ Metalworking Equipment 139 

MGT Magnetic Recording Equipment 20 

MIL Dairy Products 113 

MIN Mining 599 

MNG Management 1,732 

MSC Miscellaneous Manufacturing 180 

MSP Machine Shops 251 

MVP Motor Vehicle Parts 485 

OCM Other Chemicals 92 

OEQ Other Electric Equipment 63 

OFD Other Food Products 144 

OFM Other Fabricated Metals 196 

OIL Refined Petroleum 70 

ONM Other Non-metallic Minerals 61 

OPM Other Primary Metals 87 

OSV Other Services 5,271 

OTP Other Transportation Services 1,064 

(continued) 
A-12 

Table A-4. 2010 U.S. Employment Projections (continued) 

Industry Label Industry Description 

Projected Employment 

(1,000) 

OTQ Other Transport Equipment 36 

PAI Paints and Adhesives 60 

PAP Pulp and Paper Mills 121 

PFS Professional Services 18,989 

PIP Pipeline Transport 43 

PLS Plastic 473 

PLY Plywood and Veneer 74 

PRN Printing 248 

PUB Public Services 21,787 

R_R Rail Cars 25 

REL Real Estate 2,158 

RSN Resins 102 

RTL Retail Trade 15,283 

RTP Railroad Transportation 236 

RUB Rubber 117 

SAW Sawmills 84 

SEA Seafood 36 

SEQ Service Industry Equipment 77 

SGR Sugar 62 

SHP Ships 140 

SMI Semiconductor Equipment 245 

SOP Soap 104 

TEX Textile Mills 110 

TKB Truck Bodies 126 

TPM Textile Product Mills 32 

TTP Freight Truck Transportation 1,429 

TVQ TV Equipment 15 

WAP Wearing Apparel 153 

WHL Wholesale Trade 5,869 

WIR Springs and Wires 36 

WTP Water Transportation 67 

Total 144,100 
A-13 

A.2.2 Economic Behavior 

A.2.2.1 U.S. Supply 

In a postpolicy scenario, industry responds to changes in the new market-clearing “net” 

price for the good or service sold: 

 %Δ”net” price = %Δ market price − %Δ direct costs − %Δ indirect costs (A.2) 

The %Δ direct costs are approximated using the engineering cost analysis and baseline 

value of output. For example, a $1 billion increase in compliance costs for the electricity sector 

(ELE) would be represented in the model as follows: 

 %Δ direct costs = $1/$317= 0.03% (A.3) 

As shown in Figure A-1, the cost change provides the industry with incentives to alter 

production rates at current market prices; market prices must rise to maintain the original prepolicy production levels (Q). 

The multimarket model also simultaneously considers how the policy influences other 

important production costs (via changes in energy and other intermediate material prices). As a 

result, the multimarket model can provide additional information about how policy costs are 

transmitted through the economy. As shown in Figure A-2, the indirect cost change provides the 

industry with additional incentives to alter production rates at current market prices. 

The %Δ indirect effects associated with each input are approximated using an input “use” 

ratio and the price change that occurs in the input market. 

 %Δ indirect costs = input use ratio x %Δ input price (A.4) 

The social accounting matrix provides an internally consistent estimate of the use ratio 

and describes the dollar amount of an input that is required to produce a dollar of output. Higher 

ratios suggest strong links between industries, while lower ratios suggest weaker links. Given the 

short time horizon, we assume the input use ratio is fixed and cannot adjust their input mix; this 

is a standard assumption in public and commercial input-output (IO) and SAM multiplier models 

(Berck and Hoffmann, 2002). Morgenstern and colleagues (2004) and Ho and colleagues (2008) 

also use this assumption when examining near-term effects of environmental policy. 
A-14 

Figure A-1. Direct Compliance Costs Reduce Production Rates at Benchmark Prices 

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

Following guidance in the OAQPS economic resource manual (OAQPS, 1999), we use a 

general form for the U.S. industry supply function: 

g ε n

i

g g gi i i Q b P t (P P )



 



       1



 (A.5) 

where 

Qg

 = with-policy supply quantity (g) 

b = calibrated scale parameter for the supply price relationship 
A-15 

Pg

 = with-policy price for output (g) 

t = direct compliance costs per unit of supply 

gi = input use ratio (g using input i) 

Pi

 = with-policy input (i) price 

Pi = benchmark input (i) price 

g  = price elasticity of supply for output (g) 

The key supply parameter that controls the industry production adjustments is the price 

elasticity of supply (εg).To our knowledge, there is no existing empirical work that estimates 

short-run supply elasticities for all industry groups used in the multimarket model. As a result, 

we assume the U.S. supply elasticities are a function of econometrically estimated rest-of-world 

(ROW) export supply elasticities (see discussion in the next section). We report the values 

currently available in the model in Table A-5. 

A.2.2.2 International Competition 

International competition is captured by a single ROW supply function: 

  ROW

g ε

g Pg Q  c  (A.6) 

where 

Qg

 = with-policy supply quantity (g) 

c = calibrated scale parameter for the supply and price relationship 

Pg

 = with-policy U.S. price for output (g) 

ROW

g  = price elasticity of supply of goods from the ROW to the United States (imports) 

(g) 

The key supply parameter that controls the ROW supply adjustments is the price 

elasticity of supply ( ROW

g  ). We obtained these estimates for a variety of industry groups from a 

recently published article by Broda and colleagues (2008b). 
A-16 

Table A-5. Supply Elasticities 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

ACM Accommodations 0.7 0.7 

ADM Administrative Services 0.7 0.7 

AGR Agricultural 1.0 1.0 

ALU Aluminum 0.8 0.5 

ANM Animal Foods 1.1 0.8 

APP Appliances 0.9 0.8 

ARC Aircraft 0.9 0.6 

ART Arts 0.7 0.7 

ATP Air Transportation 0.7 0.7 

BAK Baked Goods 0.8 0.7 

BEV Beverages and Tobacco 2.9 2.9 

BOI Boilers and Tanks 1.1 0.8 

CEM Cement 0.9 0.7 

CEQ Construction and Agricultural Equipment 0.8 0.6 

CHM Chemicals and Gases 1.1 0.8 

CLY Clay 0.8 0.6 

CMQ Communication Equipment 2.5 1.0 

CNS Construction 0.7 0.7 

COL Coal 2.2 2.2 

CPP Converted Paper Products 0.9 0.7 

CPU Computers 1.0 0.7 

CRU Crude Oil Extraction 3.7 3.7 

CUT Cutlery 1.4 1.1 

EDU Education 0.7 0.7 

EEQ Engines 1.2 1.0 

EGV Engraving 1.1 0.8 

ELE Electric Generation 2.0 2.0 

ELQ Electric Equipment 0.8 0.6 

FIN Finance and Insurance 0.7 0.7 

FMP Fabricated Metals 1.2 1.1 

FRG Forging and Stamping 1.6 1.5 

FRT Fertilizer 1.0 0.7 

FRU Fruits and Vegetables 1.0 0.7 

GAS Natural Gas 12.2 12.2 

GEQ General Equipment 1.0 0.7 

GLS Glass 0.8 0.6 

GAS Natural Gas 12.2 12.2 

(continued) 
A-17 

Table A-5. Supply Elasticities (continued) 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

FUR Furniture 1.9 1.9 

GRN Grain Milling 1.7 1.5 

HLT Health Care 0.7 0.7 

HRD Hardware 1.1 0.8 

HVC HVAC Equipment 0.9 0.6 

I_S Iron and Steel 1.0 0.6 

IEQ Industrial Equipment 0.9 0.6 

INF Information 0.7 0.7 

INS Instruments 0.9 0.6 

LEA Leather 0.9 0.7 

LGT Lighting 1.1 0.7 

LIM Lime and Gypsum 0.9 0.7 

LUM Other Lumber 0.9 0.7 

M_V Motor Vehicles 1.3 0.7 

MEA Meat Products 1.2 3.9 

MED Drugs and Medicine 1.3 1.0 

MEQ Metalworking Equipment 0.7 0.5 

MGT Magnetic Recording Equipment 1.0 0.7 

MIL Dairy Products 1.1 0.9 

MIN Mining 2.2 2.2 

MNG Management 0.7 0.7 

MSC Miscellaneous Manufacturing 1.0 0.8 

MSP Machine Shops 1.1 0.8 

MVP Motor Vehicle Parts 0.9 0.6 

OCM Other Chemicals 1.1 0.6 

OEQ Other Electric Equipment 1.0 0.7 

OFD Other Food Products 1.1 0.7 

OFM Other Fabricated Metals 0.9 0.6 

OIL Refined Petroleum 1.0 0.7 

ONM Other Non-metallic Minerals 1.5 0.7 

OPM Other Primary Metals 0.7 0.5 

OSV Other Services 0.7 0.7 

OTP Other Transportation Services 0.7 0.7 

OTQ Other Transport Equipment 1.0 0.7 

PAI Paints and Adhesives 1.0 0.7 

PAP Pulp and Paper Mills 1.1 0.7 

PFS Professional Services 0.7 0.7 

(continued) 
A-18 

Table A-5. Supply Elasticities (continued) 

Industry Label Industry Description 

Rest of World 

(ROW) U.S. 

PIP Pipeline Transport 2.0 2.0 

PLS Plastic 1.0 0.7 

PLY Plywood and Veneer 1.3 1.3 

PRN Printing 1.0 0.7 

PUB Public Services 0.7 0.7 

R_R Rail Cars 1.8 0.7 

REL Real Estate 0.7 0.7 

RSN Resins 1.0 0.7 

RTL Retail Trade 0.7 0.7 

RTP Railroad Transportation 0.7 0.7 

RUB Rubber 1.3 1.1 

SAW Sawmills 0.8 0.6 

SEA Seafood 1.1 0.8 

SEQ Service Industry Equipment 0.8 0.6 

SGR Sugar 1.1 0.8 

SHP Ships 1.0 0.7 

SMI Semiconductor Equipment 1.2 1.0 

SOP Soap 0.8 0.6 

TEX Textile Mills 1.0 0.7 

TKB Truck Bodies 3.2 3.1 

TPM Textile Product Mills 0.8 0.6 

TTP Freight Truck Transportation 0.7 0.7 

TVQ TV Equipment 5.8 5.4 

WAP Wearing Apparel 1.2 0.8 

WHL Wholesale Trade 0.7 0.7 

WIR Springs and Wires 1.9 0.8 

WTP Water Transportation 0.7 0.7 

Note: RTI mapped Broda et al. data for their industry aggregation to the multimarket model’s 100 industries. 

Domestic supply elasticities are typically assumed to be within one standard deviation of the sample of supply 

elasticities used for the ROW. In selected cases where this information is not available, the U.S. supply elasticity 

is set equal to the ROW. 

Source: Broda, C., N. Limao, and D. Weinstein. 2008a. “Export Supply Elasticities.” 

http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/ 

TradeElasticities.html. Accessed September 2009. 

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 
A-19 

comparable with the 100-sector model. The first step was to match the Harmonized Trade 

System (HS) elasticities estimated in the article to the appropriate NAICS codes. Many of the HS 

codes correspond with a detailed NAICS codes (5- and 6-digit level), while the multimarket 

sector industries typically correspond with more aggregated sectors (NAICS 2-, 3-, or 4-digit 

levels). To adapt these labels to our model, we combined the 5- and 6- digit NAICS under their 

3- and 4-digit codes and calculated an average inverse elasticity value for codes that fell within 

the multimarket model’s aggregate industrial sectors. This gives a crude way to account for the 

variety of products detailed in the original data set. We also restricted the elasticity sample to 

those that Broda et al. classify as “medium” and “low” categories; these categories tend to have 

lower elasticity values that are consistent with the multimarket model’s modeling horizon (i.e., in 

the short run importers are likely to have less flexibility to respond to price changes and 

elasticities are low).2

Our ideal preference was to use an exact 3- or 4-digit match from the medium category if 

one was available. If the multimarket model had a 4-digit code for which there was no direct 

match, we aggregated up a level and applied the relevant 3-digit elasticity. If a multimarket code 

was not covered in the medium set of elasticities, we used the low elasticity category. This 

method was sufficient for mapping the majority of the sectors in the model. After applying our 

inverse elasticity values to the multimarket sectors, we calculated the inverse of the value to 

arrive at the actual supply elasticity. Since Broda et al.’s article focused on industrial production 

goods, some of the multimarket sectors were not covered in the elasticity data. These sectors 

included mainly service industries, transportation, and energy sources. 

In order to fill these gaps, we turned to the source substitution elasticities from Purdue 

University’s Global Trade Analysis Project (GTAP).3

 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 structure4), the parameters provide us 

with some additional information about the relative trade elasticity differences between industry 

sectors. To use the GTAP information to develop assumptions about the multimarket model 

sectors with missing elasticities, we chose a base industrial sector (iron and steel) for which we 

had parameter value from Broda et al. Next, we developed industry-specific ratios for missing 

industries using the corresponding GTAP sector trade elasticities and the GTAP iron and steel 

 

2

 Broda et al.’s intent was to use these categories to describe or proxy for domestic market power. 3

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

https://www.gtap.agecon.purdue.edu/resources/download/4184.pdf; see Table 14.2 for elasticities. 4

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-20 

sector. We multiplied the resulting ratio by the Broda et al. iron and steel parameter (1.0). For 

example, the GTAP trade elasticity for coal (6.10) is approximately 2.2 times the trade elasticity 

for iron and steel (2.95). As a result, the multimarket import supply elasticity for coal is 

computed as 2.2 (2.2 x 1.0). 

A.2.2.4 Price Elasticity of Supply: United States 

We also used Broda et al.’s elasticities to derive a set of domestic supply elasticities for 

the model. We have assumed that a product’s domestic supply would be equal to or less elastic 

than other countries’ supply of imports. When we aggregated and averaged the original 

elasticities to the 3- and 4- digit NAICS level for our foreign supply elasticities, we also 

calculated the standard deviation of each 3- and 4-digit NAICS sample. By adding the standard 

deviation to the corresponding foreign supply and then taking the inverse, we were able to 

calculate a domestic supply elasticity for each sector that was lower than its foreign counterpart 

while maintaining the structure of the original elasticities. For sectors in which no standard 

deviation was available,5

 we used professional judgment to apply the closest available substitute 

from a similar industry. Without a comparable way of scaling our foreign elasticities for the 

sectors in which we used the GTAP elasticities, we elected to keep the domestic and foreign 

supply elasticities the same. 

A.2.2.5 Demand 

Uses for industry output are divided into three groups: investment/government use, 

domestic intermediate uses, and other final use (domestic and exports). Given the short time 

horizon, investment/government does not change. Intermediate use is determined by the input 

use ratios and the industry output decisions. 

Qi giQg    

 (A.7) 

Qi

 = with-policy input demand quantity (i) 

gi = input use ratio (g using input i) 

Qg

 = with-policy output quantity (g) 

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

economic resource manual (OAQPS, 1999), we use a general form for the U.S. industry demand 

function: 

 

5

No standard deviations were calculated for the 3- and 4-digit codes that had only one observation (i.e., Broda et 

al.’s model used the exact 3- or 4-digit code). 
A-21 

  g Qg a Pg

    (A.8) 

where 

Qg

 = with-policy demand quantity (g) 

a = calibrated scale parameter for the demand and price relationship 

Pg

 = with-policy price for output (g) 

g = price elasticity of demand (g) 

The key parameter that controls consumption adjustments is the price elasticity of 

demand (ηg). To approximate the response, we use demand elasticities that were simulated with a 

general equilibrium model (Ho, Morgenstern, and Shih, 2008). Table A-6 reports the values 

currently available in the model. 

A.2.2.6 Model Scope 

The multimarket model includes 100 sectors covering energy, manufacturing, and service 

applications. Each sector’s production technology requires the purchase of energy and other 

intermediate goods made by other sectors included in the model. Linking the sectors in this 

manner allows the model to trace direct and indirect policy effects across different sectors. 

Therefore, it is best used when potential economic impacts and equity effects on related markets 

might be important to stakeholders not directly affected by an environmental policy. However, 

the model can also be run in single-market partial equilibrium mode to support and provide 

insights for other types of environmental policies. 

A.2.2.7 Model Time Horizon 

The model is designed to address short-run and transitional effects associated with 

environmental policy. Production technologies are fixed; the model does not assess substitution 

among production inputs (labor, energy intermediates, and other intermediates) and assumes 

each investment cannot be changed during the time frame of the analysis. These issues are better 

addressed using other frameworks such as computable general equilibrium modeling. Similarly, 

government purchases from each sector do not adjust in response to changes in goods/service 

prices. Although, employment levels (number of jobs) adjust as production levels change, wages 

are assumed to be fixed. 
A-22 

Table A-6. U.S. Demand Elasticities 

Industry Label Industry Description 

Demand Elasticity 

ηg

ACM Accommodations −0.7 

ADM Administrative Services −0.7 

AGR Agricultural −0.8 

ALU Aluminum −1.0 

ANM Animal Foods −0.6 

APP Appliances −2.6 

ARC Aircraft −2.5 

ART Arts −0.7 

ATP Air Transportation −0.8 

BAK Baked Goods −0.6 

BEV Beverages and Tobacco −0.6 

BOI Boilers and Tanks −0.5 

CEM Cement −0.8 

CEQ Construction and Agricultural Equipment −1.7 

CHM Chemicals and Gases −1.0 

CLY Clay −0.8 

CMQ Communication Equipment −2.6 

CNS Construction −0.8 

COL Coal −0.1 

CPP Converted Paper Products −0.7 

CPU Computers −2.6 

CRU Crude Oil Extraction −0.3 

CUT Cutlery −0.5 

EDU Education −0.7 

EEQ Engines −1.7 

EGV Engraving −0.5 

ELE Electric Generation −0.2 

ELQ Electric Equipment −2.6 

FIN Finance and Insurance −0.7 

FMP Fabricated Metals −0.5 

FRG Forging and Stamping −0.5 

FRT Fertilizer −1.0 

FRU Fruits and Vegetables −0.6 

FUR Furniture −0.7 

(continued) 
A-23 

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

Industry Label Industry Description 

Demand Elasticity 

ηg

GAS Natural Gas −0.3 

GEQ General Equipment −1.7 

GLS Glass −0.8 

GRN Grain Milling −0.6 

HLT Health Care −0.7 

HRD Hardware −0.5 

HVC HVAC Equipment −1.7 

I_S Iron and Steel −1.0 

IEQ Industrial Equipment −1.7 

INF Information −0.7 

INS Instruments −2.6 

LEA Leather −1.1 

LGT Lighting −2.6 

LIM Lime and Gypsum −0.8 

LUM Other Lumber −0.7 

M_V Motor Vehicles −2.5 

MEA Meat Products −0.6 

MED Drugs and Medicine −1.0 

MEQ Metalworking Equipment −1.7 

MGT Magnetic Recording Equipment −2.6 

MIL Dairy Products −0.6 

MIN Mining −0.6 

MNG Management −0.7 

MSC Miscellaneous Manufacturing −1.7 

MSP Machine Shops −0.5 

MVP Motor Vehicle Parts −2.5 

OCM Other Chemicals −1.0 

OEQ Other Electric Equipment −2.6 

OFD Other Food Products −0.6 

OFM Other Fabricated Metals −0.5 

OIL Refined Petroleum −0.1 

ONM Other Non-metallic Minerals −0.8 

OPM Other Primary Metals −1.0 

OSV Other Services −0.7 

OTP Other Transportation Services −0.8 

(continued) 
A-24 

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

Industry Label Industry Description 

Demand Elasticity 

ηg

OTQ Other Transport Equip −2.5 

PAI Paints and Adhesives −1.0 

PAP Pulp and Paper Mills −0.7 

PFS Professional Services −0.7 

PIP Pipeline Transport −0.8 

PLS Plastic −1.0 

PLY Plywood and Veneer −0.7 

PRN Printing −0.7 

PUB Public Services −0.7 

R_R Rail Cars −2.5 

REL Real Estate −0.7 

RSN Resins −1.0 

RTL Retail Trade −0.7 

RTP Railroad Transportation −0.8 

RUB Rubber −1.0 

SAW Sawmills −0.7 

SEA Seafood −0.6 

SEQ Service Industry Equipment −1.7 

SGR Sugar −0.6 

SHP Ships −2.5 

SMI Semiconductor Equipment −2.6 

SOP Soap −1.0 

TEX Textile Mills −1.1 

TKB Truck Bodies −2.5 

TPM Textile Product Mills −1.1 

TTP Freight Truck Transportation −0.8 

TVQ TV Equipment −2.6 

WAP Wearing Apparel −2.4 

WHL Wholesale Trade −0.7 

WIR Springs and Wires −0.5 

WTP Water Transportation −0.8 

Note: RTI assigned an elasticity using the most similar industry from Ho and colleagues’ industry aggregation. 

Source: Ho, M. S, R. Morgenstern, and J. S. Shih. 2008. “Impact of Carbon Price Policies on US Industry.” RFF 

Discussion Paper 08-37. http://www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?. 

Publicationid=20680. Accessed August 2009. Table B.6. 
B-1 

APPENDIX B 

INDUSTRY PROFILES 

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

rule. The industries were selected based on high facility population counts within 3-digit NAICs 

industries reported in the combustion facility survey. The purpose is to give the reader a general 

understanding of economic aspects and industry trends to provide additional context for the 

economic impact analysis. 

B.1 Wood Product Manufacturing 

B.1.1 Introduction 

The wood product industry is not forecasted to earn high profits in the near future. 

According to a report by Standard & Poor’s (O’Reilly, 2008), a number of factors are shaping 

the current economic environment for wood products, including, but not limited to, the housing 

slump, high input costs, low prices for lumber and other building materials, and a weak dollar. 

Table B-1 shows that revenues in this industry are variable, exhibiting a decrease in shipment 

revenue between 1997 and 2002, an increase to within $5 billion of the 1997 value in 2006 and 

another decline to within $14 billion of the 2006 value in 2007. Upon closer review, one also 

notices a rise in the cost of labor (shipment revenue per dollar of payroll—$5.54 in 2002, $6.20 

in 2006, and $5.84 in 2007) and material inputs during this same time period, making high profit 

margins difficult to predict. 

While total payroll dropped 3% over this time, annual payroll per employee rose 6.5% 

from 1997 to 2007 because of the decline in the number of employees (Table B-2). Shipments 

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

The U.S. Census Bureau categorizes this industry’s facilities into three categories: 

“sawmills and wood preservation”; “veneer, plywood, and engineered wood product 

manufacturing”; and “other wood product manufacturing.” These are further divided into the 

following types of facilities as defined by the Census Bureau: 

 Sawmills and Wood Preservation 

– Sawmills and Wood Preservation (NAICS 32111): This industry comprises 

establishments primarily engaged in one or more of the following manufacturing 

activities: (a) sawing dimension lumber, boards, beams, timber, poles, ties, 

shingles, shakes, siding, and wood chips from logs or bolts; (b) sawing round 

wood poles, pilings, and posts and treating them with preservatives; and 
B-2 

(c) treating wood sawed, planed, or shaped in other establishments with creosote 

or other preservatives to prevent decay and to protect against fire and insects. 

Sawmills may plane the rough lumber that they make with a planing machine to 

achieve smoothness and uniformity of size. 

Table B-1. Key Statistics: Wood Product Manufacturing (NAICS 321) 

 1997 2002 2006 2007 

Shipments ($2007, 106) $110,956 $102,721 $115,390 $101,879 

Payroll ($2007, 106) $17,959 $18,528 $18,623 $17,439 

Employees 570,034 543,459 536,094 519,651 

Establishments 17,367 17,255 NA 14,862 

NA = Not available. 

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

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

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

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

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

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

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

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

December 27, 2009. 

Table B-2. Industry Ratios: Wood Product Manufacturing (NAICS 321) 

Industry Ratios 1997 2002 2006 2007 

Total shipments ($2007, 106) $110,956 $102,721 $115,390 $101,879 

Shipments per establishment ($103) $25,613 $5,953 NA $6,855 

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

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

Annual payroll per employee ($2007) $31,504 $34,093 $34,738 $33,558 

Employees per establishment 33 31 NA 35 

NA = Not available. 

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

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

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

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

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

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

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

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

December 27, 2009. 
B-3 

 Veneer, Plywood, and Engineered Wood Product Manufacturing 

– Veneer, Plywood, and Engineered Wood Product Manufacturing (NAICS 32121): 

This industry comprises establishments primarily engaged in one or more of the 

following manufacturing activities: (a) veneer and/or plywood, (b) engineered 

wood members, and (c) reconstituted wood products. This industry includes 

manufacturing plywood from veneer made in the same establishment or from 

veneer made in other establishments, and manufacturing plywood faced with nonwood materials, such as plastics or metal. 

 Other Wood Product Manufacturing 

– Millwork (NAICS 32191): This industry comprises establishments primarily 

engaged in manufacturing hardwood and softwood cut stock and dimension stock 

(i.e., shapes); wood windows and wood doors; and other millwork including wood 

flooring. Dimension stock or cut stock is defined as lumber and worked wood 

products cut or shaped to specialized sizes. These establishments generally use 

woodworking machinery, such as jointers, planers, lathes, and routers to shape 

wood. 

– Wood Container and Pallet Manufacturing (NAICS 32192): This industry 

comprises establishments primarily engaged in manufacturing wood pallets, wood 

box shook, wood boxes, other wood containers, and wood parts for pallets and 

containers. 

– All Other Wood Product Manufacturing (NAICS 32199): This industry comprises 

establishments primarily engaged in manufacturing wood products (except 

establishments operating sawmills and wood preservation facilities; and 

establishments manufacturing veneer, plywood, engineered wood products, 

millwork, wood containers, or pallets). 

Figure B-1 shows that the industry proportion of the value of shipments for other wood 

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

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

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. 
B-4 

Veneer, Plywood, &

Engineered Wood

ProductMfg (NAICS

3212), 22%

Sawmills & Wood

Preservation (NAICS

3211),

27%

Other Wood

ProductMfg (NAICS

3219),

51%

Figure B-1. Distribution of Value of Shipments within Wood Product Manufacturing 

(NAICS 322): 2007 

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

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

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

Other Wood

ProductMfg (NAICS

3219),

60%

Sawmills & Wood

Preservation (NAICS

3211), 20%

Veneer, Plywood, &

Engineered Wood

ProductMfg (NAICS

3212),

20%

Figure B-2. Distribution of Employment within Wood Product Manufacturing 

(NAICS 322): 2007 

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

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

<http://factfinder.census.gov>. Accessed on December 27, 2009. [Source for 2007 numbers] 
B-5 

B.1.2 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. 

B.1.2.1 Goods and Services Used in Wood Product Manufacturing 

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

wood product manufacturing industry (Table B-3). 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%. 

Table B-3. Costs of Goods and Services in Wood Product Manufacturing (NAICS 321) 

Industry Ratios 2005 Share 2006 Share 2007 Share 

Total shipments ($2007, 106) $118,705 100% $115,390 100% $102,002 100% 

Total compensation ($2007, 106) $23,327 20% $23,306 20% $22,513 22% 

Annual payroll ($2007) $18,884 16% $18,623 16% $17,444 17% 

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

Total employees 538,890 536,094 524,212 

Average compensation per employee 

($2007) 

$43,286 $43,473 $42,947 

Total production workers’ wages 

($2007, 106

) 

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

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

Total production hours (103

) 911,332 887,613 837,074 

Average production wages per 

hour ($2007) 

$15 $15 $14 

Total cost of materials ($2007, 103

) $71,808 60% $69,892 61% $60,682 59% 

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

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

Purchased fuel ($2007) $810 1% $835 1% $843 1% 

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

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

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

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

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

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

Accessed on December 27, 2009. [Source for 2007 numbers] 
B-6 

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

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

(Table B-4). The largest comes from the wood product industry itself. This is quite 

understandable, since the descriptions of the various industries within wood product 

manufacturing imply that they supply each other with products in order to add value and 

distribute their products to the broader market. The top five inputs are rounded out by forestry 

and logging products, wholesale trade, management of companies and enterprises, and truck 

transportation, which together make up 70% of the total cost of input. 

Table B-4. Key Goods and Services Used in Wood Product Manufacturing (NAICS 321) 

($2007, 106

) 

Description BEA Commodity Code Wood Products 

Wood products 3210 $20,989 

Forestry and logging products 1130 $18,914 

Wholesale trade 4200 $5,417 

Management of companies and enterprises 5500 $2,853 

Truck transportation 4840 $2,542 

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

Other fabricated metal products 332B $1,310 

Nonmetallic mineral products 3270 $1,110 

Real estate 5310 $799 

All other administrative and support services 561A $748 

Architectural and structural metal products 3323 $725 

Rail transportation 4820 $723 

Other inputs $14,650 

Total intermediate inputs T005 $72,169 

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

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

B.1.2.1.1 Energy. The Department of Energy (DOE) categorizes wood product 

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

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

between 2006 and 2030 (DOE, 2008). 
B-7 

Table B-5 shows that total energy use decreased between 1998 and 2002 by 26 and then 

between 2002 and 2006, total energy use rose by 19%. However, Figure B-3 shows that 

electrical power use decreased rapidly during this period, including a steady decline in the latter 

part of 2000. Following a rapid decrease at the end of 2002, electric power use has been 

increasing steadily since then. 

Table B-5. Energy Used in Wood Product Manufacturing (NAICS 321) 

Fuel Type 1998 2002 2006 

Total (trillion BTU) 504 375 445 

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

Residual fuel oil (million bbl) * * 1 

Distillate fuel oilb

 (million bbl) 2 2 3 

Natural gasc

 (billion cu ft) 71 56 84 

LPG and NGLd

 (million bbl) 1 1 1 

Coal (million short tons) * * Q 

Coke and breeze (million short tons) — — * 

Othere (trillion BTU) 341 229 228 

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

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

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

fuel (for example, coal). 

b

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

c

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

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

d

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

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing 

plants, including plants that fractionate raw natural gas liquids (NGLs). 

e

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

that respondents indicated was used to produce heat and power. 

* Estimate less than 0.5. 

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

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

Manufacturers—Data Tables.” Tables 3.2 and N3.2. <http://www.eia.doe.gov/emeu/mecs/mecs2002/ 

data02/shelltables.html>. Washington, DC: DOE. 

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

Manufacturers—Data Tables.” Tables 3.1. <http://www.eia.doe.gov/emeu/mecs/mecs2006/ 

2006tables.html>. [Source for 2006 numbers] 
B-8 

85

90

95

100

105

110

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Jan‐00

Jul‐00

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Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Index Value (Jan‐97=100)

Manufacturing Electric Power Use for NAICS 321

Figure B-3. Electrical Power Use Trends in the Wood Product Manufacturing Industry 

(NAICS 321): 1997–2005 

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

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

B.1.2.2 Uses and Consumers 

Table B-6 shows that three of the top four consumers of wood products are represented 

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

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

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

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

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

transportation, plastics and rubber products manufacturing, and other, use these products. 

B.1.3 Firm and Market Characteristics 

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

basis for further analysis, including regulatory flexibility analyses, as well as a complete picture 

of the recent historical trends of production and pricing. 
B-9 

Table B-6. Demand by Sector: Wood Product Manufacturing (NAICS 321) ($2007, 106

) 

Sector BEA Code 3210 Wood Products 

New residential construction 2302 $19,997 

New nonresidential construction 2301 $11,854 

Furniture and related product manufacturing 3370 $8,197 

Maintenance and repair construction 2303 $4,048 

Motor vehicle body, trailer and parts manufacturing 336A $2,516 

Real estate 5310 $2,335 

Food services and drinking places 7220 $2,307 

Other miscellaneous manufacturing 3399 $1,311 

Wholesale trade 4200 $1,284 

Rail transportation 4820 $1,138 

Retail trade 4A00 $1,047 

Plastics and rubber products manufacturing 3260 $877 

General state and local government use S007 $3,116 

Owner occupied dwelling S008 $11,209 

Private fixed investment F020 $7,933 

Exports of goods and services F040 $3,978 

Total final uses (gross domestic product [GDP]) T004 $3,719 

Total commodity output T007 $101,753 

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

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

B.1.3.1 Location 

As Figure B-4 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. 

B.1.3.2 Production Capacity and Utilization 

Capacity utilization of the wood product manufacturing industry has been experiencing 

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

manufacturing industries combined. The decline in wood product manufacturing is similar to 

total manufacturing between 1997 and 2002. However, capacity utilization in total 

manufacturing, which peaked in 2006, started increasing at a faster rate than wood product 

manufacturing, but decreased sharply after its peak. Wood product manufacturing experienced 

its own rapid decrease in capacity utilization between 2007 and 2009, though not at the same rate 

as total manufacturing (Figure B-5). 
B-10 

Establishments by State

Less than 100

100 - 199

200 - 399

400 - 600

More than 600

Figure B-4. Establishment Concentration in the Wood Product Manufacturing Industry 

(NAICS 321): 2002 

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

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

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

0

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Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Percentage Capacity Utilization

Manufacturing Wood Products

Figure B-5. Capacity Utilization Trends in the Wood Product Manufacturing Industry 

(NAICS 321) 

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

<http://www.federalreserve.gov/datadownload/>.. 
B-11 

B.1.3.3 Employment 

California has the largest number of employees in the wood product manufacturing 

industry with over 39,000 reported in the 2002 census followed by over 32,000 in Oregon 

(Figure B-6). The states with the highest number of employees do not directly correlate with the 

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

Oregon had fewer than 600 establishments, as shown in Figure B-6, 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. 

Employemnt by State

Less than 2,000

2,000 - 4,999

5,000 - 9,999

10,000 - 20,000

More than 20,000

Figure B-6. Employment Concentration in the Wood Product Manufacturing Industry 

(NAICS 321): 2002 

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

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

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

B.1.3.4 Plants and Capacity 

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

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

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

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

though it started to dip again in 2008 and 2009 (Figure B-7). 
B-12 

0

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160

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Jan‐06

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Jul‐09

Index Value (January 1997 = 100)

Manufacturing Food

Figure B-7. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321) 

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

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

B.1.3.5 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 B-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). Although these 

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

which firms in this sector compete. 

B.1.3.6 Size Distribution 

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

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

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

were generated by companies with fewer than 500 employees (Table B-8). The number of 

employees in the small business cutoff is 500 employees for all sub-industries in the wood 

product manufacturing industry (Table B-9). 
B-13 

Table B-7. Largest U.S. Paper and Forest Products Companies: 2006 

Company Revenues ($106)

a

International Paper 21,995 

Weyerhaeuser 21,896 

Smurfit-Stone 7,157 

MeadWestvaco 6,530 

Temple-Inland 5,558 

Bowater 3,530 

Grief Inc. 2,628 

Louisiana-Pacific 2,235 

Packaging Corp. 2,187 

Plum Creek 1,627 

a

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

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

B.1.3.7 Domestic Production 

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

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

manufacturing sector since 1997 (Figure B-8). Similar to capacity utilization trends (Figure B-8), 

the index shows a faster rate of decline for wood products than the entire manufacturing sector. 

B.1.3.8 International Trade 

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

trade deficit (Figure B-9). The value of imports has fluctuated greatly since 1997; however, 

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

B.1.3.9 Market Prices 

Prices of goods in the wood product manufacturing industry have remained roughly the 

same since 2005. The prices for the entire manufacturing sector increased between 2003 and 

2008 but have decreased since August 2008. Producer price indices (PPIs) show that producer 

prices for wood products increased by 6% from 2004 to 2007, while producer prices for all 

manufacturing goods increased by roughly 34% at its peak and was at 23% in November 2008 

and 24% in November 2009 (Figure B-10). 
B-14 

Table B-8. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 32

 Enterprises with 

Variable Total 

1 to 20 

Employeesa

20 to 99 

Employees 

100 to 499 

Employees 

500 to 749 

Employees 

750 to 

Emplo

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

Employment 534,011 65,423 132,612 118,910 19,784 11,9Receipts ($103) $88,649 $8,204 $18,276 $19,717 $3,192 $1,9Receipts/firm ($103) $5,833 $842 $5,572 $24,927 $50,673 $70,4Receipts/establishment ($103) $5,199 $841 $5,249 $15,513 $19,231 $20,9Receipts/employment ($) $166,006 $125,393 $137,818 $165,814 $161,363 $159,2

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

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

<http://www.census.gov/csd/susb/download_susb02.htm>. 
B-15 

Table B-9. Small Business Size Standards: Wood Product Manufacturing (NAICS 321) 

NAICS NAICS Description Employees 

321113 Sawmills 500 

321114 Wood Preservation 500 

321211 Hardwood Veneer and Plywood Manufacturing 500 

321212 Softwood Veneer and Plywood Manufacturing 500 

321213 Engineered Wood Member (except Truss) Manufacturing 500 

321214 Truss Manufacturing 500 

321219 Reconstituted Wood Product Manufacturing 500 

321911 Wood Window and Door Manufacturing 500 

321912 Cut Stock, Resawing Lumber, and Planing 500 

321918 Other Millwork (including Flooring) 500 

321920 Wood Container and Pallet Manufacturing 500 

321991 Manufactured Home (Mobile Home) Manufacturing 500 

321992 Prefabricated Wood Building Manufacturing 500 

321999 All Other Miscellaneous Wood Product Manufacturing 500 

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

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

<http://www.sba.gov/services/contractingopportunities/sizestandardstopics/size/index.html>. 
B-16 

60

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Jul-05

Jan-06

Jul-06

Jan-07

Jul-07

Jan-08

Jul-08

Jan-09

Jul-09

Index Value (Jan-97=100)

Manufacturing Wood Products

Figure B-8. Industrial Production Trends in the Wood Product Manufacturing Industry 

(NAICS 321): 1997–2009 

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

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

0

5,000

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Jul-08

$1,000,000 (2007)

Wood Product Exports Wood Product Imports

Figure B-9. 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). 
B-17 

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PPI (Dec‐03=100)

Wood Products PPI All Manufacturing PPI

Figure B-10. Producer Price Trends in the Wood Product Manufacturing Industry 

(NAICS 321) 

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

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

B.2 Paper Manufacturing 

B.2.1 Introduction 

The paper manufacturing subsector is an essential component of all business operations 

worldwide. Broadly speaking, paper and paperboard are manufactured by converting timber or 

other recycled material into products such as printing and writing papers, newsprint, tissue, and 

containerboard (Benwart, 2006). The subsector has been experiencing a decline in shipments as 

of late. From 1997 to 2007, shipments in the industry declined 7%, and employment declined by 

27% (Table B-10). While total payroll dropped 26% over this time, annual payroll per employee 

rose 2% from 1997 to 2007 because of the decline in the number of employees). Shipments per 

employee grew 28% from 1997 to 2007, with much of that growth taking place between 2002 

and 2006 (Table B-11). 
B-18 

Table B-10. Key Statistics: Paper Manufacturing (NAICS 322) 

1997 2002 2006 2007 

Shipments ($2007, 106) $188,496 $175,983 $174,887 $175,806 

Payroll ($2007, 106) $27,983 $24,561 $21,188 $20,804 

Employees 574,274 489,367 414,049 416,886 

Establishments 5,868 5,495 NA 4,803 

NA = Not available. 

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

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

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

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

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

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

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

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

December 28, 2009. [Source for 2007 numbers] 

Table B-11. Industry Ratios: Paper Manufacturing (NAICS 322) 

Industry Ratios 1997 2002 2006 2007 

Total shipments ($2007, 106) $188,496 $175,983 $174,887 $175,806 

Shipments per establishment ($2007, 103) $32,123 $32,026 NA $36,603 

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

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

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

Employees per establishment 98 89 NA 87 

NA = Not available. 

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

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

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

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

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

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

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

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

<http://factfinder.census.gov>. Accessed on December 28, 2009. [Source for 2007 numbers] 
B-19 

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 : 

 Pulp, Paper, and Paperboard: 

– Pulp Mills (NAICS 32211): This industry comprises establishments primarily 

engaged in manufacturing pulp without manufacturing paper or paperboard. The 

pulp is made by separating the cellulose fibers from the other impurities in wood 

or other materials, such as used or recycled rags, linters, scrap paper, and straw. 

– Paper Mills (NAICS 32212): This industry comprises establishments primarily 

engaged in manufacturing paper from pulp. These establishments may 

manufacture or purchase pulp. In addition, the establishments may convert the 

paper they make. The activity of making paper classifies an establishment into 

this industry regardless of the output. 

– Paperboard Mills (NAICS 32213): This industry comprises establishments 

primarily engaged in manufacturing paperboard from pulp. These establishments 

may manufacture or purchase pulp. In addition, the establishments may also 

convert the paperboard they make. 

 Converted Paper Products: 

– Paperboard Containers Manufacturing (NAICS 32221): This industry comprises 

establishments primarily engaged in converting paperboard into containers 

without manufacturing paperboard. These establishments use corrugating, cutting, 

and shaping machinery to form paperboard into containers. Products made by 

these establishments include boxes; corrugated sheets, pads, and pallets; paper 

dishes; and fiber drums and reels. 

– Paper Bag and Coated and Treated Paper Manufacturing (NAICS 32222): This 

industry comprises establishments primarily engaged in one or more of the 

following manufacturing activities: cutting and coating paper and paperboard; 

cutting and laminating paper and paperboard and other flexible materials (except 

plastics film to plastics film); bags or multiwall bags or sacks of paper, metal foil, 

coated paper, or laminates or coated combinations of paper and foil with plastics 

film; laminated aluminum and other converted metal foils from purchased foils; 

and surface coating paper or paperboard. 

– Stationary Product Manufacturing (NAICS 32223): This industry comprises 

establishments primarily engaged in converting paper or paperboard into products 

used for writing, filing, art work, and similar applications. 

– Other Converted Paper Products (NAICS 32229): This industry comprises 

establishments primarily engaged in one of the following manufacturing 

activities: 

 converting paper and paperboard into products (except containers, bags, 

coated and treated paper and paperboard, and stationery products), or 
B-20 

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

egg cartons, food trays, and dishes. 

Figure B-11 shows that the value of shipments for converted paper products was slightly 

higher in 2007, 54%, than the value of shipments for pulp, paper, and paperboard products in that 

year, 46%. However, Figure B-12 indicates that significantly more employees worked in the 

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

more labor intensive. 

PPP (3221)

CPP (3222) 46%

54%

Figure B-11. Distribution of Value of Shipments within Paper Manufacturing 

(NAICS 322): 2007 

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

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

Accessed on December 28, 2009. 

PPP (3221)

30%

CPP (3222)

70%

Figure B-12. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007 

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

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

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

2.2.2 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 
B-21 

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

uses and consumers of paper manufacturing products. 

B.2.2.1 Goods and Services Used in Paper Manufacturing 

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

paper manufacturing industry (Table B-12). Total compensation of employees represented 15% 

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

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

Table B-12. Costs of Goods and Services Used in the Paper Manufacturing Industry 

(NAICS 322) 

Variable 2005 Share 2006 Share 2007 Share 

Total shipments ($2007, 106) $171,477 100% $174,887 100% $176,018 100% 

Total compensation ($2007, 106) $28,846 17% $27,791 16% $27,150 15% 

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

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

Total employees 426,748 414,049 417,367 

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

Total production workers wages ($2007, 106

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

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

Total production hours (103

) 716,963 691,134 680,732 

Average production wages per hour $21 $21 $21 

Total cost of materials ($2007, 103

) $91,897 54% $92,452 53% $94,029 53% 

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

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

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

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

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

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

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

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

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

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

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 
B-22 

Analysis (BEA) data (Table B-13). Inputs for pulp, paper, and paperboard products are notably 

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

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

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

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

from pulp, paper, and paperboard products. 

Table B-13. Key Goods and Services Used in the Paper Manufacturing Industry 

(NAICS 322) ($106

, $2007) 

Description BEA Code 

NAICS 3221 

Pulp, Paper, and 

Paperboard 

NAICS 3222 

Converted 

Paper Products Total 

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

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

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

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

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

Electric power generation, transmission, 

and distribution 

2211 $2,690 $913 $3,603 

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

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

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

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

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

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

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

B.2.2.1.1 Energy. The Department of Energy (DOE) categorizes paper manufacturing 

(NAICS 322) as an energy-intensive subsector. The 2008 Annual Energy Outlook predicts that 

the paper-producing subsector will be one of four subsectors experiencing positive average 

growth of delivered energy consumption between 2006 and 2030 (DOE, 2008). 

Energy generation from the recovery boiler is often insufficient for total plant needs, so 

facilities augment recovery boilers with fossil fuel–fired and wood waste–fired boilers (hogged 

fuel) to generate steam and often electricity. Industry-wide, the use of pulp wastes, bark, and 
B-23 

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

companies (U.S. EPA, 2002). 

Likewise, Table B-14 shows that total energy use decreased between 1998 and 2006 by 

14%. Figure B-13 indicates that total electrical power use changed sporadically between 2002 

and 2004 but decreased consistently and rapidly after 2004. 

Table B-14. Energy Used in Paper Manufacturing (NAICS 322) 

Fuel Type 1998 2002 2006 

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

Net electricitya (million kWh) 70,364 65,503 72,518 

Residual fuel oil (million bbl) 24 16 15 

Distillate fuel oilb

 (million bbl) 2 2 2 

Natural gasc

 (billion cu ft) 570 490 461 

LPG and NGLd

 (million bbl) 1 2 1 

Coal (million short tons) 12 11 10 

Coke and breeze (million short tons) — * — 

Othere

 (trillion BTU) 1,476 1,276 1,303 

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

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

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

fuel (for example, coal). 

b

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

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

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

d

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

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing 

plants, including plants that fractionate raw natural gas liquids (NGLs). 

e

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

that respondents indicated was used to produce heat and power. 

* Estimate less than 0.5. 

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

Manufacturers—Data Tables.” Tables 3.2 and N3.2. <http://www.eia.doe.gov/emeu/mecs/mecs2002/ 

data02/shelltables.html>. Washington, DC: DOE. 

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

Manufacturers—Data Tables.” Table 3.1. <http://www.eia.doe.gov/emeu/mecs/mecs2006/ 

2006tables.html>. Accessed on December 27, 2009. [Source for 2006 numbers] 
B-24 

90

92

94

96

98

100

102

104

106

108

Jan‐97

Jul‐97

Jan‐98

Jul‐98

Jan‐99

Jul‐99

Jan‐00

Jul‐00

Jan‐01

Jul‐01

Jan‐02

Jul‐02

Jan‐03

Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Index Value (Jan‐97=100)

Manufacturing Paper

Figure B-13. Electrical Power Use Trends in the Paper Manufacturing Industry: 

1997–2005 

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

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

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 B-15). During the 1972–1999 period, the proportion of total 

industry power generated from the combination of woodroom wastes, spent liquor solids, and 

other self-generated methods increased from about 41% to about 56%, while coal, fuel oil, and 

natural gas use decreased from about 54% to about 36% (U.S. EPA, 2002). 

B.2.2.2 Uses and Consumers 

Products manufactured in the NAICS groups 3221 and 3222 have different, but 

complementary, consumer profiles. NAICS 3221 supplies a significant portion of NAICS 3222 

demand (37% of total commodity output). Both industries specialize in products with 

intermediate uses, with an average of 92% of sales between the two going toward this purpose. 

NAICS 3222 has a very diverse assortment of subsector groups from which it receives demand. 

Food manufacturing makes up 21% of the demand, making members of this industry the largest 

consumer of converted paper products (Table B-16). Pulp, paper, and paperboard products have 

a large trade deficit, while converted paper products have a very small trade surplus. 
B-25 

Table B-15. Estimated Energy Sources for the U.S. Pulp and Paper Industry 

Energy Source 1972 1979 1990 1999 

Purchased steam 5.4% 6.7% 7.3% 1.5% 

Coal 9.8% 9.1% 13.7% 12.5% 

Fuel oil 22.3% 19.1% 6.4% 6.3% 

Natural gas 21.5% 17.8% 16.4% 17.6% 

Other purchased energy — — — 6.7% 

Waste wood and wood chips (hogged fuel) and bark 6.6% 9.2% 15.4% 13.5% 

Spent liquor solids 33.7% 37.3% 39.4% 40.3% 

Other self-generated power 0.6% 0.8% 1.2% 1.6% 

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

Project. <http://www.epa.gov/Compliance/resources/publications/assistance/sectors/notebooks/ 

index.html>. 

Table B-16. Demand by Sector: Paper Manufacturing Industry (NAICS 322) ($106, $2007) 

Sector BEA Code

3221 

Pulp, Paper, and 

Paperboard 

3222 

Converted 

Paper Products Total 

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

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

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

General state and local government services S007 $6,065 $7,792 $13,857 

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

Newspaper, periodical, book, and directory 

publishers 

5111 $4,851 $168 $5,018 

Plastics and rubber products manufacturing 3260 $1,249 $3,403 $4,651 

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

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

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

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

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

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

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

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

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

Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA. 
B-26 

B.2.3 Firm and Market Characteristics 

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

basis for further analysis, including regulatory flexibility analyses, and give a complete picture of 

the recent historical trends of production and pricing. 

B.2.3.1 Location 

As Figure B-14 illustrates, California is home to the most paper manufacturing 

establishments in the United States, followed by Illinois and some bordering northeastern states. 

The location of establishments in the paper manufacturing industry varies a great deal by 

subsector. Wisconsin and New York have the most pulp, paper, and paperboard establishments, 

while California dominates with over 500 converted paper product establishments. Overall, the 

United States has 561 pulp, paper, and paperboard establishments and 4,956 converted paper 

product establishments. 

Establishments

Fewer than 50

50 - 99

100 - 199

200 - 300

More than 300

Figure B-14. Establishment Concentration in Paper Manufacturing Industry 

(NAICS 322): 2002 

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

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

Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov>; (July 23, 2008). 
B-27 

B.2.3.2 Production Capacity and Utilization 

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

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

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

industries (Figure B-15). 

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Jan‐97

Jul‐97

Jan‐98

Jul‐98

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Jul‐99

Jan‐00

Jul‐00

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Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Percentage Capacity Utilization

Manufacturing Paper (NAICS 322)

Figure B-15. Capacity Utilization Trends in the Paper Manufacturing Industry 

(NAICS 322) 

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

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

B.2.3.3 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 B-16). 

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

paper, and paperboard group, 67. 
B-28 

Employment

Fewer than 3,000

3,000 - 5,999

6,000 - 11,999

12,000 - 24,000

More than 24,000

Figure B-16. Employment Concentration in the Paper Manufacturing Industry 

(NAICS 322): 2002 

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

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

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

B.2.3.4 Plants and Capacity 

While the manufacturing sector has been growing consistently since 1997, the paper 

manufacturing sector has not experienced the same amount of success in the same period. 

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

subsector’s capacity has declined to as much as 7% below 1997 capacity levels (Figure B-17). 

B.2.3.5 Firm Characteristics 

In 2006, the top 10 paper and forest product companies produced over $1.6 billion in 

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

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

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

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

which firms in this sector compete. 
B-29 

90

100

110

120

130

140

150

1997‐011997‐071998‐011998‐071999‐01

1999‐072000‐012000‐072001‐012001‐072002‐012002‐07

2003‐012003‐072004‐012004‐072005‐012005‐072006‐01

2006‐072007‐012007‐072008‐012008‐072009‐012009‐07

Index Value (Jan‐97=100)

Manufacturing Paper (NAICS 322)

Figure B-17. Capacity Trends in the Paper Manufacturing Industry (NAICS 322) 

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

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

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

Company Revenues ($106)

a

International Paper 21,995 

Weyerhaeuser 21,896 

Smurfit-Stone 7,157 

MeadWestvaco 6,530 

Temple-Inland 5,558 

Bowater 3,530 

Grief Inc. 2,628 

Louisiana-Pacific 2,235 

Packaging Corp. 2,187 

Plum Creek 1,627 

a

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

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

 U.S. and international sales data from company reports. 
B-30 

B.2.3.6 Size Distribution 

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

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

2002, large companies dominated revenue-generating transactions in the paper manufacturing 

subsector; 80% of receipts were generated by companies with 500 employees or more (Table B18). 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 B-19). The cutoff for all subsectors in the pulp, 

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

product groups is 500 employees. 

B.2.3.7 Domestic Production 

Similar to industry capacity rates, subsector production rates for paper manufacturing 

have witnessed a decreasing rate of production compared to the steady increase in production for 

the manufacturing sector since 1997 (Figure B-18). It seems that the paper manufacturing sector 

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

experienced a downward production trend since then. 

Table B-18. Distribution of Economic Data by Enterprise Size: Paper Manufacturing 

(NAICS 322) 

 Enterprises with 

Variable Total 

1 to 20 

Employeesa 

20 to 99 

Employees

100 to 499 

Employees

500 to 749 

Employees 

750 to 999 

Employees 

1,000 to 

1,499 

Employees

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

Establishments 5,546 1,488 1,271 755 83 69 138

Employment 495,990 11,325 52,334 78,402 13,293 12,496 23,283

Receipts ($106) $154,746 $2,218 $9,483 $17,620 $3,034 $3,951 $6,798

Receipts/firm ($103) $43,738 $1,497 $7,903 $37,017 $70,561 $179,577 $206,001

Receipts/establishment 

($103

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

Receipts/employment 

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

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

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

Employment Sizes: 2002.” <http://www.census.gov/csd/susb/download_susb02.htm>. 
B-31 

Table B-19. Small Business Size Standards: Paper Manufacturing (NAICS 322) 

NAICS NAICS Description Employees 

322110 Pulp Mills 750 

322121 Paper (except Newsprint) Mills 750 

322122 Newsprint Mills 750 

322130 Paperboard Mills 750 

322211 Corrugated and Solid Fiber Box Manufacturing 500 

322212 Folding Paperboard Box Manufacturing 750 

322213 Setup Paperboard Box Manufacturing 500 

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

322215 Non-Folding Sanitary Food Container Manufacturing 750 

322221 Coated and Laminated Packaging Paper Manufacturing 500 

322222 Coated and Laminated Paper Manufacturing 500 

322223 Coated Paper Bag and Pouch Manufacturing 500 

322224 Uncoated Paper and Multiwall Bag Manufacturing 500 

322225 Laminated Aluminum Foil Manufacturing for Flexible, Packaging Uses 500 

322226 Surface-Coated Paperboard Manufacturing 500 

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

322232 Envelope Manufacturing 500 

322233 Stationery, Tablet, and Related Product Manufacturing 500 

322291 Sanitary Paper Product Manufacturing 500 

322299 All Other Converted Paper Product Manufacturing 500 

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

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

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

B.2.3.8 International Trade 

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

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

(Figure B-19). Imports and exports have been changing at similar rates since 1999. 
B-32 

75

85

95

105

115

125

135

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Jul‐97

Jan‐98

Jul‐98

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Jul‐99

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Jul‐00

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Jul‐01

Jan‐02

Jul‐02

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Jul‐03

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Index Value (Jan‐97=100)

Manufacturing Paper (NAICS 322)

Figure B-18. Industrial Production Trends in the Paper Manufacturing Industry 

(NAICS 322): 1997–2009 

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

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

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 B-19. International Trade Trends in the Paper Manufacturing Industry 

(NAICS 322) 

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

Consumption.” <http://dataweb.usitc.gov/scripts/user_set.asp>. 
B-33 

B.2.3.9 Market Prices 

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

manufacturing products (Figure B-20). Producer price indices (PPIs) show that producer prices 

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

goods increased by roughly 27%. 

90

95

100

105

110

115

120

125

130

135

140

Dec‐03

Jun‐04

Dec‐04

Jun‐05

Dec‐05

Jun‐06

Dec‐06

Jun‐07

Dec‐07

Jun‐08

Dec‐08

Jun‐09

PPI (Dec‐03=100)

Manufacturing PPI Paper PPI

Figure B-20. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222) 

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

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

B.3 Chemical Manufacturing 

B.3.1 Introduction 

The chemical manufacturing industry produces over 70,000 chemical substances, many 

of which are ubiquitous in American life. Broadly speaking, chemical manufacturing operates by 

converting feedstocks into chemical products that can serve as intermediate goods or final 

products such as medicine, soap, and printer ink. From 1997 to2007, shipments in the industry 

grew 42%, while employment declined by 8% (Table B-20). 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 B-21). Shipments per employee grew 54% from 1997 to 

2007, with much of that growth taking place between 2002 and 2006 (Table B-21). 
B-34 

Table B-20. Key Statistics: Chemical Manufacturing (NAICS 325) 

 1997 2002 2006 2007 

Shipments ($2007, 106) $521,251 $531,173 $675,223 $738,303 

Payroll ($2007, 106) $49,961 $51,317 $46,981 $49,648 

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

Establishments 13474 13,475 NA 12,937 

NA = Not available. 

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

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

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

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

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

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

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

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

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

Table B-21. Industry Ratios: Chemical Manufacturing (NAICS 325) 

Industry Ratios 1997 2002 2006 2007 

Total shipments ($2007, 106) $521,251 $531,173 $675,223 $738,303 

Shipments per establishment ($103) $38,686 $39,419 NA $57,069 

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

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

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

Employees per establishment 66 63 NA 63 

NA = Not available. 

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

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

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

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

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

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

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

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

<http://factfinder.census.gov>. Accessed on December, 27, 2009. [Source for 2007 numbers] 
B-35 

Chemical manufacturing (NAICS 325) covers a diverse set of industry groups, which we 

have aggregated into the following three groups: 

 Bulk Chemicals—Includes the most energy-intensive industry groups as aggregated 

by the Department of Energy (DOE) (DOE/EIA-0554, 2008): Basic Chemical 

Manufacturing (NAICS 3251); Resin, Rubber, and Artificial Fibers Manufacturing 

(NAICS 3252); and Agricultural Chemical Manufacturing (NAICS 3253). 

 Pharmaceutical and Medicine Manufacturing (NAICS 3254)—Consists primarily of 

pharmaceutical preparation manufacturing. This industry group is the largest importer 

of goods within chemical manufacturing. 

 Other Chemical Manufacturing: Consists of Paint, Coating, and Adhesive 

Manufacturing (NAICS 3255); Soap, Cleaning Compound, and Toiletry 

Manufacturing (NAICS 3256); and Other Chemical Product and Preparation 

Manufacturing (NAICS 3259). 

In 2007, each of these groups generated approximately one-third of the total employment in 

chemical manufacturing (Figure B-21). The bulk chemicals group accounted for the biggest 

share of chemical manufacturing’s total value of shipments (Figure B-22). 

Bulk Chemicals,

35%

Pharmaceuticals,

31%

Other Chemicals,

34%

Figure B-21. Distribution of Employment within Chemical Manufacturing (NAICS 325): 

2007 

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

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

date: October 30, 2009. Accessed on December 27, 2009. 
B-36 

Bulk Chemicals,

50%

Pharmaceuticals,

26%

Other Chemicals,

24%

Figure B-22. Distribution of Total Value of Shipments within Chemical Manufacturing 

(NAICS 325): 2007 

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

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

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

B.3.2 Supply and Demand Characteristics 

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

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

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

the major uses and consumers of chemical manufacturing products. 

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

2002 accounted for 71% of the total intermediate inputs (Table B-22). Bulk chemicals’ 

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

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

natural gas distribution. 

B.3.2.1 Goods and Services Used in Chemical Manufacturing 

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

value (Table B-22). Total compensation to employees represented 9% of total shipment value, 

down from 10% in 2005. 

B.3.2.1.1 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). 
B-37 

Table B-22. Key Goods and Services Used in Chemical Manufacturing (NAICS 325) 

($2007, 106

) 

Good or Service BEA Code 

Bulk 

Chemicals Pharmaceuticals 

Other 

Chemicals Total 

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

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

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

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

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

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

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

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

Electric power generation, transmission, and 

distribution 

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

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

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

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

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

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

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

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

chemical manufacturing’s total value of shipments (Table B-23). 

As a whole, chemical manufacturing has become less energy intensive over the last 10 

years. According to DOE, natural gas use by the chemical manufacturing industry dropped 30% 

from 1998 to 2006, and electricity use fell 10% (Table B-24). From 1997 to 2005, when data 

ceased to be available, chemical manufacturing became less electricity intensive faster than the 

manufacturing sector as a whole (Figure B-23). 
B-38 

Table B-23. Costs of Goods and Services Used in Chemical Manufacturing (NAICS 325) 

Variable 2005 Share 2006 Share 2007 Share 

Total shipments ($2007) $646,895 100% $675,223 100% $722,494 100% 

Total compensation ($2007, 106) $62,669 10% $61,683 9% $63,591 9% 

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

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

Total employees 756,078 747,134 801,567 

Average compensation per 

employee ($2007) 

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

Total production workers’ wages 

($2007, 106

) 

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

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

Total production hours (103

) 899,499 885,993 948,244 

Average production wages per 

hour 

$25 $25 $24 

Total cost of materials ($103

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

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

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

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

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

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

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

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

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

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

Accessed on December, 27, 2009. 

B.3.2.2 Uses and Consumers 

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

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

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

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

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

manufacturing products, and a large trade deficit (Table B-25). 
B-39 

Table B-24. Energy Used in Chemical Manufacturing (NAICS 325) 

Fuel Type 1998 2002 2006 

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

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

Residual fuel oil (million bbl) 8 7 4 

Distillate fuel oilb

 (million bbl) 2 2 2 

Natural gasc

 (billion cu ft) 1,931 1,634 1,349 

LPG and NGLd

 (million bbl) 15 9 2 

Coal (million short tons) 13 14 8 

Coke and breeze (million short tons) * * * 

Othere (trillion BTU) 748 1,158 1,045 

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

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

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

fuel (for example, coal). 

b

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

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

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

d

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

ethane-propane mixtures, propane-butane mixtures, and isobutene produced at refineries or natural gas processing 

plants, including plants that fractionate raw natural gas liquids (NGLs). 

e

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

that respondents indicated was used to produce heat and power. 

* Estimate less than 0.5. 

Sources: U.S. Department of Energy, Energy Information Administration. 2007b. “2006 Energy Consumption by 

Manufacturers—Data Tables.” Table 3.1. Washington, DC: DOE. <http://www.eia.doe.gov/emeu/mecs/ 

mecs2006/2006tables.html>. [Source for 2006 numbers] 

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

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

<http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>. 
B-40 

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Jul‐03

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Index Value (Jan‐97=100)

Manufacturing Chemical Manufacturing

Figure B-23. Electric Power Use Trends in Chemical Manufacturing (NAICS 325): 

1997–2005 

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

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

B.3.3 Firm and Market Characteristics 

This remaining subsection describes geographic, production, and market data. These data 

provide the basis for further analysis, including regulatory flexibility analyses, and give a 

complete picture of the recent historical trends of production and pricing. 

B.3.3.1 Location 

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

States, followed by Texas and New Jersey (Figure B-24). The composition of establishments in 

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

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

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

B.3.3.2 Production Capacity and Utilization 

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

the manufacturing sector (Figure B-25). 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. 
B-41 

Table B-25. Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007 106

) 

Sector 

BEA 

Code 

Bulk 

Chemicals Pharmaceuticals 

Other 

Chemicals Total 

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

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

Pharmaceutical and medicine 

manufacturing 

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

Resin, rubber, and artificial fibers 

manufacturing 

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

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

General state and local government 

services 

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

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

Other chemical product and preparation 

manufacturing 

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

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

Soap, cleaning compound, and toiletry 

manufacturing 

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

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

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

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

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

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

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

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

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

B.3.3.3 Employment 

The geographic distribution of employment in chemical manufacturing differs largely 

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

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

in the Gulf Coast states comes from bulk chemicals manufacturing (Figure B-26). 

B.3.3.4 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 B-27). 
B-42 

Establishments

Fewer than 100

100 - 249

250 - 474

475 - 750

More than 750

Figure B-24. Establishment Concentration in Chemical Manufacturing (NAICS 325): 

2002 

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

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

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

0

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Percentage Capacity Utilization

Manufacturing Chemical Manufacturing

Figure B-25. Capacity Utilization Trends in Chemical Manufacturing (NAICS 325) 

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

<http://www.federalreserve.gov/datadownload/>. 
B-43 

Employment

Fewer than 5,000

5,000 - 13,999

14,000 - 29,999

30,000 - 40,000

More than 40,000

Figure B-26. Employment Concentration in Chemical Manufacturing (NAICS 325): 2002 

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

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

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

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150

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Jul‐07

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Index Value (Jan‐97=100)

Manufacturing Chemical Manufacturing

Figure B-27. Capacity Trends in Chemical Manufacturing (NAICS 325) 

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

<http://www.federalreserve.gov/datadownload/>. 
B-44 

B.3.3.5 Firm Characteristics 

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

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

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

operations outside of the United States (Table B-26). The largest chemical manufacturing 

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

States (Dow Chemical Company, 2008). 

Table B-26. Top Chemical Producers: 2007 

 Chemical Sales ($106) % of Total Sales % of Sales in United States 

Dow Chemical 53,513 100% 35% 

ExxonMobil 36,826 9% 38% 

DuPont 29,218 100% 38% 

Lyondella

 16,165 57% 80% 

Chevron Phillips 12,534 100% 86% 

PPG Industriesa

 10,025 90% 56% 

Huntsman Chemical 9,651 100% 50% 

Praxair 9,402 100% 43.5% 

Air Productsa

 8,820 88% 51% 

Rohm & Haasb

 7,837 88% 49% 

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

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

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

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 B-27). 

B.3.3.6 Size Distribution 

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

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

provided in Table B-28. In 2002, enterprises with fewer than 500 employees accounted for 27% 

of employment and 15% of receipts within the chemical manufacturing industry (Table B-29). 
B-45 

Table B-27. 2007 Corporate Income and Profitability (NAICS 325) 

Industry 

Number of 

Corporations

Number of 

Corporations 

with Net 

Income 

Total 

Receipts 

($103

) 

Business 

Receipts 

($103) 

BeforeTax Profit 

Margin 

After-Tax 

Profit 

Margin 

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

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

Resin, synthetic rubber, 

and artificial synthetic 

fibers and filaments 

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

Pharmaceutical and 

medicine 

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

Paint, coating, and 

adhesive 

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

Soap, cleaning compound, 

and toilet preparation 

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

Other chemical product 

and preparation 

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

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

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

B.3.3.7 Domestic Production 

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

chemical manufacturing component (Figure B-28). Following the recession of 2001, however, 

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

manufacturing production caused by the hurricane season of 2005. 

B.3.3.8 International Trade 

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

chemical manufacturing products (Figure B-29). This change occurred because the trade deficit 

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

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

B.3.3.9 Market Prices 

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

having outpaced overall manufacturing since 2002 (Figure B-30). Much of this recent 

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

rapid increase in fertilizer prices. 
B-46 

Table B-28. Small Business Size Standards: Chemical Manufacturing (NAICS 325) 

NAICS Description Employees 

325110 Petrochemical Manufacturing 1,000 

325120 Industrial Gas Manufacturing 1,000 

325131 Inorganic Dye and Pigment Manufacturing 1,000 

325132 Synthetic Organic Dye and Pigment Manufacturing 750 

325181 Alkalies and Chlorine Manufacturing 1,000 

325182 Carbon Black Manufacturing 500 

325188 All Other Basic Inorganic Chemical Manufacturing 1,000 

325191 Gum and Wood Chemical Manufacturing 500 

325192 Cyclic Crude and Intermediate Manufacturing 750 

325193 Ethyl Alcohol Manufacturing 1,000 

325199 All Other Basic Organic Chemical Manufacturing 1,000 

325211 Plastics Material and Resin Manufacturing 750 

325212 Synthetic Rubber Manufacturing 1,000 

325221 Cellulosic Organic Fiber Manufacturing 1,000 

325222 Noncellulosic Organic Fiber Manufacturing 1,000 

325311 Nitrogenous Fertilizer Manufacturing 1,000 

325312 Phosphatic Fertilizer Manufacturing 500 

325314 Fertilizer (Mixing Only) Manufacturing 500 

325320 Pesticide and Other Agricultural Chemical Manufacturing 500 

325411 Medicinal and Botanical Manufacturing 750 

325412 Pharmaceutical Preparation Manufacturing 750 

325413 In-Vitro Diagnostic Substance Manufacturing 500 

325414 Biological Product (except Diagnostic) Manufacturing 500 

325510 Paint and Coating Manufacturing 500 

325520 Adhesive Manufacturing 500 

325611 Soap and Other Detergent Manufacturing 750 

325612 Polish and Other Sanitation Good Manufacturing 500 

325613 Surface Active Agent Manufacturing 500 

325620 Toilet Preparation Manufacturing 500 

325910 Printing Ink Manufacturing 500 

325920 Explosives Manufacturing 750 

325991 Custom Compounding of Purchased Resins 500 

325992 Photographic Film, Paper, Plate and Chemical Manufacturing 500 

325998 All Other Miscellaneous Chemical Product and Preparation 

Manufacturing 

500 

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

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

<http://www.sba.gov/services/contractingopportunities/sizestandardstopics/size/index.html>. 
B-47 

Table B-29. Distribution of Economic Data by Enterprise Size: Chemical Manufacturing 

(NAICS 325) 

 Enterprises with 

Variable Total 

1 to 20 

Employeesa

20 to 99 

Employees 

100 to 499 

Employees 

500 to 749 

Employees 

750 to 999 

Employees 

1,000 to 1,499 

Employees 

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

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

Employment 827,430 34,838 78,090 113,326 28,025 18,119 28,338 

Receipts ($106

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

Receipts/firm ($103) $50,124 $1,779 $10,838 $49,507 $128,603 $130,779 $207,913 

Receipts/establishment ($103) $35,752 $1,773 $9,689 $28,928 $48,869 $39,587 $53,485 

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

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

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

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

100

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Manufacturing Chemical Manufacturing

Figure B-28. Industrial Production Trends in Chemical Manufacturing (NAICS 325) 

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

<http://www.federalreserve.gov/datadownload/>. 
B-48 

0

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1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

$1,000,000 (2007)

Imports Exports

Figure B-29. 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>. 

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Manufacturing Chemical Manufacturing

Figure B-30. Producer Price Trends in Chemical Manufacturing (NAICS 325) 

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

&PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home.htm> 
B-49