1 

### December 20, 2012

MEMORANDUM
SUBJECT: Regulatory Impact Results for the Recons ideration 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 bene fits of this recons ideration final rule using the methodology that was discussed in the orig inal final rule RIA and in the preamble to the original final rule. See FR 76 15704. Changes Since 2010 Final Rule to Emissi on Reductions and Engineering Costs 

### The changes in emission reductions and annua l 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. Sin ce 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 sub category to another . Making these changes resulted in 1 additional incinerator, 14 addi tional 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 inventor y 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 milli on dollars per year in total annual costs. The changes in emission reductions and a nnual engineering costs in the final CISWI reconsideration are mainly the result of revisi ons 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 liqui d/gas burning energy recover units decreased by approximately $219,000 because a lower baseline CO emission concentration was determined2 

### 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. Incremen tal annual engineering costs for incinerators increased by about $340,000 because of a more stringent CO limit, which prompted the need for afterburner retrofit s 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 c ontrols to meet the revised limits. Table 1 shows the changes in emission reductions of directly emitted PM 2.5 

### , SO 2 

### , and NOx. Table 2 shows an estimate of the changes in monetized benefits associated with the emission reductions and engineering cost s in the final CISWI reconsideration. Table 1. Changes in Emission Reducti ons for the Final CISWI Reconsideration a

Direct PM 2.5 (tons per year) SO 2 (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 Co sts for the Final CISWI Reconsideration

Monetized Benefits in 2015 a Annual Engineering Costs b (considering fuel savings) 3% discount rate 7% discount rate 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 w ith reduced exposure to HA P, direct exposure to SO 2 , 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 reconsid eration. 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 discoun t rate and $310 million to $750 million at 7 percent discount rate. However, EPA noted that th e RIA did not incorporat e the final engineering costs and emission reductions, which would decr ease the engineering costs by approximately 22% and increase the monetized benefits by approx imately 4%. For this final reconsideration, we estimate the total monetized benef its to be $420 million to $1 bill ion 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 nationa l price increases for industrial sectors are less than 0.001 percent, while average annua l domestic production may fall by less than 0.001 percent. Because of higher domestic prices, im ports slightly rise by 0.001 percent and exports fall by 0.001 percent. The change in US surplu s 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 th is final reconsideratio n rule, which are very close to the estimated changes for the final rule RIA.4 

### Table 3. Price, Production, Import, and Expo rt 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 ni ne had cost-to-sales perc entages of more than 3 percent. For the reconsideration final only one of the five had a co st-to-sales percentage of more than 3 percent and the other four had small savi ngs. 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 em ployees 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 re ductions 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/CISWIRIA final110221_psg2.pdf5 

### mercury, and 58 grams of dioxins/furans. Please re fer to the full description in the final CISWI RIA of the unquantified benefits as well as an alysis limitations and uncertainties. These monetized benefits are approximately 18% hi gher than the final CISWI NSPS due to the increased emission reductions of PM 2.5 

### and SO 2 

### . Since the reconsider ation proposal, we have made several updates to the approach we use to estimate mortality and morb idity benefits in the PM NAAQS RIAs (U.S. EPA, 2012a,b) 2,3 

### , including updated epidemiology studies, health endpoints, and population data. Alth ough 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 unlik ely to be different than those provided below. More information on these updates can be found in the PM NAAQS propos al 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 Esti mates 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 PM 2.5 917 $230,000 $560, 000 $210, 000 $500, 000 $ 210 o $5 10 $ 190 t
o $ 460 PM 2.5 Precursors SO 2 6,262 $29,000 $72,000 $27,000 $65,000 $180 to $450 $170 to $410 NO 2 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 implementati on 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 evid ence is not yet sufficient to allow differentiation of effect estimates by particle type. The benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . The monetized benefits inco rporate 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/re gdata/RIAs/PMRIACombin edFile_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 Reducti ons in Health Incidences from PM 2.5 

### 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 ye t sufficient to allow differe ntiation 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 increa sed 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 PM 2.5 

### Benefits Estimates for the Final CISWI Reconsideration in 2015

Revised Net Benefits


### Table 6 shows the estimated costs and bene fits for the reconsideration final. The estimated net benefits are highe r than the final rule RIA, wh ich was $30 million to $470 million at 7 percent and was $60 million to $550 million at 3 percent. Table 6. Summary of Estimate d 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 Screeni ng Analysis ......................................................................... 4-1 4.1.1 Small Businesses ...................................................................................... 4-1 4.1.2 Small Entity Anal ysis ............................................................................. 4-2

5 Human Health Benefits of Emissions Reductions ........................................................ 5-1
5.1 Synopsis ............................................................................................................... 5-1
5.2 Calculation of PM 2.5 

### Human Health Benefits ...................................................... 5-2
5.3 Energy Disbenefits ............................................................................................. 5-13 5.3.1 Social Cost of Carbon and Gr eenhouse Gas Disbenefits ....................... 5-13
5.4 Unquantified Benefits ........................................................................................ 5-16 5.4.1 Carbon Monoxide Benefits .................................................................... 5-17 5.4.2 Other SO 2 

### Benefits ................................................................................. 5-17 5.4.3 HAP Benefits ......................................................................................... 5-19

### iv 5.5 Characterization of Uncertainty in the Monetized PM 2.5 

### Benefits ..................... 5-29

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

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

Appendixes A: OAQPS Multimarket Model to A ssess 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 Ch anges by Industry (Total Surplus Change = $224 million, 2008$) (MACT Floor) ........................................................ 3-6 3-2. Employment Impacts Using Morgenst ern, Pizer, Shih (2002) (1,000 FTEs) .......... 3-10

5-1. Breakdown of Monetized PM 2.5 

### Health Benefits Estimates using Mortality Function from Pope et al. (2002) ............................................................................... 5-8 5-2. Total Monetized PM 2.5 

### 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 PM 2.5 

### Precursor Pollutant and Sour ce (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 Carcinoge nic Risk (from 2002 NATA) ........................... 5-21 5-6. Estimated County Level Noncancer (Res piratory) Risk (from 2002 NATA) ......... 5-21 5-7. Percentage of Adult Population by Annual Mean PM 2.5 

### Exposure (pre- and post-policy policy) ................................................................................................... 5-31 5-8. Cumulative Distribution of Adu lt Population at Annual Mean PM 2.5 

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

6-1. Net Benefits for the CISWI NSPS and Em issions Guidelines at 3% Discount Rate .......................................................................................................................... .. 6-3 6-2. Net Benefits for the CISWI NSPS and Em issions Guidelines at 7% Discount Rate .......................................................................................................................... .. 6-4

### vi LIST OF TABLES

Number Page
1-1. Summary of the Monetized Benefits, Soci al 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 Cost s 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 Change s: 2016 (Beyond the MACT Floor) ......... 3-4 3-3. Distribution of Social Co sts (million, 2008$): 2016 .................................................. 3-5 3-4. Distribution of Social Co sts (million, 2008$): 2016 .................................................. 3-5 3-5. Employment Impacts Using Morgen stern, Pizer, Shih (2002) (FTE) ....................... 3-9

4-1. Affected Sectors and Size Standards ......................................................................... 4-1 4-2. Sales Tests Using Small Companies Identi fied in the Combustion Survey .............. 4-2

5-1. Human Health and Welfare Effects of PM 2.5 

### ............................................................. 5-2 5-2. Summary of Monetized Benefits Estima tes for the CISWI NSPS and EG in 2016 (2008$) .............................................................................................................. 5-9 5-3. Summary of Reductions in H ealth Incidences from PM 2.5 

### Benefits for the CISWI NSPS and EG in 2016 ................................................................................. 5-10 5-4. All PM 2.5 

### 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 CO 2 

### ) for 2016 ..................... 5-15 5-6. Monetized SCC-derived Disbenefits of CO 2 

### Emission Increases in 2016 (millions of 2008$) .................................................................................................. 5-16

6-1. Summary of the Monetized Benefits, Soci al 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) requireme nt to review the new source performance standards and emission guidelines every 5 years. Additionally, when revising the standards of performance and emission guidelines we consider ed the District of Columbia Circuit Court rulings on maximum achievable control technol ogy standards that were issued after promulgation of the new source performance sta ndards and emission guidelines for commercial and industrial solid waste incineration units in 20 00 and a concurrently promulgated definition of nonhazardous secondary materials as solid wast e 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 wa ste 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 incl udes an economic impact analysis (EIA) and a small entity impacts analysis and docum ents the RIA methods and results.

The RIA does not include the fi nal engineering costs and emi ssion reductions into this RIA (see Chapter 2 for more detail on the engin eering costs that were not accounted for). We estimate that incorporating these final estima tes would decrease the engineering costs by approximately 22% and increase the monetized be nefits 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 promulgate d 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-lev el 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 ec onomic 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 hi gher 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 es timate of +300 employees.

 

### Small Entity Analyses: EPA performed a screening analysis for i mpacts on small entities by comparing compliance costs to sales/revenues (e.g., sales and revenue tests). EPA’s analysis found the tests exceed ed 3% for four of nine small entities included in the screening analysis. After reviewing screening an alysis results, EPA has determined the promulgated rule will not have a SISNOSE and presumes that rule is eligible for cer tification under the RFA as ame nded 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. T hus, all monetized benefits reported reflect improvements in ambient PM 2.5 

### concentrations. As suc h, although the monetized benefits likely underestimate the total bene fits, the extent of the underestimate is unclear. In the year of full implementa tion (2016), EPA estimates the monetized PM 2.5 

### 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 PM 2.5 

### and premature mortality supplied by experts, higher and lower benefits estimates are plausible, but most of the expert-based estimates fall between these estimates. In addition, ecosystem benefits and visibility be nefits 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 $470 million, at 3% and 7% discount rates respectively (Table 1-1). All estimates ar e in 2008$ 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 a nd the results of the EIA:

 

### Section 2 describes the e ngineering 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 Guide lines in 2016 (mi llions of 2008$) a

3% Discount Rate 7% Discount Rate Option 1: MACT Floor Total Monetized Benefits b $340 to $830 $310 To $750 Total Social Costs c $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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment Option 2: Beyond the Floor Total Monetized Benefits b $430 to $1,100 $390 To $960 Total Social Costs c $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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementati on 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 PM 2.5 through

reductions of directly emitted PM 2.5 and PM 2.5 precursors such as NO x and SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume th at all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because th e scientific evidence is not yet sufficient to allow differentiation of effect estim ates 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 e ngineering costs and em ission reductions. We estimate that incorporating these final estima tes would decrease the engineering costs by approximately 22% and increase the monetized be nefits 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 revis ed 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 theref ore should not have these cost s repeated in the CISWI cost estimates. 

### This section provides an overview of the engi neering 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 imp acts 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 si gnature of the final package. These adjustments affected the bottom-line cost estimate of the fi nal rules. The cost memorandum in the docket reflects the results of the late i nventory and data revisi ons and the resulting costs. However, there was insufficient time to revise the RIA to reflect th ese 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 th e inventory to its corresponding emission limit to determine which, if any, control devices would be n eeded in order to comply with the standards. In order to fill data gaps for units having no em issions 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 as signed the appropriate emission factor for the units in that subcategory 1 

### . 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 fille d using average emission factors.

### 2-2 primary control device for particulate matter, ca dmium, 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 contro l; selective non-catalytic reduction (SNCR) for oxides of nitrogen; and afterburne r retrofits, regenerative therma l 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 th e sulfur dioxide limits. We included costs for testing and monitoring requirement s contained in the rule. Finall y, 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 incinerati on 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 inci neration unit. The resulting tota l 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 co sts for control devices, work practices, testing and monitoring. Costs include testing and monitori ng costs, as well as estimated recordkeeping and reporting costs. Based on the cost to comply w ith the rule, availability of alternatives to incineration, and historic negativ e growth in this source catego ry, we anticipate only one new incineration unit within the next five years, with an annua l compliance cost of $829,500, and up to five new small remote incinerators at an annual cost of $351,000 per unit 2



### . 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 s ources are facilities that dispose of waste and only have incineration options available.

### 2-3 annual cost of $280 million plus recordkeeping a nd reporting costs. The requirements result in industry recordkeeping and reporting burden associ ated with reviewing th e amendments for all CISWI and inspections of scrubbers, fabric filter s, and other air polluti on control devices that may be used to meet the emission limits for all CISWI. Ongoing parametric monitoring requirements for ESPs, SNCR, dry sorbent inject ion 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 perfor mance improvements to meet the emission limits and for CISWI that, prior to this action, have no t 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 recove ry 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 re quired 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 an d operation and maintenance (O&M) costs are associated with the EG monitoring requireme nts, EPA Method 22 of Appendix A-7 testing, initial stack testing, storage of data and reports, and photoc opying 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 soci al costs can then be co mpared with estimated social benefits (as presented in Se ction 5). As noted in EPA’s (2000) Guidelines for Preparing Economic Analyses , several tools are available to estimat e social costs and range from simple direct compliance cost methods to the developm ent of a more complex market analysis that estimates market changes (e.g., price and cons umption) and economic welfare changes (e.g., changes in consumer and producer surplus).
The Office of Air Quality Planning and Sta ndards (OAQPS) adopted a standard market analysis as described in the Office’s resour ce manual (U.S. EPA, 1999). The approach uses a single-period multimarket partial equilibrium mode l to compare pre-policy market baselines with expected post-policy market outcomes. The anal ysis’ time horizon is the short run; for this analysis, we use the model to approximate base line 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 output s (U.S. EPA, 1999, 5-6). The intermediate time horizon allows us to captu re important transitory stakeholder outcomes. Key measures in this analysis include industry-level changes in price levels, production and consumption, jobs, international trade, and soci al costs (changes in producer and consumer surplus). 3.1 Partial Equilibrium Analys is (Multiple Markets) The partial equilibrium analysis develops a market model that simulates how stakeholders (consumers and industries) might respond to the additi onal regulatory program costs. In this section, we provide an overview of the economic model us ed in the analysis.
3.1.1 Overview Although several tools are available to estima te social costs, cu rrent EPA guidelines suggest that multimarket models “...are best used when potential economic impacts and equity effects on related markets might be considerab le” and modeling using a computable general equilibrium model is not available or pract ical (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 sh ort- and long-term time horizons (Morgenstern and colleagues, 2004; Ho, Morgenstern, and Shi h, 2008). Aldy and Pizer (2 009) 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 co sts by changing production rates

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

– customers respond to these price increases and consumption falls

 

### Model scope

– 100 sectors are linked with each other ba sed on their use of energy and other nonenergy materials. For example, the c onstruction industry is linked with the petroleum, cement, and steel industries a nd is influenced by price changes that occur in each sector. The links allow EP A to account for indirect effects the regulation has on related markets.

– production adjustments influence employment levels

– international trade (imports/e xports) 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 quant ity adjustments includ ing the changes in international trade (Table 3-1). The Agency’s economic model suggest s 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 Quanti ty 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. st akeholders is $283 million. As U.S. prices rise, other countries are affected th rough international trade relations hips. Households that buy goods from the United States experien ce losses, while industries that sell goods to the United States benefit; the model estimates a ne t 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 So cial 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 So cial 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 sour ces (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 st andard 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 provi ded 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 la bor be used to produce a given amount of output, that additional labor is reflected in an increas e in the cost of production. Mo reover, 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 stimula tive effect that results in a net increase in overall employment.

### 3-7 With significant numbers of workers unempl oyed, the opportunity costs associated with displacing jobs in other sectors ar e 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 di rect 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 di rect effects of this regulatio n. For example, EPA is currently exploring ways to quantify the job impacts in the pollution control sector th at 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 envi ronmental 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 pr oduction costs causing firms to increase prices; higher prices reduce consump tion (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 a nd 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 work ers to operate and maintain it, just as they would hire workers to produce more output. The exte nt of this effect will depend in part on whether the operation and maintenan ce 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 an alyses and significant expenditures for assembly and installation of such equipment. These activities will be job-creating during the period before firm s 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 cont rol equipment), environmental regulations also create employment in industries that pr ovide intermediate goods to the environmental protection industry. For example, capit 

### al expenditures to redu ce air pollution involve the purchase of abatement equipment. Th e equipment manufacturers, in turn, order steel, tanks, vessels, blowers, pu mps, and chemicals to manufacture and install the equipment. On the other hand, demand for la bor will decrease in sectors that supply inputs for, or demand the out puts 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 ou tput of the regulated sectors. The estimated impacts of the final rule on empl oyment in affected sources are based on an empirically derived relationship reported in Morgenstern, Pizer and Shih (2002), a peerreviewed, published study. Estimates of the empl oyment impacts of the capital investments and other non-recurring requiremen ts 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 additi onal production with the proceeds of sales of their other (market) products. Satisfying this new demand requires add itional inputs, including labor, and may alter the relative proportions of la bor and capital used by regulat ed firms in their production processes. Thus, Morgenstern et al., decompose the ove rall 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 dema nd and supply elasticities to determine cost pass-through and reductions in output..

### 3-9  

### The “Cost Effect”: As production costs incr ease, plants use mo re of all inputs, including labor, to maintain a given level of output. For example, in order to reduce pollutant emissions while holding output leve ls 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 con ceptual relationship between environmental regulation and employment in regulated sectors, and permitted Morgenstern, et al. to provide an empirical estimate of the net impact. For pres ent purposes, the net effect is of particular interest, and is the focus of our analysis.
Morgenstern, Pizer and Shi h (2002): Empirical Results Morgenstern et al. empirically estimate a mode l for four highly polluting, regulated industries (pulp and paper, plastics, petrol eum refining and steel) to examin e the effect of higher abatement costs from regulation on employment. They co nclude 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 insi gnificant) net increase of 1.55 (+/- 2.24) jobs. 4 

### “In plastics and petroleum, [Morgenstern et al] find that increased regula tion raises employment by a small but statistically significant amount: 6.9 and 2.2 j obs 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 pollu tion 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 sector s 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 Mo rgenstern, Pizer, Shih (2002) (FTE)

Demand Effect Cost Effect Factor Shift Effect Net Effect Change in Full-Time Jobs per Million Dollars of Environmental Expenditure a − 3.56 2.42 2.68 1.55

Standard Error 2.03 1.35 0.83 2.24
EPA estimate

− 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 5.01,000JobsBase=144,100

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


### Figure 3-2. Employment Impacts Using Morg enstern, Pizer, Shih (2002) (1,000 FTEs) Limitations of the Analysis
Although the Morgenstern et al. paper provides in formation about the potential job effects of environmental protection programs, there are se veral caveats associated with using those estimates to analyze the final rule. First, the Mo rgenstern 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, re lying 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 pl ants 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 i ndirect effects and all induced effects.

### 4-1 SECTION 4 SMALL ENTITY ANALYSES
The RFA as amended by SBREFA generally requ ires 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 agen cy certifies that the rule will not have a significant economic impact on a su bstantial number of small entities (SISNOSE). Small entities include small bus inesses, small governmental jurisdictions, and small not-forprofit enterprises. EPA assessed the potential sm all entity economic impacts using a screening analysis. After reviewing screening analysis resu lts, EPA has determined the promulgated rule will not have a SISNOSE and presumes that rule is eligible for certif ication under the RFA as amended by SBREFA. We provide the fact ual basis for certification below. 4.1 Small Entity Screening Analysis
4.1.1 Small Businesses The sectors covered by the rule were identifi ed through lists of sma ll 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 f acilities with combustion units in the major source Boiler rule, we identified a ffected facility names listed as sm all, traced the ultimate parent company name to verify the facility was owne d by a small business, and collected the most recent parent company sales and employment figur es. As Table 4-2 shows, the average cost-tosales ratios for small companies are approxi mately 3.4% and 3.5% for the MACT Floor and Beyond the MACT Floor options. The median ra tios are 2.2% for the MACT Floor option and 2.3% for the Beyond the MACT Floor option. Each opti on has 4 entities that have sales test that exceeds 3%. Table 4-2. Sales Tests Using Small Companie s 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 en tities subject to this regulati on, of which 10 of them are considered to be small companies. The small en tities directly regulated by the rule are facilities engaged in industrial or commerc ial 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 enti ties, 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 fi nal engineering costs and emi ssion reductions into this RIA (see Chapter 2 for more detail on the engin eering costs that were not accounted for). We estimate that incorporating these final estima tes would decrease the engineering costs by approximately 22% and increase the monetized be nefits by approximately 4% from those shown in this RIA. 5.1 Synopsis In this section, we provide an estimate of th e monetized benefits as sociated 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 PM 2.5 

### precursors such as NO x 

### and SO 2 

### , as well as ancillary reductions from emission limits on other pollutants. The latter are often referred to as “co-benefits.” The total PM 2.5 

### 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 morb idity and premature mortality among populations exposed to the PM 2.5 

### precursors reduced by this rule making. Because we were unable to monetize the direct benef its 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 th e total monetized bene fits of the CISWI NSPS and EG (MACT floor option) to be $340 m illion to $830 million in the implementation year (2016). Using a 7% discount rate, we estim ate the total monetized benefits of the CISWI NSPS and EG (MACT floor option) to be $310 m illion 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 una ble to incorporate the final emission reductions into this RIA. We estimate that incorporating th ese final estimates would increase the monetized benefits by approximately 4% from those shown in this RIA.

These estimates reflect EPA’s most current inte rpretation of the scientific literature (U.S. EPA, 2009b). Higher or lower estimates of be nefits are possible using other assumptions; examples of this are provid ed in Figure 5-2. Methodologica l and time limitations under the court-ordered schedule prevented EPA from mone tizing the benefits from several important benefit categories, including benefits from reduc ing hazardous air pollutants, ecosystem effects, and visibility impairment. The benefits from reducing other air pollu tants 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 PM 2.5 

### Human Health Benefits This rulemaking would reduce emissions of PM 2.5 

### , SO 2, 

### and NO 2 

### . Because SO x 

### and NO 2 

### are also precursors to PM 2.5, 

### reducing these emissions would also reduce PM 2.5 

### formation, human exposure, and the incidence of PM 2.5 

### -related health effects. For th is rule, the PM reductions are the result of emission limits on PM, emission limits on PM 2.5 

### precursors such as NO x 

### and SO 2 

### , as well as ancillary reductions from emission limits on other pollutants. The total PM 2.5 

### 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 PM 2.5 

### -related benefits. Instead, we us ed the “benefit-per-ton” approach to estimate these benefits based on the methodology described in Fann, Fulc her, and Hubbell (2009). The key assumptions are described in detail below. These PM 2.5 

### benefit-per-ton estimates pr ovide the total monetized human health benefits (the sum of premature mo rtality and premature mo rbidity) of reducing one ton of PM 2.5 

### (or precursor) from a specified source. EP A has used the benefit per-ton technique in several previous RIAs, including the recent SO 2 

### NAAQS RIA (U.S. EPA, 2010b). Table 5-1 shows the quantified and unquantified benefits captured in those be nefit-per-ton estimates.
Table 5-1. Human Health and Welfare Effects of PM 2.5

Pollutant /

Effect Quantified and Monetized in Primary Estimates Unquantified Effects Changes in:
PM 2.5 Adult premature mortality Bronchitis: chronic and acute Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lower and upper respiratory illness Minor restricted-activity days Work loss days Asthma exacerbations (a sthmatic 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 PM 2.5 

### benefits estimates utilize the concentratio n-response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA’s expe rt elicitation study as a sensitivity analysis.

### 5-3  

### One estimate is based on the concentrati on-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/m 3 

### as was done in recent (2006–2009) O ffice 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 report ed by Laden et al. (2006). This study, published after the completion of the Staff Paper for the 2006 PM 2.5 

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

### NAAQS RIA and PM 2.5 

### benefits estimates in RIAs completed since the PM 2.5 

### NAAQS. When calculating the estimate, EPA applied the effect coefficient as repor ted in the study without an adjustment for assumed concentration threshold of 10 μg/m 3 

### as was done in recent (2006–2009) RIAs.

 

### Twelve estimates are based on the C-R func tions from EPA’s e xpert elicitation study (IEc, 2006; Roman et al., 2008) on the PM 2.5 

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

### NAAQS. For that study, twelve experts (labeled A through L) provi ded independent estimates of the PM 2.5 

### -mortality concentration-response function. EPA pract ice has been to develop independent estimates of PM 2.5 

### -mortality estimates corresponding to the concentration-response function provided by each of the twelve expe rts, to better characterize the degree of variability in the expert responses.
Readers interested in reviewing the genera l 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-t on values calculated therefore apply only to tons reduced from those specific po llutant/source combinations (e.g., SO 2 

### emitted from electric generating units; NO 2 

### emitted from mobile sources). In this analysis, we apply the national average benefit-per-ton estimate for a 2016 analys is year and multiply it by the corresponding emission reductions of directly emitted PM 2.5, 

### SO 2, 

### and NO x 

### to quantify the benefits of this rule. The benefit-per-ton estimates found in Fann, Fulche r, and Hubbell (2009) refl ect a specific set of key assumptions and input data. As we update these underlying assumptions to reflect the scientific literature, we re-estimate the bene fit-per-ton estimates and post the updated estimates at http://www.epa.gov/air/benmap/bpt.html. In add ition, 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 prem ature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimat es by particle type (U.S. EPA, 2009b). Directly

### 5-4 emitted PM, SO 2 

### , and NO x 

### are the dominant PM 2.5 

### precursors affected by this rule. Even though we assume that all fine particles have equivalent health effects, the benefi t-per-ton estimates vary between precursors because each ton of precursor reduced has a different propensity to form PM 2.5 

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

### tends to have a lower benefit-per-ton estimate than direct PM 2.5 

### because sulfate par ticles formed from SO 2 

### emissions can transport many miles, meaning th at higher exposures may occur over areas with low populations. On the other hand, to the extent that direct PM 2.5 

### emissions occur in high density population areas, exposures will tend to be higher there, leadi ng to higher monetized health benefits for direct PM 2.5 

### than for SO 2 

### emissions. The benefit-per-ton coefficients in this analysis were derived using modi fied versions of the health im pact functions used in the PM NAAQS Regulatory Impact Analysis . Specifically, this analysis us es the benefit-per-ton method first applied in the Portland Cement NESHAP RI A (U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an expande d geographic scope of the benefit-per-ton calculation, and the functions dire ctly from the epidemiology studi es 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 a nd the methods used in RIAs prior to the Portland Cement proposal, and we now calculate incremental benefits down to the lowest modeled PM 2.5 

### air quality levels.

EPA strives to use the best available science to support our benefits analyses, and we recognize that interpreta tion of the science rega rding air pollution and h ealth is dynamic and evolving. Based on our review of the current body of scientific literatu re, EPA now estimates PM-related mortality without ap plying 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 Comm ittee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded that the scientif ic literature consistently finds that a no-thr eshold log-linear model most adequately portrays the PM-morta lity concentration-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response function. Since then, the Health Effects Subcommitt ee (U.S. EPA-SAB, 2010) of EPA’s Council concluded, “The HES fully supports EPA’s deci sion to use a no-threshold model to estimate mortality reductions. This deci sion is supported by the data, wh ich are quite consistent in showing effects down to the lowest measured leve ls. Analyses of cohorts using data from more

1 These updates were already included in Fa nn et al. (2009). An example of the e ffect 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 concentratio ns have fallen, continue to report strong associations with mortality. Ther efore, 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 applica tion of thresholds in PM 2.5 

### concentration-response functions used in EPA’s RIAs. For a summary of these scientific review statements and the panel members commenting on thresholds since 2002, please co nsult the Technical Support Document (TSD) Summary of Expert Opinions on the Exis tence of a Threshold (U.S. EPA, 2010c).

Consistent with this recent scientific advi ce, we are replacing the previous threshold sensitivity analysis with a new “Lowest Measur ed Level” (LML) assessment. This information allows readers to determine the portion of population exposed to annual mean PM 2.5 

### levels at or above the LML of each study; in general, ou r confidence in the estimated PM mortality decreases as we consider air quality levels fu rther below the LML in major cohort studies that estimate PM-related mortality. While an LML assessm ent provides some insight into the level of uncertainty in the estimated PM mortality bene fits, EPA does not view the LML as a threshold and continues to quantify PM-related mortality imp acts using a full range of modeled air quality concentrations. It is important to emphasi ze that we have high confidence in PM 2.5 

### -related effects down to the lowest LML of the ma jor 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 availa ble due to time or resource limitations. For these rules, we are unable to estimate the percentage of premature mortality associated with this specific ru le’s emission reductions at each PM 2.5 

### level. However, we believe that it is still important to characterize the dist ribution of exposure to ba seline air quality levels. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 

### level using the most recent modeling av ailable 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 inciden ce rates population, and change in air quality. In other words, the per centage of the population exposed to air pollution below the LML is not the same as the percenta ge of the population experiencing health impacts as a result of a specific emission reduction polic y. The most important aspect, which we are unable to quantify for rules without air quality modeling, is the shif t 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 th e LML TSD (U.S. EPA, 2010d). The results of this analysis are pr ovided in Section 5.5.
As is the nature of Regulatory Impact An alyses (RIAs), the assumptions and methods used to estimate air quality benefits evolve over time to reflect the Agency’s most current interpretation of the scientif ic 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 availabl e 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-a nalysis 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 pe er-review group.
During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a repo rt 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 Ag ency consulted several times w ith the Science Advisory Board Environmental Economics Advisory Committee (SAB-EEAC) on the i ssue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, appropriate meta-analytic tech niques to combine estimates from unique data sources and different studies, including those using differe nt methodologies (i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007).
Until updated guidance is availabl e, the Agency determined that a single, peer-reviewed estimate applied consistently best reflects the SAB-EEAC advice it has re ceived. 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 curre ncy 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 st udies 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 progr ess in responding to the SAB-EEA C’s specific recommendations.
In implementing these rules, emission cont rols may lead to reductions in ambient PM 2.5 

### below the National Ambient Air Quality Standard s (NAAQS) for PM in some areas and assist other areas with attaining th e PM NAAQS. Because the PM NAAQS RIAs also calculate PM benefits, there are important differences worth no ting in the design and anal ytical 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 st rategies for different sources. In short, NAAQS RIAs hypothesize, but do not pred ict, the control strate gies that States may choose to enact when implementing a NAAQS. The setting of a NAAQS doe s not directly result in costs or benefits, and as such, the NAAQS RI As are merely illustrative and are not intended to be added to the costs and benefits of other regula tions that result in speci fic costs of control and emission reductions. However, some costs and be nefits estimated in this RIA account for the same air quality improvements as estimated in the illustrative PM 2.5 

### NAAQS RIA.
By contrast, the emission reductions for th is 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 importa nt to note that emission reductions anticipated from these rules do not result in emission incr eases elsewhere (other than potential energy disbenefits). Emission reductions achieved unde r 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 w ith 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 informati on on the relationship between the NAAQS and rules such as analyzed here, pl ease see Section 1.2.4 of the SO 2 

### NAAQS RIA (U.S. EPA, 2010f).
Figure 5-1 illustrates the relativ e breakdown of the monetized PM 2.5 

### 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% Chronic Bronchitis 4%
AMI 2% Acute Respiratory Symptoms 0.5%

Infant Mortality 0.4%

Work Loss Days 0.2%

Hospital Admissions, Cardio 0.2% Hospital Admissions, Resp 0.04% Asthma Exacerbation 0.01% Acute Bronchitis 0.01% Upper Resp Symp 0.00% Lower Resp Symp 0.00% ER Visits, Resp 0.00% Other 1%



### Figure 5-1. Breakdown of Monetized PM 2.5 

### 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 premat ure 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 th e 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 th e expert elicitation on PM mortality. Figures 6-2 through 6-4 provide a visual repres entation of the range of benef its 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 speci fic analysis year (i.e., 2016), and mo st of the PM benefits occur within that year with two excep tions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 years of follow-up medical co sts and lost wages. For premature mort ality, we assume that there is a “cessation” lag between PM exposures and the total reali zation 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 y ear, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction in PM 2.5 (U.S. EPA-SAB, 2004). Changes in the la g 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 prematur e 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 PM 2.5 710 $230,000 $560,000 $210,000 $500,000 $160 to $400 $150 to $360
PM 2.5 Precursors

SO 2 5,170 $29,000 $72,000 $27,000 $65,000 $150 to $370 $140 to $340
NO 2 5,544 $4,900 $12,000 $4,400 $11,000 $27 to $66 $24 to $59
Total $340 to $830 $310 to $750Beyond-the-Floor Direct PM 2.5 713 $230,000 $560,000 $210,000 $500,000 $160 to $400 $150 to $360
PM 2.5 Precursors

SO 2 7,496 $29,000 $72,000 $27,000 $65,000 $220 to $540 $200 to $490
NO 2 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 implementati on 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 PM 2.5 . 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 disb enefits valued at $3.8 million, but the rounded totals do not change.

### 5-10 Table 5-3. Summary of Estimated Reducti ons in Health Incidences from PM 2.5 

### Benefits for the CISWI NSPS and EG in 2016 a

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 y ear (2016) and are rounded to whole numbers with two significant figures.

These models assume that all fine particles, regardle ss of their chemical compos ition, are equally potent in causing premature mortality because the sc ientific evidence is not yet suffici ent 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 an alysis because of the benefit-per-ton methodology.

### 5-11 Table 5-4. All PM 2.5 

### Benefits Estimates for the CISWI NS PS 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 Coeffici ents Derived from Epidemiology Literature

Pope et al. $340 $310 $440 $400 Laden et al. $830 $750 $1,100 $970 Benefit-per-ton Coefficients Deri ved 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. Es timates 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 from 2 epidemiology functions and 12 expert functions 3% DR 7% DR



### Figure 5-2. Total Monetized PM 2.5 

### 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) st udy, 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 PM 2.5 

### 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 in crease 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 CO 2 

### for the floor option. Since NO x 

### and SO 2 

### are covered by capped emissions trading programs, we are only estimating the CO 2 

### 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 Sta ndards,” which is availa ble in the docket. 5.3.1 Social Cost of Carbon an d Greenhouse Gas Disbenefits EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 

### ) emissions using recent estimates of the “social cost of carbon” (S CC). The SCC is an estimate of the monetized damages associated with an in cremental increase in carbon emi ssions in a given year. It is intended to include (but is not limited to) change s in net agricultural pr oductivity, human health, property damages from increased flood risk, and the valu e 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 en tities, 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 Vehi cle Greenhouse Gas Emission Sta ndards and Corporate Average Fuel Economy Standards; see the rule’s preamb le 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 CO 2 

### emissions 8 

### in 2010, in 2007 dollars. The first three valu es are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, a nd 5 percent, respectivel y. SCCs at several discount rates are included because the literatu re 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 percen tile of the SCC from all three models at a 3 percent disc ount rate. It is included to repr esent higher-than-expected impacts from temperature change further out in the ta ils of the SCC distribution. Low probability, high impact events are incorporated in to 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 sens itivity 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 sy stems become more stressed in response to greater climatic change. Note th at the interagency group estimated the growth rate of the SCC directly using the three integr ated 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 anal ysis 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 Advi sers, 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 CO 2 emissions. Given that warming profiles and

impacts other than temperature cha nge (e.g., ocean acidifica tion) vary across GHGs, the group concluded “transforming gases into CO 2 -equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, would not result in accurate estimate s of the social costs of non-CO 2 gases” (SCC TSD, pg 13).

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

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 specifi c. SCC values represent on ly a partial accounting of

climate impacts. 

### When attempting to assess the incremen tal 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 climat e 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 limita tions to the SCC analysis, including the incomplete way in which the integrated a ssessment models capture catastrophic and noncatastrophic impacts, their in complete treatment of adapta tion 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 th ese gaps and that the SCC estimates used for regulatory analysis by the Federal government w ill continue to evolve with improvements in modeling. Additional details on these limita tions are discussed in the SCC TSD.
In light of these limitations , the interagency group has co mmitted to updating the current estimates as the science and economic understa nding of climate change and its impacts on society improves over time. Specifically, the inte ragency 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 CO 2 

### emissions for the range of policy scenarios, we estimate the dollar va lue of the climate-relate d disbenefits captured by the models for each analysis year. For inte rnal 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-d erived Disbenefits of CO 2 

### Emission Increases in 2016 (millions of 2008$) a

Discount Rate and Statistic MACT Floor Beyond-the-Floor
CO 2 (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 specifi c. SCC values represent on ly 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 ambien t fine particles. Methodological and time limitations under the court-ordered schedule preven ted EPA from quantifying or monetizing the benefits from several important benefit categories, in cluding benefits from reducing toxic emissions, ecosystem effects, and vi sibility 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 PM 2.5 

### precursor, SO 2 

### emissions also contribute to adverse effects from acidic deposition in aquati c and terrestrial ecosystems, in creased mercury methylation, as well as visibility impairment. The benefits fr om reducing other air pol lutants 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, whet her small or large, is unknown.

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

### 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 a nd the resulting ambient concentrations using fine-scale air quality modeling, we were unabl e to estimate the exposure to CO for nearby populations. Due to methodological and time limitati ons 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 photoc hemical reactions in the atmosphere. The amount of CO emitted from these reactions, re lative to carbon dioxide (CO 2 

### ), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that are dependent upon concentration and dur ation of exposure.
The Integrated Science Assessment for Ca rbon 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 co ronary heart disease. Epidemiologic studies associate short-term CO exposure with increased risk of emergency de partment visits and hospital admissions. Coronary hear t disease includes those who ha ve angina pectoris (cardiac chest pain), as well as those who have e xperienced a heart att ack. Other subpopulations potentially at risk include i ndividuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabet es, and individuals in very early or late life stages, such as older adults or the developing young. The evid ence is suggestive of a causal relationship between short-term exposure to CO and respirator y morbidity and mortality. The evidence is also suggestive of a causal relationship for birth ou tcomes and developmental effects following longterm exposure to CO, and for central nervous sy stem effects linked to short- and long-term exposure to CO.
5.4.2 Other SO 2 

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

### , SO 2 

### emissions are also associated with a variety of respiratory health effects. Unfortunately, we were unable to estimate the health benefits associated with reduced SO 2 

### exposure in this analysis because we do not have air quality modeling data available. Without knowing the location of the emission reductions and the resulting ambient concentra tions, we were unable to es timate the exposure to SO 2 

### for nearby

### 5-18 populations. Therefore, this analysis only quantifies and monetizes the PM 2.5 

### benefits associated with the reductions in SO 2 

### 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 betw een respiratory health effects and short-term exposure to SO 2 

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

### NAAQS, “the immediate effect of SO 2 

### on the respiratory system in humans is bronchocon striction” (U.S. EPA, 2009c). In addition, the REA summarized from the ISA that “asthmatic s are more sensitive to the effects of SO 2 

### likely resulting from preexisting inflammation associat ed with this disease.” A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO 2 

### at concentrations between 20 and 100 ppb, both in te rms 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 SO 2 

### ISA identified as a “causal relationship”: asthma exacer bation, respiratory-related emer gency department visits, and respiratory-related hospitalizations. The differi ng evidence and associated strength of the evidence for these different effects is described in detail in the SO 2 

### ISA. The SO 2 

### ISA also concluded that the relations hip between short-term SO 2 

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

### alone. Although the SO 2 

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

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

SO 2 

### emissions also contribute to adverse welf are effects from acidi c deposition, visibility impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which can cause a loss of biodiversity of fishes, z ooplankton, 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. Bi ological effects of acidification in terrestrial ecosystems are generally linked to al uminum toxicity, which can cause reduced root growth, which restricts the ability of the plant to take up water a nd nutrients. These direct effects can, in turn, increase the sensit ivity of these plants to stre sses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial acidification affects several im portant ecological services, incl uding declines in habitat for

### 5-19 threatened and endangered species (c ultural), declines in forest aesthetics (cultural), declines in forest productivity (provi sioning), and increases in forest so il erosion and reductions in water retention (cultural and regul ating) (U.S. EPA, 2008c).
Reducing SO 2 

### emissions and the secondary formation of PM 2.5 

### would improve the level of visibility throughout the Un ited States. Fine particles wi th significant li ght-extinction efficiencies include sulfates, nitrates, organic car bon, elemental carbon, and soil (Sisler, 1996). These suspended particles and gases degrade visi bility by scattering and absorbing light. Higher visibility impairment levels in the East are due to generally higher c oncentrations of fine particles, particularly sulfates , and higher average relative humid ity levels. In fact, particulate sulfate is the largest contributor to regional haze in th e eastern U.S. (i.e., 40% or more annually and 75% during summer). In the western U.S., particulate sulfate co ntributes to 20–50% of regional haze (U.S. EPA, 2009b). Visibility has di rect significance to pe ople’s enjoyment of daily activities and their overall sense of wellbei ng. Good visibility increa ses 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 prioriti ze air toxics, emission source t ypes 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 fo r calendar year 2002, and was released in June 2009. 11 

### NATA for 2002 includes four steps: 1. Compiling a national emissi ons inventory of air toxi cs 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 inhala tion of air toxics including both cancer and noncancer effects

10 U.S. EPA. (2009) 2002 National-Scale Air Toxi cs Assessment. http://www.epa.gov/ttn/atw/nata2002/ 11 U.S. EPA. (2009) 2002 National-Scale Air Toxi cs Assessment. http://www.epa.gov/ttn/atw/nata2002/

### 5-20 Noncancer health effects can result from chronic, 12 

### subchronic, 13 

### or acute 14 

### inhalation exposures to air toxics, and incl ude neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the immune a nd reproductive systems. According to the 2002 NATA, nearly the entire U.S. population was expose d 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 noncance r respiratory hazard from the assessment. Results from the 2002 NATA suggest that acrolein is th e primary driver for noncancer respiratory risk. 16 

### Large reductions in HAP emissi ons may not necessarily translate into significant reductions in health risk becaus e toxicity varies by pollu tant 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 emissi ons on a national basis, according to EPA’s 2005 National Emissions Inventory (NE I). However, the Integrated Risk Information System (IRIS) reference concentration (RfC) for acrolein is c onsiderably lower than that for acetaldehyde, suggesting that acrolein could be potentially more toxic than acet aldehyde. 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 maki ng 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 qua litative (e.g., quality of data, review of emission inventories) judgments can be found at h ttp://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 Nonc ancer (Respiratory) Risk (from 2002 NATA)

### 5-22 Due to methodology and time limitations under th e court-ordered schedule, we did not estimate the benefits associated with the hazardous air pollutant s 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 poten tial reductions in the incidenc es of cancer and non-cancer risk (e.g., U.S. EPA, 1995). In those analyses, EPA relied on unit risk f actors (URF) developed through risk assessment procedures. 17 

### These URFs are designed to be conservative, and as such, are more likely to represent the hi gh 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, conser vative 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 Adviso ry 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 anal yzing 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 emi ssions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure-response fu nctions, uncertainties in emissions inventories and background levels, the difficulty of extra polating risk estimates to low doses and the challenges of tracking health progress for diseas es, such as cancer, th at have long latency periods” (U.S. EPA-SAB, 2008).
In 2009, EPA convened a workshop to address th e inherent complexities, limitations, and uncertainties in current methods to quantify the benefits of reducing HAPs. Recommendations from this workshop included identifying rese arch priorities, focu sing on susceptible and vulnerable populations, and improving dose-respon se relationships (Gwinn et al., 2011).
In summary, monetization of the benefits of reductions in cancer incidences requires several important inputs, includi ng central estimates of cancer risk s, estimates of exposure to carcinogenic HAPs, and estimates of the value of an avoided case of cance r (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

17 The unit risk factor is a quantitative estimate of the ca rcinogenic potency of a pollutant, often expressed as the probability of contracting cancer from a 70-year lifetime continuous exposure to a concentration of one μg/m 3 of a pollutant.

### 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 explor e additional concepts of benef its, 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/fu rans each year in the primary approach. 5.4.3.1 Mercury Mercury is a highly neurotoxic contaminant th at enters the food web as a methylated compound, methylmercury (U.S. EPA, 2008c). The contaminant is concentrated in higher trophic levels, including fish eat en by humans. Experimental ev idence 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 mercur y is present, increased sulfate deposition very likely results in methylmerc ury accumulation in fish (Drevnick et al., 2007; Munthe et al, 2007). The SO 2 

### ISA concluded that evidence is sufficient to infer a casual relationship between sulfur de position and increased mercury methylation in wetlands and aquatic environments (U.S. EPA, 2008c).

In addition to the role of su lfate 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 a nd 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 me rcury, inorganic ionic mercury, and particulate bound mercury. Gaseous elemental mercury can be transported very long distances, even globally, to regions far from the emissions sour ce (becoming part of the global “pool”) before deposition occurs. Inorganic ionic and partic ulate bound mercury have a shorter atmospheric

18 Due to time limitations under the court-ordered schedule for this rule, we were unable to model mercury methylation, bioaccumulation in fish tissue, and human co nsumption of mercury-contamin ated 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 accept ed 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 underg o transformation into i onic 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, a bout 103 tons of mercury were emitted from all anthropogenic sources in th e U.S. in 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury were emitted wo rldwide 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 emi ssions 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-or dered 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 or der to estimate the human health benefits from reducing mercury emissions.
Potential exposure routes to mercury emi ssions include both direct inhalation and consumption of fish containing methylmercur y. 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 se ttles 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 so me 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 fis h, including ocean specie s and local freshwater fish, contain some methylmercury For example, in recent studies by EPA and the U.S. Geological Survey (USGS) of fish tissues, ev ery 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 pr imarily 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 subs tantial 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 methylme rcury content of fish may reduce cardioprotective effects of fish consum ption. Some of these studies al so suggest that methylmercury may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of the literature concerning methylme rcury health effects took note of two epidemiological studies that found an association betw een dietary exposure to methyl mercury 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 co ronary heart disease or cardiovascular disease. 20 

### The NRC also noted a study of 917 seven year ol d 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 exhib ited 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 app ears to be a target for methylmercury toxicity in humans and animals.” 22 

### NAS also stated that “Additional studies ar e needed to better characterize the effect of methylmercury exposure on blood pressure and cardiovascular f unction 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 possi ble 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. 20 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. 21 Sorensen, N., K. Murata, E. Budtz-Jorgensen, P. Weih e, and Grandjean, P. 1999. “Prenatal Methylmercury

Exposure As A Cardiovascular Risk Factor At Seven Years of Age,” Epidemiology, pp 370-375. 22 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.

### 5-26 methylmercury exposure. These studies include ep idemiological, toxicologi cal, and toxicokinetic investigations. Over a dozen review papers ha ve also been published. If there is a causal relationship between methylmercury exposure and a dverse 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 methodol ogies for estimating population level cardiovascular health imp acts of reduced methylmercury exposure. The report from that workshop is in preparation.
5.4.3.2 Cadmium 23 

### Breathing air with very high le vels 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 expect ed 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 sufficien tly high, may result in kidney di sease. 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 cad mium is a probable human carcinogen.
5.4.3.3 Lead 24 

### The main target for lead toxi city is the nervous system, bot h in adults and children. Longterm exposure of adults to lead at work has resu lted 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 sma ll increases in blood pressure, particularly in middle-aged and older people. Lead exposure may also cause anemia. At high levels of

23 All health effects language for this section came fro m: 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 fro m: 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. Availa ble on the Internet at <http://www.atsdr.cdc.gov/ToxProfiles/phs13.html>.

### 5-27 exposure, lead can severely damage the brain an d kidneys in adults or children and ultimately cause death. In pregnant wome n, high levels of exposure to lead may cause miscarriage. Highlevel exposure in men can damage the or gans responsible for sperm production.
We have no conclusive proof that lead cause s cancer (is carcinogeni c) in humans. Kidney tumors have developed in rats and mice that ha d 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 reason ably anticipated to be hum an 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 carci nogen. The International Agency for Research on Cancer (IARC) has determined that inorgani c 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 fr om studies in humans and in animals.

Children are more sensitive to the health eff ects of lead than adults. No safe blood lead level in children has been determined. Lead a ffects children in differe nt 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 certai n drugs that help eliminate lead from the body. If a child swallows smaller amounts of lead, such as dust c ontaining lead from paint, much less severe but still important effects on blood, de velopment, and behavior may occu r. 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 conse quences of lead exposure. At still lower levels of exposure, lead can affect a ch ild’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 persis t beyond childhood. 5.4.3.4 Hydrogen Chloride (HCl) 25 

### Hydrogen chloride gas is intensely irritati ng to the mucous membranes of the nose, throat, and respiratory tract. Brie f exposure to 35 ppm causes throat irritation, and levels of 50 to

25 All health effects language for this section came fro m: 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. Availabl e on the Internet at <http://www.atsdr.cdc.gov/Mhmi/mmg173.html>.

### 5-28 100 ppm are barely tolerable for 1 hour. The grea test impact is on the upper respir atory tract; exposure to high concentrations can rapidly lead to swelling and spasm of th e throat and suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronc hioles. Patients who have massive exposures may develop an accumulation of fluid in the lung s. Exposure to hydrogen chloride can lead to Reactive Airway Dysfunction Syndrome (RADS), a chemically- or irri tant-induced type of asthma. Children may be more vulnerable to co rrosive agents than a dults because of the relatively smaller diameter of their airways. Children may al so be more vulnerable to gas exposure because of increased minute ventilation pe r kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not b een 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 leve ls. 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 th at 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 th at 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 re ported in people exposed to rela tively high concentrations of 2,3,7,8-TCDD. Most of the effects ar e considered mild and were reversible. However, in some people these effects may last for many years. Sl ight increases in the risk of diabetes and abnormal glucose tolerance have been observe d 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 peopl e, but animal studies suggest that this is a potential health concern.
In certain animal species, 2,3,7,8-TCDD is especi ally 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 rela tively 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 fro m: 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 anim al species exposed to CDDs during pregnancy had miscarriages and the offspring of animal s exposed to 2,3,7,8-TCDD during pregnancy often had severe birth defects includi ng skeletal deformities, kidney defects, and weakened immune responses. In some studies, effects were obser ved 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, es pecially 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 fre quent lung infections, numbness, effects on the nervous system, and mild changes in the live r. Children born to exposed mothers had skin irritation and more difficulty lear ning, but it is unknown if this effect was permanent or caused by CDFs alone or CDFs and polychlor inated 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 serious ly injured. Some animals had birth defects and testicular damage, and in severe cases, some an imals died. These effects in animals were seen when they were fed large amounts of CDFs ove r a short time, or small amounts over several weeks or months. Nothing is known about the pos sible 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 th e health effects of th ese compounds can be found in EPA’s IRIS database. 28 

### 5.5 Characterization of Uncertainty in the Monetized PM 2.5 

### Benefits In any complex analysis, there are likely to be many sources of uncertainty. Many inputs are used to derive the final estimate of econo mic benefits, including emission inventories, air

27 All health effects language for this section came fro m: 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 paramete rs and inputs), epidemio logical estimates of concentration-response (C-R) f unctions, estimates of values , population estimates, income estimates, and estimates of the future state of the world (i.e., regula tions, technology, and human behavior). For some parameters or inputs it may be possible to provide a st atistical representation of the underlying uncertainty distribution. For other parameters or inputs, the necessary information is not available.
The annual benefit estimates presen ted in this analysis are also inherently variable due to the processes that govern pollutant emissions a nd ambient air quality in a given year. Factors such as hours of equipment use a nd weather are constantly variable , regardless of our ability to measure them accurately. As discussed in the PM 2.5 

### NAAQS RIA (Table 4-5) (U.S. EPA, 2006), there are a variety of uncertainties associated with these PM benef its. 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 redu ctions and specific air quality and benefits modeling assumptions. For example, these estimates do not reflect local va riability in population density, meteorology, exposure, baseline health inci dence rates, or other local factors. Use of these $/ton values to estimate benefits associat ed with different emissi on control programs (e.g., for reducing emissions from large stationary s ources 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 bene fits-per-ton over the entire Unite d States. The benefits-per-ton for emission reductions in specific locations may be very different than the estimates presented here.

PM 2.5 

### mortality benefits are the largest benef it category that we monetized in this analysis. To better characterize the uncertainty associated with mortality impacts that are estimated to occur in areas with low baseline levels of PM 2.5 

### , we included the LML assessment. 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 th e population exposed at each PM 2.5 

### level using the most recent modeling available from the recently proposed Tr ansport 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 distributi on 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 PM 2.5 

### levels at or above the LML. Using the Pope et al. (2002) study, the 85% of the population is exposed to annual mean PM 2.5 

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

### . Using the Laden et al. (2006) study, 40% of the population is exposed above the LML of 10 μg/m 3



### . As we model mortality impacts among populations exposed to levels of PM 2.5 

### that are successively lower than the LML of the lowest cohort study, our confidence in the results diminish es. However, the analysis above confirms that the great majority of the impacts occur at or abov e the lowest cohort study’s LML. It is important to emphasize that we have high confidence in PM 2.5 

### -related effects down to the lowest LML of the major cohort studies. Just because we have gr eater confidence in the benefits above the LML, this does not mean that we have no confid ence 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 PM 2.5 levels substantially, particularly among highly exposed populations. In the baseline, 85% of the population lived in areas where PM 2.5 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 Adul t Population by Annual Mean PM 2.5 

### Exposure (pre- and post-policy policy)

### 5-32 Figure 5-8. Cumulative Distribution of Adult Population at Annual Mean PM 2.5 

### levels (pre- and post-policy policy)

Above we present the estimates of the total monetized benefits, based on our interpretation of the best avai lable scientific literature a nd methods and supported by the SABHES and the NAS (NRC, 2002). The benefits estima tes are subject to a number of assumptions and uncertainties. For example, for key assu mptions underlying the estimates for premature mortality, which typically account fo r at least 90% of the total mone tized benefits, we were able to quantify include the following:
1. PM 2.5 

### benefits were derived through benefit per-ton estimates, which do not reflect local variability in popula tion density, meteorology, exposure, baseline health incidence rates, or other local factors thatmight lead to an over-estimate or underestimate of the actual benefits of control ling directly emitted fine particulates. 2. We assume that all fine particles, re gardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

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

### released from diesel engines and other industrial sources, but the scientific ev idence is not yet sufficient to allow differentiation of effect estimates by particle type. 3. We assume that the health impact functi on 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 PM 2.5, 

### including both regions that are in attainment with fine particle standard and those that do not meet the standard down to th e lowest modeled concentrations. 4. To characterize the uncertainty in the relationship between PM 2.5 

### and premature mortality (which typically accounts for 85% to 95% of total monetized benefits), we include a set of twelve estimates based on results of the expert elicitation study in addition to our core estimates. Even these multiple characterizations omit the uncertainty in air quality estimates, baseline incidence 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 PM 2.5 

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

### benefits, please consult the PM 2.5 

### NAAQS RIA (Table 4-5).
This RIA does not include the type of detail ed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2006) because we lack th e 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 de velop benefit-per-ton metrics and associated estimates of uncertainty using the benefits estim ates 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 th e 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 engin eering 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 decreas e 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 to tal 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 m onetized benefits of the CISWI NSPS and Emissions Guidelines to be $310 m illion 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 PM 2.5 

### mortality functions at discount rates of 3% and 7%. The benefits from reducing other air pollutan ts have not been monetized in this analysis, including reduci ng 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 Benefits b $340 to $830 $310 to $750 Total Social Costs c $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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment Option 2: Beyond the Floor Total Monetized Benefits b $430 to $1,100 $390 To $960 Total Social Costs c $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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementati on 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 PM 2.5 through

reductions of directly emitted PM 2.5 and PM 2.5 precursors such as NO x and SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume th at all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because th e scientific evidence is not yet sufficient to allow differentiation of effect estim ates 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 co sts 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 combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions


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

a Net benefits are quantified in terms of PM 2.5 benefits for the implementation year (2016). This graph shows 14

benefits estimates combined with the cost estimate. These m odels assume that all fine particles, regardless of their chemical composition, are equally potent in causing prematur e mortality because the scien tific 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 combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions


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

a Net benefits are quantified in terms of PM 2.5 benefits for the implementation year (2016). This graph shows 14

benefits estimates combined with the cost estimate. These m odels assume that all fine particles, regardless of their chemical composition, are equally potent in causing prematur e mortality because the scien tific 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.

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### National Ambient Air Quality St andards (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. EP A). 2010c. Summary of Expert Opinions on the Existence of a Threshold in the C oncentration-Response Function for PM 2.5 

### -related Mortality: Technical Support Document. Comp iled 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-Specifi c Air Quality Data Available: Technical Support Document. Office of Air Quality Pla nning 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. EP A). 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/RIA s/proposaltrria_final.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010f. Regulatory Impact Analysis for the SO 2 

### NAAQS. Office of Air Quality Planning and St andards, Research Triangle Park, NC. June. Available at < http://www.epa.gov/ttn /ecas/regdata/RIAs/fs o2ria100602full.pdf>. U.S. Environmental Protection Agency - Sc ience Advisory Board (U.S. EPA-SAB). 2004. Advisory on Plans for Health Effects Analys is 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-COUNCI L-ADV-04-002. March. Availabl e on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E540 0433D5D/$File/council_adv_04002.pdf>. U.S. Environmental Protection Agency - Scie nce Advisory Board (U.S. EPA-SAB). 2007. SAB Advisory on EPA’s Issues in Valuin g Mortality Risk Re duction. EPA-SAB-08-001. October. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$F ile/sab-08-001.pdf >.

### R-8 U.S. Environmental Protection Agency - Sc ience Advisory Board (U.S. EPA-SAB). 2009a. Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPA-COUNCI L-09-008. May. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb8525 6ead006be86e/73ACCA834AB44A10852575BD 0064346B/$File/EPA-CASAC-09-008- unsigned.pdf>. U.S. Environmental Protection Agency - Sc ience Advisory Board (U.S. EPA-SAB). 2009b. Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09- 009. May. Available on the Internet at <http://yosemite.epa.gov/sab/ SABPRODUCT.NSF/81e39f4c09954fc b85256ead006be86e/723FE644C5D758DF85257 5BD00763A32/$File/EPA-CAS AC-09-009-unsigned.pdf>. U.S. Environmental Protection Agency - Sc ience 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. Ava ilable 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/scri pts/user_set.asp>. U.S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Classi fication System Codes.” Effective August 22, 2008. <http://www.sba.gov/serv ices/contractingopportuni ties/sizestanda rdstopics/ 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 th e costs are distributed among stakeholders (U.S. EPA, 2000). In addition, large-scale policies requi re additional analysis to better understand how costs are passed across the ec onomy. Although several tools are av ailable 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 cons iderable” 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 appe ndix 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 a bout 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 (CO 2 

### ) emissions

– estimates of industry employment

### A-2  

### Economic behavior

– industries respond to regulatory co sts by changing production rates

– market prices rise to reflect higher energy and other non-en ergy 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 c onstruction industry is linked with the petroleum, cement, and steel industries a nd is influenced by price changes that occur in each sector. The links allow EP A to account for indirect effects the regulation has on related markets.

– Links come from input-output inform ation used from OAQPS’s computable general equilibrium model (EMPAX)

– production adjustments influence employment levels

– international trade (imports/e xports) 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 i ndustry as a national market, th e identity represents the prepolicy market-clearing condition, or benchmark “equilibrium”; supply equals demand in each market. In Table A-1, we identify the 100 i ndustries 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 co nvention” and choose units where are all prices are one in the benchmark equilibrium (Shoven and Whalley, 1995).

### A-3 Table A-1. Industry Sectors In cluded in Multimarket Model

Industry Label Description Representative NAICS a Energy Industries

COL Coal 2121 CRU Crude Oil Extraction 211111 (exc. nat gas) ELE Electric Generation 2211 GAS Natural 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 Mo del (continued)

Industry Label Description Representative NAICS a Plastics and Rubber

PLS Plastic 3261 RUB Rubber 3262 Nonmetallic Minerals

CLY Clay 3271 GLS Glass 3272 CEM Cement 3273 LIM Lime and Gypsum 3274 ONM Other Non-Metallic Minerals 3279 Primary Metals

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

FRG Forging and Stamping 3321 CUT Cutlery 3322 FMP Fabricated Metals 3323 BOI Boilers and Tanks 3324 HRD Hardware 3325 WIR Springs and Wires 3326 MSP Machine Shops 3327 EGV Engraving 3328 OFM Other Fabricated Metals 3329 Machinery and Equipment CEQ Construction and Agricultural Equipment 3331
IEQ Industrial Equipment 3332 SEQ Service Industry Equipment 3333 HVC HVAC Equipment 3334 MEQ 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 Mo del (continued)

Industry Label Description Representative NAICS a 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 fo r 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 Macroe conomic 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 rele ase or Global Insights For multimarket industries with finer NAICs detail, estimates were calcul ated by selecting a primary NAICS supersector estimate (AEO or Global Insights) and distribu ting 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 da ta. 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 sector s that use Global Insight s’ 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. Employmen t 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. Employmen t 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 anal ysis 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 pr ovides the industry with incentives to alter production rates at current market prices; market pr ices must rise to main tain the original prepolicy production levels (Q).
The multimarket model also simultaneously considers how the policy influences other important production costs (via changes in energy a nd other intermediate material prices). As a result, the multimarket model can provide addi tional 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 i nput are approximated using an input “use” ratio and the price change that occurs in the input market.

% Δ indirect costs = input use ratio x % Δ input price (A.4)
The social accounting matrix provides an intern ally consistent estimate of the use ratio and describes the dollar amount of an input that is required to produce a dollar of output. Higher ratios suggest strong links between industries, wh ile 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 commerc ial input-output (IO) and SAM multiplier models (Berck and Hoffmann, 2002). Morgenstern and co lleagues (2004) and Ho and colleagues (2008) also use this assumption when examining n ear-term effects of environmental policy.

### A-14 Figure A-1. Direct Compliance Costs Reduce Pr oduction Rates at Benchmark Prices

Figure A-2. Indirect Costs Further Reduce Pr oduction Rates at Benchmark Prices

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

g ε n

i i i gi g g 

### ) P P ( t P b Q      

 

     

# 

 1 

###  

### (A.5)
where

g Q  

### = with-policy supply quantity (g)

b 

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

### A-15 g P  

### = with-policy price for output (g)

t 

### = direct compliance costs per unit of supply

gi  

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

i P  

### = with-policy input (i) price

i P 

### = benchmark input (i) price

g  

### = price elasticity of supply for output (g) The key supply parameter that controls the industry production adjust ments is the price elasticity of supply ( ε g 

### ). To our knowledge, there is no existing empirical work that estimates short-run supply elasticities for all industry group s used in the multimarket model. As a result, we assume the U.S. supply elasticities are a f unction of econometrically estimated rest-of-world (ROW) export supply elasticities (see discussi on 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 capture d by a single ROW supply function: 

#   ROW g ε g g 

### P c Q    (A.6)
where

g Q  

### = with-policy supply quantity (g) c = calibrated scale parameter for the supply and price relationship

g P  

### = 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 Br oda 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 select ed cases where this information is no t 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/we bsite/research/unrestric ted/TradeElasticities/ TradeElasticities.html. Accessed September 2009. 

### A.2.2.3 Price Elasticity of Suppl y: Rest of World (ROW) Broda and colleagues (2008) provide an empiri cal basis for the multimarket model supply elasticities. Broda et al. provi de over 1,000 inverse elasticities that RTI organized to be

### A-19 comparable with the 100-sector model. The fi rst step was to match the Harmonized Trade System (HS) elasticities estimated in the article to the appropri ate NAICS codes. Many of the HS codes correspond with a detailed NAICS codes (5 - and 6-digit level), while the multimarket sector industries typically corres pond with more aggregated sect ors (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 calculate d 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 se t. We also restricted the elasticity sample to those that Broda et al. classify as “medium” a nd “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 le ss 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 re levant 3-digit elasticity. If a multimarket code was not covered in the medium set of elasticiti es, 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 elastic ity. Since Broda et al.’s articl e 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 substi tution elasticities from Purdue University’s Global Trade Analysis Project (GTAP). 3 

### Although the elasti cities in the GTAP model are a different type of international trade elasticity and cannot be directly applied in the multimarket model (e.g., they are based on the Armington structure 4 

### ), the parameters provide us with some additional information about the relati ve trade elasticity differences between industry sectors. To use the GTAP information to develop assumptions about the multimarket model sectors with missing elastic ities, we chose a base industrial sect or (iron and steel) for which we had parameter value from Broda et al. Next, we developed industry-spec ific ratios for missing industries using the corresponding GTAP sector tr ade 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 Documentati on 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 ma tch 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 tim es the trade elasticity for iron and steel (2.95). As a result, the multi market 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 domesti c supply elasticities for the model. We have assumed that a product’s dome stic 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 forei gn supply elasticities, we also calculated the standard deviation of each 3- a nd 4-digit NAICS sample. By adding the standard deviation to the corresponding fo reign supply and then taking the inverse, we were able to calculate a domestic supply elasticity for each se ctor 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 ap ply 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 us e (domestic and exports). Given the short time horizon, investment/government does not change. In termediate use is determined by the input use ratios and the industry output decisions.

g gi i 

### Q Q     (A.7)

i Q  

### = with-policy input demand quantity (i)

gi  

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

g Q  

### = with-policy output quantity (g) Other final use does respond to market pri ce 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

g g 

### P a Q  

###    (A.8)
where

g Q  

### = with-policy demand quantity (g)

a 

### = calibrated scale parameter for the demand and price relationship

g P  

### = with-policy price for output (g)

g  

### = price elasticity of demand (g) The key parameter that controls consumpti on adjustments is the price elasticity of demand ( η g 

### ). To approximate the response, we use dema nd elasticities that were simulated with a general equilibrium model (Ho, Morgenstern, an d 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 technol ogy requires the purchase of energy and other intermediate goods made by other sectors include d in the model. Linking the sectors in this manner allows the model to trace direct and indi rect policy effects across different sectors. Therefore, it is best used when potential economic impacts and e quity 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 pa rtial 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 effect s associated with environmental policy. Production te chnologies are fixed; the mode l does not assess substitution among production inputs (labor, energy intermedia tes, and other interm ediates) and assumes each investment cannot be changed during the time frame of the analysis. These issues are better addressed using other frameworks such as comp utable general equilibrium modeling. Similarly, government purchases from each sector do not adju st in response to changes in goods/service prices. Although, employment levels (number of jobs) adjust as pr oduction levels change, wages are assumed to be fixed.

### A-22 Table A-6. U.S. Demand Elasticities

Industry Label Industry Description Demand Elasticity η g ACM Accommodations

− 0.7 ADM Administrative Services

− 0.7 AGR Agricultural

− 0.8 ALU Aluminum

− 1.0 ANM Animal Foods

− 0.6 APP Appliances

− 2.6 ARC Aircraft

− 2.5 ART Arts

− 0.7 ATP Air Transportation

− 0.8 BAK Baked Goods

− 0.6 BEV Beverages and Tobacco

− 0.6 BOI Boilers and Tanks

− 0.5 CEM Cement

− 0.8 CEQ Construction and Agricultural Equipment

− 1.7 CHM Chemicals and Gases

− 1.0 CLY Clay

− 0.8 CMQ Communication Equipment

− 2.6 CNS Construction

− 0.8 COL Coal

− 0.1 CPP Converted 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 η g GAS Natural Gas

− 0.3 GEQ General Equipment

− 1.7 GLS Glass

− 0.8 GRN Grain Milling

− 0.6 HLT Health Care

− 0.7 HRD Hardware

− 0.5 HVC HVAC Equipment

− 1.7 I_S Iron and Steel

− 1.0 IEQ Industrial Equipment

− 1.7 INF Information

− 0.7 INS Instruments

− 2.6 LEA Leather

− 1.1 LGT Lighting

− 2.6 LIM Lime and Gypsum

− 0.8 LUM Other Lumber

− 0.7 M_V Motor Vehicles

− 2.5 MEA Meat Products

− 0.6 MED Drugs and Medicine

− 1.0 MEQ Metalworking Equipment

− 1.7 MGT Magnetic Recording Equipment

− 2.6 MIL Dairy Products

− 0.6 MIN 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 η g OTQ Other Transport Equip

− 2.5 PAI Paints and Adhesives

− 1.0 PAP Pulp and Paper Mills

− 0.7 PFS Professional Services

− 0.7 PIP Pipeline Transport

− 0.8 PLS Plastic

− 1.0 PLY Plywood and Veneer

− 0.7 PRN Printing

− 0.7 PUB Public Services

− 0.7 R_R 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 indu stry 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. Accesse d August 2009. Table B.6.

### B-1 APPENDIX B INDUSTRY PROFILES
In this section, we provide an introduction to selected indust ries that are affected by the rule. The industries were selected based on high facility population counts within 3-digit NAICs industries reported in the combusti on facility survey. The purpose is to give the reader a general understanding of economic aspects and industry tr ends 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 Standa rd & Poor’s (O’Reilly, 2008), a number of factors are shaping the current economic environment for wood produc ts, including, but not limited to, the housing slump, high input costs, low prices for lumber a nd other building material s, and a weak dollar. Table B-1 shows that revenues in this industry ar e variable, exhibiting a decrease in shipment revenue between 1997 and 2002, an increase to w ithin $5 billion of the 1997 value in 2006 and another decline to within $14 b illion of the 2006 value in 2007. Upon closer review, one also notices a rise in the cost of labor (shipm ent revenue per dollar of payroll—$5.54 in 2002, $6.20 in 2006, and $5.84 in 2007) and material inputs dur ing 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 i ndustry’s facilities into three categories: “sawmills and wood preservation”; “veneer, plywood, and engineered wood product manufacturing”; and “other wood product manufact uring.” 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 trea ting them with preservatives; and

### B-2 (c) treating wood sawed, planed, or shaped in other establishments with creosote or other preservatives to pr event 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, 10 6 ) $110,956 $102,721 $115,390 $101,879 Payroll ($2007, 10 6 ) $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 Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.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, 10 6 ) $110,956 $102,721 $115,390 $101,879 Shipments per establishment ($10 3 ) $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 Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.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 pr imarily engaged in one or more of the following manufacturing activities: (a) ve neer and/or plywood, (b) engineered wood members, and (c) reconstituted w ood products. This industry includes manufacturing plywood from veneer made in the same establishment or fromveneer 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 si zes. These establishments generally use woodworking machinery, such as jointers, planers, lathes, and routers to shape wood.

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

– All Other Wood Product Manuf acturing (NAICS 32199): This industry comprises establishments primarily engaged in manufacturing wood products (except establishments operating sawmills a nd 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 th e 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 th is industry fell under ot her wood products (60%). Veneer, plywood, and engineered wood products ha d 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 Product Mfg (NAICS 3212), 22%

Sawmills & Wood Preservation (NAICS 3211), 27% Other Wood Product Mfg (NAICS 3219), 51%



### Figure B-1. Distribution of Value of Shipm ents 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 Statis tics: 2007.” <http://factfinde r.census.gov>. Accessed on December 27, 2009. [S ource for 2007 numbers]

Other Wood Product Mfg (NAICS 3219), 60% Sawmills & Wood Preservation (NAICS 3211), 20%

Veneer, Plywood, & Engineered Wood Product Mfg (NAICS 3212), 20%



### Figure B-2. Distribution of Employmen t 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 St atistics: 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 indu stry has with other industries and people and identify the key goods a nd 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 th e number of employees in this industry decreased between 2005 a nd 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, 10 6 ) $118,705 100% $115,390 100% $102,002 100% Total compensation ($2007, 10 6 ) $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, 10 6 ) $13,363 11% $13,132 11% $12,086 12%
Total production workers 431,569 432,315 417,471 Total production hours (10 3 ) 911,332 887,613 837,074

Average production wages per hour ($2007) $15 $15 $14
Total cost of materials ($2007, 10 3 ) $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 input s to the wood product industry accounted for 80% of the total intermediate inputs accordi ng 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 desc riptions of the various i ndustries within wood product manufacturing imply that they supply each ot her 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 en terprises, 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, 10 6 

### )

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 i ndustry. 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, includi ng a steady declin e in the latter part of 2000. Following a rapid decrease at th e end of 2002, electric power use has been increasing steadily since then. Table B-5. Energy Used in Wood Pro duct Manufacturing (NAICS 321)

Fuel Type 1998 2002 2006 Total (trillion BTU) 504 375 445 Net electricity a (million kWh) 21,170 20,985 26,723 Residual fuel oil (million bbl) * * 1 Distillate fuel oil b (million bbl) 2 2 3 Natural gas c (billion cu ft) 71 56 84 LPG and NGL d (million bbl) 1 1 1 Coal (million short tons) * * Q Coke and breeze (million short tons) — — * Other e (trillion BTU) 341 229 228

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 electri city 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 fu el 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, market ers, 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, generatio n from renewables, and net transfers), and other energy

that respondents indicated was us ed to produce heat and power. * Estimate less than 0.5.

Q = Withheld because relative standa rd 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://w ww.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html>. [Source for 2006 numbers]

### B-8 85 90 95 100 105 110Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(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 Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” <http://www.f ederalreserve.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 ma intenance and repair construc tion consume 35% of the total commodity output in this industry. The top 10 co nsumers 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 t ypes of consumers, such as food services and drinking places, rail transportation, plastics and rubber products ma nufacturing, 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, includ ing 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 Pro duct Manufacturing (NAICS 321) ($2007, 10 6 

### )

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 drin king 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 num ber of wood product manufacturing establishments are disperse d 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 ma nufacturing industry has been experiencing capacity utilization increases and declines with more extreme fluctuations than those of all manufacturing industries combined. The declin e in wood product manufacturing is similar to total manufacturing between 1997 and 2002. Howe ver, capacity utilization in total manufacturing, which peaked in 2006, started in creasing at a faster rate than wood product manufacturing, but decreased sharply after it s peak. Wood product manufacturing experienced its own rapid decrease in cap acity 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 Co ncentration 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 St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census. gov>; (July 23, 2008).

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PercentageCapacityUtilization

Manufacturing Wood Products


### Figure B-5. Capacity Utilization Trends in the Wood Product Manufacturing Industry (NAICS 321) Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” <http://www.federalreserve.gov/datadownload/>..

### B-11 B.1.3.3 Employment California has the largest number of em ployees 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 direc tly 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 St atistics 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 c onsistently since 1997, the wood product manufacturing industry has expe rienced 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 20 40 60 80 100 120 140 160Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(January1997=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 produc t companies produced over $75 billion in sales, with the top two companies—Internati onal Paper and Weyerhaeuser—generating nearly $22 billion each (Table B-7). The top two companies’ revenue consis ts of 58% of the revenue of the top 10 companies in Standard & Poor’s (S&P’s) list (Benwa rt, 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 busin ess 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 em ployees 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 ($10 6 ) 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 stea dy increase in production for the manufacturing sector since 1997 (Fig ure B-8). Similar to capacity ut ilization trends (Figure B-8), the index shows a faster rate of decline for wood products than th e entire manufacturing sector.
B.1.3.8 International Trade Since 1997, the wood product manufacturing indust ry has contributed to an increasing trade deficit (Figure B-9). The value of impor ts 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 manuf acturing industry have remained roughly the same since 2005. The prices for the entire manuf acturing 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-14Table B-8. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 321)Enterprises withVariable Total1 to 20Employeesa20 to 99Employees100 to 499Employees500 to 749Employees750 to 999Employees1,000 to 1,499EmployeesFirms15,1989,7403,280791632730Establishments17,0529,7583,4821,27116691133Employment534,01165,423132,612118,91019,78411,94418,533Receipts ($103)$88,649$8,204$18,276$19,717$3,192$1,902$3,118Receipts/firm ($103)$5,833$842$5,572$24,927$50,673$70,453$103,927Receipts/establishment ($103)$5,199$841$5,249$15,513$19,231$20,904$23,442Receipts/employment ($)$166,006$125,393$137,818$165,814$161,363$159,262$168,231aExcludesStatistics of U.S. Businesses(SUSB) employment category for zero employees. These entities only operated for a fraction of the year.Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002.”<http://www.census.gov/csd/susb/download_susb02.htm>.

### 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 Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/sizestandardstopics/size/index.html>.

### B-16 60 70 80 90 100 110 120 130 140Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index Value (Jan-97=100)

Manufacturing Wood Products


### Figure 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.federalrese rve.gov/datadownload/>. Acce ssed on December 15, 2009.

0 5,000 10,000 15,000 20,000 25,000 30,000Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08$1,000,000 (2007)

Wood Product Exports Wood Product Imports 

### Figure 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/s cripts/user_set.asp>; (July 17, 2008).

### B-17 0 20 40 60 80 100 120 140Dec‐03Jun‐04Dec‐04Jun‐05Dec‐05Jun‐06Dec‐06Jun‐07Dec‐07Jun‐08PPI(Dec‐03=100)

Wood Products PPI All Manufacturing PPI


### Figure 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 esse ntial component of a ll business operations worldwide. Broadly speaking, pa per and paperboard are manufactured by converting timber or other recycled material into pr oducts such as printing and writing papers, newsprint, tissue, and containerboard (Benwart, 2006). Th e subsector has been experienci ng a decline in shipments as of late. From 1997 to 2007, shipments in the industr y declined 7%, and employment declined by 27% (Table B-10). While total pa yroll dropped 26% ove r 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: Pap er Manufacturing (NAICS 322)

1997 2002 2006 2007 Shipments ($2007, 10 6 ) $188,496 $175,983 $174,887 $175,806 Payroll ($2007, 10 6 ) $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 Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.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. [Sou rce for 2007 numbers] 

### Table B-11. Industry Ratios: Paper Manufacturing (NAICS 322)

Industry Ratios 1997 2002 2006 2007 Total shipments ($2007, 10 6 ) $188,496 $175,983 $174,887 $175,806 Shipments per establishment ($2007, 10 3 ) $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 Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfind er.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 indu stry’s facilities into two categories: pulp, paper, and paperboard manufacturing and conve rted paper product manufacturing. These are further divided into the following types of f acilities as defined by the Census Bureau :

 

### Pulp, Paper, and Paperboard:

– Pulp Mills (NAICS 32211): This industr y 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 recy cled rags, linters, scra p paper, and straw.

– Paper Mills (NAICS 32212): This industr y comprises establishments primarily engaged in manufacturing paper fr om pulp. These establishments may manufacture or purchase pulp. In additi on, the establishments may convert the paper they make. The activity of making pa per classifies an establishment into this industry rega rdless of the output.

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

 

### Converted Paper Products:

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

– Paper Bag and Coated and Treated Pa per Manufacturing (NAICS 32222): This industry comprises establishments primar ily engaged in one or more of the following manufacturing activities: cutti ng 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 comb inations of paper and foil with plastics film; laminated aluminum and other conve rted metal foils from purchased foils; and surface coating paper or paperboard.

– Stationary Product Manufacturing (NAI CS 32223): This industry comprises establishments primarily engaged in conve rting paper or paperboard into products used for writing, filing, art work, and similar applications.

– Other Converted Paper Products (NAI CS 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 paperboa rd, 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 shipment s for converted paper products was slightly higher in 2007, 54%, than the value of shipments for pulp, paper, a nd paperboard products in that year, 46%. However, Figure B-12 indicates that significantly more employees worked in the converted paper product category of the industr y, 70%, thus making converted paper products more labor intensive.

PPP (3221) 46% CPP (3222) 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 with in 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>. A ccessed on December 28, 2009. 

### 2.2.2 Supply and Demand Characteristics Next, we provide a broad overview of th e supply and demand sides of the paper manufacturing industry. We emphasize the economic interactions this indu stry has with other

### B-21 industries and people and identify the key goods a nd 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). Tota l 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 in creased 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, 10 6 ) $171,477 100% $174,887 100% $176,018 100% Total compensation ($2007, 10 6 ) $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, 10 6 ) $14,965 9% $14,689 8% $14,190 8% Total production workers 331,228 321,684 321,937 Total production hours (10 3 ) 716,963 691,134 680,732

Average production wages per hour $21 $21 $21 Total cost of materials ($2007, 10 3 ) $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 input s 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 pul p, paper, and paperboard products are notably different from inputs for converted paper produ cts because the NAICS 3221 group represents the initial step in the paper manufact uring 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 pape r 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) ($10 6 

### , $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-int ensive subsector. The 2008 Annual Energy Outlook predicts that the paper-producing subsector will be one of fo ur subsectors experiencing positive average growth of delivered energy consum ption between 2006 and 2030 (DOE, 2008).
Energy generation from the recovery boiler is often insufficient for total plant needs, so facilities augment recove ry boilers with fossil fuel–fired and wood waste–fired boilers (hogged fuel) to generate steam and often electricity. In dustry-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 en ergy use decreased between 1998 and 2006 by 14%. Figure B-13 indicates that total electrical power use ch anged sporadically between 2002 and 2004 but decreased consiste ntly 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 electricity a (million kWh) 70,364 65,503 72,518 Residual fuel oil (million bbl) 24 16 15
Distillate fuel oil b (million bbl) 2 2 2 Natural gas c (billion cu ft) 570 490 461
LPG and NGL d (million bbl) 1 2 1 Coal (million short tons) 12 11 10 Coke and breeze (million short tons) — * —
Other e (trillion BTU) 1,476 1,276 1,303

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 electri city 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 fu el 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, market ers, 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, generatio n from renewables, and net transfers), and other energy

that respondents indicated was us ed 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 fo r 2006 numbers]

### B-24 90 92 94 96 98 100 102 104 106 108Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(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 Pro duction and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” <http://www.f ederalreserve.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 pr ocesses 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 322 1 supplies a significant portion of NAICS 3222 demand (37% of total commodity output). Bo th industries speciali ze in products with intermediate uses, with an average of 92% of sales between the two goi ng toward this purpose. NAICS 3222 has a very diverse a ssortment of subsector groups from which it receives demand. Food manufacturing makes up 21% of the demand, ma king members of this industry th e 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 fo r 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/resour ces/publications/assistance/sectors/notebooks/ index.html>. 

### Table B-16. Demand by Sector: Paper Manuf acturing Industry (NAICS 322) ($10 6 

### , $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 activ ities 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, includi ng 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 borderi ng northeastern states. The location of establishments in the paper manufacturing industry va ries 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 establis hments. Overall, the United States has 561 pulp, pape r, and paperboard establishm ents and 4,956 converted paper product establishments.

Establishments
Fewer than 50 50 - 99 100 - 199 200 - 300 More than 300 

### Figure B-14. Establishment Concentrat ion 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 St atistics 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 manufactur ing 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 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PercentageCapacityUtilization

Manufacturing Paper (NAICS 322)


### Figure B-15. Capacity Utilization Trends in the Paper Manufacturing Industry (NAICS 322) Source: Federal Reserve Board. 2009. “Industrial Pro duction and Capacity Utilization: Capacity Utilization.” <http://www.federalreserve.gov/datadownload/>. 

### B.2.3.3 Employment Wisconsin has the largest number of employ ees in the paper manufacturing subsector with over 38,008 reported in the 2002 census fo llowed by 29,379 in California (Figure B-16). The converted paper products group has more em ployees per establishm ent, 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 St atistics 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 th e 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—Internati onal Paper and Weyerhaeuser—generating nearly $22 billion each (Table B-17). The top two compan ies’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poo r’s (S&P’s) list (Benwart , 2006). Although these numbers do not exclusively reflect paper product s, they do convey the market environment in which firms in this sector compete.

### B-29 90 100 110 120 130 140 1501997‐011997‐071998‐011998‐071999‐011999‐072000‐012000‐072001‐012001‐072002‐012002‐072003‐012003‐072004‐012004‐072005‐012005‐072006‐012006‐072007‐012007‐072008‐012008‐072009‐012009‐07IndexValue(Jan‐97=100)

Manufacturing Paper (NAICS 322)


### Figure B-17. Capacity Trends in the Pa per Manufacturing Industry (NAICS 322) Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Capacity.” <http://www.federalreserve.gov/datadownload/>. 

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

Company Revenues ($10 6 ) 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 busin ess 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-generati ng transactions in the paper manufacturingsubsector; 80% of receipts were generated by companies with 500 employees or more (Table B18). This was especially true in the pulp, pape r, and paperboard group, in which large companies generated 92% of receipts. The number of em ployees 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, subsect or production rates for paper manufacturing have witnessed a decreasing rate of production comp ared to the steady increase in production for the manufacturing sector since 1997 (Figure B-18). It seems that the pape r manufacturing sector was not able to return to its former levels of growth following the 2001 recession; it has experienced a downward pr oduction trend since then.
Table B-18. Distribution of Economic Data by Enterprise Size: Paper Manufacturing (NAICS 322)

Enterprises with

Variable Total 1 to 20 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees
Firms 3,538 1,482 1,200 476 43 22 33 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 ($10 6 

### ) $154,746 $2,218 $9,483 $17,620 $3,034 $3,951 $6,798 Receipts/firm ($10 3 ) $43,738 $1,497 $7,903 $37,017 $70,561 $179,577 $206,001 Receipts/establishment ($10 3 ) $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 fr om 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 Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/sizestandardstopics/size/index.html>.


### B.2.3.8 International Trade Since 1997, paper manufacturing products, bot h pulp, paper, and paperboard products and converted paper products, have contributed to an increasing trade surplus in this sector (Figure B-19). Imports and exports have been changing at similar rates since 1999.

### B-32 75 85 95 105 115 125 135Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09IndexValue(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 Produc tion 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.us itc.gov/scripts/user_set.asp>.

### B-33 B.2.3.9 Market Prices Prices of goods in paper manufacturing have be en increasing at a rate consistent with all manufacturing products (Figure B- 20). Producer price indices (PPI s) show that producer prices for paper in 2007 increased by 20% since 1997, wh ile producer prices for all manufacturing goods increased by roughly 27%.

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

Manufacturing PPI Paper PPI


### Figure 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 produ ces over 70,000 chemical substances, many of which are ubiquitous in American life. Br oadly speaking, chemical manufacturing operates by converting feedstocks into chemical products th at can serve as intermediate goods or final products such as medicine, soap, and printe r ink. From 1997 to2007, shipments in the industry grew 42%, while employment declined by 8% (T able 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). Sh ipments 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: Chemic al Manufacturing (NAICS 325)

1997 2002 2006 2007 Shipments ($2007, 10 6 ) $521,251 $531,173 $675,223 $738,303 Payroll ($2007, 10 6 ) $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 Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.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, 10 6 ) $521,251 $531,173 $675,223 $738,303 Shipments per establishment ($10 3 ) $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 Statistic s for the United States and the States (1997 NAICS Basis): 2002 and 1997.” <http://factfi nder.census.gov>; (July 8, 2008). U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007.” <http://factfinder.census.gov>. A ccessed on December, 27, 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 Chem ical Manufacturing (NAICS 3253).

 

### Pharmaceutical and Medicine Manufacturi ng (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 approximate ly 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 with in 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: Detaile d Statistics by Industry for the U.S.: 2007.” Release date: October 30, 2009. Acce ssed on December 27, 2009.

### B-36 Bulk Chemicals, 50%

Pharmaceuticals, 26% Other Chemicals, 24%



### Figure B-22. Distribution of To tal Value of Shipments with in 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: De tailed Statistics by Industry for U.S.: 2007.” <http://factfinder.census.gov>. A ccessed 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 indu stry has with other industries and people, including identifying the key goods and serv ices used by the industry and the major uses and consumers of chemical manufacturing products.
The top 10 industry groups supplying inputs to the chemical manuf acturing industry in 2002 accounted for 71% of the total intermedia te 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 compen sation 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 i ndustry. Pharmaceuticals and other chemical manufacturing are categorized as non-energy-intensive industrie s, 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 Manufactu ring (NAICS 325) ($2007, 10 6 

### )

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 f acilitate chemical proc esses 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’R eilly, 2008). These fuel costs represented 2% of chemical manufacturing’s total valu e of shipments (Table B-23).
As a whole, chemical manufacturing has b ecome less energy intensive over the last 10 years. According to DOE, natural gas use by th e chemical manufacturi ng 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 b ecame 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, 10 6 ) $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, 10 6 ) $22,643 4% $22,231 3% $23,157 3%
Total production workers 431,502 430,880 463,802 Total production hours (10 3 ) 899,499 885,993 948,244 Average production wages per

hour $25 $25 $24
Total cost of materials ($10 3 ) $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 out put and 56% of the total intermediate use of chemical manufacturing products. Pharmaceutic als has both a high level of demand from personal consumption, accounting for 67% of the total personal consumption of chemical manufacturing products, and a larg e 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 electricity a (million kWh) 169,233 153,104 151,646 Residual fuel oil (million bbl) 8 7 4
Distillate fuel oil b (million bbl) 2 2 2 Natural gas c (billion cu ft) 1,931 1,634 1,349
LPG and NGL d (million bbl) 15 9 2 Coal (million short tons) 13 14 8 Coke and breeze (million short tons) * * *
Other e (trillion BTU) 748 1,158 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 electri city 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 fu el 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, market ers, 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, generatio n from renewables, and net transfers), and other energy

that respondents indicated was us ed 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. Washin gton, 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 80 85 90 95 100 105 110Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05IndexValue(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 Pro duction 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 geographi c, production, and market data. These data provide the basis for further an alysis, including regul atory flexibility analyses, and give a complete picture of the recent histori cal 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 Ne w Jersey (Figure B-24). The composition of establishments in these states differs among th e 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 establishmen ts, and only 13% were pharm aceutical establishments. B.3.3.2 Production Capacity and Utilization Capacity utilization of the ch emical manufacturing industry ha s been broadly in line with the manufacturing sector (Figure B-25). In th e 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 dow nturn in 2001 can be s een in the capacity utilization of both manufacturing and chemical manufacturing.

### B-41 Table B-25. Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007 10 6 

### )

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 manufacturi ng has grown 33% since 1997. This growth, however, is 9% less than the growth rate for the manuf acturing 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 St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov >; (July 23, 2008).

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Jan‐97Jul‐97Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PercentageCapacityUtilization

Manufacturing Chemical Manufacturing 

### Figure B-25. Capacity Utilization Trends in Chemical Manufacturing (NAICS 325)
Source: Federal Reserve Board. 2009. “Industrial Pro duction 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 Ch emical Manufacturing (NAICS 325): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry St atistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” <http://factfinder.census.gov >; (July 23, 2008).

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

Manufacturing Chemical Manufacturing 

### Figure B-27. Capacity Trends in Ch emical Manufacturing (NAICS 325)
Source: Federal Reserve Board. 2009. “Industrial Produc tion 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 sale s had greater than $10 billion in sales. Together, their sales are greater than the next 44 highest chemi cal companies combined. These, however, are global companies, with a large portion of both sa les and production coming from operations outside of the United States (Tab le B-26). The largest chemical manufacturing company, Dow Chemicals, has 108 out of 150 manuf acturing sites located outside of the United States (Dow Chemical Company, 2008). Table B-26. Top Chemical Producers: 2007

Chemical Sales ($10 6 ) % of Total Sales % of Sales in United States Dow Chemical 53,513 100% 35% ExxonMobil 36,826 9% 38% DuPont 29,218 100% 38% Lyondell a 16,165 57% 80% Chevron Phillips 12,534 100% 86%
PPG Industries a 10,025 90% 56% Huntsman Chemical 9,651 100% 50% Praxair 9,402 100% 43.5%
Air Products a 8,820 88% 51%
Rohm & Haas b 7,837 88% 49%

a Percentage of sales in the United States calcu lated 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 manufacturi ng corporations generated net income. Including those with and without net income, chemical manufactur ers 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 busine ss 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, en terprises with fewer than 500 employees accounted for 27% of employment and 15% of recei pts within the chemical manuf acturing 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

($10 3 ) Business Receipts

($10 3 ) 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/taxs tats/article/0,,id=167415,00.html>; (January, 15, 2010).


### B.3.3.7 Domestic Production In the late 1990s, overall manufacturing pr oduction was growing much faster than the chemical manufacturing component (Figure B-28 ). Following the rece ssion of 2001, however, the components have moved broadly in line with one another, except for the drop in chemical manufacturing production cause d 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 (F igure B-29). This change occurr ed because the trade deficit in pharmaceutical manufacturing, currently at $35 billion, overwhelmed the trade surplus of bulk chemicals and other chemical manufacturi ng combined, currently at $22 billion.
B.3.3.9 Market Prices Prices of goods in chemical manufacturing ha ve accelerated rapidly in the last 2 years, having outpaced overall manufacturing since 200 2 (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 Manufactu ring (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 a nd 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 Ch emical 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 Agricultu ral 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, Plat e and Chemical Manufacturing 500 325998 All Other Miscellaneous Chem ical Product and Preparation Manufacturing 500

Source: U. S. Small Business Administration (SBA). 2008. “Table of Small Business Size Standards Matched to North American Industry Cl assification System Codes.” Effective August 22, 2008. <http://www.sba.gov/services/c ontractingopportunities/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 Employees a 20 to 99 Employees 100 to 499 Employees 500 to 749 Employees 750 to 999 Employees 1,000 to 1,499 Employees Firms 9,341 5,413 1,974 790 95 56 71 Establishments 13,096 5,433 2,208 1,352 250 185 276 Employment 827,430 34,838 78,090 113,326 28,025 18,119 28,338 Receipts ($10 6 ) $468,211 $9,631 $21,394 $39,111 $12,217 $7,324 $14,762 Receipts/firm ($10 3 ) $50,124 $1,779 $10,838 $49, 507 $128,603 $130,779 $207,913 Receipts/establishment ($10 3 ) $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. Busi nesses: U.S. Detail Employment Sizes: 2002.” <http://www.census.gov/csd/ susb/download_susb02.htm>.

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

Manufacturing Chemical Manufacturing 

### Figure B-28. Industrial Production Trends in Chemical Manufacturing (NAICS 325)
Source: Federal Reserve Board. 2009. “Industrial Produc tion and Capacity Utilization: Industrial Production.” <http://www.federalreserve.gov/datadownload/>.

### B-48 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000

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

Imports Exports 

### Figure 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.us itc.gov/scripts/user_set.asp>.

90 100 110 120 130 140 150 160 170Jan‐98Jul‐98Jan‐99Jul‐99Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09PPI(Jan‐98=100)

Manufacturing Chemical Manufacturing


### Figure 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