### 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 27711iii 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 Anal ys is
................................ ................................ . 2
- 1 3 Economic Impact Anal ys is
................................ ................................ ........................... 3
- 1 3.1 Partial Equilibrium Analy sis (Multiple Markets)
................................ ................ 3
- 1 3.1.1 Overview
................................ ................................ ................................ .. 3
- 1 3.1.2 Economic Impact Anal ys is Results
................................ ......................... 3
- 3 4 Small E ntity

Anal ys es ................................ ................................ ................................ ... 4
- 1 4.1 Small Entity

Screening Analy sis
................................ ................................ ......... 4
- 1 4.1.1 Small Businesses
................................ ................................ ...................... 4
- 1 4.1.2 Small Entity

Anal ys is
................................ ................................ ............. 4
- 2 5 Human Health Benefits of Emissions Reductions
................................ ........................ 5
- 1 5.1 Sy nopsis
................................ ................................ ................................ ............... 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 Greenhouse 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
- 19iv

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 Appendi x es A : OAQPS Multimarket Model to Assess the Eco nomic I mpacts of Environmental Regulation
................................ ................................ .................. A
- 1 B : Industry

Profiles
................................ ................................ ................................ ....... B
- 1v LIST OF FIGURES Number Page 3
- 1. Distribution of Total Surplus Changes b y Industry (Total Surplus Change = $224 million, 2008$) (MACT Floor)
................................ ........................ 3
- 6 3
- 2. Employ

ment Impacts Using Morgenstern, Pizer, Shih (20 02) (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 CI SWIN SPS and EG in 2016
................................ ................................ ................................ .......................... 5
- 12 5
- 3. Breakdown of Monetized Benefits Estimates for the CI SWI NSPS and EG by PM 2.5 Precursor Pollutant and Source (MACT floor option)
................................ ... 5
- 12 5
- 4. Breakdown of Monetized Benefi ts Estimates for the CI SWI NSPS and EG by Subcategory

(MACT floor option)
................................ ................................ .......... 5
- 13 5
- 5. Estimated County L evel Carcinogenic Risk (from 2002 NATA)
........................... 5
- 21 5
- 6. Estimated County L evel Noncancer (Respirator y) Risk (from 2002 NATA)
......... 5
- 21 5
- 7. Percentage of Adult Population by Annual Mean PM 2.5 Ex posure (pre
- and post - policy

policy )
................................ ................................ ................................ ... 5
- 31 5
- 8. Cumulative Distribution of Adult Population at Annual Mean P M 2.5 levels (pre
- and post
- policy

policy )
................................ ................................ .................... 5
- 32 6
- 1. Net Benefits for the CISWINSPS and Emissions Guidelines at 3% Discount Rate
................................ ................................ ................................ ............................ 6
- 3 6
- 2. Net Benefits for the CISWINSPS and Emissions Guidelines at 7% Discount Rate
................................ ................................ ................................ ............................ 6
- 4vi LIST OF TABLES Number Page 1
- 1. Summary

of the Monetized Benefits, Social Costs, and Net Benefits for the CI SWINSPS and Emissions Guidelines in 2016 (millions of 2008$)
...................... 1
- 4 2
- 1. Summary

of Capital and Annual Costs for Existing CISWISources
........................ 2
- 2 3
- 1. Market
- Level Price and Quantity Changes: 2016 (MACT Floor)
............................. 3
- 3 3
- 2. Market
- Level Price and Quantity Changes: 2016 (Bey ond the MACT Floor)
......... 3
- 4 3
- 3. Distribution of Social Costs (million, 2008$): 2016
................................ .................. 3
- 5 3
- 4. Distribution of Social Costs (million, 2008$): 2016
................................ .................. 3
- 5 3
- 5. Employ

ment Impacts Using Morgenstern, Pizer, Shih (2002) (FTE)
....................... 3
- 9 4
- 1. Affected Sectors and Size Standards
................................ ................................ ......... 4
- 1 4
- 2. Sales Tests Using Small Companies I dentified 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 Estimates for the CI SWI NSPS and EG in 2016 (2008$)
................................ ................................ ................................ .............. 5
- 9 5
- 3. Summary

of Reductions in Health Inc idences from PM 2.5 Benefits for the CI SWINSPS 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$)
................................ ......................... 5
- 11 5
- 5. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 ) for 2016
..................... 5
- 15 5
- 6. Monetized SCC
- derived Disbenefits of CO 2 Emission I ncreases in 2016 (millions of 2008$)
................................ ................................ ................................ .. 5
- 16 6
- 1. Summary

of the Mon etized Benefits, Social Costs, and Net Benefits for the CI SWINSPS and EG in 2016 (millions of 2008$)
................................ ................... 6
- 21
- 1 SECTION 1 I NTRODUCTION The U.S. Environmental Protection Agency (EPA) is pro mulgating new standards of performance and emission guidelines based on a review of the standards and guidelines as part of the Clean Air Act Section 129(a)(5) requirement to review the new source performance standards and emission guidelines every 5 y ears. Additionally , when revising the standards of performance and emission guidelines we considered the District of Columbia Circuit Court rulings on maximum achievable control technology standards that were issued after promulgation of the new source performance standards and emission guidelines f or commercial and industrial solid waste incineration units in 2000 and a concurrently promulgated definition of nonhazardous secondary

materials as solid waste under the Resource Conservation and Recovery Act. EPA is also promulgating other amendments tha t EPA believes are necessary to adequatel y address air emissions from commercial and industrial solid waste incineration units and to clarify certain portions of the rules. As part of the regulatory process , EPA is required to develop a regulatory

impact anal ys is (RI A). The RIA includes an economic impact analy sis (EIA) and a small entity

impacts analy sis and documents the RIA methods and results. The RIA does not include the final engineering costs and emission reductions into this RI A (see Chapter 2 for more detail on the engineering costs that were not accounted for)
. We estimate that incorporating these final estimates would decrease the engineering costs by approximately 22 % and increase the monetized benefits by approximately

4% from those shown in th is RIA
. 1.1 Executive Summary The key results of the RIA are as follows:

 Engineering Cost Analysis: EPA estimates the promulgated rule’s total annualized costs will be $ 2 80 million (2008$).

 Market Analysis: Under the promulgated rule, the Agency ’s econom ic model suggests t he average national market
- level variables (prices, production
- levels, consumption, international trade) will not change significantl y (e.g., are less than 0. 1% ).

 Social Cost Analysis: The estimated social cost is approximately $ 2 8 0 mill ion (2008$). In the near term, the Agency ’s economic model suggests that industries are able to pass approximately $ 76 million of the rule’s costs to consumers (e.g., marginally

higher market prices). Domestic ind ustries’ surplus falls b y $ 207 million, whi le other countries on net benefit from higher prices (a net increase in rest
- of
- the1
- 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 est imated emplo ym ent changes range between

− 500 to 1,000 employ ees, with a central estimate of +300 employ ees.

 Small Entity Analyses: EPA performed a screening anal ys is for i mpacts on small entities by

comparing compliance costs to sales/revenues (e.g., sales and revenue tests). EPA’s anal ys is found the tests exceeded 3 % for four of nine small entities included in the screening anal ys is. After reviewing screening anal ys is results, EPA has determined the promulgated rule will not have a SI SNOSE and presumes tha t rule is eligible for certification under the RFA as amended by SBREFA. We provide the factu al basis for certification in Section 4
.
 Benefits Analysis: The benefits from reducing some air pollutants have not been monetized in this analy sis, including redu cing 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 ye ar. We assess the benefits of these emission reductions qualitatively

in this analy sis
. Thus , all monetized benefits reported reflect improvements in ambient PM 2.5 concentrations
. As such, although the monetized benefits likely

underestimate the total benefits, the extent of the underestimate is unclear
. In the y ear of full implementation ( 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 respectivel y. All estimates are in 2008$
. Using alternate relationship s 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, ecosy stem benefits and visibility benefits have not been monet ized in this anal ys is
.
 Net Benefits: The net benefits for the NSPS and Emission Guidelines are $6 0 million to $ 550 million and $ 3 0 million to $ 470 million, at 3% and 7% discount rates respectivel y ( Table 1
- 1) . All estimates are in 2008$ for the ye ar 2016
. These results are shown in Tables 1
- 1 1.2 Organization of this Report The remainder of this report supports and details the methodology and the results of the EI A:

 Section 2 describes the engineering cost analy sis.

 Section 3 describes the economic impact anal ys is.

 Section 4 describes the small entity anal ys es.

 Section 5 presents the benefits estimates.

 Section 6 presents the net benefits.1
- 3  Appendix A describes the multimarket model used in the economic analy sis.

 Appendix B describes the affected Industry pro files.1
- 4

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

CISWI NSPS and Emissions Guidelines in 2016 (millions of 2008$) a 3% Discount Rate 7% Discount Rate Option 1: MACT Floor Total Monetized Benefits b $340 to $830 $310 T o $750 Total Social Costs c $280 $280 Net Benefits $60 to $550 $3 0 T o $470 Non
- monetized Benefits 25,00 0 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 He alth 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 $13 0 to $770 $90 T o $66 0 Non
- monetized Ben efits 25,00 0 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 estimat es are for the implementation year ( 2016 ), and are rounded to tw o 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 associa ted 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 mo netized benefits include many but not all health effects associated w ith PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006) . T hese models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature morta lity because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type
. These estimates include energy disbenefits valued at $ 3.8 million
. c The methodology used to estimate social costs for one year in th e multimarket model using surplus changes results in the same social costs for both discount rates.2
- 1 SECTION 2 OVERVIEW OF THE ENGINEERING COST ANA LYSIS This RI A does not incorporate the final engineering costs and emission reductions
. We estimate that in corporating these final estimates would decrease the engineering costs by approximately

22% and increase the monetized benefits by approximately

4% from those shown in this RIA
. The changes made that were not included in the RIA are the following:

 The cost estimates for existing units were revised to reflect removal of several units from the CISW I population, including one energy recovery unit, several cyclonic burn barrels, and a few cem ent kilns.  - For the remaining kilns, we identified ones that will like ly install regenerative thermal oxidizers to me et the NESHAP limits, and therefore should not have these costs repeated in the CIS WI cost estimates. This section provides an overview of the engineering cost analy sis used to estimate the private expenditur es industry may make in order to comply with the rule. A detailed discussion of the methodology

used to estimate cost impacts is presented in “ Revised Compliance Cost Analy sis for Existing and New CI SWI Units” in the EPA
- HQ
- OAR
- 2003
- 0119 Docket. Note that there were some very late adjustments made to the CI SWI inventory and carbon monoxide test data that were performed shortly before signature of the final package. These adjustments affected the bottom
- line cost estimate of the final rules. The cost memoran dum in the docket reflects the results of the late inventory and data revisions and the resulting costs. However, there was insufficient time to revise the RIA to reflect these late revisions, so the cost totals presented below differ from those presented in “ Revised Compliance Cost Analy sis for Existing and New CI SWIUnits” in the EPA
- HQ
- OAR
- 2003
- 0119 Docket
. To estimate the national cost impacts of the rule for existing sources, EPA compared the maximum three
- run average value for each pollutant measured in tests (i.e., the highest result of an emission test performed) of each unit in the inventory to its corresponding emission limit to determine which, if an y, control devices would be needed in order to comply with the standards. In order to fill data gap s for units having no emissions data, these maximum test averages were averaged over each subcategory to develop an emission factor for each pollutant and each subcategory
. If identical units were operated at the facilit y but emissions data were available for only one unit , the emissions data from the unit with data were applied to the identical units. For the remaining units that did not report emissions data, we assigned the appropriate emission factor for the units in that subcategory 1 . The control anal y sis 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 baselin e emission concentration data gaps were filled using average emission factors.2
- 2

primary control device for particulate matter, cadmium, and lead control; packed bed scrubbers and dry sorbent injection for h ydrogen chloride and sulfur dioxide control; activated carbon injection for mercury

and di oxins/furans control; selective non
- catal yt ic reduction (SNCR) for oxides of nitrogen; and afterburner retrofits, regenerative thermal oxidizers, tune - ups, advanced combustion controls, and oxidation cataly sts for carbon monoxide controls. We also consider ed whether existing control devices could be improved to achieve the limits, such as adding more lime to duct sorbent injection sy stems to meet the sulfur dioxide limits. We included costs for testing and monitoring requirements contained in the rule. Fina lly , we analyzed the costs of waste segregation practices and alternative disposal options, such as diverting waste to a landfill to see if less expensive options to incineration were available. In certain cases, such as incinerators, our data suggest that sending waste to a landfill may likel y be less costly

than operating and maintaining an incineration unit. The resulting total national cost impact of the rule is 706 million dollars in capital expenditures and 280 million dollars per y ear in total annual costs. The total capital and annual costs include costs for control devices, work practices, testing and monitoring. Costs include testing and monitoring costs, as well as estimated recordkeeping and reporting costs. Based on the cost to comply with the r

ule, availability of alternatives to incineration, and historic negative growth in this source category , we anticipate only one new incineration unit within the next five y ears, with an annual compliance cost of $829,500, and up to five new small remote in cinerators at an annual cost of $351,000 per unit 1
. Table 2 - 1. Summary of Capital and Annual Costs for Existing CISWI Sources

Subcategory Number of Affected Units Capital Costs

(m illions of 2008$) Annualized Costs (m illions of 2008$) Energy Recovery Unit s
– Solids 3 1 507.6 194.3 Energy Recovery Units

– Liquid/Gas 6 32.3 7.6 Cement Kilns 16 157.7 73.7 Incinerators 28 5.7 3.3 Sm all remote incinerators 19 2.8 1.2 Based on this analy sis, EPA anticipates an overall total capital investment of $ 706 millio n plus the cost of recordkeeping and reporting required b y this rule, with an associated total

1 For new units, EPA fully anticipates new kilns and boilers would choose fuels in such a way that w ould not subject to CISWI regulation. The only new sources are facilities tha t dispose of w aste and only have incineration options available.2
- 3

annual cost of $2 80 million plus recordkeeping and reporting costs . The requirements result in industry recordkeeping and reporting burden associated with review ing the amendments for all CI SWIand inspections of scrubbers, fabric filters, and other air pollution control devices that may be used to meet the emission limits for all CISWI . Ongoing parametric monitoring requirements for ESPs, SNCR, dry sorbent inject ion and activated carbon injection are also required of all CI SWI units. Stack testing and development of new parameter limits would be necessary for CISWI that need to make performance improvements to meet the emission limits and for CISWIthat, prior to this action, have not been required to demonstrate compliance with certain pollutants. Visual emissions tests of ash handling would be required for all subcategories except kilns on an annual basis. Energy recovery units would be required to continuously m onitor opacity , and units larger than 250 MMBtu/hr would be required to monitor PM emissions using a PM CEMS. Kilns would be required to continuously monitor Hg emissions using an Hg CEMS. An y new CI SWI would also be required to continuously monitor CO emi ssions. Annualized capital/startup costs and operation and maintenance (O&M) costs are associated with the EG monitoring requirements, EPA Method 22 of Appendix A
- 7 testing, initial stack testing, storage of data and reports, and photocopy ing and postage.3
- 1 SECTION 3 ECONOMIC IMPACT ANAL YSIS EPA prepares an EIA to provide a measure of the social costs of using resources to comply with a program (U.S. EPA , 2000 ). The social costs can then be compared with estimated social benefits (a s presented in Se ction 5 ). As noted in EPA’s (2000) Guidelines for Preparing Economic Analyses , several tools are available to estimate social costs and range from simple direct compliance cost methods to the development of a more comple x market anal ys is that estimates market changes (e.g., price and consumption) and economic welfare changes (e.g., changes in consumer and producer surplus). The Office of Air Quality Planning and Standards (OAQPS) adopted a standard market anal ys is as des cribed in the Offi ce’s resource manual (U.S. EPA , 1999 ). The approach uses a single
- period multimarket partial equilibrium model to compare pre - policy market baselines with expected post - policy market outcomes. The anal ys is’ time hor izon is the short run; for this anal ys is, we use the model to approximate baseline conditions for 2016. In this analy sis, s ome production factors are fixed and some are variable and is distinguished from the very short run where all factors are fixed and p roducers cannot adjust inputs or outputs (U.S. EPA , 1999, 5
- 6 ). The intermediate time horizon allows us to capture important transitory stakeholder outcomes. Key measures in this analy sis include industry
- level changes in price levels, production and consumption, jobs, international trade , and social costs (changes in producer and consumer surplus). 3 .1 Partial Equilibrium Analysis (Multiple Markets) The partial equilibrium analy sis develops a market model that simulates how stakeholders (consumers and industries) might respond to the additional regulatory program costs. I n this section, we provide an overview of the economic model used in the anal ys is
. 3 .1.1 Overview Although several tools are available to estimate social costs, current EPA guidelines suggest that multimarket models “...are best used when potential economic impacts and equity effects on related markets might be considerable” and modeling using a computable general equilibrium model is not ava ilable 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 anal ys es can complement full dy

namic general equilibrium analy sis. For example, Morgenstern and3
- 2

colleagues examine carbon price policies with short
- and long
- term time horizons (Morgenstern and colleagues, 2004 ; Ho, Morgenstern, and Shih, 2008 ). Aldy and Pizer (2009) assess near - term competitiveness effects of a domestic cap
- and
- trade program to address stakeholder concerns about shifts in economic activity and jobs to other countries. A single
- period multimarket partial equilibrium model contains the following features:

 Industry

sectors and benchmark data set

– All industries aggregated to 100 industry sectors

– a single benchmark year (2010) 1

– estimates of industry

employ ment

 Economic behavior

– industries respond to r egulatory costs by changing production rates

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

– customers respond to these price increases and consumption falls

 Model scope

– 100 sectors are linked with each other based on their use of energy and other non
energy materials. For example, the construction industry is linked with the petroleum, cement, and steel industr ies and is influenced b y price changes that occur in each sector. Th e links allow EPA to account for indirect effec ts the regulation has on related markets.

– production adjustments influence employ ment levels

– international trade (imports/exports) behavior considered

 Model time horizon (“short run”)

– fixed production resources (e.g ., capital) lead to an upward
- sloping in dustry 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 behavi oral assumptions, data, parameters, and model equations.

1 For this analysis, we use the model to approximate baseline conditions for 2016.3
- 3

3 .1.2 Economic Impact Analysis Results 3 .1.2.1 Market

-Level Results Market
- level impacts include price and quantity adjustments including the changes in international trade ( Table 3
- 1)
. The Agency ’s economic model suggests t he average national market
- level variables (prices, production
- levels, consumption, international trade) will not significantl y change (e.g., are less than 0.1%). Similar results are present for th e Bey ond 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% Transp ortation Equipment 0.00002%

− 0.00019% 0.00004%

− 0.00011%

− 0.00004% Other 0.00058%

− 0.00130% 0.00098%

− 0.00044%

− 0.00054% Wholesale and Retail Trade

− 0.00027%

− 0.00018%

− 0.00018%

− 0.00018% 0.00020% Transportation Services

− 0.00052%

− 0.00045%

− 0.00026%

− 0 .00031% 0.00043% Other Services 0.00001%

− 0.00018%

− 0.00006%

− 0.00018%

− 0.00002%3
- 4

Table 3 - 2 . Market - Level Price and Quantity Changes: 2016 ( Beyond the MACT Floor ) Industry Sector Prices Production Imports Consumption Exports Energy 0.00254%

− 0.00090% 0.00531%

− 0.00068%

− 0.00042% Nonmanufacturing 0.00243%

− 0.00058% 0.00153%

− 0.00043%

− 0.00187% Manufacturing Food, beverages, and textiles 0.00071%

− 0.00096% 0.00118%

− 0.00053%

− 0.00044% Lumber, paper, and printing 0.03158%

− 0.01319% 0.03217%

− 0.00761%

− 0.02204% Chemicals 0.00022%

− 0.00156% 0.00027%

− 0.00118%

− 0.00021% Plastics and Rubber 0.00064%

− 0.00124% 0.00069%

− 0.00097%

− 0.00063% Nonmetallic Minerals 0.04872%

− 0.01157% 0.01783%

− 0.00723%

− 0.04027% Primary Metals 0.00093%

− 0.00128% 0.0 0089%

− 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 E quipment 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 , i nd ustries are able to pass on $ 76 m illion (2008$) the costs to U.S. househol ds in the form of higher pri ces ( Table 3
- 3 )
. In 2016, e xisting U.S. industries’ surplus f alls by $ 207 m illion, and the net loss for U.S. stakeholders is $ 2 83 million . As U.S. prices rise, other countries are affected through international trade relationships. Households that buy good s from the United States experience losses, while industries that sell goods to the United States benefit; the mo del estimates a net gain of $ 3 million . After accounting for international trade effects, the Agency ’s economic model projects the net total su rplus loss associa ted with the rule is $ 2 80 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 anal ys is 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 . Dis tribution of Social Costs (m illion, 2008$): 2016 Me thod MACT Floor Partial Equilibrium Model (Multiple Markets) Change in U.S. consumer surplus −$ 76 Change in U.S. producer surplus −$ 207 Change in U.S. surplus −$283 Direct Compliance Costs Metho d Total annualized costs, new sources (not modeled) $1 Change in U.S. Surplus −$284 Net change in rest of world surplus $3 Net change in total surplus −$281
Table 3 - 4 . Distribution of Social Costs (million, 2008$): 2016 Me thod 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 To tal annualized cost savings , unknown sources (not modeled) Less than $1 million Change in Total Surplus −$299 Net change in rest of world surplus $4 Net change in total surplus −$2953
- 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% Lu mb er, p ap er, a nd pr int ing

Other Se rv ice s Nonm eta llic

M ine ra ls No nm anufa ct uring En erg y In dust ri es Che mi cals Food, be ve ra ge s, and tex ti le s

Pr im ar y Me ta ls Wh ole sale a nd Re ta il Tr ade

Ot her Pl ast ics and Ru bbe r Tr anspor ta ti on Eq uipm en t Ele ct ronic

E quipm en t Tr an sp ort at io n Se rv ice s Fabr icat ed M eta ls Mach in ery

a nd Equipm en t 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 employ ment impacts of the final rule based on Morgenstern, Pizer and Shih (2002). A stand
alone anal ys is of jobs is not included in a standard cost
- benefit anal ys is. Executive Order 13563 , however, states, “ Our regulatory s ys tem must protect public health, welfare, safet y, and our environment while promoting economic gr owth, innovation, competitiveness, and job creation ” (emphasis added). Therefore, we have provided this anal ys is to inform the discussion of job impacts. EPA continues to explore the relevant theoretical and empirical literature and to seek public comme nts in order to ensure that such estimates are as accurate as possible. From an economic perspective labor is an input into producing goods and services; if regulation requires that more labor be used to produce a given amount of output, that additional labor is reflected in an increase in the cost of production. Moreover, when the econom y is near full employ

ment, jobs created in one industry as a result of regulation displace jobs in other industries. On the other hand, in periods of high unemploy ment, an increase in labor demand due to regulation may

have a stimulative effect that results in a net increase in overall employ ment.3
- 7

With significant numbers of workers unemploy ed, the opportunity costs associated with displacing jobs in other sectors are lik el y to be much smaller. For this reason, this RI A looks carefull y at a subset of the employment consequences of this final rule. I t is important to note that EPA has estimated only a portion of the employ

ment effects
-- namely , those associated with the d irect impacts on employ ment in the regulated industry . A full analy sis 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 employ ment throughout th e econom y as a whole in response to changes in output and factor prices. We expect that the rule’s direct impact on emplo yment will be small. The Agency ’s anal ys is does not include all the direct effects of this regulation. For example, EPA is currentl y ex ploring way s to quantify the job impacts in the pollution control sector that result from these and future regulations
. Furthermore, we have not quantified the rule’s indirect or induced impacts. What follows is an overview of the various way s that enviro nmental regulation can affect emplo ym ent, followed b y a discussion of the estimated impacts of this rule. An environmental regulation can affect the demand for labor in several way s:

 Direct Effects :

– Increased prices for industry output may reduce the dema nd for labor: Environmental regulations increase production costs causing firms to increase prices; higher prices reduce consumption (and production), thus reducing demand for labor within the regulated industry . The extent of this effect will depend on t he extent of the price increase and the elasticity of the demand curve.

– Regulated firms demand labor workers to operate and maintain pollution controls within those firms. Once pollution control equipment is installed, regulated firms may

hire workers to operate and maintain it, just as they would hire workers to produce more output. The extent of this effect will depend in part on whether the operation and maintenance of pollution controls are labor intensive

– Increased demand for pollution control equipme nt 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 anal ys es and significant expenditures for assembly and installation of such equipment. These activities will be job
- creating during the period before firms must comply with the rule, at which point all pollution contr ol equipment must be installed and operating.3
- 8 . Indirect and Induced Effects:

– Environmental regulations create employment in many basic industries . I n addition to the increase in employ ment in the environmental protection industry (increased orders for pollution cont rol equipment), environmental regulations also create employ ment in industries that provide intermediate goods to the environmental protection industry. For example, capit a l expenditures to reduce air pollution involve the purchase of abate ment equipment. The equipment manufacturers, in turn, order steel, tanks, vessels, blowers, pumps, and chemicals to manufacture and install the equipment. 1 On the other hand, demand for labor will decrease in sectors that supply

inputs for, or demand the o utputs of the regulated industry
. None of these impacts is accounted for in the current anal ys is
. 2 We also do not estimate employ

ment impacts “induced” by increased output of the environmental protection sector, or decreased output of the regulated sector s. The estimated impacts of the final rule on emplo yment in affected sources are based on an empirically

derived relationship reported in Morgenstern, Pizer and Shih (2002) , a peer reviewed, published study . Estimates of the employ ment impacts of the capi tal investments and other non
- recurri ng requirements of the rule are derived from the cost anal ysis developed for the regul a t ion. 3 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 legall y compelled to satisfy this new demand, regulated firms have to finance this addition al production with the proceeds of sales of their other (market) products. Satisfy ing this new demand requires additional inputs, including labor, and may alter the relative proportions of labor and capital used by regulated firms in their production proce sses. Thus, Morgenstern et al., decompose the overall effect of a regulation on employ ment into the following three sub components:

3 Richard D. Morgenstern, William A. Pizer, and Jhih
- Shy ang Shih, Journal of Environmental Economics and M anagement | May 2002 | Vol. 43, no. 3 | pp. 412
- 436. 3 Richard D. Morgenstern, William A. Pizer, and Jhih
- Shy ang Shih, Journal of Environmental Economics and M anagement | May 2002 | Vol. 43, no. 3 | pp. 412
- 436. 3 Richard D. Morgenstern, William A. Pizer, and Jhih
- Shy ang Shih, Journal of Environmental Economics and M anagement | May 2002 | Vol. 43, no. 3 | pp. 412
- 436.3
- 9  The “Demand Effect”: higher production costs raise market prices, reducing consumption (and production), thereb y reducing de mand for labor within the regulated industry 1 ;

 The “Cost Effect”: As production costs increase, plants use more of all inputs, including labor, to maintain a given level of output. For example, in order to reduce pollutant emissions while holding output levels constant, regulated firms may require additional labor;

 The “Factor
- Shift Effect”: Regulated firms’ production technologies may be more or less labor intensive after comply ing with a regulation (i.e., more/less labor is required per dollar of outpu t). Decomposing the overall employ ment impact of environmental regulation into three subcomponents clarifies the conceptual relationship between environmental regulation and employ ment in regulated sectors, and permitted Morgenstern, et al. to provide an e mpirical estimate of the net impact. For present purposes, the net effect is of particular interest, and is the focus of our anal ys is. Morgenstern, Pizer and Shih (2002): Empirical Results Morgenstern et al. empirically estimate a model for four highl y pol luting, regulated industries (pulp and paper, plastics, petroleum refining and steel) to examine the effect of higher abatement costs from regulation on employ ment. They

conclude that increased abatement expenditures generall y do not cause a significant c hange in employ ment. More specifically , their results show that, on average across the four industries, each additional $1 million spending on pollution abatement results in a (statistically insignificant) net increase of 1.5 5 (+/
- 2.24) jobs. 2 “In plastic s and petroleum, [Morgenstern et al] find that increased regulation raises employ ment by a small but statistically

significant amount: 6.9 and 2.2 jobs per million dollars of regulatory expense, respectivel y. In pulp and paper and steel, the estimates ar e even smaller and insignificantl y different from zero.” 3 By apply ing these estimates to pollution abatement costs , w e estimated the net employ

ment effect for major and areas sources to range from
- 4,100 to +8,500 jobs in the directly affected sectors wit h a central estimate of +2,200 (Table 4
- 3). 4 , 5

1 The Morgenstern et al. results rely on industry demand and supply elasticities to determine cost pass
- through and reductions in output.. 2 These results are similar to Berman and Bui, who find that wh ile sharply increased air quality regulation in Los Angeles to reduce NOx emissions resulted in large abatement costs they did not result in substantially reduced em ployment. "Environmental regulation and labor demand: evidence from the South Coast Air Bas in." Journal of Public Economics 79(2): 265
- 295. 3 Morgenstern, Pizer and Shih, p. 413. 4 Since Morgenstern’s analysis reports environmental expenditures in $1987, w e m ake an inflation adjustment the engineering cost analysis using GDP implicit price defla tor (64.76/108.48) = 0.60) 5 Net employment effect = 1 .5 5 × $ 2,4 00 million × 0.603
- 10

Table 3 - 5 . Employment Impacts Using Morgenstern, Pizer, Shih (2002) (FTE) Demand Effect Cost Effect Factor Shift Effect Net Effect Change in Full
- Time Jobs per Million Dollars of Environmental Expenditu re 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. 01,000 JobsBase=144,100

De mand Effec t: Es ti mate de ri ve d fr om Morge nstern, et al . Cost Ef fec t: E sti mate d er iv ed f rom Morge nste rn, et a l. Factor Shif t Effec t: Es ti mate de ri ve d fr om Morge nste rn, et a l. Net E ffect: Esti mate d er ived fr om Morge nste rn, et al . Figure 3 - 2. Employment Impacts Using Morgenstern, Pizer, Shih (2002) ( 1,000 FT E s ) Limitations of the Analysis Although the Morgenstern et al. paper provides information about the potential job effects of environmental protection programs, there are sev eral caveats associated with using those estimates to anal yz e the final rule
. First, the Morgenstern et al. estimates presented in Table 4
- 33
- 11

and used in EPA ’s anal ys is represent the weighted average parameter estimates for a set of manufacturing industries (pulp and paper, plastics, petroleum, and steel) . This set of industries only partiall y overlaps with the sectors affected by this rule
. Second , rel yi ng on Morgenstern et al. implicitly assumes that estimates derived from 1979

– 1991 data are still applica ble. Third, the methodology used in Morgenstern et al. assumes that regulations affect plants in proportion to their total costs. I n other words, each additional dollar of regulatory burden affects a plant b y an amount equal to that plant's total costs re lative 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 e t al. does not include most indirect effects and all induced effects.4
- 1 SECTION 4 S MALL ENTITY A N ALYSES The RFA as amended b y SBREFA generall y requires an agency to prepare a regulatory flexibility anal ys is of an y rule subject to notice and comment rule making requirements under the Administrative Procedure Act or an y other statute, unless the agency certifies that the rule will not have a significant economic impact on a substantial number of small entities ( SI SNOSE )
. Small entities include small busines ses, small governmental jurisdictions, and small not
- for
profit enterprises. EPA assessed the potential small entity economic impacts using a screening anal ys is. A fter reviewing screening anal ys i s results, EPA has determined the promulgated rule will not h ave a SI SNOSE and presumes that rule is eligible for certification under the RFA as amended b y SBREFA. We provide the factual basis for certification below
. 4 .1 Small Entity Screening Analysis 4 .1.1 Small Businesses The sectors covered b y the rule were ide ntified through lists of small entities provided b y the engineering anal ys is
. Table 4
- 1 provides a list of the sectors affected (3
- digit NAICS) and the range of SB A size definitions
. Table 4 - 1. Affected Sectors and Size Standards 2007 NAICS Description Siz e 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 Ty pically 500 to 1,500 employees 325 Chemical Manufacturing 500 to 1,000 employees 326 Plastics and Rubber Products Manufacturing Ty pically 500 to 1,000 employees 337 Furniture and Related Product Manufacturing 500 employees 562 Waste Management and Remediation Serv ices Ty pically $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 i nf

ormation collection effort for f ac ilities with combustion u nits in the major source Boiler rule, w e id entified affected facilit y names listed as small, traced the ultimate parent compan y name to verify the facility was owned by a small business, and collected the most recent parent compan y sales and employment figures. As Table 4
- 2 shows, the average cost
- to sales ratios for small companies are approximately 3.4% and 3.5% for the MACT Floor and Be yond the MACT Floor options . The median ratios are 2.2% for the MACT Floor option and 2.3% for the Bey ond the MACT Floor option
. Each option has 4 entities that ha ve sales test that exceeds 3%. Table 4 - 2 . Sales Tests Using Small Companies Identified in the Combustion Survey

Sample Statistic MACT Floor Beyond the MACT Floor Mean 3.4 % 3.5% Median 2.2 % 2.3% Maximum 17.3 % 17.3% Minimum

− 2.4 %

− 2.5% Ultimate parent com

pany observations 1 0 1 0 Ultimate parent company observations with sales data 9 9 Ultimate parent companies with Sale Tests Exceeding 3% 4 4
We estimate that there are 88 entities subject to this regulation, of which 10 of them are considered to be s mall companies. The small entities directly regulated by

the rule are facilities engaged in industrial or commercial operations, such as paper and paperboard manufacturing and utility providers. The average cost
- to - sales ratios for small companies are b elow 3.5 percent. The median ratio is 2.2 percent. Onl y four entities, which are in 3 different industries, have a sales test that exceeds 3 percent. For the purposes of this rulemaking, four is not considered a "substantial number" of small entities. Al though 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 BENEFIT S OF EMISSIONS REDUC TIONS The RIA does n ot include the final engineering costs and emission reductions into this RI A (see Chapter 2 for more detail on the engineering costs that were not accounted for). We estimate that incorporating these final estimates would decrease the engineering costs by approximately

22% and increase the monetized benefits by approximately

4% from those shown in this RIA
. 5 .1 Synopsis In this section, we provide an estimate of the monetized benefits associated with reducing particulate matter (PM) for the CI SWI 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 emissi on limits on other pollutants. The latter are often referred to as “co
- benefi ts.” 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 morbidit y and premature mortality among pop ulations exposed to the PM 2.5 precurso rs reduced b y this rulemaking. Because we were unable to monetize the direct benefits associated with reducing HAPs, the monetized benefits estimate is an underestimate of the total benefits. The extent of this underes timate, whether small or large, is unknown
. Using a 3% discount rate, we estimate the total monetized benefits of the CI SWI NSPS and EG (MACT floor option) to be $340 million to $830 million in t he implementation ye ar ( 2016 ). Using a 7% discount rate, we e stimate the total monetized benefits of the CI SWI NSPS and EG (MACT floor option) to be $310 million to $750 million in the implementation ye ar . All estimates are in 2008$ and include an y energy disbenefits from additional energy usage . Due to last minute changes, we were unable to incorporate the final emission reductions into this RI A
. We estimate that incorporating these final estimates would increase the monetized benefits b y approximately 4% from those shown in this RI A
. These estimates reflect EPA’s most current interpretation of the scientific literature (U.S. EPA, 2009b )
. Higher or lower estimates of benefits are possible using other assumptions; examples of th is are provided in Figure 5
- 2
. M ethodological and time limitations under the court - ordered schedule prevented EPA from monetizing the benefits from several important benefit categories, including benefits from reducing hazardous air pollutants, ecos ys tem effec ts, and visibility

impairment. The benefits from reducing othe r air pollutants have not been monetized in this analy sis, 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 ye ar. We assess the bene fits of these emission reductions qualitatively in this anal ys is. 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 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

reducti ons from emissi on limits on other pollutants. The total PM 2.5 reductions are the consequence of the technologies installed or waste diversion to meet these mul tiple limits. Due to analy

tical limitations, it was not possible to provide a comprehensive estim ate of PM 2.5 - related benefits. Instead, we used the “benefit
- per - ton” approach to estimate these benefits based on the methodology described in Fann, Fulcher, and Hubbell (2009) . The key assumptions are described i n detail below. These PM 2.5 benefit
- per - ton estimates provide the total monetized human health benefits (the sum of premature mortality and premature morbidity ) of reducing one ton of PM 2.5 (or precursor) f rom a specified source. EPA has used the benefit p er - ton technique in several previous RIAs, including the recent S O 2 NAAQS RIA (U.S. EPA, 2010b ). Table 5
- 1 shows the quantified and unquantified benefits captured in those benefit
- per - ton estimates. Table 5 - 1 . Human Health and Welfa re Effects of PM 2.5 Pollutant / Effect Quantified and Monetized in Prim ary Estimates Unquantified Effects Changes in: PM 2.5 Ad ult premature mortality

Bronchitis: chronic and acute Hospital admissions: respiratory and cardiovascular Em ergency room visit s for asthma Nonfatal heart attacks (myocardial infarction) Lo wer and upper respiratory illness Minor restricted
- activity days Work loss days Asthma exacerbations (asthmatic population) Infant mortality Subchronic bronchitis cases Lo w birth weight Pre
- term births Pulmonary function Nonfatal cardiovascular outcomes other than myocardial infarctions Chronic respiratory diseases other than chronic bronchitis Non
- asthm a respiratory emergency room visits Visibility Household soiling Consistent with the Portlan d Cement NESHAP (U.S. EPA, 2009a ), the PM 2.5 benefits estimates utilize the concentration
- response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA ’ s expert elicitation study as a sensi tivity

analy sis.5
- 3  One estimate is based on the concentration
- response (C
- R) function developed from the extended anal ys is of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002) , a stud y that EPA has previously u sed to generate its primary benefits esti mate. When calculating the estimate, EPA applied the effect coefficient as reported in the stud y without an adjustment for assume d concentration threshold of 10 μg/m 3 as was done in recent (2006

– 2009) Office of Air and Radiation RIAs.

 One estimate is based on the C
- R function developed from the extended analy sis of the Harvard Six Cities cohort, as reported b y Laden et al. (2006)
. This study , published after the completion of the Staff Pap er 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 RI As completed since the PM 2.5 NAAQS. When calculating the estimate, EPA applied the effect coefficient as reported in the study wi thout an adjustment for assumed concentration threshold of 10 μg/m 3 as was done in recent (2006

– 2009) RI As.

 Twelve estimates are based on the C
- R functions from EPA ’ s expert elicitation study (IEc, 2006 ; Roman et al., 2008 ) on the PM 2.5 - mortality relationship and interpreted for benefits anal ys is in EPA ’ s final RIA for the PM 2.5 NAAQS. For that study , twelve experts (labeled A through L ) provided independent estimates of the PM 2.5 - mortality concentration
- respo nse function. EPA practice has been to develop independent estimates of PM 2.5 - mortality estimates corresponding to the concentration
- response function provided b y each of the twelve experts, to better characterize the degree of variability

in the expert r esponses. Readers interested in reviewing the general methodology for creating the benefit
- per - ton estimates used in this analy sis should consult Fann, Fulcher, and Hubbell (2009) . As described in Fann, Fulcher, an d Hubbell (2009) , benefit
- per - ton estimates are developed for selected pollutant/source category combinations. The per
- ton values calculated therefore appl y only to tons reduced from those specific pollutant/source combinations (e.g., SO 2 emitted from electric generating units; NO 2 emitted from mobile sources). In this anal ys is , we apply the national average benefit
- per - ton estimate for a 2016 analy sis y ear and multiply it by the corresponding emission reductions of directl y emitted PM 2.5 , SO 2, and NO x to quantify the benefits of this rule
. The benefit
- per - ton estimates found in Fann, Fulcher, and Hubbell (2009) reflect a specific set of k ey assumptions and input data. As we update these underl yi ng assumptions to reflect the scientific literature, we re
- estimate the benefit
- per - ton estimates and post the updated estimates at http:// www.epa.gov/air/benmap/bpt.html
. Inadditio n, we adjust these estimates to match the currency y ear f or the costs in this analy sis. 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 y et sufficient to allow differentiation of effect estimates by particle t yp e (U.S. EPA, 2009b)
. Directl y5
- 4

emitted PM, SO 2 , and NO x are the dominant PM 2.5 precursors affected b y t his rule. Even though we assume that all fine particles have equivalen t health effects, the benefit
- 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 , resulting geographic distribution of exposure
. When more people are exposed, the benefits per ton are greater
. For example, SO 2 tends to have a lower benefit
- per - ton estimate than direct PM 2.5 because sulfate particles formed from SO 2 emissions can transport many miles, meaning that higher exposures may occur over areas w ith 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, leading to higher monetized health benefits for direct PM 2.5 than for SO 2 emissions. The benef it
- per - ton coefficients in this anal ys is were derived using modified versions of the health impact functions used in the PM NAAQS Regulatory

I mpact Anal ys is. Specifically, this analy sis uses the benefit
- per - ton method first applied in the Portland Cement N ESHAP RIA (U.S. EPA, 2009a ), which incorporated three updates: a new population dataset, an expanded geographic scope of the benefit
- per - ton calculation, and the functions directly from the epidemiology

studies without an adjustment f or an assumed threshold. 1 Removing the threshold assumption is a key difference between the method used in this analysis of PM benefits and the methods used in RIAs prior to the Portland Cement proposal , and we now calculate incremental benefits down to th e lowest modeled PM 2.5 air quality

levels. EPA strives to use the best available science to support our benefits anal ys es, and we recognize that interpretation of the science regarding air pollution and health is dy namic and evolving. Based on our review of the current body of scientific literature, EPA now estimates PM
- related mortalit y without applying an assumed concentration threshold. EPA ’ s I ntegrated Science Assessment for Particulate Matter (U.S. EPA, 2009b ), which was recent ly reviewed by EPA ’ s Clean Air Scientific Advisory Committee (U.S. EPA
- SAB, 2009a ; U.S. EPA
- SAB, 2009b ), concluded that the scientific literature consistently finds that a no
- threshold log
- linear mode l most adequatel y portray s the PM
- mortality concentration
- response relationship while recognizing potential uncertainty about the exact shape of the concentration
- response function. Since then, the Health Effects Subcommittee (U.S. EPA
- SAB, 2010 ) of EPA ’ s Council concluded, “The HES fully supports EPA ’ s decision to use a no
- threshold model to estimate mortality reductions. This decision is supported by the data, which are quite consistent in showing effects down to the lowest meas ured levels. Analy ses of cohorts using data from more
1 These updates w ere already included in Fann et al. (2009). An example of the effect of these updates is available in the Portland Cement proposal RIA (U.S. EPA, 2009a). The benefit
- per - ton estimates have also been updated since the Portland Cement proposal RIA (U.S. EPA, 2009a) to incorporate a revised VSL, as discussed on the next page.5
- 5

recent ye ars, during which time PM concentrations have fallen, continue to report strong associations with mortality . Therefore, there is no evidence to support a truncation of the CRF.” In conjunction with the underly ing scientific literature, this document provided a basis for reconsidering the application of thresholds in PM 2.5 concentration
- response functions used in EPA ’ s RI As. For a summary of these scientific review statements and the panel member s commenting on thresholds since 2002, please consult the Technical Support Document (TSD) Summary of Expert Opinions on the Existence of a Threshold (U.S. EPA, 2010c )
. Consistent with this recent scientific advice, we are replacing the previous threshold sensitivity

anal ys is with a new “Lowest Measured L evel” (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, our confidence in the estimated PM mortality decreases as we consider air quality levels further below the LML in major cohort studies that estimate PM
- related mortality . While an LML assessment provides some insight into the level of uncertaint y in the estima ted PM mortality benefits, EPA does not view the LML as a threshold and continues to quantify PM
- related mortalit y impacts using a full range of modeled air quality concentrations. Itis important to emphasize that we have high confidence in PM 2.5 - related effects down to the lowest L ML of the major cohort studies. J ust 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 anal ys is, policy
- specific air quali ty data is not available due to time or resource limitations. For these rules, we are unable to estimate the percentage of premature mortalit y associated with this specific rule ’ s emission reductions at each PM 2.5 level. However, we believe that it is stil l important to characterize the distribution of exposure to baseline air qualit y 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 available from the recently proposed Transport Rule (U.S. EPA, 2010e ). It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population, and change in air quality . I n ot her words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emission reduction policy . The most important aspect, which we are unable to quantify

for rules without air quality modeling, is the shift in exposure associated with this specific rule. Therefore, caution is warranted when interpreting the LML assessment. For more information on the data and conclusions in the L ML assess ment for rules without policy
-5
- 6

specific air quality

modeling, please consult the LMLTSD (U.S. EPA, 2010d ) . The results of this analy sis are provided in Section 5. 5
. As is the nature of Regulatory I mpact Anal ys es (RI As), the assumpti ons and methods used to estimate air quality benefits evolve over time to reflect the Agency ’ s most current interpretation of the scientific and economic literature. For a period of time (2004

– 2008), the Office of Air and Radiation (OAR) valued mortality r isk reductions using a value of statistical life (VSL) estimate derived from a limited anal ys is of some of the available studies. OAR arrived at a VSLusing a range of $1 million to $10 million (2000$) consistent with two meta
- anal ys es of the wage
- risk lit erature. The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) meta - anal ys is of 33 studies. The $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta anal ys is 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 - ana ly sis. However, the Agency neither changed its official guidance on the use of VSL in rule
- makings nor subjected the interim estimate to a scientific peer
- review process through the Science Advisory Board (SAB) or other peer
- review group. During this time , the Agency continued work to update its guidance on valuing mortalit y risk reductions, including commissioning a report from meta
- anal yt ic experts to evaluate methodological questions raised b y EPA and the SAB on combining estimates from the various data sources. In addition, the Agency consulted several times with the Science Advisory Board Environmental Economics Advisory Committee (SAB
- EEAC) on the issue. With input from the meta - anal yt ic experts, the SAB
- EEAC advised the Agency to update its guidance using specific, appropriate meta
- anal yt ic techniques to combine estimates from unique data sources and different studies, including those using different methodologies (i.e., wage
- risk and stated preference) (U.S. EPA
- SAB, 2007 ). Until updated guidance is available, the Agency determined that a single, peer
- reviewed estimate applied consistently best reflects the SAB
- EEAC advice it has received. Therefore, the Agency has decided to apply

the VSL that was vetted and endorsed b y 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 adjust ing the VSL to account for a different currency year (2008$) and to acco un t for income growth to 2016, the $5.5 mi llion VSL is $7.9 million. 3 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008 b ), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortal ity risk valuation guidance would be f orthcoming in the near future. Therefore, this report does not represent final agency polic y.5
- 7

derived from 26 labor marke t and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 4 The Agency is committed to using scientifically

sound, appropriatel y reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB
- EEAC ’ s specific recommendations. In implementing these rules, emission controls may lead to reductions in ambient PM 2.5 below the National Ambient Air Quality Standards (NAAQS) for PM in some areas and assist other areas with attaining the PM NAAQS. Because the PM NAAQS RIAs also calculate PM benefits, t here are important differences worth noting in the design and analy tical objectives of each RIA. The NAAQS RI As illustrate the potential costs and benef its of attaining a new air quality standard nationwide based on an array of emission control strategies for different sources. In short, NAAQS RIAs hypothesize, but do not predict, the control strategies that States may choose to enact when implementing a NAAQS. The setting of a NAAQS does not directl y result in costs or benefits, and as such, the NAAQS RIAs are merel y illustrative and are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and em ission reductions. However, some costs and benefits estimated in this RIA account for the same air qualit y improvements as estimated in the illustrative PM 2.5 NAAQS RI A
. By contrast, the emission reductions for this rule are from a specific class of well
characterized sources. In general, EPA is more confident in the magnitude and location of the emission reductions for these rules. It is important to note that emission reductions anticipated from these rules do not result in emission increases elsewhere ( other than potential energy disbenefits). Emission reductions achieved under these and other promulgated rules will ultimately

be reflected in the baseline of future NAAQS anal ys es, which would reduce the incremental costs and benefits associated with atta ining the NAAQS. EPA remains forward looking towards the next iteration of the 5
- ye ar review cy cle for the NAAQS, and as a result does not issue updated RIAs for existing NAAQS that retroactively update the baseline for NAAQS implementation. For more infor mation on the relationship between the NAAQS and rules such as anal yz ed here, please see Section 1.2.4 of the SO 2 NAAQS RIA (U.S. EPA, 2010 f ). Figure 5
- 1 illustrates the relative breakdown of the monetized PM 2.5 he alth benefits.

4 In this analysis, we adjust the VSL to account for a different currency year (2008$) and to acco unt for income grow th to 2016. After applying these adjustments to the $6.3 million value, the VSL is $9.1 m illion
.5
- 8 Adult Mortality - Po pe et al. 93% Chroni c Bronchiti s 4% AMI 2% Ac ute Re spi ratory Symptom s 0. 5%

Infa nt Mor tality 0 .4 %

Wo rk L oss Days 0. 2% Ho spi tal Admi ssi ons, Cardio

0. 2% Hospital Admi ssi ons, Re sp 0. 04% Asthma Ex ace rb ation 0. 01% Ac ute Bronchitis 0. 01% Upper R es p Symp 0. 00% Low er R es p Symp 0. 00% ER Vi si ts, Re sp 0. 00% Othe r 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 breakdow n is illustrative, using the results based on Pope et al. (2002) as an example. Using t he Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult mo rtality wo uld be 97%. This chart shows the breakdown using a 3% discount rate, and the results would be similar i f a 7% discount rate was used. Table 5
- 2 provides a general summary of the primary

approach results by

pollutant, including the emission reductions and monetized benefits
- per - ton at discount rates of 3% and 7%. 5 Table 5
- 3 provides a summary of the reducti ons in health incidences as a result of the polluti on reductions. In Table 5
- 4, we provide the benefits using our anchor points of Pope et al. and Laden et al. as well as the results from the expert elicitation on P M mortality
. Figures 6
- 2 through 6
- 4 provide a visual representation of the range of benefits estimates and the pollutant breakdown of the monetized benefits
.

5 To comply with Circular A
- 4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003 ). These benefit s are estimated for a specific analysis year (i.e., 2016 ), and most of the PM benefits occur within that year with two exceptions: acute myocardial infarctions (AMIs) and premature m ortality. For AMIs, we assume 5 years of follow
- up medical costs and lost wages. For premature mortality, we assume that there is a “cessation” lag between PM exposures and the total realization of changes in health effects. Although the structure of the lag is uncertain, EPA follows the advice of the SAB
- HES to assume a segment ed lag structure characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction in PM 2.5 (U.S. EPA
- SAB, 2004 ). Changes in the lag assumptions do not chang e the total number of estimated deaths but rath er the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis year and the valuation of premature mortalities that oc cur after the analysis year. As such, the monetized be nefits 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

P ollutant 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 (m illions 2008$ at 3%) Total

Monetized Benefits (m illions 2008$ at 7%)MACT Floor D irect 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 Tota l $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 im plementation year (2016 ), and are rounded to tw o 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 m onetized benefits incorporate the conversion from precursor emissions to ambient fine particles. Confidence intervals are unavailable for this analysis because of the benefit
- per - ton methodology. These estimates do not include the energy disbenefits valued at $3.8 million, but the rounded totals do not change.5
- 10

Table 5 - 3 . Summary of Estimated Reductions in Health Incidences from PM 2.5 B enefits

for the CISWI NSPS and EG in 2016 a MACT Floor Beyond - the - Floor Avoided Prem ature Mortality Pope et al. 38 49 Laden et al. 98 130 Avoided Morbidity Chronic Bronchitis 26 33 Ac ute Myocardial Infarction 61 78 Hospital Admissions, Respiratory 9 12 Hospital Admissions, Cardiovascula r 19 25 Em ergency Room Visits, Respiratory 36 46 Ac ute Bronchitis 62 80 Work Loss Days 5,100 6,500 Asthma Exacerbation 680 870 Minor Restricted Activity Days 30,000 39,000 Lo wer Respiratory Symptoms 740 950 Upper Respiratory Symptoms 560 720

a All estimates are for the analysis year ( 2016 ) and are rounded to whole numbers with two significant figures. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the s cientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type
. The m onetized benefits incorporate the conversion from precursor emissions to am bient fine particles. Confidence intervals are unavailable for this ana lysis because of the benefit
- per - ton methodology.5
- 11

Table 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$ ) a MACT Floor Beyond - the - Floor 3% 7% 3% 7% Benefit - per - ton Coefficients Der ived fromEpide miology Literature Pope et al. $340 $310 $440 $400 Laden et al. $830 $750 $1,100 $970 Benefit - per - ton Coefficients Derived fromExpert Elicitation Expert A $880 $800 $1,130 $1,020 Expert B $680 $610 $870 $790 Expert C $670 $610 $86 0 $780 Expert D $480 $430 $610 $550 Expert E $1,090 $990 $1,400 $1,260 Expert F $610 $560 $790 $710 Expert G $400 $370 $520 $470 Expert H $510 $460 $650 $590 Expert I $670 $600 $850 $770 Expert J $540 $490 $700 $630 Expert K $130 $120 $170 $160 Expert L $490 $450 $630 $570

a All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they were derived through the benefit
- per - ton technique described above. The benefits estimates from the expert elicit ation 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 m ethodology. 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$)

Benef its estimates der iv ed from 2 epidemiology f unctions and 12 exper t f unctions

3% D R 7% D R

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 coeffici ents derived from the Pope et al. (2002) study and the Laden et al
. (2006) study, as w ell 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 . Br eakdown 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 Di sbenefits In this section, we provide an estimate of the energy disbenefits associated with the increased emissions from additional energy usage. 6 Electricity

usage associated with the operation of control devices is anticipated to increase emissions of p ollutants 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 generati ng 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 b y capped emissions trading programs, we are onl y estimating the CO 2 emission increases from the increased electricit y demand. The methodology used to calculate these emission increases is described “Secondary Impacts of Control Options for the CI SWI Standards , ” which is available in the docket
. 5 .3.1 Social Cost of Carbon and Greenhouse Gas Disbenefits EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 ) emissions using recent estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given y ear. Itis intended to include (but is not limited to) changes in net agricultural productivity , human health, propert y damages from increased flood risk, and the value of ecos ys tem services due to climate change. The SCC estimates used in this analy sis were developed through an interagency process that included EPA and other executive branch entities, and concluded in February 2010. EPA first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking
6 As we use the term “energy disbenefits” in this analysis, w e are not referring to the cost of purchasing additional electricity or fuel
.5
- 14

to establish L ig ht
- Dut y Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Econom y Standards; see the rule ’ s preamble for discussion about application of SCC (75 FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a complete discussion of the methods used to deve lop these SCC estimates. 7 The interagency

group selected four SCC values for use in regulatory anal yses, which we have applied in this anal ysis: $5, $21, $35, and $65 per metric ton of CO 2 emissions 8 in 2010, in 2007 dollars. The first three values are ba sed on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent, respectively
. SCCs at several discount rates are included because the literature shows that the SCC is quite sensitive to assumptions about the disc ount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all three models at a 3 percent discount rate. It is included to represent higher
- than
- expect ed impacts from temperature change further out in the tails of the SCC distribution. L ow probability , high impact events are incorporated into all of the SCC values through explicit consideration of their effects in two of the three models as well as the u se of a probability density function for equilibrium climate sensitivity
. Treating climate sensitivity probabilistically

results in more high temperature outcomes, which in turn lead to higher projections of damages. The SCC increases over time because fut ure emissions are expected to produce larger incremental damages as phy sical and economic sy stems become more stressed in response to greater climatic change. Note that the interagency group estimated the growth rate of the SCC directly using the three int egrated 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 ys is years of 2016 , in 2008$ are provided in Tabl e 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, w ith participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Also available at http://www.epa.gov/otaq/climate/regulations.htm 8 The interagency group decided that these estimates apply only to CO 2 emissions. Given that w arming profiles and im pacts other than temperature change (e.g. , ocean acidification) vary across GHGs, the group concluded “transforming gases into CO 2 - equivalents using GWP, and then multiply ing the carbon
- equivalents by the SCC, wo uld not result in accurate estimates 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
- y ear specific. SCC values represent only a partial accounting of climate impacts. When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analy st faces a number of serious challenges. A recent report from the National Aca demies of Science (NRC, 200 9 ) points out that any assessment will suffer from uncertaint y, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and fu ture emissions on the climate sy stem, (3) the impact of changes in climate on the phy sical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, an y effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional. The interagency

group noted a number of limitations to the SCC analy sis, including the incomplete way

in which the integrate d assessment models capture catastrophic and non
catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertaint y in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of research linking climate impacts to economic damages makes the interagency

modeling exercise even more difficult. The interagency group hopes that over time researchers and modelers will work to fill these gaps and that the SCC estimates used fo r regulatory

anal ys is by

the Federal government will continue to evolve with improvements in modeling. Additional details on these limitations are discussed in the SCC TSD. In light of these limitations, the interagency group has committed to updating the current estimates as the science and economic understanding of climate change and its impacts on society improves over time. Specificall y, the interagency group has set a preliminary goal of revisiting the SCC values within two y ears or at such time as sub stantially updated models become available, and to continue to support research in this area.5
- 16

Apply ing the global SCC estimates to the estimated increases in CO 2 emissions for the range of policy

scenarios, we estimate the dollar value of the climate
- rela ted disbenefits captured by the models for each analy sis y ear. For internal consistency , the annual disbenefits are discounted back to NPV terms using the same discount rate as each SCC estimate (i.e. , 5%, 3%, and 2.5%) rather than 3% and 7%. 9 These estima tes are provided in Table 5
- 6
. Table 5 - 6. Monetized SCC - derived 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 specific. SCC values represent only a partial accounting of climate impacts. 5 . 4 Unquantified Benefits The monetized benefits estimated in this RI A onl y ref lect the portion of benefits attributable to the health effect reductions associated with ambient fine particles. M ethodological and time limitations under the court
- ordered schedule prevented EPA from quantify ing or monetizing the benefits from several important benefit categories, including benefits from reducing toxic emissions, ecos ys tem effects, and visibility impairment. The health benefits from reducing hazardous air pollutants (HAPs) and carbon monoxide have not been monetized in this anal ys is. In a ddition to being a PM 2.5 precursor, SO 2 emissions also contribute to adverse effects from acidic deposition in aquatic and terrestrial ecosy stems, increased mercury methy lation, as well as visibility

impairment. The benefits from reducing other air polluta nts have not been monetized in this analy sis, 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 ye ar. Because we were unable to monet ize the direct benefits associated with reducing HAPs, the monetized benefits estimate is an underestimate of the total benefits. The extent of this underestimate, whether small or large, is unknown
.

9 It is possible that other benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at ra tes that differ from those used to develop the SCC estimates.5
- 17

5 .4.1 Carbon Monoxide Benefits Carbon monoxide (CO) exp osure is associated with a variet y of health effects. Without knowing the location of the emission reductions and the resulting ambient concentrations using fine
- scale air quality

modeling, we were unable to estimate the exposure to CO for nearby populatio ns. Due to methodological and time limitations under the court
- ordered schedule , we were unable to estimate the benefits associated with the reductions in CO emissions that would occur as a result of this rule. Carbon monoxide in ambient air is formed pri marily by the incomplete combustion of carbon
- containing fuels and photochemical reactions in the atmosphere. The amount of CO emitted from these reactions, relative to carbon dioxide (CO 2 ), is sensitive to conditions in the combustion zone, such as fuel o xy gen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory s ystem to the blood, which can cause h ypoxia (reduced ox ygen availability ). Carbon monoxide can elicit a broad range of effects in multiple tissues and or gan s ys tems that are dependent upon concentration and duration of exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a ) concluded that short
- term exposure to CO is “likely to have a causal relationship” with cardiovascular morbidity , particularly

in individuals with coronary heart disease. Epidemiologic studies associate short
- term CO exposure with increased risk of emergency department visits and hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac chest pain), as well as those who have experienced a heart attack. Other subpopulations potentially

at risk include individuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as older adults or the developing young. The evidence is suggestive of a causal relationship between short
- term exposure to CO and respiratory morbidity

and mortality . The evidence is also suggestive of a causal relationship for birth outcomes and developmental effects following long
term exposure to CO, and for central nervous s ys tem effects linked to short
- and long
- term exposure to CO. 5 .4.2 Other SO 2 B enefits In addition to being a precursor to PM 2.5 , SO 2 emissions are also associated with a variet y of respiratory

health effects. Unfortunatel y, 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 concentrations, we were unable to estimate the exposure to SO 2 for nearb y5
- 18

populations. Therefore, this analy sis onl y quantifies and monetizes the PM 2.5 benefits associated w ith 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 between respiratory health effects and short
- term exposure t o SO 2 (U.S. EPA, 2008 a ). 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

s ys tem in humans is bronchoconstriction” (U.S. EPA, 2009c ). In addition, the REA summarized from the I SA that “asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease.” A clear concentration
response relationship has been de monstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversel y affected (U.S. EPA, 2009c). Based on our review of this information, we identified four short
- term morbidity endpoints that the SO 2 IS A identified as a “causal relationship”: asthma exacerbation, respiratory
- related emergency department visits, and respiratory
- related hospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in the SO 2 IS A. The SO 2 IS A also concluded that the relationship between short
- term SO 2 ex posure and premature mortality was “suggestive of a causal relationship” because it is di fficult to attribute the mortality risk effects to SO 2 alone. Although the SO 2 IS A stated that studies are generally consistent in reporting a relationship between SO 2 exposure and mortality , there was a lack of robustness of the observed associations to a djustment for pollutants. SO 2 emissions also contribute to adverse welfare effects from acidic deposition, visibility impairment, and enhanced mercury methy lation. Deposition of sulfur causes acidification, which can cause a loss of biodiversity of fishes , zooplankton, and macro invertebrates in aquatic ecos ys tems, as well as a decline in sensitive tree species, such as red spruce ( Picea rubens ) and sugar maple (

Acer saccharum ) in terrestrial ecos ystems . Inthe northeastern United States, the surface water s affected by acidification are a source of food for some recreational and subsistence fishermen and for other consumers and support several cultural services, including aesthetic and educational services and recreational fishing. B iological effects of aci dification in terrestrial ecos ys tems are generall y linked to aluminum toxicity , which can cause reduced root growth, which restricts the ability of the plant to take up water and nutrients. These direct effects can, in turn, increase the sensitivity of the se plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy

trees. Terrestrial acidification affects several important ecological services, including declines in habitat for5
- 19

threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in forest productivit y (provisioning), and increases in forest soil erosion and reductions in water retent ion (cultural and regulating) (U.S. EPA, 2008 c )
. Reducing SO 2 emissions and the secondary formation of PM 2.5 would improve the level of visibility

throughout the United States. Fine particles with significant light
- extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996 ). These suspended particles and gases degrade visibility by s cattering and absorbing light. Higher visibility impairment levels in the East are due to generall y higher concentrations of fine particles, particularl y sulfates, and higher average relative humidity levels. In fact, particulate sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually and 75% during summer). Inthe western U.S., particulate sulfate contributes to 20

– 50% of regional haze (U.S. EPA, 2009 b ). Visibility has direct significance to people’s enjoy ment of daily activities and the ir overall sense of wellbeing. Good visibility increases the quality

of life where individuals live and work, and where they enga ge in recreational activities. 5 .4.3 HAP B enefits 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 ex posed vary depending on where people live and work and the kinds of activities in which they engage. In order to identify

and prioritize air toxics, emission source 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 for calendar ye ar 2002, and was released in June 2009. 11 NATA for 2002 includes four steps: 1
. Compiling a national emissions inventory of air toxics emissions from outdoor sources 2
. E stimating ambient concentrations of air toxics across the United States 3
. Estimating population exposures across the United States 4
. Characterizing potential public health risk due to inhalation of air toxics including both cancer and noncancer effects

10 U.S. EPA. (2009) 2002 National
- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 11 U.S. EPA. (2009) 2002 National
- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/5
- 20

N oncancer health effects can result from chronic, 12 subchronic, 13 or acute 14 inhalation exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the immune and reproductive sy stems. Accordi ng to the 2002 NATA, nearl y the entire U.S. population was exposed to an average concentration of air toxics that has the potential for adverse noncancer respiratory health effects. 15 Figures 6
- 5 and 6
- 6 depict estimated count y - level carcinogenic risk and noncancer respiratory hazard from the assessment. Results from the 2002 NATA suggest that acrolein is the primary driver for noncancer respiratory

risk. 16 La rge reductions in HAP emissions may not necessaril y translate into significant reductions in healt h risk because toxicity varies by pollutant and whether or not there are exposures at or above levels of concern is not known
. For example, acetaldeh yde mass emissions are more than double acrolein emissions on a national basis, according to EPA’s 2005 Nat ional Emissions I nventory (NEI). However, the Integrated Risk I nformation Sy stem (IRI S) reference concentration (RfC) for acrolein is considerabl y lower than that for acetaldeh yde, suggesting that acrolein could be potentially more toxic than acetaldeh yde
. 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 frame work 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 w ebsite. Even so, this mo deling framework is very useful in identifying air toxic pollutants and so urces of greatest concern, setting regulatory priorities, and informing the decision making process. U.S. EPA. (2009) 2002 National
- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 16 Detai ls about the overall confidence of certainty ranking of the individual pieces of NATA assessments including both quantitative (e.g., model
- to - mo nitor ratios) and qualitative ( e.g., quality of data, review of emission inventories) judgments can be found at http://www.epa.gov/ttn/atw/nata/roy/page16.html
.5
- 21

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

Figu re 5 - 6. Estimated County Level Noncancer (Respiratory) Risk (from 2002 NATA)5
- 22

Due to methodology and time limitations under the court
- ordered schedule , we did not estimate the benefits associated with the hazardous air pollutants that would be reduced as a result of these rules. In a few previous anal ys es of the benefits of reductions in HAPs, EPA has quantified the benefits of potential reductions in the incidences of cancer and non
- cancer risk (e.g., U.S. EPA, 1995 ). In those anal ys e s, EPA relied on unit risk factors (URF) developed through risk assessment procedures. 17 These URFs are designed to be conservative, and as such, are more likely

to represent the high end of the distribution of risk rather than a best or most likely estimat e of risk. As the purpose of a benefit anal ys is is to describe the benefits most likely to occur from a reduction in pollution, use of high
- end, conservative risk estimates would overestimate the benefits of the regulation. While we used high
- end risk esti mates in past anal ys es, advice from the EPA’s Science Advisory Board (SAB) recommended that we avoid using high
- end estimates in benefit anal ys es (U.S. EPA, 2002 ). Since this time, EPA has continued to develop better methods for anal yz ing the benefits of reductions in HAPs. In the Second Prospective 812 Anal ys is, 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 Analy sis concluded that “the challenges for assessing progress in health improvement as a result of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure
- response functions, uncertainties in emissions inventories and background levels, the difficult y of extrapolating risk estimates to low doses and the challenges of tracking health progress for diseases, such as cancer, that have long latency periods” (U.S. EPA
- SAB, 2008 ). In 2009, EPA convened a workshop to address the inherent complexities, limitations, and uncertainties in current methods to quantify the benefits of reducing HAPs. Recommendat ions from this workshop included identify ing research priorities, focusing on susceptible and vulnerable populations, and improving dose
- response relationships (Gwinn et al., 2011 ). In summary , monetization of the benefits of re ductions in cancer incidences requires several important inputs, including central estimates of cancer risks, estimates of exposure to carcinogenic HAPs, and estimates of the value of an avoided case of cancer (fatal and non
- fatal). Due to methodology and time limitations under the court
- ordered schedule , we do not have sufficient information on emissions from specific sources and thus are unable to model changes

17 The unit risk factor is a quantitative estimate of the carcinogenic potency of a pollutant, often expressed as the probability of contracting cancer from a 70
- year lifetime continuous exposure to a concentr ation 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 analy sis. 18 In stead, we provide a qualitative analy sis of the health effects associated with the HAPs anticipated to be reduced b y these rules. EPA remains committed to improving methods for estimating HAP benefits b y continuing to explore additional concepts of benefits, including changes in the distribution of risk. Available emissions data show that several different HAPs are emitted from CI SWI
. This rule is anticipated to reduce , 470 tons of HCl, 4.1 tons of lead, 0.95 tons of cadmium, 260 pounds of mercury , and 92 grams of total dioxins/furans each y ear in the primary approach. 5 .4.3.1 Mercury Mercury is a highl y neurotoxic contaminant that enters the food web as a methy lated co mpound, methy lmercury (U.S. EPA, 2008c )
. The contaminant is concentrated in higher trophic levels, i ncluding fish eaten b y humans. Experimental evidence has established that only inconsequential amounts of methy lmercury can be produ ced in the absence of sulfate (U.S. EPA, 2008 c )
. Current evidence indicates that in watersheds where mercury is present, increased sulfate deposition very

likel y results in methy lmercury accumulation in fish (Drevnick et al., 2007 ; Munthe et al, 2007 ). The SO 2 IS A concluded that evidence is sufficient to infer a casual relationship between sulfur deposition and increased mercury methy lation in wetlands and aquatic environments (U.S. EPA, 2008 c ). In addition to the role of sulfate deposition on methy lation, th is rule would also reduce mercury emissions
. Mercury

is emitted to the air from various man - made and natural sources. These emissions transport thro ugh the atmosphere and eventuall y de posit to land or water bodies. This deposition can occur locally , regionall y, or globally, depending on the form of mercury emitted and other factors such as the weather. The form of mercury emitted varies depending on t he source t ype and other factors. Available data indicate that the mercury emissions from these sources are a mixture of gaseous elemental mercury , inorganic ionic mercury , and particulate bound mercury
. Gaseous elemental mercury can be transported very

lo ng distances, even globall y, to regions far from the emissions source (becoming part of the global “po ol”) before deposition occurs. Inorganic ionic and particulate bound mercury have a shorter atmospheric

18 Due to time limitations under the court
- ordered schedule for this rule , w e were unable to model mercury methylation, bioaccumulation in fish tissue, and human consumption of mercury
- contaminated fish that would be needed in order to estimate the human health benefits from reducing mercury emissions. How ever, it is important to emphasize, that we generally have more data and accepted methods to estimate mercury benefits than we have for other HAPs.5
- 24

lifetime and can deposit to land or water bodies c loser to the emissions source. Furthermore, elemental mercury

in the atmosphere can undergo transformation into ionic mercury , providing a significant pathway

for deposition of emitted elemental mercury
. This source category emitted about 0.5 tons of mercu ry in the air in 2008 in the U.S. Based on the EPA’s National Emission I nventory , about 103 tons of mercury were emitted from all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury were em itted worldwide by anthropogenic sources in 2005. We believe that total mercury emissions in the U.S. and globall y in 2008 were abo ut the same magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 0.5% of the total anthropogen ic mercury emissions in the U.S. and about 0.03% of the global emissions. Overall, this rule would reduce mercury emissions by

about 260 pounds per y ear from current levels, and therefore, contribute to reductions in mercury exposures and health effects. D ue to time limitations under the court
- ordered schedule , we were unable to model mercury dispersion, deposition, methy lation, bioaccumulation in fish tissue, and human consumption of mercury
contaminated fish that would be needed in order to estimate the human health benefits from reducing mercury

e missions. Potential exposure routes to mercury emissions include both direct inhalation and consumption of fish containing meth yl mercury . In the U.S., the primary route of human exposure to mercury

emissions from industrial sources is generall y indirectly through the consumption of fish containing meth yl mercury . As described above, mercury that has been emitted to the air eventually settles into water bodies or onto land where it can either move directly o r be lea ched into waterbodies. Once deposited, certain microorganisms can change it into methy lmercury , a highl y toxic form that builds up in fish, shellf ish and animals that eat fish. Consumption of fish and shellfish are the main sources of methy lmercury exposur e to humans. Methy lmercury

builds up more in some ty pes of fish and shellfish than in others. The levels of methy lmercury

in fish and shellfish vary widel y depending on what they eat, how long they live, and how hi gh they

are in the food chain. Most fish, including ocean species and local freshwater fish, contain some methylmercury For example, in recent studies b y EPA and the U.S. Geological Survey

(USGS) of fish tissues, every fish sample d from 291 streams across the country contained some methy lmercury ( Scudder, 2009 ). The majority

of fish consumed in the U.S. are ocean species. The methy lmercury concentrations in ocean fish species are primaril y influenc ed b y the global mercury pool. However, the meth yl mercury found in local fish can be due, at least partl y, to mercury emissions from local sources. Research shows that most people’s fish consumption does not cause a5
- 25

m ercury - related health concern. However, certain people may be at higher risk because of their routinely high consumptio n of fish (e.g., tribal and other subsistence fishers and their families who rel y heavil y on fish for a s ubstantial part of their diet). It has been demonstrated that high levels of meth yl mercury

in the bloodstream of unborn babies and y oung children may h arm the developing nervous s ys tem, making the child less able to think and learn. Moreover, mercury exposure at high levels can harm the brain, heart, kidney s, lungs, and immune sy stem of people of all ages. Several studies suggest that the methy lmercury c ontent of fish may

reduce cardio
protective effects of fish consumption. Some of these studies also suggest that methy lmercury may cause adverse effects to the cardiovascular s ystem. For example, the NRC (2000) review of the literature c oncerning methy lmercury health effects took note of two epidemiological studies that found an association between dietary exposure to methy lmercury and cardiovascular effects. 19 I n a study

of 1,833 males in Finland aged 42 to 60 ye ars, Solonen et al. (1995) observed a relationship between methy lmercury exposure via fish consumption and acute my ocardial infarction (AM I or heart attacks), death from coronary heart disease or cardiovascular disease
. 20 The NRC also noted a study of 9 17 seven y ear old children in the Faroe Is lands, whose initial exposure to methy lmercury was in utero although post natal exposures may have occurred as well. At seven ye ars of age, these children exhibited an increase in blood pressure and a decrease in h eart rate variabilit y. 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 s ys tem appears to be a target for methy lmercury tox icity in humans and animals .” 22 NAS also stated that “Additional studies are needed to better characterize the effect of methy lmercury

exposure on blood pressure and cardiovascular function at various stages of life.” Since publication of the NRC report the re have been some 30 published papers presenting the findings of studies that have examined the possible cardiovascular effe cts of

19 National Research Coun cil (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. Weihe, 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 Acade mies Press. Washington, DC. p. 229.5
- 26

methy lmercury

exposure. These studies include epidemiological, toxicological, and toxicokinetic investigations. Over a dozen review pap ers have also been published. If there is a causal relationship between methy lmercury exposure and adverse cardiovascular effects, then reducing exposure to methy lmercury would result in public health benefits from reduced cardiovascular effects
. In earl y 2010, EPA sponsored a workshop in which a group of experts were asked to assess the plausibility

of a causal relationship between methy lmercury exposure and cardiovascular health effects and to advise EPA on methodologies for estimating populatio n level cardiovascular health impacts of reduced methy lmercury exposure. The report from that workshop is in preparation. 5 .4.3.2 Cadmium 23 Breathing air with very

high levels of cadmium can severel y damage the lungs and may cause death. In the United State s, where proper industrial hy giene is generally practiced, inhaling very high levels of cadmium at work is expected to be rare and accidental. Breathing air with lower levels of cadmium over long periods of time (for ye ars) results in a build
- up of cadmium in the kidney , and if sufficiently high, may

result in kidney

disease. Lung cancer has been found in some studies of workers exposed to cadmium in the air and studies of rats that breathed in cadmium. The U.S. Department of Health and Human Services (DHHS ) has determined that cadmium and cadmium compounds are known human carcinogens. The International Agency for Research on Cancer (IARC) has determined that cadmium is carcinogenic to humans. The EPA has determined that cadmium is a probable human carcinoge n. 5 .4.3.3 Lead

24 The main target for lead toxicity is the nervous s ys tem, both in adults and children. L ong term exposure of adults to lead at work has resulted in decreased performance in some tests that measure functions of the nervous s ys tem. Lead expos ure may also cause weakness in fingers, wrists, or ankles. Lead exposure also causes small increases in blood pressure, particularl y 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 from: Agency for Toxic Substances and Disease Registry (ATSDR). 2008
. Public Health Statement for Cadmium
. CAS# 1306
- 19
- 0. Atlanta , GA: U.S. Departme nt 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 fr om : Agency for Toxic Substances and Disease Registry (ATSDR). 2007 . P ublic Health Statement for Lead. CAS# : 7439
- 92
- 1 . Atlanta , GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at < http://www.atsdr.cdc.gov/ToxProfiles/phs13.html >.5
- 27

exposure, lead can severely damage the brain and kidney s in adults or children and ultimatel y cause death. In pregnant women, high levels of exposure to lead may cause miscarriage. High
level exposure in men can damage the organs responsible for sperm production. We have no conclusive proo f that lead causes cancer (is carcinogenic) in humans. Kidney tumors have developed in rats and mice that had been given large doses of some kind of lead compounds. The Department of Health and Human Services (DHHS) has determined that lead and lead compou nds are reasonabl y anticipated to be human carcinogens based on limited evidence from studies in humans and sufficient evidence from animal studies, and the EPA has determined that lead is a probable human carcinogen. The International Agency for Research on Cancer (IARC) has determined that inorganic lead is probably carcinogenic to humans. I ARC determined that organic lead compounds are not classifiable as to their carcinogenicity in humans based on inadequate evidence from studies in humans and in animal s. Children are more sensitive to the health effects of lead than adults. No safe blood lead level in children has been determined. Lead affects children in different ways depending on how much lead a child swallows. A child who swallows large amounts of l ead may develop anemia, kidney damage, colic (severe “stomach ache”), muscle weakness, and brain damage, which ultimately

can kill the child. I n some cases, the amount of lead in the child’s body can be lowered b y giving the child certain drugs that help e liminate lead from the body . If a child swallows smaller amounts of lead, such as dust containing lead from paint, much less severe but still important effects on blood, development, and behavior may occur. In this case, recovery is likely once the child i s removed from the source of lead exposure, but there is no guarantee that the child will completely

avoid all long
- term consequences of lead exposure. At still lower levels of exposure, lead can affect a child’s mental and phy sical growth. Fetuses exposed to lead in the womb, because their mothers had a lot of lead in their bodies, may be born prematurel y and have lower weights at birth. Exposure in the womb, in infancy , or in earl y childhood also may slow mental development and cause lower intelligence later in childhood. There is evidence that these effects may

persist bey ond childhood. 5 .4.3.4 Hydrogen Chloride ( HCl ) 25 Hy drogen chloride gas is intensely irritating to the mucous membranes of the nose, throat, and respiratory

tract. Brief exposure to 35 pp m causes throat irritation, and levels of 50 to

25 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR)
. Medical Managemen t Guidelines for Hydrogen Chloride (HCl) . CAS# : 7647
- 01
- 0 . Atlanta , GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at < http://www.atsdr.cdc.gov/Mhmi/ mmg173.html >.5
- 28

100 ppm are barel y tolerable for 1 hour. The greatest impact is on the upper respiratory tract; exposure to high concentrations can rapidly lead to swelling and spasm of the throat and suffocation. Most serio usly exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures may develop an accumulation of fluid in the lungs. Exposure to hy drogen chloride can lead to Reac tive Airway

Dy sfunction Sy

ndrome (RADS), a chemically
- or ir ritant
- induced ty

pe of asthma. Children may

be more vulnerable to corrosive agents than adults because of the relativel y smaller diameter of their airways. Children may also be more vulnerable to gas exposure because of increased minute ventilation per kg and failure to evacuate an area promptly when exposed. Hy drogen chloride has not been classified for carcinogenic effects. 5 .4.3.5 Dioxins (Chlorinated dibenzodioxins (CDDs) 26 A number of effects have been observed in people exposed to 2,3,7,8
- TCDD levels that are at least 10 times higher than background levels. The most obvious health effect in people exposure to relatively

large amounts of 2,3,7,8
- TCDD is chloracne. Chloracne is a severe skin di sease with acne
- like lesions that occur mainl y on the face and upper bod y. Other skin effects noted in people exposed to high doses of 2,3,7,8
- TCDD include skin rashes, discoloration, and excessive body

hair. Changes in blood and urine that may indicate li ver damage also are seen in people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglobin, lipids, sugar, and protein have been reported in people exposed to relativel y high concentrations of 2,3,7,8
- TCDD
. Most of the effects are consid ered mild and were reversible. However, in some people these effects may

last for many

y ears. Slight increases in the risk of diabetes and abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8
- TCDD
. We do not have enough information to know if exposure to 2,3,7,8
- TCDD would result in reproductive or developmental effects in people, but animal studies suggest that this i s a potential health concern. In certain animal species, 2,3,7,8
- TCDD is especially harmful and c an 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 s ys tem. In man y species of animals, 2,3,7,8
- TCDD weakens the immune sy stem and ca uses a decrease in the s ystem's ability to fight bacteria and viruses at relatively low levels (approximately 10 times higher than human background bod y burdens) . In other animal studies, exposure to 2,3,7,8
- TCDD has caused

26 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR). 1999
. ToxFAQs for Chlorinated Dibenzo
- p - dioxins (CDDs) (CAS# : 2,3,7,8
- TCDD 1746
- 01
- 6 ). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at < http://www.atsdr.cdc.gov/tfacts104.html >.5
- 29

reproductive damage and birth de fects. Some animal species exposed to CDDs during pregnancy had miscarriages and the offspring of animals exposed to 2,3,7,8
- TCDD during pregnancy often had severe birth defects including skeletal deformities, kidney defects, and weakened immune responses. In some studies, effects were observed at body burdens 10 times higher than human background levels. 5 .4.3.6 Furans (Chlorinated dibenzofurans (CDFs)) 27 Most of the information on the adverse health effects comes from studies in people who were accidental ly

exposed to food contaminated with CDFs. The amounts that these people were exposed to were much higher than are likel y from environmental exposures or from a normal diet. Skin and ey e irritations, especiall y severe acne, darkened skin color, and swollen ey elids with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the nervous s ys tem, and mild changes in the liver. Children bor n to exposed mothers had skin irritation and more difficulty learning , but it is unknown if this effect was permanent or caused by CDFs alone or CDFs and poly chlorinated bipheny ls in combination
. Many of the same effects that occurred in people accidentall y exposed also occurred in laboratory

animals that ate CDFs. Animals also had severe weight loss, and their stomachs, livers, kidney s, and immune sy stems were seriously injured. Some animals had birth defects and testicular damage, and in severe cases, som e animals died. These effects in animals were seen when they

were fed large amounts of CDFs over a short time, or small amounts over several weeks or months. Nothing is known about the possible health effects in animals from eating CDFs over a lifetime. 5 .4.3.7 Other Air Toxics In addition to the compounds described above, other compounds from SI RI CE would be affected b y this rule. Information regarding the health effects of these compounds can be found in EPA’s I RI S database. 28 5 . 5 Characterization of Unc ertainty in the Monetized PM 2.5 Benefits In an y complex anal ys is, there are likel y to be many sources of uncertainty. Many inputs are used to derive the final estimate of economic benefits, including emission inventories, air

27 All health effects language for this section came from: Agency f or 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/iris5
- 30

quality models (with their ass ociated parameters and inputs), epidemiological estimates of concentration
- response (C
- R) functions, estimates of values, population estimates, income estimates, and estimates of the future state of the world (i.e., regulations, technology , and human behav ior). For some parameters or inputs it may be possible to provide a statistical representation of the underl yi ng uncertainty distribution. For other parameters or inputs, the necessary information is not available. The annual benefit estimates presented i n this analy sis are also inherentl y variable due to the processes that govern pollutant emissions and ambient air quality in a given y ear. Factors such as hours of equipment use and weather are constantly variable, regardless of our ability to measure them accuratel y. As discussed in the PM 2.5 NAAQS RI A ( Table 4
- 5) (U.S. EPA, 2006 ), there are a variet y of uncertainties associated with these PM benefits. Therefore, the estimates of annual benefits should be viewed as representative of the magnitude of benefits expected, rather than the actual benefits that would occur every y ear. It is important to note that the monetized benefit
- per - ton estimates used here reflect specific geographic patterns of emissions reductions and specific air q uality and benefits modeling assumptions. For example, these estimates do not reflect local variability in population density , meteorology , exposure, baseline health incidence rates, or other local factors. Use of these $/ton values to estimate benefits as sociated with different emission control programs (e.g., for reducing emissions from large stationary sources like EGUs) may lead to higher or lower benefit estimates than if benefits were calculated based on direct air quality modeling. Great care should be taken in appl yi ng these estimates to emission reductions occurring in an y specific location, as these are all based on national or broad regional emission reduction programs and therefore represent average benefits
- per - ton over the entire United States. The benefits
- per - ton for emission reductions in specific locations may be very different than the estimates presented here. PM 2.5 mortality

benefits are the largest benefit category that we monetized in this anal ys is. To better characterize the uncertain ty associated with mortality impacts that are estimated to occur in areas with low baseline levels of PM 2.5 , we included the L ML assessment. Without policy
- specific air quality modeling, we are unable to quantify the shift in exposure associated with this specific rule. For this rule, as a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 level using the most recent modeling available from the recently proposed Transport Rule (U.S. EPA, 2010e ). A very large proportion of the population is exposed at or above the lowest LML of the cohort studies (Figures 6
- 7 and 6
- 8 ), increasing our confidence in the PM mortality anal ysis. Figure 5
- 7 shows5
- 31

a bar chart of the p ercentage of the popu lation exposed to various air quality levels in the pre
and post
- policy

policy
. Figure 5
- 8 shows a cumulative distribution function of the same data. Both figures identify

the LML for each of the major cohort studies. As the policy shifts the distribution of air quality levels, fewer people are exposed to 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 LMLof 7.5 μg/m 3

. Using th e 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 successivel y lower than the LML of the lowest cohort study , our confidence in the results diminishes. However, the anal ys is above confirms that the great majorit y of the impacts occur at or above the lowest cohort study ’ s L ML
. It is important to emphasize that we have high confidence in PM 2.5 - related effects d own to the lowest L ML of the major cohort studies. Just because we have greater confidence in the benefits above the LML, this does not mean that we have no confidence that benefits occur below the L ML
.

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 . Percentag e of Adult 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 be nefits, based on our interpretation of the best available scientific literature and methods and supported by the SAB
HES and the NAS (NRC, 2002 ). The benefits estimates are subject to a number of assumptions and uncertainties. For example, for key assumptions underly ing the estimates for premature mortality , which t ypically account for at least 90% of the total monetized benefits, we were able to quantify

include the following: 1. PM 2.5 benefits were derived through benefit per
- ton estimate s, which do not reflect local variability

in population density , meteorology , exposure, baseline health incidence rates, or other local factors that might lead to an over
- estimate or under
estimate of the actual benefits of controlling directly emitted fin e particulates. 2. We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality . This is an important assumption, because PM 2.5 produced via transported precursors emitted from EGUs may dif fer significantl y from direct PM 2.5 released from diesel engines and other industrial sources, but the scientific evidence is not y et sufficient to allow differentiation of effect estimates b y particle type
. 3. We assume that the health impact function fo r fine particles is linear down to the lowest air quality

levels modeled in this analy sis. Thus, the estimates include health5
- 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 the lowest modeled concentrations. 4. To characterize the uncertainty in the relationship between PM 2.5 and premature mortality (which t ypically accounts for 85% to 95% of total m onetized 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 uncertaint y in air quality estimates, baseline incidence rates, popu lations 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 uncertaint y in the PM 2.5 estimates. This information should be interpreted within the context of the larger uncertaint y surrounding the entire anal ys is. For more information on the uncertainties associated with PM 2.5 benefits, please consult the PM 2.5 NAAQS RI A ( Table 4
- 5). This RI A does not include the ty pe of det ailed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2006 ) because we lack the necessary air quality

input and monitoring data to run the benefits model. Inaddition, we have not conducted any air quality

modeling for th is rule. Moreover, it was not possible to develop benefit
- per - ton metrics and associated estimates of uncertainty

using the benefits estimates from the PM RIA because of the significant differences between the sources affected in that rule and those regula ted here. However, the results of the Monte Carlo anal ys es of the health and welfare benefits presented in Chapter 5 of the PM RI A can provide some evidence of the uncertainty surrounding the benefits results presented in this anal ys is.6
- 1 SECTION 6 COMPARIS ON OF BENEFITS AND C OSTS This RI A does not include the final engineering costs and emission reductions (see Chapter 2 for a description of the changes that have not been accounted for)
. We estimate that incorporating these final estimates would decrease th e engineering costs by approximately 22 % and increase the monetized benefits b y approximately 4% from those shown in this RIA
. Using a 3% discount rate, we estimate the total monetized benefits of the CI SWINSPS and Emissions Guidelines to be $ 340 million to $ 830 million in the implementation y ear ( 2016 ) ( Table 6
- 1 ) . Using a 7% discount rate, we estimate the total monetized benefits of the CI SWI NSPS and Emissions Guidelines to be $ 310 million to $ 750 million. The annualized costs are $2 80 million at a 7% interest rate. 1 Thus, net benefits are $ 6 0 million to $ 550 million at a 3% discount rate for the benefits and $ 3 0 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 pollutants have not been monetized in this analy sis, including reducing 25,000 tons of c arbon 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 ye ar.

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 E G 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 $3 0 to $470

Non
- monetized Benefits 25,00 0 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 Benefi ts b $430 to $1,100 $390 To $960 Total Social Costs c $ 300 $300 Net Benefits $13 0 to $770 $90 to $66 0 Non
- monetized Benefits 25,00 0 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 implementation year ( 2016 ), and are rounded to tw o significant figures. These results include units anticipated to come o nline 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 importa nt to note that the mo netized benefits include many but not all health effects associated w ith PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their ch emical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type
. These estimates include energy disbenefits valued at $ 3.8 million
. c The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both discount rates.6
- 3 - $200 - $100 $0 $100 $200 $300 $400 $500 $600 $700 $800 $900

Pope etal. La den etal.Millions (2008$)

Cost esti mate c ombine d wi th total moneti zed benefi ts esti mate s der iv ed f rom 2 epi demi ology functions and 12 e xper t func ti ons Figure 6 - 1 . Net Benefits for the CISWI NSPS and Emissions Guidelines at 3% Discount

Rate a
a Net benefits are qua n tified in terms of PM 2.5 benefits for the implementation year ( 2016 ). This graph shows 14 benefits estimates combined with the cost estimate. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow

differentiation of effect estimates by particle type
. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles
. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at $ 3.8 million
.6
- 4 - $200 - $100 $0 $100 $200 $300 $400 $500 $600 $700 $800

Pope etal. La den etal.Millions (2008$)

Cost esti mate c ombine d wi th total moneti zed benefi ts esti mate s der iv ed f rom 2 epi demi ology functions and 12 e xper t func ti ons Figure 6 - 2 . Net Benefits for the CISWI NSPS and Emissions Guidelines at 7% Discount

Rate a
a Net benefits are qua n tified in terms of PM 2.5 benefits for the implementation year ( 2016 ). This graph shows 14 benefits estimates combined with the cost estimate. These models assume that all fine particles, regardless of their chemical c om

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

differentiation of effect estimates by particle type
. The monetized benefits incorporate the conversion from precursor emissions t o am bient fine particles
. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at $ 3.8 million
.R
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- 76.A
- 1 APPENDIX A OAQPS MULTIMARKET MO DEL TO ASSESS THE EC ONOMIC

IMPACTS OF ENVIRONME NTAL

REGULATION

A.1 Introduction An economic impact analy sis (EIA) provides inf ormation about a policy ’s effects (i.e., social costs); emphasis is also placed on how the costs are distributed among stakeholders (U.S. EPA , 2000 ). In addition, large
- scale policies require additional anal ys is to better understand how costs are passed across the econom y. Although several tools are available to estimate social costs, current EPA guidelines suggest that multimarket models “...are best used when potential economic impacts and equity effects on related markets might be co nsiderable” 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 s hort
- run analy ses can complement full dy namic general equilibrium analy sis. The multimarket model described in this appendix is a new addition to the Office of Air quality Planning and Standards’ (OAQPS’s) economic model tool kit; it is designed to be used as a transparent tool that can respond quickl y to requests about how stakeholders in 100 U.S. industries might respond to new environmental policy . Next, we provide an overview of the model, data, and parameters. A.2 Multimarket Model The multimarket mod el 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

employ mentA
- 2  Economic beh avior

– industries respond to regulatory costs by changing production rates

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

material costs

– customers respond to these price increases and consumption falls

 Model scope

– 100 sectors are linked w ith each other based on their use of energy and other nonenergy

materials. For example, the construction industry is linked with the petroleum, cement, and steel industries and is influenced b y price changes that occur in each sector. The links allow EPA t o account for indirect effects the regulation has on related markets.

– Li nks come from input
- output information used from OAQPS’s computable general equilibrium model (EMPAX)

– production adjustments influence employ ment levels

– international trade (imports/ exports) behavior considered

 Model time horizon (“short
- run”)

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

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

– pric e 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 ye ar, the model uses information from OAQPS’s computable genera l equilibrium model’s balanced social accounting matrix (SAM) and the following accounting identity holds (U.S. EPA , 2008 ):

Output + I mports = Consumption + I nvestment + Government + Exports ( A .1 ) If we abstract and treat each indus try as a national market, the identity represents the pre
policy market
- clearing condition, or benchmark “equilibrium”; supply equals demand in each market. In Table A
- 1, we identify the 100 industries for the multimarket model; Table A
- 2 provides the 2010 benchmark data set. Since the benchmark data are reported in value terms, we also use the common “Harberger convention” and choose units where are all prices are one in the benchmark equilibrium (Shoven and Whalley , 1995 ).A
- 3

Table A - 1. Industry Sectors Included in Multimarket Model

Industry Label Description Representative NAICS a Energy Industries COL Coal 2121 CRU Crude Oil Extraction 211111 (exc. nat gas) ELE Electric Generation 2211 GAS Natural Gas 211112 2212 4862 OIL Refined Petroleum 324 Nonm anufacturing 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 Ferti lizer 3253 MED Drugs and Medicine 3254 PAI Paints and Adhesives 3255 SOP Soap 3256 OCM Other Chemicals 3259 (continued)A
- 4

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

Industry Label Description Representative NAICS a Plastics an d 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 Hardw are 3325 WIR Springs and Wires 3326 MSP Machine Sh ops 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 Metalw orking Equip ment 3335 EEQ Engines 3336 GEQ General Equipment 3339 Electronic Equipment CPU Com puters 3341 CMQ Communication Equipment 3342 TVQ TV Equipment 3343 SMI Sem iconductor Equipment 3344 INS Instruments 3345 MGT Magnetic Recording Equipment 3346 LGT Lighting 3351 APP Appliances 3352 (continued)A
- 5

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

Industry Label Description Representative NAICS a Electronic Equipment (continued) ELQ Electric Equipment 3353 OEQ Other Electric Equip me nt 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 Man ufacturing 339 Services Wholesale and Retail Trade WHL Wholesale Trade 42 RTL Retail Trade 44

– 45 Transportation Services AT P 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 Ad ministrative Services 56 EDU E ducation 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 rel ies 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 Ad ministrative 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 AT P 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 Constr

uction 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 Com

puters $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 $3 0 $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 F RG 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 Export s 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 Hardw are $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 $4 4 $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 Metalw orking

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 F ood 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 Re tail 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 Suga r $34 $6 $36 $2 $3 SHP Ships $36 $6 $13 $29 Less than $1 SMI Sem iconductor

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 T TP 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 $1 9 $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 employ ment foreca sts for each of the 100 sectors. Employ ment estimates are based on data from three sources: the AEO 2009 estimates of employ ment (AEO supplemental Table 126 and indicators of Macroeconomic Activity ), and Global I nsights, Inc., and the Bureau of Labor Stati stics (BL S) 2008 end
- of year - employ ment (Current Employ

ment Statistics

— CES [National]). Ty picall y, 3
- digit NAICS sectors’ employ ment estimates are either directly reported in the updated AEO 2009 release or Global Insights For multimarket industries with f iner NAICs detail, estimates were calculated by selecting a primary

NAI CS supersector estimate (AEO or Global I nsights) and distributing total employ ment from the larger NAICS supersectors across more detailed NAICS sectors within the super
- sector. The dis tributions were determined using observed 2008 BLS emplo ym ent data. In the last step, In order to match aggregate U.S. employ

ment numbers reported in the AEO 2009 release (140.1 million), a single adjustment factor was applied to all sectors that use Globa l Insights’ supersector data. 1 Table A
- 4 reports the baseline employ ment 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 Employm ent (1,000) ACM Accommodations 11,239 ADM Ad ministra tive Services 9,274 AGR Agricultural 1,607 ALU Aluminum 87 ANM Animal Foods 45 APP Appliances 33 ARC Aircraft 449 ART Arts 1,939 AT P 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 Com puters 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 F UR Furniture 327 (continued)A
- 11

Table A - 4. 2010 U.S. Employment Projections (continued) Industry Label Industry Description Projected Employm ent (1,000) GAS Natural Gas 98 GEQ General Equipment 198 GLS Glass 71 GRN Grain Milling 55 HLT Health Care 15,19 0 HRD Hardw are 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 Prod ucts 450 MED Drugs and Medicine 279 MEQ Metalw orking 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 Transpor tation Services 1,064 (continued)A
- 12

Table A - 4. 2010 U.S. Employment Projections (continued) Industry Label Industry Description Projected Employm ent (1,000) OTQ Other Transport Equipment 36 PAI Paints and Adhesives 60 PAP Pulp and Paper Mills 121 PFS Pr ofessional 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 2 36 RUB Rubber 117 SAW Sawmills 84 SEA Seafood 36 SEQ Service Industry Equipment 77 SGR Sugar 62 SHP Ships 140 SMI Sem iconductor 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 144,100A
- 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 analy sis and baseline value of output. For example, a $1 billion increase in compliance costs for the electricit y s ector (ELE) would be represented in the model as follows: %Δ direct costs = $1/$317= 0.03% ( A .3 ) As shown in Figure A
- 1, the cost change provides the industry with incentives to alter production rates at current market prices; market prices must rise to m aintain the original pre
policy production levels (Q). The multimarket model also simultaneously considers how the policy influences other important production costs (via changes in energy and other intermediate material prices). As a result, the multimark et model can provide additional information about how policy costs are transmitted through the economy . As shown in Figure A
- 2, the indirect cost change provides the industry with additional incentives to alter production rates at current market prices. T he %Δ indirect effects associated with each input are approximated using an input “use” ratio and the price change that occurs in the input market.

%Δ indirect costs = input use ratio x %Δ input price ( A .4 ) The social accounting matrix provides an interna lly consistent estimate of the use ratio and describes the dollar amount of an input that is required to produce a dollar of output. Higher ratios suggest strong links between industries, while lower ratios suggest weaker links. Given the short time horizo n, we assume the input use ratio is fixed and cannot adjust their input mix; this is a standard assumption in public and commercial input
- output (I O) and SAM multiplier models (Berck and Hoffmann, 2002 ). Morgenstern and col leagues (2004 ) and Ho and colleagues (2008) also use this assumption when examining near
- term effects of environmental policy
.A
- 14

Figure A - 1. Direct Compliance Costs Reduce Production Rates at Benchmark Prices

Figure A - 2. Indirect Costs Further Reduce Production Rates at Benchmark Prices Following guidance in the OAQPS economic resource manual (OAQPS, 1999 ), we use a general form for the U.S. industry suppl y fu nction:

g ε n i i

i gi g g ) P P ( t P b Q      





     

## 

 1  ( A .5 ) where g Q  = with
- policy

suppl y quantity

(g) b = calibrated scale parameter for the suppl y price relationshipA
- 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 elast icit y of suppl y for output (g) The key suppl y parameter that controls the industry production adjustments is the price elasticity of suppl y ( ε g ). To our knowledge, there is no existing empirical work that estimates short
- run supply

elasticities for all ind

ustry groups used in the multimarket model. As a result, we assume the U.S. supply elasticities are a function of econometricall y estimated rest
- of
- world (ROW) export supply

elasticities (see discussion in the next section). We report the values currentl y available in the model in Table A
- 5. A.2.2.2 International Competition International competition is captured b y a single ROW supply function: 

#   ROW

g ε g g 

### P c Q    ( A .6 ) where g Q  = with
- policy

suppl y quantity

(g) c = calibrated scale parameter for the suppl y and price relationship g P  = with
- policy

U.S. price for output (g) ROW g 

###  = price elasticit y of suppl y of goods from the ROW to the United States (imports) (g) The key suppl y parameter that contro ls the ROW supply adjustments is the price elasticity of suppl y ( ROW

g 

###  ). We obtained these estimates for a variet y of industry groups from a recentl y published article by Broda and colleagues (2008b ).A
- 16

Table A - 5. Supply Elasticities Industry Label Industry Description Rest of World (ROW) U.S. ACM Accommodations 0.7 0.7 ADM Ad ministrative 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 AT P 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 G ases 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 Com puters 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 Hardw are 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 LI M 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 Metalw orking 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 Pl ywood 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 Se afood 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 Sem iconductor 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 Tr ansportation 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 mo del’s 100 industries. Dom estic supply elasticities are typically assumed to be within one standard deviation of the sample of supply elasticities used for the ROW. In selected cases where this information is not available, the U.S. supply elasticity is s et equal to the ROW. Source: Broda, C., N. Limao, and D. Weinstein. 2008a . “Export Supply Elasticities.” http://faculty.chicagobooth.edu/christian.broda/w ebsite/research/unrestricted/TradeElasticities/ TradeElasticities.html . Accessed September 2009. A. 2.2.3 Price Elasticity of Supply: Rest of World (ROW) Broda and colleague s (2008) provide an empirical basis for the multimarket model supply elasticities. Broda et al. provide over 1,000 inverse elasticities that RTI organized to beA
- 19

comparable with the 100
- sector model. The first step was to match the Harmonized Trade Sy stem (HS) elasticities estimated in the article to the appropriate NAICS codes. Many of the HS codes correspond with a detailed NAICS codes (5
- and 6
- digit level), while the multimarket sector industries ty

pically correspond w ith more aggregated sectors (NAICS 2
- , 3
- , or 4
- digit levels). To adapt these labels to our model, we combined the 5
- and 6
- digit NAI CS under their 3
- and 4
- digit codes and calculated an average inverse elasticity value for codes that fell within the mult imarket model’s aggregate industrial sectors. This gives a crude way to account for the variet y of products detailed in the original data set. We also restricted the elasticity sample to those that Broda et al. classify as “medium” and “low” categories; th ese categories tend to have lower elasticit y values that are consistent with the multimarket model’s modeling horizon (i.e., in the short run importers are likely to have less flexibility

to respond to price changes and elasticities are low). 1 Our ideal pr eference was to use an exact 3
- or 4
- digit match from the medium category if one was available. If the multimarket model had a 4
- digit code for which there was no direct match, we aggregated up a level and applied the relevant 3
- digit elasticity . If a mult imarket code was not covered in the medium set of elasticities, we used the low elasticity category . This method was sufficient for mapping the majority of the sectors in the model. After appl yi ng our inverse elasticit y values to the multimarket sectors, w e calculated the inverse of the value to arrive at the actual supply elasticity . Since Broda et al.’s article focused on industrial production goods, some of the multimarket sectors were not covered in the elasticity data. These sectors included mainly

ser vice industries, transportation, and energy sources. In order to fill these gaps, we turned to the source substitution elasticities from Purdue University ’s Global Trade Anal ys is Project (GTAP)
. 2 Although the elasticities in the GTAP model are a different ty

pe of international trade elasticity and cannot be directly

applied in the multimarket model (e.g., they are based on the Armington structure 3 ), the parameters provide us with some additional information about the relative trade elasticity differences be tween industry sectors. To use the GTAP information to develop assumptions about the multimarket model sectors with missing elasticities, we chose a base industrial sector (iron and steel) for which we had parameter value from Broda et al. Next, we develop ed industry
- specific ratios for missing industries using the corresponding GTAP sector trade elasticities and the GTAP iron and steel
1 Broda et al.’s intent was to use these categories to describe or proxy for domestic market pow er. 2 See Chapter 14 of the GTAP 7 Database Documentation for the full description of the parameters at https://w ww.gtap.agecon.purdue.edu/resources/download/4184.pdf ; see Table 14.2 for elasticities. 3 Detailed documentation of the entire GTAP 7 Database is available at https://www.gtap.agecon .purdue.edu/databases/v7/v7_doco.asp
. T he GTAP also uses a unique system of categorizing commodities that does not match the NAICS or HS system exactly.A
- 20

sector. We multiplied the resulting ratio by the Broda et al. iron and steel parameter (1.0). For example, the GTAP trade elasticity for coal (6.10) is approximately 2.2 times the trade elasticity for iron and steel (2.95). As a result, the multimarket import supply elasticity for coal is computed as 2.2 (2.2 x 1.0). A. 2.2.4 Price Elasticity of Supply: United States We also u sed Broda et al.’s elasticities to derive a set of domestic supply elasticities for the model. We have assumed that a product’s domestic supply would be equal to or less elastic than other countries’ supply of imports. When we aggregated and averaged the o riginal elasticities to the 3
- and 4
- digit NAICS level for our foreign suppl y elasticities, we also calculated the standard deviation of each 3
- and 4
- digit NAICS sample. By adding the standard deviation to the corresponding foreign supply and then taking the inverse, we were able to calculate a domestic supply elasticit y for each sector that was lower than its foreign counterpart while maintaining the structure of the original elasticities. For sectors in which no standard deviation was available, 1 we use d professional judgment to appl y 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 supp ly elasticities the same. A. 2.2.5 Demand Uses for industry

output are divided into three groups: investment/government use, domestic intermediate uses, and other final use (domestic and exports). Given the short time horizon, investment/government does no t change. Intermediate 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 price changes. Following guidance in the OAQPS economic resource manual (OAQPS, 1999 ), we use a general form for the U.S. industry demand funct ion:

1 No standard deviations were calculated for the 3
- and 4
- digit codes that had only one observation (i. e., Broda et al .’s model used the exact 3
- or 4
- digit code).A
- 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 elasticit y of demand (g) The key parameter that controls consumption adjustments is the price elasticity of demand (η g ). To approximate the response, we use demand elasticities that were simulated with a general equilibrium model (Ho, Morgenstern, and Shih, 2008 ). Table A
- 6 reports the values currentl y available in the model. A.2.2.6 Model Scope The multimarket model includes 100 sectors covering energy , manufacturing, and service applications. Each sector’s production technology requires the purchase of energy and other intermediate goods made by other sectors included in the model. L inking the sectors in this manner allows the model to trace direct and indirect policy effects across different sectors. Therefore, it is best used when potentia l economic impacts and equity effects on related markets might be important to stakeholders not directl y affected b y an environmental policy. However, the model can also be run in single
- market partial equilibrium mode to support and provide insights for o ther t ypes of environmental policies. A.2.2.7 Model Time Horizon The model is designed to address short
- run and transitional effects associated with environmental policy . Production technologies are fixed; the model does not assess substitution among produ ction inputs (labor, energy intermediates, and other intermediates) and assumes each investment cannot be changed during the time frame of the anal ys is. These issues are better addressed using other frameworks such as computable general equilibrium modelin g. Similarly , government purchases from each sector do not adjust in response to changes in goods/service prices. Although, emplo yment levels (number of jobs) adjust as production levels change, wages are assumed to be fixed.A
- 22

Table A - 6. U.S. Demand Elastic ities Industry Label Industry Description Dem and Elasticity η g ACM Accommodations −0.7 ADM Ad ministrative 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 AT P Air Trans portation −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 Construc tion −0.8 COL Coal −0.1 CPP Converted Paper Products −0.7 CPU Com puters −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 Finan ce 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 Dem an d 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 Hardw are −0.5 HVC HVAC Equipment −1.7 I_S Iron and Steel −1.0 IEQ Industrial Equipment −1.7 INF Information −0.7 INS I nstruments −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 Metalw orking 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 Fab ricated 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 Labe l Industry Description Dem and 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 Print ing −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 Sem iconductor Equipment −2.6 SOP Soap −1.0 TEX Textile Mills −1.1 TKB Truck Bodies −2.5 TPM Textile Product Mills −1.1 TTP Freight Truck Transportation −0.8 TVQ TV Equipment −2.6 WAP Wearing Apparel −2.4 WHL Wholesale Trade

− 0.7 WIR Springs and Wires −0.5 WTP Water Transportation −0.8 Note: RTI assigned an elasticity using the most similar industry from Ho and colleagues’ industry aggregation. Source: Ho, M. S, R. Morgenstern, and J. S. Shih. 2008. “Impact of Carbon Price Policies on US Industry.” RFF Discussion Paper 08
- 37. http://www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?
. Publicationid=20680. Accessed August 2009. Table B.6.B
- 1 APPEN DIX B INDUSTRY PROFILES In this section, we provide an introduction to selected industries that are affected b y the rule. The industries were selected based on high facility population counts within 3
- digit NAICs industries reported in the combustion facil ity survey . The purpose is to give the reader a general understanding of economic aspects and industry trends to provide additional context for the economic impact analy sis. 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 b y Standard & Poor’s (O’Reilly , 2008) , a number of factors are shaping the current economic environment for wood products, including, but not limited to, th e housing slump, high input costs, low prices for lumber and other building materials, and a weak dollar. Table B
- 1 shows that revenues in this industry are variable , exhibiting a decrease in shipment revenue between 1997 and 2002 , an increase to within $5 billion of the 1997 value in 2006 and another decline to within $14 billion of the 2006 value in 2007. Upon closer review, one also notices a rise in the cost of labor (shipment revenue per dollar of pay roll

— $5.54 in 2002, $6.20 in 2006, and $5.84 in 2007 ) and material inputs during this same time period, making high profit margins difficult to predict. While total pay

roll dropped 3% over this time, annual pay roll per employ ee rose 6.5% from 1997 to 2007 because of the decline in the number of emplo ye es ( T able B
- 2). Shipments per emplo yee grew 10.6% from 1997 to 2006 and dropped 8.9% from 2006 to 2007 ( Table B
- 2). The U.S. Census Bureau categorizes this industry ’s facilities into three categories: “sawmills and wood preservation”; “veneer, ply wood, and engi neered wood product manufacturing”; and “other wood product manufacturing.” These are further divided into the following t ypes of facilities as defined b y the Census Bureau:

 Sawmills and Wood Preservation

– Sawmills and Wood Preservation (NAI CS 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 woo d poles, pilings, and posts and treating them with preservatives; andB
- 2 (c) treating wood sawed, planed, or shaped in other establishments with creosote or other preservatives to prevent decay and to protect against fire and insects. Sawmills may

plane the r ough 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 Em ploy ees 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 Interna tional; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997.” < http://factfinder.census .gov >; (July 8, 2008)
. U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy
- Wide Key Statistics: 2007.” Acc essed 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 empl oy ee ($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 Em ploy ees per establishment 33 31 NA 35 NA = Not available. Sources: U.S. Ce nsus 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://factfind er.census.gov >; (July 8, 2008)
. U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAIC S Basis): 2002 and 1997.” < http://factfinder.census.gov >; (July 8, 2008)
. U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sect or 00: EC0700A1: All Sectors: Geographic Area Series: Economy
- Wide Key Statistics: 2007.” Accessed on December 27, 2009
.B
- 3  Veneer, Ply

wood, and Engineered Wood Product Manufacturing

– Veneer, Ply

wood, and Engineered Wood Product Manufacturing (NAICS 32121): This industry

comprises establishments primarily engaged in one or more of the following manufacturing activities: (a) veneer and/or ply wood, (b) engineered wood members, and (c) reconstituted wood products. This industry includes manufacturing pl ywood from veneer made in the sam e establishment or from veneer made in other establishments, and manufacturing pl ywood faced with non
wood materials, such as plastics or metal.

 Other Wood Product Manufacturing

– Millwork (NAICS 32191): This industry comprises establishments primarily engag ed 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 spe cialized sizes. These establishments generall y use woodworking machinery, such as jointers, planers, lathes, and routers to shape wood.

– Wood Container and Pallet Manufacturing (NAI CS 32192): This industry comprises establishments primarily engaged in manuf

acturing wood pallets, wood box shook, wood boxes, other wood containers, and wood parts for pallets and containers.

– All Other Wood Product Manufacturing (NAICS 32199): This industry comprises establishments primarily

engaged in manufacturing wood products (except establishments operating sawmills and wood preservation facilities; and establishments manufacturing veneer, ply wood, engineered wood products, millwork, wood containers, or pallets). Figure B
- 1 shows that the industry proportion of the value of s hipments for other wood product manufacturing (51%) was greater than the value of shipments for sawmills and wood preservation (27%) and veneer, pl ywood, and engineered wood products (22%). Figure B
- 2 indicates that the majority of employ ees in this indust ry fell under other wood products (60%). Veneer, ply

wood, and engineered wood products had the same percentage (20%) of employ ees as sawmills and wood preservation (20%), even though it contributed to a lesser portion of the value of shipments.B
- 4 Ve neer, P lywood, & En gine ered Wood Pr oduc t Mfg (N AI CS 3212), 2 2%

Sa wmil ls & Wood Pres erv ati on (N AI CS 3211), 27% Ot her Wo od Pr oduc t Mfg (N AI CS 3219) , 51%

Figure B - 1. Distribution of Value of Shipments within Wood Product Manufacturing

(NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy
- Wide Key Statistics: 2007.” < http://factfinder.census.gov >. Accessed on December 27, 2009. [Source for 2007 numbers]

Ot her Wood Product M fg (NA IC S 3219), 60% Sa wmills & Wood Pres erva tion (N AI CS 3211) , 20%

Ve neer, P lywood, & En gine ered Wood Pr oduc t Mfg (N AI CS 3212) , 20%
Figure B - 2. Distribution of Employment within Wood Product Manufacturing

(NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy
- Wide Key Statistics: 2007” Release Date: 12/22/09. < http://factfinder.census.gov >. Accessed on De cem ber 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 inter actions this industry has with other industries and people and identify the key goods and services used b y the industry and the major uses and consumers wood products. B .1.2.1 Goods and Services Used in W ood Product Manufacturing In 2007, the cost of mate rials comprised 59% of the total shipment value of goods in the wood product manufacturing industry ( Table B
- 3). Total compensation of employ ees represented 22% of the total value in 2007. Both the number of total shipments and the number of emplo ye es in t his industry

decreased between 2005 and 2007

—the former b y 14% and the latter b y 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 1 00% $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 Ave rage compensation per employee ($2007) $43,286 $43,473 $42,947 Total production w orkers’ wages ($2007, 10 6 ) $13,363 11% $13,132 11% $12,086 12% Total production w orkers 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% Pur chased 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: EC0731I 1: 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 supply ing inputs to the wood product industry acco unted for 80% of the total intermediate inputs according to 2008 Bureau of Economic Analy sis data ( Table B
- 4). The largest comes from the wood product industry itself. This is quite understandable, since the descriptions of the various industries within wo od product manufacturing impl y that they suppl y each other with products in order to add value and distribute their products to the broader market. The top five inputs are rounded out by forestry and logging products, wholesale trade, management of compani es and enterprises, and truck transportation, which together make up 70% of the total cost of input. Table B - 4. Key Goods and Services Used in Wood Product Manufacturing (NAICS 321)

($2007, 10 6 ) Description BEA Commodity Code Wood Products Wood products 3 210 $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 pow er generation, transmission, and distribution 2211 $1,388 Other fabric ated metal products 332B $1,310 Nonmetallic mineral products 3270 $1,110 Real estate 5310 $799 All other administrative and support services 561A $748 Architectural and structural metal products 3323 $725 Rail transportation 4820 $723 Other inputs $ 14,650 Total intermediate inputs T005 $72,169 Source: U.S. Bureau of Economic Analysis (BEA). 2008 . “2002 Benchmark Input
- Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA. B .1.2.1.1 Energy. The Department of Energy (DOE) categorizes wood product manufacturing (NAICS 321) as a non
- energy - intensive industry . The 2008 Annual Energy Outlook predicts that the wood product industry will be one of five (out of eight) non
- energy intensive 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 betwee n 2002 and 2006, total energy use rose by

19%. However, Figure B
- 3 shows that electrical power use decreased rapidly during this period, including a steady decline in the latter part of 2000. Following a rapid decrease at the end of 2002, electric power us e has been increasing steadil y since then. Table B - 5. Energy Used in Wood Product Manufacturing (NAICS 321)

Fuel Type 1998 2002 2006 Total (trillion BTU) 504 375 445 Net electricity a (million kWh) 21,170 20,985 26,723 Residual fuel oil (million bbl) * * 1 Distillate fuel oil b (m illion bbl) 2 2 3 Natural gas c (billion cu ft) 71 56 84 LPG and NGL d (m illion 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 obta ined by summing purchases, transfers in, and generation from noncombustible renewable resources, minus quantities sold and transferred out. It does not include electricity inputs from on
- site cogeneration or generation from combustible fuels because that e nergy has already been included as generating fuel (for example, coal). b Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels. c Natural gas includes natural gas obtained from utilities, local distribution companies, and any other supplier(s), such as independent gas producers, gas brokers, marketers, and any marketing subsidiaries of utilities. d Examples of liquefied petroleum gases (LPGs) are ethane, ethylene, propane, propylene, normal butane, butylene, ethane
- pro pane mixtures, propane
- butane mixtures, and isobutene produced at refineries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs). e Other includes net steam (the sum of purchases, generation from rene wables, and net transfers), and other energy that respondents indicated w as used to produce heat and power. * Estimate less than 0.5. Q = Withheld because relative standard error is greater than 50%. Sources: U.S. Department of Energy, Energy Information Adminis tration. 2007 a . “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, D C: DOE
. U.S. Department of Energy, Energy Information Administr ation. 2007b. “2006 Energy Consumption by Manufacturers
— Data Tables.” Tables 3.1. < http://www.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html >. [Source for 2006 numbers]B
- 8 85 90 95 100 105 110Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Index Value (Jan-97=100)

Man ufa ct ur ing Ele ct ri c Pow er Use f or N AI CS 321 Figure B - 3. Electrical Power Use Trends in the Wood Product Manufacturing I ndustry

(NAICS 321): 1997

– 2005 Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” < http://www.federalreserve.gov/data dow nload/ >. . B .1.2.2 Uses and Consumers Table B
- 6 shows that three of the top four consumers of wood products are represented by the construction sector of the economy (NAICS 23). New residential construction, new nonresidential construction, and maintenance and repair construction consume 35% of the total commodity

output in this industry . The top 10 consumers of wood products make up 54% of the demand for wood products. Although man y of the top consumers deal with construction, repair, or real estate services, other ty pes of consumers, such as food services and drinking places, rail transportation, plastics and rubber products manufacturing, and other, use these products. B .1.3 Firm and Market Characteristics This section describes geographic, production, and market data. These data provide the basis for further analy sis, including regulatory flexibility

anal ys es, as well as a complete picture of the recent historical trends of production and pricing.B
- 9

Table B - 6. Demand by Sector: Wood Product Manufacturing (NAICS 321) ($2007, 10 6 )

Sector BEA Cod e 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 ma nufacturing 336A $2,516 Real estate 5310 $2,335 Food services and drinking places 7220 $2,307 Other miscellaneous manufacturing 3399 $1,311 Wholesale trade 4200 $1,284 Rail transportation 4820 $1,138 Retail trade 4A00 $1,047 Plastics and rubber pr oducts manufacturing 3260 $877 General state and local government use S007 $3,116 Owner occupied dw elling S008 $11,209 Private fixed investment F020 $7,933 Exports of goods and services F040 $3,978 Total final uses (gross domestic product [GDP]) T004 $3,719 Total commodity output T007 $101,753 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input
- Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA
. B .1.3.1 Location As Figure B
- 4 illustrates, the states with the largest number of wood product manufacturing establishments are dispersed throughout the country , with a significant concentration of establishments in the northeastern states. Other states with many establishments include California, Texas, and North Carolina. B .1.3.2 Production Capacity and U tilization Capacity utilization of the wood product manufacturing industry has been experiencing capacity utilization increases and declines with more extreme fluctuations than those of all manufacturing industries combined. The decline in wood product manufacturing is similar to total manufacturing between 1997 and 2002. However, capacity utilization in total manufacturing, which peaked in 2006, started increasing at a faster rate than wood product manufacturing, but decreased sharpl y after its peak. Wood product manufacturing experienced its own rapid decrease in capacity utilization between 2007 and 2009, though not at the same rate as total manufacturing ( Figure B
- 5).B
- 10 Establishments by State

Less than 100 100 - 199 200 - 399 400 - 600 More than 600 Figure B - 4. Establishment Concentration in the Wood Product Manufacturing Industry

(N AICS 321): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Pla ces: 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-09Percentage Capacity Utilization

Man ufa ct uri ng Wo od Product s Figure B - 5. Capacity Utilization Trends in the Wood Product Manufacturing Industry

(NAICS 321) Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Capacity Utilization.” < http://www.federalreserve.gov/datadow nload/ >..B
- 11

B .1.3.3 Employment California has the largest number of emplo ye es in the wood product manufacturing indust ry 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 employ ees do not directly correlate with the states with the highest number of establishments. States such as Indiana, Geo rgia, Arkansas, and Oregon had fewer than 600 establishments, as shown in Figure B
- 6, but had more than 20,000 employ ees, whereas states such as Ohio and New York had fewer than 20,000 employ ees 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 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 Statistics for the States, Metropoli tan and Micropolitan Statistical Areas, Counties, and Places: 2002.” < http://factfinder.census.gov >; (July 23, 2008)
. B .1.3.4 Plants and Capacity While the ca

pacit y of the manufacturing sector has been growing consistently since 1997, the wood product manufacturing industry has experienced inconsistent growth. After a small amount of growth in capacity between 1997 and 2001, the wood product manufacturing indus try ’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-09Index Value (January 1997 = 100)

Manufac tu ri ng Food Figure B - 7. Capacity Trends in the Wood Product Manufacturing Industry (NAICS 321) Source: Federal Re serve Board. “Industrial Production and Capacity Utilization: Industrial Capacity.” < http://www.federalreserve.gov/datadow nload/ >. . B .1.3.5 Firm Characteristics In 2006, the top 10 paper and forest product companies produced over $75 billion in sales, with the top two companies

— International Paper and Wey erhaeuser

— generating nearl y $22 billion each ( Table B
- 7). The top two companies’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor’s (S&P’s) list (Benwart, 2006 ). Al though these numbers do not exclusively reflect wood products, they do convey

the market environment in which firms in this sector compete. B. 1.3.6 Size Distribution The primary

criterion for categorizing a business as small is the number of employ ees, usi ng definitions by

the SBA for regulatory

flexibility

anal ys es. According to SUSB reports for 2002, small companies were the recipients of the majority of receipts in 2002; 53% of receipts were generated b y companies with fewer than 500 employees ( Table B
- 8 ). The number of employ ees in the small business cutoff is 500 employ ees 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 Internation al Paper 21,995 Weyerhaeuser 21,896 Smurfit
- Stone 7,157 MeadWestvaco 6,530 Tem ple
- Inland 5,558 Bow ater 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: Benw art , 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 woo d product manufacturing have decreased since 2006 compared to the stead y increase in production for the manufacturing sector since 1997 ( Figure B
- 8). Similar to capacit y utilization trends ( Figure B
- 8), the index shows a faster rate of decline for wood pro ducts than the entire manufacturing sector. B .1.3.8 International Trade Since 1997, the wood product manufacturing industry has contributed to an increasing trade deficit ( Figure B
- 9). The value of imports has fluctuated greatly since 1997; however, export s have remained fairly constant, with seasonal changes, since 1997. B .1.3.9 Market Prices Prices of goods in the wood product manufacturing industry have remained roughl y the same since 2005. The prices for the entire manufacturing sector increased between 2003 and 2008 but have decreased since August 2008. Producer price indices (PPI s) show that producer prices for wood products increased b y 6% from 2004 to 2007, while producer prices for all manufacturing goods increased b y roughl y 34% at its peak and was at 23% in November 2008 and 24% in November 2009 ( Figure B
- 10).B-14 Table B - 8. Distribution of Economic Data by Enterprise Size: Wood Product Manufacturing (NAICS 321) Enterprises with Variable Total 1 to 20 Employees a 20 to 99

Employees 100 to 499

Employ ees 500 to 749

Employees 750 to 999

Employees 1,000 to 1,499

Employees Firms 15,198 9,740 3,280 791 63 27 30 Establishments 17,052 9,758 3,482 1,271 166 91 133 Em ployment 534,011 65,423 132,612 118,910 19,784 11,944 18,533 Receipts ($10 3 ) $88,649 $8,20 4 $18,276 $19,717 $3,192 $1,902 $3,118 Receipts/firm ($10 3 ) $5,833 $842 $5,572 $24,927 $50,673 $70,453 $103,927 Receipts/establishment ($10 3 ) $5,199 $841 $5,249 $15,513 $19,231 $20,904 $23,442 Receipts/employment ($) $166,006 $125,393 $137,818 $165,814 $161,363 $159,262 $168,231

a Excludes Statistics of U.S. Businesses (SUSB) employment category for zero employees. These entities only operated for a fraction of the year. Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Busin esses: 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 Hardw ood Veneer and Plyw ood 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 Manufactur ing 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) Manufac turing 500 321992 Prefabricated Wood Building Manufacturing 500 321999 All Other Miscellaneous Wood Product Manufacturing 500 Source: U.S. Small Business Administration (SBA). 2008. “ Ta ble of Small Business Size Standards Matched to North American Indus try Classification System Codes.” Effective August 22, 2008. < http://www.sba.gov/services/contractingopportunities/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)

Manuf ac tu ri ng Wo od Pr odu cts Figure B - 8. Industrial Production Trends in the Wood Product Manufacturing Industry

(NAICS 321): 1997

– 2009 Source: Federal Reserve Board. “Industrial Production and Capacity Utilization: Industrial Production.” < http://www.federalreserve.gov/datadow nload/ >. Accessed 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)

Wo od Pro duct E xport s Wo od Product I mp or ts Figure B - 9. International Trade Trends in the Wood Product Manufacturing Industry

(NAICS 321) Source: U.S. International Trade Commission. 2008a. “U.S. Domestic Exports” & “U.S. Imports for Consumption.” < http://dataweb.usitc.gov/scripts/user_set.asp >; (July 17, 2008)
.B
- 17 0 20 40 60 80 100 120 140Dec-03Jun-04Dec-04Jun-05Dec-05Jun-06Dec-06Jun-07Dec-07Jun-08PPI (Dec-03=100)

Wood Pro duct s PPI Al l Manufac tu ring PPI Figure B - 10. Producer Price Trends in the Wood Product Manufacturing Industry

(NAICS 321) Source: U.S. Bureau of Labor Statistics (BLS). 2009 a . “Producer Price Index.” Series ID: PCU321

— 321

— & PCUOMFG

— OMF G

— . < http://www.bls.gov/ppi/home.htm >. Accessed on January 8, 2010
. B .2 Paper Manufacturing B. 2.1 Introduction The paper manufacturing subsector is an essential component of all business operations worldwide. Broadl y speaking, paper and paperboard are manufactured b y converting timber or other recy cled material into products such as printing and writing papers, newsprint, tissue, and containerboard (Benwart, 2006 ). The subsector has been experiencing a decline in shipments as of late. From 1997 to 2007, shipmen ts in the industry declined 7%, and employ ment declined by 27% ( Table B
- 10). While total pay roll dropped 26% over this time, annual pay roll per employ ee rose 2% from 1997 to 2007 because of the decline in the number of emplo yees). Shipments per employ ee gr ew 28% from 1997 to 2007, with much of that growth taking place between 2002 and 2006 ( Table B
- 11).B
- 18

Table B - 10. Key Statistics: Paper Manufacturing (NAICS 322) 1997 2002 2006 2007 Shipments ($2007, 10 6 ) $188,496 $175,983 $174,887 $175,806 Payroll ($2007 , 10 6 ) $27,983 $24,561 $21,188 $20,804 Em ploy ees 574,274 489,367 414,049 416,886 Establishments 5,868 5,495 NA 4,803 NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” < http://factfinder.census.gov >; (July 8, 2008)
. U.S. Census Bureau; generated by RTI International ; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997.” < http://factfinder.census.gov > ; (July 8, 2008)
. U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy
- Wide Key Statistics: 2007.” Accessed on December 28, 2009. [Source for 2007 numbers] Table B - 11. Industry Ratios: Paper Manufacturing (NAICS 322)

Industry Ratios 1997 2002 2006 2007 Total shipments ($2007, 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 Em ploy ees per establishment 98 89 NA 87 NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” < http://factfinder.census.gov >; (July 8, 2008)
. U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997.” < http://factfinder.census.gov >; (July 8, 2008)
. U.S. Census Bureau; generated by Kapur Energy a nd Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Series: Economy
- Wide Key Statistics: 2007.” < http://factfinder.census.gov >. Accessed on December 28, 2009. [Source for 2007 numbers]B
- 19

The U.S. Census Bureau categorizes this industry ’s facilities into two categories: pulp, paper, and paperboard manufacturing and converted paper product manufacturing. These are further divided into the f

ollowing ty

pes of facilities as defined by the Census Bureau :

 Pulp, Paper, and Paperboard:

– Pulp Mills (NAI CS 32211): This industry comprises establishments primarily engaged in manufacturing pulp without manufacturing paper o r 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, scrap paper, and straw.

– Paper Mills (NAICS 32212): This industry comprises establishments primar ily engaged in manufacturing paper from pulp. These establishments may manufacture or purchase pulp. I n addition, the establishments may convert the paper they

make. The activity

of making paper classifies an establishment into this industry

regardless of the output.

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

 Converted Paper Products:

– Paperboard Containers Manufacturing (NAICS 32221): This industry comprises establishments primarily

engaged in converting paperboard into containers without manufacturing paperboard. These establishments use corrugat ing, cutting, and shaping machinery

to form paperboard into containers. Products made by these establishments include boxes; corrugated sheets, pads, and pallets; paper dishes; and fiber drums and reels.

– Paper Bag and Coated and Treated Paper Manufacturing (NAICS 32222): This industry comprises establishments primarily engaged in one or more of the following manufacturing activities: cutting and coating paper and paperboard; cutting and laminating paper and paperboard and other flexible materials (except pl astics film to plastics film); bags or multiwall bags or sacks of paper, metal foil, coated paper, or laminates or coated combinations of paper and foil with plastics film; laminated aluminum and other converted metal foils from purchased foils; and surfac e coating paper or paperboard.

– Stationary

Product Manufacturing (NAICS 32223): This industry comprises establishments primarily

engaged in converting paper or paperboard into products used for writing, filing, art work, and similar applications.

– Other Conv erted Paper Products (NAICS 32229): This industry comprises establishments primarily

engaged in one of the following manufacturing activities:
 converting paper and paperboard into products (except containers, bags, coated and treated paper and paperboard, and stationery products), orB
- 20  converting pulp into pulp products, such as disposable diapers, or molded pulp egg cartons, food tray s, and dishes. Figure B
- 11 shows that the value of shipments for converted paper products was slightl y higher in 2007, 54%, t han the value of shipments for pulp, paper, and paperboard products in that ye ar, 46%. However, Figure B
- 12 indicates that significantly more employees worked in the converted paper product category of the industry , 70%, thus making converted paper product s more labor intensive.

PPP ( 3221) 46% CP P (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: In dustry Series: Detailed Statistics by Industry for the United States: 2007.” Accessed on December 28, 2009
.

PPP ( 3221) 30% CP P (3 222) 70%
Figure B - 12. Distribution of Employment within Paper Manufacturing (NAICS 322): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Envir onm ent; using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” < http://factfinder.census.gov >. Accessed on December 28, 2009
. 2.2.2 Supply and Demand Characteristics Next, we provide a broad overview of the supply and demand sides of the paper manufacturing industry . We emphasize the economic interactions this industry has with otherB
- 21

industries and people and identify the key goods and services used b y the industry and the major uses and consumers of paper manufacturing products. B .2.2.1 Goods and Services Used in Paper Manufacturing In 2007, the cost of materials made up 53% of the total shipment value of goods in the paper manufacturing industry ( Table B
- 12). Total compensation of employees represented 15% of the total value in 2007, down from 17% in 2005. The total number of employ ees dropped b y 2%, between 2005 and 2007, while shipments increase d 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 T otal production w orkers wages ($2007, 10 6 ) $14,965 9% $14,689 8% $14,190 8% Total production w orkers 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 mater ials ($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% $4,7 55 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 supply ing inputs to the paper manufacturing subsector accounted for 70% of the total intermediate inputs according to 2008 Bureau of EconomicB
- 22

Analy sis (BEA) data ( Table B
- 13). Inputs for pulp, paper, and paperboard products are notably different from inputs for converted p aper products because the NAICS 3221 group represents the initial step in the paper manufacturing process; thus, its inputs include more raw resources such as wood products, forestry and logging products, natural gas, and electricity. This becomes evident when observing inputs for converted paper products: 49% of the cost of inputs comes from pulp, paper, and paperboard products. Table B - 13. Key Goods and Services Used in the Paper Manufacturing Industry

(NAICS 322) ($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 pow er 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 N atural 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 Stan dard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA
. B.2 .2.1.1 Energy. The Department of Energy (DOE) categorizes paper manufacturing (NAICS 322) as an energy
- intensive subsector. The 2008 Annual Energy Outlook predicts that the paper
- producing subsector will be one of four subsectors experiencing posit ive average growth of delivered energy consumption between 2006 and 2030 (DOE, 2008 ). Energy generation from the recovery boiler is often insufficient for total plant needs, so facilities augment recovery boilers with fossil fuel

– fir ed and wood waste

– fired boilers (hogged fuel) to generate steam and often electricity . I ndustry
- wide, the use of pulp wastes, bark, andB
- 23

other papermaking residues supplies 58% of the energy requirements of pulp and paper companies (U.S. EPA , 2002 ). Likewise, Table B
- 14 shows that total energy use decreased between 1998 and 2006 by 14%. Figure B
- 13 indicates that total electrical power use changed sporadically between 2002 and 2004 but decreased consistently and rapidl y after 2004. Tab le 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 (m illion bbl) 2 2 2 Natural gas c (billion cu ft) 570 490 461 LPG and NGL d (m illion 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, tran sfers in, and generation from noncombustible renewable resources, minus quantities sold and transferred out. It does not include electricity inputs from on
- site cogeneration or generation from combustible fuels because that energy has already been included as generating fuel (for example, coal). b Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels. c Natural gas includes natural gas obtained from utilities, local distribution companies, and any other supplier(s), such as independent gas producers, gas brokers, marketers, and any marketing subsidiaries of utilities. d Examples of liquefied petroleum gases (LPG) are ethane, ethylene, propane, propylene, normal butane, butylene, ethane
- propane mixtures, propane
- butane mi xtures, and isobutene produced at refineries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs). e Other includes net steam (the sum of purchases, generation from rene wables, and net transfers), and other en ergy that respondents indicated w as used to produce heat and power. * Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007 a . “2002 Energy Consumption by Manufacturers

— Data Tables.” Tables 3.2 and N3.2. < 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.” Ta ble 3.1. < http://www.eia.doe.gov/emeu/mecs/mecs2006/ 2006tables.html >. Accessed on December 27, 2009. [Source for 2006 numbers]B
- 24 90 92 94 96 98 100 102 104 106 108Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Index Value (Jan-97=100)

Man ufac tu ri ng Pa pe r Figure B - 13. Electrical Power Use Trends in th e Paper Manufacturing Industry:

1997

– 2005 Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” <http://www.federalreserve.gov/datadow nload/>
. Over the last 25 ye ars, the pulp and paper subsector has changed its energy generation methods from fossil fuels to a greater use of processes such as increases in the use of wood wastes in place of fuel ( Table B
- 15). During the 1972

– 1999 period, the proportion of total industry power generated from the combination of woodroom wastes, spent liquor solids, and other self
- generated methods increased from about 41% to about 56%, while coal, fuel oil, and natural gas use decreased from about 54% to about 36% (U.S. EPA , 2002 ). B .2.2.2 Uses and Consumers Products manufactured in the NAICS groups 3221 and 3222 have different, but complementary , consumer profiles. NAICS 3221 supplies a significant portion of NAI CS 3222 demand (37% of total commodity output). Both industries specialize in products with intermediate uses, with an average of 92% of sales between the two going toward this purpose. NAI CS 3222 has a very

diverse assortment of subsector groups from which it receives demand. Food manufacturing makes up 21% of the demand, making members of this industry the largest consumer of converted paper products ( Table B
- 16). Pulp, paper, and paperboard products have a large trade deficit , while converted paper products have a very small trade surplus.B
- 25

Table B - 15. Estimated Energy Sources for the U.S. Pulp and Paper Industry Energy Source 1972 1979 1990 1999 Purchased steam 5.4% 6.7% 7.3% 1.5% Coal 9.8% 9.1% 13.7% 12.5% Fuel oil 22.3% 1 9.1% 6.4% 6.3% Natural gas 21.5% 17.8% 16.4% 17.6% Other purchased energy

— — — 6.7% Waste w ood and w ood 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 pow er 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/re sources/publications/assistance/sectors/notebooks/ index.html > . Table B - 16. Demand by Sector: Paper Manufacturing 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 Foo d manufacturing 3110 $638 $18,782 $19,421 Printing and related support activities 3230 $13,320 $3,874 $17,194 General state and local government services S007 $6,065 $7,792 $13,857 Pulp, paper, and paperboard mills 3221 $4,155 $1,415 $5,569 Newspaper, periodical, book, and directory publishers 5111 $4,851 $168 $5,018 Plastics and rubber products manufacturing 3260 $1,249 $3,403 $4,651 Wholesale trade 4200 $990 $2,619 $3,609 Food services and drinking places 7220 $1,510 $2,597 $4,107 Total intermedia te 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 Im ports 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, produc tion, and market data. These data provide the basis for further analy sis, including regulatory flexibility

anal ys es, and give a complete picture of the recent historical trends of production and pricing. B .2.3.1 Location As Figure B
- 14 illustrates, Califor nia is home to the most paper manufacturing establishments in the United States, followed b y Il linois and some bordering northeastern states. The location of establishments in the paper manufacturing industry varies a great deal by subsector. Wisconsin and New York have the most pulp, paper, and paperboard establishments, while California dominates with over 500 converted paper product establishments. Overall, the United States has 561 pulp, paper, and paperboard establishments and 4,956 converted paper pro duct establishments.

Establishments Fewer than 50 50 - 99 100 - 199 200 - 300 More than 300 Figure B - 14. Establishment Concentration in Paper Manufacturing Industry

(NAICS 322): 2002 Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Indu stry Statistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” < http://factfinder.census.gov >; (July 23, 2008)
.B
- 27

B .2.3.2 Production Capacity and Utilization Capacity utilization of the paper manufacturing subsector has been experiencing a steady decline, similar to the decline of the total manufacturing sector. However, paper manufacturing has managed to use its cap acity at a consistentl y 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-09Percentage Capacity Utilization

Manufac tu ri ng Pape r (N AI CS 322) Figure B - 15. Capacity Utilization Trends in the Paper Manufacturing Industry

(NAICS 322) Source: Federal Reserve Board. 2009. “Industrial Production and Capa city Utilization: Capacity Utilization.” < http://www.federalreserve.gov/datadow nload/ > . B .2.3.3 Employment Wisconsin has the largest number of emplo ye es in the paper manufacturing subsector with over 38,008 reported in the 2002 census followed b y 29,379 in California ( Figure B
- 16). The convert ed paper products group has more employ ees per establishment, 283, than the pulp, paper, and paperboard group, 67.B
- 28 Employment Fewer than 3,000 3,000 - 5,999 6,000 - 11,999 12,000 - 24,000 More than 24,000 Figure B - 16. Employment Concentration in the Paper Manufacturing Industry

(NAICS 322): 2002 Source: U.S. Census Bureau; generated by RTI In ternational; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” < http://f actfinder.census.gov >; (July 23, 2008)
. B .2.3.4 Plants and Capacity While the manufacturing sector has been growing consistently since 1997, the paper manufacturing sector has not experienced the same amount of success in the same period. Despite a small amount of growth in capacit y between 1997 and 2001, the paper manufacturing subsector’s capacit y has declined to as much as 7% below 1997 capacit y l evels ( Figure B
- 17). B .2.3.5 Firm Characteristics In 2006, the top 10 paper and forest product companies produced over $1.6 billion in sales, with the top two companies

— International Paper and Wey erhaeuser

— generating nearl y $22 billion each ( Table B
- 17). The top two companies’ revenue consists of 58% of the revenue of the top 10 companies in Standard & Poor’s (S&P’s) list (Benwart, 2006 ). Although these numbers do not exclusively reflect paper products, they do convey

the market environment in which firms in this sector compete.B
- 29 90 100 110 120 130 140 1501997-011997-071998-011998-071999-011999-072000-012000-072001-012001-072002-012002-072003-012003-072004-012004-072005-012005-072006-012006-072007-012007-072008-012008-072009-012009-07Index Value (Jan-97=100)

Man ufa ct uri ng Pa per (N AI CS 322) Figure B - 17. Capacity Trends in the Paper Manufacturing Industry (NAICS 322) Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial Capacity.” < http://www.federalr eserve.gov/datadow nload/ > . Table B - 17. Largest U.S. Paper and Forest Products Companies: 2006 Co mpany Revenues ($10 6 ) a International Paper 21,995 Weyerhaeuser 21,896 Smurfit
- Stone 7,157 MeadWestvaco 6,530 Tem ple
- Inland 5,558 Bow ater 3,530 Grief Inc. 2,628 Louisiana
- Pacific 2,235 Packaging Corp. 2,187 Plum Creek 1,627

a Includes revenues fr om operations other than paper and forest products in certain cases. Sources: Benw art, S.J. 2006 . “Paper & Forest Products. Standard and Poor’s Industry Surveys.” 176(28). U.S. and international sales data from company reports.B
- 30

B .2. 3.6 Size Distribution The primary

criterion for categorizing a business as small is the number of employ ees, using definitions by

the SBA for regulatory

flexibility

anal ys es. According to SUSB reports for 2002, large companies dominated revenue
- generating transactions in the paper manufacturing subsector; 80% of receipts were generated b y companies with 500 employees or more ( Table B
18). This was especially

true in the pulp, paper, and paperboard group, in which large companies generated 92% of receipts. T he number of employees in the small business cutoff varies according to six
- digit NAI CS codes ( Table B
- 19). The cutoff for all subsectors in the pulp, paper, and paperboard group is 750 employ ees, while the cutoff for most converted paper product groups is 500 employ ees. B .2.3.7 Domestic Production Similar to industry

capacity rates, subsector production rates for paper manufacturing have witnessed a decreasing rate of production compared to the stead y increase in production for the manufacturing sector sin ce 1997 ( Figure B
- 18). I t seems that the paper manufacturing sector was not able to return to its former levels of growth following the 2001 recession; it has experienced a downward production trend since then. Table B - 18. Distribution of Economic Data by Enterprise Size: Paper Manufacturing

(NAICS 322) Enterprises with
Variable Total 1 to 20 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 Establi shm ents 5,546 1,488 1,271 755 83 69 138 Em ployment 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 Receip ts/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 from the Statistics of U.S. Businesses: U.S. Detail Em ployment Sizes: 2002.” < http://www.census.gov/csd/susb/download_ susb02.htm > .B
- 31

Table B - 19. Small Business Size Standards: Paper Manufact uring (NAICS 322) NAICS NAICS Description Employees 322110 Pulp Mills 750 322121 Paper (except New sprint) Mills 750 322122 Newsprint Mills 750 322130 Paperboard Mills 750 322211 Corrugated and Solid Fiber Box Manufacturing 500 322212 Folding Paperboa rd 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 Manufac turing 500 322222 Coated and Laminated Paper Manufacturing 500 322223 Coated Paper Bag and Pouch Manufacturing 500 322224 Uncoated Paper and Multiw all 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 M anufacturing 500 322299 All Other Converted Paper Product Manufacturing 500 Source: U.S. Small Business Administration (SBA). 2008. “ Ta ble of Small Business Size Standards Matched to North American Industry Classification System Codes.” Effective August 22, 2008. < http://www.sba.gov/services/contractingopportunities/sizestandardstopics/size/index.html > . B .2.3.8 International Tra de Since 1997, paper manufacturing products, both pulp, paper, and paperboard products and converted paper products, have contributed to an increasing trade surplus in this sector ( Figure B
- 19). Imports and exports have been changing at similar rates since 1999.B
- 32 75 85 95 105 115 125 135Jan-97Jul-97Jan-98Jul-98Jan-99Jul-99Jan-00Jul-00Jan-01Jul-01Jan-02Jul-02Jan-03Jul-03Jan-04Jul-04Jan-05Jul-05Jan-06Jul-06Jan-07Jul-07Jan-08Jul-08Jan-09Jul-09Index Value (Jan-97=100)

Man ufa ct uri ng Pa per (N AI CS 322) Figure B - 18. Industrial Production Trends in the Paper Manufacturing Industry

(NAICS 322): 1997

– 2009 Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial Production.” < http://www.federalreserve.gov/datadow nload/ > .

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)

Yea r Pa pe r Ex port s Pa pe r Impo rt s Figure B - 19. Internatio nal Trade Trends in the Paper Manufacturing Industry

(NAICS 322) Source: U.S. International Trade Commission. 2008b. “U.S. Dom estic Exports” & “U.S. Imports for Consumption.” < http://dataweb.usitc. gov/scripts/user_set.asp > .B
- 33

B .2.3.9 Market Prices Prices of goods in paper manufacturing have been increasing at a rate consistent with all manufacturing products ( Figure B
- 20). Producer price indices (PPIs) show that producer prices for paper in 2007 increased by 20% since 1997, while producer prices for all manufacturing goods increased b y rough ly 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)

Man ufac tu ri ng P PI Pa per PPI Figure B - 20. Producer Price Trends in the Paper Manufacturing Industry (NAICS 222) Source: U.S. Bureau of Labor Statistics (BLS). 2009 b . “Producer Price Index.” Series ID: PCU322

– 322

– & PCUOMFG

– OMFG

– . < http://www.bls.gov/ppi/home.htm > . B .3 Chemical Manufacturing B .3.1 Introduction The chemical manu facturing industry produces over 70,000 chemical substances, man y of which are ubiquitous in American life. Broadl y speaking, chemical manufacturing operates b y converting feedstocks into chemical products that can serve as intermediate goods or final prod ucts such as medicine, soap, and printer ink. From 1997 to2007, shipments in the industry grew 42%, while employment declined b y 8% ( Table B
- 20). While total pay roll dropped 0.6% over this time, annual pay roll per employ ee rose 7.8% from 1997 to 2007 becau se of the decline in the number of emplo yees ( Table B
- 21). Shipments per emplo ye e grew 54% from 1997 to 2007, with much of that growth taking place between 2002 and 2006 ( Table B
- 21).B
- 34

Table B - 20. Key Statistics: Chemical Manufacturing (NAICS 325) 1997 2 002 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 Em ploy ees 882,645 853,224 747,134 814,024 Establishments 13474 13,475 NA 12,937 NA = Not available. Sources: U.S. Census Bure au; generated by RTI International; using American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” < http://factfinder.census .gov >; (July 8, 2008)
. U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997.” < http://factfinder.census.gov >; (July 8, 2008)
. U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC 0700A1: 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 Manufacturi ng (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 Shipme nts 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 Em ploy ees per establishment 66 63 NA 63 NA = Not available. Sources: U.S. Census Bureau; generated by RTI International; using A merican FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” < http://factfinder.census.gov >; (July 8, 2008)
. U. S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All Sectors: Core Business Statistics Series: Comparative Statistics for the United States and the States (1997 NAICS Basis): 2002 and 1997.” < http://factfinder.census.gov >; (July 8, 2008)
. U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic Area Serie s: Economy
- Wide Key Statistics: 2007.” < http://factfinder.census.gov >. Accessed on December, 27, 2009. [Source for 2007 numbers]B
- 35

Chemical manufacturing (NAICS 325) covers a d iverse 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 ): Basi c Chemical Manufacturing (NAICS 3251); Resin, Rubber, and Artificial Fibers Manufacturing (NAICS 3252); and Agricultural Chemical Manufacturing (NAI CS 3253).

 Pharmaceutical and Medicine Manufacturing (NAICS 3254)

— Consists primarily of pharmaceutical preparation manufacturing. This industry group is the largest importer of goods within chemical manufacturing.

 Other Chemical Manufacturing: Consists of Paint, Coating, and Adhesive Manufacturing (NAICS 3255); Soap, Cleaning Compound, and Toiletry Manufacturing (NAICS 3256); and Other Chemical Product and Preparation Manufacturing (NAICS 3259). In 2007, each of these groups generated approximately one - third of the total employ ment in chemical manufacturing ( Figure B
- 21). The bulk chemicals group accounted for t he biggest share of chemical manufacturing’s total value of shipments ( Figure B
- 22).

Bulk Che mic als, 35%

Pharm ac eut ica ls, 31% Ot her Chem ica ls, 34%

Figure B - 21. Distribution of Employment within Chemical Manufacturing (NAICS 325):

2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using Ame rican FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the U.S.: 2007.” Release date: October 30, 2009. Accessed on December 27, 2009
.B
- 36 Bu lk Chem ica ls, 50%

Pharm ac eut ic als, 26% Ot her Chem ica ls, 24%

Figure B - 22. Distribution of Total Value of Shipments within Chemical Manufacturing

(NAICS 325): 2007 Source: U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31:EC0731I1: Manufacturing Industry Series: Detailed Statistics by Industry for U.S.: 2007.” < http://factfinder.census.gov >. Accessed on December 27, 2009
. B .3.2 Supply and Demand Characteristics Next, we provide a broad overview of the supply and demand side of the chemical manufacturing industry . We emphasize the economic interactions this industry has with other industries and people, including identifying the key goods and services used by the industry

and the major uses and consumers of chemical manufacturing products. The top 10 indu stry

groups supply ing inputs to the chemical manufacturing industry in 2002 accounted for 71% of the total intermediate inputs ( Table B
- 22). Bulk chemicals’ production was the most energy intensive, using 79% of the chemical manufacturing inputs from petro leum 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 v alue ( Table B
- 22). Total compensation to employees represented 9% of total shipment value, down from 10% in 2005. B .3.2.1.1 Energy. The Department of Energy (DOE) classifies bulk chemical manufacturing as an energy
- intensive industry . Pharmaceuticals and o ther chemical manufacturing are categorized as non
- energy - intensive industries, grouped together with other industry groups under the “Balance of Manufacturing” category (DOE, 2008 ).B
- 37

Table B - 22. Key Goods and Services Used in Chemica l Manufacturing (NAICS 325)

($2007, 10 6 )
Good or Service BEA Code Bulk Chemicals Pharm aceuticals 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 Pharm aceuticals 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 a nd 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 pow er generation, transmission, and distribution 2211 $6,025 $716 $807 $7,548 Natural gas distribution 2212 $6,390 $154 $390 $6,934 Total intermediate use T005 $167,699 $82,403 $91,833 $341,935 Source: U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark Input
- Output Accounts: 2002 Standard Make and Use Tables at the Summary Level.” Table 2. Washington, DC: BEA
. Fuel used in chemical production can either facilitate chemical processes or p rovide the feedstock to derive value
- added chemicals. In 2007, 70% of chemical manufacturing’s energy bill was spent on fuel used as feedstocks (O’Reill y, 2008 ). These fuel costs represented 2% of chemical manufacturing’s total value of shipments ( Table B
- 23). As a whole, chemical manufacturing has become less energy intensive over the last 10 ye ars. According to DOE, natural gas use b y the chemical manufacturing industry dropped 30% from 1998 to 2006, and electricity use fell 10% ( T able B
- 24). From 1997 to 2005, when data ceased to be available, chemical manufacturing became less electricit y 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 $1 4,510 2% $14,702 2% $14,811 2% Total employees 756,078 747,134 801,567 Average compensation per em ploy ee ($2007) $82,887 $82,559 $79,333 Total production w orkers’ wages ($2007, 10 6 ) $22,643 4% $22,231 3% $23,157 3% Total production w orkers 431,50 2 430,880 463,802 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% $2 91,656 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: Ann ual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2006 and 2005.” < http://factfinder.census.gov >; (July 8, 2008)
. U.S. Census Bureau; generated by Kapur Energy and Environment; using American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007.” Accessed on December, 27, 2009
. B .3.2.2 Uses and Consumers Products manufactured in the groups bulk chemicals, pharmaceuticals, and other chemicals have very

different consumer profiles. Bulk chemicals is dominated by intermediate use, representing 93% of its total commodity output and 56% of the total intermediate use of chemical manufacturi ng products. Pharmaceuticals has both a high level of demand from personal consumption, accounting for 67% of the total personal consumption of chemical manufacturing products, and a large trade deficit ( Table B
- 25).B
- 39

Table B - 24. Energy Used in Chemical Man ufacturing (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 (m illion bbl) 2 2 2 Natural gas c (billion cu ft) 1 ,931 1,634 1,349 LPG and NGL d (m illion 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 electricity inputs from on
- site cogeneration or generation from combustible fuels because that energy has already been included as generating fuel (for exam

ple, coal). b Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2, and 4 diesel fuels. c Natural gas includes natural gas obtained from utilities, local distribution companies, and any other supplier(s), such as independent gas producers , gas brokers, marketers, and any marketing subsidiaries of utilities. d Examples of liquefied petroleum gases (LPGs) are ethane, ethylene, propane, propylene, normal butane, butylene, ethane
- propane mixtures, propane
- butane mixtures, and isobutene produc ed at refineries or natural gas processing plants, including plants that fractionate raw natural gas liquids (NGLs). e Other includes net steam (the sum of purchases, generation from rene wables, and net transfers), and other energy that respondents indica ted w as used to produce heat and power. * Estimate less than 0.5. Sources: U.S. Department of Energy, Energy Information Administration. 2007b. “2006 Energy Consumption by Manufacturers

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

— Data Tables.” Tables 3.2 and N3.2. Washington, DC: DOE. < http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html > .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-05Index Value (Jan-97=100)

Man ufac tu ri ng Che mi ca l Manufac tu ri ng Figure B - 23. Electric Power Use Trends in Chem ical Manufacturing (NAICS 325):

1997

– 2005 Source: Federal Reserve Board . 2009. “Industrial Production and Capacity Utilization: Electric Power Use: Manufacturing and Mining.” < http://www.federalreserve.gov/datadow nload/ > . B .3.3 Firm and Market Characteristics This remaining subsection describes geographic, production, and market data. These data provide the basis for further anal ys is, including regulatory flexibility

anal yses, and give a complete picture of the recent historical trends of production and pricing. B .3.3.1 Location In 2002, California had the most chemica l manufacturing establishments in the United States, followed by

Texas and New Jersey

( Figure B
- 24). The composition of establishments in these states differs among the different industry groups. Despite the fact that each group employ ed an approximately e qual share of people in 2002, 54% of the total establishments were other chemicals establishments, and only 13% were pharmaceutical establishments. B .3.3.2 Production Capacity and Utilization Capacity utilization of the chemical manufacturing industry has been broadly

in line with the manufacturing sector ( Figure B
- 25). In the second half of 2005, the chemical manufacturing industry ’s capacity

utilization fell dramaticall y because of the multiple hurricanes affecting the Gulf Coast states. The impact of the economic downturn in 2001 can be seen in the capacity utilization of both manufacturing and chemical manufacturing.B
- 41

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

Sector BEA Code Bulk Chemicals Pharm aceuticals Other Chemical s 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 Pharm aceutical and medicine manufacturing 3254 $4,778 $25,125 $462 $30,365 Resin, rubber, and artificial fiber s 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 pro duct 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 P ersonal 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 Im ports 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, D C: BEA
. B .3.3.3 Employment The geographic distribution of employ ment in c hemical manufacturing differs largel y among the different groups. I n California, 52% of the chemical manufacturing employ ment comes from the pharmaceutical industry , while 60% of the chemical manufacturing employ ment in the Gulf Coast states comes from bul k chemicals manufacturing ( Figure B
- 26). B .3.3.4 Plants and Capacity Production capacit y in chemical manufacturing has grown 33% since 1997. This growth, however, is 9% less than the growth rate for the ma nufacturing industry as a whole ( Figure B
- 27).B
- 42 Establishments Fe wer than 100 100 - 249 250 - 474 475 - 750 More than 750 Fi gure B - 24. Establishment Concentration in Chemical Manufacturing (NAICS 325):

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

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

Man ufac tu ri ng Chem ic al Manufac tu ri ng Figure B - 25. Capacity Utili zation Trends in Chemical Manufacturing (NAICS 325) Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Capacity Utilization.” < http://www.federalreserve.gov/datadow nload />
.B
- 43 Employment Fewer than 5,000 5,000 - 13,999 14,000 - 29,999 30,000 - 40,000 More than 40,000 Figure B - 26. Employment Concentration in Chemical Manufacturing (NAICS 325): 2002 So urce: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Geographic Area Series: Industry Statistics for the States, Metropolitan and Micropolitan Statistical Areas, Counties, and Places: 2002.” < http://factfinder.census.gov >; (July 23, 2008)
.

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

Manufa ct ur ing Che mi ca l Manufac tu ri ng Figure B - 27. Capacity Trends in Chemical Manufacturing (NAICS 325) Source: Federal Reserve Board. 2009. “Industr ial Production and Capacity Utilization: Industrial Capacity.” < http://www.federalreserve.gov/datadow nload />
.B
- 44

B .3.3.5 Firm Characteristics In 2007, the top six companies by chemical sales had greater than $10 billion in sales. Together, their sales are greater than the next 44 highest chemical companies combined. These, however, are global companies, with a large portion of both sales and production coming from operations outside of the United States ( Table B
- 26). The largest chemical manufacturing compan y, Dow Chemicals, has 108 out of 150 manufacturing sites located outside of the United States (Dow Chemical Company , 2008 ). Table B - 26. Top Chemical Producers: 2007 Chemical Sales ($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% Lyo ndell a 16,165 57% 80% Chevron Phillips 12,534 100% 86% PPG Industries a 10,025 90% 56% Huntsman Chemical 9,651 100% 50% Praxair 9,402 100% 43.5% Air Products a 8,820 88% 51% Rohm & Haas b 7,837 88% 49%

a Percentage of sales in the United States calculated from total sales, not chemical sales. b Percentage of sales in the United States is actually percentage of sales in North America. Sources: O’Reilly, R. 2008. “Chemicals.” Standard and Poor’s Indu stry Surveys . 176(28)
. In 2007, 58%of U.S. chemical manufacturing corporations generated net income. In cluding those with and without net income, chemical manufacture rs had an average before
- tax profit margin of 10.24%. Profitability is highest for pharmaceutical and medicine corporations ( Table B
- 27). B .3.3.6 Size Distribution The primary

criterion for categorizing a business as small is number of employ ees, using def initions by

the SBA for regulatory

flexibility

analy ses. The data describing size standards are provided in Table B
- 28. In 2002, enterprises with fewer than 500 employ ees accounted for 27% of employ

ment and 15% of receipts within the chemical manufacturing industry ( Table B
- 29).B
- 45

Table B - 27. 2007 Corporate Income and Profitability (NAICS 325)

Industry Number of

Corporations Number of

Corporations with Net Income Total

Receipts ($10 3 ) Business

Receipts ($10 3 ) Before Tax 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%
Pharm aceutical 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. 2008 b . “Corporation Source Book: Data File 2007.” < http://www.irs.gov/taxstats/article/0,,id=167415,00.html >; (January, 15, 2010)
. B .3.3.7 Domestic Production In the late 1990s, overall manufacturing production was growing much faster than the chemical manufacturing component ( Figure B
- 28). Following the recession of 2001, however, the comp onents have moved broadl y in line with one another, except for the drop in chemical manufacturing production caused b y the hurricane season of 2005. B .3.3.8 International Trade In the ye ar 2000, the United States moved from having a trade surplus to a trad e deficit in chemical manufacturing products ( Figure B
- 29). This change occurred because the trade deficit in pharmaceutical manufacturing, currentl y at $35 billion, overwhelmed the trade surplus of bulk chemicals and other chemical manufacturing combined, currentl y at $22 billion. B .3.3.9 Market Prices Prices of goods in chemical manufacturing have accelerated rapidly in the last 2 y ears, having outpaced overall manufacturing since 2002 ( Figure B
- 30). Much of this recent acceleration seen in the industry P PI is due to the bulk chemicals segment, largel y reflecting the rapid increase in fertilizer prices.B
- 46

Table B - 28. Small Business Size Standards: Chemical Manufacturing (NAICS 325) NAICS Description Em ployees 325110 Petrochemical Manufacturing 1,000 325120 Industrial Gas Manufacturing 1,000 325131 Inorganic Dye and Pigment Manufacturing 1,000 325132 Synthetic Organic Dye and Pigment Manufacturing 750 325181 Al kalies and Chlorine Manufacturing 1,000 325182 Carbon Black Manufacturing 500 325188 All Other Basic Inorganic Chemical Manufacturing 1,000 325191 Gu m and Wood Chemical Manufacturing 500 325192 Cy clic Crude and Intermediate Manufacturing 750 325193 Ethyl Alcohol Manufacturing 1,000 325199 All Other Basic Organic Chemical Manufacturing 1,000 32 5211 Plastics Material and Resin Manufacturing 750 325212 Synthetic Rubber Manufacturing 1,000 325221 Cellulosic Organic Fiber Manufacturing 1,000 325222 Noncellulosic Organic Fiber Manufacturing 1,000 325311 Nitrogenous Fertilizer Manufacturing 1,000 325312 Phosphatic Fertilizer Manufacturing 500 325314 Fertilizer (Mixing Only) Manufacturing 500 325320 Pesticide and Other Agricultural Chemical Manufacturing 500 325411 Medicinal and Botanical Manufacturing 750 325412 Pharmaceutical Preparation Manu facturing 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 Manufactu ring 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 Compo unding of Purchased Resins 500 325992 Photographic Film, Paper, Plate and Chemical Manufacturing 500 325998 All Other Miscellaneous Chemical Product and Preparation

Manufacturing 500 Source: U. S. Small Business Administration (SBA). 2008. “ Table of Sma ll Business Size Standards Matched to North American Industry Classification System Codes.” Effective August 22, 2008. < http://www.sba.gov/services/contra ctingopportunities/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 Em ployees a 20 to 99

Em ployees 100 to 499

Em ployees 500 to 749

Em ployees 750 to 999

Em ployees 1,000 to 1,499

Em ployees 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. Businesse s: 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-09Index Value (Jan-97=100)

Manufa ct ur ing Che mi ca l Manufac tu ri ng Figure B - 28. Industrial Production Trends in Chemical Manufacturing (NAICS 325) Source: Federal Reserve Board. 2009. “Industrial Production and Capacity Utilization: Industrial Production.” < http://www.federalreserve.gov/datadow nload />
.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)

Imp ort s Ex port s Figure B - 29. International Trade Trends in Chemical Manufacturing (NAICS 325) Source: U.S. International Trade Commission. 2008a. “U.S. Domestic Exports” & “U.S. Imports for Consumption.” < http://dataweb.usitc.gov/scripts/user_set.asp > .

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)

Manufa ct ur ing Che mi ca l Manufac tu ri ng Figure B - 30. P roducer Price Trends in Chemical Manufacturing (NAICS 325) Source: U.S. Bureau of Labor Statistics (BLS). 2009 c . Producer Price Index. Series ID: PCU325

— 325

— &PCUOMFG

— OMFG

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