6
- 1 **CHAPTER 6 EMPLOYMENT**  **AND ECONOMIC IMPACT**  **ANALYSIS** In this chapter, we estimate select employment effects of the rule , for both the regulated industry (electric power industry) and the environmental control industry. **6 .**  **1 Empl**  **oyment Impacts for the MA T** **S** In addition to addressing the cos ts and benefits of the MATS , EPA has estimated certain impacts of this on employment , which are presented in this section. 1 While a standalone analysis of employment impacts is not included in a standard cost
- benefit analysis, such an analysis is of particular concern in the current economic climate of sustained unemployment. Executive Order 13563, states, “ Our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovatio n, competitiveness, and job creation ” (emphasis added). Therefore, the Agency ha s provided this analysis to inform the discussion of labor demand and employment impacts. The estimate s include the employment impacts on the regulated industry over time. Als o provided are certain short
-term and on
- going employment impacts (increase in labor demand) associated with the construction of needed pollution control equipment , and other activities, to comply with the regulation. The Agency ha s not quantified the rule ’ s effects on all labor in other sector s not regulated by the MATS , or the effects induced by changes in workers ’ incomes. What follows is an overview of the various ways that environmental regulation can affect employment, followed by a discussion of the estimated impacts of this rule. EPA continues to explore the relevant theoretical and empirical literature and to seek public comments in order to ensure that such estimates are as accurate, useful and informative as possible. From an economic perspectiv e , labor is an input into producing goods and services . I f regulation requires that more labor be used to produce a given amount of output, th e additional labor is reflected by an increase in the cost of production. 2 Moreover, when the economy is at full employment, we would not expect an environmental regulation to have an impact on overall employment because labor is being shifted from one sector to another. On the other hand, in periods of high unemployment, an increase in labor demand due to

1 See the employment impacts appendix included in this RIA. 2 It should be noted that if more labor must be used to produce a given amount of output, then this implies a decrease in labor productivity. A decrease in labor productivity will cause a short
- run aggregate supply curve to shift to the left, and businesse s will produce less, all other things being equal.6
- 2

regulati on may result in a short
-term net increase in overall employment due to the potential hiring of previously unemployed workers by the regulated sector to help meet new requirements (e.g., to install new equipment) or by the environmental protection sector t o produce new abatement capital
. With significant numbers of workers unemployed, the opportunity costs associated with displacing jobs in other sectors are likely to be smaller. To provide a partial picture of the employment consequences of this rule, EPA takes two approaches. First, the analysis uses the results of Morgenstern, Pizer, and Shih (2002) to estimate the effects of the regulation on the regulated industry, the electric power industry in this case. This approach has been used by EPA previousl y in recent Regulatory Impact Analyses. Second, EPA uses information derived from engineering studies and projections of pollution controls from the power sector modeling to generate estimates of employment impacts to the pollution control sector. EPA is interested in public comments on the merits of including information derived in this fashion for assessing the employment consequences of regulations. Section 6.2 discusses the estimates of the employment consequences in the electricity sectors, using the Morgenst er n, et al. approach. Section 6.3 estimates the employment consequences in the environmental protection sector. **6.2 Employment Impacts Primarily on the Regulated Industry: Morgenstern, Pizer, and**  **Shih (2002)** EPA examined possible employment effe cts within the electric utility sector using a peer- reviewed, published study that explores historical relationships between industrial employment and environmental regulations (Morgenstern, Pizer, and Shih, 2002). For context, in 2007, the electric power generation, transmission and distribution sector (NAICS 2211) had approximately 510,000 paid employees (a ccord ing to the 2007 Economic Census)
. Estimates from Morgenstern et. al. study have been applied in recent RIAs to derive the employment effects of ne w regulations within the regulated industry. (See, for example, the Regulatory Impact Analyses for the proposed MATS and final CSAPR regulations). We believe this study is applicable to this employment analysis, as it was for the MATS proposal, since the p ollution control strategies or measures that form the basis of the cost inputs in the Morgenstern et al. analysis are primarily add
- on or end
- of
- line pollution controls, in general covering more than 70% of the abatement expenditures in most years and indu strie s analyzed as shown in Table 6
- 1 . The analysis of control strategies presented in Chapter 3 of this RIA are composed entirely of add
- on or end
- of
- line pollution controls. Thus, the cost inputs in the Morgenstern et al. analysis are consistent with the cost inputs that enter into this analysis of employment impacts within the regulated industry for MATS.6
- 3

**Table 6** **- 1**  **. Percent of Abatement Expenditures**  **in Different PACE Studies from Add -**  **On or**

**End -**  **of -**  **Line Control Measures 3** 
**Industry Percent**  **1979 1983**  **1988 1991**  Pulp and Paper 84 80 61 47 Plastics 85 88 75 67 Petroleum Refining 72 57 63 61 Iron and Steel 96 93 94 92
Determining the direction of employment effects in the regulated industry is challenging due to competing effects. The fundamental insight of Morgenstern, et al. is that environmental regulations can be understood as requiring regulated firms to add a new output (environmental quality) to their product mixes. Although legally compelled to satisfy this new demand, regul ated firms have to finance this additional production with the proceeds of sales of their other (market) products. Satisfying this new demand requires additional inputs, including labor, and may alter the relative proportions of labor and capital used by r egulated firms in their production processes. Thus, Morgenstern et al. decompose the overall effect of a regulation on employment into the following three subcomponents:

 The “Demand Effect”: higher production costs raise market prices, reducing consumptio n (and production), thereby reducing demand for labor within the regulated industry (an unambiguously negative effect);

 The “Cost Effect”: Assuming that the capital/labor ratio in the production process is held fixed, 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 (an unambiguously positive effect) ;

 The “Factor
- Shift Effect”: Regulated firms’ production technologies may be more or less labor intensive after complying with a regulation (i.e., more/less labor is required per dollar of output). “ E nvironmental activities may be more labor intensive than conve ntional production,” meaning that “the amount of labor per dollar of output will rise.” However, activities may, instead, be less labor

3 U.S. Bureau of the Census. Pollution Abatement Costs and Expenditures . Washington, DC: U.S. Government Printing Office, various years. The pulp and paper industry is defined by SIC 2611 & 2621, plastics by SICE 282, petroleum refining by SIC 2911, and iron and steel by SIC 332. For pulp and paper and iron and steel industries in 1983 the data is partially estimated based on non reported data due to disclosure reasons. The 1984 (1989) data for plastics (pulp and paper) is used instead of 1983 (1988) due to a lack of reported data in the original year.6
- 4 intensive because “cleaner operations could involve automation and less employment, for example.” (p. 416) (ambiguous effect) Decomposing the overall employment impact of environmental regulation into three subcomponents clarifies the conceptual relationship between environmental regulation and employment in regulated sectors, and permitted Morgenstern, et al. to provide an empirical estimate of the net impact. For present purposes, the net effect is of particular interest, and is the focus of our analysis. The demand effect is expected to have a n unambiguously negative effect on employment, the cost effect to have a n unamb iguously positive effect on employment, and the factor
- shift effect to have an ambiguous effect on employment. Without more information with respect to the magnitudes of these three competing effects, it is not possible to predict the net employment effect in the regulated sector
. Using plant
- level Census information between the years 1979 and 1991, Morgenstern et al. estimate the size of each effect for four highly polluting and regulated industries (petroleum, plastic material, pulp and paper, and steel). On average across the four industries, each additional $1 million ($1987) spending on pollution abatement results in a (statistically insignificant) net increase of 1.55 (+/

- 2.24) jobs. As a result, the authors conclude that increases in pollution abatem ent expenditures do not necessarily cause economically significant employment changes. The conclusion is similar to Berman and Bui (2001) who found that increased air quality regulation in Los Angeles did not cause in large employment changes. Since the M orgenstern, et al. parameter estimates are expressed in jobs per million ($1987) 4 of environmental compliance expenditures, their study offers a transparent and simple way to transfer estimates for other employment analysis. For each of the three job effec ts outlined above, EPA used the Morgenstern et. al. four industry average parameters and standard errors along with the estimated private compliance costs to provide a range (based on the 95th percentile of results) of employment effects in the electricity sector associated with the rule. By applying these estimates to annualized cost for the final rule for the electric power sector as shown in Chapter 3 of this RIA ($9. 60 billion in 2007$) , the Agency estimated each effect. The results are:

4 The Morgenstern et al. analysis uses “production worker” as defined in the US Census Bureau’s Annual Survey of Manufactures (ASM) in order t o define a job. This definition can be found on the Internet at http://www.census.gov/manufacturing/asm/definitions/index.html6
- 5  Demand effect: −39 ,000 to +2,000 jobs in the directly affected sector with a central estimate of −18,000;

 Cost effect: +4,000 to +21 ,000 jobs in the directly affected sector with a central estimate of +12,000; and

 Factor
- shift effect: +200 to +27 ,000 jobs in the directly affected secto r with a central estimate of +14 ,000. EPA estimates the net employment effect to range from −1 5 ,000 to + 30 ,000 jobs in the directly affected sector with a central estimate of +8,000. 5 , 6 EPA recognizes there will be other employment effects which are not considered in the Morgenstern et al. study. For example, employment in environmental protection industries may increase as firms purchase more pollution control equipment and services to meet the rule’s requirements. EPA does provide such an estimate of employment change later in this section in a separate analysis. A defensible methodology for evaluating the employment impacts beyond the environmental protection and regulated sectors is not yet available, though as noted before , net effects on employment are expected to be at or very close to zero for the economy overall under full employment. Attempts to estimate such effects usually rely on input
- output methodologies that hold technol ogies and the proportion of various inputs constant over time, making them inappropriate for estimating long run impacts of regulation. **6.2 .1**  **Limitations** The Morgenstern et al. approach to employment analysis has the advantage of carefully controlling for many possibly confounding effects in order to separate the effect of changes in regulatory costs on employment. Although the Morgenstern et al. paper provides information about the potential job effects of environmental protection programs, however, there are several caveats associated with using those estimates to analyze the final rule. First, the Morgenstern et al. estimates presented in Table 6
- 2 and used in EPA’s analysis represent the weighted average parameter estimates for a set of manufacturing in dustries (pulp and paper, plastics, petroleum, and steel). Unfortunately this set of industries does not overlap directly with the electric utility sector. Second, relying on Morgenstern et al. implicitly assumes that the

5 Since Morgenstern’s analysis reports environmental expenditures in $1987, we make an inflation adjustment the IPM costs using the ratio of the annual consumer price index, U.S. city, all items reported by the U.S. Bureau of Labor Statistics: CPI 1987/ CPI 2007 = (113.6/207.3) = 0.55. 6 Net employment effect = 1 .5 5 × $9,6 00 million × 0. 55. Given the 95% confidence interval for this effect, this estimated net result is not statistically different from zero.6
- 6

employment estimates derived from 1979– 1991 data are still applicable. Third, the methodology used in Morgenstern et al. assumes that regulations affect plants in proportion to their total costs. In other words, each additional dollar of regulatory burden affects a plant by an amount equal to that plant’s total costs relative to the aggregate industry costs. By transferring the estimates, EPA assumes a similar distribution of regulatory costs by plant size and that the regulatory burden does not disproportionately fall on smaller or larger plants. Fourth, the Morgenstern et al. analysis makes particular assumptions about the role of imports and the effect of previous regulation on plant closures. While imports are not an issue for MATS , the stringency of the current regulation is expected to result in a number of power plant closures due to early retirement of coal
- fired EGU capacity in 2015, as indicated in Chapter 3 of this RIA. Finally, the Morgenstern et.al. methodology does not examine the effects of regulation on employment in sect ors related to , but outside of the regulated sector, but it does suggest that it is not appropriate to assume that the employment impact in any sector is directly proportional to increased costs
. **Table 6 -**  **2 .**  **Employment Impacts Within the Regulated Industry**  **Using Peer -**  **Reviewed Study**

**Estimates using Morgenstern et al. (2002) Estimates Using Morgenstern, et. al (2002)**  **Demand Effect Cost Effect**  **Factor Shift Effect Net Effect**  Change in Full
-Time Jobs per Million Dollars of Environmental Expenditure a −3.56 2.42 2.68 1.55 Standard Error 2.03 0.83 1.35 2.24 EPA Estimate for Rule b −39 ,000 to +2,000 +4,000 to +21 ,000 +200 to +27 ,000 −15,000 to +30 ,000 c

a Expressed in 1987 dollars
. Adjustment of dollars from 2007 to 1987 is accomplished through use of the annual Consumer Price Index
– All Urban Consumers, found on the Internet at ftp://ftp.bls.gov/pub/special.request s/cpi/cpiai.txt . U.S. Department of Labor, Bureau of Labor Statistics
. Washington, D.C. bAccording to the 2007 Economic Census, the electric power generation, transmission and distribution sector (NAICS 2211) had approximately 510,000 paid employees. Both the midpoint and range for each effect are reported in the last row of the table.

c EPA has used this st udy to estimate the mean net employment impact of this rule, and provided the 95% confidence interval results to reflect the high degree of uncertainty regarding the effect on employment within the regulated industry . T he confidence interval includes zero indicating we are uncertain as to the sign of the effect , but the interval itself does reveal information on the magnitude of the effect.6
- 7

**6.3 Employm**  **ent Impacts of the MATS -**  **Environmental Protection Sector Approach by 2015**  **7** Regulations set in motion new orders for pollution control equipment and services. New categories of employment have been created in the process of implementing regulations to make our air safer to breathe. When a regulation is promulgated, one typical response of industry is to order pollution control equipment and services in order to comply with the regulation when it becomes effective. With such a response by industry as a basi s , t his section presents estimates for short term employment needed to design, construct, and install the control equipment in the three or four year period leading up to the compliance date. Revenue and employment in the environmental technology industry have grown steadily between 2000 and 2008, reaching an industry total of approximately $300 billion in revenues and 1.7 million employees in 2008. 8 While these revenues and employment figures represent gains for the environmental technologies industry, t hey are costs to the regulated industries required to install the equipment. As with any pool of labor, the gross size of the labor pool does not reflect the net impact on overall employment after adjusting for shifts in other sectors. Regulated firms hire workers to design and build pollution controls. Once the equipment is installed, regulated firms hire workers to operate and maintain the pollution control equipment
– much like they hire workers to produce more output. Of course, these firms may also r eassign existing employees to do these activities. Environmental regulations also support employment in many basic industries. In addition to the increase in employment in the environmental protection industry ( to fill increased orders for pollution contr ol equipment placed by the regulated sector ), environmental regulations also support employment in industries that provide intermediate goods to the environmental protection

7 EPA expects that the installation of retrofit control equipment in response to the requirements of th is proposal will primarily take place within 3 years of the effective date of the final rule, but there may be a possibility that some installations may occur within 4 years of the effective date. 8 In 2008, the industry totaled approximately $315 billion in revenues and 1.7 million employees including indirect employment effects, pollution abatement equipment production employed approximately 4.2 million workers in 2008. These indirect employment effects are based on a multiplier for indirect employment = 2.24 (1982 value from Nestor and Pasurka
- approximate middle of range of multipliers 1977
- 1991). Environmental Business International (EBI), Inc., San Diego, CA. Environmental Business Journal, monthly (copyright). http://www.ebiusa.com/ EBI data taken from the Department of Commerce International Trade Administration Environmental Industries Fact Sheet from April 2010: http://web.ita.doc.gov/ete/eteinfo.nsf/068f3801d047f26e85256883006ffa54/4878b7e2fc08ac6d85256883006c45 2c?OpenDocument . The d efinition of the environmental technology industry from the Department of Commerce’s ITA FY 2010 environmental technologies industries assessment is not according to NAICS code but instead is “all goods and services that generate revenue associated with en vironmental protection, assessment, compliance with environmental regulations, pollution control and prevention, waste management, renewable energy, remediation of contaminated property, design and operation of environmental infrastructure, and the provisi on and delivery of environmental resources. ”6
- 8

industry. For example, an investment in capital expenditures to reduce air pollut ion involves the purchase of abatement equipment. The equipment manufacturers, in turn, order steel, tanks, vessels, blowers, pumps, and chemicals to manufacture and install the equipment. A study by Bezdek, Wendling, and DiPernab (2008) found that “ investments in environmental protection create jobs and displace jobs, but the net effect on employment is positive. ” 9 The focus of this part of the employment analysis is on short
-term employment related to the compliance actions of the affected entities. Thi s analysis estimates the employment impacts due to the increased demand for pollution control equipment in response to MATS. 10 Results indicate that the MATS has the potential to result in a net increase of labor in these industries, driven by the high dem and for new pollution controls. Overall, the results of the environmental protection sector approach indicate that the MATS could support an increase of about 46 ,000 job
- years 11 by 2015. **6. 3** **.1 Overall Approach and Methodology for Environmental Protection**  **Sector Approach** EPA developed estimates of the potential employment changes for the Environmental Protection Sector using a bottom
- up engineering based methodology combined with macroeconomic data on industrial output and productivity, to estimate employme nt impacts. The approach relies heavily on the projections and cost ing analysis from the IPM model, which uses industry specific data and assumptions to derive compliance costs and energy impacts (See Chapter 3 )
. Central to t he approach are prior EPA stu dies on similar issues, and in particular, data and information from extensive engineering studies that the Agency has commissioned
. 12 The analysis develops employment estimates by relying on IPM projections from the MATS analysis for the specific types of pollution control technologies expected to be installed to comply with the rule. 13 More specifically, the analysis includes estimates for the labor needed

9 Environmental protection, the economy, and jobs: National and regional analyses , Roger H. Bezdek, Robert M. Wendling and Paula DiPerna, Journ al of Environmental Management Volume 86, Issue 1 **,** January 2008, Pages 63
- 79. 10 For more detail on methodology, approach,and assumptions, see TSD as part of the Toxics Rule Docket:
“ Employment Estimates of Direct Labor in Response to the Proposed Toxics Rul e in 2015. ” 11 Numbers of job years are not the same as numbers of individual jobs, but represents the amount of work that can be performed by the equivalent of one full
-time individual for a year (or FTE). For example, 25 job years may be equivalent to fiv e full
-time workers for five years, twenty
-five full
-time workers for one year, or one full
-time worker for twenty
-five years. 12 Engineering and Economic Factors Affecting the Installation of Control Technologies for Multipollutant Strategies EPA
- 600/R

- 02/073 (2002) and Engineering and Economic Factors Affecting the Installation of Control Technologies
– An Update (2011)
. 13 Detailed results from IPM for the MATS can be found in Chapter 3 of the RIA.6
- 9

to design, manufacture and install the needed pollution control equipment over the 3 to 4 years lead ing up to compliance in 2015. For construction labor, the labor needs are derived from an update to a 2002 EPA resource analysis for building various pollution controls (FGD
– Flue Gas Desulfurization or scrubbers, SCR
- selective catalytic reduction, ACI
– activated carbon injection, DSI
- dry sorbent injection, and FF
- Fabric Filters) and are further classified into different labor categories. These categories include boilermakers, engineers and a catch
- all “ other” installation labor. For the inputs neede d (e.g., steel), the updated 2002 resource study was used to determine the steel demand for each MW of additional pollution control, combined with labor productivity data from the Economic Census and BLS for relevant industries. More detail on methodology, assumptions, and data sources can be found in Appendix 6A for this RIA. Projections from IPM were used to estimate the incremental retrofit capacities projected in response to the final rule. These additional pollution controls are shown in Table 6
- 3 , and reflect the added pollution controls needed to meet the requirements of the rule. Additional information on the power sector impacts can be found in Chapter 3 of the RIA. **Table 6 -**  **3 .**  **Increased Pollution Control Installations due to MATS, by 2015 (GW)**

**Retrofit Type IPM Projected Additional Pollution Control**  FGD 1 7 ACI 9 9 DSI 44 FF 10 2
**6. 3** **.2 Summary of Employment Estimates from Environmental Protection Sector Approach**  Table 6
-4 shows the results of employment impacts resulting from the additional demand for the aforementioned pollution controls. The results indicate that MATS could support or create roughly 46 ,000 one -time job
- years of increased cost of direct labor, driven by the need to design and build the pollution control retrofits.6
- 10

**Table 6 -** **4 .**  **Employment Effects Using the Environmental Protection Sector Approach for the**

**MATS (in Job -** **Years) 14** 

**Employment Incremental Employment**  One
-Time Employment Changes for Construction 1. Boilermakers 20,000 2. Engineers 5,000 3. General Construction 21,000 **Total 4**  **6 ,000** 

**6. 3** **.3 Other Employment Impacts of**  **MATS** There are also likely to be other employment impacts associated with MATS
. These include changes resulting from additional labor needed to operate the needed pollution controls, increased demand for materials used in pollution control operation, shifts in demand for fuel in response to the rule, changes in employment resulting from additional coal retirements, and c hanges in other industries due to changes in the price of electricity and natural gas. The EPA has provide d estimates of some of these effects below, which can be found in Appendix 6A. The most notable of the ones that the Agency is unable to estimate is the impacts on employment as a result of the increase in electricity and other energy prices in the economy. Nor is the Agency able to quantify all the employment changes in industries that support and supply the pollution control industry. Because of t his inability to estimate all the important employment impacts, EPA neither sums the impacts that the Agency is able to estimate for the se other employment impacts or make any inferences of whether there is a net gain or loss of employment across the se cat egories
. A summary of the other employment impacts can be found in Table 6
- 5 , with additional detail provided in Appendix 6A.

14 Numbers are rounded to nearest thousand.6
- 11

**Table 6 -**  **5 . Other Employment Impacts of MATS (in Job**  **- Years)**  Employment Impacts from Increased Demand for Pollution Control O perating Inputs Lime (FGD) 2 8 0 Activated Carbon (ACI) 460 Trona (DSI) 3,1 3 0 Baghouse material (FF) 20 Employment Impacts from Pollution Control Operation 4,3 2 0 Employment Impacts from Retirements of Existing Coal Capacity ( 2,500 ) Employment Impacts from Changes in Coal Demand ( 430 ) Employment Impacts from Changes in Natural Gas Demand 6 7 0
Note: See Appendix 6A for more detail. **6.4 Summary of Employment Impacts**  The employment approaches used by EPA rely on different analytical techniques and are applied to different industries during different time periods, and they use different units of analysis. These estimates should not be summed because of the different metrics, length and methods of analysis. The Morgenstern estimates are used for the ongoing employment impacts for the regulated entities (the electric power sector). The short term estimates for employment needed to design, construct, and install the control equipment in the three or four year period leading up to the compliance da te are also provided. Finally some of the other types of employment impacts that will be ongoing are estimated. In Table 6
- 6 , we show the employment impacts of the MATS as estimated by the environmental protection sector approach and by the Morgenstern app roach. **Table 6 -**  **6 .**  **Estimated Employment Impact Table for the MATS**
**Annual (Reoccurring) One Time (Construction**  **During Compliance Period)** Environmental Protection Sector approach a Not Applicable 46 ,000 Net Effect on Electric Utility Sector Employment from Morgenstern et al. approach c 8,000 b

- 15, 000 to +3 0 ,000 d Not Applicable

a These one
-time impacts on employment are estimated in terms of job
-years. bThis estimate is not statistically different from zero. cThese annual or recurring employment impacts are estimated in terms of production workers as defined by the US Census Bureau ’ s Annual Survey of Manufacturers (ASM). d 95% confidence interval6
- 12

**6.5 Potential**  **Effect of Electricity Price Increase on Downstream Sectors**  **6.5.1 Introduction**  As with any input into production, the new price of electricity, reflecting the costs of MATS, will be absorbed in some fraction by industries that use electricity in their operations. This section contains a screening analysis that presents upper
- bound estimates of the incre ase in per unit production expenses of industries other than the electricity sector. These cost increases represent credible upper
- bounds on price increases due to higher electricity prices because they assume that production, consumption , and input level s do not change in the economy. In reality, we know that producers and consumers can always substitute for less electricity
- intensive goods and services to avoid a significant portion of these costs. EPA conducted this scoping or “upper
- bound” analysis t o determine if the electricity price increase in 2015 attributable to MATS will lead to substantial increases in the average production expenses of any other goods and services produced in the U.S. In this section, using readily available data collected and prepared by the Bureau of Economic Analysis (BEA), we estimate an upper

- bound on percentage increase in the per unit production costs attributable to higher electricity prices due to MATS. By upper
- bound, we mean an estimate of the increase in the per unit production costs making the extreme assumption that there are no changes in the production and use of any goods, services, and intermediate inputs in the economy. 15 Note that we are only performing simple calculations to estimate these upper
- bound inc reases in per unit production costs. A modeling approach would require assumptions about behavioral response to price changes, and we are assuming for this analysis that there is no behavioral response to higher electricity prices. If quantities of product ion, consumption, and input use changed in response to a higher electricity price, the effects on average production expenses would be less than the upper
- bound estimate we calculate. We show that the upper
- bound increase in average production expenses of goods and services across the economy attributable to the increase in the electricity prices as a result of MATS across the economy is 0.023 % (using a total output weighted average), and is no greater than 0.241% for any industrial sector . Taking into acc ount the fact that these numbers represent an upper bound with no adjustments by firms to higher electricity prices, EPA does not expect that increases in average production expenses in this range are sufficient to cause significant shifts in economic acti vity outside the electricity sector and its major input markets.

15This means that all other inputs, including capital, labor, and materials are assumed to be fixed when estimating an upper bound per unit production expense for the industries included in this analysis.6
- 13

**6.5.2 Bureau of Economic Analysis**  **Benchmark Input -**  **Output Accounts**  With the exception of the forecast change in the national average electricity price in 2015, all of the data used to calcul ate an upper - bound on the increase in average production expenses are drawn from the 2002 BEA benchmark input
- o utput (I/O) summary- level accounts (BEA 2002a, 2002b). The benchmark I/O accounts are intended to “facilitate the study of economic activity by providing a highly detailed looks at interindustry activity” (Stewart et al., 2007). The BEA’s benchmark I/O summary- level data includes information on the share of expenditures by industry spent on 135 commodity categories for 133 different sectors. These data provide a “ comprehensive picture of inner workings of the economy“ (Ibid.). That is, they represent a full balancing of the use an d production of goods and services in the entire U.S. economy. For each sector, the sum of expenditure shares across all 135 commodities equals 1. The entire output of each sector is also accounted for. That is, the output of each sector is the sum of prod uction for inputs used by other sectors, production for final demand, and production for export. BEA’s sector and commodity classification system for the benchmark I/O accounts is described in BEA 2007a and 2009. The most recent benchmark I/O data available that are suitable for calculating an upper
bound on the increase in per unit production costs reflect economic conditions in 2002 and were published in 2007. 16 In the benchmark I/O summary data one of the commodities is “electricity genera tion, transmission and distribution”. As discussed in Chapter 2 of this RIA, g eneration, transmission and distribution are the three main services used to provide electricity to consumers, and therefore are the three main services reflected in electricity rates. For brevity, in this section we collective refer to the average price for these three services as the “electricity price” and expenditures on these three services as “electricity expenditures”.

16 BEA also reports I
- O accounts data on an annual b asis ( Stewart et al., 2007 ). The most recent annual I/O accounts data represent economic activity in 2010. We cannot use the annual I/O accounts data because they do not report the share of electricity consumption at the sector level independent of other c ommodities. Rather, expenses on electricity are combined with expenses on other utilities such as water and sewage treatment in the annual I/O accounts data. Aggregating utility expenditures together prevents us from using more recent expenditure data as i t is not reasonable to assume that all utility expenses reflect electricity expenses exclusively. Furthermore, the 2002 benchmark I/O data are the most recent benchmark I/O data available. The benchmark I/O data are based on data collected on the Economic Census, which is conducted by the U.S. Census Bureau and occurs every five years. The publication of the benchmark I/O data by BEA lags the Economic Census by five years. Therefore benchmark I/O data for the 2007 Economic Census, the most recent conducted, have not been published yet by BEA.6
- 14

For each sector, our calculation of the upper
- bound in crease in average production expenses accounts for the increase in expenditures from direct electricity consumption. It does not account for changes in factor prices attributable to higher electricity prices. 17 The share of direct electricity expenditures for each sector is reported in the Commodity
- by
- Industry Direct Requirements after Redefinitions, 2002 table (BEA, 2007b). The share of production expenditures on electricity for each of the 133 sectors is reported in Table 6B
- 1 in Appendix 6B of this RIA. The share of expenditures on electricity as a share of total expenditures ranges from 0.010% for Software Publishers to 8.029% for Metal Ores Mining. On an expenditures
- weighted basis, electricity comprises only 1.7% of total production expenditures across all sectors in 2002 (BEA, 2007c). **6.5.3 Upper**

**- bound Increase in Per Unit Production Expenses from Electricity Purchases**  In order to estimate an upper
- bound percentage increase in per unit production expenditures in 2015, for each sector we multiply the forecast percent change in electricity price in 2015 by the share of production expenditures on electricity. As reported in Chapter 3, the Retail Electricity Price Model forecasts a 3.1% increase in the contiguous U.S. electricity price in 2015 (see Table 3
- 12) as a result of MATS
. 18 The share of production expenditures on electricity in each of the 133 sectors, as described above, comes from BEA. For example, according to the 2002 benchmark I/O accounts, in the Forging and Stamping sector 1.42% of producti on expenditures are on electricity. Again, EPA forecasts that MATS will increases average electricity prices by 3.1% in 2015
. Therefore, the upper bound on the percentage increase in production costs in this sector (from direct electricity expenses) is 0.0 44% ( =[0.031 * 0.01423]*100% ). Because we assume that production, consumption and input use does not change in this analysis, the calculated percent change in total production expenses equals the upper
- bound

17 For sectors that use energy
- intensive inputs the effect of higher electricity prices on the prices of other factors might be important. However, applying the BEA data to estimate an upper
- bound that account s for el ectrici ty price pass
-through on factor prices would rel y even more heavily on the assumption that factor mix es do not change in response to relative factor prices changes , and thus we would not find the upper
- bound increase in average production costs calc ulated in this way credible
. See Section 6.5.4 for further discussion. 18 We use the national average electricity price change for this calculation because we do not have sufficiently detailed regional production expenditure data from BEA to calculate re gional sectoral estimates of upper
- bounds on increases in average production expenses.6
- 15

on the percent change in per
- unit or average production. By average production expenditures we mean the change in production expenditure per unit of output by the sector. 19 Note that this per unit percent cost increase does not reflect potential other economic effects of this rule. For example, the increased expenditures on pollution abatement equipment could create more demand for labor in those industries. Alternatively, as producers switch away from electricity
- intensive inputs, the demand for other inputs may increase, changing the cost of produ ction for those factors of production. Table 6
- 7 summarizes the upper
- bound average production expense increase for the thirteen largest private sectors, which r epresent just over 50% of total private economic activity, in terms of value added. We focus on the private sector as the effect of MATS on private economic activity is of significant public interest. We focus on the largest sectors by BEA’s categorizatio n given their collective importance to the U.S. economy. The average of the upper - bound change in average production expenditure, weighted by total output, for these sectors is 0.024%
. Table 6 B
- 1 reports the total output of these sectors.

19 Output in these sectors is measured in dollars as output needs to be normalized for product and price heterogeneity within the sector as described in BEA, 2009
.6
- 16

**Table 6 -**  **7 .**  **Share of Production Expenditures on Electricity and Upper** **- B**  **ound Percentage** 

**Increase in Production Expenditures for 13 Largest Sectors in Terms of Value Added**  **(2002)**

**Sector Share of** 

**Production Expenditures on**  **Electricity a**  **Upper -**  **Bound Increase** 

**in Average Pro duction**  **Expenditures b**  **Share of 2002 Total**

**Value Added from Private Sector**  **c** Real estate 1.783% 0.055% 7.39% Retail trade 1.328% 0.041% 7.08% Wholesale trade 0.450% 0.014% 6.97% Monetary authorities, credit intermediation and related activities 0.096% 0.003% 5.23% Ambulatory health care services 0.350% 0.011% 3.82% Management of companies and enterprises 0.602% 0.019% 3.14% Food services and drinking places 1.894% 0.059% 2.88% Insurance carriers and related activities 0.073% 0.002% 2.79% New nonresidential construction 0.363% 0.011% 2.77% Telecommun ications 0.334% 0.010% 2.71% Hospitals 1.142% 0.035% 2.45% New residential construction 0.325% 0.010% 2.19% Computer systems design and related services 0.811% 0.025% 1.99%

a Source: BEA 2007b. Electricity expenses are those on “e lectric power generation, transmission, and distribution " which is BEA commodity sector 2211. The correspondence between BEA’s sector classification and NAICS codes is available from BEA 2007a
. b The forecast change in average production expenses is an upper
- bound and assumes that production and use of all goods and services in the economy remain unchanged. See chapter discussion for details. c Source: BEA 2007c . These percentages are calculated by dividing the total value added for the sector by the total value added for the sectors that are not government and not owner
- occupied dwellings. See footnote 20 for further explanation. Table 6B
- 1 reports the upper
- bound per unit production cost increase for all 133 sectors in the BEA benchmark I/O data. The sector with the highest average production expense6
- 17

increase, 0.241% (less than 3 tenths of 1 percent), is m etal ores mining . The next highest is n onmetallic mineral mining and quarrying, which is 0.125%. Seven se ctors have an upper
bound average production expense increase greater than 0.100%. Twenty two have an upper
bound average production expense greater than 0.050%. Table 6
- 8 reports the upper
- bound per unit production cost increase for the 10 sectors with th e highest upper
- bounds, along with their share of value added in 2002. These 10 sectors represent 2.38% of total value added from the private sector in 2002. 20 The average of the upper
- bound change in average production expenditures for all 133 sectors, wei ghted by total output, is 0.023%.

20 Ther e is no clear distinction in the BEA data between private and public sector activities. However, we use “private sector” as shorthand to indicate that we are excluding those sectors that BEA characterizes as “government industries”. These are sectors Feder al Government enterprises, State and local government enterprises, General Federal defense government services, General Federal nondefense government services, and General state and local government services. We also exclude the owner
- occupied dwellings se ctor in what we are calling the private sector. The maximum percent change in average production expenditures for the government industries and owner
- occupied dwellings sectors is reported in Table 6B
- 1 in the Appendix to this chapter.6
- 18

**Table 6 -**  **8 .**  **Share of Production Expenditures on Electricity and Upper** **- B**  **ound Percentage** 

**Increase in Production Expenditures for 10 Sectors with Largest Upper -**  **Bound Average Production**  **Expenditures Increase**  **(2002)**

**Sector Share of** 

**Production Expenditures on**  **Electricity a**  **Upper -**  **Bound Increase** 

**in Average Production Expenditures**  **b Share of 2002 Total** 

**Value Added from Private Sector**  **c** Metal ores mining 8.029% 0.241% 0.048% Nonmetallic mineral mining and quarrying 4.158% 0.125% 0.121% Basic chemical manufacturing 3.519% 0.106% 0.243% Iron and steel mills and manufacturing from purchased steel 3.412% 0.102% 0.187%
Nonferrous metal production and processing 3.396% 0.102% 0.148%
Pulp, paper, and paperboard mills 3.346% 0.100% 0.326% Accommodation 3.141% 0.094% 0.737% Foundries 2.905% 0.087% 0.130% Warehousing and storage 2.615% 0.078% 0.296% Textile mills 2.409% 0.072% 0.146% a Source: BEA 2007b. Electricity expenses are those on “e lectric power generation, transmission, and distribution ” which is BEA commodity sector 2211. The correspondence between BEA’s sector classification and NAICS codes is available from BEA 2007a
. b The forecast change in average production expenses is an upper
- bound and assumes that production and use of all goods and services in the economy remain unchanged. See chapter discussion for details. c Source: BEA 2007c . These percentages are calculated by dividing the total value added for the sector by the total value added for the sectors that are not government and not owner
- occupied dwellings. See footnote 20 for further explanation.
Tables 6
- 7 and 6
- 8 illustrate that, even when considering an unrealistic worst case scenario where the economy fails to respond in any way to the change in electri city prices, the change in average production expenditures for most sectors is modest. Averaged over the 13 largest sectors , which represent over half of total output, th e average electricity price impact of the rule leads to , at most, an only 0.024% incr ease in average production expenditures. Averaged a cross all sectors, the upper - bound increase in average production expenditures would be a very similar 0.023%. Such small changes are unlikely to lead to significant impacts6
- 19

but rather ones which likely cannot be detected in the noise and variability of the real national economy. **6.5.4 Limitations**  This approach suffers from a number of limitations. First, these estimates reflect an upper - bound on the change the average production cost o f goods and servic es prices because the calculation used to estimate these changes assume that production, consumption and input use will not change in response to higher electricity prices. For example, s ectors that use electricity may mitigate the impact of the higher pr ice of electricity by shifting to other inputs
. T his limitation is why we characterize this approach provides an upper bound on the potential increase in average production costs outside the electricity sector. Second, t his analysis also does not account for the effect that higher electricity prices have on the factors used by other sectors (e.g. the cost of components and other inputs ). For sectors that use both electricity and other energy
- intensive inputs the effect of higher electricity prices on inp ut prices can be important but applying the BEA data to account for the effect of electricity price pass
- through on factors relies even more heavily on assumptions about the inability of sectors to change their factor mix in response to relative factor pri ces changes. Third , important differences across regions and consumer classes may be mas k ed by the nationwide estimates of upper
- bound average expenditure changes. However, because the Retail Electricity Price Model does not estimate price changes for di fferent customer classes, and because the BEA data does not provide a regional decomposition of the economic accounts suitable to calculating regional upper
- bounds on per unit production expenses by sector, regional and consumer class price differences can not be calculated . Similarly, there may be subsectors that would have a meaningfully higher or lower maximum increase in average production expenditures than their corresponding BEA sector, but we cannot identify these cases as we are limited to using BEA’ s allocation of industries to sectors for this analysis. Such regional impacts are highly sensitive to the actual pathway to compliance taken by firms as well as the steps individual sectors take to adjust to any changes in electricity price. Fourth , the share of electricity used in each sector may have changed since 2002. In general , electricity consumption per dollar of gross domestic product fell from 2002 through 2009 , but electricity expenditures relative to gross domestic product rose slightly over this time (EIA, 2011; BEA 2011). Not accounting for this change over time in expenditures on electricity6
- 20

may lead to a slight under estimate of the upper bound increase in average production expenditures, averaged across the entire economy, as a result of t his rule. Fifth , MATS is expected to meaningfully change a number of factor demands in the electricity market in addition to electricity suppl y as described in Chapter 3. These changes in factor demands may in turn affect average production expenditures o f goods in sectors that are not regulated, but only the effect on average production expenditures associated with the electricity price increase is accounted for in the upper bound calculations. Finally, our calculation of upper bounds on average productio n cost increases does not account for the effect of health benefit impacts of the regulation on prices and output (e.g., health care consumers and effects on markets from changes in labor productivity from reduced health damages) , though this is a common l imitation for other economic tools for estimating price changes in response to regulations (e.g. partial equilibrium models, CGE models). While the re are several caveats to this approach, this screening analysis has the virtue of being transparent and p rovides an upper bound on the effect of MATS on average production expenses attributable to higher electricity expenditures within a sector as a result of the rule. The results suggest that electricity prices under MATS will not have a large impact on pro duction costs across the entire economy. This is consistent with the overall history of the implementation of the Clean Air Act (Jaffe et al., 1995). **6.5.5 Impacts of Electricity Price Induced Changes in Production Costs on Employment in Downstream Sec** **tors** With respect to the impact of electricity prices , the analysis using the benchmark I/O data from BEA suggests that the MATS will only have modest impacts on production costs across the entire economy. These upper - bound estimates of average production cost increases cannot be used to estimate changes in employment as a result of the regulation, either nationally or for individual sectors. First, as noted above, these calculations overstate the actual impact of retail electricity prices by failing to a ccount for the ability of the real economy to adjust to changes in price through input substitution, technological innovation , or other means . It is necessary to account for changes in production, consumption, and input use to estimate the change in total employment. Second, this approach does not account for changes in consumer and producer behavior as they adjust the quantity of goods and services supplied or demanded in all of the markets affected by the regulation.6
- 21

**6.6 Alternative**  **Approach to Estimating Social Cost and Economic Impacts** In the Transport Rule proposed in the summer of 2010 and in other rulemakings, EPA used a different model to estimate the social cost and economic impacts of the regulatory approach than the model applied in this RIA. That model, EPA’s EMPAX, is a CGE mode l that dynamically cascades the cost of a regulation through the entire economy. Since that rule was proposed, a different version of EMPAX was used to estimate the social cost of the Clean Air Act in a new EPA report entitled “The Benefits and Costs of th e Clean Air Act from 1990 to 2020” (EPA, 2010, herein referred to as the Section 812 report). This version of EMPAX accounts for the benefits of reducing pollution on labor productivity and on the demand for health care, which significantly influenced the model’s estimates of the social cost and economic impacts of the Clean Air Act relative to an analysis using EMPAX in which these benefits
- related effects were not accounted for. In December 2010, in its review of the 812 Report EPA’s Science Advisory Boar d (SAB) found that “The inclusion of benefit

- side effects (reductions in mortality, morbidity, and health
- care expenditures) in a computable general equilibrium (CGE) model represents a significant step forward in benefit
- cost analysis” (SAB, 2010). A desc ription of the changes to the model and implications are described in detail in chapter 8 of the Section 812 report. EPA has determined that it needs to update the EMPAX model version used for RIAs to account for these beneficial effects of reducing pollut ion prior to its use in any additional regulatory analysis. The EMPAX model version used for the Section 812 report cannot be used for this rulemaking because it contains energy and economic data that are consistent with the multi
- year timeframes and energy scenarios of the 812 study but not with the single target year, analysis timeframe, and energy scenario most appropriate for this current rulemaking analysis. For example, much of the energy data in the EMPAX model employed in the Section 812 report is f rom the Energy Information Administration’s Annual Energy Outlook 2005. With these impacts of reducing pollution on labor productivity and the demand for health care now in the process of being incorporated into the model, the SAB’s perspective on the desi rability of accounting for these effects in the CGE analysis for the 812 study, and the typical practice by EPA’s Office of Air and Radiation of having analyses within RIAs to be consistent in design with those included in the most recent available Section 812 report, EPA will not use EMPAX for this RIA. **6. 7**  **References** Berman, E., and L. T. M. Bui. 2001. Environmental Regulation and Labor Demand: Evidence from the South Coast Air Basin. Journal of Public Economics 79(2):265

- 295.6
- 22

Bezdek, Roger H. , Robert M . Wendling and Paula DiPerna, Environmental protection, the economy, and jobs: National and regional analyses
. Journal of Environmental Management Volume 86, Issue 1 **,** January 2008, Pages 63
- 79. Environmental Business International (EBI), Inc., San Diego, CA. Environmental Business Journal, monthly (copyright). http://www.ebiusa.com/ Jaffe , Adam B., Steven R. Peterson, Paul R. Portney, Robert N. Stavins
. (1995). “ Environmental Regulation and the Competitiveness of U. S. Manufacturing: What Does the Evidence Tell Us? ” Journal of Economic Literature, 33 (1) , pp. 132
- 163 Morgenstern, R. D., W. A. Pizer, and J. S. Shih. 2002. Jobs versus the Environment: An Indu stryLevel Perspective. Journal of Environmental Economics and Management 43(3):412

- 436. Stewart, Ricky L., Jessica Brede Stone and Mary L. Streitweisser. 2007. “U.S. Benchmark Input
Output Accounts, 2002.” Survey of Current Business. October, 2007. US B ureau of Economic Analysis (BEA) . 20 07a
. Appendix B.
-- Classification of Value Added and Final Uses in the 2002 Benchmark Input
- Output Accounts
. Available in: 2002 Summary Tables, 2002 Benchmark Input
- Output Data. Retrieved from http://www.bea.gov/industry/io_benchmark.htm#2002data
. US Bureau of Economic Analysis (BEA) . 20 07b
. Commodity
- by
- Industry Direct Requirements after Redefinitions, 2002
. Available in: 2002 Summary Tables, 2002 Be nchmark Input
Output Data. Retrieved from http://www.bea.gov/industry/io_benchmark.htm#2002data
. US Bureau of Economic Analysis (BEA) . 20 07c
. “The Use of Commodities by Industries after Redefinitions, 2002 .” Available in: 2002 Summary Tables, 2002 Benchmark Input
- Output Data. Retrieved from http://www.bea.gov/industry/io_benchmark.htm#2002data
. US Bureau of Eco nomic Analysis (BEA)
. 2009. Concepts and Methods of the U.S. Input
- Output Accounts. Retrieved from: http://www.bea.gov/papers/pdf/IOmanual_092906.pdf
. US Bureau of Economic Analysis (BEA) . 2011. Gross Domestic Product. In National Incom e and Product Accoun t Tables (Table 1.1.5). Retrieved From http://www.bea.gov//national/nipaweb/DownSS2.asp
. US Bureau of the Census. 2007 Economic Census, Washington, DC: U.S. Government Printing Office, http://www.census.gov/econ/census07/
. US Bureau of the Census. Pollution Abatement Costs and Expenditures Washington, DC: U.S. Government Printing Office, various years.6
- 23

US Bureau of Labor Statistics, United States Department of Labor. ―Industry Labor Productivity and Cost Data Tables, Annual Percent Changes. (2010). US Energy Information Administration (EIA), Annual Energy Review 2010. ―Coal Mining Productivity By State and Mine Type. US Energy Information Agency (EIA) . 2011. Electricity End Use, 1949
- 2010. In Annual Energy Review 2010 (Table 8.9). Retrieved From http://www.eia.gov/totalenergy/data/annual/index.cfm#electricity US Environmental Protection Agency. Engineering and Economic Factors Affecting the Installation of Control Tec hnologies for Multipollutant Strategies, EPA
- 600/R

- 02/073 (2002).6A
- 1 **APPENDIX 6A EMPLOYMENT ESTIMATES**  **OF DIRECT LABOR IN R ESPONSE TO THE MERCU**  **RY AND AIR TOXICS STANDARDS IN 2015**  This appendix presents the short
- term employment estimates of the Mercury and Air Toxics Standards (MATS), henceforth referred to as the final MATS. The focus of the employment analysis in this study is only on the first order employment impacts related to the compliance actions of the affected coal
- fired entities withi n the power sector. 21 It does not include the ripple effects of those impacts on the broader economy (i.e., the “multiplier” effect), nor does it include the wider economy

- wide effects of the changes to energy markets (such as higher electricity prices). 22 Moreover, this study provides only a static snapshot of the impacts for 2015 and does not account for the dynamic adjustments of the affected entities as they adapt to the final MATS, such as those arising from technological innovation and learning
by
- do ing. This analysis is also independent of other techniques used by the U.S. EPA to estimate certain employment effects of particular regulations. The estimates of the employment impacts are divided into several categories: job gains due to the increased d emand for pollution control equipment; job losses due to retirements of coal capacity; and job shifts due to changes in demand for fuels. The various employment metrics can also be distinguished by one
-time employment changes (e.g., pollution control cons truction), and ongoing employment changes (e.g., fuel use changes and pollution control operation or coal retirements). Results indicate that the final MATS has the potential to provide significant short
-term employment opportunities, primarily driven by the high demand for new pollution control equipment. The employment gains related to the new pollution controls are likely to be tempered by some losses due to certain coal retirements, although, as discussed below, some of these workers who lose their jo bs due to plant retirements could find replacement employment operating the new pollution controls at nearby units. Finally, job losses due to reduced coal demand are expected to be offset by job gains due to increased natural gas demand, resulting in very small positive (i.e., less than three hundred) net change in employment due to fuel demand changes. Overall, the preliminary results indicate that the final MATS could support a net of slightly over 46,000 one
- time job
- years and a net of about 6, 0 00 ong oing job
- years in 2015. These results are summarized in Table 6A
- 1 below.

21 This analysis does not include potential employment effects resulting from projected impacts on oil/gas

-fired units. 22 For more detail on the economic impacts of the proposed rule, see Chapter 3 of the Regulatory Impact Analysis accompanying final MATS.6A
- 2

**Table 6A** **- 1: Net Employme**  **nt Changes for 2015 (job -** **years) a,b** 
**One Time Ongoing**  New Pollution Control Equipment 46,120 8, 2 1 0 Retirements of Generating Units - (2,500) Changes in Fuel Use - 240 **Net Effect 46,120**  **5,950** a Total job years of labor for controls projected to be installed by 2015. b Totals may not add due to rounding
. The job
- years estimated here is a snapshot of the first order employment effect of the final MATS in 2015. While there is no temporal dimension to this study, some of these jobs are likely to be spread over several years, and some will last longer. Most of the construction related labor demand, for example, is expected to provide a short
-term, temporary boost to employment that could last two or three years, along with any “multiplier effects” (i.e., secondary employment supported in upstream sectors) tha t are not included in these job
- year estimates. Most of the operational labor needs and labor shifts resulting from fuel changes are likely to be longer term. Thus, in terms of the impacts of the final MATS on economy
- wide employment over time, this anal ysis shows there could be a significant temporary increase to employment levels starting well before 2015, which would likely recede thereafter as the construction phases for the needed pollution controls wind down. Over time, the operational jobs will co ntinue to provide a small boost to employment over “business as usual” baseline employment levels. Note that this synopsis does not account for other employment impacts of the final MATS, such as those resulting from higher energy prices. **6A.1 Overall**  **Approach** The estimates for the near
- term employment effects of the final MATS utilize studies conducted by EPA on engineering and resource requirements for various compliance activities, such as installing pollution control equipment, switching fuels, o r ceasing plant operations as they become uneconomic. Some of the information used here was obtained from a 2002 EPA engineering study for multi
- pollutant control strategies. 23 This study was also the basis for the employment analysis for the proposed MATS. For the final MATS, EPA has undertaken a separate study to update the 2002 analysis in order to refine and update the Agency’s understanding of the resource requirements (labor and materials) of various compliance activities, including estimates for ne wer pollution control equipment that were not included in

23 Engineering and Economic Factors Affecting the Installation of Control Technologies for Multi
-pollutant Strategies . EPA
- 600/R

- 02/073 (October, 2002)
.6A
- 3

the 2002 analysis. 24 For example, the 2002 study focused on pollution control technologies that directly address SO 2 and NO x emissions, while the updated study also includes pollution control techno logies that reduce mercury emissions and hazardous air pollutants as well. Collectively, these studies are referred to as EPA pollution control studies in this appendix. This employment analysis is based on information from the updated study where availa ble, as well as data from the original 2002 study, where updated information was unavailable. The basic approach involved using power sector projections and various energy market implications under the final MATS from modeling using EPA’s data and assump tions with the Integrated Planning Model (IPM), along with data from secondary sources, to estimate the first order employment impacts for 2015. Throughout this analysis, incremental employment is measured in job
- years, since there is no temporal dimensio n to this analysis. 25 Also, this appendix does not include estimates of total employment impacts over time, though there is a distinction between short
- term construction related labor needs and more long
-term operational labor needs for new pollution contr ols (though these operational labor requirements are also measured in 2015 job
- years only). **6A.1.2 Employment Changes due to New Pollution Control Equipment**  EPA’s IPM projections for the final MATS policy case were used to estimate the incremental pollu tion control demand. These are shown in Table 6A
- 2 below: 26 Note that the capacity estimates shown in Table 6A
- 2 do not include EPA’s projections for ESP and FGD upgrades on existing units. Because the engineering studies used in this employment analysis do not include resource estimates for these technologies, EPA chose not to analyze the employment impacts for these technologies. This exclusion is likely to understate the total employment impacts for the final MATS.

24 Engineering and Economic Factors Affecting the Installation of Control Technologies
– An Update . Andover Technology Partners and ICF International. October 20, 2011. 25 A job
-year is defined as the amount of work that can be performed by the equivalent of one full
-time individual for one year (or FTE). 26 According to IPM, there is some overlap between the different types of pollution control equipment demand at individual facilities. To the extent that there could be some efficiency gains at plants installing multiple controls due to economies of scale, the job estimates presented here could overstate the impacts.6A
- 4

**Table 6A** **- 2: Increased**  **Pollution Control Demand due to the final MATS, 2015 (GW)**

**Pollution Control Type IPM Projected Additional Pollution Control**  **Scrubbers (FGD) 27**  1 7 **Activated Carbon Injection (ACI)** 9 9 **Dry Sorbent Injection (DSI)** 44 **Fabric Filter (FF) 28**  10 2 The employment impacts due to increased pollution control demand are divided into three categories, one of which is associated with the construction and installation labor requirements, while the remaining two are associated with the resources required for the ongoing operation of the pollution control equipment. The labor needed for constructing and installing these controls are for construction
- related sectors, such as boilermakers, engineers, and other installation labor. The two categories of labor ne eds for ongoing resource requirements include employment in sectors that supply resources needed to run these pollution controls (such as reagents); and utility sector jobs to operate the control equipment. The following sections discuss the approach for each:

 For the construction labor estimates, per
- unit labor needs were taken from the pollution control studies, which included man
- hours required per MW for each of the control technologies listed above. The total installation labor was then sub
- divided i nto different labor categories, such as boilermakers, engineers and a catch
- all “other installation labor”, using estimated shares of the different labor types in the EPA pollution control studies.

 For the longer term labor associated with operating the pollution controls , per- unit estimates of the main resources needed for the particular types of equipment (see Table 6A
- 3 below for a list of the resources) are also taken from EPA’s pollution control studies. Resources needed for FF (such as the filter b ags) were estimated from the

27 In addition to the scrubber capacity shown in Table 2, EPA also projects an additional 2.6 GW of dry scrubbers on units burning waste coal and pet. coke and have existing baghouses. This capacity is not included in the table above, however, employment impacts associated with these controls are included in this appendix as discussed below. 28 This number includes the total incremental fabric filters, as reported in Chapter 3. For the purpose of estimating the construction jobs from fabric filters this appendix uses an incremental capacity of 84 GW (i.e., incremental fabric filters that are standalone, or installed with DSI or ACI+Toxecon). To avoid double counting, the remaining 18 GW of fabric filters that represent those installed on units with dry scrubbers are excluded from the employment analysis, under the assumption that the labor estimates for dry scrubbers include resources required for both the scrubber as well as the fab ric filter that goes with it.6A
- 5

incremental Variable Operation and Maintenance (VOM) costs from EPA’s IPM modeling results. 29 These were then multiplied by the incremental GW for each pollution control to obtain the total (physical) quantity of resources need ed. Total tonnage for each resource was then converted to dollars of increased economic output for these resources using price estimates developed by Sargent & Lundy for EPA’s IPM Base Case v4.10 modeling assumptions (see notes at the end of Table 6A
- 3 be low). Finally, the labor productivity for each particular sector was used to estimate the number of job
years these could create in 2015. Labor productivities for each sector were adjusted to account for increased worker productivity in 2015. Data for b aseline worker productivity and corresponding growth rates to account for future productivities came from the Economic Census and the Bureau of Labor Statistics (BLS) estimates. 30

 The final employment vector estimated was for the utility sector labor needed to operate these pollution control equipment. This estimate was based on the incremental Fixed Operation and Maintenance (FOM) costs from EPA’s IPM modeling results, excluding costs due to retirements. Thus, this study assumes that the FOM costs are a reasonable proxy for the payroll costs that are part of the FOM costs in EPA’s modeling (FOM costs are defined as the operating and maintenance costs incurred by the utility, such as those for payroll, irrespective of whether the equipment is operated). The FOM costs were then translated into employment based on estimates of payroll per worker for the power sector taken from the 2007 Economic Census and BLS estimates. 31

29 Because FF requiremen ts are not endogenously determined in IPM, it required a different approach than the other controls. 30 Total value of shipments in 2007 and total employees were taken from 2007 Economic Census, Statistics by Industry for Mining and Manufacturing sectors. The average annual growth rate of labor productivity was taken from the Bureau of Labor Statistics. Average growth rate calculated for years 1992
- 2007, applied to 2007 productivity to determine 2015 estimates of productivity. See the Detailed Methodology section at the end for more details about the data used for these calculations. 31 Same sources as other productivity estimates (2007 Economic Census and BLS), however, uses employees and total payroll rather than revenue or value of shipments.6A
- 6

**Table 6A** **- 3: Estimated Pollution Control Resource Needs (Quan**  **tity and Prices Used)**
**Amount of Resource Used Price Used ($/unit)**  **Lime, FGD (tons)** 1,490,391 $95 32 **Activated Carbon, ACI (tons)** 184,771 $1,500 **Trona, DSI (tons)** 10,667,613 $150 Price Sources:  Sargent & Lundy, “IPM Model
– Revisions to Cost and Performance for APC Technologies Mercury Control Cost Development Methodology FINAL”, March 2011, Project 12301
- 009, Perrin Quarles Associates, Inc.  Sargent & Lundy, “IPM Model
– Revisions to Cost and Performance for APC Technologies Dry Sorbent Injection Cost Development Methodology FINAL”, August 2010, Project 12301
- 007, Perrin Quarles Associates, Inc.  Sargent & Lundy, “IPM Model
– Revisions to Cost and Performance for APC Technologies SDA FGD Cost Development Me thodology FINAL”, August 2010, Project 12301
- 007, Perrin Quarles Associates, Inc.
**6A.1. 3**  **Results** Table 6A
-4 presents the estimated employment impacts in 2015 resulting from the additional pollution controls needed to meet the final MATS requirements. According to this analysis, these investments could provide the opportunity to support about 54,500 job
- years to design, construct, and operate the needed pollution control equipment in 2015. Note, some of these jobs are expected to start before, and continue beyond 2015 (such as the resource related job
- years), but this analysis only provides a snapshot for 2015
.

32 For F GD this study uses the price for Lime (Dry FGD) which is significantly greater than the Limestone (Wet FGD) price. This price was used because EPA’s modeling indicates most of the incremental FGD units are likely to be dry scrubbers.6A
- 7

**Table 6** **A -** **4: Jobs Due to Pollution Control Equipment under the fina l MATS**  **(Job -** **years in 2015)**  **Jobs for Construction Incremental Employment**  1. Boilermakers 20,190 2. Engineers 5,060 3. General Construction 20,870 **Sub -** **Total: 46,120**  **Jobs for Operation Jobs from Increased Operating Resource Use**  1. Lime (FGD) 280 2. Activated Carbon (ACI) 460 3. Trona (DSI) 3,130 4. Baghouse material (FF) 20 **Sub -** **Total: 3,890**  **Jobs for Pollution Control Operation 4,320**  **Total Labor: 54,**  **330** Note: Totals may not add due to rounding The number of job
- years estimated for pollution control installation (i.e., “Jobs for Construction” in Table 6A
- 4 above) is driven in large part by the demand for new FFs used in EPA’s modeling. As shown in Table 6A
- 2, up to 84 GW of new FF capa city is projected to come online in 2015 due to the final MATS that are relevant for this employment analysis. The demand for new FFs is estimated to contribute nearly 70 percent of the new employment resulting from the installation of pollution controls. Moreover, of the labor needed due to increased resource use, the Trona required for DSI is estimated to support higher number of jobs than the other resources. This is because the DSI technology requires significantly higher quantities of reagents than the other pollution controls, based on EPA’s engineering estimates and pollution control studies. The second highest resource
- related employment gains would likely come from the activated carbon needed in response to the final MATS. Of the roughly 54,50 0 job
- years estimated in Table 6A
- 4, about 4,300 job
-years, or about 8 percent, are estimated to occur within the utility sector for labor needed to operate the pollution controls (referred to as “Jobs for Pollution Control Operation” in Table 6A
- 4). The rest of the labor demand will benefit the pollution control industry and other economic sectors. The increased demand for resources and chemicals needed to operate the pollution controls will result in increased employment in sectors such as mining, chemi cals, and other6A
- 8

manufacturing sectors. The majority of these first order employment effects, however, are likely to benefit construction
- related sectors, such as construction, boilermaker, heavy engineering, and other heavy construction sectors, resulting from the construction and installation of the new pollution controls at affected sources throughout the country. 6A.1. 3 .1 Employment Changes due to Coal Retirements Employment changes due to incremental coal plant retirements were estimated by first identifying the retiring coal units 33 from EPA’s modeling results (for the base and the final MATS policy cases). EPA projects roughly 4.7 GW of additional coal retirements by 2015 with the final MATS in place. 34 In order to convert the retired coal capac ity into potential employment losses, it was assumed that changes in the operating costs for the retired coal units can be used as a proxy for payroll expenditures and the lost economic output due to coal retirements. Thus, the changes in the FOM costs for these particular retiring units were derived using EPA’s IPM modeling results, and converted to lost jobs using data from the Economic Census and BLS output/worker estimates for the utility sector. 35 Employment losses due to plant retirements will not onl y affect those that are directly working at the plant (i.e., plant operators), but would also affect administrative and other “back
- office” workers for those utilities and their support organizations. This appendix assumes that the FOM costs related to re tiring plants are a good proxy for these types of job losses. **Table 6A** **- 5: Annual Job Losses due to Coal Capacity Retirements for 2015**  FOM Decrease from Retirements (million) $288 Workers Per Million$ in payroll 8.7 **Workers lost due to retirements (job**  **- year):**  **2,500**

33 Oil and gas steam unit emissions requirements, and potential retirements, were not directly included in EPA’s IPM modeling under the MATS policy scenario. An analysis of these units was conducted separately, and to the extent that there may be some retirements of oil and gas units, then the estimates of potential job losses due to retirements provided here will understate the employment losses. 34 Retirement estimates are based on IPM System Summary Reports from EPA’s modeling runs . Where applicable, data from IPM parsed o utputs were adjusted to account for partial retirements reported in the parsed outputs. 35 The same specific sources as cited before, however, used workers and total payroll.6A
- 9

Results indicate there could potentially be about 2,500 job losses (measured in job

- years for 2015, but any net job losses under this category are likely to be permanent), due to coal retirements. However, two mitigating factors could reduce the negative employment impacts due to retirements. First, many of the retiring units are at plants that are likely to have other units operating under the policy scenario. In such cases, some of the excess labor pool at the retiring units could well be absorbed at other units within the same firm. Second, as Table 6A
-4 indicates, utilities are expected to have the need to f ill about 4,300 additional job slots to operate the pollution controls needed to meet the requirements of the final MATS. If workers with experience at existing coal facilities become available through plant retirements, some of these workers could be abs orbed in operating these new pollution controls. 6A.1. 3 .2 Employment Changes due to Changes in Fuel Use Employment impacts due to projected fuel use changes (coal and natural gas production shifts) were estimated using EPA’s modeling results. First, emp loyment losses due to reductions in coal demand were estimated using an approach similar to EPA’s coal employment analyses under Title IV of the Clean Air Act Amendments. 36 Using this approach, EPA’s projected coal demand changes (in short tons) for variou s coal supplying regions were converted to job
- years using EIA data on regional coal mining productivity (in short tons per employee hour), using 2008 labor productivity estimates. 37 , 38 Results of the coal employment impacts of the final MATS are presented in Table 6A
- 6 below.

36 Impacts of the Acid Rain Program on Coal Industry Employment. EPA 430
- R
- 01
- 0 02 March 2001
. 37 From US Energy Information Administration (EIA) Annual Energy Review, Coal Mining Productivity Data. Used 2008. 38 Unlike the labor productivity estimates for various equipment resources which were forecasted to 2015 using BLS average gro wth rates, we used the most recent historical productivity estimates for fuel sectors. In general, labor productivity for the fuel sectors (both coal and natural gas) showed a significantly higher degree of variability in recent years than the manufacturi ng sectors, which would have introduced a high degree of uncertainty in forecasting productivity growth rates for future years.6A
- 10

**Table 6A** **- 6: Annual Employment Impacts Due To Changes in Coal Use for 2015**  **Coal by Region Change in Coal Demand**  **(MM Tons) Labor Productivity**  **Job -** **year Change** Appalachia (11.8) 2.91 (1,950) Interior 19.9 4.81 1,990 West (17.3) 19.91 (420) Waste Coal (0.7) 5.96 (60) **Net Total (9.9)**  **-- (430)**  Notes: Used US national coal productivity for waste coal Totals may not add due to rounding For natural gas production, labor productivity per unit of natural gas was unavailable, unlike coal labor productivities used above. Most secondary data sources (such as Census and EIA) provide estimates for the combined oil and gas extra ction sector. This appendix thus uses an adjusted labor productivity estimate for the combined oil and gas sector that accounts for the relative contributions of oil and natural gas in the total sector output (in terms of the value of energy output in MMBt u). This estimate of labor productivity is then used with the incremental natural gas demand for the final MATS to estimate the job
- years for 2015. **Table 6A** **- 7: Annual Employment Impact due to Changes in Fuel Use (2015)**  **Fuel Type Employment**  **Coal Job Years Lost**  **(430) Natural Gas**  Incremental Natural Gas Use (MMBtu) 175,786,505 Labor Productivity (MMBtu/job

-year) 261,840 **Job -** **years gained 670**  **Net Employment Effects of Fuel use changes 240**  Note: Totals may not add due to rounding Thus, about 430 job losses in the coal mining sector are likely to be offset by about 670 job gains in the natural gas production related sectors, for a net effect of about 240 job
- year gains due to the changes in fuel use. The changes in coal mining employment is driven by a significant increase in demand for Interior coal which leads to about the same amount of job gains as is lost due to the decreased demand for Appalachian coal (see Table 6A
- 6 above). This,6A
- 11

c oupled with the fact that there is likely to be some job gains due to increased demand for natural gas, results in a small net job gain due to fuel use changes for the final MATS. **6A.2 Results Summary**  Overall, the final MATS is expected to provide an increase to short
- term employment resulting from substantial investments in new pollution control equipment. For 2015, the results indicate the final MATS could support or create around 46,000 job
-years driven by the need to design and construct the need ed equipment. While there could be some employment losses due to coal retirements that will likely have a negative effect on some utilities and the coal mining sector, employment gains in pollution control operation activities and the natural gas sector a re likely to offset some of those losses. As previously discussed, this assessment does not account for the long
- run economy- wide effects of the final MATS. **6A.3 Detailed Methodology**  This section provides more details on the data and approaches used t o estimate the employment impacts discussed above. The section also details the sources for individual data elements. **6A.3.1 Pollution Control Equipment Labor**  6A.3.1.1 Installation Labor **Table 6A** **- 8: Installation Labor Requirement**  **39 Pollution**  **Control Type**  **Incremental GW**

**Installed Man**  ****

**hours/MW Boilermakers (%)**  **Engineers (%) Others (%)**  **FGD 40**  1 7 1730 40 20 40 **ACI** 9 9 10 50 17 33 **DSI** 44 55 50 17 33 **FF 41**  10 2 780 45 7 48

39 See Chapter 3 for more detail. 40 EPA also projects 2.6 GW of dry scrubbers on waste coal and pet. coke units with existing b aghouses, which are not shown in this table. Employment impacts from these units, however, are included in the pollution control construction figures, calculated using the capital cost for these controls ($220.7 MM) and estimated manhours/$ capital cost (0 .00598) developed from the same example dry scrubbers used to find the manhours/MW of capacity. 41 This number includes the total incremental fabric filters. For the purpose of estimating the construction jobs from fabric filters, this analysis uses an inc remental capacity of 84 GW. The remaining 18 GW represent fabric filters installed with dry FGD units, which are excluded because the labor estimates for d ry scrubbers includes the labor for fabric filters that is installed in conjunction.6A
- 12

Source: Engineering and Economic Factors affecting the Installation of Control Technologies
– An Update, Andover Technology Partners, 2011 Installation labor is estimated by using the incremental GW installed for each pollution control type from EPA’s modeling using IPM. This was then converted into total man
- hours needed for installation using estimates of man
- hours/MW primarily from EPA’s 2011 update on pollution control technology,. Total man
- hours for each pollution control type were then converted into man
- years assuming 2,080 working hours per year. Total man
- years for each pollution control type were then broken down into various sectors using the percentages, shown in Table 6A
- 8. These percentages were estimated from the 2002 study, updated from the 2011 study, where applicable. 6A.3.1.2 Operating Resource Labor **Table 6A** **- 9: Resources Needed for Operation**  **Pollution Control**  **Type Incremental** 

**Total GW Resource** 

**(Units in parenthesis)**  **Usage**

**Estimates Price** 

**($/unit) Industry Assumed** 

**for Productivity Calculations**  **Productivity***
**FGD** 1 7 Lime (Tons/MWh) 0.013 95 Lime Manufacturing 2.0
**ACI** 9 9 Activated Carbon (Tons/MWh) 0.00025 1,500 Other Chemical Product Manufacturing 1.6

**DSI** 44 Trona (Tons/MWh) 0.033 150 Potash Soda and Borate Mineral Mining 2.0
**FF** 10 2 Bag
- house Resources *Resource Labor

determined Using VOM cost for FFs Plastics Material and Resin Manufacturing 0.6 *Workers/$Million in Output, Forecasted to 2015 Sources: Usage:  Sargent & Lundy, “IPM Model
– Revisions to Cost and Performance for APC Technologies Mercury Control Cost Development Methodology FINAL”, March 2011, Project 12301
- 009, Perrin Quarles Associa tes, Inc.  Sargent & Lundy, “IPM Model
– Revisions to Cost and Performance for APC Technologies Dry Sorbent Injection Cost Development Methodology FINAL”, August 2010, Project 12301
- 007, Perrin Quarles Associates, Inc.  Sargent & Lundy, “IPM Model
– Revisions to Cost and Performance for APC Technologies SDA FGD Cost Development Methodology FINAL”, August 2010, Project 12301
- 007, Perrin Quarles Associates, Inc. Labor related to resources used in operating pollution control equipme nt was estimated using the total incremental GW of pollution control capacity which was first converted to total MWh of incremental capacity assuming 85 percent capacity factor. For each pollution control type, the next step involved choosing the primary operating resource. This approach is6A
- 13

consistent with prior EPA’s analyses on similar topics. The next step involved estimating the resource needs by each control type, generally in tons of material using the resource usage estimates as shown in Table 6A
- 9. Using the total usage for each pollution control input (in tons) and associated average prices, total expenditure by each resource type was then calculated. This total expenditure was then converted to labor using workers per $Million in total output for the industry associated with producing each respective input material. 42 6A.3.1.3 Operating Labor **Table 6A** **- 10**  **: Operating Labor Assumptions** Incremental FOM from IPM Parsed ($ Billion) 2.20 FF and other Capital Costs Included in FOM ($ Billion) 1.70 Remaining FOM used to find O&M Labor ($ Million) 496.5 Productivity* 8.7 *Workers per $Million in Payroll for Electricity Generating Sector, Forecast to 2015 Sources: Productivity from 2007 Economic Census and Growth Rate from BLS. Labor requirement to operate the controls is estimated for all equipment types combined, using the incremental FOM costs from IPM. The IPM incremental FOM cost estimate included capital costs for fabric filters and scrubber improvement costs, which were f irst subtracted to obtain the true FOM costs ($496.5 million). Resulting FOM cost estimate was then converted to labor needs using the workers/$ Million in total payroll for the Electric Generating Sector. **6A.3.2 Retirement Labor**  **Table 6A** **- 11: Inputs**  **to Labor from Retirements** Capacity of Incremental Retirements in SSR (MW) 4
. 7 O&M Decrease scaled to SSR Retirements ($MM) (To account for Partial Retirements) 288.2 Workers Per $Million in payroll, forecast to 2015 8.7 Sources: Productivity from 2007 Economic Census and Growth Rate from BLS.

42 Fabric filters follow a different pattern. Instead of a resource usage estimate, we used the VOM cost for FFs and converted this to jobs using the workers per million dollars output for the relevant manufacturing industry sector.6A
- 14

Retirement labor was estimated by first identifying the retiring units from EPA’s modeling using IPM parsed outputs (using incremental retirements in the policy case). The next step involved estimating the capac ity of incremental retirements as well as the change in the FOM costs due to these retirements. Because of the discrepancies between partial retirements in EPA’s parsed outputs and System Summary Reports (SSR), FOM costs were scaled proportionately to ref lect the lower SSR
- based estimates, as shown in Table 6A
- 11 above. FOM cost decreases were then converted to job
- years lost due to retirements using workers per $Million in payroll. **6A.3.3 Fuel Use Labor**  **Table 6A** **- 12: Inputs to Labor for Fuel Use** 
**Coal by Region**  **2015 Incremental Fuel USE (Tons) 2008 Short Tons/Employee hour**  **Appalachia**
- 11,770,000 2.9 **Interior** 19,870,000 4.8 **West**
- 17,260,000 19.9 **Waste Coal**
- 700,000 6.0

**Natural Gas** EIA Total Natural Gas Production 2007 (TCF) 24.664 EIA Total Crude Oil Production 2007 (Barrels) 1,848,450,000 EIA Natural Gas Heat Content 2007 (Btu/cf) 1,027 EIA Petroleum Heat Content (MMBtu/Barrel) 6.151 Total Crude Oil and Natural Gas Production (MMBtu) 36,699,744,000 Economic Census 2007 Oil and Gas Extraction Employees 140,160 MMBtu per Man
-year for Oil and Gas Extraction 261,842 Incremental Natural Gas from IPM (TCF) 0.171 Incremental Natural Gas from IPM (Converted to MMBtu) 175,786,505 *Workers per $Million in Payroll for Electricity Generating Sector, Forecast to 2015 Note: Heat Contents from EIA are assumed to be for fuels used in Electric Power Sector Sources: Short Tons per hour from US EIA, Coal Industry Annual. Total Production from 2009 EIA Annual Energy Review. Heat Contents from EIA, Heat Content of Natural Gas Consumed and 2009 Annual Energy Review. Employment Data from 2007 Economic Census.6A
- 15

Fuel use related employment impacts were estimated by using IPM results for incremental changes in coal and natural gas use (policy case over the base case). For coal, estimates of coal use in tons by region from IPM were used in conjunction with labor productivity estimates from the EIA for each region (in short tons/ employee hour), to calculate the change in job
- hours needed. These we re then converted to job
- years, assuming 2,080 working hours per year. As discussed above, because of the high variability in coal mining labor productivity in recent years, no attempt was made to forecast coal (and natural gas, for consistency) productiv ities, instead the most recent historical estimates were used in this appendix (which was the 2008 labor productivity for coal). For natural gas, the first step was estimating labor productivity since such information was not available directly from any reliable source. EIA production data from the Annual Energy Review for natural gas and crude oil (in TCF and barrels, respectively), along with EIA heat content estimates were used to find total crude oil and natural gas production in MMBtu for 2007. Lab or productivity in MMBtu per job
- year for the Oil and Gas Extraction sector was then estimated using data from the Census on oil and gas extraction employment. Then, the incremental natural gas demand from EPA’s IPM modeling results (in TCF) was converted to MMBtu of natural gas demand using EIA data on natural gas heat content. This was then used with the labor productivity estimated above to calculate the total job
- years needed for increased natural gas demand for the final MATS.6A
- 16

**6A.4 References**  Sargent & Lundy, “IPM Model
– Revisions to Cost and Performance for APC Technologies Mercury Control Cost Development Methodology FINAL”, March 2011, Project 12301
- 009, Perrin Quarles Associates, Inc. Sargent & Lundy, “IPM Model
– Revisions to Cost and P erformance for APC Technologies Dry Sorbent Injection Cost Development Methodology FINAL”, August 2010, Project 12301
- 007, Perrin Quarles Associates, Inc. Sargent & Lundy, “IPM Model
– Revisions to Cost and Performance for APC Technologies SDA FGD Cost D evelopment Methodology FINAL”, August 2010, Project 12301
- 007, Perrin Quarles Associates, Inc. United States Department of Labor. Bureau of Labor Statistics. Industry Labor Productivity and Cost Data Tables, Annual Percent Changes. 2010. US Census Bureau . 2007 and 2002 Economic Census, 2000 Annual Survey of Manufacturers, Manufacturing and Mining: Detailed Statistics by Industry for the US and Utilities: Summary Statistics for the US. US Energy Information Administration (EIA). Annual Energy Review 2009, Assorted Data for Coal, Natural Gas, and Petroleum. 2009. US Energy Information Administration (EIA). Natural Gas 2009, Heat Content of Natural Gas Consumed 2009. US EPA. Clear Skies Act, Technical Package, Section D. “Projected Impacts on Generation and Fuel Use”. 2003. US EPA. Clean Air Interstate Rule, Technical Support Document. “Boilermaker Labor Analysis and Installation Timing.” OAR
- 2003
- 0053 (March, 2005). US EPA. “Impacts of the Acid Rain Program on Coal Industry Employment.” EPA 430
- R
- 01
- 002 (March, 2001). US EPA, Office of Research and Development. “Engineering and Economic Factors Affecting the Installation of Control Technologies for Multi
- pollutant Strategies.” EPA
- 600/R

- 02/073 (October, 2002). US EPA, Engineering and Economic Factors Affecting the Installation of Control Technologies: An Update. Prepared by Andover Technology Partners and ICF International. October, 2011.6B
- 1 **APPENDIX 6B POTENTIAL EFFECT OF**  **ELECTRICITY PRICE IN CREASE ON AVERA** **GE PRODUCTION EXPENSES** **, DETAILED RESULTS Table 6B**  **- 1**  **. 2002 Share of Production Expenditures on Electricity and Maximum Percentage** 

**Increase in Production Expenditures**
**Sector Share of**  **Production Expenditures on**  **Electricity a**  **Upper -**  **Bound**

**Increase in Average Pro**  **duction Expenditures**  **b Total Value** 

**Added in 2002**  **(MM 2002$) c**  **Total Industry**

**Output in 2002**  **(MM 2002$) d**  Crop production 2.243% 0.070% $46,172 $119,523 Animal production 1.636% 0.051% $27,504 $100,916 Forestry and logging 0.033% 0.001% $11,396 $32,259 Fishing, hunting and trapping 0.074% 0.002% $2,955 $5,424 Support activities for agriculture and forestry 0.424% 0.013% $7,556 $13,059 Oil and gas extraction 1.467% 0.045% $52,741 $104,084 Coal mining 2.090% 0.065% $10,431 $20,390 Metal ores mining 8.029% 0.249% $4,184 $7,976 Nonmetallic mineral mining and quarrying 4.158% 0.129% $10,542 $19,750 Support activities for mining 0.325% 0.010% $14,122 $32,356 Electric power generation, transmission, and distribution e 0.001% 0.000% $152,179 $224,934 Natural gas distribution 0.033% 0.001% $27,426 $83,256 Water, sewage and other systems 0.307% 0.010% $4,923 $7,519 New nonresidential construction 0.363% 0.011% $240,950 $445,033 New residential construction 0.325% 0.010% $190,199 $438,434 Maintenance and repair construction 0.262% 0.008% $78,440 $148,896 Food manufacturing 1.168% 0.036% $97,176 $451,935 Beverage manufacturing 0.796% 0.025% $20,992 $71,627 Tobacco manufacturing 0.252% 0.008% $35,407 $47,465 Textile mills 2.409% 0.075% $12,697 $44,684 Textile product mills 0.788% 0.024% $10,109 $29,891 Apparel manufacturing 0.567% 0.018% $14,720 $39,559 Leather and allied product manufacturing 0.688% 0.021% $1,809 $5,786 Wood product manufacturing 1.363% 0.042% $25,910 $88,746 Pulp, paper, and paperboard mills 3.346% 0.104% $28,322 $69,961 Converted paper product manufacturing 0.979% 0.030% $26,547 $81,164 Printing and related support activities 0.993% 0.031% $45,594 $98,453 Petroleum and coal products manufacturing 0.781% 0.024% $21,047 $210,398 Basic chemical manufacturing 3.519% 0.109% $21,144 $105,185 Resin, rubber, and artificial fibers manufacturing 2.115% 0.066% $10,905 $59,517 Agricultural chemical manufacturing 1.550% 0.048% $5,311 $18,6616B
- 2 **Sector Share of**  **Production Expenditures on**  **Electricity a**  **Upper -**  **Bound**

**Increase in Average Pro**  **duction Expenditures**  **b Total Value** 

**Added in 2002**  **(MM 2002$) c**  **Total Industry**

**Output in 2002**  **(MM 2002$) d**  Pharmaceutical and medicine manufacturing 0.452% 0.014% $63,756 $135,402 Paint, coating, and adhesive manufacturing 0.590% 0.018% $6,723 $26,356 Soap, cleaning compound, and toiletry manufacturing 0.382% 0.012% $26,468 $60,511 Other chemical product and preparation manufacturing 0.871% 0.027% $9,720 $35,968 Plastics and rubber products manufacturing 1.836% 0.057% $63,199 $169,013 Nonmetallic mineral product manufacturing 2.218% 0.069% $39,717 $92,407 Iron and steel mills and manufacturing from purchased steel 3.412% 0.106% $16,231 $61,396 Nonferrous metal production and processing 3.396% 0.105% $12,846 $49,644 Foundries 2.905% 0.090% $11,348 $26,039 Forging and stamping 1.423% 0.044% $7,137 $21,033 Cutlery and handtool manufacturing 0.956% 0.030% $5,089 $10,056 Architectural and structural metals manufacturing 0.624% 0.019% $21,469 $58,094 Boiler, tank, and shipping container manufacturing 1.116% 0.035% $7,627 $22,955 Ordnance and accessories manufacturing 1.150% 0.036% $3,016 $5,158 Other fabricated metal product manufacturing 1.217% 0.038% $56,484 $122,911 Agriculture, construction, and mining machinery manufacturing 0.478% 0.015% $14,080 $44,654 Industrial machinery manufacturing 0.708% 0.022% $10,039 $28,165 Commercial and service industry machinery manufacturing 0.498% 0.015% $7,272 $19,765 HVAC and commercial refrigeration equipment manufacturing 0.550% 0.017% $11,874 $30,838 Metalworking machinery manufacturing 1.072% 0.033% $11,506 $24,246 Engine, turbine, and power transmission equipment manufacturing 0.537% 0.017% $13,264 $34,721 Other general purpose machinery manufacturing 0.647% 0.020% $19,782 $53,959 Computer and peripheral equipment manufacturing 0.231% 0.007% $17,951 $67,600 Audio, video, and communications equipment manufacturing 0.278% 0.009% $19,749 $70,744 Semiconductor and other electronic component manufacturing 1.112% 0.034% $43,181 $108,1916B
- 3 **Sector Share of**  **Production Expenditures on**  **Electricity a**  **Upper -**  **Bound**

**Increase in Average Pro**  **duction Expenditures**  **b Total Value** 

**Added in 2002**  **(MM 2002$) c**  **Total Industry**

**Output in 2002**  **(MM 2002$) d**  Electronic instrument manufacturing 0.515% 0.016% $33,232 $89,668 Manufacturing and reproducing magnetic and optical media 1.130% 0.035% $2,470 $7,373 Electric lighting equipment manufacturing 0.667% 0.021% $5,282 $11,757 Household appliance manufacturing 0.469% 0.015% $7,911 $21,073 Electrical equipment manufacturing 0.537% 0.017% $13,219 $29,252 Other electrical equipment and component manufacturing 1.091% 0.034% $14,582 $35,712 Motor vehicle manufacturing 0.208% 0.006% $51,523 $237,729 Motor vehicle body, trailer, and parts manufacturing 0.651% 0.020% $65,811 $222,484 Aerospace product and parts manufacturing 0.528% 0.016% $45,155 $121,440 Other transportation equipment manufacturing 0.458% 0.014% $14,760 $41,125 Furniture and related product manufacturing 0.711% 0.022% $29,629 $73,343 Medical equipment and supplies manufacturing 0.491% 0.015% $32,265 $58,729 Other miscellaneous manufacturing 0.616% 0.019% $27,230 $64,817 Wholesale trade 0.450% 0.014% $606,156 $871,529 Retail trade 1.328% 0.041% $616,158 $905,320 Air transportation 0.085% 0.003% $43,101 $98,612 Rail transportation 0.051% 0.002% $19,621 $40,537 Water transportation 0.514% 0.016% $7,745 $26,389 Truck transportation 0.250% 0.008% $92,509 $203,596 Transit and ground passenger transportation 0.772% 0.024% $17,889 $31,425 Pipeline transportation 1.127% 0.035% $5,603 $22,316 Scenic and sightseeing transportation and support activities 0.487% 0.015% $34,859 $63,843 Couriers and messengers 0.465% 0.014% $35,669 $61,599 Warehousing and storage 2.615% 0.081% $25,729 $42,563 Newspaper, periodical, book, and directory publishers 0.376% 0.012% $70,937 $139,878 Software publishers 0.010% 0.000% $64,378 $104,043 Motion picture and sound recording industries 0.486% 0.015% $49,890 $84,176 Radio and television broadcasting 0.212% 0.007% $21,790 $49,756 Cable networks and program distribution 0.155% 0.005% $11,360 $25,639 Internet publishing and broadcasting 0.094% 0.003% $4,339 $8,445 Telecommunications 0.334% 0.010% $236,169 $433,191 Internet service providers, web search portals, and data processing 0.110% 0.003% $43,267 $71,8476B
- 4 **Sector Share of**  **Production Expenditures on**  **Electricity a**  **Upper -**  **Bound**

**Increase in Average Pro**  **duction Expenditures**  **b Total Value** 

**Added in 2002**  **(MM 2002$) c**  **Total Industry**

**Output in 2002**  **(MM 2002$) d**  Other information services 0.820% 0.025% $4,550 $6,777 Monetary authorities, credit intermediation and related activities 0.096% 0.003% $454,896 $681,748 Securities, commodity contracts, investments, and related activities 0.212% 0.007% $159,906 $285,116 Insurance carriers and related activities 0.073% 0.002% $242,318 $452,443 Funds, trusts, and other financial vehicles 0.088% 0.003% $21,539 $88,019 Real estate 1.783% 0.055% $642,766 $815,660 Owner
- occupied dwellings 0.000% 0.000% $703,125 $959,446 Automotive equipment rental and leasing 0.277% 0.009% $22,491 $38,811 Consumer goods and general rental centers 1.220% 0.038% $19,448 $32,612 Commercial and industrial machinery and equipment rental and leasing 0.288% 0.009% $25,915 $46,901 Lessors of nonfinancial intangible assets 1.121% 0.035% $101,280 $124,372 Legal services 0.186% 0.006% $152,002 $205,482 Accounting, tax preparation, bookkeeping, and payroll services 0.285% 0.009% $76,034 $101,979 Architectural, engineering, and related services 0.194% 0.006% $109,962 $176,744 Specialized design services 0.315% 0.010% $15,156 $21,611 Computer systems design and related services 0.811% 0.025% $172,781 $254,150 Management, scientific, and technical consulting services 0.119% 0.004% $86,247 $127,971 Scientific research and development services 0.606% 0.019% $59,858 $105,529 Advertising and related services 0.368% 0.011% $53,337 $80,504 Other professional, scientific, and technical services 0.325% 0.010% $48,877 $73,753 Management of companies and enterprises 0.602% 0.019% $272,922 $440,898 Employment services 0.236% 0.007% $113,200 $138,221 Travel arrangement and reservation services 0.223% 0.007% $16,564 $27,997 All other administrative and support services 0.371% 0.012% $167,088 $275,522 Waste management and remediation services 0.346% 0.011% $29,525 $53,150 Educational services 2.167% 0.067% $80,329 $141,829 Ambulatory health care services 0.350% 0.011% $332,366 $519,818 Hospitals 1.142% 0.035% $213,215 $380,4726B
- 5 **Sector Share of**  **Production Expenditures on**  **Electricity a**  **Upper -**  **Bound**

**Increase in Average Pro**  **duction Expenditures**  **b Total Value** 

**Added in 2002**  **(MM 2002$) c**  **Total Industry**

**Output in 2002**  **(MM 2002$) d**  Nursing and residential care facilities 1.396% 0.043% $84,168 $127,094 Social assistance 0.672% 0.021% $59,063 $102,033 Performing arts, spectator sports, museums, zoos, and parks 0.698% 0.022% $42,786 $75,962 Amusements, gambling, and recreation 1.931% 0.060% $59,972 $92,438 Accommodation 3.141% 0.097% $64,137 $107,298 Food services and drinking places 1.894% 0.059% $250,895 $468,666 Automotive repair and maintenance 0.869% 0.027% $98,087 $176,375 Electronic, commercial, and household goods repair 0.649% 0.020% $62,000 $95,436 Personal and laundry services 1.125% 0.035% $79,997 $127,208 Religious, grantmaking, giving, and social advocacy organizations 0.199% 0.006% $38,982 $91,988 Civic, social, professional and similar organizations 1.351% 0.042% $22,731 $63,418 Private households 0.000% 0.000% $12,516 $12,516 Federal Government enterprises 0.405% 0.013% $64,552 $83,473 State and local government enterprises 0.932% 0.029% $63,668 $161,513 General Federal defense government services 0.407% 0.013% $219,737 $380,797 General Federal nondefense government services 0.314% 0.010% $124,580 $209,856 General state and local government services 0.591% 0.018% $797,163 $1,240,559
a Source: BEA 2007b . “ Electric power generation, transmission, and distribution “ is BEA commodity sector 2211. The correspondence between BEA’s sector classification and NAICS codes is available from BEA 2007a
. b The forecast change in average production expenses is an upper
- bound and assumes that production and use of all goods and services in the economy remain unchanged. See chapter discussion for details. c Source: BEA 2007c. This information is used in the calculation of percentage of total value added in Tables 6
- 7 and 6
- 8. d Sour ce: BEA 2007c . This information is used in the calculation of output
- weighted average maximum average production expense increases reported in Section 6. 5.3 e The upper
- bound increase in average production expenditures in the Electric power generation, tr ansmission, and distribution sector reported in this table are strictly those due to higher electricity prices and are calculated using the BEA benchmark I/O accounts as described in Section 6.5.3. Chapter 3 reports EPA’s estimate of the effect of MATS on production costs in the electricity sector.