5 - 1

**CHAPTER 5: COST, EC ONOMIC, ENERGY, AND**  **EMPLOYMENT IMPACTS Overview**  This chapter reports the compliance cost, emissions, economic, energy and employ ment impact anal ys e s performed for the proposal to update CSAPR . The EPA used the Integrated Planning Model (IPM), developed b y IC F International, to conduct most of the anal ys is discussed in this Chapter. The EPA conducted additional analy sis using the IPM estimates of changes in the power and fuels sector as a basis for e xamining the potential direct labor impacts in those sectors. IPM is a d ynamic linear programming model of the power sector that can be used to examine air pollution control policies affecting NO X , CO 2 , SO 2 , Hg, HCl, and other air pollutants throughout the contiguous United States for the entire power s ys tem. Future - ye ar electricity demand levels are based on projections from the Energy Information Administration (EIA). This chapter of the RIA presents anal ys es of the proposed rule that include assumptions about the possible actions that EGUs may pursue as they reduce their NO X emissions to comply with the proposed EGU NO X ozone - season emissions budgets in the 23 - state region. Over the past two decades , the EPA has used IPM and other anal yt ical methods descr ibed in this chapter to conduct extensive analy ses of proposed and final federal environmental regulatory actions affecting the power sector. These previous anal yt ical efforts support the Agency ’s understanding of key variables that influence the effects o f a policy and provide the framework for how the Agency estimates the costs and benefits associated with its actions. **5.1 Power Sector Modeling Framework**  IPM is a state - of

- the - art, peer - reviewed, d ynamic linear programming model that can be used to project power sector behavior under future business - as - usual conditions and examine prospective air pollution control policies throughout the contiguous United States for the entire electric power s ys tem. EPA used IPM to project likely future electricit y market c onditions with and without the proposed updates to CSAPR. IPM is a multi - regional, dy namic, deterministic linear programming model of the contiguous U.S. electric power sector. It provides estimates of least cost capacity expansion,5 - 2

electricit y dispatch, and emission control strategies while meeting energy demand and environmental, transmission, dispatch, and reliability constraints. The EPA has used IPM for over two decades to better understand power sector behavior under future business - as - usual conditio ns and to evaluate the economic and emissions impacts of prospective environmental policies. The model is designed to reflect electricity markets as accuratel y as possible. The EPA uses the best available information from utilities, industry experts, gas and coal market experts, financial institutions, and government statistics as the basis for the detailed power sector modeling in IPM. The model documentation provides additional information on the assumptions discussed here as well as all other model assumptions and inputs. 1 The model incorporates a detailed representation of the fossil - fuel suppl y s ystem that is used to estimate equilibrium fuel prices. The model includes an endogenous representation of the North American natural gas suppl y s ys tem through a natural gas module that reflects a partial supply /demand equilibrium of the North American gas market, accounting for varying levels of potential power sector and non - power sector gas demand and corresponding gas production and price levels. 2 This module consists of 118 supply , demand, and storage nodes and 15 liquefied natural gas re - gasification facility locations that are tied together by a series of linkages (i.e., pipelines) that represent the North American natural gas transmission and distribution network. IPM also endogenously

models the partial equilibrium of coal supply and EGU coal demand levels throughout the contiguous U.S., taking into account assumed non - power sector demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal gr ades, and the coal transport network, which consists of over four thousand linkages representing rail, barge, and truck and convey er linkages. The coal supply curves in IPM were developed during a thorough bottom - up, mine - by - mine approach that depicts the coal choices and associated supply costs that power plants would face if selecting that coal over the modeling time horizon. The IPM

1 Detailed information and documentation of EPA’s Base Case using IPM (v5.15), including all the underlying assumptions, data sources, and architecture parameters can be found on EPA’s website at: http:/ /www.epa.gov/ airmarkets/pow ersectormodeling.html 2 See Chapter 10 of EPA’s Base Case using IPM (v5.15) documentation, available at: http://www.epa.gov/ airmarkets/pow ersectormodeling.html5 - 3

documentation outlines the methods and data used to quantify the economically

recoverable coal reserves, characterize their cost, and build the 36 coal regions’ suppl y curves. 3 To estimate the annualized costs of additional capital investments in the power sector, the EPA uses a conventional and widel y accepted approach that applies a capital recovery factor (CRF) multiplier to capital investments and adds that to the annual incremental operating expenses. The CRF is derived from estimates of the power sector’s cost of capital (i.e., private discount rate), the amount of insurance coverage required, local propert y taxes, and the life of capital. 4 Itis important to note that there is no single CRF factor applied in the model; rather, the CRF varies across technologies, book life of the capital investments, and regions in the model in order to better simulate power sector decisi on - making. The EPA has used IPM extensively over the past two decades to anal yz e options for reducing power sector emissions. Previously , the model has been used to estimate the costs, emission changes, and power sector impacts for the Clean Air Interstate Rule, the 2011 Cross State Air Pollution Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), the Clean Power Plan (CPP) for Existing Power Plants, and the Carbon Pollution Standards for New Power Plants. EPA has also used IPM to estimate the air po llution reductions and power sector impacts of water and waste regulations affecting EGUs, including Cooling Water Intakes (316(b)) Rule, Disposal of Coal Combustion Residuals from Electric Utilities (CCR) and Steam Electric Effluent Limitation Guidelines (ELG). The model and EPA's input assumptions undergo periodic formal peer review. The rulemaking process also provides opportunity for expert review and comment by a variet y of stakeholders, including owners and operators of capacit y in the electricit y sec tor that is represented b y the model, public interest groups, and other developers of U.S. electricity sector models. The feedback that the Agency receives provides a highl y - detailed review of key input assumptions, model representation, and modeling resul ts. IPM has received extensive review by energy and environmental modeling experts in a variet y of contexts. For example, in the late

3 See Chapter 9 of the IPM v.5.15 documentation. The documentation for EPA’s Base Case v.5.15 using IPM consists of a comprehensive document for IPM v. 5.13, and an incremental update document for both v.5.14 and v.5.15. All are available at available at: http://www.epa.gov/airmarkets/powersectormodeling.html 4 See Chapter 8 of EPA’s Base Case documentation.5 - 4

1990s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 prospective studies that are peri odically conducted. The model has also undergone considerable interagency

scrutiny when it was used to conduct over a dozen legislative analy ses (performed at Congressional request) over the past decade. The Agency has also used the model in a number of co mparative modeling exercises sponsored b y Stanford Universit y’s Energy Modeling Forum over the past 15 ye ars. IPM has also been employ ed by states (e.g., for RGGI, the Western Regional Air Partnership, Ozone Transport Assessment Group), other Federal and s tate agencies, environmental groups, and industry . **5.2 EPA’s Power Sector Modeling Base Case for the Proposal to Update CSAPR**  The IPM “Base Case” for any regulatory

impact analy sis is a business - as - usual scenario that would be expected under market and regulatory conditions in the absence of the rule. As such, an IPM base case represents the baseline for this RIA. 5 The EPA frequently updates the IPM base case to reflect the latest available electricity demand forecasts from the U.S. Energy In formation Agenc y (EIA) as well as expected costs and availability of new and existing generating resources, fuels, emission control technologies, and regulatory requirements. Our anal ys is of the proposal to update CSAPR involved two different IPM base cases. The EPA use d IPM version 5.14 (I PM v . 5.14 ) to provide power sector emission s data for air quality modeling . Specifically , IPM v.5.14 was used for the air qualit y modeling of the 2017 baseline and 2017 illustrative control case. This air quality modeling and IPM v.5.14 are detailed in Chapter 3. IPM v.5.14 was also used for the air quality modeling to quantify

upwind state c ontributions , described in Section V of the preamble for this proposal. After these air qualit y m odeling scenarios were underway , the EPA rele ased an updated IPM base case, version 5.15 (IPM v.5.15) , and the final Clean Power Plan (CPP). The EPA used IPM v. 5.15 for developing the proposed state NO X emissions budgets discussed in Chapter 4 of this RIA, and for analyzing the proposed rule’s cost , benefits and impacts . The EPA relied on

5 As described in Chapter 5 of EPA’s Guidelines for Preparing Economic Analyses, the baseline “ should incorporate assumptions about exogenous changes i n the economy that may affect relevant benefits and costs (e.g., changes in demographics, economic activity, consumer preferences, and technology), industry compliance rates, other regulations promulgated by EPA or other government entities, and behavioral responses to the proposed rule by firms and the public. “ (USEPA, 2010).5 - 5

IPM v.5.15 for these analy ses so that the base case for this proposal would reflect all on - the books policies , including the Clean Power Plan, as well as the most current power sector modeling data . Using IP M v . 5.15 for these anal ys e s provides EPA with the best information available to develop the proposed rule and to provide the public with the most current information possible. 5.2.1 EPA’s IPM Base Case v.5.15 When the EPA began using the current version of IPM (version 5), the EPA developed a comprehensive updated base case (v.5.13), as well as a companion updated database of EGU units (the National Electricity Energy Data S ys tem, or NEEDS v.5.13) that is used in EPA’s modeling applications of IPM. The EPA base case is updated periodicall y to reflect the annual electricit y demand forecasts from the EIA, among other data updates. EPA’s IPM modeling platform used to anal yz e this proposed rule (v.5.15) incorporates the version of the model used to anal yz e the potent ial impacts of the CPP, which was finalized in August, 2015. As discussed below in Section 5.2.2, the base case for the proposal to update CSAPR also includes certain revisions to the v.5.15 base case used in the CPP analy sis. The updates to the base case between v.5.14 and v.5.15 include an update to the natural gas suppl y as well as routine calibrations with the EIA’s Annual Energy Outlook (AEO), such as updating the electric demand forecast consistent with the AEO 2015. 6 Additional updates, based on the most up - to - date information and/or public comments received b y the EPA, include unit level specifications (e.g., pollution control configurations), planned power plant construction and closures, and updated cost and performance for onshore wind and utility - scale solar technologies. The IPM v.5.15 modeling platform incorporates federal and most state laws and regulations whose provisions were either in effect or enacted and clearl y delineated b y March 2015. This update also includes two non - air federal rules affecting EGUs: Cooling Water Intakes (316(b)) Rule and Combustion Residuals from Electric Utilities (CCR). Additionally , all new capacit y projected b y the model is compliant with Clean Air Act 111(b) standards, including the final standards of performanc e for GHG emissions from new sources. For a detailed account of all
6 The naming conventions used for EPA’s IPM modeling (such as v.5.15) reflect both the major overall version of IPM (v.5) and which iteration of the AEO (currently AEO 2015) that su pplies the electricity demand forecast used in IPM.5 - 6

updates made to the v.5.15 modeling platform, see the Incremental Documentation for EPA Base Case v.5.15 Using IP M. 7 EPA also updated the National Electric Energy Data Sy stem (NEEDS). Thi s database contains the unit - level data that is used to construct the "model" plants that represent existing and committed units in EPA modeling applications of IPM. NEEDS includes detailed information on each individual EGU, including geographic, operatin g, air emissions, and other data on every generating unit in the contiguous U.S. 5.2. 2 The IPM Base Case Used to Analyze the Proposal to Update CSAPR As discussed above, IP M Base Case v.5.15 was used as the base case in EPA’s anal ys is of the final Clean Po wer Plan, which was finalized in August, 2015. We are using a different IPM base case for this analy sis of the proposal to update CSAPR to reflect the fact that the Clean Power Plan is now a finalized and promulgated federal regulation. The Clean Power Plan is now a part of the business - as - usual scenario that would be expected under market and existing regulatory

condit ions in the absence of this proposal. The base case used to analyze the proposal to update CSAPR is the IPM configuration EPA used to anal y

ze one of the final CPP illustrative policy options. Specifically , the base case used in the current anal ys is is the IPM configuration for the CPP’s “rate - based” illustrative plan approach. Using the CPP rate - based IPM configuration for the proposed CSAPR analy sis assures that the IPM results are the incremental impacts on the power sector of the proposal to update CSAPR alone, and avoids “double counting” the impacts of the CPP as well as all other federal and state regulations included the v.5.15 base cas e. Extensive information on the IPM configuration and detailed results of the CPP rate - based anal ys is is available in the Clean Power Plan Final R

ule

– Regulatory

I mpact Anal ys is 8 for the CPP as well as at EPA’s Power Sector Modeling website. 9

7 Available at: http://www.epa.gov/ airmarkets/powersectormodeling.html / 8 Available at: http://www2.epa.gov/cleanpow erplan/clean - pow er - plan - final - rule - regulatory - im pact - analysis 9 Available at: http://www.epa.gov/ airmarkets/powersectormodeli ng.html /5 - 7

The 2017 baseline EGU emissions include impacts from the Final Mercury and Air Toxics Standards announced on December 21, 2011 10 and CSAPR issued on July 6, 2011. The EPA notes that because the modeling for the proposal was performed prior to the D.C. Circuit’s issu ance of EME Homer City I I, 11 that modeling assumed in its baseline for all states the emission reductions associated with the CSAPR NO X ozone - season phase 2 emissions budgets. **5.3 Evaluating the Regulatory Control**  **Alternatives** To estimate the costs, benefi ts, and economic and energy market impacts of the proposal to update CSAPR, the EPA conducted quantitative analy sis of three regulatory control alternatives: the proposed EGU NO X ozone - season emissions budgets that reflect EGU NO X control strategies repres ented b y a uniform NO X cost of $1,300 per ton; and more and less stringent alternative EGU NO X ozone - season emissions budgets that reflect EGU NO X control strategies represented by uniform NO X costs of $3,400 per ton and $500 per ton, respectivel y. Details about these regulatory

control alternatives, including state - specific EGU NO X ozone - season emissions budgets for each alternative, are provided in Chapter 4 of this RIA. Before undertaking power sector anal ys is to evaluate compliance with the regulatory control alternatives, the EPA first considered available EGU NO X mitigation strategies that could be implemented for the first compliance period (i.e., the 2017 ozone season) assuming that this rule is finalized in the summer of 2016. The EPA considered al l widely used EGU NO X control strategies: fully

operating existing Selective Catalytic Reduction (SCR) and Selective Non Cataly tic Reduction (SNCR)

– including optimizing NO X removal b y existing, operational SCRs and SNCRs as well as turning on and optimiz ing existing idled SCRs and SNCRs; installation of (or upgrading to) state - of - the - art NO X combustion controls; shifting generation to units with lower NO X emission rates within the same state; and installing new SCRs and SNCRs. EPA determined that the powe r sector could implement all of these NO X mitigation strategies, except installation of new SCRs or SNCRs, for the 2017 ozone - season, assuming this proposed rule is finalized in the summer of 2016. As to the installation of new SCRs or SNCRs, the amount of
10 In Michigan v. EPA, the Supreme Court reversed on narrow grounds a portion of the D.C. Circuit decision upholding MA TS , finding that EPA erred by not considering cost when determining that regulation of EGUs was "appropriate" pursuant to CAA section 112(n)(1). 135 S.Ct. 192 (2015). The case w as remanded to the D.C. Circuit for further proceedings, and MATS currently remai n in place. 11 EME Homer City Generation, L.P., v. EPA, No. 11 - 1302, slip op. at 19, 36 (July 28, 2015)5 - 8

time from contract award through commissioning for retrofit with new SCR or SNCR exceeds 18 and 12 months, respectively . It would therefore not be feasible to retrofit new SCR or SNCR to achieve EGU NO X reductions in the 2017 ozone season. For more detail s on these assessments , including the assessment of EGU NO X mitigation costs and feasibility , please refer to the EGU NO X Mitigation Strategies TSD , in the docket for this rule. The EPA notes that, due to limitations on model size, IPM v.5.15 does not have the capacity to determine, within the model, whether or not to operate existing EGU post combustion NO X controls (i.e., SCR or SNCR) that are idle in the base case. 12 In order to evaluate compliance with the regulatory control alternatives (including the proposal and more and less stringent alternatives), the EPA determined, outside the model, whether or not it would be reasonabl y expected for these controls to operate in order to compl y with each of the evaluated regulatory

control alternatives. After imposing the requirement to operate these control sy stems, IPM then estimated the associated NO X reductions and costs. The EGU NO X mitigation strategies that are assumed to operate or be available to reduce NO X in order to comply

with each of the regulatory control alternatives are shown in Table 5 - 1; more information about the estimated costs of these controls can be found in the EGU NO X Mitigation Strategies TSD. **Table 5 -**  **1 NO**  **X Mitigation Strategies Implemented for Compliance with the Regulatory** 

**Control Alternatives Regulatory Control**  **Alternative NO**  **X Controls Imple**  **mented** Less Stringent Alternative (1) Fully operating existing SCRs to achieve 0.075 lb/MMBtu NO X emission rate (outside IPM) (2) Shift generation to minimize costs (w ithin IPM ) Proposal (All controls above ) (3) Turn on idled SCRs ( within IPM) (4) Install or upgrade c om bustion controls (outside IPM ) More Stringent Alternative ( All controls above) (5) Turn on idled SNCRs ( outside IPM )

12 The EPA notes that EGUs with idled SCR or SNCR in the base represent a small percentage (less than 10 percent) of the EGU fleet that is equipped with N O X post - com bustion controls.5 - 9

5.3.1 Emission Reduction Assessment As discussed in Chapter 3 the EPA determined that NO X emissions in 23 eastern states affect the ability

of downwind states to attain and maintain the 2008 ozone NAAQS. For these 23 eastern states, the EPA is proposing to issue Federal Implementation Plans (FIPs) that generall y update the existing CSAPR NO X ozone - season emission budgets for EGUs and implement these budgets via the CSAPR NO X ozone - season allowance trading program. The EPA anal yz ed ozone - season NO X emission reductions from implementing each of the three regulatory

control alternatives. Specifically , the EPA conducted I PM modeling of each of the regulatory

control alternatives to evaluate the corresponding power sector emissions reduc tions from compl yi ng with the EGU NO X ozone - season emissions budgets that are provided in Chapter 4 of this RIA. The NO X emissions reductions are presented for two time periods

– 2017 (the principal ye ar of interest for the proposal to update CSAPR) and 20 20. However, this version of IPM was not structured to simulate 2017 directly . To evaluate the 2017 ozone season EGU NO X reductions from compliance with the regulatory control alternatives, EPA developed estimates of emissions for 2017 by

adjusting IPM’s d irect estimates for 2018 to account for a number of known differences. For example, these adjustments account for emissions from EGUs that are expected to operate in 2017, but that have announced plans to retire b y 2018 (and whose future ye ar emissions are thus not otherwise represented in I PM’s direct 2018 estimates). The EPA has only made such adjustments for ozone season NO X emissions given the air quality objective of the proposal to address transported ozone pollution by the 2017 ozone season. 13 For co - pollutant EGU emissions (i.e., annual NO X , SO 2 , and CO 2 ), the EPA assumes that the 2018 I PM results are representative of 2017 with no further adjustment. The EPA believes that this is reasonable given that the overall magnitude of the adjustments to ozone season NO X emissions for 2017 is relativel y small and that the adjustments generally affect both the base case and the regulatory control scenarios and therefore have an even smaller influence on the emissions difference between the base case and the regu latory control scenarios.

13 The 2018 emissions output from IPM were adjusted to reflect 2017 emissions levels as described in http://www.epa.gov/airmarkets/documents/ipm/Adjust ed2017.pdf5 - 10

Table 5 - 2 presents the reduction in EGU NO X emissions resulting from compliance with the regulatory

control alternatives (i.e., emissions budgets) in the 23 - state region, as well as the impact on states not in the region. The emi ssion reductions follow an expected pattern: the less stringent alternative produces substantially smaller emission reductions than EPA’s proposed emissions budgets, and the more stringent alternative results in modestly more NO X reductions. **Table 5 -**  **2 EGU**  **Ozone Season NO X**  **Em ission Reductions (tons) for the Proposal**  **and More**

**and Less Stringent Alternatives**  Ozone Season NO X Proposal More Stringent Alternative Less Stringent Alternative 2017 23 - State Region 8 5,000 88,0 00 24,0 00

Non - Region - 400 - 5 00 - 3 00 Total 85,000 87,000 24,000 2020 23 - State Region 83,000 84,000 24,000

Non - Region - 300 - 460 - 260 Total 83,000 84,000 24,000 In addition to the ozone season NO X reductions, there will also be reductions of other air emissions emitted by

EGUs burning fossil fuels (i.e., co - pollutants). These other emissions include the annual total changes in emissions of NOx, SO 2 and CO 2 . The co - pollutant emission reductions are presented in Table 5 - 3. **Table 5 -**  **3 EGU Annual Emission Reductions (tons) for SO**  **2 and CO**  **2 for the Proposal and** 

**More and Less Stringent Alternatives Annual NOx**  Proposal More Stringent Alternative Less Stringent Alternative 2017 23 - State Region 91,000 93,000 24,000

Non - Region - 560 - 700 - 500 Total 90,000 93,000 24,000 2020 23 - State Region 92,000 94,000 24,000

Non - Region - 1,000 - 1,700 - 700 Total 91,000 92,000 24,000 **Annual SO 2**  2017 23 - State Region 2,000 2,160 1,800

Non - Region - 820 - 920 - 710 Total 1,200 1,200 1,100 2020 23 - State Region 1,700 1,600 1,300

Non - Region - 1,100 - 1,500 - 910 Total 610 100 360 **Annual CO 2**  2017 23 - State Region 1,300,000 1,600,000 1,300 ,000

Non - Region - 670,000 - 920,000 - 520 ,0005 - 11

**Annual NOx** Proposal More Stringent Alternative Less Stringent Alternative Total 660,000 710,000 770 ,000 2020 23 - State Region 1,700,000 2,200,000 1,400 ,000

Non - Region - 1,400,000 - 2,400,000 - 1,100 ,000 Total 270,000 - 250,000 360 ,000,000 5.3.2 Compliance Cost Assessment This section describes EPA’s approach to quantify the costs for compliance with the regulatory

control alternatives. IPM directly estimated the costs for three of the NO X mitigation strategies: turning on idled SCRs, turning on idled SNCRs, and shifting ge neration to lower - NO X emitting EGUs. However, the costs of increasing the use and optimizing the performance of existing and operating SCRs and SNCRs, and for installing or upgrading NO X combustion controls, were estimated using an incremental analy sis ste p that EPA developed to quantify the additional costs of these controls. These methods and anal ys is relies on data and methods used within IPM, including NO X control cost equations used in IPM. Therefore, this analy sis is consistent with IPM and provides the best available quantification of the costs of these NO X mitigation strategies, consistent with IPM. The following steps illustrate this anal yt ical method and demonstrate the application of this method to the EPA’s cost estimate of compliance with the p roposal:

Analy tical Method Application to Estimate the Costs of Compliance with the Proposal  Id entify

the IPM modeled EGU NO X reductions that are attributable to NO X mitigation strategies with costs that are not included in the model.  Maximizing the use of existing SCRs accounts for approximately

33,000 tons of ozone season EGU NO X reductions.
 Installing or upgrading NOX combustion controls accounts for approximately 7,800 tons of ozone season EGU NO X reductions.  Estimate the costs associated with these EGU NO X mitigation strategies.  Maximizing the use of existing SCRs costs approximately

$500 per ton.  Installing or upgrading NOX combustion controls costs approximately

$1,200 per ton.  Calculate the cost of each EGU NO X mitigation strategies as applied to reduce emissions within the model.  The cost of reductions from maximizing the use of existing SCRs is approximately $17,000,000 annually for 2017.  The cost of reduction from installing or upgrading5 - 12 Analy tical Method Application to Estimate the Costs of Compliance with the Proposal NOX combustion controls is approximately $9,400,000 annua lly

for 2017. The total costs of compliance with the regulatory control alternatives are estimated as the sum of the costs that are modeled within IPM and the costs that are calculated outside the model. The results of EPA’s IPM analy sis show that, with respect to compliance with the proposed EGU NO X emissions budgets, turning on idle existing SCRs provides the largest amount of ozone season NO X emission reductions (51 percent), and maximizing the use of existing SCRs produces an additional 39 percent of the total ozone season NO X reductions. Combustion controls (9 percent) and generation shifting (1 percent) make up the remainder of the ozone season NO X reductions. In the more stringent alternative, compliance by turning on idle existing SNCRs makes up 3 percent of the total reductions, while the shares attributed to the other four mitigation measures are similar to the shares for compliance with the proposed EGU NO X emissions budgets. The estimates of the changes in the cost of supplying electricit y for the regulatory control alternatives are presented in Table 5 - 4. The costs associated with compliance with monitoring, recordkeeping, and reports requirements are not included within the estimates in this table and can be found in Chapter 7. **Table 5 -**  **4 Cost**  **Estimates (millions of 2011$) for Proposal and More and Less Stringent**

**Alternatives** Proposal More Stringent Alternative Less Stringent Alternative Annualized* $92 .9 $95 . 7 $4 . 7 Annual 2017 $71 .6 $63 .4 - $47 . 6 Annual 2020 $48 .6 $20 .3 - $27 . 6 *Levelized annualized costs over the period 2016 through 2040, discounted using the 4.77 discount rate used in IPM’s objective function of minimizing the NPV of the stream of total costs of electricity generation . There are several notable aspects of the results presented on Table 5 - 4. The most notable result in Table 5 - 4 is that the estimated annual compliance costs for the less stringent alternative are negative (i.e., a cost reduction) in 2017 and 2020, although this regulatory control alternative5 - 13

reduc es NO X emissions by over 25,000 tons as shown in Table 5 - 3. The estimate of the cost of controls determined outside the model are positive. Therefore, the finding of net negative annual compliance costs derives from the portion of the annual compliance cos t estimate that comes from IPM. While seemingl y counter - intuitive, such an estimated cost reduction is quite possible given the d yn amic, perfect foresight linear programing structure of IPM. IPM’s objective function is to minimize the discounted NPV of a s tream of annual total cost of generation over a multi - decadal time period. For example, IPM’s perfect foresight structure makes it possible that the least cost way

of complying may

be to delay

a new investment otherwise expected to occur in the base case. Such a delay could result in a lowering of annual cost in an earl y time period and increases in later time periods. A better understanding of the cause of the negative costs for the less stringent alternative can come from considering the entire time path of costs, rather than focusing on the 2017 and 2020 estimates in isolation. Because IPM onl y reports estimates for certain years, it is necessary to estimate the cost values for the years between the years directl y simulated. A simple but straightforward m ethod is to use a linear interpolation between the annual cost estimates for directly simulated y ears to estimate a potential annual compliance cost time series across all ye ars in a given period. Figure 5 - 1 shows the annual cost estimates, which are the s um of the annual compliance cost estimated by I PM and the annual compliance costs estimated outside the model, for each directly

simulated y ear from 2016 through 2040. It also shows the linearl y interpolated annual cost between those ye ars for all three of the regulatory control alternatives. 14

14 EPA estimated year - specific outside - the - model cost adjustments only for 2017 and 2020. For this analysis of the time series of costs, the outside - the - model compliance cost adjustments estimated for 2020 are added to all years betw een 2021 and 2040. While this approach is a relatively rougher estimate of the likely cost of the exogenously im posed NOx controls for those years, it is consistent with an assumption that once the CSAPR update is im plemented it will continue to have similar costs and emission reductions throughout the rest of the model’s forecast horizon.5 - 14

**Figure 5 -**  **1 Time series of annual costs for the Proposal and More and Less Stringent**

**Alternatives** Figure 5 - 1 shows that while two of the IPM y ear cost estimates (2017 and 2020) in the less stringent alternative are negative, the rest of the annual costs are positive, resulting in the majority of the ye ars between 2016 and 2040 having positive costs. All y ears have positive costs under compliance with the proposed EGU NO X ozone - season emissions budgets and the more s tringent alternative. Using the estimated annual cost time series, it is possible to use a standard NPV calculation to estimate an annualized cost (i.e., levelized annual cost) of the annual cost stream associated with compliance with each regulatory control alternative. 15 For this analysis we first calculated the NPV of the stream of costs from 2016 through 2040. We calculate the NPV in 2016 of each cost stream using a 4.77 percent discount rate. 16 While EPA ty pically uses a 3 and a 7 percent discount rate to discount future y ear benefits and costs in regulato ry impact anal ys es (USEPA 2010) , in this cost annualization approach we use a 4.77 percent rate to be consistent with the optimization approach used within IPM. IPM’s cost minimization objective functi on

15 The XNPV() function in Microsoft Excel 2013 w as used to calculate the NPV of the variable stream of costs. 16 While IPM also reports the costs for 2050, w e do not include the 2050 estimates in this analysis. In this situation, extending the present value calculations to include 2050 does not materially change the results.

-$50 $0 $50 $100 $150 $200

2016 2020 2024 2028 2032 2036 2040AnnualCosts(Millionsof2011$)

Less Stringent Proposal More Stringent5 - 15

estimates the lowest possible NPV, and uses a 4.77 percent discount rate to calculate the NPV. IPM v.5.15 uses a 4.77 percent discount rate as an estimate of the real opportunity cost of capital in the power sector. 17 After calculating the NPV of the cos t streams, the same 4.77 percent discount rate and 2016 - 2040 time period is used to calculate the levelized annualized cost estimates shown in Table 5 - 3. 18 Figure 5 - 2 depicts the annualized cost estimates on top of the time stream of annual costs previously

presented on Figure 5 - 1.

**Figure 5 -**  **2 Time series of annual costs and**  **annualized costs for the Proposal and More and**

**Less Stringent Alternatives** As can be seen in Table 5 - 3 and Figure 5 - 2, the annualized costs are positive for compliance wi th each of the three regulatory control alternatives. Furthermore, the annualized
17 The IPM Base Case documentation (Section 8.2.1 Introduction to Discount Rate Calculations) states “ The real di scount rate fo r expenditures (e.g., capital, fuel, variable operations and maintenance, and fixed operations and maintenance costs) in the EPA Base Case v.5.13 is 4.77%. This serves as the default discount rate for all expenditures. ” Section 8.2 in that documentation de scribes the rationale for using a real opportunity cost of capital specific to the power sector, and describes the data used to derive the 4.77 percent rate. 18 The PMT() function in Microsoft Excel 2013 is used to calculate the level annualized cost from the estimated NPV. -$50

$0 $50 $100 $150 $200

2016 2020 2024 2028 2032 2036 2040AnnualCosts(Millionsof2011$)

Less Stringent Proposal More Stringent

Annualized Annualized Annualized5 - 16

costs have the expected relationship; the annualized costs are lowest for the less stringent alternatives, and highest for the more stringent alternative. 5.3.3 Impacts on Fu el Use, Prices and Generation Mix The proposal to update CSAPR is estimated to have a variety of different impacts to the power sector. While all the impacts are relativel y small in percentage terms, considering the potential impacts in addition to the cos t and emissions estimates presented previously is an important component of assessing the overall impact of the proposal. In this section of the RIA we discuss the estimated changes in fuel use, prices of fuel and retail electricity , generation by fuel ty p e, and capacity

by fuel ty pe. Table 5 - 5 presents the percentage changes in national coal and natural gas usage by EGUs in 2017. Percent changes in fuel prices are also shown. As will be seen for the other measures of impacts on the power sector, the fuel use estimates in Table 5 - 5 estimates reflect a modest shift to natural gas from coal. **Table 5 -**  **5** **Percent Changes* in Coal and Natural Gas Usage by EGUs for the Proposal and**

**More and Less Stringent Alternatives for 2017** Proposal More Stringent Alternative Less Stringent Alternative Change in Fuel Quantity (TBtu) Coal - 0.077% - 0.072% - 0.091% Natural Gas 0.105% 0.090% 0.134% Change in Fuel Prices Coal** - 0.211% - 0.279% - 0.321% Natural Gas*** 0.002% - 0.004% 0.004% * Fuel use changes measured in TBtu * *Coal price changes are minemouth prices per MMBtu * **Natural gas price changes are delivered gas prices per MMBtu Table 5 - 6 presents the projected percentage changes in the amount of electricity generation nationally in 2017 by

3 major ty

pes of electricity generation: coal, gas, and conventional h ydro. The amount generated b y nuclear energy is unchanged in all three alternatives. **Table 5 -**  **6 will be expanded to include details by prime mover type (in addition to by fuel type already shown).**  **Table 5**  **- 6**  **Percent Changes* in Generation by Major Type for the Proposal and More and**

**Less Stringent Alternatives for 2017** Proposal More Stringent Alternative Less Stringent Alternative5 - 17 Proposal More Stringent Alternative Less Stringent Alternative Coal - 0.084% - 0.079% - 0.103% Natural Gas 0.108% 0.101% 0.127% Hy dro 0.062% 0.064% 0.068% Total Generation** 0.009% 0.008% 0.009% * Changes in GWh generated in 2017. **Changes in total generation is from all types of generation nationally, including fossil, non - fossil and renewable EGUs dispatching electricity to the grid. Table 5 - 7 presents the projected percentage changes in the amount of generating capacity in 2017 for coal, natural gas and total capacit y. **Table 5 -**  **7** **Changes* in Generating Capacity by** **Major Type for the Proposal and More** 

**and Less Stringent Alternatives in 2017** Proposal More Stringent Alternative Less Stringent Alternative **Percent Change** Coal - 0.144% - 0.136% - 0.162% Natural Gas - 0.006% - 0.006% - 0.006% Total Capacity - 0.006% - 0.004% - 0.015% **Capacity Change (MW)** Coal - 282 - 318 - 266 Natural Gas - 24 - 95 - 22 Total Capacity - 60 - 146 - 36 * Changes in generating capacity available in 2017. The EPA estimated the change in the retail price of electricity (2011$) using the Retail Price Model (RPM). 19 The RPM was developed b y IC F International for the EPA, and uses the IPM estimates of changes in the cost of generating electricit y to estimate the changes in average retail prices. The prices are average prices over consumer classes (i .e.., consumer, commercial and industrial) and regions , weighted b y the amount of electricit y used by each class and in each region . The RPM combines the I PM cost estimates in each of the 64 IPM regions with EIA electricit y market data for each of the 22 electricity supply regions in the electricit y market module of the National Energy Modeling Sy stem (NEMS). 20 Table 5 - 8 presents the projected percentage changes in the retail price of electricit y for the proposal and more and less stringent alternatives. Es timates are presented for both 2017 and 2020. While all of the estimated changes in prices are quite small, Table 5 - 8 includes both positive and negative retail electricit y price changes. This is consistent with the pattern of 19 See documentation available at: http://www.epa.gov/powersectormodeling/ 20 See documentation available at: http://www.eia.gov/forecasts/aeo/nems/documentation/electricity/pdf/m068(2014).pdf5 - 18

positive and negative annual cost estimates discussed above in Section 5.3.3, and also reflects some influence from aggregating results for the 22 NEMS regions into separate estimates for the 23 State region affected by the proposal to update CSAPR, and the rest of the country . **Table 5**  **- 8**  **Retail Electricity Price for the Proposal and More and Less Stringent**

**Alternatives for 2017 & 2020**

Base Case Percent Change in Retail Electricity Price Price (cents/kWh) Proposal More Stringent Alternative Less Stringent Alternative 2017 National Price 9.93 - 0.069% - 0.099% - 0.025% 23 State Region 9.69 - 0.161% - 0.371% - 0.047% Non - Region 10.75 - 0.154% - 0.371% - 0.027% 2020 National Price 10.3 3 0.031% 0.034% 0.009% 23 State Region 10.26 0.027% 0.028% 0.022% Non - Region 11.11 0.018% 0.022% - 0.001% **5.4 Employment Impacts**  Executive Order 13563 directs federal agencies to consider regulatory impacts on job creation and employ

ment. According to the Executive Order, “our regulatory sy stem must protect public health, welfare, safet y, and our environment while promoting economic growth, innovation, competitiveness, and job creation. It must be based on the best available science” (Executive Order 13563, 2011). Although standard benefit - cost anal ys es have not ty picall y included a separate anal ysis of regulation - induced employ ment impacts, 21 we ty

picall y conduct employ ment anal ys es for economicall y significant rules. This section discusses and projects pote ntial employ

ment impacts relate d to today ’s propos al, but the employ ment impacts have not been quantified . 22 Section 5.5.1 describes the theoretical framework used to anal yz e regulation - induced employ ment impacts, discussing how economic theory alone canno t predict whether such impacts are positive or negative. Section 5.5.2 presents an overview of the peer - reviewed

21 Labor expenses do, how ever, contribute toward total costs in the EPA’s standard benefit - cost analyses. 22 The employment analysis in this RIA is part of EPA’s ongoing effort to “conduct continuing evaluations of potential loss or shifts of employment which may result from the administration or enforce ment of [the Act]” pursuant to CAA section 321(a).5 - 19

literature relevant to evaluating the effect of environmental regulation on employ ment. Section 5.5.3 provides background regarding recent empl oym ent trends in the electricity generation, coal and natural gas extraction sectors. Section 5.5.4 discusses the potential direct emplo ym ent impacts in these sectors. The direct employ ment impacts are not quantified in this RIA. 5.4.1 Economic Theory and Employment Regulatory

employ ment impacts are difficult to disentangle from other economic changes affecting employ

ment decisions over time and across regions and industries. L abor market responses to regulation are complex. They depend on labor demand and supply elasticities and possible labor market imperfections (e.g., wage stickiness, long - term unemploy ment, etc.). The unit of measurement (e.g., number of jobs, ty pes of job s, hours worked, and earnings) may affect observability

of that response. Net emp loy ment impacts are composed of a mix of potential declines and gains in different areas of the economy (e.g., the directly

regulated sector, the environmental protection sector, upstream and downstream sectors, etc.) over time. In light of these difficult ies, economic theory provides a constructive framework for anal ys is. Microeconomic theory

describes how firms adjust their use of inputs in response to changes in economic conditions. 23 Labor is one of man y inputs to production, along with capital, energy, and materials. In competitive markets, firms choose inputs and outputs to maximize profit as a function of market prices and technological constraints . 24 , 25 Berman and Bui (2001) adapt this model to analyze how environmental regulations affect labor demand. 26 They model environmental regulation as effectivel y requiring certain factors of production, such as pollution abatement capital, at levels that firms would not otherwise choose. Berman and Bui (2001) model two components that drive changes in firm - level l abor demand: output effects and substitution effects. 27 Regulation affects the profit - maximizing quantity of output by

changing the marginal cost of production. If a regulation causes marginal production cost to increase, it will place upward pressure on output prices, leading to a decrease in demand, and resulting in a decrease in production. The output effect describes how, holding labor intensity constant, a decrease in production causes a decrease in labor demand. As noted b y Berman and Bui, although man y assume that regulations must increase marginal cost, in some cases they may decrease it. A regulation could induce a firm to upgrade to less polluting and more efficient equipment that lowers the marginal cost of production. In such a case, output could increase after firms comply

with the regulation. An unregulated profit - maximizing firm may not have chosen to install such an efficiency - improving technology if the return on investment were too low, but once the technology

is in place it lowers marginal p roduction costs.

23 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9. 24 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost - min imization. 25 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs ) . 26 Morgenstern, Pizer, and Shih (2002) develop a similar model. 27 The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 2) a cost effect; and 3) a factor - shift effect.5 - 20

The substitution effect describes how, holding output constant, regulation affects the labor intensity of production. Although increased environmental regulation may increase use of pollution control equipment and energy to operate that eq

uipment, the impact on labor demand is ambiguous. For example, equipment inspection requirements, specialized waste handling, completing required paperwork, or pollution technologies that alter the production process may affect the number of workers necess ary to produce a unit of output. Berman and Bui (2001) model the substitution effect as the effect of regulation on pollution control equipment and expenditures required b y the regulation and the corresponding change in the labor - intensity of production. In summary , as output and substitution effects may be positive or negative, economic theory alone cannot predict the direction of the net effect of regulation on labor demand. Inaddition, the empirical literature illustrates difficulties with estimation o f net employ ment impacts . Th e most commonly

used empirical methods, for example, Greenstone (2002), likely overstate employ ment impacts because they rely on relative comparisons between more regulated and less regulated counties, which can lead to “double counting” of impacts when production and employ ment shift from more regulated towards less regulated areas. Thus these empirical methods cannot be used to estimate net employ ment effects. 28 The conceptual framework described thus far focused on regulatory e ffects on plant - level decisions within a regulated industry , but e mployment impacts at an individual plant do not necessarily

represent impacts for the sector as a whole. At the industry - level, labor demand is more responsive if: (1) the price elasticity o f demand for the product is high, (2) other factors of production can be easil y substituted for labor, (3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total production costs. 29 For example, if all firms in an indus try are faced with the same regulatory compliance costs and product demand is inelastic, then industry output may

not change much, and output of individual firms may change slightly . 30 In addition to changes to labor demand in the regulated industry , net e mploy ment impacts encompass changes in other related sectors , for example, the environmental protection sector. This proposal may

increase demand for the nitrogenous reagent (t ypically ammonia or urea) used in SCRs and SNCRs to reduce NOx, which may increa se revenue and employ ment in the firms providing these chemicals . If the U.S. econom y is at full employ ment, even a large - scale environmental regulation is unlikely to have a noticeable impact on aggregate net employ ment. 31 In stead, labor in affected sectors would primaril y be reallocated from one productive use to another (e.g., from producing electricit y to manufacturing, installing, or operating and maintaining pollution - abatement equipment), and net national employ ment e ffects from environmental regulation would be small and transitory

(e.g., as workers move from one job to another). 32 Some workers may retrain or relocate in anticipation of new requirements or require time to search for new jobs, while shortages in some se ctors or regions could bid up wages to attract workers. These adjustment costs can lead to local labor disruptions. 28 See Greenstone (2002) p. 1212. 29 See Ehrenberg & Smith, p. 108. 30 This discussion draws from Berman and Bui (2001 ), pp. 293. 31 Full employment is a conceptual target for the economy where everyone who wants to w ork and is available to do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero. 32 Arrow et al. 1996; see discu ssion on bottom of p. 8. In practice, distributional impacts on individual workers can be important, as discussed in later paragraphs of this section.5 - 21

If, on the other hand, the econom y is operating at less than full employment, economic theory does not clearl y indicate the direction or ma gnitude of the net impact of environmental regulation on employ

ment; it could cause either a short - run net increase or short - run net decrease (Schmalansee and Stavins, 2011). For example, the Congressional Budget Office considered EPA’s Mercury

Air Toxics Standards and regulations for industrial boilers and process heaters as potentially

leading to short - run net increases in economic growth and employ ment, driven b y capital investments for compliance with the regulations (Congressional Budget Office, 2011). Environmental regulation may also affect labor supply

and productivity . In particular, reducing pollution and other environmental risks may imp rove labor productivity or employ ees’ abilit y to work. 33 While the theoretical framework for anal yz ing labor supp ly effects is analogous to that for labor demand, it is more difficult to study empirically . There is a small emerging literature that uses detailed labor and environmental data to assess these impacts. To summarize, economic theory provides a framework fo r anal yz ing the impacts of environmental regulation on employ ment. The net employ

ment effect incorporates expected employ ment changes (both positive and negative) in the regulated sector and other related sectors including the environmental protection sect or . Labor demand impacts for regulated firms, and also for the regulated industry , can be decomposed into output and substitution effects which may be either negative or positive. Estimation of net employ ment effects for regulated sectors is possible when data of sufficient detail and qualit y are available. Finally , economic theory suggests that labor supply effects are also possible. In the next section, we discuss the empirical literature . 5.4.1.2 Current State of Knowledge Based on the Peer
-Reviewed Lite rature The peer - reviewed empirical literature specifically estimating employ ment effects of environmental regulations is limited but growing. We summarize it briefly in this section. 5.4.1.3 Regulated Sector Several empirical studies, including Berman a nd Bui (2001) and Ferris, Shadbegian, and Wolverton (2014), suggest that regulation - induced net employ ment impacts may

be zero or slightly positive, but small in the regulated sector. Gray et al (2014) find that pulp mills that had to comply

with both the air and water regulations in EPA’s 1998 “Cluster Rule” experienced relativel y small, and not always statistically significant, decreases in employment. Other research on regulated sectors suggests that employ ment growth may

be lower in more regulated areas (Greenstone 2002, Walker 2011, 2013). However , since these latter studies compare more regulated to less regulated counties , this methodological approach likel y overstates emplo ym ent impacts to the extent that regulation causes plants to locate in one are a of the country rather than

33 E.g. Graff Zivin and Neidell (2012).5 - 22

another, which would lead to “double counting” of the employ ment impacts. L ist et al. (2003) find some evidence that this ty pe of geographic relocation may be occurring. 5.4 .1. 4 Economy
- Wi de Given the difficult y noted above w ith estimating national impacts of regulations, EPA has not generall y estimated economy - wide employ ment impacts of its regulations in its benefit - cost anal ys es. However, in its continuing effort to advance the evaluation of costs, benefits, and economic impacts associated with environmental regulation, EPA has formed a panel of experts as part of EPA’s Science Advisory Board (SAB) to advise EPA on the technical merits and challenges of using economy - wide economic models to evaluate the impacts of its regulations, including the impact on net national employ ment. 34 Once EPA receives guidance from this panel , it will carefully

consider this input and then decide if and how to proceed on econom y wide modeling of employment impacts of its regulations. 5.4.1.5 Lab or Supply Impacts The empirical literature on environmental regulatory employ ment impacts focuses primaril y on labor demand. However, there is a nascent literature focusing on regulation - induced effects on labor supply . 35 Although this literature is limited by empirical challenges, researchers have found that air qualit y improvements lead to reductions in lost work days (e.g., Ostro, 1987). Limited evidence suggests worker productivity may also improve when pollution is reduced. Graff Zivin and Neidell (2012 ) used detailed worker - level productivity data from 2009 and 2010, paired with local ozone air quality monitoring data for one large California farm growing multiple crops, with a piece - rate pay ment structure. Their quasi - experimental structure identifies an effect of daily

variation in monitored ozone levels on productivity . They find “ozone levels well below federal air quality standards have a significant impact on productivity : a 10 parts per billion (ppb) decreases in ozone concentrations increases wor ker productivity

by 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654). 36 5.4.1.6 Conclusion This section has outlined the challenges associated with estimating regulatory effects on both labor demand and supply for specific sectors. These challenges make it difficult to estimate net national employ

ment estimates that would appropriatel y capture the way in which costs, compliance spending, and environmental improvements propagate through the macro - econom y . 5.4.2 Recent Employment Trends The U.S. electric ity

s ys tem includes employ ees that support electric power generation, transmission and distribution; the extraction of fossil fuels; renewable energy generation; and 34 For further information see: http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED?OpenDocument 35 For a recent review

see Graff

- Zivin and Neidell (2013). 36 The EPA is not quantifying produ ctivity impacts of reduced pollution in this rulemaking using this study. In light of this recent research, however, the EPA is considering how best to incorporate possible productivity effects in the future.5 - 23

supply - side and demand - side energy efficiency . This section describes recent employ ment tr ends in the electricit y s ys tem. 5.4.2.1 Electric Power Generation In 2014, the electric power generation, transmission and distribution sector (NAICS 2211) employ ed about 390,000 workers in the U.S. Installation, maintenance, and repair occupations accounted for the largest share of workers (25 percent). These categories include inspection, testing, repairing and maintaining of electrical equipment and/or installation and repair of cables used in electrical power and distribution sy stems. Other major occupation categories include office and administrative support (18 percent), production occupations (16 percent), architecture and engineering (10 percent), business and financial operations (7 percent) and management (7 percent). As shown in Figure 5 - 3 , employ ment in the Electric Power Industry averaged about 420,000 workers 2000 to 2005, declining to an average of about 400,000 workers for the rest of the decade, and then declining to about 390,000 workers in 2014.

**Figure 5 -**  **3 Electric Power Industry**  **Employment**

0 50 100 150 200 250 300 350 400 450 500 

### Po wer Generation and Supply Employment

(NAICS = 2211, Annual Average, 1000s of Employees)5 - 24

5.4.2.2 Fossil Fuel Extraction **Coal Mining.** The coal mining sector (NAICS 2121) is primarily engaged in coal mining and coal mine site development, excluding metal ore mining and nonmetallic mineral mining and quarrying. In 2014, BLS reported a bout 74,000 c oal mining employ ees (Figure 5 - 4 ). During the 2000 to 2014 period, coal mining employ ment peaked in 2011 at about 87,000 employ ees.

**Figure 5 -**  **4 Coal Production Employment**  **Oil and Gas Extraction.** In2014, there were close to 200,000 emplo ye es in the oil and gas extraction sector (NAI CS 211). This sector includes production of crude petroleum, oil from oil shale and oil sands, production of natural gas, sulfur recovery from natural gas, and recovery of hy drocarbon liquids. Activities includ e the development of gas and oil fields, exploration activities for crude petroleum and natural gas, drilling, completing, and equipping wells, and other production activities. Incontrast with coal, Figure 5 - 5 shows there has been a sharp increase in empl oy

ment in this sector over the past decade.5 - 25

**Figure 5 -**  **5 Oil and Gas Extraction Employment** 5.4.3 Power and Fuels Sector Direct Employment Impacts As described above, a ffected EGUs may respond to the CSAPR update by upgrading or improving performance of existing combustion controls, or by upgrading, improving, or utilizing post - combustion NO X sy stems alread y in place . Inaddition, some net generation may shift from higher NO X - emitting EGUs to units with lower NO X emission rates. All of these changes will likely involve some amount of change in various ty pes of amount of labor needed in different parts of the fuels and utility power sector s. There also may be other labor impacts in sectors that provide products and materials used in reducing NO X emissions at EGUs, such as cataly sts used in SCR control sy stems. These direct labor impacts will likely include both increased demand for certain t ypes of labor in some portions of the affected sectors, and reduced demand for labor in other portions of the affected sectors. Installing and operating new equipment could change labor demand in the electricit y generating sector itself, as well as associated equipment and services sectors. Specificall y, the direct emplo ym ent effects in the power sector that could occur be cause of actions taken by the 2017 ozone season include:

0 50 100 150 200 250 

### Oil and Gas Extraction Employment

(NAICS = 211, Annual Average, 1000s of Employees)5 - 26

 Optimizing NO X removal from existing and operational SCR and SNCR sy stems;
 Turning on and optimizing idled SCR and SNCR sy stems;
 Installing or upgrading combustion - side improvements resulting in redu ced NO X emissions;
 Shifting generation from units with higher NO X emission rates to units with lower emission rates. In a ddition , there could be directl y induced employ ment impacts (both positive and negative) in the labor demand in the fossil fuels industry suppl yi ng fuels to the power sector. Once implemented, both the potential increases in operating efficiency and NO X reductions, as well as and shifting generation to lower NO X emitting assets, could impact the utility power sec tor’s demand for fossil fuels , and hence the demand for labor needed in the coal mining and gas extraction sectors. The direct net employ

ment impacts of this proposed rule, however, are anticipated to be relativel y small. This is consistent with the relatively small estimated changes in the power sector’s overall cost of generation, as well as relatively small changes i n generation, fuel use, capacity , and the percent of total generation produced by each t ype of fuel. For example, for the proposal in 2017, the estimated impacts relevant to changes in labor demand include:

 The overall total national cost of generation i n 2017 increases by 0.046 percent (the levelized annualized total cost of generation increases by 0.01 percent);
 Total net generation increases b y 0.009 percent (coal generation decreases by .084 percent, and natural gas generation increases by 0.11 percent);
 The power sector’s total tons of coal used for electricity generation decreases by

0.04 percent (or 0.09 percent increase in BTUs);
 Total natural gas increases by 0.11 percent. While the results of the power sector modeling suggest the direction and m agnitude of the potential labor impacts, because of the very small changes in the power and fuels sector, the direct labor impacts are not quantified in this report.5 - 27

**5.5 Social Costs** As discussed in the EPA Guidelines for Preparing Economic Anal ys es, soci al costs are the total economic burden of a regulatory action (USEPA, 2010). This burden is the sum of all opportunity

costs incurred due to the regulatory action, where an opportunity cost is the value lost to society

of an y goods and services that will n ot be produced and consumed as a result of reallocating some resources towards pollution mitigation. Estimates of social costs may be compared to the social benefits expected as a result of a regulation to assess its net impact on society . The social costs of a regulatory action will not necessaril y be equivalent to the expenditures associated with compliance. Nonetheless, here we use compliance costs as a prox y for social costs. The cost estimates for the three alternative NO X cost threshold scenarios pre sented in this chapter are the change in expenditures required b y the electricit y sector for compliance under each alternative, with a focus on the proposed remedy scenario. The change in the expenditures required b y the electricity sector to maintain comp liance reflect the changes in electricit y production costs due to changes in electricity demand resulting from application of the control strategies described in Chapter 4 and changes in electricity production costs required to achieve the additional emiss ion reductions necessary to comply

with the emissions budgets that are described earlier in this RIA. The changes in electricity production costs and changes in demand are estimated b y IP M as described earlier in Chapter 5, and the change in expenditures r equired by the electricit y sector to maintain compliance are also described earlier in Chapter 5. **5.6 Secondary Economic Impacts** The e nergy

sector impacts presented earlier in Chapter 5 of this RIA include potential changes in the prices for electricit y, natural gas, and coal potentially resulting from the proposal . This chapter addresses the impact of these potential changes on other markets outside of the power sector and discusses some of the determinants of the magnitude of these impacts. We refer to these changes as secondary market impacts. The anal ys is of costs for this proposal includes strategies for affected states to reduce NO X emissions from EGUs to comply with the proposed EGU NO X ozone - season emissions budgets in 2017 . Ultimately , g iven the f lexibilities afforded EGUs and states in complying with the5 - 28

proposed rule , the benefits, cost and economic impacts reported in this RIA are illustrative of actions that states may

take . The implementation approaches adopted b y the states, and the strategie s adopted b y affected EGUs, will ultimately drive the magnitude and timing of secondary impacts from changes in the price of electricit y, and the demand for inputs by the electricit y sector, on other markets that use and produce these inputs. To estimate the costs, benefits, and impacts of implementing the proposal, the EPA modeled an illustrative compliance approach for the proposal and more and less stringent alternatives. Chapter 4 provides a description of the proposal and the more and less stringent al ternatives considered. This chapter provides a quantitative assessment of the energy price impacts for these illustrative approaches and a qualitative assessment of the factors that will in part determine the timing and magnitude of effects in other market s. 5.6.1 Methods One potential quantitative approach to evaluating the secondary market impacts is to use a computable general equilibrium (CGE) model. CGE models are able to provide aggregated representations of the whole econom y in equilibrium in the bas eline and potentially with regulation in place. As such, a CGE model may be able to capture interactions between economic sectors and provide information on changes outside of the directly regulated sector. In support of previous rulemakings, such as the 2 008 Final Ozone NAAQS (U.S. EPA 2008) and the 2010 Transport Rule proposal (U.S. EPA 2010a), the EPA used the Economic Model for Policy Anal ys is (EMPAX) CGE model to estimate the secondary market effects based on the cost impacts projected b y the Integrate d Planning Model (IPM) for the directly regulated sector. When considering the secondary market impacts of a regulation, the effects of the costs, the benefits of improved air quality , and their interaction may be relevant. Therefore, in the Second Prospe ctive Analy sis under Section 812 of the Clean Air Act Amendments, the EPA incorporated a set of health benefits arising from air quality improvement into the EMPAX CGE model when studying the econom y - wide impacts of the Clean Air Act (U.S. EPA 2011). While the external Council on Clean Air Compliance Analy sis (Council) review of this study stated that inclusion of benefits in an econom y - wide model “represent[ed] a significant step forward in benefit - cost anal ys is” (Hammitt 2010), the EPA recognizes that ser ious technical challenges5 - 29

remain when attempting to evaluate the benefits and costs of potential regulatory actions using econom y - wide models . In light of these challenges, the EPA has established a Science Advisory Board (SAB) panel on econom y - wide modeling to consider the technical merits and challenges of using this anal yt ical tool to evaluate costs, benefits, and economic impacts in regulatory development. In addition, EPA is asking the panel to i dentify potential paths forward for improvements that could address the challenges posed when using econom y - wide models to evaluate the effects of regulations. The final panel membership was announced in March 2015 and the first of multiple face - to - face me etings of the SAB panel has been scheduled for October 2015. The EPA will use the recommendations and advice of this panel as an input into its process for improving benefit cost and economic impact analy ses used to inform decision - making at the agency . T he advice from the Science Advisory Board (SAB) panel formed specifically to address the subject of economy - wide modeling was not available in time for this final action. Given the ongoing SAB panel on economy - wide modeling , and the ongoing challenges of a ccurately representing costs, benefits, energy efficiency improvements in econom y - wide modeling, this chapter considers the energy impacts associated with the proposed alternatives anal yz ed and a qualitative assessment of the factors that will, in part, de termine the timing and magnitude of effects in other markets. 5.6.2 Summary of Secondary Market Impacts of Energy Price Changes Electricity , natural gas, and coal are important inputs to the production of other goods and services. Therefore, changes in the price of these commodities will shift the production costs for sectors that use electricity , natural gas, and coal in the production of other goods and services. Changes in the t yp es and levels of inputs used by producers in response to electricit y and fu el price changes may

mitigate the production cost changes in these sectors. Such changes in production costs may

lead to changes in the quantities and/or prices of the goods or services produced and changes in imports and exports . The EPA used IPM to estim ate e lectricit y, natural gas, and coal price changes based on compliance with the regulatory control alternatives for this rule. IPM is a multi - regional, dy namic, deterministic linear programming model of the U.S. electric power sector described in5 - 30

more de tail earlier in Chapter 5. The Retail Price Model (RPM) uses estimated changes in wholesale prices to estimate changes in average retail prices. The prices are average prices over consumer classes and regions weighted b y the amount used. Table 5 - 9 shows th ese estimated price changes for the proposal in 2017 and, for illustrative purposes onl y, 2020. For other results generated b y IPM and the RPM, please refer to earlier sections in Chapter 5. There are man y factors influencing th e projected natural gas prices. IPM (and its integrated gas resource and suppl y module) models natural gas natural gas supplies based on a multitude of factors. Since the model simulates perfect foresight, it anticipates future demand for natural gas and respond s accordingl y. In addition, IPM (and the natural gas module) are viewing a very long time horizon (through 2050 ) , such that the impacts in certain ye ars may be responsive to other modeling assumptions or drivers. The modeling framework is simultaneously solving for all of these key market and policy

parameters (both electric and natural gas), resulting in the impacts discussed in previous sections and shown in Table 5 - 9 . **Table 5**  **- 9.**  **Estimated Percentage Changes in Average Energy Prices**  **by Energy Type for** 

**the Proposed Alternat ive** * Proposed Rule 20 17 202 0 Electricity Price Change - 0 . 1 % - 0 . 2 % Delivered Natural Gas Price Change 0.01 % - 0.1 % Delivered Coal Price Change - 0.3 % - 0.4 % *A minus sign in front of the number denotes a negative change, or a price decrease . For ye ars when the price of electricity , natural gas, or coal increased, one would expect decreases in production and increases in market prices in sectors for which these commodities are inputs, ceteris paribus. Conversely , for ye ars when prices of these inp uts decreased, one would expect increases in production and decreases in market prices within these sectors. Smaller changes in input price changes would lead to smaller impacts within secondary markets. For compliance with the proposal and more and less s tringent alternatives these price changes are quite small, or even negative, in 2017. Thus, one could readily surmise that the impacts of this proposed rule upon secondary

markets should be minimal. As seen in Table 5 - 9, most of the price changes are negat ive, thus showing an estimated decrease in energy prices, albeit very

small. The variation for these prices at the regional level is not substantially different from the national - level price changes, and the variation of energy prices related to the propos al and the regional level can be found among the IPM outputs in the public docket for this rulemaking. However, a number of factors, in addition to the magnitude and sign of the energy price changes, influence the magnitude of the impact on production and market prices for sectors using electricit y, natural gas, or coal as inputs to production. These factors are discussed below.5 - 31

5 .6.3 Share of Total Production Costs The impact of energy

price changes in a particular sector depends, in part, on the share of total production costs attributable to those commodities. For sectors in which the directl y affected inputs are only

a small portion of production costs, the impact will be smaller than for sectors in which these inputs make up a greater portion of total p roduction costs. Therefore, more energy - intensive sectors would potentially experience greater cost increases when electricity , natural gas, or coal prices increase, but would also experience greater reduced costs when these input prices decrease. 5.6.4 Ab ility to Substitute between Inputs to the Production Process The ease with which producers are able to substitute other inputs for electricity , natural gas, or coal, or even amongst those commodities, influences the impact of price changes for these inputs . Those sectors with a greater abilit y to substitute across energy inputs or to other inputs will be able to, at least partiall y, offset the increased cost of these inputs resulting in smaller market impacts. Similarly , when prices for electricity , natural gas, or coal decrease, some sectors may

choose to use more of these inputs in place of other more costly substitutes. 5.6.5 Availability of Substitute Goods and Services The a bility of producers in sectors experiencing changes in their input prices to pas s along the increased costs to their customers in the form of higher prices for their products depends , in part , on the availabilit y of substitutes for the sectors’ products. Substitutes may be either other domestic products or foreign imports. If close su bstitutes exist, the demand for the product will in general be more elastic and the producers will be less able to pass on the added cost through a price increase. Such substitution can also take place between foreign and domestic goods within the same sec tor. Changes in the price of electricit y, natural gas, and coal can influence the quantities of goods imported or exported from sectors using these inputs. When the cost of domestic production increases due to more expensive inputs, imports may increase as consumers substitute towards relativel y less costly foreign - produced goods , and vice versa .5 - 32

5.6.6 Effect of Changes in Input Demand from Electricity Sector Section 5.7.2 focuses on the effects of changes in energy prices, and possible responses to those price changes, on sectors outside of the electricity sector. A change in demand for inputs in the electricit y sector, as well as changes in demand for energy efficiency services and products, will also influence economic activity in other sectors of the economy . For example, there could be changes in the demand for new generation sources such as natural gas combined cy cle units, and therefore sectors producing technologies such as these may expand. 5.6.7 Conclusions Changes in the price of electricity , natural gas, and coal can affect markets for goods and services produced b y s ectors that use these energy inputs in the production process. The direction and magnitude of these impacts are influenced b y a number of factors. For example, the more producers in these sectors are able to substitute away from the use of these energy inputs, the smaller the effect of energy prices changes will be on their production cos t. Changes in cost of production may

lead to changes in price, quantity produced, and profitability of firms within secondary markets. Furthermore, the demand inputs in the electricity sector will also affect secondary markets. If regulation results in changes in domestic markets that lead to an increase in imports, then domestic producers may experience less demand from their consumers, and vice versa. Modeling choices in I PM influence the estimated changes in electricity , natural gas, and coal prices in this RIA. Actual market conditions will ultimately influence the price changes of these energy

inputs and consequent effects on secondary markets, as will the plan approaches that states adopt. **5.7 References**  Arrow, K. J.; M. L . Cropper; G. C. Eads; R. W. Hahn; L . B. Lave; R. G. Noll; Paul R. Portney ; M. Russell; R. Schmalensee; V. K. Smith and R. N. Stavins. 1996. “Benefit - Cost Analy sis in Environmental, Health, and Safety Regulation: A Statement of Principles.” American Enterprise Institute, the Annapolis Center, and Resources for the Future; AEI Press. Available at : <http://www.hks.harvard.edu/fs/rstavins/Papers/Benefit Cost Analysis in Environmental.AEI.1996.pdf>. Accessed June 5, 2015.5 - 33

Berman, E. and L. T. M. Bui. 2001. “Environmental Regulation and La bor Demand: Evidence from the South Coast Air Basin.” Journal of Public Economics. 79(2): 265 - 295. Congressional Budget Office (2011), Statement of Douglas W. Elmendorf, Director, before the Senate Budget Committee, “Policies for Increasing Economic Growth and Employ ment in 2012 and 2013” (November 15)<http://www.cbo.gov/sites/default/files/11 - 15 - Outlook_Stimulus_Testimony .pdf> Ehrenberg, R. G. and R. S. Smith. 2000. Modern Labor Economics: Theory and Public Policy. Addison Wesley

L ongman, Inc., Chapter 4. Executive Order 13563 (January 21, 2011). “Improving Regulation and Regulatory Review. Section 1. General Principles of Regulation.” Federal Register 76(14): 3821 - 3823. Ferris, A. E., R. J. Shadbegian, A. Wolverton. 2014. “ The Effect of Environmental Regulation on Power Sector Emplo ym ent: Phase Iof the Title IV SO 2 Trading Program.” Journal of the Association of Environmental and Resource Economists. 1(4): 521 - 553. Graff Zivin J. and M. Neidell. 2012. “The Impact of Pollution on Worker Productivity .” Ame rican Economic Review . 102(7):3652 - 73. Gray , W., R. J. Shadbegian, C. Wang and M. Meral. 2014 “Do EPA Regulations Affect Labor Demand? Evidence from the Pulp and Paper Industry ”, Journal of Environmental Economics and Management. 68: 188 - 202. Greenstone, M . 2002. “The Impacts of Environmental Regulations on Industrial Activity : Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures.” Journal of Political Economy. 110(6): 1175 - 1219. Hamermesh, D. S. 1993. Labor Demand. Princ eton, NJ: Princeton University Press. Chapter 2. La yard, P.R.G. and A. A. Walters. 1978. Microeconomic Theory . McGraw - Hill, Inc. Chapter 9. List, J. A.; D. L . Millimet; P. G. Fredriksson and W. W. McHone. 2003. “Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator.” The Review of Economics and Statistics. 85 (4): 944 - 952. Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal of Environmental Economics Man agement . 14:87 - 98. Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analy sis for the Transport Rule.” White Paper. Boston, MA. Exelon Corp.5 - 34

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>. Accessed 9/17/2015. U.S. EPA. 2010. EPA Guidelines for Preparing Economic Analyses . Available at: <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/guidelines.htm>. Accessed 9/21/2015. U.S. EPA. 2010a. Regulatory Impact Analysis for the Proposed Federal Transport Rule Analyses . Available at: < http://www3.epa.gov/ttnecas1/ria.html >. Accessed 9/21/2015.5 - 35