5-1

CHAPTER 5: COST, ECONOMIC, ENERGY, AND EMPLOYMENT IMPACTS

Overview This chapter reports the compliance cost, emissions , economic, energy and employment impact analyses performed for the proposal to updat e CSAPR. The EPA used the Integrated Planning Model (IPM), developed by ICF Internationa l, to conduct most of the analysis discussed in this Chapter. The EPA conducted additi onal analysis using the IPM estimates of changes in the power and fuels sector as a basis fo r examining the potential direct labor impacts
in those sectors.

IPM is a dynamic linear programming model of the po wer 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 ent ire power system. Future-year electricity demand levels are based on projections from the Ene rgy Information Administration (EIA).

This chapter of the RIA presents analyses of the pr oposed 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 reg ion. Over the past two decades, the EPA has used IPM and other an alytical methods described in this chapter to conduct extensive analyses of proposed and final federal environmental regulatory actions affecting the power sector. These previous analytic al efforts support the Agency’s understanding of key variables that influence the effects of a po licy 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, dynamic l inear programming model that can be used to project power sector behavior under future business-as-usual conditions and examine prospective air pollution control policies througho ut the contiguous United States for the entire electric power system. EPA used IPM to project like ly future electricity market conditions with and without the proposed updates to CSAPR.

IPM is a multi-regional, dynamic, deterministic lin ear programming model of the contiguous U.S. electric power sector. It provides estimates of least cost capacity expansion,5-2

electricity dispatch, and emission control strategi es while meeting energy demand and environmental, transmission, dispatch, and reliabil ity constraints. The EPA has used IPM for over two decades to better understand power sector behavior under future business-as-usual
conditions and to evaluate the economic and emissio ns impacts of prospective environmental policies. The model is designed to reflect electric ity markets as accurately as possible. The EPA uses the best available information from utilities, industry experts, gas and coal market experts, financial institutions, and government statistics a s the basis for the detailed power sector modeling in IPM. The model documentation provides a dditional information on the assumptions discussed here as well as all other model assumptio ns and inputs. 1

The model incorporates a detailed representation of the fossil-fuel supply system that is used to estimate equilibrium fuel prices. The model includes an endogenous representation of the North American natural gas supply system 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 dem and and corresponding gas production and price levels. 2 This module consists of 118 supply, demand, and st orage 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 natura l gas transmission and distribution network.

IPM also endogenously models the partial equilibriu m of coal supply and EGU coal demand levels throughout the contiguous U.S., takin g into account assumed non-power sector demand and imports/exports. IPM reflects 36 coal su pply regions, 14 coal grades, and the coal transport network, which consists of over four thou sand linkages representing rail, barge, and truck and conveyer linkages. The coal supply curves in IPM were developed during a thorough bottom-up, mine-by-mine approach that depicts the c oal 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/powersectormodeling.html
2 See Chapter 10 of EPA’s Base Case using IPM (v5.15) documentati on, available at: http://www.epa.gov/airmarkets/powersectormodeling.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’ supply curves. 3

To estimate the annualized costs of additional capi tal investments in the power sector, the EPA uses a conventional and widely accepted approac h that applies a capital recovery factor (CRF) multiplier to capital investments and adds th at 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 re quired, local property taxes, and the life of capital. 4 It is important to note that there is no single CR F factor applied in the model; rather, the CRF varies across technologies, book life of the ca pital investments, and regions in the model in order to better simulate power sector decision-maki ng.
The EPA has used IPM extensively over the past two decades to analyze options for reducing power sector emissions. Previously, the mo del has been used to estimate the costs, emission changes, and power sector impacts for the Clean Air Interstate Rule, the 2011 CrossState Air Pollution Rule (CSAPR), the Mercury and A ir 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 p ollution reductions and power sector impacts of water and waste regulations affecting EGUs, incl uding 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 perio dic formal peer review. The rulemaking process also provides opportunity for ex pert review and comment by a variety of stakeholders, including owners and operators of cap acity in the electricity sector that is represented by the model, public interest groups, a nd other developers of U.S. electricity sector models. The feedback that the Agency receives provi des a highly-detailed review of key input assumptions, model representation, and modeling res ults. IPM has received extensive review by energy and environmental modeling experts in a vari ety of contexts. For example, in the late

3 See Chapter 9 of the IPM v.5.15 documentation. The documentatio n 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.e pa.gov/airmarkets/powersectormodeling.html
4 See Chapter 8 of EPA’s Base Case documentation.5-4

1990s, the Science Advisory Board reviewed IPM as p art of the CAA Amendments Section 812 prospective studies that are periodically conducted . The model has also undergone considerable interagency scrutiny when it was used to conduct ov er a dozen legislative analyses (performed at Congressional request) over the past decade. The Ag ency has also used the model in a number of comparative modeling exercises sponsored by Stanfor d University’s Energy Modeling Forum over the past 15 years. IPM has also been employed by states (e.g., for RGGI, the Western Regional Air Partnership, Ozone Transport Assessmen t Group), other Federal and state agencies, environmental groups, and industry.

5.2 EPA’s Power Sector Modeling Base Case for the P roposal 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 elect ricity demand forecasts from the U.S. Energy Information Agency (EIA) as well as expected costs and availability of new and existing generating resources, fuels, emission control techn ologies, and regulatory requirements.

Our analysis of the proposal to update CSAPR involv ed two different IPM base cases. The EPA used IPM version 5.14 (IPM v.5.14) to provide p ower sector emissions data for air quality modeling. Specifically, IPM v.5.14 was used for the air quality modeling of the 2017 baseline and 2017 illustrative control case. This air qualit y modeling and IPM v.5.14 are detailed in Chapter 3. IPM v.5.14 was also used for the air qua lity modeling to quantify upwind state contributions, described in Section V of the preamb le for this proposal.

After these air quality modeling scenarios were und erway, the EPA released an updated IPM base case, version 5.15 (IPM v.5.15), and the f inal 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 cos t, benefits and impacts. The EPA relied on

5 As described in Chapter 5 of EPA’s Guidelines for Prepari ng Economic Analyses, the baseline “should incorporate assumptions about exogenous changes in the economy tha t may affect relevant benefits and costs (e.g., changes in demographics, economic activity, consumer preferen ces, and technology), industry compliance rates, other regulations promulgated by EPA or other government entities, a nd behavioral responses to the proposed rule by firms and the public.“ (USEPA, 2010).5-5

IPM v.5.15 for these analyses so that the base case for this proposal would reflect all on-thebooks policies, including the Clean Power Plan, as well as the most current power sector modeling data. Using IPM v.5.15 for these analyses provides EPA with the best information available to develop the proposed rule and to provi de 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 a s a companion updated database of EGU units (the National Electricity Energy Data System, or NEEDS v.5.13) that is used in EPA’s modeling applications of IPM. The EPA base case is updated periodically to reflect the annual electricity demand forecasts from the EIA, among ot her data updates. EPA’s IPM modeling platform used to analyze this proposed rule (v.5.15 ) incorporates the version of the model used to analyze the potential impacts of the CPP, which was finalized in August, 2015. As discussed below in Section 5.2.2, the base case for the propo sal to update CSAPR also includes certain
revisions to the v.5.15 base case used in the CPP a nalysis.

The updates to the base case between v.5.14 and v.5 .15 include an update to the natural gas supply as well as routine calibrations with the EIA’s Annual Energy Outlook (AEO), such as updating the electric demand forecast consistent wi th the AEO 2015. 6 Additional updates, based on the most up-to-date information and/or public co mments received by the EPA, include unitlevel specifications (e.g., pollution control confi gurations), planned power plant construction and closures, and updated cost and performance for onsh ore wind and utility-scale solar technologies. The IPM v.5.15 modeling platform incorporates feder al and most state laws and regulations whose provisions were either in effect or enacted a nd clearly delineated by March 2015. This update also includes two non-air federal rules affe cting EGUs: Cooling Water Intakes (316(b))
Rule and Combustion Residuals from Electric Utiliti es (CCR). Additionally, all new capacity projected by the model is compliant with Clean Air Act 111(b) standards, including the final

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 supplies the electricity demand forecast used in IPM.5-6

standards of performance for GHG emissions from new sources. For a detailed account of all updates made to the v.5.15 modeling platform, see t he Incremental Documentation for EPA Base Case v.5.15 Using IPM. 7

EPA also updated the National Electric Energy Data System (NEEDS). This database
contains the unit-level data that is used to constr uct 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 Proposa l to Update CSAPR
As discussed above, IPM Base Case v.5.15 was used a s the base case in EPA’s analysis of the final Clean Power Plan, which was finalized in August, 2015. We are using a different IPM base case for this analysis of the proposal to update CSAPR to reflect the fact that the Clean Power Plan is now a finalized and promulgated feder al regulation. The Clean Power Plan is now a part of the business-as-usual scenario that would be expected under market and existing regulatory conditions in the absence of this propos al.

The base case used to analyze the proposal to upda te CSAPR is the IPM configuration EPA used to analyze one of the final CPP illustrati ve policy options. Specifically, the base case used in the current analysis is the IPM configurati on for the CPP’s “rate-based” illustrative plan approach. Using the CPP rate-based IPM configuratio n for the proposed CSAPR analysis 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 impac ts of the CPP as well as all other federal and state regulations included the v.5.15 base case . Extensive information on the IPM configuration and detailed results of the CPP rate- based analysis is available in the Clean Power Plan Final Rule – Regulatory Impact Analysis 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/cleanpowerplan/clean-power-plan-final-ru le-regulatory-impact-analysis
9 Available at: http://www.epa.gov/airmarkets/powersectormodeling.html/5-7

The 2017 baseline EGU emissions include impacts fro m 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 wa s performed prior to the D.C. Circuit’s issuance of EME Homer City II, 11 that modeling assumed in its baseline for all stat es 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, benefits, and economic and e nergy market impacts of the proposal to update CSAPR, the EPA conducted quantitative analys is of three regulatory control alternatives: the proposed EGU NO X ozone-season emissions budgets that reflect EGU NO X control strategies represented by a uniform NO X cost of $1,300 per ton; and more and less stringen t 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, respecti vely. Details about these regulatory control alternatives, including state-sp ecific EGU NO X ozone-season emissions budgets for each alternative, are provided in Chapt er 4 of this RIA.

Before undertaking power sector analysis to evaluat e compliance with the regulatory control alternatives, the EPA first considered avai lable 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 co nsidered all widely used EGU NO X control strategies: fully operating existing Selective Cata lytic Reduction (SCR) and Selective NonCatalytic Reduction (SNCR) – including optimizing N O X removal by existing, operational SCRs and SNCRs as well as turning on and optimizing exis ting 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 installi ng new SCRs and SNCRs. EPA determined that the power sector could implement al l of these NO X mitigation strategies, except installation of new SCRs or SNCRs, for the 2017 ozo ne-season, assuming this proposed rule is
finalized in the summer of 2016. As to the installa tion of new SCRs or SNCRs, the amount of

10 In Michigan v. EPA, the Supreme Court reversed on narrow gr ounds a portion of the D.C. Circuit decision upholding MATS, finding that EPA erred by not considering cost whe n determining that regulation of EGUs was "appropriate" pursuant to CAA section 112(n)(1). 135 S.Ct. 192 (2015). The case was remanded to the D.C. Circuit for further proceedings, and MATS currently remain in place.
11 EME Homer City Generation, L.P., v. EPA, No. 11-1302, sl ip 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 deta ils 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 siz e, IPM v.5.15 does not have the capacity to determine, within the model, whether ornot to operate existing EGU postcombustion 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 alt ernatives (including the proposal and more and less stringent alternatives), the EPA determine d, outside the model, whether or not it would be reasonably expected for these controls to operat e in order to comply with each of the evaluated regulatory control alternatives. After im posing the requirement to operate these control systems, 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 con trol 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 w ith the Regulatory

Control Alternatives Regulatory Control Alternative NO X Controls Implemented

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 (within IPM)
Proposal (All controls above) (3) Turn on idled SCRs (within IPM) (4) Install or upgrade combustion 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 NO X post-combustion controls.5-9

5.3.1 Emission Reduction Assessment
As discussed in Chapter 3 the EPA determined that N O 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 Fe deral Implementation Plans (FIPs) that generally 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 analyzed ozone-season NO X emission reductions from implementing each of the three regulatory control alternatives. Specifically , the EPA conducted IPM modeling of each of the regulatory control alternatives to evaluate the corresponding power sector emissions reductions from complying 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 pe riods – 2017 (the principal year of interest for the proposal to update CSAPR) and 2020. However, this version of IPM was not structured to simulate 2017 directly. To evalua te the 2017 ozone season EGU NO X reductions from compliance with the regulatory cont rol alternatives, EPA developed estimates of emissions for 2017 by adjusting IPM’s direct estima tes for 2018 to account for a number of known differences. For example, these adjustments a ccount for emissions from EGUs that are expected to operate in 2017, but that have announce d plans to retire by 2018 (and whose futureyear emissions are thus not otherwise represented i n IPM’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 IPM results are representative of 2017 with no further adjustme nt. The EPA believes that this is reasonable given that the overall magnitude of the adjustments to ozone season NO X emissions for 2017 is relatively small and that the adjustments generally affect both the base case and the regulatory
control scenarios and therefore have an even smalle r influence on the emissions difference between the base case and the regulatory control sc enarios.

13 The 2018 emissions output from IPM were adjusted to reflect 201 7 emissions levels as described in http://www.epa.gov/airmarkets/documents/ipm/Adjusted2017.pdf5-10

Table 5-2 presents the reduction in EGU NO X emissions resulting from compliance with the regulatory control alternatives (i.e., emission s budgets) in the 23-state region, as well as the impact on states not in the region. The emission re ductions follow an expected pattern: the less stringent alternative produces substantially smalle r emission reductions than EPA’s proposed emissions budgets, and the more stringent alternati ve results in modestly more NO X reductions.

Table 5-2 EGU Ozone Season NO X Emission Reductions (tons) for the Proposal and Mo re

and Less Stringent Alternatives

Ozone Season NO X Proposal More Stringent Alternative Less Stringent Alternative

2017 23-State Region 85,000 88,000 24,000

Non-Region -400 -500 -300 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 NO x, 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 t he costs for compliance with the regulatory control alternatives. IPM directly estim ated the costs for three of the NO X mitigation strategies: turning on idled SCRs, turning on idled SNCRs, and shifting generation 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 inst alling or upgrading NO X combustion controls, were estimated using an incremental analy sis step that EPA developed to quantify the
additional costs of these controls. These methods a nd analysis relies on data and methods used within IPM, including NO X control cost equations used in IPM. Therefore, thi s analysis 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 analytical meth od and demonstrate the application of this method to the EPA’s cost estimate of compliance wit h the proposal:

Analytical Method Application to Estimate the Costs of Compliance wit h the Proposal

• Identify 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 2017.5-12 Analytical Method Application to Estimate the Costs of Compliance wit h the Proposal

• The cost of reduction from installing or upgrading NOX combustion controls is approximately $9,400,000 annually for 2017.

The total costs of compliance with the regulatory c ontrol alternatives are estimated as the
sum of the costs that are modeled within IPM and th e costs that are calculated outside the model.

The results of EPA’s IPM analysis show that, with r espect to compliance with the proposed EGU NO X emissions budgets, turning on idle existing SCRs p rovides the largest
amount of ozone season NO X emission reductions (51 percent), and maximizing t he use of existing SCRs produces an additional 39 percent of the total ozone season NO X reductions. Combustion controls (9 percent) and generation shif ting (1 percent) make up the remainder of
the ozone season NO X reductions. In the more stringent alternative, com pliance 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 s hares for compliance with the proposed EGU
NO X emissions budgets.

The estimates of the changes in the cost of supplyi ng electricity for the regulatory control alternatives are presented in Table 5-4. The costs associated with compliance with monitoring, recordkeeping, and reports requirements are not inc luded within the estimates in this table and can be found in Chapter 7.

Table 5-4 Cost Estimates (millions of 2011$) for Pr oposal 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 o f total costs of electricity generation

.

There are several notable aspects of the results pr esented on Table 5-4. The most notable
result in Table 5-4 is that the estimated annual co mpliance costs for the less stringent alternative5-13

are negative (i.e., a cost reduction) in 2017 and 2 020, although this regulatory control alternative
reduces 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 an nual compliance cost estimate that comes from IPM. While seemingly counter-intuitive, such a n estimated cost reduction is quite possible given the dynamic, perfect foresight linear program ing structure of IPM. IPM’s objective
function is to minimize the discounted NPV of a str eam of annual total cost of generation over a multi-decadal time period. For example, IPM’s perfe ct foresight structure makes it possible that the least cost way of complying may be to delay a n ew investment otherwise expected to occur in the base case. Such a delay could result in a lo wering of annual cost in an early 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 c osts, rather than focusing on the 2017 and 2020 estimates in isolation. Because IPM only repor ts estimates for certain years, it is necessary to estimate the cost values for the years between t he years directly simulated. A simple but straightforward method is to use a linear interpola tion between the annual cost estimates for directly simulated years to estimate a potential an nual compliance cost time series across all years in a given period. Figure 5-1 shows the annua l cost estimates, which are the sum of the annual compliance cost estimated by IPM and the ann ual compliance costs estimated outside the model, for each directly simulated year from 2016 t hrough 2040. It also shows the linearly interpolated annual cost between those years for al l three of the regulatory control alternatives. 14

14 EPA estimated year-specific outside-the-model cost adjust ments only for 2017 and 2020. For this analysis of the time series of costs, the outside-the-model compliance cost adjus tments estimated for 2020 are added to all years between 2021 and 2040. While this approach is a relatively rougher estimate of the likely cost of the exogenouslyimposed NOx controls for those years, it is consistent with an assumption that once the CSAPR update is implemented it will continue to have similar costs and emi ssion reductions throughout the rest of the model’s forecast horizon.5-14

Figure 5-1 Time series of annual costs for the Pro posal and More and Less Stringent

Alternatives

Figure 5-1 shows that while two of the IPM year cos t 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 years between 2016 and 2040 having positive costs. All years have positive costs under compliance with the proposed EGU NO X ozone-season emissions budgets and the more stringent alternative.

Using the estimated annual cost time series, it is possible to use a standard NPV calculation to estimate an annualized cost (i.e., l evelized annual cost) of the annual cost stream associated with compliance with each regulatory con trol 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 disco unt rate. 16 While EPA typically uses a 3 and a 7 percent discount rate to discount future year ben efits and costs in regulatory impact analyses (USEPA 2010), in this cost annualization approach w e use a 4.77 percent rate to be consistent with the optimization approach used within IPM. IPM ’s cost minimization objective function

15 The XNPV() function in Microsoft Excel 2013 was used to calculat e the NPV of the variable stream of costs. 16 While IPM also reports the costs for 2050, we 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 2040Annual Costs (Millions of 2011$)

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 cost streams, the same 4.77 percent discount rate and 2016-2040 time period is used to calculate the leve lized 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 annual ized costs for the Proposal and More and

Less Stringent Alternatives

As can be seen in Table 5-3 and Figure 5-2, the ann ualized costs are positive for compliance with each of the three regulatory contro l alternatives. Furthermore, the annualized

17 The IPM Base Case documentation (Section 8.2.1 Introduction to Discount Rate Calculations) states “The real discount rate for 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%. Thi s serves as the default discount rate for all expenditures.” Section 8.2 in that documentation describes the r ationale 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 2040Annual Costs (Millions of 2011$)

Less Stringent Proposal More Stringent

Annualized Annualized Annualized5-16

costs have the expected relationship; the annualize d costs are lowest for the less stringent alternatives, and highest for the more stringent al ternative.

5.3.3 Impacts on Fuel Use, Prices and Generation Mi x The proposal to update CSAPR is estimated to have a variety of different impacts to the power sector. While all the impacts are relatively small in percentage terms, considering the potential impacts in addition to the cost and emiss ions 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, price s of fuel and retail electricity, generation by fuel type, and capacity by fuel type.

Table 5-5 presents the percentage changes in nation al coal and natural gas usage by EGUs in 2017. Percent changes in fuel prices are also sh own. 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 Alternati ve 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 MMBt u

Table 5-6 presents the projected percentage changes in the amount of electricity generation nationally in 2017 by 3 major types of electricity generation: coal, gas, and conventional hydro. The amount generated by nuclear energy is unchanged in all three alternatives.

Table 5-6 will be expanded to include details by pr ime mover type (in addition to by fuel type already shown).5-17

Table 5-6 Percent Changes* in Generation by Major T ype for the Proposal and More and

Less Stringent Alternatives for 2017 Proposal More Stringent Alternative Less Stringent Alternati ve Coal -0.084% -0.079% -0.103% Natural Gas 0.108% 0.101% 0.127% Hydro 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 capacity.

Table 5-7 will be expanded to include all coal prod uction regions

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 Alternati ve 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 by ICF International for the EPA, and uses the IPM estimates of changes in the cost of generating electricity to estimate the changes in average retail prices. The prices are average prices over c onsumer classes (i.e.., consumer, commercial and industrial) and regions, weighted by the amount of electricity used by each class and in each region. The RPM combines the IPM cost estimates in each of the 64 IPM regions with EIA electricity market data for each of the 22 electric ity supply regions in the electricity market module of the National Energy Modeling System (NEMS ). 20

19 See documentation available at: http://www.epa.gov/power sectormodeling/
20 See documentation available at: http://www.eia.gov/forecasts/aeo/nems/documentation/electric ity/pdf/m068(2014).pdf5-18

Table 5-8 presents the projected percentage changes in the retail price of electricity for the proposal and more and less stringent alternativ es. Estimates 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 electricity price chan ges. This is consistent with the pattern of positive and negative annual cost estimates discuss ed 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 Percent Changes* in 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 10.33 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% *Changes in GW capacity available in 2017.

5.4 Employment Impacts Executive Order 13563 directs federal agencies to c onsider regulatory impacts on job creation and employment. According to the Executive Order, “our regulatory system must protect public health, welfare, safety, and our env ironment while promoting economic growth, innovation, competitiveness, and job creation. It m ust be based on the best available science” (Executive Order 13563, 2011). Although standard be nefit-cost analyses have not typically included a separate analysis of regulation-induced employment impacts, 21 we typically conduct employment analyses for economically significant ru les. This section discusses and projects

21 Labor expenses do, however, contribute toward total costs in the E PA’s standard benefit-cost analyses. Formatted Table5-19

potential employment impacts related to today’s pro posal, but the employment impacts have not been quantified. 22

Section 5.5.1 describes the theoretical framework u sed to analyze regulation-induced employment impacts, discussing how economic theory alone cannot predict whether such impacts are positive or negative. Section 5.5.2 pre sents an overview of the peer-reviewed literature relevant to evaluating the effect of env ironmental regulation on employment. Section 5.5.3 provides background regarding recent employme nt trends in the electricity generation, coal and natural gas extraction sectors. Section 5.5.4 d iscusses the potential direct employment impacts in these sectors. The direct employment imp acts are not quantified in this RIA.

5.4.1 Economic Theory and Employment Regulatory employment impacts are difficult to dise ntangle from other economic changes affecting employment decisions over time and across regions and industries. Labor market responses to regulation are complex. They depend on labor demand and supply elasticities and possible labor market imperfections (e.g., wage sti ckiness, long-term unemployment, etc.). The unit of measurement (e.g., number of jobs, types of jobs, hours worked, and earnings) may affect observability of that response. Net employment impa cts are composed of a mix of potential declines and gains in different areas of the econom y (e.g., the directly regulated sector, the environmental protection sector, upstream and downs tream sectors, etc.) over time. In light of these difficulties, economic theory provides a cons tructive framework for analysis. Microeconomic theory describes how firms adjust the ir use of inputs in response to changes in economic conditions. 23 Labor is one of many inputs to production, along w ith capital, energy, and materials. In competitive markets, firms choose inputs and outputs to maximize profit as a function of market prices and technological constra ints. 24,25 Berman and Bui (2001) adapt this model to analyze how environmental regulations affe ct labor demand. 26 They model environmental regulation as effectively requiring c ertain factors of production, such as pollution abatement capital, at levels that firms would not o therwise choose. Berman and Bui (2001) model two components that drive changes in firm-lev el labor 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 ca uses marginal production cost to increase, it

22 The employment analysis in this RIA is part of EPA’s ong oing effort to “conduct continuing evaluations of potential loss or shifts of employment which may result f rom the administration or enforcement of [the Act]” pursuant to CAA section 321(a).
23 See Layard and Walters (1978), a standard microeconomic t heory 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-minimization. 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 mod el.
27 The authors also discuss a third component, the impact of regu lation on factor prices, but conclude that this effect is unlikely to be important for large competitive factor mar kets, such as labor and capital. Morgenstern, Pizer and Shih (2002) use a very similar model, but they break the employme nt effect into three parts: 1) a demand effect; 2) a cost effect; and 3) a factor-shift effect.5-20

will place upward pressure on output prices, leadin g 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 d emand. As noted by Berman and Bui, although many assume that regulations must increase marginal cost, in some cases they may decrease it. A regulation could induce a firm to up grade to less polluting and more efficient equipment that lowers the marginal cost of producti on. In such a case, output could increase after firms comply with the regulation. An unregulated pr ofit-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 production costs. The substitution effect describes how, holding outp ut constant, regulation affects the laborintensity of production. Although increased environ mental regulation may increase use of pollution control equipment and energy to operate t hat equipment, the impact on labor demand is ambiguous. For example, equipment inspection requir ements, specialized waste handling, completing required paperwork, or pollution technol ogies that alter the production process may affect the number of workers necessary to produce a unit of output. Berman and Bui (2001) model the substitution effect as the effect of regu lation on pollution control equipment and expenditures required by the regulation and the cor responding 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 effec t of regulation on labor demand. In addition, the empirical literature illustrates difficulties with estimation of net employment impacts. The most commonly used empirical methods, for example, Green stone (2002), likely overstate employment impacts because they rely on relative co mparisons between more regulated and less regulated counties, which can lead to “double count ing” of impacts when production and employment shift from more regulated towards less r egulated areas. Thus these empirical methods cannot be used to estimate net employment e ffects. 28 The conceptual framework described thus far focused on regulatory effects on plant-level decisions within a regulated industry, but employme nt impacts at an individual plant do not necessarily represent impacts for the sector as a w hole. At the industry-level, labor demand is more responsive if: (1) the price elasticity of dem and for the product is high, (2) other factors of production can be easily substituted for labor, (3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total producti on costs. 29 For example, if all firms in an industry 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 regul ated industry, net employment impacts encompass changes in other related sectors, for exa mple, the environmental protection sector. This proposal may increase demand for the nitrogeno us reagent (typically ammonia or urea) used in SCRs and SNCRs to reduce NOx, which may increase revenue and employment in the firms providing these chemicals.

28 See Greenstone (2002) p. 1212. 29 See Ehrenberg & Smith, p. 108.
30 This discussion draws from Berman and Bui (2001), pp. 293.5-21

If the U.S. economy is at full employment, even a l arge-scale environmental regulation is unlikely to have a noticeable impact on aggregate n et employment. 31 Instead, labor in affected sectors would primarily be reallocated from one pro ductive use to another (e.g., from producing electricity to manufacturing, installing, or operat ing and maintaining pollution-abatement equipment), and net national employment effects fro m 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 req uire time to search for new jobs, while shortages in some sectors or regions could bid up w ages to attract workers. These adjustment costs can lead to local labor disruptions. If, on the other hand, the economy is operating at less than full employment, economic theory does not clearly indicate the direction or magnitud e of the net impact of environmental regulation on employment; it could cause either a short-run ne t increase or short-run net decrease (Schmalansee and Stavins, 2011). For example, the C ongressional 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 e conomic growth and employment, driven by capital investments for compliance with the regulat ions (Congressional Budget Office, 2011). Environmental regulation may also affect labor supp ly and productivity. In particular, reducing pollution and other environmental risks may improve labor productivity or employees’ ability to work. 33 While the theoretical framework for analyzing labo r supply effects is analogous to that for labor demand, it is more difficult to study emp irically. There is a small emerging literature that uses detailed labor and environmental data to assess these impacts. To summarize, economic theory provides a framework for analyzing the impacts of environmental regulation on employment. The net emp loyment effect incorporates expected employment 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 decompo sed into output and substitution effects which may be either negative or positive. Estimation of n et employment effects for regulated sectors is possible when data of sufficient detail and quality are available. Finally, economic theory suggests that labor supply effects are also possibl e. In the next section, we discuss the empirical
literature.

5.4.1.2 Current State of Knowledge Based on the Pee r-Reviewed Literature The peer-reviewed empirical literature specificall y estimating employment effects of environmental regulations is limited but growing. W e summarize it briefly in this section.

31 Full employment is a conceptual target for the economy where everyone who wants to work and is available to do so at prevailing wages is actively employed. The unemployme nt rate at full employment is not zero.
32 Arrow et al. 1996; see discussion on bottom of p. 8. In practice, distributional impacts on individual workers can be important, as discussed in later paragraphs of this section.
33 E.g. Graff Zivin and Neidell (2012).5-22

5.4.1.3 Regulated Sector Several empirical studies, including Berman and Bui (2001) and Ferris, Shadbegian, and Wolverton (2014), suggest that regulation-induced n et employment impacts may be zero or slightly positive, but small in the regulated secto r. Gray et al (2014) find that pulp mills that had to comply with both the air and water regulations i n EPA’s 1998 “Cluster Rule” experienced relatively small, and not always statistically sign ificant, decreases in employment. Other research on regulated sectors suggests that employment growt h may be lower in more regulated areas (Greenstone 2002, Walker 2011, 2013). However, sinc e these latter studies compare more regulated to less regulated counties, this methodol ogical approach likely overstates employment impacts to the extent that regulation causes plants to locate in one area of the country rather than another, which would lead to “double counting” of t he employment impacts. List et al. (2003) find some evidence that this type of geographic rel ocation may be occurring. 5.4.1.4 Economy-Wide Given the difficulty noted above with estimating na tional impacts of regulations, EPA has not generally estimated economy-wide employment impacts of its regulations in its benefit-cost analyses. However, in its continuing effort to adva nce the evaluation of costs, benefits, and economic impacts associated with environmental regu lation, EPA has formed a panel of experts as part of EPA’s Science Advisory Board (SAB) to ad vise 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 employment. 34 Once EPA receives guidance from this panel, it will carefully consider this input and th en decide if and how to proceed on economywide modeling of employment impacts of its regulati ons. 5.4.1.5 Labor Supply Impacts The empirical literature on environmental regulator y employment impacts focuses primarily on labor demand. However, there is a nascent litera ture focusing on regulation-induced effects on labor supply. 35 Although this literature is limited by empirical c hallenges, researchers have found that air quality improvements lead to reducti ons in lost work days (e.g., Ostro, 1987). Limited evidence suggests worker productivity may a lso 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 payment structure . Their quasi-experimental structure identifies an effect of daily variation in monitored ozone lev els on productivity. They find “ozone levels well below federal air quality standards have a sig nificant impact on productivity: a 10 parts per billion (ppb) decreases in ozone concentrations inc reases worker productivity by 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654). 36

34 For further information see: http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004 F87ED?OpenDocument 35 For a recent review see Graff-Zivin and Neidell (2013).
36 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this study. In light of this recent research, however, the EPA is considering how be st to incorporate possible productivity effects in the future.5-23

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 employment estimates that would approp riately capture the way in which costs, compliance spending, and environmental improvements propagate through the macro-economy.

5.4.2 Recent Employment Trends
The U.S. electricity system includes employees that support electric power generation, transmission and distribution; the extraction of fo ssil fuels; renewable energy generation; and supply-side and demand-side energy efficiency. This section describes recent employment trends in the electricity system.

5.4.2.1 Electric Power Generation In 2014, the electric power generation, transmissio n and distribution sector (NAICS 2211) employed about 390,000 workers in the U.S. Installa tion, maintenance, and repair occupations accounted for the largest share of workers (25 perc ent). These categories include inspection, testing, repairing and maintaining of electrical eq uipment and/or installation and repair of cables used in electrical power and distribution systems. Other major occupation categories include office and administrative support (18 percent), pro duction occupations (16 percent), architecture and engineering (10 percent), business and financia l operations (7 percent) and management (7 percent). As shown in Figure 5-3, employment in the Electric Power Industry averaged about 420,000 workers 2000 to 2005, declining to an avera ge of about 400,000 workers for the rest of the decade, and then declining to about 390,000 wor kers in 2014.5-24

Figure 5-3 Electric Power Industry Employment

5.4.2.2 Fossil Fuel Extraction Coal Mining. The coal mining sector (NAICS 2121) is primarily en gaged in coal mining and coal mine site development, excluding metal ore min ing and nonmetallic mineral mining and quarrying. In 2014, BLS reported about 74,000 coal mining employees (Figure 5-4). During the 2000 to 2014 period, coal mining employment peaked in 2011 at about 87,000 employees.

0 50 100 150 200 250 300 350 400 450 500 

### Power Generation and Supply Employment

(NAICS = 2211, Annual Average, 1000s of Employees)5-25

Figure 5-4 Coal Production Employment

Oil and Gas Extraction. In 2014, there were close to 200,000 employees in t he oil and gas extraction sector (NAICS 211). This sector includes production of crude petroleum, oil from oil shale and oil sands, production of natural gas, sul fur recovery from natural gas, and recovery of hydrocarbon liquids. Activities include the develop ment of gas and oil fields, exploration activities for crude petroleum and natural gas, dri lling, completing, and equipping wells, and other production activities. In contrast with coal, Figure 5-5 shows there has been a sharp increase in employment in this sector over the past decade.5-26

Figure 5-5 Oil and Gas Extraction Employment

5.4.3 Power and Fuels Sector Direct Employment Impa cts As described above, affected EGUs may respond to th e CSAPR update by upgrading or improving performance of existing combustion contro ls, or by upgrading, improving, or utilizing post-combustion NO X systems already in place. In addition, some net ge neration 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 typ es of amount of labor needed in different parts of the fuels and utility power sectors. There also may be other labor impacts in sectors that provide products and materials used in reducing NO X emissions at EGUs, such as catalysts used in SCR control systems. These direct labor impacts will likely include both increased demand for certain types of labor in some portions of the affe cted sectors, and reduced demand for labor in other portions of the affected sectors.

Installing and operating new equipment could change labor demand in the electricity generating sector itself, as well as associated equ ipment and services sectors. Specifically, the direct employment effects in the power sector that could occur because 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)5-27

• Optimizing NO X removal from existing and operational SCR and SNCR systems;

• Turning on and optimizing idled SCR and SNCR system s;

• Installing or upgrading combustion-side improvement s resulting in reduced NO X emissions;

• Shifting generation from units with higher NO X emission rates to units with lower emission rates.
In addition, there could be directly induced employ ment impacts (both positive and negative) in the labor demand in the fossil fuels i ndustry supplying fuels to the power sector. Once implemented, both the potential increases in o perating efficiency and NO X reductions, as well as and shifting generation to lower NO X emitting assets, could impact the utility power sector’s demand for fossil fuels, and hence the dem and for labor needed in the coal mining and gas extraction sectors.

The direct net employment impacts of this proposed rule, however, are anticipated to be relatively small. This is consistent with the relat ively small estimated changes in the power sector’s overall cost of generation, as well as rel atively small changes in generation, fuel use, capacity, and the percent of total generation produ ced by each type of fuel.

For example, for the proposal in 2017, the estimate d impacts relevant to changes in labor
demand include:

• The overall total national cost of generation in 20 17 increases by 0.046 percent (the levelized annualized total cost of generation incre ases by 0.01 percent);

• Total net generation increases by 0.009 percent (co al generation decreases by .084 percent, and natural gas generation increases by 0.11 percen t);

• The power sector’s total tons of coal used for elec tricity 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 sugg est the direction and magnitude 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 rep ort.5-28

5.5 Social Costs As discussed in the EPA Guidelines for Preparing Ec onomic Analyses, social 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 ac tion, where an opportunity cost is the value lost to society of any goods and services that will not be produced and consumed as a result of reallocating some resources towards pollution mitig ation. Estimates of social costs may be compared to the social benefits expected as a resul t of a regulation to assess its net impact on society. The social costs of a regulatory action wi ll not necessarily be equivalent to the expenditures associated with compliance. Nonetheles s, here we use compliance costs as a proxy
for social costs.

The cost estimates for the three alternative NO X cost threshold scenarios presented in this chapter are the change in expenditures required by the electricity sector for compliance under each alternative, with a focus on the proposed reme dy scenario. The change in the expenditures required by the electricity sector to maintain comp liance reflect the changes in electricity production costs due to changes in electricity dema nd resulting from application of the control strategies described in Chapter 4 and changes in el ectricity production costs required to achieve the additional emission reductions necessary to com ply with the emissions budgets that are described earlier in this RIA. The changes in elect ricity production costs and changes in demand are estimated by IPM as described earlier in Chapte r 5, and the change in expenditures required by the electricity sector to maintain compliance ar e also described earlier in Chapter 5.

5.6 Secondary Economic Impacts The energy sector impacts presented earlier in Chap ter 5 of this RIA include potential changes in the prices for electricity, natural gas, and coal potentially resulting from the proposal. This chapter addresses the impact of these potentia l 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 analysis of costs for this proposal includes st rategies 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, given the flexibilities afford ed EGUs and states in complying with the5-29

proposed rule, the benefits, cost and economic impa cts reported in this RIA are illustrative of actions that states may take. The implementation ap proaches adopted by the states, and the strategies adopted by affected EGUs, will ultimatel y drive the magnitude and timing of secondary impacts from changes in the price of elec tricity, and the demand for inputs by the electricity sector, on other markets that use and p roduce these inputs.

To estimate the costs, benefits, and impacts of imp lementing the proposal, the EPA modeled an illustrative compliance approach for the proposal and more and less stringent alternatives. Chapter 4 provides a description of t he proposal and the more and less stringent alternatives considered. This chapter provides a qu antitative assessment of the energy price impacts for these illustrative approaches and a qua litative assessment of the factors that will in part determine the timing and magnitude of effects in other markets.

5.6.1 Methods One potential quantitative approach to evaluating t he secondary market impacts is to use a computable general equilibrium (CGE) model. CGE mod els are able to provide aggregated representations of the whole economy in equilibrium in the baseline and potentially with regulation in place. As such, a CGE model may be ab le 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 2008 F inal Ozone NAAQS (U.S. EPA 2008) and the 2010 Transport Rule proposal (U.S. EPA 2010a), the EPA used the Economic Model for Policy Analysis (EMPAX) CGE model to estimate the s econdary market effects based on the cost impacts projected by the Integrated Planning M odel (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 int eraction may be relevant. Therefore, in the Second Prospective Analysis under Section 812 of th e 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 economy-wide impacts of the Clean Air Act (U.S. EPA 2011). While the external Council on Clean Air Compliance Analys is (Council) review of this study stated that inclusion of benefits in an economy-wide model “rep resent[ed] a significant step forward in benefit-cost analysis” (Hammitt 2010), the EPA reco gnizes that serious technical challenges5-30

remain when attempting to evaluate the benefits and costs of potential regulatory actions using economy-wide models.

In light of these challenges, the EPA has establish ed a Science Advisory Board (SAB) panel on economy-wide modeling to consider the tech nical merits and challenges of using this analytical tool to evaluate costs, benefits, and ec onomic impacts in regulatory development. In addition, EPA is asking the panel to identify poten tial paths forward for improvements that could address the challenges posed when using economy-wid e models to evaluate the effects of regulations. The final panel membership was announced in March 2 015 and the first of multiple face-to-face meetings of the SAB panel has been sch eduled for October 2015. The EPA will use the recommendations and advice of this panel as an input into its process for improving benefitcost and economic impact analyses used to inform de cision-making at the agency.

The advice from the Science Advisory Board (SAB) pa nel formed specifically to address the subject of economy-wide modeling was not availa ble in time for this final action. Given the ongoing SAB panel on economy-wide modeling, and the ongoing challenges of accurately representing costs, benefits, energy efficiency imp rovements in economy-wide modeling, this chapter considers the energy impacts associated wit h the proposed alternatives analyzed and a qualitative assessment of the factors that will, in part, determine 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 in puts 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 types and levels of inputs used by p roducers in response to electricity and fuel price changes may mitigate the production cost chan ges in these sectors. Such changes in production costs may lead to changes in the quantit ies and/or prices of the goods or services produced and changes in imports and exports.

The EPA used IPM to estimate electricity, natural g as, and coal price changes based on compliance with the regulatory control alternatives for this rule. IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector described in5-31

more detail earlier in Chapter 5. The Retail Price Model (RPM) uses estimated changes in wholesale prices to estimate changes in average ret ail prices. The prices are average prices over consumer classes and regions weighted by the amount used. Table 5-9 shows these estimated price changes for the proposal in 2017 and, for ill ustrative purposes only, 2020. For other results generated by IPM and the RPM, please refer to earli er sections in Chapter 5.

There are many factors influencing the projected na tural gas prices. IPM (and its integrated gas resource and supply module) models natural gas natural gas supplies based on a multitude of factors. Since the model simulates perfect foresigh t, it anticipates future demand for natural gas and responds accordingly. In addition, IPM (and the natural gas module) are viewing a very long time horizon (through 2050), such that the impacts in certain years may be responsive to other modeling assumptions or drivers. The modeling frame work is simultaneously solving for all of these key market and policy parameters (both electr ic 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 Alternative*

Proposed Rule 2017 2020 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 years when the price of electricity, natural ga s, or coal increased, one would expect decreases in production and increases in market prices in sec tors for which these commodities are inputs, ceteris paribus. Conversely, for years when prices of these inputs decreased, one would expect increases in production and decreases in market pri ces within these sectors. Smaller changes in input price changes would lead to smaller impacts w ithin secondary markets. For compliance with the proposal and more and less stringent alter natives these price changes are quite small, or even negative, in 2017. Thus, one could readily sur mise that the impacts of this proposed rule upon secondary markets should be minimal. As seen i n Table 5-9, most of the price changes are negative, thus showing an estimated decrease in ene rgy prices, albeit very small. The variation for these prices at the regional level is not subst antially different from the national-level price changes, and the variation of energy prices related to the proposal and the regional level can be found among the IPM outputs in the public docket fo r this rulemaking. However, a number of factors, in addition to the magnitude and sign of t he energy price changes, influence the magnitude of the impact on production and market pr ices for sectors using electricity, natural gas, or coal as inputs to production. These factors are discussed below.5-32

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 commod ities. For sectors in which the directly affected inputs are only a small portion of product ion costs, the impact will be smaller than for sectors in which these inputs make up a greater por tion of total production costs. Therefore, more energy-intensive sectors would potentially experien ce greater cost increases when electricity, natural gas, or coal prices increase, but would als o experience greater reduced costs when these input prices decrease.

5.6.4 Ability to Substitute between Inputs to the P roduction Process
The ease with which producers are able to substitut e other inputs for electricity, natural gas, or coal, or even amongst those commodities, in fluences the impact of price changes for these inputs. Those sectors with a greater ability to substitute across energy inputs or to other inputs will be able to, at least partially, 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 p lace of other more costly substitutes.

5.6.5 Availability of Substitute Goods and Services The ability of producers in sectors experiencing ch anges in their input prices to pass along
the increased costs to their customers in the form of higher prices for their products depends, in part, on the availability of substitutes for the se ctors’ products. Substitutes may be either other domestic products or foreign imports. If close subs titutes exist, the demand for the product will in general be more elastic and the producers will b e less able to pass on the added cost through a price increase.

Such substitution can also take place between forei gn and domestic goods within the same sector. Changes in the price of electricity, natura l 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 relatively less costly foreign-produced goo ds, and vice versa.5-33

5.6.6 Effect of Changes in Input Demand from Elect ricity 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 elec tricity sector. A change in demand for inputs in the electricity sector, as well as changes in deman d for energy efficiency services and products, will also influence economic activity in other sect ors of the economy. For example, there could be changes in the demand for new generation sources such as natural gas combined cycle units, and therefore sectors producing technologies such a s these may expand.

5.6.7 Conclusions Changes in the price of electricity, natural gas, a nd coal can affect markets for goods and services produced by sectors that use these energy inputs in the production process. The direction and magnitude of these impacts are influenced by a number of factors. For example, the more producers in these sectors are able to substitute a way from the use of these energy inputs, the smaller the effect of energy prices changes will be on their production cost. Changes in cost of production may lead to changes in price, quantity p roduced, and profitability of firms within secondary markets. Furthermore, the demand inputs i n 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 IPM influence the estimated cha nges in electricity, natural gas, and coal prices in this RIA. Actual market conditions w ill ultimately influence the price changes of these energy inputs and consequent effects on secon dary markets, as will the plan approaches that states adopt.

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