Demonstrating NOx Emission Reduction Benefits of State-Level Renewable Energy and Energy Efficiency Policies
I. Background 
EPA encourages air agencies to consider a multi-pollutant approach when assessing compliance options for ozone reasonable further progress (RFP) and attainment demonstration SIPs. Multi-pollutant approaches include the use of energy efficiency (EE) and renewable energy (RE) programs. EPA has provided resources to help air planners include these types of programs in a NAAQS State Implementation Plan (SIP) or Tribal Implementation Plan (TIP), and we have used these resources to conduct the analysis presented in this Technical Support Document (TSD). 
For the purpose of demonstrating the emission reduction benefits of state-level Renewable Portfolio Standards (RPS) and energy efficiency policies, such as Energy Efficiency Resource Standards (EERS) and Public Benefit Funded (PBF) programs, EPA developed an illustrative analysis to determine the nitrogen oxide (NOx) emission increases that would take place if these policies were not been in place from 2015 to 2020. The analysis encompasses counties in four states (Connecticut, New Jersey, New York and Texas) that were in nonattainment for the 2008 standard and are at risk of being designated in nonattainment for the 2015 ozone standard. An overview of the analysis can be found in Section V.E of the Preamble. 
II. Incorporating EE/RE Programs in SIPs/TIPs
EPA has provided several resources to help state air planners include EE and RE in a SIP, including the "Roadmap for Incorporating EE/RE Programs and Policies in NAAQS SIPs/TIPs," which provides four pathways for incorporating EE/RE policies and programs. The methodology that EPA describes in this TSD is consistent with one of those pathways, the baseline emissions projection pathway, which is.
Projecting future emissions from the electric power sector normally requires an electricity demand forecast (a baseline forecast) that is used as a basis for predicting how generation requirements will change over time. There are a variety of demand forecasts available, including the U.S. Energy Information Administration's (EIA) Annual Energy Outlook (AEO). It is important to understand the assumptions made in a forecast, including which EE/RE programs have already been incorporated.
In general, EPA's suggested approach to estimating the electricity effects of EE/RE policies is described in six steps:  Step 1: Choose a baseline forecast for electricity demand projections. Step 2: Document EE/RE policies already included in baseline electricity demand projections. Step 3: Identify "on-the-books" EE/RE policies not included in AEO or the chosen baseline electricity demand forecast. Step 4: Estimate the incremental electricity savings of the "on-the-books" EE/RE policies not included in the chosen baseline electricity demand forecast. Step 5: Incorporate the incremental electricity impacts of EE/RE policies and lower projected electricity demand in the model. Step 6: Project the change in power sector emissions attributable to the incremental effects of "on-the-books" EE/RE policies for future attainment year(s).  Again, these steps, along with additional information, are detailed in the U.S. EPA's "Roadmap for Incorporating EE/RE Policies and Programs in SIPs/TIPs."
III. Overview of Illustrative Analysis
To conduct this illustrative analysis of the NOx impacts of EE and RE policies, EPA first generated a "business-as-usual" base case reflecting projected changes in electricity demand and the expected impacts of "on-the-books" energy efficiency and renewable energy policies in Connecticut, New Jersey, New York, and Texas in 2020. EPA then considered a set of counterfactual policy cases in which existing EE and/or RE policies in these states were not in place -- cases in which, compared to the base case, electricity demand was higher and/or renewable electricity generation was lower due to the removal of EE/RE polices. Specifically, EPA tested three counterfactual policy cases: removal of EE policies; removal of RE policies; and removal of EE and RE policies. The changes in electricity demand and RE generation in these cases are expected to be offset by increased fossil-fired generation; therefore, EPA used its AVoided Emissions and geneRation Tool (AVERT) to compare average NOx emissions between the base case and these policy cases on the ten highest temperature days. EPA found higher levels of NOx emissions in each policy case examined.
Table 1 summarizes the state policies used in this analysis. For the energy efficiency portion of the analysis, EE policy targets or program funding levels were used to derive energy savings in 2020, which were then translated into percent changes in the amount of energy generated by fossil sources. For the renewable energy portion of this analysis, renewable generation requirements phrased as a percentage of electricity sales were used to find the amount of electricity (in GWh) expected to be generated by wind- and solar-powered facilities in order to satisfy these requirements in each state in 2020. This amount of electricity was then converted to an equivalent capacity (in MW) of wind and solar facilities. 
The methodology used by EPA to determine the EE savings and RE generation under these scenarios is described in sections IV through VIII below. Appendix 1 shows EPA's calculations for this analysis, and Appendix 2 contains individual AVERT runs for base and policy cases in each applicable AVERT region.   
Only the incremental electricity savings of "on-the-books" EE/RE policies from a baseline year to a future year would be used for SIP credit. In developing SIPs to address ozone planning obligations, a common exercise is to evaluate emissions changes between a base year and a future year, such as the year by which the air quality standard must be met. The period of our analysis of EE/RE policies is 2015 to 2020, meaning that we examined the emissions impact of the incremental EE/RE requirements that will be in effect in 2020 as compared to the requirements in 2015. In other words, our analysis tests the emissions reductions impacts that are expected to be realized in years 2016 through 2020 due to the implementation of energy efficiency and renewable energy policies in these years.
We used this timeframe to facilitate the inputs available for the AVERT modeling run (i.e., 2015 is the most recent data year available at the time of this writing), and because it is reasonably contemporaneous to the SIP planning timeframe of a moderate nonattainment area under the 2015 ozone standard, which as described in the Preamble, is expected to run from a base year of 2017 to an attainment year of 2023. To illustrate the broader impacts these policies have on emissions, EPA also illustrates the full benefits (i.e., since the policies' initial implementation) of RE policies as well. 
Table 1. Overview of EE/RE Policies Analyzed
State
EERS, EE Goal, or EE Program Funding
RE Requirement or RPS
AVERT Region(s)
CT
Requirement to procure all cost-effective energy efficiency resources, which equates to about 1.7% of annual retail sales savings based on approved CT DEEP budgets in IRP
27% of retail load by 2020
Northeast
NJ
$265 Million in annual funding for EE
19% of retail sales by 2020
Great Lakes/Mid-Atlantic and Northeast
NY
600 trillion Btu increase in statewide energy efficiency by 2030
50% of retail load by 2030
Northeast
TX
Energy savings calculated using a capacity factor of 20%, and reductions in annual growth in residential and commercial demand of 20% in 2010 and 2011, 25% in 2012, and 30% in 2013 and thereafter.
Additional 10,000 MW of wind between 2015 and  2020 
Texas 
a. Study Area and Period

This analysis was conducted for a subset of counties in four states -- Connecticut, New Jersey, New York, and Texas -- that were in nonattainment for the 2008 standard and are also at risk of being designated in nonattainment for the 2015 ozone standard. These counties are listed in Table 2.

Table 2. Counties Included in Study Area

Dallas-Ft. Worth, TX 
2008 Ozone Nonattainment Area
New York-N. New Jersey-Long Island, NY-NJ-CT 2008 Ozone Nonattainment Area
 Collin County, TX
 Dallas County, TX
 Denton County, TX
 Ellis County, TX
 Johnson County, TX
 Kaufman County, TX
 Parker County, TX
 Rockwall County, TX
 Tarrant County, TX
 Wise County, TX
 Fairfield County, CT
 Middlesex County, CT
 New Haven County, CT
 Bergen County, NJ
 Essex County, NJ
 Hudson County, NJ
 Hunterdon County, NJ
 Middlesex County, NJ
 Monmouth County, NJ
 Morris County, NJ
 Passaic County, NJ
 Somerset County, NJ
 Sussex County, NJ
 Union County, NJ
 Warren County, NJ
 Bronx County, NY
 Kings County, NY
 Nassau County, NY
 New York County, NY
 Queens County, NY
 Richmond County, NY
 Rockland County, NY
 Suffolk County, NY
 Westchester County, NY

In conducting its analysis, EPA analyzed the impacts of noted EE and RE policies on average NOX emissions in these at-risk counties on the 2015 top ten high temperature days. EPA grouped these counties into two nonattainment areas (Dallas-Ft. Worth, TX and New York-N. New Jersey-Long Island, NY-NJ-CT) and analyzed the impact of policy changes on NOx emissions in each area. 
b. Use of AVERT
EPA used its AVoided Emissions and geneRation Tool (AVERT) to conduct this analysis. AVERT is a tool designed to estimate changes in emissions resulting from changes in the amount of electric demand that must be met by fossil-fired generation. In order to analyze the impacts of a given RE or EE measure on emissions, users select an AVERT region for analysis and then input a regional data file (RDF), which is based on historical generation from a recent year and is used in AVERT's main module. In addition, users input a desired change in renewable energy or energy efficiency that will effect a change in the amount of generation that must be supplied by fossil-fired electric generating units (EGUs). Changes in renewable energy are input as additions or subtractions of wind- or solar-powered capacity. Changes in energy efficiency are input as a set amount of energy or percent changes in demand, divided equally throughout every hour of the year, or as percentage change to a percentage of hours. Changes in demand in general can also be simulated using these inputs.
AVERT divides the contiguous 48 states of the United States into ten regions, as shown in Figure 1, and models these regions as independent in terms of their demand for and supply of electricity. AVERT assumes that changes in demand within a region are responded to only by EGUs within that region, implying that any transmission between regions is constant over time and regardless of the implementation of EE, RE, or other changes in demand. 
The boundaries of the AVERT regions are based on NERC regions of the contiguous United States electrical grid. However, electrical boundaries do not follow state boundaries.  Only 26 states lie entirely within one AVERT region. The remaining 22 states are split across AVERT boundaries.  For states covered by multiple regions, EPA recommends conducting analyses for all applicable AVERT regions, using an apportionment based on how much of that state's fossil-fired generation is found in each relevant AVERT region. Table 3 below summarizes the AVERT regions into which the states in this analysis fall and the percentage of each state's fossil-fired electric generation found those AVERT regions. 

Figure 1. Map of AVERT Regions

Table 3. State Apportionment in AVERT Regions, Based on Generation from 2010-2013
                                       
                                 AVERT Regions
                                     State
                                   Northeast
                                 Great Lakes/
                                 Mid Atlantic
                                   Southeast
                                 Lower Midwest
                                     Texas
                                      TX
                                       -
                                       -
                                      6%
                                      12%
                                      82%
                                      CT
                                     100%
                                       -
                                       -
                                       -
                                       -
                                      NY
                                     100%
                                       -
                                       -
                                       -
                                       -
                                      NJ
                                      23%
                                      77%
                                       -
                                       -
                                       -
IV. Base Case Methodology
Using an appropriate base case is key to obtaining meaningful results in a counterfactual analysis and estimating the emissions impacts of EE/RE policies. Because this analysis uses AVERT RDFs based on 2015 generation, generating a "business-as-usual" base case requires projecting fossil-fired EGU generation in 2020 resulting from expected changes in demand and the results of existing policies. As such, the base case factors in changes that are expected to occur between 2015 and 2020 given "on-the-books" RPS, EERS, and PBF policies described in Table 1 above. The counterfactual policy cases examine the impacts of EE and RE programs; therefore, the definition of the base case must include both a projection of future demand and a projection of how much of that demand will be met by fossil-fired resources.
Because EPA's analysis uses AVERT, the base case was developed on a regional basis using the regions defined in AVERT. As discussed above, AVERT treats the contiguous United States as a set of independent regions. Importantly, in this framework, changes in demand for electricity within a given region are satisfied by supply resources located only within that AVERT region. However, within an AVERT region, demand at any location can be satisfied by supply resources from throughout the AVERT region. This has important implications for setting an appropriate base case in AVERT: because generators from any state within a region are expected to respond to changes in demand in any state within that region, the base case must consistently represent expected changes for the entire AVERT region. Because we are looking at Connecticut, New Jersey, New York and Texas, EPA's base case includes a projection of future demand and fossil-fired generation for three AVERT regions: the Northeast (NE) region, the Great Lakes/Mid-Atlantic (EMW) region, and the Texas (TX) region.
a. Calculating the Base Case
To formulate its 2020 base case, EPA projected changes in demand for fossil-fired electricity generation in light of the savings in overall demand expected from "on-the-books" EE and the reduction in demand for fossil energy specifically due to RE policies. This projection generated inputs for AVERT reflecting demand for electricity and installed RE generation capacity in 2020, relative to 2015. 
First, EPA projected total demand for electricity in 2020. EPA's electricity demand projection was based on the AEO 2013 reference case, which includes some, but not all, EE policy and program savings. It was therefore necessary to adjust AEO values to include the entire expected outcome of EE policies. EPA previously developed a revised demand projection, which is taken as an input for this analysis; EPA provides greater detail on its methodology in the "Roadmap for Incorporating Energy Efficiency/Renewable Energy Policies and Programs into State and Tribal Implementation Plans." 
Expected changes in demand were calculated by summing expected demand for every state in a given region in both 2015 and 2020 (in accordance with the regional apportionment scheme described in the AVERT manual and Table 3 above). Demand for a given region in 2015 is denoted Do while future demand in 2020 for that region in the base case (with EE policies in place) is denoted Df. The projected change in demand in percentage terms (df) is simply the expected difference in demand between 2020 and 2015 (Df  -  Do) divided by Do:
      df = (Df  -  Do) / Do
Second, EPA needed to translate the expected change in demand from 2015 to 2020 into an expected change in fossil-fired generation. EPA made the simplifying assumption that the percentage of demand that is met by fossil-fired resources will stay constant on the regional scale apart from the impact of incremental renewable energy as required by RPS policies in the states of interest. In other words, if fossil-fired generation met 90% of demand in a given region in 2015, EPA assumed that fossil-fired generation will also meet 90% of demand in that region in 2020 (less the additional renewable energy  expected to be added due to RPS policies, which is accounted for separately in the base case as discussed below). The validity of this assumption will vary for different regions. Thus, EPA assumed that the percentage change in demand (df) would also be the percentage change in fossil generation. As such, baseline fossil-fired generation in 2020 (Ff) can be estimated as: 
	Ff = Fo x (1+df)
where Fo represents fossil-fired generation in 2015, as found in the 2015 AVERT regional data files. EPA further assumed that the percentage change in fossil generation would be spread evenly throughout the year. In other words, it was assumed that the amount of fossil generation in every hour would change by df percent. 
Third, EPA needed to determine the incremental amount of renewable energy generation capacity that would be installed between 2015 and 2020 to satisfy the requirements of each state's "on-the-books" RE policies. EPA's approach to calculating the amount of RE required in the base case (as well as in cases testing the impact of the incremental and full impact of states' RPS policies) is described in Section VI.
Based on these calculations, the expected change in fossil demand and additional RE capacity input into AVERT for each region for this base case is presented in Table 4.
Table 4. Effects of Incremental EE/RE Policies in Base Case

                                   Northeast
                                     (NE)
                           Great Lakes/Mid-Atlantic 
                                     (EMW)
                                    Texas 
                                     (TX)
EE Policy
RE Policy
                                   % growth
                                   Wind (MW)
                                      PV 
                                     (MW)
                                   % growth
                                     Wind
                                     (MW)
                                      PV
                                     (MW)
                                   % growth
                                     Wind
                                     (MW)
                                      PV
                                     (MW)
Incremental effects of noted EE policies in 2015-2020 (df)

Incremental effects of noted RE policies in 2015-2020 (Rinc)

                                    -4.72%
                                     3,757
                                      764
                                     0.64%
                                      5 
                                    1,840 
                                     4.55%
                                    10,000
                                       0
V. Effect of EE Goals, EE Program Funding and EERS Policies
To estimate the potential emissions that would be expected to be emitted if not for states' "on-the-books" EE policies, EPA developed a counterfactual policy case that removes current state-level EE policies and programs in Connecticut, New Jersey, New York, and Texas. EPA estimated cumulative incremental impacts (in megawatt-hours) from 2015 to 2020 of EE policies listed in Table 1. It was assumed that the entirety of increased demand due to the absence of EE policies would be met by increased generation from fossil-fired EGUs. An illustration of the policy and base cases is presented in Figure 2.
Figure 2. Approach to Removal of State EE Policies in Electricity Demand Forecast

To simulate in AVERT the expected change in demand between 2015 and 2020 and the result of removing these EE policies, EPA began with the amount of fossil-fired generation expected in 2020, as determined in the base case (Ff). EPA then added the amount of increased generation that must be supplied by fossil-fired EGUs in the absence of EE policies (denoted FEE) to the amount of fossil-fired generation expected in the base case:
	Fn = Ff + FEE
This amount of fossil-fired generation (Fn) can be understood as the amount of generation in 2020 that would be required had demand grown without modifications from the implementation of energy efficiency policies. Finally, EPA found the percentage change between fossil generation in 2015 and expected fossil generation in 2020 in the absence of EE programs and policies:
      dn = (Fn - Fo) / Fo 
The result of these calculations are the regional growth rates in Table 4. 
Table 5. 2020 Regional Electricity Demand Growth with and without EE Policies
                                       
                               New England (NE)
                               Great Lakes (EMW)
                                  Texas (TX)
EE Policy
                                   % growth
                                   % growth
                                   % growth
Base Case with "on-the-books" policies in place in 2020 (df)
(Included in the Base Case, as noted in Table 4)
                                    -4.72%
                                    0.64 %
                                     4.55%
Policy Case removing EE policies in NJ, NY, CT and TX in 2020 (dn)
                                     5.48%
                                     1.41%
                                     5.06%







VI. Effect of RE Requirements and RPS Policies 
To estimate the potential emissions that would be expected to be emitted if not for states' "on-the-books" RPS policies, EPA developed a second counterfactual policy case that removes the incremental impacts of  current state-level RE requirements in Connecticut, New Jersey, and New York. EPA also tested the emissions reductions impact that would be expected if Texas doubled its currently "on-the-books" RE requirement. Finally, as described below, EPA also tested the full impacts if these policies had never been implemented at all. 
Connecticut, New Jersey, and New York have RPS policies that set forth renewable energy generation goals as a percentage of electric sales in those states. These policies commenced in different years but all were in place by 2015. As a result, some renewable energy generation was required by all three states in 2015. This percentage is denoted as ro. In each of the three states, the percentage of sales that must be met by renewable energy in 2020 (denoted as rf) is higher than that required in 2015. Therefore, it is expected that RE generation will increase to meet these higher standards. In accordance with AVERT's regional allocation scheme, it was assumed that the renewable generation needed to meet each state's RPS is and will continue to be located within the AVERT region(s) containing each state. For example, it was assumed that all of the renewable generation necessary to meet the New York RPS would be located in the Northeast AVERT region and that the renewable generation necessary to meet the New Jersey RPS would be split between the Northeast and Great Lakes/Mid-Atlantic AVERT regions according to the apportionment described in Table 3 above. In calculating the amount of renewable generation for this analysis, EPA used the most recent RPS policies that are "on the books" for New Jersey and Connecticut and anticipated an RPS update for New York that is likely to be "on the books" before a reasonable further progress (RFP) ozone SIP submittal. 
Texas' RPS is unlike that of the other three states and requires a certain amount of RE capacity (10,000 MW) to be installed by 2025 rather than requiring that a percentage of sales be met by RE generation. The amount of RE capacity installed in Texas in 2015 more than meets this requirement and, as such, it is not expected that any increase in RE generation between 2015 and 2020 will be attributable to the RPS currently "on the books." Therefore, Texas was treated differently than the other states in this counterfactual analysis. Instead of estimating the impact of the RE capacity required by the current RPS, EPA tested the effect of a "what-if" scenario in which Texas amends its RPS to require an additional 10,000 MW of RE capacity by 2020, doubling the current requirement. EPA assumed that all of the additional RE capacity used to meet this requirement would be wind-powered and would be located in ERCOT (which is represented in AVERT as the Texas region).
a. Incremental Effect of RE Requirements and RPS Policies
For all states, the amount by which required RE generation in 2020 is expected to be higher than required RE generation in 2015 is referred to here as the incremental RPS requirement. The entire amount of RE generation required by the RPS policies in 2020, regardless of what was required in 2015, is referred to as the full RPS requirement. Because the incremental RPS requirement implies a smaller amount of RE generation than the full RPS requirement, the emissions savings associated with the incremental RPS requirement (the incremental RPS savings) are smaller than the emissions savings associated with the full RPS requirement (the full RPS savings). As discussed above, for SIP purposes only the incremental savings and RE generation would be used for SIP credit.
In examining the impact of RPS policies in isolation, the amount of RE generation expected under each state's full RPS requirement (Rfull) was calculated as:
	Rfull = rf x Df
where the full amount of RE generation required in 2020 is the product of demand in 2020 (Df) and the percentage of sales that must be met by RE in 2020 (rf). 
In order to calculate the amount of RE generation expected to be added under each state's incremental RPS requirement (Rinc), EPA first estimated the amount of RE required by RPS policies in 2015 (Ro) as the product of the RPS requirement in 2015 (ro) and total demand in 2015 (Do):
      Ro = ro x Do
The incremental RPS requirement (Rinc) was then calculated as the difference between the 2020 requirement (Rfull) and 2015 requirement:
	Rinc = Rfull  -  Ro
In New Jersey, the expected amounts of RE generation were apportioned between the Northeast and Great Lakes/Mid-Atlantic regions as described above. Expected amounts of renewable generation were translated into equivalent amounts of wind- and solar-powered capacity in two steps. First, the total amount of expected RE generation was apportioned by technology using the regional distribution coefficients found by LBNL/NREL in their retrospective study on RPS compliance. Then, expected wind- and solar-powered generation was converted to an equivalent capacity using AVERT's default capacity factors for these technologies, which vary by region. EPA treated all solar-powered generation in this analysis as utility-scale solar rather than dividing such generation between the utility- and distributed-scale. 
Because the incremental RE requirement is expected to be added between 2015 and 2020, incremental RE requirements determined using this methodology were added to a given region in the base case. 
In order to test the impact of these incremental RE additions, EPA developed a policy case that adjusted fossil demand to account for expected changes between 2015 and 2020 with EE policies in place (as described in Section IV) but that did not add RE capacity to the system. Without incremental RE, demand on the fossil system is increased, resulting in increased emissions. The impact of incremental RPS requirements was calculated based on the difference in emissions with and without incremental additions of RE. Table 6 presents the expected changes in RE capacity for each region in the base case, reflecting these incremental RE requirements. 
b. Full Effect of RE Requirements and RPS Policies
EPA also tested the impact of the full RPS policies in Connecticut, New Jersey, and New York by creating a policy case in which fossil generation was increased to supply the entire amount of energy required by RPS policies in 2020. This scenario tests a case in which the full amount of RE required by RPS policies in 2020 (Rfull) was removed from the system and was compared to the base case. Because the base case includes the incremental RE required by RPS policies in 2020, EPA's method of testing the full RPS was to perform a run in which the incremental RE (Rinc) was not added and the amount of RE required in 2015 (Ro) was removed from the system. The difference in RE between the base case and the policy case testing the full RPS requirement (Rfull) is therefore the sum of Rinc and Ro:
      Rfull = Rinc + Ro
EPA assumed that all RE removed from the system would be compensated for by increased generation by fossil-fired EGUs, resulting in increased emissions. Table 5 presents the expected changes in RE capacity for each region in this policy case.
Table 6. 2020 Renewable Energy Capacity Based on State RE Requirements

                                   Northeast
                                     (NE)
                           Great Lakes/Mid-Atlantic 
                                     (EMW)
                                    Texas 
                                     (TX)
Resource Type
                                     Wind
                                     (MW)
                                      PV
                                     (MW)
                                   Wind (MW)
                                      PV
                                     (MW)
                                     Wind
                                     (MW)
                                      PV
                                     (MW)
Incremental amount of renewable energy added between 2015 and 2020 as required by state policies (Rinc)
(Included in the base case, see Table 4)
                                     3,757
                                      764
                                       5
                                     1,840
                                    10,000
                                       -
Full amount of renewable energy required by state policies in 2020 (Rfull)
                                    24,498
                                     2,864
                                      15
                                     5,022
                                       -
                                       -
Amount of renewable energy required by state policies in 2015 removed from system to simulate the full effects of state policies (Ro)
                                    20,741
                                     2,100
                                       9
                                     3,182
                                       -
                                       -
VII. Joint Effects of EE and RE Policies
To estimate the potential emissions that would be expected to be emitted if not for both states' "on-the-books" RPS policies and "on-the-books" EE policies, EPA developed a third counterfactual policy case that removes state-level EE and RE policies. When testing these policies in concert, an adjustment must be made to account for the fact that RPS policies are stated in terms of electricity sales, and electricity sales are expected to be higher in the absence of the savings associated with EE policies.  
Similar to the previous policy case, the amount of RE generation needed to satisfy the full RPS requirement in 2020 was calculated as:
	R'full = rf x Dn
In other words, the amount of renewable generation required by the full RPS (R'full) is simply the product of the RPS requirement in 2020 (rf, which is independent of any EE policies) and the amount of demand expected in the absence of EE policies (Fn, as elaborated upon in section 5 above). 
As before, the amount of RE generation expected under each state's incremental RPS requirement (Rinc) was calculated as the difference between the full amount of RE generation required by state RPS policies (R'full) and the full amount of RE generation required by state RPS policies in 2015 (Ro):
      R'inc = R'full  -  Ro  
The same process outlined in Section VI was used to convert this amount of RE generation into equivalent wind and PV capacity. Table 7 shows the full set of input into AVERT to simulate this policy case.

Table 7. 2020 Electricity Demand Growth and Renewable Energy Capacity Displacement in the Absence of State EE and RE Policies

                                   Northeast
                                     (NE)
                           Great Lakes/Mid-Atlantic 
                                     (EMW)
                                    Texas 
                                     (TX)
EE Policy
RE Policy
                                   % growth
                                   Wind (MW)
                                    PV (MW)
                                   % growth
                                   Wind (MW)
                                    PV (MW)
                                   % growth
                                   Wind (MW)
                                    PV (MW)
Removal of EE policies in NJ, NY, CT and TX in 2020 (dn)
Incremental amount of renewable energy added between 2015 and 2020 as required by state policies (R'inc)
(also included in Table 4)
                                     5.48%
                                     5,289
                                     976 
                                     1.41%
                                      7 
                                    2,264 
                                     5.06%
                                    10,000 
                                       -
Removal of EE policies in NJ, NY, CT and TX in 2020 (dn)

Full amount of renewable energy required by state policies in 2020 (R'full)
                                     5.48%
                                    26,030
                                    3,077 
                                     1.41%
                                      16 
                                    5,445 
                                     5.06%
                                       -
                                       -

VIII. Results of Illustrative Counterfactual Analysis
As discussed above, we wanted this analysis to quantify the increase in average daily emissions of NOX that would be expected given the absence of EE/RE programs that are either presently or potentially required by states. Specifically, EPA sought to estimate the average emissions increases that would be expected on the top ten highest temperature days in selected counties that are at risk of nonattainment status for the 2015 ozone standard.  
In order to arrive at final results for this analysis, EPA began with hourly, unit-specific NOX outputs from AVERT for each base and policy case for each AVERT region. These outputs represent expected changes in hourly, unit-specific NOX emissions as compared to the 2015 input data. Outputs for all runs were aggregated by county and day to allow EPA to isolate the values for the counties and days of interest. Values obtained from runs on the Northeast AVERT region were summed with those from runs on the Great Lakes/Mid-Atlantic region to obtain results for the entire NY/NJ/CT nonattainment area. 
Finally, the output of the base case AVERT run was subtracted from outputs of the policy cases AVERT runs. This subtraction allowed the outputs of policy runs to be compared to the 2020 base case. As such, the final results represent the expected emission increases due to the removal of EE/RE policies as compared to the 2020 base case -- a scenario in which "on-the-books" EE/RE policies are kept in place. The final results are summarized in Table 8.
Table 8. Estimated Expected Increases in Tons of NOX Emitted per Day (on Average for Top Ten Highest Temperature Days) if not for EE and RE Requirements
                                   NY/NJ/CT
         Expected emissions increase in 2020 (tons NOx/day) without...
EE Policies alone
                15.1 
Inc. RPS alone
                  3.8 
Full RPS alone
                22.5 
EE Policies and Inc. RPS
                20.5 
EE Policies and Full RPS
                39.8 
                                      TX
         Expected emissions increase in 2020 (tons NOx/day) without...
EE Policies alone
                  0.3 
Add'l 10 GW Wind alone
                  2.3 
EE Policies and Add'l 10 GW Wind
                  2.5 

EPA hopes environmental officials find this illustrative analysis to be a useful example of the NOx emission reduction benefits of state-level EE and RE policies as well as the methodology of how to include "on-the-books" programs into ozone RFP and attainment demonstration SIPs.
