Revenue Decoupling                                       14                                        1                             Performance Incentives                                       29                                        2                              Program Cost Recovery                                       46                                        0 Source: IEI 2014 These approaches have been widely used for over a decade, with performance incentives approved by Connecticut as early as 1988 (IEI 2014).  In 2006, the need for such policies was recognized by the National Action Plan for Energy Efficiency Leadership Group of gas and electric utilities, state agencies, energy consumers, energy service providers, environmental groups, and energy efficiency organizations.  Over 100 states, utilities, and key stakeholders across 49 states made aggressive commitments to energy efficiency under the Action Plan and endorsed the Action Plan five key policy recommendations which included modifying utility incentives (EPA and DOE 2006).  In 2014, the Edison Electric Institute (EEI) and the Natural Resources Defense Council (NRDC) issued a joint statement urging state utility regulators to adopt a number of policies to sustain utility financial health given the greater investment being made in both energy efficiency and customer-sited electricity generation (NRDC 2014). The need for states to adopt utility financial incentive policies is due in part to how traditional ratemaking allows utilities to recovery costs based on the volume of electricity sold. This creates an incentive to maximize the volume of sales (often referred to as the "throughput incentive"), which in turn creates a disincentive to invest in energy efficiency since such programs will reduce sales volume.  A decoupling policy counteracts the financial incentive to increase sales by fixing a utility's revenue for a specific term, in order to match the amount of anticipated costs incurred plus an appropriate profit. If the utility can reduce its costs during the term through energy efficiency, the utility will be able to increase its profits. Alternately, lost revenue adjustment mechanisms may be used to diminish the throughput incentive by allowing utilities to directly recover the lost revenue associated with not selling energy due to energy efficiency programs.   Reduced electricity sales may affect utility finances in additional ways, including reducing the utility's opportunity to earn additional profit from the regulated rate of return on new capital projects which would otherwise be needed to meet higher load growth, such as new generators and transformers upgrades. As an alternative, performance incentive policies are used to allow utilities to earn a profit for achieving high levels of energy savings, recognizing that multiple benefits of energy efficiency resources that are not otherwise explicitly accounted for in traditional regulatory models.  Performance incentive policies come in different forms, such as earning bonuses and allowing a utility to earn a higher rate of return on capital invested in energy efficiency.  The level of capital investment made by a utility is often informed by electricity resource planning. Most states require one or more forms of electricity resource planning, such processes used to inform long-term integrated resources plans (IRPs) and regulatory approvals for large projects often referred to as Certificates of Public Convenience and Necessity (CPCNs).  IRPs are required or present in more than 30 states, including most vertically integrated states, and at least 19 states have some form of CPCN (EPA 2015).  Incorporating energy efficiency into electricity resource planning enables the utility to consider a broad range of electricity resource options and avoid capital investment in more expensive electricity supply or delivery infrastructure. Due in part to the shifts in long-term incentives from regulators, utilities are also increasingly seeing energy efficiency as part of their business model. According to a 2015 survey of utility executives, 71% of utilities are developing new business models for energy efficiency and demand response (UtilityDIVE 2015).  A significant number of the utilities surveyed see their business model changing from a traditional vertically integrated utility, to an energy services or smart integrator businesses model.  Further, states such as New York and Hawaii have initiated actions at their public utility commissions to examine and encourage changes to the utility business model. Both of these state are experiencing growth in distributed electricity generation and can build upon their existing energy efficiency regulatory frameworks which include energy efficiency program cost recovery, decoupling, and performance incentives. Energy Efficiency Certificates In the U.S., extensive consideration of tradable energy efficiency credits (EEC) developed almost a decade ago, most often framed as a potential compliance mechanism for state energy efficiency resource standards (EERS), a widely adopted and effective policy mechanism for achieving cost-effective electricity savings through utility energy efficiency programs. State EERS as a key policy strategy and the use of EECs for compliance were concepts borrowed from the earlier adoption of renewable portfolio standards (RPS) and the use of renewable energy credits (RECs) for compliance.  Building from the successful experience with EERS/RECs, several significant policy papers discussed how the generation, tracking, and trading of energy efficiency credits could work as a means for supporting compliance with EERS or as a mechanism for achieving greenhouse gas reductions (NREL 2008; CRS 2007; IEA 2012a). A few states put regulations in place that allowed for the use of EECs for EERS or RPS compliance. Consistent with these developments, several of the regional tracking systems developed to support the use of RECs for compliance with state RPS also included a capability to perform the same functions for the use of EECs for potential compliance with state policies. However, these capabilities have yet to be widely used as significant levels of EEC transactions have not developed in the U.S. There are, however, some voluntary markets for energy savings credits. For example, North American Renewable Registry extended its certificate tracking system to energy efficiency, and registered the first voluntary energy efficiency certificates for IBM Corporation in March 2010 (NARR 2010).  Sterling Planet, a retailer of RECs and carbon offsets, also offers White Tags(R) trading instruments that recognize the meter-measured, performance-based results of energy efficiency projects. In June 2012, Sterling Planet completed the first White Tags transaction by purchasing Michigan's Selfridge Air National Guard Base's 2011 White Tags in order to fulfill energy efficiency compliance requirements for a Michigan utility (Sterling Planet 2012). The Climate Registry has a program called "Cool Planet Energy Efficiency" which provides assistance to utility company business customers with measuring and managing their energy and carbon outputs (TCR 2015).  Even though experience with EECs in the U.S. is limited, their use internationally has been significant. Nine energy efficiency policies across seven countries outside of the United States allow for trading of the energy savings used for compliance (IEA 2012a).  Trading has been incorporated in the design of these policies to help identify cost-effective opportunities from a broader pool of participating projects and parties.  Table 9 summarizes the types of energy savings and trading included in policy design. Over half of these policies allow for trading through the creation and sale of EECs. Table 9. International Examples of Trading Energy Savings                              Country (Jurisdiction)                             Eligible Energy savings                                 Type of Trading                          Australia  (New South Wales) Preapproved projects implemented by obligated and accredited non-obligated parties EECs                          Australia  (South Australia) Activities undertaken by, or on behalf of, obligated energy retailer Limited transfer of credits among obligated parties                              Australia  (Victoria) Installation of preapproved energy efficiency products by obligated and accredited non-obligated parties EECs                                      China Achieved by grid companies and energy service companies subsidiaries of grid companies Obligated parties may purchase savings from customers and ESCOs under bilateral contracts                                     Denmark Distributors must engage third parties to achieve energy savings across energy types Energy savings may only be traded among obligated energy distributors                                     France Produced by obligated parties, local authorities, and social housing landlords Over-the-counter trading of EECs                                      Italy Produced by obligated distributors ad accredited energy service providers Over-the-counter and spot market trading of EECs                                     Poland Projects selected through annual auctions EECs fully tradable on the Polish Power Exchange                                 United Kingdom Projects implemented by obligated parties or their contractors Transfers of emissions reductions and trading of obligations allowed among obligated parties   Source: IEA 2012a Two good international examples are provided by programs in Italy and in Australia (New South Wales) that have been trading EECs successfully and at significant scale over the past decade (Pavan 2008; NSW 2015). Italy uses EECs as part of its White Certificates system, a key policy supporting their international climate commitments (Italy 2014). In implementing this policy, electricity and gas distribution utilities use EECs to meet established energy savings targets. These utilities may either generate their own EECs or buy from third parties through the market. Approximately one-third of EECs are generated by third parties and subsequently trade on the open market.  EECs are also exchanged between energy efficiency providers and utilities through bilateral contracts. The majority of energy savings from the Italian White Certificates system are realized from reducing electricity usage through a set of pre-established eligible project types (IEA 2012a). In Australia, the state of New South Wales initially incorporated EEC trading as part of their Greenhouse Gas Emissions Trading scheme. In 2009, New South Wales began trading EECs via the Energy Savings Scheme recognizing that unique barriers to energy efficiency persisted. Obligated parties, which include electric utilities, are required to acquire and surrender EECs to meet the energy savings targets of the Energy Savings Scheme. Other organizations also use EECs to voluntarily reduce their greenhouse gas emissions. Energy efficiency projects across residential, commercial and industrial sectors are used to create EECs in New South Wales. Active trading of EECs also exists in New South Wales. Trading markets have been established by the private sector with some organizations, such as the Australian Financial Markets Association and individual brokers of environmental commodities, providing regular updates on EEC wholesale market prices (NSW 2015). Even though most EECs are traded via bilateral contracts, trading of EECs has also developed across spot, forward, and options contracts. Since any party who is an "Accredited Certificate Provider" may create EECs, the Energy Savings Scheme policy has fostered the development of a robust energy services industry of firms implementing energy efficiency projects supported in part by revenues from the sale of associated EECs. Demand-Side EE Plan Scenario: Magnitude and Timing of Savings Approach Chapter 3 of the Regulatory Impacts Analysis (RIA) presents illustrative analysis of the final rule by making assumptions about the approaches that states might pursue as they develop their state plans. These approaches are embodied in the illustrative plan scenarios developed by EPA for the RIA and used to estimate the costs, benefits, and impacts of the final rule. The final CPP provides the states the flexibility to use demand reductions resulting from demand-side EE as a component of their state plan strategy, either directly recognized towards compliance with a rate-based goal or as a complimentary approach for achieving a mass-based goal (see section VIII, State Plans, of the final rule preamble for details). The EPA has included in the illustrative plan scenarios (both rate- and mass-based) a level of demand reduction that could be achieved, and the associated costs incurred, through implementation of demand-side EE measures. In this section, the EPA provides the basis for the illustrative demand-side EE plan scenario that is included as a component of the plan scenarios presented in the RIA and, then, in the following section, the basis for associated EE cost estimates are provided. The demand-side EE plan scenario is derived from state and utility experience with ratepayer-funded EE programs and the resulting energy savings, as described in preceding sections of this TSD. Demand-side EE is included in the illustrative analysis contained in the RIA for several reasons including the current use of such programs in every state, the substantial savings relative to other opportunities to reduce CO2 emissions from the power sector, overall cost-effectiveness, and the quality of available information on their impacts and associated costs. The ratepayer-funded EE programs are the basis for the levels of demand-side EE that are included as part of the RIA, although there are other strategies and measures that states may employ for purposes of the final CPP. For example, the final rule provides states the flexibility to use building codes, appliance standards, and energy service performance contracting (to name a few examples), and those strategies also offer substantial, low-cost opportunities to reduce CO2 emissions from affected EGUs. Inputs The following steps were taken to establish the inputs for development of the illustrative demand-side EE plan scenario for each state, tribal area, and territory: Step 1: Determine current level of demand-side EE performance Step 2: Determine EE plan scenario level of performance Step 3: Determine start year for EE plan scenario Step 4: Determine start year level performance Step 5: Determine pace of improvement from start year to EE plan scenario level of performance Step 6: Determine average portfolio measure life and distribution of measure lives Step 7: Determine sustainability of EE plan scenario level of performance Step 1: Determine Current Level of Demand-Side EE Performance A fundamental indicator of the level of EE program performance is incremental annual savings (also known as first-year savings) as a percent of retail sales. This is a common metric defining savings levels for state EERS and is readily calculated from EIA Form 861 data for each state, tribal area and territory (hereafter, just referred to as "states"). Incremental annual savings are also more directly estimated and evaluated than are cumulative savings. To determine the current level of demand-side EE performance, the EPA aggregated the most recent (2013) EIA Form 861 data to the state level to establish their current level of performance. Estimated incremental savings results are presented in Table 4. Step 2: Determine EE Plan Scenario Level of Performance As discussed previously (section 4.2.3), achievable demand-side EE potential exists at significant and comparable levels (on the basis of total cumulative potential over a period of ten to twenty years) in all regions of the country. While varied regional characteristics (e.g., avoided power system costs, economic growth, sectoral mix, climate, and level of past EE efforts) affect estimates of achievable potential, ongoing improvements in energy-efficient technologies and practices, economic growth, population increases, and continually improving strategies for program delivery have resulted in persistent and substantial levels of achievable potential regardless of specific regional characteristics. Direct indicators of the achievable incremental levels of energy savings performance is provided by past performance at the state and utility levels, and by requirements states have put in place for levels of savings to be achieved by 2020. As discussed, these requirements are often in the form of EERS or similar savings goals that are applied to utilities in the state. Table 10 summarizes incremental savings levels as a percentage of retail sales from EIA Form 861 (2013) data, aggregated to the state level, and categorized into four ranges of savings levels (< 0.5%, 0.5% to 0.99%, 1.0% to 1.49%, and >= 1.5%). As shown, three states achieved the highest level of performance (> 1.5%) and an additional fourteen states achieved the second highest level of performance (1.0% to 1.49%). Table 10. 2013 State Levels of Incremental Annual Savings Incremental Savings as  % of Retail Sales # of States States >= 1.5% 3 AZ, RI, VT 1.0% to 1.49% 14 CA, CT, IL, IA, ME, MD, MA, MI, MN, NY, OH, OR, WA, WI 0.5% to 0.99% 9 CO, ID, IN, MT, NJ, NM, NC, PA, UT < 0.5% 25 AL, AK, AR, DE, DC, FL, GA, HI, KS, KY, LA, MS, MO, NE, NV, NH, ND, OK, SC, SD, TN, TX, VA, WV, WY   Source: EPA calculation using EIA 2013 In addition to the state levels of performance represented in Table 10, numerous utilities of all ownership types and in all regions of the country have achieved significant levels of incremental savings. Tables 11, 12, and 13 provide examples of investor-, cooperative-, and municipal-owned utilities and their incremental savings levels for 2013 based on EIA Form 861 data. Table 11. 2013 Incremental Annual Savings  -  Investor-Owned Utilities Investor-Owned Utility State Incremental Savings as  % of Retail Sales Arizona Public Service Company AZ 1.6% Commonwealth Edison Company IL 1.2% DTE Electric Company MI 1.3% Niagara Mohawk Power Corporation NY 2.2% Pacific Gas & Electric Company CA 1.7% Potomac Edison Company MD 1.8% Source: EPA calculation using EIA 2013 Table 12. 2013 Incremental Annual Savings  -  Cooperative-Owned Utilities Cooperative-Owned Utility State Incremental Savings as  % of Retail Sales French Broad Electric Membership Corporation NC 2.2% Jackson Electric Cooperative Inc. WI 2.6% KEM Electric Cooperative Inc. ND 1.6% Southern Maryland Electric Cooperative Inc. MD 1.4% Southern Pine Electric Power Association MS 2.4% Umatilla Electric Cooperative Association OR 1.5% Source: EPA calculation using EIA 2013 Table 13. 2013 Incremental Annual Savings  -  Municipal-Owned Utilities Municipal-Owned Utility State Incremental Savings as  % of Retail Sales Austin Energy TX 0.9% City of Fort Collins CO 2.2% City of Oberlin OH 2.6% City of Seattle WA 1.5% City of Saint Peter MN 2.1% Jacksonville Electric Authority FL 0.8% Los Angeles Department of Water & Power CA 0.9% Source: EPA calculation using EIA 2013 Table 14 summarizes incremental savings levels required by state EERS and categorized into the same four ranges as Table 10 (ACEEE 2015b). Seven states are required to achieve the highest level of performance (> 1.5%) and an additional eight states are required to achieve the next highest level of performance (1.0% to 1.49%). Table 14. Levels of Incremental Savings Required by State EERS Incremental Savings as  % of Retail Sales # of States States >= 1.5% 7 AZ, ME,  MA, MN, RI, VT, WA 1.0% to 1.49% 8 CO, CT, HI, IA, MD, MI, NY, OR 0.5% to 0.99% 4 AR, CA, IL, WI < 0.5% 1 TX Source: ACEEE 2015b For the illustrative demand-side EE plan scenario level of performance, the EPA has determined that a 1.0% incremental savings, as a percentage of retail sales, is appropriate. This level was achieved by seventeen states and by numerous utilities of all ownership types in 2013. An additional two states (CO and HI), accounting for overlap, are expected to achieve this level by 2020 as shown in Table 14. Thus, nineteen states have either achieved or are required to achieve this level of performance. Step 3: Determine Start Year for EE Plan Scenario For construction of the EE plan scenario, the EPA has determined that 2020 is appropriate as the first year for state demand-side EE efforts. The EPA believes this is reasonable for the illustrative plan scenario as efforts are ramped up in advance of the 2022-2029 interim plan performance period. This allows sufficient time  -  more than four years  -  after issuance of the final rule for planning of these efforts. This reflects a three year delay from the 2017 start year used for the proposal. Step 4: Determine Start Year Level of Performance For construction of the best practices scenario, the EPA has set each state's level of performance in the start year (2020) to the level of performance observed in 2013, based on the aggregate state-level incremental electricity savings based on EIA Form 861 data (Table 4). This reflects the experience of states and utilities in ramping up EE program efforts over time, with less experienced program administrators requiring time to increase savings from current levels. Step 5: Determine Pace of Improvement from Start Year to EE Plan Scenario Level of Performance To determine a reasonable trajectory of incremental savings levels from the 2020 level to the EE plan level of performance, the EPA considered past performance of individual program administrators as well as requirements of existing state EERS. For the past performance of individual program administrators, the EPA first screened the data and divided them into moderate and high performing sub-groups. The moderate group (47 entities) was defined as programs that achieved from 0.8% to 1.5% maximum incremental savings levels and the high group (26 entities) was defined as programs that achieved greater than 1.5% maximum incremental savings levels. The EPA then calculated the rate at which each entity had increased savings over time and calculated average values for each sub-group. For the moderate group, the average rate of improvement of incremental annual savings rate was 0.30% per year. For the high group, the average rate of improvement of incremental annual savings rate was 0.38% per year. See Appendix 2 for supporting data and analysis. EPA also considered requirements of existing state EERS and evaluated the rate at which their incremental savings levels increase over time. For several EERS, the EPA was unable to clearly identify ramp-up schedules. The EPA identified ten states with clear schedules and calculated the average rate of improvement for each. The average rate of improvement of incremental annual savings rate required for these ten states is 0.21% per year. See Appendix 2 for supporting data and analysis. Based on these results, the EPA has chosen 0.2% per year for the demand-side EE plan scenario rate of improvement. These values are at the low end of the range in comparison with our analysis of past state performance and state requirements. Step 6: Determine Average Portfolio Measure Life and Distribution of Measure Lives The next step in defining the EE plan scenario entails projecting the cumulative future impacts of the annual incremental savings levels for each state. The incremental (or first-year) savings impacts in a year reflect the savings from EE measures put in place in that year. The cumulative incremental savings in a given year represents the total impacts of all EE measures, those put in place in that year and all prior years, that still have remaining savings impacts in the given year. The cumulative savings account for the continuing impacts of EE measures that remain in place for a period of time (the "measure life") before being replaced. For example, the purchase of a high-efficiency refrigerator may lead to savings for twelve years, before being replaced with a new model. To estimate cumulative impacts of a series of years of incremental savings, the industry uses the concept of an average measure life for the entire portfolio of EE programs being administered. Rather than use a single, average measure life to represent a diverse portfolio of programs, that range in measure lives from less than ten years (e.g., commercial lighting technologies and applications, residential behavioral feedback) to as long as twenty years or more (e.g., residential HVAC, residential building insulation), the EPA is assuming a distribution of measure lives around the average to account for future impacts of incremental savings levels. To approximate a distribution of measure lives across a portfolio of EE programs represented by the EE plan scenario, EPA relied on a recent analysis by the Lawrence Berkeley National Laboratory (LBNL) of the distribution of EE program lifetimes (LBNL 2015a). The analysis was based on the LBNL DSM Program Database. The database consists of program-level data, including first-year savings and program savings lifetimes, reported directly by the EE program administrators since 2009. Across the database of nearly 6,000 program years of data, program savings lifetimes are available for about 1,600 program years (27% of the program years). More than 50 utilities and other EE program administrators in 25 states contributed to those data through their regulatory filings, statewide databases, and other sources. The weighted average of EE measure lifetimes for the entire population in the LBNL analysis is 10.2 years. The LBNL analysis further partitions the measure lifetimes of the EE programs into four tiers based on a cluster analysis  -  a statistical approach for grouping values based on their similarity. The four-tier distribution of EE program measure lifetimes, as well as the associated weighted-average measure lifetime and the shares of first-year savings of each tier, are used in the analysis. Figure 5 presents LBNL's four-tier distribution of the EE program measure lifetimes, and the associated weighted average measure lifetime and share of first-year savings for each tier. Figure 5. The Four-Tier Distribution of Demand-Side EE Program Measure Lifetime                                         Source: LBNL 2015a The weighted-average EE program measure lifetime of 10.2 years for the entire population of data in the LBNL analysis is roughly consistent with a 2014 ACEEE study that found an average measure life of 10.6 years based on electricity EE program data across all sectors in 20 states (ACEEE 2014d). A more common approach in other studies is to assume a portfolio with no diversity of measure lives, with the entirety of incremental savings being realized in each year from the first through the full average measure life and then dropping to zero in the following year. By comparison, the approach used here is a conservative one, leading to the same quantity of total energy savings, but with a greater portion of the savings occurring in later years than occur with the more common, simpler approach of using a single average measure life to represent the entire portfolio of EE programs. Step 7: Determine Sustainability of EE Plan Scenario Level of Performance For construction of the best practices scenario, once a state achieves the best practices level of performance, the EPA has kept the level of performance constant through the analysis period. Limited empirical data suggests the reasonableness of this approach; however, comprehensive data, across all regions and states, does not exist because these levels of performance have not been achieved and sustained nationwide previously. The Northwest Power Conservation Council (NPCC) provides one such example. NPCC has been conducting the most consistent and long-running series of evaluations of achievable cost-effective potential in the country, updated every five years, as part of their five-state regional energy resource plans (NPCC 2010). These analyses have become more detailed, reliable, and purposeful over time. Since 1998, NPCC's estimates of achievable potential have more than tripled even as evaluated electricity savings from EE programs have increased rapidly, more than quadrupling between 1998 and 2010 (while levelized costs of saved energy achieved have remained flat), and exceeding plan targets every year since 2005. A study of the NPCC results concludes: "our research shows that when programs invest in higher levels of efficiency, this helps drive measurement improvements and technical innovation, resulting in larger and more reliable conservation supply estimates" (NPCC 2008). Table 15 summarizes the NPCC's achievable potential estimates and evaluated savings since 1998 (NPCC 2010). Table 15. NPCC Achievable EE Potential and Achieved Incremental Savings (1998-2010)

Year

1998 2005 2010 Achievable Potential over 20 Years (GWh) 13,447 24,651 51,684 Achieved Incremental Savings  from EE Programs  (GWh) 547 1,184 2,248 Source: NPCC 2010 Additional substantiation of this approach is provided by average annual achievable rates from reviewed studies, as discussed previously, and comparison of those with the rates resulting from the best practices scenario. We address this in a later section, 8.4.2, after presenting those results. Summary of the Demand-Side EE Plan Scenario Construction Table 16 provides a summary of inputs for the demand-side EE plan scenario. The pace of improvements, average measure life, and distribution of measure lives are each conservative and, therefore, lead to lower cumulative savings than would otherwise result. Similarly, the EE plan scenario, being based solely on results from and requirements of EE programs, is less stringent than a level would be that accounted for potential impacts of other EE strategies such as building energy codes, state appliance standards, or energy services performance contracting.  Table 16. Summary of Demand-Side EE Plan Scenario Inputs Input Demand-Side EE Plan Scenario Current Level of Performance (incremental savings as % of retail sales) Calculated using data from EIA 861 (2013) EE Plan Scenario Level of Performance (incremental savings as % of retails sales) 1.0% Start Year 2020 Start Year Level of Performance 2013 level of performance Pace of Improvement (increase in incremental savings rate per year) 0.20% per year Average Measure Life and Distribution of Measure Lives 10.2 years with a 4-tier  distribution of measure lifetimes (LBNL database and analysis) Continued Performance Once achieved, EE plan scenario savings level is sustained Source: Section 8.2 Calculations This section presents the calculations for determining cumulative savings levels for each state, for each year based on the demand-side EE plan scenario. The cumulative savings levels are derived based upon the key inputs summarized in Table 16. Calculating the net cumulative savings of the demand-side EE plan scenario involves six steps. For each state, for each year (2020-2030) the following steps are taken:       1. Determine annual business-as-usual (BAU) sales Determine annual incremental EE savings as a percentage of sales Determine annual incremental EE savings (GWh) Determine annual expiring EE savings (GWh) Determine net cumulative EE savings (GWh) Determine sales after net EE (GWh) Determine net cumulative EE savings as a percentage of BAU sales To illustrate these calculations, each step is described and results provided for one state (using South Carolina as an example) for 2020 through 2027. We truncate the results at 2027 for simplicity, but all calculations and full results are presented for all states for all years in the spreadsheet file included as Appendix 3. Step 1: Determine Annual Business as Usual (BAU) Sales BAU sales are derived by aggregating 2013 sales to the state level using EIA Form 861 data and increasing the value for each subsequent year by the average annual growth rate (AAGR) from the AEO 2015 Reference Case for the region corresponding to the state. Specifically, the AAGR from AEO 2015 for 2013-2040 is used to derive BAU sales for 2014 through 2040, and the AAGR from AEO 2015 for 2030-2040 is used to derive BAU sales for 2041 through 2050. For South Carolina the corresponding region is SRVC and the AAGR from 2013-2040 is 0.88% per year and from 2030-2040 is 0.76% per year. The resulting values are provided in Table 17 for South Carolina. Table 17. BAU Sales for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 BAU Sales (GWh) 83,601 84,341 85,087 85,840 86,599 87,365 88,138 88,918

Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales Next, the 2020 value for annual incremental EE savings as a percentage of sales is set at the 2013 value based upon aggregated EIA-861 reported data (see Table 4). For South Carolina that value is 0.46%. This value is then increased by the pace of improvement of 0.2% per year until the goal level of 1.0% is reached and then held constant. The resulting values are provided in Table 18 for South Carolina. Table 18. Annual Incremental EE Savings as a Percentage of Sales for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 Annual Incremental EE Savings (% sales) 0.46% 0.66% 0.86% 1.00% 1.00% 1.00% 1.00% 1.00%

Step 3: Determine Annual Incremental EE Savings (GWh) Annual incremental EE savings (also known as first-year savings) are calculated by multiplying the annual incremental savings as a percentage of sales times the prior year sales after net EE. The resulting values are provided in Table 19 for South Carolina. Table 19. Annual Incremental EE Savings (GWh) for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 Annual Incremental EE Savings (GWh) 385 553 721 834 833 833 832 832

Step 4: Determine Annual Expiring EE Savings (GWh) As discussed above (section 8.2.6), the annual incremental (or first-year) EE savings of the portfolio decline (or expire) over time according to the four-tier distribution of measure lives. Expiring EE savings are calculated as the sum of all expired savings in a given year from all prior years' incremental (first-year) savings. The resulting values for expiring EE savings are provided in Table 20 for South Carolina. Table 20. Expiring EE Savings for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 Expiring EE Savings (GWh) 0 0 0 0 0 0 69 165

Step 5: Determine Net Cumulative EE Savings (GWh) Net cumulative EE savings in a given year are equal to annual incremental savings for that year minus total expiring savings for that year plus net cumulative savings for the prior year. The resulting values are provided for South Carolina in Table 21. Table 21. Net Cumulative EE Savings for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 Net Cumulative EE Savings (GWh) 385 938 1,659 2,493 3.327 4,159 4,922 5,589

Step 6: Determine Sales after Net EE (GWh) Sales after net EE are calculated by subtracting net cumulative savings from BAU sales. The resulting values are provided in Table 22 for South Carolina. Table 22. Sales after Net EE Savings for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 Sales after Net EE Savings (GWh) 83,216 83,403 83,428 83,346 83,272 83,206 83,216 83,329

Step 7: Determine Net Cumulative EE Savings as a Percentage of BAU Sales Net cumulative EE savings as a percentage of BAU sales are equal to net cumulative savings divided by BAU sales. The resulting values are summarized for South Carolina in Table 23. Table 23. Net Cumulative EE Savings as a Percentage of BAU Sales for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 Net Cumulative EE Savings as % of BAU Sales 0.46% 1.11% 1.95% 2.90% 3.84% 4.76% 5.58% 6.29%

Summary of Calculation Formulas and Results by Step for South Carolina Table 24 provides summaries of the results for South Carolina for each step, respectively. Table 24. Summary of Results by Step for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 BAU Sales (GWh) 83,601 84,341 85,087 85,840 86,599 87,365 88,138 88,918 Annual Incremental EE Savings (% sales) 0.46% 0.66% 0.86% 1.00% 1.00% 1.00% 1.00% 1.00% Annual Incremental EE Savings (GWh) 385 553 721 834 833 833 832 832 Annual Expiring EE Savings (GWh) 0 0 0 0 0 0 69 165 Net Cumulative EE Savings (GWh) 385 938 1,659 2,493 3.327 4,159 4,922 5,589 Sales after Net EE (GWh) 83,216 83,403 83,428 83,346 83,272 83,206 83,216 83,329 Net Cumulative EE Savings as % of BAU Sales 0.46% 1.11% 1.95% 2.90% 3.84% 4.76% 5.58% 6.29% Results Summary of Results As discussed, the EE plan scenario results in reduced electricity demand in each state, in each year of the plan scenario. Table 25 provides the results for 2020, 2025, and 2030, as electricity demand reduced in gigawatt-hours and as a percentage of BAU sales for the year. Full results are presented for all states for all years in the spreadsheet file included as Appendix 3. Table 25. Demand-Side EE Plan Scenario: Magnitude and  Timing of Net Cumulative Savings States Net Cumulative Savings

2020 2025 2030

GWh % of BAU Sales GWh % of BAU Sales GWh % of BAU Sales Alabama 91 0.10% 3,287 3.37% 7,258 7.13% Arizona 797 0.99% 4,786 5.65% 7,195 8.09% Arkansas 241 0.48% 2,519 4.81% 4,279 7.80% California 2,735 0.99% 16,310 5.70% 24,310 8.19% Colorado 524 0.91% 3,367 5.58% 5,116 8.06% Connecticut 305 1.00% 1,805 5.77% 2,658 8.34% Delaware 1 0.01% 352 2.98% 849 7.06% District of Columbia 51 0.45% 556 4.81% 938 7.99% Florida 389 0.17% 9,054 3.69% 18,673 7.31% Georgia 357 0.26% 5,889 4.07% 11,314 7.48% Idaho 137 0.54% 1,327 4.98% 2,195 7.91% Illinois 1,458 1.00% 8,633 5.76% 12,742 8.31% Indiana 1,006 0.92% 6,347 5.69% 9,459 8.29% Iowa 484 0.99% 2,878 5.73% 4,270 8.25% Kansas 2 0.01% 1,265 2.95% 3,080 6.97% Kentucky 337 0.37% 4,232 4.50% 7,544 7.68% Louisiana 23 0.02% 2,905 3.02% 6,983 6.92% Maine 121 1.00% 717 5.77% 1,056 8.34% Maryland 632 1.00% 3,729 5.78% 5,482 8.36% Massachusetts 566 1.00% 3,344 5.77% 4,925 8.34% Michigan 1,055 1.00% 6,232 5.77% 9,176 8.34% Minnesota 712 0.99% 4,230 5.73% 6,275 8.25% Mississippi 119 0.23% 2,159 3.95% 4,244 7.40% Missouri 420 0.49% 4,324 4.89% 7,230 7.97% Montana 79 0.53% 770 4.98% 1,274 7.91% Nebraska 49 0.15% 1,205 3.65% 2,500 7.35% Nevada 176 0.47% 1,862 4.81% 3,162 7.84% New Hampshire 17 0.15% 425 3.67% 875 7.42% New Jersey 531 0.69% 4,202 5.40% 6,514 8.24% New Mexico 138 0.56% 1,300 5.02% 2,140 7.87% New York 1,477 1.00% 8,637 5.86% 12,537 8.51% North Carolina 814 0.59% 7,385 5.12% 11,984 7.95% North Dakota 3 0.02% 517 3.00% 1,244 7.00% Ohio 1,545 1.00% 9,150 5.76% 13,506 8.31% Oklahoma 168 0.26% 2,744 4.07% 5,276 7.45% Oregon 500 0.99% 2,986 5.69% 4,460 8.17% Pennsylvania 1,351 0.90% 8,677 5.70% 12,915 8.33% Rhode Island 80 1.00% 471 5.77% 693 8.34% South Carolina 385 0.46% 4,159 4.76% 7,117 7.79% South Dakota 16 0.13% 464 3.54% 986 7.28% Tennessee 320 0.31% 4,586 4.26% 8,503 7.57% Texas 801 0.20% 16,359 3.82% 32,959 7.32% Utah 257 0.81% 1,823 5.53% 2,789 8.12% Vermont 57 1.00% 338 5.77% 498 8.34% Virginia 28 0.02% 3,707 3.02% 8,907 6.94% Washington 975 0.99% 5,822 5.69% 8,696 8.17% West Virginia 79 0.24% 1,347 4.06% 2,580 7.59% Wisconsin 710 1.00% 4,201 5.76% 6,197 8.31% Wyoming 31 0.17% 700 3.73% 1,433 7.33% Navajo 0 0.00% 21 2.90% 53 6.85% Ute 0 0.00% 17 2.91% 41 6.90% Fort Mojave 0 0.00% 2 2.90% 4 6.85% Continental U.S. 23,150 0.59% 194,126 4.81% 327,092 7.83% Alaska 2 0.04% 161 3.13% 376 7.04% Hawaii 3 0.04% 284 3.10% 668 7.03% Puerto Rico 0 0.00% 528 2.97% 1,271 7.14% Guam 0 0.00% 46 2.97% 112 7.14% U.S. Total 23,156 0.59% 195,145 4.80% 329,518 7.82% Source: Appendix 3 Results in Context To provide context for state cumulative savings results provided in Table 26, the average annual savings were calculated for each state through 2030, starting from 2020. Table 22 summarizes the results. Table 26. Average Annual Savings Rates of Demand-Side EE Plan Scenario Years Number of Years Range of Cumulative Savings (% of Sales) across States in 2030 Range of Average Annual Savings Rates across States National Average Annual Savings Rate 2020-2030 11 6.92% to 8.36% 0.63%/year to 0.76%/year 0.71% per year Source: EPA calculations based on Table 25 The state range and national value for the average annual savings rate represented in the demand-side EE plan scenario are below the range of values found in recent utility, state, and regional studies (0.9% to 1.3% per year) as summarized in Table 6. These results provide additional support for the reasonableness and feasibility of the demand-side EE plan scenario and associated state-specific results. Representation of Demand-Side EE in Modeling of the Illustrative Plan Scenario To reflect the implementation of the illustrative energy efficiency plan scenario in power sector modeling, as discussed in the RIA (Chapter 3), the IPM base case electricity demand was adjusted exogenously to reflect the estimated future-year demand reductions described and summarized above. State-level demand reductions were scaled up to account for transmission losses and applied to base case generation demand in each model year to derive adjusted demand for each state, reflecting the energy efficiency plan scenario energy reductions. The demand adjustments were applied proportionally across all segments (peak and non-peak) of the load duration curve. This approach to allocating annual electricity reductions is consistent with the significant levels of savings represented in the illustrative demand-side plan scenario. Achieving 1.0% incremental savings typically require robust EE program portfolios, employing a diverse set of measures across all sectors and most end-use categories. The impacts of such comprehensive portfolios occur across the full annual load duration curve with higher periods of demand providing proportionally greater EE potential. In other words, potential demand reductions through EE measures tend to mirror the underlying load duration curve which, in turn, is formed from the demands from all end uses, across all end-use sectors. Appendix 1 provides summary results from more than 50 recent EE potential studies  -  conducted between 2009 and 2014  -  from across the U.S. The detailed results of these studies indicate the breadth of end-uses, across all end-use sectors, which provide cost-effective EE potential. Demand-Side EE Plan Scenario: Assessment of Costs Approach This section provides the basis for estimating the costs that would be incurred to achieve the magnitude and timing of EE savings represented by the EE plan scenario. Chapter 3 of the Regulatory Impacts Analysis (RIA) presents illustrative analysis of the final rule by showing approaches that states might pursue as they develop their state plans. The scenarios used in the RIA are the basis for costs, benefits, and other power sector impacts of the final rule. The final rule allows states to use demand-side EE as a component of their state plan strategy, either directly recognized towards compliance with a rate-based goal or as a complimentary approach for achieving a mass-based goal. The EPA has included in the illustrative plan scenarios (both rate- and mass-based) a level of demand reduction that could be achieved, and the associated costs incurred, through implementation of demand-side EE measures as shown in this TSD. In this section, the EPA provides the basis for the costs of the illustrative demand-side EE plan scenario that is included as a component of the plan scenarios presented in the RIA. The electricity savings resulting from the demand-side EE methodology was used to adjust electricity demand levels in an exogenous manner, applied to the power sector modeling for the illustrative plan scenarios of the RIA. In other words, the degree to which EE is employed is not determined endogenously within the power sector modeling based upon optimization of costs but, rather, "hard wired" into the illustrative plan scenarios. The electricity savings represented in the demand-side EE plan scenario lead to substantial reductions in power system costs due to the reductions in specified electricity demand. Since EE is not represented endogenously as an abatement measure within the power sector modeling, the costs associated with the EE plan scenario must be estimated outside of the power sector modeling and integrated with the results from that modeling. These EE cost estimates, their basis, and calculations are addressed in the following sections. Inputs The following steps were taken to establish the inputs for development of the EE cost estimates for each state in each year. Step 1: Determine incremental electricity savings Step 2: Determine first-year program costs and cost factors Step 3: Determine the ratio of program to participant costs Step 1: Determine Incremental Electricity Savings Results from section 8 provide the starting basis for estimation of EE costs used in the RIA. The resulting incremental savings (MWh) by state and by year provide the first step in calculating EE plan scenario costs. Step 2: Determine First-Year Program Costs and Cost Factors First-year program costs refer to the full costs (e.g., incentive payments, administration,  technical support, marketing, evaluation, measurement and verification (EM&V), information to consumers, etc.) incurred by a utility or other administrator of EE programs in a given year that lead to EE measures (technologies and practices) put in place in that year, and result in reductions in electricity demand in that and future years (driven by the mix of measure lives across the portfolio of EE programs employed). First-year program costs are represented as these costs divided by the incremental (or first-year) savings for that year. Unlike participant costs, program costs are readily known by the administrator of EE programs and are, therefore, an appropriate starting point for cost analysis of the demand-side EE plan scenario.  Recent studies have collected and analyzed first-year program cost data directly from EE program administrators from across the U.S. In 2009, ACEEE conducted a national review of data on EE program costs from program annual reports, evaluation reports, and information compiled from contacts at program administers in 14 states (ACEEE 2009). The program cost data were compiled from multiple EE program administrators over multiple years in each state. ACEEE found average first-year net program costs of $275/MWh (2011$). Two newer national analyses have found lower program costs than the 2009 ACEEE study. In 2014, ACEEE updated their analysis from 2009, expanding the number of states to 20, and including a greater number of program administrators and years (ACEEE 2014d). In this analysis ACEEE found average first-year net program costs of $230/MWh (2011$). In 2014, an LBNL study presented results from a uniquely comprehensive study of EE program costs (LBNL 2014b). The LBNL analysis reviewed program-level data from over 100 program administrators in 31 states. Data were collected from over 1,700 individual programs for up to three years (2009-2011), covering more than 4,000 individual program-years data points. Because of the broad scope of their study and the lack of net savings information for many programs, LBNL focused on gross, rather than net, savings values.  LBNL found national average first-year program cost of gross savings of $162/MWh (2012$). Applying an average net-to-gross ratio of 0.9 and deflating costs at 1.8%, results in an estimated national average first-year cost of net savings of $177 (2011$). The results cited from these three studies represent average first-year costs from program administrators across the U.S. There are two factors that would influence first-year EE costs as higher levels of performance are achieved. Economies of scale in the operation of larger EE programs and larger portfolios of EE programs, and learning and expertise gained over time from the continued implementation of programs, are two factors that would lower costs as programs scale up and expand to realize higher levels of performance (Jaffe et al. 2003). However, the limited supply of EE abatement measures and the need to employ higher cost measures, over time, to reach higher levels of performance, and to sustain high levels of performance, are factors that would increase costs as higher levels of performance are achieved. Several analyses of EE program costs and savings based on empirical data are consistent with this, indicating significantly declining costs up to incremental savings levels of 1.0% or higher, and then level or increasing costs as higher savings levels are achieved (Synapse 2008; GEEG 2012; LBNL 2015b). Based on analysis like these, a recent LBNL study adopted an approach that generically represented declining costs at lower savings levels and increasing costs at higher savings levels (Barbose et al. 2013). Counter to these studies, other analysis of empirical data has indicated only steadily rising costs as savings levels increase even from levels below 0.5% (ACEEE 2014d). In consideration of the above discussion, the EPA has chosen to use a three-tier approach to generically represent declining first-year program costs through the maximum savings level of 1.0% represented in the EE plan scenario. The first tier is used for incremental savings levels less than 0.5%; the second tier is used for incremental savings levels equal to or greater than 0.5% and less than 1.0%; and the third tier is used for incremental savings levels of 1.0% (the highest level assumed for demand-side EE plan scenario). To reflect the significantly higher costs found at the lowest savings levels, program costs for the first tier are set at two times the level found in the 2009 ACEEE study. This results in first-year program costs of $550/MWh ($2011) for the first tier. For the second and third tiers, these costs are reduced by 20% and 40%, respectively, resulting in first-year program costs of $440/MWh and $330/MWh. The approach results in reasonable but conservative cost assumptions for analyzing the demand-side EE plan scenario. Step 3: Determine the Ratio of Program to Participant Costs As noted above, while program costs are readily known and consistently tracked and reported by program administrators, participant costs are not easily known, and are less consistently estimated and reported. The ratio between program and participant costs will vary significantly from one program to the next within a utility's portfolio. To determine an appropriate ratio for the impacts assessment of the CPP proposed rule, EPA conducted research and analysis of industry data (annual EE program reports from administrators in 22 states) and found that on average program costs represented 53% of total measured costs (with direct participant costs representing the remaining 47%) (EPA 2014a). Based on this analysis, the EPA used a ratio of 1-to-1 for program to participant costs for the EE cost estimates contained in the CPP proposed rule. In April 2015 the Lawrence Berkeley National Laboratory (LBNL) published a technical brief that analyzed program and participant costs for EE programs based on their extensive LBNL DSM Program Database (LBNL 2015b). The database contains information on approximately 1,700 individual EE programs across multiple years from 34 states. The LBNL summary findings indicate a ratio of program to participant costs of 51% to 49%.  These results are consistent with the earlier analysis and support the use of the 1-to-1 ratio for the current analysis. This ratio is used to derive participant and total costs based upon program costs.  Summary of Inputs for Demand-Side EE Cost Analysis Table 27 provides a summary of inputs for the EE cost analysis including incremental electricity savings, first-year costs and cost factors, and ratio of program to participant costs.  Table 27. Summary of EE Cost Analysis Inputs Input Source or Value Incremental  Electricity Savings Results from demand-side EE plan scenario First-Year Program Cost $550/MWh (2011$) Ratio of Program to Participant Costs 1:1 First-Year Participant Cost $550/MWh (2011$) First-Year Total Cost $1100/MWh (2011$) Cost Factors and Total Costs by Level of Incremental Savings Incremental Savings Rate

< 0.5% 0.5% to < 1.0% 1.0%

100% of first-year costs $1100/MWh (2011$) 80% of base costs: $880/MWh (2011$) 60% of base costs: $660/MWh (2011$) Source: Section 9.2 Calculations This section addresses the methodology for calculating the cost associated with the EE levels included in the illustrative plan scenarios. Specifically, three values are calculated (annual first-year costs, levelized cost of saved energy (LCSE), and annualized costs); for each, program and participant components are then calculated using the 1:1 ratio (i.e., 50% of total for each) derived above. Specific results from prior sections on timing and magnitude of savings, and costs inputs are used for these calculations. For each state, the following steps are taken for each year:       2. Calculate annual first-year costs of saved energy Calculate levelized cost of saved energy (LCSE) Calculate annualized costs To illustrate these calculations, each step is described and results are provided for one state (using South Carolina as an example) for 2020 through 2027 (truncated for simplicity).  The full national results (for 2020, 2025, and 2030) are presented following the step-by-step example for South Carolina. Step 1: Calculate Annual First-Year Costs Annual total first-year costs are calculated by multiplying annual total incremental savings (MWh) (from Table 19) by the first-year total cost of saved energy (from Table 27, using the corresponding incremental savings rate). Program and participant portions of the first-year costs are then calculated as 50% of total first-year costs for each per Table 27. The resulting values are summarized for South Carolina in Table 28. Table 28. Calculation of Annual First-Year Costs for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 Annual Incremental Savings (GWh) 385 553 721 834 833 833 832 832 First-Year Total Cost of Saved Energy (2011$/MWh) 1100 880 880 660 660 660 660 660 First-Year Total Cost (millions 2011$) 423.4 486.6 634.5 550.6 550.1 549.6 549.2 549.2 First-Year Program (millions of 2011$) 211.7 243.3 317.3 275.3 275.0 274.8 274.6 274.6 First-Year Participant (millions of 2011$) 211.7 243.3 317.3 275.3 275.0 274.8 274.6 274.6 Step 2: Calculate Levelized Cost of Saved Energy The LCSE calculation algorithm is based on the 2002 California Standard Practice Manual (California GOPR 2002). This approach was adopted in order to appropriately allocate the cost of a single year EE program over declining lifetime savings. The net present value of all savings from a single year's EE activities (i.e., over the entire distribution of program lifetimes) is calculated using the real discount rate rates of 3% and 7%. The levelized cost of saved energy is then calculated by dividing the annual first-year costs (from Table 28) by the net present value of the savings. The resulting values are summarized for South Carolina in Table 29. Table 29. Levelized Cost of Saved Energy for South Carolina (at 3% discount rate)

Year

2020 2021 2022 2023 2024 2025 2026 2027 Levelized Savings (GWh) 3,279 4,710 6,141 7,105 7,099 7,092 7,087 7,087 First-Year Total Cost (millions 2011$) 423.4 486.6 634.5 550.6 550.1 549.6 549.2 549.2 Total LCSE (2011$/MWh) 129.2 103.3 103.3 77.5 77.5 77.5 77.5 77.5 Program LCSE (2011$/MWh) 64.6 51.7 51.7 38.7 38.7 38.7 38.7 38.7 Participant LCSE (2011$/MWh) 64.6 51.7 51.7 38.7 38.7 38.7 38.7 38.7

Step 3: Calculate Annualized Costs Annualized costs are calculated by multiplying the LCSE for each year by the estimated savings of the demand-side EE plan scenario in that year through the full distribution of measure lifetimes. For a given year in the analysis, the total annualized costs resulting from all current and past investments are summed to calculate the total annualized costs for that year. The resulting values are summarized for South Carolina in Table 30. Table 30. Annualized Costs for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027 Annualized Total Costs (millions 2011$) 49.7 106.9 181.4 246.0 310.6 375.1 430.7 476.4 Annualized Program Costs (millions 2011$) 24.9 53.4 90.7 123.0 155.3 187.6 215.3 238.2 Annualized Participant Costs (millions of 2011$) 24.9 53.4 90.7 123.0 155.3 187.6 215.3 238.2

Summary of General Formulas and Results by Step for South Carolina Table 31 provides results for South Carolina for each step. Table 31. Summary of Results by Step for South Carolina.

Year

2020 2021 2022 2023 2024 2025 2026 2027 Total First-Year Costs (millions 2011$) 423.4 486.6 634.5 550.6 550.1 549.6 549.2 549.2 Total LCSE (2011$/MWh) 129.2 103.3 103.3 77.5 77.5 77.5 77.5 77.5 Annualized Total Costs (millions 2011$) 49.7 106.9 181.4 246.0 310.6 375.1 430.7 476.4

Results Summary of National Results Tables 32 and 33 provide the national first-year and annualized EE costs for the best practices EE scenario for 2020, 2025, and 2030. Table 34 provides the estimated national LCSE for the same years. Each of the three tables includes values for program, participant, and total costs. Table 32. First-Year Demand-Side EE Costs (billions 2011$) (Continental U.S.)

Year

2020 2025 2030 Program 9.1 12.7 12.7 Participant 9.1 12.7 12.7 Total 18.1 25.4 25.3

Table 33. Annualized Demand-Side EE Costs (3% discount rate, billions 2011$) (Continental U.S.)

Year

2020 2025 2030 Program 1.1 8.4 13.2 Participant 1.1 8.4 13.2 Total 2.1 16.7 26.3

Table 34. Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh) (Continental U.S.)

Year

2020 2025 2030 Program 46.0 43.1 40.3 Participant 46.0 43.1 40.3 Total 92.0 86.3 80.5

Full calculations and results are presented for all states for all years in the spreadsheet file included as Appendix 3. The file includes results for LCSE (total, program and participant, discounted at 3% and 7%), first-year costs (total, program and participant), and annualized costs (total, program and participant, discounted at 3% and 7%). Results in Context To provide context for the pace of increase in EE program spending levels represented by the demand-side EE plan scenario, we consider the compound annual growth rate (CAGR) of the recent rapid increase in historic investment (2006 to 2011) and the CAGR from 2013 (the most recent historic value) through 2020, 2025, and 2030 represented by program costs of the plan scenario. Historic data is from Table 2 and EE plan scenario data is from Table 32. Table 35 provides a summary of the results. The CAGRs represented by the EE plan scenario through 2020, 2025, and 2030 vary from 4% to 6%. The historic growth rate reflecting the rapid recent growth in EE program spending is 30%, roughly six times the EE plan scenario values. The pace of increase in EE program spending represented by the demand-side EE plan scenario is modest and much lower than recently achieved performance. Table 35. Annual Growth Rates of Demand-Side EE Program Spending  -  Historic and Demand-Side EE Plan Scenario Time Period (Years) Compound Average Growth Rate Historic (2006-2011) 29.8% Demand-Side EE Plan Scenario (2013-2020) 5.4% Demand-Side EE Plan Scenario (2013-2025) 6.0% Demand-Side EE Plan Scenario (2013-2030) 4.2%

Costs per Tonne CO2 Reduced For the CPP proposed rule, the EPA analyzed a scenario incorporating the reductions in electricity demand associated with the proposal's "EE scenario" and compared the results with the base case scenario (EPA 2014a). Both analyses were conducted using the Integrated Planning Model (IPM) described in RIA. Combining the resulting power system cost reductions with the energy efficiency cost estimates associated with the EE scenario, net cost impacts were derived for 2020, 2025, and 2030. Dividing these net cost impacts by the associated CO2 reductions for each year, the average cost of the CO2 reductions achieved were found to range from $16 to $24 per metric tonne of CO2. These results confirmed the general cost-effectiveness of demand-side EE at the levels and costs of the proposal, and relative to the IPM base case used at that time (since updated). The final rule incorporates changes to the IPM base case, and the levels (significantly lower) and costs (higher in 2020, lower in 2025 and 2030) of demand-side EE incorporated into the illustrative plan scenario. These changes are likely to result in similar or lower average costs per tonne of CO2 achieved through the demand-side EE plan scenario in the final rule. Analysis Considerations Two considerations are worth noting in regards to the analysis described in the previous two sections (9 and 10) which characterize the energy savings and costs of the demand-side EE plan scenario: 1) the implicit representation of demand-side EE policies and programs in the base case electric demand forecast and 2) Form EIA-861 as a data source. State EE Policies in the Baseline Electricity Demand The AEO 2015 reference case provides the basis for the base case electric demand forecast used for EPA's power sector modeling of the final rule (as discussed in the RIA) and also serves as a basis for estimates of electricity savings for the demand-side EE plan scenario. The AEO 2015 reference case does not explicitly represent existing EE programs, the continuation of EE programs or policies, or future requirements (e.g., EERS) to achieve savings through such programs. For example, existing state EERS are not evaluated and represented in the AEO 2015 reference case. However, to some degree, AEO 2015 may implicitly reflect a continuation of the effects of past or existing EE programs in the electricity demand projections represented in the reference case. This implicit representation is captured in part through a calibration process that is affected by several historic factors including reported electricity sales and sectoral energy consumption surveys. As noted, the estimates of electricity savings for the demand-side EE plan scenario do rely on AEO 2015 as a basis for base case electricity demand projections. The underlying mechanics of the calculations are such that modest changes in base case demand growth rates lead to minor impacts on cumulative savings levels relative to other inputs such as 2020 starting savings levels (based on 2013 EIA 861 data), the target savings level (i.e., 1.0% incremental annual savings), and the pace of improvement from the starting to target savings levels (i.e., 0.2% per year). Any implicit representation of the effects of existing EE programs in the base case demand forecast would also, to some degree, affect estimates of the impacts of the final rule in the RIA. The effects of these considerations would have some degree of influence on the assessment of demand-side EE, however, for purposes of the RIA, the methodology for determining levels of demand-side EE, and associated costs, is appropriate in the context of the final rule. Energy Information Administration Form EIA-861 as Data Source Comprehensive national data on electricity savings are limited for evaluating and comparing energy efficiency programs and their effectiveness at the utility, state, and national scale. Issues related to the lack of standardized definitions and reporting, and data quality are noted to limit evaluation of EE programs (MJ Bradley 2011). The EIA Form 861, "Annual Electric Power Industry Report," remains the most comprehensive effort that collects data annually on demand-side management (DSM) programs, including their spending and energy savings impacts, nationally. The form is requested for electric utilities, electric power producers, energy service providers, wholesale power marketers, and all DSM program managers and entities responsible to estimate the DSM activity for the reporting year using their best available data, including costs and electricity savings from EE and load management programs.  This analysis uses only two EIA-861 data variables. Specifically, we use the 2013 sales data and reported incremental annual electricity savings of EE programs to estimate the current performance of EE programs (aggregated to the state level) to inform the illustrative demand-side EE plan scenario discussed in this chapter. EPA notes potential concerns associated with consistency and quality of reported DSM program data in Form EIA-861. Specifically, the data are self-reported by utilities and other DSM program administrators. The definitions and data categories may not be consistently applied across different program administrators, utilities, and states, and may vary by data year. Over time, however, the data quality has improved significantly and there is increased standardization in data reporting and more detailed and up-to-date data categories are being reported. For instance, in 2011, EIA began collecting data from third-party administrators of programs and now requires that entities operating programs in multiple states report results disaggregated to the state level. While now comprehensive, outside entities have found that the EIA-861 data can be improved through supplementation with publicly available annual EE program reports.

                                                                                Appendices

Appendix 1: Summary of Recent Energy Efficiency Potential Studies (2009-2014)

Appendix 2:  Incremental Electricity Savings Pace of Improvement Analysis

Appendix 3:  Demand-Side Energy Efficiency Plan Scenario: Data, Calculations and Results

Appendix 1: Summary of Recent Energy Efficiency Potential Studies (2009-2014) The EPA conducted an extensive review of energy efficiency potential studies of electricity savings. Studies were identified from recent published meta-analyses (Sreedharan 2013; ACEEE2014b) and supplemented with additional studies identified through research. Comprising results from more than 50 potential studies conducted between 2009 and 2014, the resulting database of results is one of the most comprehensive of its kind.  See the attached file for detailed information on the sources, data, calculations, and results: GHG Abatement TSD  -  Demand-Side EE Potential Studies.xlsx

Appendix 2: Incremental Electricity Savings Pace of Improvement Analysis This appendix summarizes and analyzes data to characterize the pace of improvement of incremental (or first-year) savings as a percentage of retail sales for electricity energy efficiency (EE) programs. We considered two different perspectives: 1) historical data reflecting achieved savings of EE programs and 2) requirements of existing state energy efficiency resource standards (EERS). For the historical perspective, we reviewed data from the Energy Information Administration's Form EIA-861 on EE program electricity savings (supplemented as needed with program administrator reports) and identified the pace at which entities reaching higher savings levels have historically increased energy savings over time. Specifically, we reviewed the historical savings data in the following two groups of energy efficiency program administrators.       3. Top saver 1% - a group with 47 entities that achieved a maximum first-year savings level of 0.8% to 1.5%. Top saver 2% - a group with 26 entities that achieved a maximum first-year savings level of 1.5% to 3.0%. For the existing state requirements perspective, we reviewed energy savings ramp-up schedules established under EERS for states that provide clear ramp-up schedules. According to ACEEE's 2013 State Energy Efficiency Scorecard, there are a total of 26 states that have mandatory EERS policies (ACEEE 2013a). Our analysis contains 10 states for which clear ramp-up schedules were identifiable. Our research findings on historical savings performance are: The "Top Saver 1%" group (savings between 0.8% and 1.5%) exhibits a trend that these entities took or would take about 3.4 years on average to increase first-year savings by 1% (with a range of 1.6 years to 10 years) (see Table 2-1). The entities in this group have increased the level of first-year savings by 0.30% per year on average from their minimum to their maximum first-year savings levels (with a range of 0.10% per year to 0.63% per year).  The "Top Saver 2%" group (savings between 1.5% and 3%) exhibits a trend that took or would take about 2.6 years on average to increase savings by 1% (with a range of 0.8 years to 7.3 years) (see Table 2-1). The entities in this group have increased the level of first-year savings by 0.38% per year on average from the minimum to the maximum first-year savings levels (with a range of 0.14% per year to 1.28% per year).  Table 2-1. Energy savings ramp-up trends in first-year savings  for "Top Saver 1%" and "Top Saver 2%" groups   Top Saver 1% Top Saver 2%

Average Annual First-Year Savings Increase Estimated Years to Gain Incremental 1% Average Annual First-Year Savings Increase Estimated Years to Gain Incremental 1% Average 0.30% 3.4 0.38% 2.6 Median 0.29% 3.4 0.34% 3.0 Max 0.63% 1.6 1.28% 0.8 Min 0.10% 10 0.14% 7.3 # of sample entities 47 26 Sources: EIA 2012a and third-party program administrator data  Our research findings on incremental electricity savings ramp-up based on existing state EERS policies are: The states with EERS policies which exhibit savings ramp-up schedules are requiring increases in first-year energy savings at a pace that ranges from 0.11% (Colorado and Oregon) to 0.40% (Rhode Island) as shown in Table 2-2.  The first-year savings pace of increase averages 0.21% per year across the 10 states. This savings level translates to about 4.7 years to achieve an incremental 1% first-year savings increase. Table 2-2. First-Year Energy Savings Ramp-up Review of State EERS Policies State Minimum Target Maximum Target Climb Time (years) Annual Average % Increase Years to Achieve 1% Increase

Min Year Max Year

a b c d e=d-b f=(c-a)/e g=1/f Arizona 1.25% 2011 2.5% 2016 5 0.25%  4.0  Arkansas 0.25% 2011 0.9% 2015 4 0.16%  6.2  Colorado 0.80% 2011 1.7% 2019 8 0.11%  9.3  Illinois 0.20% 2008 2.0% 2015 7 0.26%  3.9  Indiana 0.30% 2010 2.0% 2019 9 0.19%  5.3  Massachusetts 1.4% 2010 2.6% 2015 5 0.24%  4.2  Michigan 0.3% 2009 1.0% 2012 3 0.23%  4.3  Ohio 0.3% 2009 1.2% 2019 10 0.17%  5.9  Oregon 0.8% 2010 1.0% 2013 3 0.07%  15.0  Rhode Island 1.7% 2011 2.5% 2013 2 0.40%  2.5  Average

0.21%  4.8  Sources: ACEEE 2014c and APSC 2013

Appendix 3: Demand-Side Energy Efficiency Plan Scenario: Data, Calculations and Results See attached files for comprehensive data, calculations, and results supporting the illustrative demand-side energy efficiency (EE) plan scenario. These files address the magnitude and timing of savings, and the assessment of costs of the demand-side energy efficiency included in the illustrative plan scenario of the Regulatory Impacts Assessment (RIA). The two files reflect costs discounted at 3% and 7%, respectively: GHG Abatement Measures TSD  -  Demand-Side EE @ 3%.xlsx GHG Abatement Measures TSD  -  Demand-Side EE @ 7%.xlsx

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