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### Chapter 5: Demand-side Energy Efficiency (EE)
Introduction This chapter provides information on dema nd-side energy effici ency (EE) as an abatement measure for reducing carbon dioxide (CO 2 ) emissions from fossil fuel-fired electric generating units (EGUs). Specifi cally, this chapter addresses EE as a component of both the “best system of emission reduction” (BSER) and state goals, and the incl usion of EE within the impacts assessment. Support is provided in this chapter for the discussion of the EE abatement measure throughout the preamble (most extensivel y in these sections: Building Blocks for Setting State Goals and Considera tions, State Goals, State Plans, and Impacts of the Proposed Rule) and its representation within the RIA. Results from this chapter feed into the technical support document (TSD) on Goal Computation. EE is also addressed in TSDs on state plan considerations and projecti ng emissions performance.

This chapter is organized as follows:

 Background

– EE Technologies and Practices

– Barriers to EE Investment

– EE Policies

– EE Programs

 The EE Opportunity

– Rapid Growth in EE

– EE Program Impacts

– EE Potential

– Costs and Cost-Effectiveness of State EE Policies

– EE as an Abatement Measure

 State Goal Setting

– Approach

– Inputs

– Calculations

– Results

 Impacts AssessmentDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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– Approach

– Inputs

– Calculations

– Results

 Appendices

 References

Background
As discussed in the State Plan Considerati ons TSD (Appendix: “Survey of Existing State Policies and Programs that Reduce Power Sector CO 2 Emissions”), demand-side energy efficiency policies and programmatic efforts have existed for decades and are now used in all 50 states. These strategies are intended to help states achieve energy sa vings goals, reduce the environmental impacts (including carbon dioxide (CO 2 ) emissions) of meeting energy service needs, save energy and money for consumers, and provide a significant resource for meeting power system capacity requirements. EE policies curre ntly in place are considered by states to be cost-effective strategies for contributing to these policy objectives 1 . Moreover, states – through their utilities, primarily – have been rapidly increasing their funding of EE programs in recent years, more than tripling budgets in the five years from 2006 to 2011, from $1.6 billion to $5.9
billion i . In 2012, the cumulative imp acts of these programs represented a 3.7% reduction in national electricity demand ii . And, EE spending is projected to c ontinue to grow at a substantial rate. A recent study by Lawrence Berkeley Nati onal Laboratory (LBNL) projects EE program spending to reach $8.1 billion to $12.2 billion (“Me dium Case” and “High Case,” respectively) in 2025 even “without considering possible major new policy developments,” such as requirements under Clean Air Act, Section 111(d) 2iii

. This section provides relevant background for th e subsequent sections that address the EE opportunity, EE as a component of BSER, EE within state goal setting, and the integration of EE within the benefit, cost, and impacts assessments as reported in the RIA and elsewhere. This section begins with a discussion of EE technologie s and practices, and then describes the market

1 See below for discussion of cost-effectiveness and rela ted cost tests used by states to evaluate EE programs. 2 Specifically, the LBNL study states: “By virtue of limiting th e analysis to current energy efficiency policies, we do not consider the potential impact of major new federal (or state) policy initiatives (e.g., a national energy efficiency resource standard, clean energy standard , or carbon policy) that could result in customer-funded energy efficiency program spending and savings that exceed the values in our High Case.”DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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failures that limit cost-effective EE investments. We then summarize EE policy objectives and discuss policy types, their relative impacts, a nd discuss in more detail the key strategy of employing EE programs.

EE Technologies and Practices
Energy efficiency is using less energy to provi de the same or greater level of service. Demand-side energy efficiency refers to an ex tensive array of technologies, practices and measures that are applied thr oughout all sectors of the economyto reduce energy demand while providing the same, and sometimes better, level and quality of service. Ut ilities’ employ a large array of strategies in implementing energy e fficiency programs, these include financial incentives such as rebates and loans, technical services such as audi ts and retrofits, and educational campaigns about the benefits of en ergy efficiency improvements. The purpose of these EE programs is to induce EE investments and practices that would not otherwise occur. In the residential sector, examples of EE activities include the purchase of more efficient products and equipment (e.g., ENERGY STAR labeled), the upgrading of insulation in attics and walls, sealing of air leaks, and undert aking home energy audits lead ing to customized whole home retrofits. Opportunities for cost-effective EE in commercial buildings in clude optimization of heating, ventilation, and air c onditioning (HVAC) systems, upgrades of windows, and use of more efficient office equipment at replacement. In the industrial sector key EE strategies include motor upgrades and maintenance programs, recovery of waste heat streams, and optimization of processes through modern instrume ntation and controls systems. The opportunity presented for economic invest ment in EE is dynamic, growing over time as technologies and practices advance, as populat ions grow, and as investment occurs in the construction of new homes, buildings, and indus trial facilities. As ne w policies are enacted, leading to the acceleration of investment in EE, an additional portion of the expanding opportunity is realized. After decades of experience implementing policies to accelerate investment in cost-effective energy efficiency, st ates are finding renewed opportunities as they develop more sophisticated and effective strate gies, evolving from a focus on individual enduses and products to whole-building and syst ems-based strategies that account for the interactions between the many energy end-uses in buildings and industry iv . As will be discussed, the experience in the U.S. has been that on ba lance, a persistent and large potential forDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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achievable and cost-effective EE has remained ev en as the impact of past and ongoing efforts
have accumulated.

Barriers to EE Investment
Despite the persistent and larg e potential for elec tricity savings through investment in EE technologies and practices, market failures, as well as non-market failures, limit the realization of the many benefits of these investments. Several ma rket failures that lead to inefficiencies in energy use are well recognized by analysts and pract itioners, and are discussed extensively in the
economic literature. v Some of the most common examples of these market failures include:

 Pollution externalities. Energy consumption is associated with negative externalities, such as
emissions of CO 2 , SO 2 , and NO x that cause human health and environmental damages. Energy prices that do not correctly reflect thes e externalities lead to investments in energy efficiency below the socially optimal levels.

 Imperfect information. Energy users often lack accurate information about energy savings
and other attributes of ener gy efficient products or practi ces to understand the costs and
benefits of EE investments. Market failure due to information imperfection leads to underinvestment in energy efficiency by consumers.

 Split incentives (or the “principal-agent problem”). Incentives of individuals who make EE investment decisions are not always aligned with incentives of those who use and pay for energy. Examples include misalignment between landlords and tenants, and between builders and homeowners. Split incentives also persist with in organizations and institutions that lead
to underinvestment in EE in both public and the private entities. vi

 Credit constraints . Limited access to credit may prevent some consumers, especially lowincome consumers, from making cost-effective EE improvement decisions due to the higher upfront cost of energy effici ent products or practices.

 Under-provision of research and development (R&D). Because of the public good nature of knowledge, technology innovation invest ed by one firm likely spills over to other firms. As a result, firms involved in technology developmen t may be less willing to invest in R&D, leading to sub-optimal levels of EE i nvestments from a social perspective. viiDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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 Supply market imperfections. Market for energy efficient products is incomplete. Manufacturers do not have perfect information about consumer preferences and may supply limited menu of products to consumers. High st art-up costs and the existence of patents may
create barriers to en try in markets and result in ol igopolistic or monopolistic behavior. Supply chains of EE products is fragmented, leading to underinvestment in innovation and energy efficiency by suppliers. In addition, supply chain fragmentation may also add complexity to the purchase and installation of otherwise economically rational investments, thereby slowing the adoption of EE technologies.

 Behavioral impediments . Behavioral economics and psychol ogy have identified potential behavioral phenomena that lead to consumers to deviate from the standa rd theory of welfare maximizing in consumption and other decisions , including energy effici ency investments. Behavioral economics posits possible explanati ons, including bounded rationality, heuristic decision-making, and non-sta ndard preference and belief. viii In the presence of market failures, users of electricity, or those making energy efficiency investments, face prices or incentives that prevent them from weighing the social benefits and
costs of their investments and thus under-invest in approaches to reduce electricity consumption. The behavioral impediments di scussed above explain why indi viduals do not always make energy efficiency investments that are seemingly in their own best interest to reduce their total expenditure, given preva iling electricity prices.
In addition to market failures and behavioral impediments, other factors, such as hidden costs, risk and uncertainty experienced by bot h consumers and suppliers of energy efficient products, and heterogeneity among consumers, producers and markets, also influence EE
investment decisions 3 . Examples of such factors include:

 Risk and uncertainty . Adopting an unfamiliar, typically more expensive EE technology can be an uncertain undertaking given the lack of credible information on product performance

3 It has been recognized that there is a difference between cost-effective energy efficiency investment levels, based on cost-minimizing consideration, and observed levels of energy efficiency. This phe nomenon, also termed ‘energy paradox,’ or ‘energy efficiency gap,’ has been studied exte nsively in the literature. See, for example, Jaffe, AB, and RN Stavins. 1994. “The Energy Paradox and the Diffusion of Conservation Technology.” Resource and Energy Economics 16(2): 91–122; Sanstad, A. H. and R. B. Howarth. 1994. ‘Normal’ markets, market imperfections and energy efficiency, Energy Policy , 22: 811-818; DeCanio 1998; DeCanio, SJ and WE Watkins. 2008. Investment in Energy Efficiency: Do the Ch aracteristics of Firms Matter? The Review of Economics and Statistics , 80: 95-107; Allcott, H, and M. Greenstone. 2012. Is There an Energy Efficiency Gap? Journal of Economic Perspectives 26 (1):3-28.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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and future energy prices, and th e irreversibility of the invest ment. Imperfect or asymmetric information can exacerbate the perceived risk of energy efficiency investments and help explain why consumers and firms do not always invest in EE measures . Suppliers also face risk and uncertainty, without perfect information of cons umer preferences for energy efficiency. In the presence of risk and uncer tainties, consumers and suppliers alike will
underinvest in EE.

 Transaction costs . Consumers face transaction costs in searching, assessing and acquiring energy efficient technologies and services. It can be time-consuming and difficult for consumers to estimate lifetime operating costs of a product. The complexity of the search process puts many efficient produc ts at a disadvantage relative to less-effi cient products with lower upfront costs.

 Capital market barriers . Consumers sometimes face higher interest rates to finance EE investments compared to other investments. Le nders can be reluctant to invest in EE loan portfolios in part because energy efficiency loans may lack standard ization and financial markets have difficulty ascertaining the likely payoff from such investments. EE policies and programs can play an important role in correcting market failures and addressing the barriers to the investment and a doption of socially benefi cial energy efficiency opportunities. Examples of eff ective EE policies and programs in clude public funding of R&D, information programs (such as energy labe ling, the voluntary ENERGY STAR Program, and consumer education), rebates for high-effi ciency products, product energy performance standards, financing and loan progr ams, and technical assistance.

EE Policies 4 Objectives and Role in Reducing CO 2 Emissions from the Power Sector EE policies are implemented by states to meet a number of closel y related policy goals ix including:

- Reducing costs to electricity customers

- Providing a significant resource for m eeting power system capacity needs

- Meeting energy savings goals

- Stimulating local economic development and new jobs

4 Existing state EE policies are described extensiv ely in the State Plan Considerations TSD.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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- Reducing the environmental impacts of meeting electricity service needs EE policies currently in place are considered by states to be cost-eff ective strategies for contributing to each of these policy objectives x . While each of these objectives, and others, contribute to the motivation of state policymaker s to pursue EE policies, reducing energy costs over the long term is the leadi ng objective in pursui ng these policies. In addition, EE policies are central to meeting state objectives for reducing CO 2 emissions from the power sector. As noted in the State Plan Considerations TSD, EE policies are a leading tool for achieving CO 2 reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in

ten states 5 with statutory requirements for greenhouse gas reductions. Economy-wide studies of climate mitigation scenar ios confirm that energy efficiency plays a critical role in reducing the costs and enhanci ng the flexibility of meeting long-term climate stabilization targets xi . Analysis by the International Energy Ag ency (IEA) suggested that in order to stabilize carbon concentration in the atmosphere at 450 ppm, as much as 44% of the estimated global abatement potential in 2035 derives from greater energy efficiency in the world

economy xii . Several recent Energy Modeling Forum (EMF) studies have investigated the role of technology in achieving climate policy objectives in the U.S. (“EMF 24” and “EMF 25” studies) and globally (“EMF 27” study). 6 These studies concluded that compared to business-as-usual energy efficiency, improvements in energy effici ency in various economic sectors would slow
the increases of GHG emissions in the shor t run, substantially reduce the costs of GHG mitigation (on average, by about 50% 7 ), and ease the technology transformation pathways to achieve long-term carbon reduction goals xiii,xiv

. Several economic studies (includi ng EMF25 studies) examined the role of energy efficiency policies (such as energy efficiency standards and subsidies) in relation to other climate policy instruments (such as carbon taxes). These studie s found that when energy efficiency policies address market failures, they are welfare improving and can complement climate policy. xv In

5 States with GHG reduction laws include: California, Connecticut, Hawaii, Maine, Maryland, Massachusetts, Minnesota, New Jersey, Oregon, and Washington. 6 Energy Modeling Forum (EMF) is a consortium of energy economists and energy economic modeling teams that

was established in 1976. Through ad hoc working groups, the EMF has focused on a series of energy and environmental topics that are of interest to policy decisions. In recent years, the EMF is recognized for its contribution to the advancement of economics of climate change and the reports of the Intergovernmental Panel

on Climate Change (IPCC).
7 It should be noted that these energy
‐ economy modeling studies do not typically include the costs of implementing energy efficiency measures or would treat such costs as exogenous.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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addition, EE policies are recognized to be an a ppropriate response to demonstrated market failures and behavioral impediments, particularly in contexts where these failures have broader societal implications such as environmental externalities. xvi In addition to providing cost-effective oppor tunities for reducing GHG emissions, energy efficiency is recognized to provide other co-b enefits, including air qua lity and public health benefits, waste reductio n from energy generation, energy secu rity, energy system reliability, community economic and social development, and consumer amenities. xvii Energy efficiency investments and policies are also found to s pur productivity growth, technology learning and
innovation. xviiixix Recently, more attention has been pa id to developing methods for recognizing these co-benefits and integrating them into the cost-benefit analysis framework used by state utility commissions and administrators of EE programs xx . These co-benefits have not been fully accounted for in the EPA analysis.

Policy Types EE policies come in many forms. The most pr ominent and impactful EE policies in most
states include those that drive de velopment and funding of EE programs 8 , and building energy codes. Other policies that ar e leading to significant impact s include state appliance and equipment standards, building energy disclosure requirements, innovative financing strategies (e.g., Property Assessed Clean Energy or “PACE”), state tax policies, and “lead by example” strategies targeting energy use in state operations. Comparing th e relative impact (achieved or potential) of the different policy types is cha llenging, particularly to do so comprehensively, across all states, and at the national level. EE pr ograms are the only state EE approach that has comprehensive and detailed repor ting of impacts, costs, and ot her characteristics from all 50

states 9 . This information is generally based upon measurement and verification studies submitted annually, most commonly to state utility comm issions, and reported to the Energy Information Administration (EIA) for all program administrator types (all utility types, third-parties, and

8 EE programs are described in more detail in the following section of this chapter and in the State Plan Considerations TSD. 9 This is a reference to data collected by the Energy Information Administration (EIA) through Form EIA-861. In 2011, EIA began collecting data from third-party administrators of programs. Prior to 2011, this was a significant shortcoming in the breadth of the data collected. The br eadth and quality of information collected through Form EIA-861 has improved over time, however, outside entities (e.g., ACEEE) have f ound that the data can be improved through expert review and supplementation with other data sources. While now fairly comprehensive, the EIA data can be improved further with regards to data quality and consistency.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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government agencies). EE program data reporte d to EIA includes incremental and cumulative energy and peak demand savings, program co sts broken down by component, and composition by end-use sector (reside ntial, commercial, industrial). In 2012, utilities and other program administrators in 48 states reported savings from EE programs to EIA through form EIA-861. At a national level, the EPA is not aware of a co mprehensive dataset repo rted by states of the achieved impacts of strategies othe r than those that lead to inve stment in EE programs. However, state and regional-level information does exis t. For example, the Northwest Power and Conservation Council (NPCC) has been compiling the impacts of EE policies (including utility and third-party EE programs, state building ener gy codes, and federal appliance standards) across their member states (ID, MT, OR, WA) for more than three decades. For the past decade, EE programs have accounted for more than 75% of the cumulative energy savings from state EE policies for NPCC, with building energy c odes accounting for the remaining savings xxi

. Another representation of the relative oppor tunity provided by different state EE strategies is presented by eval uations of EE achievable potential or projections of the impacts of EE policies. The results from two recent evaluations at a national level are presented in Table 5- 1. EE programs account for 77% and 82% of achievable savings in ACEEE xxii and Georgia
Tech xxiii studies, respectively. These studies indicate that the substantial majority of potential savings from state EE efforts are available th rough EE programs, and that state and local building energy codes can make a significant ad ditional contribution. Massachusetts provides a state example of the impacts of EE program s relative to other state EE policies. The Massachusetts Global Warming Solutions Ac t of 2008 established statewide limits on greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. To achieve this target, Massachusetts is relying upon an integrated portfolio of clean energy policies. State EE policies are expected to provide the largest cont ribution to meeting the 25 percent target with utility sponsored EE programs and state bui lding energy codes accounting for 76% and 17%, respectively, of those policies xxiv . In their 2013 progress report, Mass achusetts indicates that they are generally on track for meeting or exceeding these projections. xxvDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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TABLE 5-1 Relative Opportunities Provided by Key EE Programs and Building Codes Study Year EE Programs Building Codes Other
ACEEE 2030 77% 13% 10% Georgia Tech 2035 82% 18% 0%

The full range of EE policies are addressed in greater detail (including designs, authority, obligated parties, measurement and verificati on (M&V), penalties for non-compliance, and implementation status) in the State Plan C onsiderations TSD. Because EE programs have provided the majority of state EE-policy electricity savings to-d ate and offer the majority of potential savings going forward, we next summar ize key characteristics of this strategy.

EE Programs
EE programs (actually portfolios of programs) are comprised of numerous measures and measure types that are applied across all s ectors of electricity end-users. Figure 5-1 xxvi illustrates the multi-level composition and breadth of EE program portfolios. The diversity represented by a typical portfolio of EE programs implemented by a utility (or other program administrator) is an important characteristic relevant to analysis of EE policies. Every deta iled program type (as illustrated in the lower half of the figure) represents a unique set of characteristics including costs of energy saved, ratio of program to part icipant costs, investment life, scale, M&V approach, etc. 10

10 See following sections for di scussion of these factors.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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FIGURE 5-1 Energy Efficiency Program Portfolio

Administrators
EE programs are administered by a variety of entities (“program administrators”) including utilities of all owne rship types (investor-owned, mu nicipals, and cooperatives), nonprofit and for-profit third-partie s (e.g., Vermont Energy Investment Corporation), and state and local government agencies (e.g., NYSERDA). Most EE programs (including all investor-owned utilities which account for more than 75% of reported savings xxvii ) are overseen by state utility commissions, which review and approve program plans, projected impacts, and associated budgets; and establish annual repo rting and M&V requirements.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Policy Drivers
EE programs result from a number of diffe rent policy approaches or “drivers.” 11 These include energy efficiency resource standards (EERS) (26 states) xxviii , system benefit charges (14 states), integrated resource planning (IRP) requ irements (34 states), demand-side management plan or multi-year energy efficiency budget (28 st ates), and statutory requirement to acquire “allcost-effective EE” (6 states) xxix12 . EERS is a more recently developed strategy and has quickly become the leading driver of the rapid growth in EE programs due to thei r clear goals and proven success as a policy tool xxx . These policy drivers lead to the evaluation, planning, and adoption of EE programs and associated budgets, whic h are supported through different funding
mechanisms.

Funding Sources
Funding sources for EE programs are varied but for most states are dominated by revenues collected from ratepayers through electricity su rcharges, typically ranging from $1 to $4 per megawatt-hour xxxi . More recently adopted funding sources include proceeds from the auction of allowances in the Regional Greenhouse Gas In itiative (RGGI) states a nd from EE resources bid into the forward capacity market operated by the New England Independent System Operator (NE-ISO). Ratepayer-funding acc ounts for more than 90% of total EE program support nationally.

The EE Opportunity
As discussed, states are employing a number of EE strategies with EE programs yielding the most significant impacts both histori cally as well as in terms of future potential. Furthermore, EE programs are unique among state EE strategies in the comprehensiveness and transparency of their reported impacts, funding, a nd other characteristics. In this section we address the rapid growth in EE programs, estimated impacts of EE programs to-date and projections of the impacts

11 These policies are discussed in depth in State Plan Considerations TSD. 12 The number of EERS states is from ACEEE (see endnote) and includes states with explicit EERS, those with long-term energy savings targets for individual program administrators, and those w ith EE incorporated as an eligible resource in a renewable portfolio standard. The numbers for the other policy approaches are from LBNL (see endnote).DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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of existing EE programs and trends, and the el ectricity savings potential achievable throughexpanded use of EE policies and programs. Finally, we will discuss the costs and costeffectiveness of EE programs, specifically.

Rapid Growth in EE
Funding for EE programs has increased rapidl y in recent years driven by recent policy innovations and increasing eviden ce of the effectiveness of th ese new strategies. Table 5-2 presents levels of EE program funding in the U.S. since 2006 xxxii . In the previous five years, funding increased by more than 200%, from $1.6 billion in 2006 to $5.9 billion in 2011.

TABLE 5-2 U.S. Electric Utility EE Program Funding (2006-2011)
Year
2006 2007 2008 2009 2010 2011 Electric Efficiency Program Budgets (billions of $s, nominal) 1.6 2.2 2.6 3.4 4.6 5.9

Key new state policies that ha ve helped to drive these ra pid increases in EE program funding include EERS, electricity savings goals , and “all cost-effectiv e energy efficiency” requirements. The adoption of EERS, in particul ar, increased through this period and clearly has been the primary driving force behind the increa sing success of and investment in EE programs.
Table 5-3 shows the number of states adopting EERS by year xxxiii

.

TABLE 5-3 U.S. State Adoption of Energy Efficiency Resource Standards
Year States Adopting an EERS Total
1997-2004 California, Hawaii, Texas, Vermont 4
2005 Nevada, Pennsylvania 2
2006 Rhode Island, Washington 2DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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2007 Colorado, Connecticut, Illinois, Minnesota, North Carolina 5
2008 Maryland, Michigan, New Me xico, New Year, Ohio 5
2009 Arizona, Indiana, Iowa, Maine, Massachusetts 5
2010 Arkansas, Oregon 2
2011 Wisconsin 1
1997-2011 26

EE Program Impacts
Impacts to-date
The primary sources for EE program inform ation (including costs and impacts) are annual EE program reports required by utility commi ssions, or cooperative or municipal utility boards of directors. These reports are based on M&V studies of indivi dual EE programs within the program portfolio. The Energy Information Ad ministration (EIA) has be en collecting data on EE programs for more than three decades with an increasing degree of breadth and detail over time. For example, third-party-administered prog rams were not initially required to report but were added beginning in 2011. Data fields have been added over the years to reflect industry trends (e.g., EE programs are now reported separately from load management programs). Outside organizations have taken the EIA da ta, supplemented it with additional sources including surveys of utility commissions and program administrators, and published their own annual reports that capture EE program impacts xxxivxxxv

. The EPA has relied on the EIA dataset for id entifying historic impacts of EE programs by state. EIA data is reported by program administ rator (e.g., utility, third- party, or state agency) and requires the disaggregation of reported data by state for administrators with programs in multiple states (e.g., multi-state investor-owned util ities). Program administrators in 48 states reported savings in 2012. The EPA has compiled this information and aggregated key data to the state level. Table 5-4 provides a summary of this data by state for the 2012 reporting year, the
most recent available. At the national level, incremental electricity savings 13 in 2012 was 0.58%

13 Incremental savings (also known as first-year savings) represent the reduction in electricity use in a given year associated with EE activities in that same year.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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of retail sales with individua l state values ranging from 0.00% to 2.19%. Cumulative electricity savings 14 (representing the remaining impacts of progr ams from all prior ye ars) reported at the national level for 2012 represent 3.7 4% of retail sales wi th individual state values ranging from
0.0% to 15.44%.

TABLE 5-4 2012 Reported Electricity Savings by State

State Incremental Savings as a % of Retail Sales (2012) Cumulative Savings as a % of Retail Sales (2012)

Alabama 0.07% 0.78%
Arizona 1.61% 5.39%
Arkansas 0.11% 0.39%
California 1.24% 13.67%
Colorado 0.84% 4.67%
Connecticut 1.05% 13.37%
Delaware 0.00% 0.00%
District of Columbia 0.00% 0.57%
Florida 0.27% 3.60% Georgia 0.18% 0.67%
Idaho 0.79% 6.20%
Iowa 1.05% 7.80%
Illinois 0.93% 2.15%
Indiana 0.58% 1.72%
Kansas 0.02% 0.24% Kentucky 0.23% 1.04%
Louisiana 0.00% 0.00%
Maine 1.96% 5.42% Maryland 0.89% 2.47%

14 Cumulative savings (also known as annual savings) represent the reduction in electricity use in a given year from EE activities in that year and all preceding years, taki ng into account the lifetimes of installed measures.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Massachusetts 0.94% 6.27% Michigan 1.01% 2.77%
Minnesota 1.12% 13.10% Mississippi 0.08% 0.50%
Missouri 0.12% 0.55%
Montana 0.66% 5.85%
Nebraska 0.30% 0.99%
Nevada 0.54% 6.19% New Hampshire 0.48% 4.90% New Jersey 0.03% 1.04%
New Mexico 0.60% 1.86%
New York 0.93% 6.89%
North Carolina 0.37% 1.26%
North Dakota 0.07% 0.22%
Ohio 0.87% 3.20%
Oklahoma 0.21% 0.70% Oregon 1.09% 7.72% Pennsylvania 1.06% 3.08%
Rhode Island 0.78% 11.22%
South Carolina 0.35% 1.12%
South Dakota 0.13% 0.33%
Tennessee 0.31% 1.76%
Texas 0.19% 1.54%
Utah 0.74% 6.59%
Vermont 2.19% 15.44% Virginia 0.03% 0.30% Washington 0.93% 7.37% West Virginia 0.18% 0.20%
Wisconsin 1.05% 6.61% Wyoming 0.14% 0.71%DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Continental U.S. Total 0.58% 3.75%
Alaska 0.02% 0.10%
Hawaii 0.04% 0.25%
U.S. Total 0.58% 3.74%

Projected Spending and Savings from EE Programs
In 2013, Lawrence Berkeley National Laborator y (LBNL) published an update to a 2009 analysis and projected future spending levels and savings through 2025 from energy efficiency programs funded by electric and ga s utility customers in the Unite d States under three scenarios (high, medium, and low cases) xxxvi . The scenarios represent “a ra nge of potential outcomes under the current policy environment” and were based on detailed, bottom-up analysis of existing state energy efficiency policies. Significantly, the study presumes no new major policy developments such as a “national energy efficiency standa rd, clean energy standar d, or carbon policy” and specifies that such policy changes could “resu lt in customer-funded energy efficiency program spending and savings that exceed the values in our High Case.” The study concludes that effici ency programs are “poised for dramatic growth over the course of the next 10 to 15 years” with the mo st significant increases o ccurring in regions with lower levels of program spending, historicall y, including the Midwest an d South. For example, under the medium scenario total U.S. spending on electric efficiency programs increase by 40% to $8.1 billion in 2025 from 2012 levels. Under th e high scenario, spending more than doubles from 2012 levels to $12.2 billion in 2025. Incremen tal savings levels grow commensurately, to
0.8% and 1.1% of sales under the medium and hi gh scenarios, respectively. The study results indicate that under the high scen ario 20 states would be achie ving 1.5% or higher levels of incremental savings, with 11 of those reaching or exceeding 2.0% 15 . Table 5-5 summarizes the results of the LBNL analysis.

15 LBNL provided these unpublished results from their analysis.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Table 5-5 Summary of Impacts: Scenarios of Future Utility Customer- Funded Energy Efficiency Programs

Case 2025 Incremental Savings (% of Sales) Program Costs (billions of $, nominal) Programs Costs (% of Revenues)
Low 0.5% 5.5 1.1%
Medium 0.8% 8.1 1.7% High 1.1% 12.2 2.7%

EE Potential
Evaluations of EE Potential
Energy efficiency potential studies are a comm on tool for informing the development of EE program plans and budgets, as well as supporting th e development of electricity savings targets, required savings levels under an EERS, or “al l cost-effective” EE re quirement. In conducting these studies, states and utilities have deve loped a standardized methodology that is often described as a “bottom-up, e ngineering-based” approach xxxvii . EE potential studies are conducted at various geographic scopes (nati onal, regional, state, and utility service territory level) and at different degrees of aggregation (e.g., economy-wi de, sectoral, and program), and can be broadly grouped into a few types: techni cal, economic, market, and program. 16

 Technical potential represents the theoretical maximum amount of energy use that could be displaced by efficiency, without regard to non-engineering constraints such as costs and the willingness of energy consumers to adopt the efficiency measures. It often assumes immediate implementation of all technologically feasible energy saving measures, with additional efficiency opportunities assumed as they arise.

 Economic potential refers to the subset of the technical potential that is economically cost-effective. Definition of “economic pot ential” can vary to some degree by study.

16 The definitions discussed below largely follow that outlined in the Guide for Conducting Energy Efficiency Potential Studies (NAPEE 2007) but the variations in definition are also discussed (e.g., Sathaye and Murtishaw 2004; Huntington 2011).DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Some estimate economic potential by evalua ting technology upfront cost, operating costs that considers energy prices, product life time and discount rate, compared to a
conventional alternative or the supply-side energy res ources. Others incorporate consideration of consumer preferences in addition to consumers’ out-of-pocket expenditure when evaluati ng the economic potential. Bo th technical and economic potential estimates assume immediate implem entation of efficiency measures without regard to technology adoption process or re al-life program implementation. In addition, these estimates do not always reflect market failures or barriers that impede energy efficiency and often fail to capture tran saction costs (e.g., admi nistration, marketing, analysis, etc.) beyond the costs of efficiency measures.

 Market potential (or “achievable” potential ) refers to the subset of economic potential
that reflects the estimated am ount of energy savings that can realistically be achieved, taking into account factors such as tec hnology adoption process, market failures or barriers that inhibit technology adoption, transaction costs, co nsumer preferences, social and institutional constraints, and possibly the capability of programs and administrators to ramp up program activity over time.

 Program potential refers to the subset of market pot ential that can be realized given specific program funding levels and designs. Program potenti al studies can consider scenarios ranging from a single progra m to a full portfolio of programs 17

. As mentioned, the EE industry standard for potential studie s is the bottom-up, engineering evaluation of energy efficiency potential of indi vidual end-use technologies and

measures xxxviii . Bottom-up analyses all employ a simila r methodology but can vary significantly in key assumptions (e.g., breadth of sectors and end-uses consid ered, study period, discount rate, pattern of technology penetra tion, whether economically jus tified early replacement of technologies is allowed for, and whether continue d improvement in efficiency of technology is provided for). As a result, estimated efficien cy potential can vary significantly among studies 18

.

17 Each subsequent potential estimate described above is a subset of the previous potential estimate, e.g., the market potential is a subset of the economic potential, and the ec onomic potential is a subset of the technical potential. 18 Because of the complex consumer behavior, energy market and macroeconomic drivers of energy use and energy efficiency, and in some cases due to the lack of consistent data, quantifying energy efficiency potential and energy savings from policies and programs remains a challenging analytical task. Assumptions about consumerDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Overview of Results
Studies of energy efficiency potential are numerous. In recent years, dozens of studies have been conducted at regional, st ate, and utility levels. This s ection reviews rece nt studies and presents a summary of findings. We first addre ss meta-analyses that summarize results from multiple utility, state, and regional studies, and then we address the few national studies that have been conducted. To normalize results of analyses addressing different st udy periods, we present average annual achievable potential by dividing cumula tive percentage savings in the last year of the study by the duration (in years) of th e study period. This is a common method of normalization for energy effi ciency potential studies. At the regional and state level, two meta-analyses, Sreedharan (2013) xxxix and Eldridge et al. (2008) xl , captured numerous studies conducted between 2001 and 2009. The meta-analysis conducted by Sreedharan (2013) presents average annual values of 4.1% per year in technical potential, 2.7% per year in economic potenti al, and 1.2% per year in maximum achievable potential. In comparison, Eldridge et al. (2008) estimated average annual values of 2.3% per year in technical potential, 1.8% pe r year in economic potential, a nd 1.5% per year in achievable potential. To supplement these studies with mo re recent data, the EPA has conducted a metaanalysis of twelve studies conduc ted between 2010 and 2014 at the ut ility, state or regional level (see Appendix 5-1). The EPA revi ew indicates an average annu al achievable pot ential of 1.5% per year across the reviewed studies. See Appendix 5-2 (Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies) fo r complete results from the EPA research.

technology adoption behavior, market barriers and failures, and how technology diffusion occurs can also affect estimated potential.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Table 5-6 presents a summary of thes e three meta-analyses of EE potential.

TABLE 5-6 Summary of Meta-Analyses of EE Potential at Utility, State, and Regional Levels

Study Dates of Studies Number of
Studies Average Annual Achievable
Potential Sreedharan (2013) 2001-2009 10 1.2%/year

Eldridge (2008) 2001-2007 20 1.5%/year EPA (2014) 20010-2014 12 1.5%/year

In addition to the numerous studies conducted at the utility, state, or regional levels since 2001, a number of studies have evaluated efficiency potential at the national level, applying a generally consistent methodology and employing a common data set, across all regions of the country. Sreedharan (2013) evalua ted four major energy efficien cy potential studies at the national level, namely, McKinsey and Co. ( 2007), McKinsey and Co. (2009), EPRI (2009), and AEO (2008) Energy Efficiency Side Case. All four studies used the AEO 2008 reference case as
the baseline but differed in othe r key respects (e.g., breadth of end-uses, assumed technology improvement over time, and definition of cost test for economic potential screening). These studies suggest technical elec tricity savings potential in th e range of 25-40% and economic potential in the range of 10-25%, as a percentage of total demand in 2020. Of these studies, only EPRI provided an estimate of achievable poten tial. On a normalized basis, the EPRI 2009 study provides an achievable annualized potential rang e of 0.2-0.4% per year (realistically achievable and maximum achievable potential, respectivel y) through 2030 at the national level. Two more recent studies also provide natio nal estimates of achievable EE potential: EPRI (2014) xli updates their 2009 analysis, using a conventional bottom-up engineering approach, and ACEEE (2014) xlii , using a top-down, policy-based approach derived from state experience and their evaluated results. EPRI ( 2014) results show an average annual achievable potential range of 0.5% to 0.6% per year (achievable and high achievable potential, respectively). ACEEE found average annual achievable potential of 1.5% per year. The results of
the EPRI and ACEEE studies ar e summarized in Table 5-7.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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TABLE 5-7 Summary of National EE Potential Studies Study Study Type Average Annual Achievable
Potential
EPRI (2009) Bottom-up, engineering 0.2%-0.4%/year
(realistic to maximum achievable)
EPRI (2014) Bottom-up, engineering 0.5%-0.6%/year
(achievable to high achievable)
ACEEE (2014) Top-down, policy-based 1.6%/year

Notably, each of these national potential st udies show significant potential in every region of the country including regions with lower electricity prices like the southeast, regions with historically high levels of EE program budget s like the northeast and west coast, and across regions with varied sectoral composition (e .g., higher manufacturing regions like the midwest and south, as well as higher service industry regions like the northeast and California). Both
EPRI studies illustrate the substantial and sim ilar scale opportunity across all regions. For instance, EPRI (2014) shows achieva ble potential ranging from 8% to 14% relative to baseline in 2035 across the thirteen regions of their analysis as well as significant opportunity in the residential, commercial and i ndustrial sectors in every regi on. The ACEEE (2014) study also shows consistently large potential across all st ates and regions through 2030, with an average potential of 24% and a range of 20% to 36% across 50 states.

Costs and Cost-Effectiveness of State EE Policies

EE Cost-Effectiveness States enact EE policies to meet multiple polic y objectives including reduction of customer electricity bills, lower costs of meeting electricity supply need s, energy reduction, environment and health benefits, and local economic development benefits xliii . Most states evaluate their EE policy options through the applicati on of cost tests, weighing th e projected benefits with theDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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costs of the energy efficiency technologies and practices xliv xlv . Each state determines their own policies for the specific costs and benefits to incl ude in these tests. The costs and benefits are compared on an equal footing by using present value analysis. This is necessary because EE typically requires primarily upfront expend itures (e.g., a whole home retrofit) while the economic benefits (e.g., electricity bill savings) a ccrue over the life of the investment (“measure life”) which can range from a few to twenty or more years. As such, the choice of discount rate and the estimation of measure life are significant determinants of the cost-effectiveness results. Most states employ multiple tests, adjusting cost and benefit catego ries depending upon the economic perspective of interest (e.g., utility, ratepayer, prog ram participant, society), and consider the results from each one, usually with an identified primary test type xlvi . Policies that
are selected are those that are f ound to be cost-effective, with be nefits greater than costs, as determined by the utility applying methods defined by their stat e utility commission. There are five primary cost-effective tests used in the U.S.: (1) Participant cost test from the perspective of the customer installing the measure. Costs may include incremental equipment and installati on costs; benefits include incentive payments, bill savings, and applicable tax credits or incentives. (2) Utility/program admin istrator cost test from the perspective of utility, government agency or third-party implem enting the program. Costs may include program incentive, installation, and overhead costs; benefits ma y include avoided energy and capacity costs - including generation, transmission a nd distribution - by the utility. (3) Ratepayer impact measure test from the perspective of utility ratepayers not participating in available energy efficiency programs. This text includes the costs and benefits that will affect utility rates, including program and administration costs, as we ll as “lost revenues” to the utility; benefits include avoided energy and capacity costs, and additional resource savings. (4) Total resource cost test from the perspective of all util ity customers in the service area. Costs may include the full incremental cost of the measure, program installation and overhead costs; benefits may include avoided energy and ca pacity costs, and addi tional resource savings. (5 ) Societal cost test from the social perspective. In addi tion to benefits considered in total resource cost test, may also include non-moneti zed benefits such as environmental and health
benefits.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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While many states consider more than one co st test in evaluating EE programs, the most commonly used (29 states) primary te st is the total resources cost test. This test is considered to
be the best measure of the interest s of all utility customers. The u tility and societal cost tests are the next most commonly used primary tests, used by five states each. The utility cost test is considered to be the most comparable metric to compare with supply-side resource investments from a utility resource planning perspective. Economic and modeling analyses of clim ate change policy su ggests that energy efficiency presents a large pot ential in reducing greenhouse gas emissions and plays a critical role in offsetting the costs and enhancing the flexibility to achieve long-term GHG reduction targets xlvii . Consistently, evaluations of the economic potential for carbon dioxide reductions from the United States’ power sect or identify demand-side energy e fficiency as the lowest cost strategy (typically, as noted above , with positive net present value) as well as the strategy having the greatest reduction potential xlviii . For example, McKinsey (2007) xlix found that EE accounted for more than 60% of their mid-range potential for greenhouse gas reductions from the U.S. power sector and that it was available at positiv e net present value if “persistent barriers to market efficiency” could be addressed.

Costs of Saved Energy A common metric for comparing alternative electricity reso urce options within utility resource plans is the levelized cost of energy (L COE) or, for EE resources, the levelized cost of saved energy (LCSE) l . LCSE EE is often compared favorab ly with LCOE of alternative new generation sources such as fossil-fueled or nucle ar power plants, or renewable energy resources like wind or solar-power generati on. The energy efficiency analysis literature reports average LCSE in the range of 1-6 cents/kWh based on program administrator cost. 19 A recent review by ACEEE (2014) examined studies across 20 stat es between 2009 and 2012, and estimated LCSE for electricity energy efficiency programs in th e range of 1.3-5.6 cents/kWh, with a mean value
of 2.8 cents/kWh. li Earlier reviews of u tility EE programs identified a similar range of LCSE. Friedrich et al. (2009) reviewed 14 utility studies of LCSE and found a range from 1.6 to 3.3 cents/kWh, with a mean value of 2.5 cents/kWh. lii An earlier ACEEE study (2004) reviewed

19 Unless otherwise noted, estimates of LCSE discussed in this section refer to program administrator cost (also known as utility cost). The discount rates, average measur e lives, and other assumptions affecting the calculation of LCSE were not consistent or not reported in all studies.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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cost-effectiveness analysis results in nine states and suggested that reported utility LCSE ranged
between 2.3-4.4 cents/k Wh, with a mean value of 3 cents/kWh. liii
The economic literature also evaluates the LCSE from EE measures using various techniques (e.g., empirical econometrical, top-do wn modeling). Some recent analyses suggest estimated LCSE in a similar range as in the ener gy efficiency analysis li terature. Gillingham et al. (2004) estimated an average cost of 3.4 cents per kWh saved from utility EE programs. liv Using a top-down approach that evaluates the sa vings potential of EE investments using stateand region-specific price elasticity, Paul et al. (2011) estima ted that electricity savings were available at an average util ity LCSE of 2.5-3.5 cents/kWh. lv In an econometric analysis of utility rate-payer funded demand-side management a nd energy efficiency programs between 1992 and 2006, Arimura et al. (2009) found that the estimat ed energy savings in electricity consumption were achieved at an expected average cost to utilities of approximately 5 cents/kWh. lvi A number of analytical and data considera tions related to LCSE estimation are also discussed in the literature, including th e issue of “free riders” in EE programs lvii , and the accuracy of utility reporte d costs and energy savings lviii . Energy efficiency practitioners also recognize the need to consider “free rider” a nd “spillover” effects in program evaluation. A slight majority of states adjust for free riders hip in energy savings esti mates, leading to higher
LCSE values than otherwise would be the case. A smaller number of states adjust for spillover
effects which reduce LCSE values when addressed. lix

EE as an Abatement Measure
Demand-side energy efficiency is a technically viable, broadly applicable, and generally cost-effective measure for achieving significant re ductions in utilization of affected EGUs. Moreover, this measure has been adopted by near ly every state and most utilities across the country, typically through multiple policy approach es. Increased use of, and impacts from, state energy efficiency policies is a leading industr y trend over recent years and the trend of increasing investment in EE programs is projected to continue for the next decade, at least. These findings support the inclusion of demand-side en ergy efficiency as an abatement measure for reducing carbon dioxide (CO 2 ) emissions from fossil fuel-fired electric generating units (EGUs). In the next section, we address the setting of st ate-specific goals for electricity savings levels resulting from state demand-side en ergy efficiency efforts. In the final section, the integration ofDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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these goals into the impacts assessment is presen ted and we consider the cost-effectiveness of this building block within the context of the broader proposal.

State Goal Setting Approach
To estimate the potential CO 2 reductions at affected EGUs that could be achieved through implementation of demand-side energy effici ency policies as a part of state goals, the EPA developed a “best practices” demand-side energy efficiency scenario. This scenario provides an estimate of the potential for states to implement policies that increase investment in cost-effective demand-side energy efficiency tec hnologies and practices, a nd projects the annual impacts of the scenario for each state. It does no t represent an EPA forecast of business-as-usual impacts of state energy efficiency policies or an EPA estimate of the full potential of end-use energy efficiency available to the power system, but rather is intended to represent a feasible policy scenario showing the reductions of CO 2 emissions from fossil fuel-fired EGUs resulting from accelerated use of energy efficiency policies in all states, generally consistent with ongoing industry trends. The scenario uses a level of performance that ha s already been demonstrated or required by policies (e.g., energy e fficiency resource standards) of many leading states, considers each state’s unique existing level of performance, and allows appropriate time for each state to increase from their current level of performance to the identified best practices level. The best practices scenario is derived from state experience with, and reliance on, policies that drive investment in energy efficiency programs, and the energy savings that result from those efforts. We focus on energy efficiency programs for several reasons:

 EE programs have achieved significant levels of savings and are being used in almost every state,

 EE program spending and savings levels ar e reported by utility or other program administrator, by state, and compiled nati onally, using standardized elements and definitions, and

 EE program savings are projected and eval uated under requiremen ts established and overseen by state utility commi ssions, and by municipal and c ooperative utility boards of
directors.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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While the approach is derived from info rmation about energy efficiency programs overseen by state utility commissi ons, other state energy efficien cy policies are available to realize a state’s goals 20 such as building energy codes, appl iance standards, and building energy benchmarking requirements. All policies included in a state plan will need to meet established requirements or guidance for EM&V 21

. The following steps were taken to establis h the inputs for development of the best practices scenario for each state:

 Step 1: Determine current level of performance

 Step 2: Determine best practices level of performance

 Step 3: Determine start year for state efforts

 Step 4: Determine start ye ar level of performance

 Step 5: Determine pace at which states improve from start year to be st practices level of performance

 Step 6: Determine average portfolio measur e life and distributi on of measure lives

 Step 7: Determine sustainability of best practices level of performance

Inputs Step 1: Determine Current Level of Performance

A fundamental indicator of the level of energy efficiency program performance is incremental annual savings as a percent of retail sales. This is a common metric defining savings levels for energy efficiency res ource standards and is readily calculated fro m EIA Form 861 data
for each state. Incremental annual savings are also more directly estimated and evaluated than are cumulative savings 22 . For the best practices scenario, we aggregated the most recent year of
EIA Form 861 data to the state le vel to establish each state’s curre nt level of performance. These results were presented pr eviously in Table 5-4.

20 See State Plan Considerations TSD. 21 See State Plan Considerations TSD. 22 Estimates of cumulative savings impacts in a given year are derived from incremental savings values and information on measure lives. Information on measure lives is less consistently gathered than is information on incremental savings values.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Step 2: Determine Best Prac tices Level of Performance
As discussed previously, achievable demand-si de energy efficiency 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 regi onal characteristics (e.g., avoided power system costs, economic grow th, sectoral mix, climate, and le vel of past energy efficiency efforts) affect estimates of achievable pot ential, ongoing improvements in energy-efficient technologies and practices, economic growth, popul ation increases, and continually improving strategies for program delivery have resulted in persistent and substantia l levels of achievable potential regardless of speci fic regional characteristics.
A direct indicator of the achievable incremen tal levels of energy savings performance is provided by past performance at th e state and utility levels, and by requirements states have put in place for levels of savings to be achiev ed by 2020. As discussed, these requirements are typically in the form of energy efficiency resour ce standards or similar savings goals that are applied to utilities in the state 23

. Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA Form 861 (2012) data lx , aggregated to the state level, and ca tegorized into four ranges of savings levels (> 0.5%, 0.5% to 0.99%, 1.0% to 1.49%, a nd >= 1.5%). As shown, three states achieved the highest level of performance (> 1.5%) and an additional eight stat es achieved the second highest level of perfor mance (1.0% to 1.49%).

TABLE 5-8 2012 Reported State Levels of Incremental Annual Savings Incremental Savings as % of
Retail Sales # of States States

>= 1.5% 3 AZ, ME, VT
1.0% to 1.49% 8 CA, CT, IA, MI, MN, OR, PA, WI
0.5% to 0.99% 14
< 0.5% 25

23 See State Plan Considerations TSD for more information.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Table 5-9 summarizes incremental savings le vels required by state policy on or before 2020 and categorized into the same four ranges lxi . Eleven states are required to achieve the highest level of performance (> 1.5%) and an add itional five states are required to achieve the next highest level of pe rformance (1.0% to 1.49%).

TABLE 5-9 Levels of Incremental Savings Required by State Policy on or before 2020 Incremental Savings as %
of Retail Sales # of States States

>= 1.5% 11 AZ, CO, IL, IN, MA, MN, NY, OH, RI, VT, WA
1.0% to 1.49% 5 HI, IA, ME, MI, OR
0.5% to 0.99% 3 AR, CA, WI
< 0.5% 1 TX

For the best practices leve l of performance for Option 1 24 , the EPA has chosen 1.5% incremental savings as a percentage of retail sa les. This level was achie ved by three states (AZ, ME, and VT) in 2012 and an additional nine st ates (CO, IL, IN, MA, MN, NY, OH, RI, and WA), accounting for overlap, are ex pected to achieve this leve l by 2020. Thus, twelve states have either achieved or are required to achieve this level of performance by 2020. For Option 2 25 , the EPA has chosen 1.0% incrementa l savings as a percentage of retail sales as the best practices level of performan ce for this alternate ap proach. This level was achieved by eleven states in 2012 and an additional twelve states are expected to achieve this level by 2020. In total, twenty states (accounting for duplication between th e two sets of states) have either achieved or are required to achieve this level of performance by 2020. Step 3: Determine Start Year for State Efforts
For construction of the best practices scen ario, the EPA has used 2017, the year following the required state plan submittal 26 , as the first year for state efforts.

24 See Preamble and Goal Computation TSD for description of Option 1. 25 See Preamble and Goal Computation TSD for description of Option 2. 26 See Preamble and State Plan Considerations TSD for desc riptions of the schedule for state plan submittals.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Step 4: Determine Start Ye ar Level of Performance
For construction of the best practices scen ario, the EPA has set each state’s level of performance (incremental savings) in the start ye ar (2017) to its current level of performance (aggregated to the state-level fr om reported EIA Form 861 data). This approach reflects neither improvement nor decline in performance between 2012 and 2017. Any improvement in EE savings performance between 2012 and 2017 will bene fit a state in meeting its state EE goals for the 2020-2029 interim compliance period 27

.

Step 5: Determine Pace at Which States Improve from Start Year to Best Practices Level of Performance
To determine a trajectory of incremental sa vings levels from the 2017 level to the best practices level, the EPA considered past pe rformance of individual program administrators 28 as well as requirements of existing state energy e fficiency resource standards. For the past performance of individual program administrators, we first screened the data and divided them into moderate and high performing sub-groups. Th e moderate group (47 entities) was defined as programs that achieved from 0.8% to 1.5% maxi mum incremental saving s levels and the high group (26 entities) was defined as programs that achieved greater than 1.5% maximum incremental savings levels. We then calculate d the rate at which eac h 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 5-3 for s upporting data and analysis. The EPA also considered requirements of ex isting state EERS and evaluated the rate at which their incremental savings levels increase over time. For several EERS, we were unable to clearly identify ramp-up schedules . We 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 st ates is 0.21% per years. See Appendix 5-3 for supporting data and analysis.

27 See Preamble for description of interim and final compliance periods. 28 EIA 861 was the primary data source; however, we supplemented EIA 861 data with data for third-party program administrators because prior to 2011 EIA did not co llect data from third-part y program administrators.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Based on these results, for the best practices rate of improvement the EPA has chosen 0.2% per year and 0.15% per year for Options 1 and 2, respectively. These values are conservative by comparison with our analysis of past state performance and future state requirements.

Step 6: Determine Average Portfolio Measur e Life and Distributi on of Measure Lives
The next step in defining the best practices scenario requires projecting the cumulative future impacts of the annual incremental savings levels for each state. The incremental savings impacts reflect the savings from EE measures put in place in that year, driven by EE program activities in that year . The cumulative annual savings repr esent the total impacts of all EE measures put in place in that year and all pr ior years, due to EE program activities. The cumulative savings account for the continuing impact s of energy efficiency measures that remain in place for a period of time (the “measure lif e”) before being replaced. For example, the purchase of a high-efficiency refrigerator may le ad to savings for twelve years, before being replaced with a new model. To estimate cumulativ e impacts of a series of years of incremental savings, the industry uses the c oncept of an average measure lif e for the entire portfolio of EE programs. Rather than use a single, average meas ure life to represent a diverse portfolio of programs, that range in measure lives from as little as a few years (e.g., certain lighting technologies and applications) to as long as fifteen or twenty y ears (e.g., adding insulation to an attic), the EPA is assuming a distribution of measure lives around the average to account for future impacts of incremental savings levels lxii

. In 2014, ACEEE updated their 2004 and 2009 nati onal reviews of EE program costs and related program characteristics, including measure lives. They reviewed electricity EE program data from 20 states and summarized average measure lifetimes by state and customer class.
Table 5-10 summarizes the results from the ACEEE study and shows an average across all sectors for these states of 10.6 years.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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TABLE 5-10 Average Electricity Measure lifeti mes by state and customer class
Sector
All Sectors Residential Commercial/Business Industrial Average 8.1 12.5 9.5 10.6

Other studies have found slightly higher valu es for average measure life for EE portfolios, ranging from 10 to 13 years lxiii . Our assumption of 10 years is conservative by comparison and leads to lower cumulative impacts over time and correspondingly lower state goals. To approximate a distribution of measure live s across an EE portfolio, consistent with an average measure life of ten years, we have assu med an even distribution from one year in length to two times the average measure life (twenty y ears) in length. Our a pproach is generally supported by the substantial range in measure liv es reviewed and summarized in a 2014 study by LBNL which shows an interquartile range fr om five to 25 years across twelve program categories (e.g., low income, residential new cons truction, commercial/indus trial custom, etc.). Our approach represents a first- order approximation of the distri bution of measure lives across a diverse portfolio of programs. The more common approach in other studies is to assume a portfolio with no diversity of m easure lives whatsoever, 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. Our approach is a c onservative one, leading to the same quantity of total energy savings, but with a gr eater portion of the savings occurr ing in later years than occurs with the more common, and simpler, approach. Th is results in lower cumulative impacts in earlier years and correspondingl y lower state goals through 2030.

Step 7: Determine Sustainability of Best Practices Level of Performance
For construction of the best practices scenar io, once a state achieves the best practices level of performance, the EPA has kept the level of performance constant through 2030. For
states with lower levels of current performance (and, hence, later achievement of the best practices level of performance – as late as 2025 in some instances), this requires sustaining the target level for as little as five years. For states currently at or above th e best practices level ofDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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performance, this reflects an ability to sustai n the target level for thirteen years (2017 through 2030). Limited empirical data suggests the r easonableness of this approach; however, comprehensive data, across all regions and stat es, does not exist because these levels of performance have not been achieved and sustai ned 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 29 regional energy resource plans lxiv

. These analyses have become more detaile d, reliable, and purposeful over time. Since 1998, NPCC’s estimates of achievable potential have more than tripled even as evaluated electricity savings from energy efficiency programs have increased rapidly, more than quadrupling between 1998 and 2010 (while levelized co sts 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 hi gher levels of efficiency, this helps drive measurement improvements and technical innovati on, resulting in larger and more reliable conservation supply estimates.” lxv Table 5-11 summarizes the NPCC’s achievable potential estimates and evaluated savings since 1998 lxvi

.

TABLE 5-11
NPCC Achievable EE Potential and Ac hieved 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

29 NPCC’s resource plans cover Idaho, Oregon, and Washington in their entirety, and western regions of Montana and Wyoming.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Additional substantiation of this approach is provided by average annual achievable rates from reviewed studies, as discussed previously, and comparison of those w ith the rates resulting from the best practices scenario. We address this in a later section, Results in Context , after presenting those results.

Summary of Best Practices Scenarios Construction Table 5-12 provides a summary of inputs for the best practi ces scenarios for Options 1 and 2. The pace of improvements, average measur e life, and distribution of measure lives are each conservative and, therefore, contribute lo wer state goals than would otherwise result. Similarly, the best practices level of performa nce, being based solely on results from and requirements of EE programs, is le ss stringent than a le vel would be that accounted for potential impacts of other state EE policies such as bu ilding energy codes, build ing energy benchmarking requirements, and state appliance standards. The use of 2012 level of performance for the 2017 start year, allows states that increase their use of effective EE policies prior to submitting their implementation plan to benefit.

Table 5-12 Summary of EE Best Practices Scenario Inputs Input Option 1 Option 2
Current Level of Performance

(incremental savings as % of retail sales) Data from 2012 EIA 861 (2012) Data from 2012 EIA 861 (2012)

Best Practices Level of
Performance (incremental savings as % of retails sales) 1.5% 1.0%

Start Year 2017 2017
Start Year Level of
Performance Data from 2012 EIA 861 (2012) Data from 2012 EIA 861 (2012) Pace of Improvement (increase in incremental 0.20% per year 0.15% per yearDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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savings rate per year) Average Measure Life and
Distribution of Measure Lives 10 years; evenly distributed across 20 years 10 years; evenly distributed across 20 years

Continued Performance Once achieved, best practices level sustained through 2030 Once achieved, best practices level sustained through 2025

Calculations
This section addresses the calculations fo r determining cumulative savings levels (cumulative savings as a percentage of baseline sales) for each stat e, for each year of the interim and final compliance periods for Options 1 and 2. The cumulative savings levels are derived based upon the key inputs summarized in Table 5- 12. These levels represent the demand-side EE component of the state goals for each state. See the Goal Computation TSD for a detailed description of how the demand-si de EE component is used as one of several inputs to the
calculation of interim and fina l state emission rate goals. Calculating the net cumulative savings as a perc ent of electricity sale s for each state involves six steps. For each state, for each year ( 2017-2030 for Option 1 and 2017-2025 for Option 2) the following steps are taken:

1. Determine annual business as usual (BAU) sales
2. Determine annual incremental EE savi ngs as a percentage of sales
3. Determine annual incremental EE savings (GWh) and sales after net EE (GWh)
4. Determine annual expiring EE savings (GWh)
5. Determine net cumulative EE savings (GWh)
6. 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 exam ple) for 2017 through 2025 for Option 1. We
truncate the results at 2025 for si mplicity, but full results are pr esented for all states in the
section.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Step 1: Determine the Annual Business as Usual (BAU) Sales BAU sales are derived by taking 2012 sales from EIA Form 861 data for the state and increasing them for each subsequent year by the average annual growth rate from the AEO 2013 Reference Case for the region corres ponding to the state. For South Carolina the corresponding region is SERC and the av erage annual growth rate from 2012 to 2040 is 1.10% per year. The resulting values are su mmarized in Table 5-13 for South Carolina.

TABLE 5-13
BAU Sales for South Carolina
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
BAU Sales (GWh) 82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005

Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales As discussed, the 2017 value for annual in cremental EE savings as a percentage
of sales is set at the 2012 value based upon EIA-861 reported data. For South Carolina
that value is 0.34%. This value is then incr eased by the pace of improvement of 0.2% per year (for Option 1) un til the goal level of 1.50% (for Option 1) is reached and then held constant. The resulting values are summa rized in Table 5-14 for South Carolina.

TABLE 5-14 Annual Incremental EE Savings as a Percentage of Sales for South Carolina
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Annual
Incremental EE Savings

(% sales) 0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50%

Step 3: Determine Annual Incremental EE Savings (GWh) and Sales after net EEDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Annual incremental EE savings are calculated by multiplying the annual incremental savings as a percentage of sales times the prior year sales after net EE. Sales after net EE are calculated by subtracting ne t cumulative savings from BAU sales. The resulting values are summarized in Table 5-15 for South Carolina.

TABLE 5-15 Annual Incremental EE Savings and Sales after Net EE Savings for South Carolina
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Annual
Incremental EE Savings (GWh) 274 440 608 777 945 1,113 1,250 1,249 1,249

Sales after

Net EE (GWh) 82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346

Step 4: Determine Annual Expiring EE Savings (GWh) Expiring EE savings are calculated as the sum of all expired savings in a given year from all prior years’ incremental (fir st-year) savings based upon an average measure life of 10 years and a linear de cline in first-year savings over twenty years. As an example, Figure 2 illustrates the decline in fi rst-year savings from EE measures installed in 2017. The resulting values for expiring EE savings are summarized in Table 5-16 for
South Carolina.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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FIGURE 1 Generalized Distribution of First-Year Savings over Time.

TABLE 5-16 Expiring EE Savings for South Carolina
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025 Expiring EE Savings (GWh) 0 14 38 70 110 160 219 285 350

Step 5: Determine the 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 summarized for South Carolina in
Table 5-17.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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TABLE 5-17 Net Cumulative EE Savings for South Carolina
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Net
Cumulative EE Savings (GWh) 274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659

Step 6: Determine the 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. Th e resulting values are summarized for South
Carolina in Table 5-18.

TABLE 5-18 Net Cumulative EE Savings as a Percentage of BAU Sales for South Carolina
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Net
Cumulative EE Savings

as % of
BAU Sales 0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40%

Summary of General Formulas and Results by Step for South Carolina Tables 5-19 and 5-20 provide summaries of the generic formulas and results for South Carolina for each step.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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TABLE 5-19 Summary of Calculation Formulas by Step
Step Result Formula

1 BAU Sales (GWh) BAU Sales year i = BAU Sale year i-1 * Annual Average Sales Growth Rate

2 Annual Incremental EE Savings (% of Sales) Annual Incremental EE Savings 2017 = Annual Incremental EE Savings 2012 ; Annual Incremental EE Savings year i = Annual Incremental EE Savings year

i-1 + annual pace of improvement (until goal level is reached)

3 Annual Incremental EE Savings (GWh) Annual Incremental Savings year i = Annual Incremental Savings as a
Percent of Sales year i * Sales After Net EE year i-1

3 Sales after Net EE (GWh) Sales After Net EE year i = BAU Sales year i – Net Cumulative Savings year i

4 Expiring EE Savings (GWh) Expiring Savings year i = Σ Expiring measures from all prior program years (10-year average measure life with linearly decline over 20 years)

5 Net Cumulative Savings (GWh) Net Cumulative Savings year i = Σ Annual Incremental Savings YTD – Expiring Savings year i

6 Net Cumulative Savings (% of Sales) Net Cumulative Savings year i = Net Cumulative Savings year i / BAU Sales

year i

TABLE 5-20 Summary of Results by Step for South Carolina.
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
BAU Sales

(GWh) 82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005

Annual
Incremental 0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50%DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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EE Savings

(% sales)

Annual
Incremental EE Savings (GWh) 274 440 608 777 945 1,113 1,250 1,249 1,249

Sales after

Net EE (GWh) 82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346

Expiring EE Savings (GWh) 0 14 38 70 110 160 219 285 350

Net
Cumulative EE Savings (GWh) 274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659

Net
Cumulative EE Savings
as % of BAU
Sales 0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40%

Results Summary of Results As discussed, the EE goals for each state ar e represented as cumulative savings as a percentage of retail sales by year for each op tion. Table 5-21 summarizes these values for the first and last year of the interim compliance period for Options 1 and 2. See Appendix 5-4 for comprehensive results by state, for years 2017 through 2030, including both annual incremental and cumulative savings as a percentage of retail sales, for each options.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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TABLE 5-21 Summary of State EE Goals for Options 1 and 2

State EE State Goal Cumulative Savings as a % of Retail Sales

Option 1 Option 2

2020 2029 2020 2024

Alabama 1.36% 9.48% 1.07% 3.86%
Arizona 1.52% 9.71% 1.24% 4.10%
Arkansas 5.24% 11.42% 3.52% 5.98%
California 4.95% 11.56% 3.55% 6.08%
Colorado 3.92% 11.01% 3.32% 5.87%
Connecticut 4.71% 11.88% 3.61% 6.25%
Delaware 1.14% 9.47% 0.86% 3.59%
District of Columbia 1.14% 9.47% 0.86% 3.59%
Florida 2.03% 9.98% 1.75% 4.65% Georgia 1.76% 9.83% 1.48% 4.36%
Idaho 4.36% 11.63% 3.52% 6.15%
Iowa 3.80% 11.10% 3.28% 5.88%
Illinois 3.20% 11.11% 2.89% 5.70%
Indiana 4.65% 11.66% 3.58% 6.17%
Kansas 1.22% 9.52% 0.94% 3.70% Kentucky 1.91% 10.02% 1.63% 4.55%
Louisiana 1.14% 9.33% 0.85% 3.56%
Maine 4.43% 11.77% 3.55% 6.21% Maryland 4.21% 11.51% 3.47% 6.10%
Massachusetts 5.37% 12.13% 3.61% 6.25% Michigan 4.59% 11.77% 3.59% 6.22%
Minnesota 4.80% 11.72% 3.58% 6.17%DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Mississippi 1.58% 9.92% 1.29% 4.20%
Missouri 1.40% 9.59% 1.12% 3.93%
Montana 3.36% 10.90% 3.01% 5.69%
Nebraska 2.84% 11.00% 2.56% 5.49%
Nevada 2.37% 10.26% 2.09% 4.98% New Hampshire 1.25% 9.58% 0.96% 3.74% New Jersey 3.10% 10.60% 2.81% 5.50%
New Mexico 4.42% 11.76% 3.54% 6.20%
New York 1.39% 9.71% 1.11% 3.95%
North Carolina 2.20% 10.40% 1.91% 4.89%
North Dakota 2.95% 10.69% 2.67% 5.45%
Ohio 4.17% 11.56% 3.47% 6.12%
Oklahoma 1.86% 9.97% 1.57% 4.49% Oregon 4.66% 11.41% 3.55% 6.06% Pennsylvania 4.67% 11.69% 3.58% 6.18%
Rhode Island 3.90% 11.56% 3.35% 6.06%
South Carolina 2.32% 10.23% 2.04% 4.94%
South Dakota 1.60% 9.91% 1.32% 4.22%
Tennessee 2.21% 10.26% 1.93% 4.86%
Texas 1.78% 9.91% 1.50% 4.40%
Utah 3.62% 11.03% 3.19% 5.82%
Vermont 1.23% 9.33% 0.95% 3.67% Virginia 5.37% 12.13% 3.61% 6.25% Washington 4.24% 11.26% 3.45% 6.00% West Virginia 4.68% 11.79% 3.60% 6.22%
Wisconsin 1.77% 10.11% 1.49% 4.44% Wyoming 1.61% 9.73% 1.33% 4.19%
Continental U.S. 3.05% 10.66% 2.44% 5.18%
Alaska 1.22% 9.45% 0.94% 3.69%
Hawaii 1.29% 9.52% 1.01% 3.79%DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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U.S. Total 3.04% 10.65% 2.43% 5.17%

Results in Context
To provide context for state cumulative savings results presented in Table 5-21 and Appendix 5-4, the average annual savings were calculated for each state through 2025 and 2030, starting from 2017. Table 5-22 summarizes the results.

TABLE 5-22 Summary of Average Annual Savings Ra tes of Best Practices Scenario

Option Years Number
of Years Range of Cumulative Savings (% of Sales)
across States in Last

Year (2025/2030) Range of Average Annual Savings
Rates across States National Average Annual

Savings Rate

1 2017-
2030 13 9.9% to 12.5% 0.76%/year to 0.96%/year 0.86% per year

2 2017-
2025 8 4.3% to 6.8% 0.54%/year to 0.85%/year 0.72% per year

The state range and national values for the av erage annual savings rate represented in the EE best practices scenario are below the range of values found in rece nt utility, state, and regional studies (1.2% to 1.5% per year) as summ arized in Table 5-6, an d within the range of
values found in the 2014 national studies from EPRI and ACEEE (0.5%/0.6% to 1.6% per year) as summarized in Table 5-7. These results provide additional support for the feasibility of the EE best practices scenario and asso ciated state-specific EE goals.

Impacts Assessment Approach In the Goal Computation TSD, state-specifi c EE goals from the previous section are integrated with the other building blocks and used to set state-specific emission rate goals for the interim and final compliance periods. These state emission rate goals are then represented asDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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requirements within the power sector modeling fo r the Regulatory Impacts Analysis (RIA). In addition, the EE state goals, resulting from the EE best practices scenario , are used to adjust electricity demand levels used as exogenous inputs to power sector mode ling for the illustrative compliance scenarios. In other words, the de gree to which EE is employed as an abatement resource is not determined endogenously w ithin the power sector modeling based upon optimization of costs but, rather, “hard wired” into the illustrative compliance scenarios. Thisapproach is taken because the EPA has determined, as discussed previously, that EE is costeffective at the established EE goal levels. The EE goal levels were constrained by practical considerations of state EE policy implemen tation, specifically, the current levels of EE performance and the pace at which states can f easibly improve their leve ls of performance over
time. The EE goals represented in the illustrative compliance scenarios lead to substantial reductions in power system costs due to the redu ctions in specified electricity demand. Since EE is not represented endogenously as an abatemen t measure within the power sector modeling, the costs associated with the EE best practice 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 addr essed in the following sections.

Inputs The following steps were taken to establish the inputs for development of the EE cost
estimates for each state.

 Step 1: Determine state-specifi c electricity savings by year

 Step 2: Determine first-year program costs of saved energy

 Step 3: Determine the ratio of program to participant costs

 Step 4: Determine the escalation rate of EE costs

Step 1: Determine State-Specific Electricity Savings by Year Results from the previous section, State Goal Setting, provide the starting point for estimation of EE costs. From those results, stat e-specific annual incremental savings (MWh) and yearly distribution of associated continuing saving s (MWh) in future years are used as inputs to
the cost estimation calculations.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Step 2: Determine First-Year Program Costs of Saved Energy First-year program costs refer to the full costs (e.g., administration, incentive payments, marketing, information to consumers, etc.) incu rred by a utility or othe r administrator of EE programs in a given year that lead to EE measur es (technologies and practices) put in place in that year and resulting in reducti ons in electricity demand in that and future years (driven by the
mix of measure lives across the portfolio of EE programs employed). Unlike participant costs, program costs are readily known by the administrator of EE programs and are, therefore, an appropriate starting point for EE program cost analysis. In 2009, AC EEE conducted a national review of data on EE program costs from pr ogram annual reports, evaluation reports, and information compiled from contacts at program administers in 14 states. Compiled data was sourced from multiple EE program administrators in each state and over multiple years of data for each administrator. ACEEE found average first-year net 30 costs of $275/MWh (2011$). The EPA has used this value for our analysis. Two recent national analyses have found slightly lower 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 admi nistrators and years. In this analysis ACEEE found average first-year net costs of $230/ MWh (2011$). In 2014, an LBNL study presented results from a uniquely comprehe nsive study of EE program costs. The LBNL analysis reviewed program-level data from over 100 pr ogram administrators in 31 st ates. Data was collected from over 1,700 individual programs for up to three years (2009-2011), covering more than 4,000 individual program years of data points. Because of the broad scope of th eir study and the lack of net savings information for many programs, LBNL focused on gross 31 , rather than net, savings values. LBNL found national average first-year cost of gross savings of $162/MWh (2012$). Applying an average net-to-gross ratio of 0.9 and de flating costs at 3%, results in an estimated national average first-year cost of net savings of $175 (2011$). The up-to-date, more comprehensive results from the ACEEE and LBNL studies, indicate that the value of $275/MWh used for this analysis is conser vative, resulting in comparatively higher total costs than would be the case based upon the newer studies.

30 “Net costs” refers to costs per electricity saved after accounting for effects of free-ridership on those savings. Depending upon the state, spillover effects may also be accounted for in net costs. 31 “Gross savings” re fers to electricity savings before any accoun ting for effects of free-ridership or spillover.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Step 3: Determine the Ratio of Program to Participant Costs As noted above, while program costs are r eadily known and consistently reported by the program administrator, particip ant costs require significant effort to estimate, and are less consistently estimated and reported. The ratio be tween program and participant costs will vary significantly from one program to the next with in a utility’s portfolio. The EPA has used a generic approach to estimate the ratio of program to participant costs across an entire portfolio, thus providing for the estimation of total costs on ce program costs are determined. To derive the ratio, the EPA reviewed 2012 EE annual reports fr om program administrators in 22 states identified as leaders in EE programs 32 based upon their magnitude of savings or their savings as a percentage of retail sales. Complete info rmation on full portfolio participant costs were
available for nine of the 22 states. Across the ni ne states, the average program and participant costs as a percentage of total costs were 53% and 47%, respectively. See Appendix 5-3 for the data and analysis documenting th is review. Based on this review , the EPA has taken a slightly conservative approach 33 and used a ratio of 1:1 between pr ogram and participant costs. We use this ratio to derive participant and total cost s based upon program costs. Starting from program CSE of $275/MWh and applying th e 1:1 ratio, we estimate participant CSE of $275/MWh and total CSE of $550/MWh (all values 2011$).

Step 4: Determine the escalation rate of EE costs The level of EE program impacts represente d in the state EE goals are substantial and represent a scenario that has not previously been achieved and sust ained at a national level in the U.S. Thus, even though the EPA has taken a cons ervative approach (i.e., leading to higher estimates of costs) to the devel opment of the EE state goals as well as to other factors that affect the EE cost estimates, we have also chosen to take a cautious approach to the escalation of EE costs at higher levels of performance (i.e., as states improve from thei r historic levels of incremental savings to the best practices leve l of 1.5% of retails sa les). Economic theory suggests two mechanisms that would change EE costs as higher levels of performance are

32 Leaders were identified using results from the 2013 AC EEE State Energy Efficiency Scorecard based on energy savings as a percentage of retail sales or total savings. 33 If we had used the 53% and 47% values, starting from program costs, total costs would have been slightly lower than calculated with the 1:1 split used.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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achieved. Economies of scale in th e operation of larger EE programs and larger portfolios of EE programs, and learning and expertise gained ove r time from the continued implementation of programs, are two factors that would lower co sts as programs scale up and expand to realize higher levels of performance. 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. Analysis based upon lim ited empirical data does provide support for significant economies of scale and/or cost reduc tions over time as learning and expertise are gained lxvii . “Supply curves” of EE as an energy resource, as well as EE as a measure represented within a GHG abatement curve, provide support for es calating costs as higher levels of savings
are realized lxviii . In a recent analysis, La wrence Berkeley National La boratory (LBNL) adopted an approach that generically repr esented both effects discussed above lxix . LBNL changed EE costs (first-year program costs) as a function of EE savings leve ls, decreasing costs at savings levels up to 0.5%, leaving costs c onstant at the base level at sa vings levels from 0.5% to 1.5%, and increasing costs at savings levels above 1.5%. Another recent analysis, by ACEEE, provides weak statistical support for a cost increase of 20% when going from 0.5% to 1.0% savings rate
and an additional cost increase of 20% when going from 1.0% to 1.5% savings rate lxx

. In consideration of the above discussion, the EPA has chosen to escalate EE costs over three steps as a function of increm ental savings (as a percentage of electricity sales) at the state
level. Until a state reaches a 0.5% savings level, their costs are se t at the base level; for savings levels between 0.5% and 1.0%, stat e costs are escalated to 120% of the base level; and for savings levels over 1.0%, state cost s are escalated to 140% of the ba se level. This approach leads to higher costs relative to the one used by LBNL when applied to EPA’s EE best practices
scenario.

Summary of Inputs for EE Cost Analysis Table 5-23 provides a summary of inputs for the EE cost anal ysis including first-year costs of saved energy, ratio of program to particip ant costs, and escalation of costs as a function of the rate of incremental savings. Each of these factors incorporates some level of conservatism, leading to higher costs than would otherwise result.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Table 5-23 Summary of EE Cost Analysis Inputs Input Source or Assumption State-Specific Electricity Savings by Year Results from state goal setting First-Year Program Cost of Saved Energy $275/MWh (2011$) Ratio of Program to Participant Costs 1:1 First-Year Participant Cost of Saved Energy $275/MWh (2011$) First-Year Total Cost of Saved Energy $550/MWh (2011$)

Escalation of Costs Incremental savings rate
0.5% - 1.0% > 1.0%
120% of base costs: $660/MWh (2011$) 140% of base costs: $770/MWh (2011$)

Calculations
This section addresses the calculations for es timating the costs associated with the statespecific EE goals discussed above. The results of th ese calculations are then used within the RIA and preamble. Specifically, three values are calculate d (annual first-year cost s, levelized cost of saved energy (LCSE), and annualized costs); fo r each, program and participant components are then calculated using the 1:1 ra tio (i.e., 50% of total for each) derived above. Specific results from prior sections on state goal setting and im pacts assessment inputs are used as inputs for these calculations. For each state, the following steps are taken for each year (2017-2030) and for each option. Calculations for steps 2 and 3 are done using real discount rates of 3% and 7%. The steps are:
1. Calculate annual first-year costs
2. Calculate levelized cost of saved energy (LCSE)
3. Calculate annualized costs To illustrate these calculations, each step is described and results are provided for one state (using South Carolina as an exam ple) for 2017 through 2025 for Option 1. The results are truncated at 2025 for simplicit y, but full national resu lts (through 2030) are presented below.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Step 1: Calculate Annual First-Year Costs Annual total first-year costs are calculat ed by multiplying annual total incremental savings (MWh) (from Table 5-15) by the firs t-year total CSE (from Table 5-23 with escalation based upon results from Table 5-14). Program and particip ant portions of the first-year costs are then calculated as 50% of total first- year costs for each per Table 5-23. The resulting values are summarized for South Carolina in Table 5-24.

TABLE 5-24
Calculation of Annual First-Yea r Costs for South Carolina
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Annual
Incremental Savings (GWh) 274 440 608 777 945 1,113 1,250 1,249 1,249

First-Year
Total Cost of Saved Energy (2011$/MWh) $550 $660 $660 $660 $770 $770 $770 $770 $770

First-Year
Total Cost

(millions

2011$) 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7

First-Year

Program (millions of 2011$) 75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9

First-Year Participant (millions of 2011$) 75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Step 2: Calculate Levelized Cost of Saved Energy Levelized costs of saved energy (LCSE) are based on leveliza tion of all savings (first and future years) resulting from EE activities in a given year. The levelization algorithm is based on the 2002 California Standard Practice Manual lxxi . The net present value of all savings from a single year’s EE acti vities (i.e., over the entire distribution of program lifetimes) is calculated using the real discount rate. The levelized cost of saved energy is then calculated by di viding the annual first-year co sts (from Table 5-24) by the levelized savings. The resulting values are summarized for South Carolina in Table 5-25.

TABLE 5-25 Levelized Cost of Saved Energy for South Carolina (at 3% discount rate)
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Levelized

Savings (GWh) 2,313 3,720 5,139 6,563 7,987 9,405 10,562 10,553 10,554

First-Year
Total Cost

(millions

2011$) 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7

Total
LCSE 6.51 7.81 7.81 7.81 9.11 9.11 9.11 9.11 9.11

Program

LCSE 3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56

Participant
LCSE 3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56

Step 3: Calculate Annualized Costs Annualized costs are calculated by multip lying the LCSE for each year by the remaining savings in each year through the full distribution of measure lifetimes. ForDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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each year in the analysis, the annualized costs resulting from all current and past
investments are summed to calculate the tota l annualized costs. The resulting values are
summarized for South Carolina in Table 5-26.

TABLE 5-26
Annualized Costs for South Carolina
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Annualized
Total Costs

(millions

2011$) 17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2

Annualized

Program

Costs (millions

2011$) 8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1

Annualized Participant
Costs (millions of 2011$) 8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1

Summary of General Formulas and Results by Step for South Carolina Tables 5-27 and 5-28 provide summaries of the generic formulas and results for South Carolina for each step.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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TABLE 5-27 Summary of Calculation Formulas by Step
Step Result Formula

1 Annual FirstYear Costs

(2011$) Annual First-Year Costs year i = Annual Incremental Sa vings x First-Year CSE year i First-Year CSE = f(incremental savings rate) per Table 5-23

2 Levelized Savings (GWh) Levelized Savings i = NPV [Lifetime Savings from EE Investments year i at Discount Rate]

2 LCSE (2011$/MWh) Levelized Cost of Saved Energy year i = Incremental Cost year i / Levelized Savings year i

3 Annualized Costs (2011$) Annualized Cost of Saved Energy year i = (LCSE year i x Remaining Savings year i ) + (Levelized Cost of Saved Energy year i-1 x Remaining Savings year i-1 ) + ... + (Levelized Cost of Saved Energy year i-n x Remaining Savings year i-n )

TABLE 5-28 Summary of Results by Step for South Carolina.
Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Total FirstYear Costs (millions 2011$) 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7

Total LCSE (2011$/MWh) 65.1 78.1 78.1 78.1 91.1 91.1 91.1 91.1 91.1

Annualized
Total Costs (millions 2011$) 17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Results Summary of National Results
Tables 5-29 and 5-31 summarize the national first-year and annualized EE costs for Option 1 for 2018, 2020, 2025, and 2030. Table 5-30 su mmarizes national LCSE for Option 1 for the same years. Each of the three tables includes values fo r program, participant, and total

costs. TABLE 5-29 First-Year EE Costs (3% discount rate, billions 2011$) (Continental U.S.)
Year
2018 2020 2025 2030 Program 10.2 15.4 21.8 21.8 Participant 10.2 15.4 21.8 21.8
Total 20.5 30.7 43.6 43.5

TABLE 5-30 Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh) (Continental U.S.)
Year
2018 2020 2025 2030 Program 42 43 45 45 Participant 42 43 45 45
Total 83 85 89 90DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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TABLE 5-31 Annualized EE Costs (3% discount rate, billions 2011$) (Continental U.S.)
Year
2018 2020 2025 2030 Program 2.0 5.1 14.4 21.4 Participant 2.0 5.1 14.4 21.4
Total 4.1 10.2 28.9 42.7

See Appendix 5-4 for comprehensive data sheets of EE cost results at the national level by year for Options 1 and 2, and at discount rates of 3% and 7% . These data sheets provide results of LCSE (total, program a nd participant), first-year costs (total, program and participant), and annualized costs (total, program and participant).
Results in Context To provide context for the pace of increase in EE program spending levels represented by Option 1, we consider the compound annual growth rate (CAGR) of the recent rapid increase in historic investment (2006 to 2011) and the CAGR from 2011 through 2018, 2020, and 2025 represented by Option 1 program cost s. Historic data is from Tabl e 5-2 and Option 1 data is from Table 5-29. Table 5-32 provides a summary of the results. The CAGRs represented by Option 1 through 2018, 2020, and 2025 vary from 8% to 11%. The historic growth rate reflecting the rapid recent growth in EE program spending is 30%, roughly three times the Option 1 values.
TABLE 5-32 Comparison of Historic and Projected (Option 1) Annual Growth Rates

Time Period (Years) Compound Average Growth Rate Historic (2006-2011) 29.8% Option 1 (2011-2018) 8.1% Option 1 (2011-2020) 11.3% Option 1 (2011-2025) 9.8%DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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### Appendices

Appendix 5-1: Summary of Recent (2010-2014) El ectric Energy Efficiency Potential Studies Appendix 5-2: Incremental Electricity Savings Pace of Improvement Analysis Appendix 5-3: Review of the Ratio of Program to Participant Costs Appendix 5-4: Comprehensive Results: St ate Goal Setting and Impacts AssessmentDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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### Appendix 5-1 Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies The following table summarizes estimates of economic and achievable energy efficiency potential from a number of recent studies (2010-2 014) for states, utilities, and other agencies across the U.S. Study periods ranged from five to twenty-one years in leng th. As Table 1 shows,
across the eleven studies that re ported achievable potential, results for average annual achievable potential range from 0.8% per year to 2.9% per year (of baseline sales) with an average of 1.5%

per year.

TABLE 1 Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies

State Client Analyst Study
Year Study
Period End-year Projected
Potential as % of

Baseline Sales Average Annual Projected
Potential as % of Baseline
Sales
Economic Achievable Economic Achievable

Arizona Salt River Project Cadmus Group 2010 2012-
2020 29% 20% 3.2% 2.2%

California California Energy Commission California Energy Commission 2013 2014-
2024 Not reported 9.6% N/A 0.9%

Colorado Xcel Energy Kema, Inc. 2010 2010-
2020 20% 15% 1.8% 1.4%

Delaware Delaware
DNR/DEC Optimal Energy,
Inc. 2013 2014-
2025 26.3% Not reported 2.2% N/A

Illinois ComEd ICF International 2013 2013-
2018 32% 10% 5.3% 1.7%

Michigan Michigan PSC GDS Associates 2013 2013-
2023 33.8% 15% 3.1% 1.4%DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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New Jersey Rutgers University EnerNOC Utility
Solutions 2012 2010-
2016 12.8% 5.90% 1.8% 0.8%

New Mexico State of New
Mexico Global Energy
Partners 2011 2012-
2025 14.7% 11.1% 1.1% 0.8%

New York ConEd Global Energy
Partners 2010 2010-
2018 26% 15% 2.9% 1.7%

Pacific
Northwest

(Idaho, Montana, Oregon, Washington) US Department of Energy Lawrence Berkeley
National Laboratory 2014 2011-
2021 11% Not reported 1.9% Not reported

Pennsylvania Pennsylvania
PUC GDS Associates
and Nexant 2012 2013-
2018 27.2% 17.3% 4.5% 2.9%

Tennessee Tennessee Valley Authority Global Energy
Partners 2011 2009-
2030 24.8% 19.8% 1.1% 0.9%

Range 0.8% - 2.9%

per year

Average 1.5% Per year

References Arizona: The Cadmus Group, Inc. 2010. Salt Rive r Project 2012-2017 Energy Efficiency Plan – Final Report. Prepared for Salt River Project, April 16.

Colorado: Kema, Inc. 2010. Colorado DSM Market Potential Assessment – Final Report. Prepared for Xcel Energy, March 12.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Delaware: Optimal Energy, Inc. 2013. Delawa re Economic Energy Efficiency Potential. Prepared for the Delaware Department of Natu ral Resources and Environmental Control, May
24.

Illinois: ICF International. 2013. ComEd Ener gy Efficiency Potential Study Report, 2013-2018. August 20.

Michigan: GDS Associates, Inc. 2013. Michigan Electric and Na tural Gas Energy Efficiency Potential Study – Final Report. Prepared fo r the Michigan Public Service Commission,
November 1.

New Jersey: EnerNOC Utility Solutions C onsulting. 2012. New Jersey Energy Efficiency
Market Potential Assessment , Volume 1: Executive Summary. Report Number 1401, Prepared for Rutgers, The State University of New Jersey, October 17, 2012.

New Mexico: Global Energy Partners. 2011. Energy E fficiency Potential Study for the State of New Mexico, Volume 2: Electric En ergy Efficiency Analysis. June 30.

New York: Global Energy Partners, LLC. 2010. Energy Efficiency Potential Study for Consolidated Edison Company of New York, Inc., Volume 2: Electric Po tential Report. March.

Pacific Northwest states (com bined): Barbose, Galen, and Al an Sanstad, Charles Goldman, Stuart McMenamin, Andy Sukenik. 2014. Incorpor ating Energy Efficiency into Western Interconnection Transmission Planning. Draft re port, Lawrence Berkeley National Laboratory, January.

Pennsylvania: GDS Associates, Inc., and Nexant . 2012. Electric Energy Efficiency Potential for Pennsylvania – Final Report. Prepared for the Pennsylvania Public Utilities Commission, May
10.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Tennessee: Global Energy Partne rs. 2011. Tennessee Valley Author ity Potential Study, Final Report, Volume 1: Executive Summa ry. Report Number 1360, December 21.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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### Appendix 5-2 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 tw o different perspectives: 1) hist orical data reflecting achieved savings of EE programs and 2) requirements of existing state energy efficiency resource standards (EERS). For the historic al perspective, we reviewed da ta from the Energy Information Administration’s Form EIA-861 on EE program elect ricity savings (supplemented as needed with program administrator reports) and identifi ed the pace at which entities reaching higher savings levels have historically increased energy savings over time. 34 Specifically, we reviewed the historical savings data in the follow ing two groups of energy efficiency program
administrators.

1. Top saver 1% - a group with 47 entities that achieved a maximum firs t-year savings level
of 0.8% to 1.5%.

2. Top saver 2% - a group with 26 entities that achieved a maximum firs t-year savings level
of 1.5% to 3.0%. 35

For the existing state requirements perspec tive, we reviewed energy savings ramp-up
schedules established under EERS for states that provi de clear ramp-up schedules. According to ACEEE’s 2013 State Energy Efficiency Scorecard lxxii , there are a total of 26 states that have

34 The EIA 861 was the main data source. However, we have supplemented the EIA 861 with third-party program administrator data because the EIA 861 just started to colle ct third-party administrator data in 2011. The third-party entities included in our analysis are Efficiency Vermont, Ener gy Trust of Oregon, Efficiency Maine Trust, and Cape Light Compact. In addition, we supplemented the EIA 861 database with additional data for two utilities that we found achieved high energy savings, but did not report savings data in the EIA 861 data for one or two years. These entities are Burlington Electric and Massachusetts El ectric Company (now part of National Grid). 35 In addition to these maximum first-year savings threshol ds, we screened program administrators for the following conditions: (a) the maximum savings levels occurs after the minimum savings levels; (b) sufficient amounts of increase in first-year savings are provide d to evaluate reasonable ramp-up schedul es to gain an incremental 1% firstyear savings; and (c) savings data series are continuous between the years for the minimum and maximum savings levels.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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mandatory EERS policies. 36 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 1 ). The entities in this group have increased the level of first-year saving s 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). 37

 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 1 ). The entities in this group have increased the level of first-year savings by 0.38% pe r year on average from the minimum to the maximum firstyear savings levels (with a range of 0.14% per year to 1.28% per year). 38

Table 1. Energy savings ramp-up trends in first-year savings for “Top Saver 1%” and “Top Saver 2%” groups 39

Top Saver 1% Top Saver 2% Average
Annual
First-Year

Savings Estimated
Years to

Gain
Incremental Average
Annual
First-Year

Savings Estimated
Years to

Gain
Incremental

36 ACEEE, 2013 State Energy Efficiency Sc orecard, Appendix B, November 2013, 37 This is a simple average estimate of the annual averag e increase in first-year savi ngs from each entity in this group. 38 This is the simple average estimate of the annual aver age increase in first-year sa vings from each entity in this group. 39 Data sources: EIA-861; ISO New England, “Compilation of raw PA data for the 2013 EE Forecast,” Jun 21, 2013; energy efficiency program administrators’ 2012 energy efficiency annual reports for the following entities: Burlington Electric Department, Efficiency Vermont, Efficiency Maine, Energy Trust of Oregon, Cape light Compact, and Wisconsin Focus on Energy.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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Increase 1% Increase 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

Our research findings on incremental electric ity savings ramp-up based on existing state EERS policies are:

 The states with EERS policies which exhibi t savings ramp-up schedules are requiring increases in first-year ener gy savings at a pace that ra nges from 0.11% (Colorado and Oregon) to 0.40% (Rhode Isla nd) as shown in Table 2.

 The first-year savings pace of increase averag es 0.21% per year across the 10 states. This savings level translates to about 4.7 years to achieve an incrementa l 1% first-year savings
increase.

Table 2. First-Year Energy Savings Ramp -up Review of State EERS Policies 40

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

40 Data sources: ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, Arkansas Public Service Commission, Docket Nos. 13-002-U. Order No. 7, September 9, 2013.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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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

References MidAmerican Energy Company, "Energy Efficien cy Plan Docket No. EEP-08-2, 2012 Annual Report to the Iowa Utilities Board," May 1, 2013, available at: http://www.state.ia.us/governme nt/com/util/energy/energy_effi ciency/ee_plans_reports.html Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012- Annual-Report.pdf National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas Energy Efficiency Plan," Docket N o. 12-109, November 2, 2012, available at: http://www.env.state.ma.us/dpu/ docs/electric/13-120/8113ngptapb.xlsx PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission, for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215, November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at: http://www.efficiencyvermont.com/docs/about_e fficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdfDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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### Appendix 5-3
Review of the Ratio of Pr ogram to Participant Costs

Introduction and Summary This appendix summarizes and analyzes data on EE costs (program and participant) to develop a ratio to enable the estimation of participant costs from known program costs. We reviewed cost data from leading EE program admi nistrators in 22 states . Our research findings
are as follows:

 A 1:1 ratio between program and particip ant costs is a reasonable and slightly conservative (i.e., slightly higher total costs) basis for estimating participant costs from known program costs.

 Reported data was reviewed from 22 states; however, program administrator reports from only nine states contained sufficient informa tion (participant costs across entire portfolio of EE programs) to inform the analysis.

 Participant cost data from ten program admini strators in nine states indicate that the weighted average and simple average particip ant costs were 47 percent of total costs.

Participant Cost Analysis We first identified states having high incr emental electricity savings rates or high absolute savings levels based upon 2013 AC EEE State Energy Efficiency Scorecard. 41 These states represent a large portion of total EE savi ngs in the U.S. We identified 22 states meeting these criteria and collected publicly availa ble EE program reports for major program
administrators within each state. From th ese program reports we identified 10 program
administrators across nine states where we were able to identify both pr ogram administrator and participant costs across their full portfolio of EE programs. The table below provides the 2012 portfolio-level program administrator and part icipant costs for the nine states. Program administrator costs represent the program administrator’s program development and

41 For the purpose of this research, we have defined leading or high impact states as th e top 15 states in the 2013 ACEEE State Energy Efficiency Sc orecard in terms of incremental savings as a percentage of retail sales or absolute annual energy savings in terms of total annual MWh saving s. These criteria resulted in a total of 22 states which include Arizona, California, Connecticut, Florida, Hawaii, Illinois, Indiana, Iowa, Maine, Massachusetts, Michigan, Minnesota, New Jersey, New York, North Carolina, Ohio, Oregon, Pennsylvania, Rhode Island, Texas, Vermont, and Washington.DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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implementation costs, and the participant costs re present the customer costs to partake in the program. Total program costs are the sum of both costs. Each state’s program administrator and participant costs are presented as a percentage of total program costs in Table 3. The weighted and simple average program and participant cost s across all nine states are presented as a percentage of total program costs. The wei ghted average cost shares were based on each program’s spending by administ rator and participants.

Table 1 2012 Participant and Program Cost Info rmation from Reported Entities

In our analysis, the weighted average pr ogram and participant costs are 53.4% and 46.6%, respectively, of total costs. On a simple average basis, program and participant costs are 52.6% and 47.4%, respectively, of tota l costs. Participant cost results range fr om a low of 17% of 2012 Portfolio Costs

State Program Administrator Program Costs (Million $s) Participant Costs (Million $s) Total Costs (Million $s) Program Costs as Percent of Total Cost (%) Participant Costs as Percent of Total Cost (%) A b c = a + b a / c b / c

California Southern California

Edison $ 316 $ 269 $ 585 54.0% 46.0% Hawaii Hawaii Energy $ 31 $ 37 $ 68 45.6% 54.4%

Iowa MidAmerican Energy Company $ 50 $ 70 $ 120 41.5% 58.5%

Maine Efficiency Maine Trust $ 24 $ 36 $ 60 39.8% 60.2% Massachusetts National Grid $ 173 $ 54 $ 227 76.3% 23.7%

Minnesota Xcel Energy $ 53 $ 98 $ 151 35.1% 64.9% Pennsylvania PECO $ 68 $ 109 $ 178 38.5% 61.5% Rhode Island National Grid $ 63 $ 13 $ 75 83.2% 16.8%

Vermont Efficiency Vermont; Burlington

Electric Department $ 34 $ 23 $ 57 59.3% 40.7% Weighted Average 53.4% 46.6%

Simple Average 52.6% 47.4%DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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total costs (National Grid in Rhode Island) to a high of 65% (Xcel Energy in Minnesota). When deriving participant costs from program costs, using a ratio of 1:1 is consistent with these results and slightly conservative, leadi ng to slightly higher total costs than the precise average values would provide.

References MidAmerican Energy Company, "Energy Efficien cy Plan Docket No. EEP-08-2, 2012 Annual Report to the Iowa Utilities Board," May 1, 2013, available at: http://www.state.ia.us/governme nt/com/util/energy/energy_effi ciency/ee_plans_reports.html Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012- Annual-Report.pdf National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas Energy Efficiency Plan," Docket N o. 12-109, November 2, 2012, available at: http://www.env.state.ma.us/dpu/ docs/electric/13-120/8113ngptapb.xlsx PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission, for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215, November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at: http://www.efficiencyvermont.com/docs/about_e fficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf Burlington Electric Department , "2012 Energy Efficiency Annual Report," 2012, available at: https://www.burlingtonelectric.com/ELBO/a ssets/2012%20DSM%20Annual%20Report%20Mas ter.pdf Hawaii Energy, “Annual Report Program Year 2012 July 1, 2012 - June 30, 2013,” 2013,
available at http://www.hawaiienergy.c om/information-reports Xcel Energy, "2012 Status Report & Associated Compliance Filings Minnesota Electric and Natural Gas Conservation Improvement Progr am Docket No. E,G002/CIP-09-198, " April 1, 2013, available at http://www.xcelenergy.com/sta ticfiles/xe/Regulatory/Regul atory%20PDFs/MN-DSM-CIP-2012- Status-Report.pdfDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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National Grid, "2012 Energy Efficiency Y ear-End Report," May 31, 2013, available at http://www.nationalgridus.com/non_htm l/eer/ri/4295-YearEnd%20Rept%20(PUC%205-31- 13).pdf Southern California Edison, "2013 Energy Effici ency Annual Report," June 2013, available at http://eega.cpuc.ca.gov/Documents.aspx http://eega.cpuc.ca.gov/Documents.aspxDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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### Appendix 5-4 Comprehensive Results: State Go al Setting and Impacts Assessment

See attached file, “Abatement Measures TSD Appendix 5-5.xlsx,” containing the following:

Goal Setting Sheets

 Option 1 – Incremental Savings as % of Sales by State (2017-2030)

 Option 1 – Cumulative Savings as % of Sales by State (2017-2030)

 Option 2 – Incremental Savings as % of Sales by State (2017-2030)

 Option 2 – Cumulative Savings as % of Sales by State (2017-2030)

Impacts Assessment Sheets

 Option 1 – National Costs at 3% Discount Rate (2017-2030)

o Levelized Cost of Saved Energy, Firs t-year Costs, and Annualized Costs

 Option 1 – National Costs at 7% Discount Rate (2017-2030)

o Levelized Cost of Saved Energy, Firs t-year Costs, and Annualized Costs

 Option 2 – National Costs at 3% Discount Rate (2017-2030)

o Levelized Cost of Saved Energy, Firs t-year Costs, and Annualized Costs

 Option 2 – National Costs at 7% Discount Rate (2017-2030)

o Levelized Cost of Saved Energy, Firs t-year Costs, and Annualized CostsDRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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References

i American Council for an Energy-Efficient Economy (A CEEE). November 2013. The 201 3 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-policy/scorecard

. ii U.S. Energy Information Administration Form EIA-861 data files. 2012. Available at http://www.eia.gov/electricity/data/eia861/

. iii Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend

. iv Seth Nowak, Martin Kushler, Patti Witte, and Dan York. Leaders of the Pack: ACEEE’s Third National Review of Exemplary Energy Efficiency Programs. American Council for an Energy-Effi cient Economy (ACEEE). Research Report U132. Available at http://www.aceee.org/research-report/u132

. v See reviews of market failures and barriers related to energy efficiency in Gillingham, K, R Newell, and K Palmer. 2009. Energy Efficiency Economics and Policy. Annual Review of Resource Economics. Annual Review of Resource Economics 1: 597-619 and Gillingham and Palmer (2 013). “Bridging the Energy Efficiency Gap: insights for policy from economic theory and empirical analysis,” Resour ces for the Future DP 13-02. vi For example, see DeCanio, S. 1998. The efficiency pa radox: bureaucratic and organizational barriers to profitable energy-saving investments. Energy Policy 26(5): 441-458; McKinsey & Co and The Conference Board. 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? pp. (52-53). vii See discussion in Jaffe, A.B., R.G. Newell, and R.N. St avins. 2003. Technological Change and the Environment. Chapter 11 in the Handbook of Environmental Economics. Volume 1, Edited by K.-G. Maler and J.R. Vincent. Elsevier Science B.V. 461-516. viii See discussion in Gillingham, K and K Palmer. 2014. Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Analysis. Review of Environmental Economics & Policy, 8(1): 18-38. ix U.S. EPA and U.S. DOE. July 2006. National Ac tion Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf

. x U.S. EPA and U.S. DOE. July 2006. National Ac tion Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf

. xi Kriegler, E., J. P. Weyant, G. J. Blanford et al. 20 14. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change

. January 2014; Clarke, L, A Fawcett, J Weyant et al. Technology and U.S. Emissions Reductions Goals: Results of the EMF 24 Modeling Exercise. [forthcoming] xii International Energy Agency (IEA). 2012. World Energy Outlook 2012. Paris. xiii Kriegler, E., J. P. Weyant, G. J. Blanford et al. 20 14. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change

. January 2014. xiv Kyle P., L. Clarke, S. Smith et al. 2011. The Value of Advanced End-Use Energy Tec hnologies in Meeting U.S. Climate Policy Goals. The Energy Journal, 32: 61-87.

xv See, for example, Comstock, O, and E Boedecker. 2011. Energy and Emissions in the Building Sector: A Comparison of Three Policies and Their Combinations. The Energy Journal , 32: 23-41; Fischer, C. (2005) “On the importance of the supply side in demand side management." in Energy Economics , 27: 165-180; Fischer, C. 2010. Imperfect Competition, Consumer Behavior, and the Prov ision of Fuel Efficiency in Light-Duty Vehicles. Resources for the Future DP 10-60. Washington, DC. xvi E.g., Gillingham, K, R Newell, and K Palmer. 2009. Energy Efficiency Economics and Policy. Annual Review of Resource Economics. Annual Review of Resource Economics 1: 597-619. xvii Woolf, T. W. Steinhurst, E. Malone, K. Takahashi. 2012. “Energy Efficiency Cost-Effectiveness Screening: How to Properly Account for ‘Other Progr am Impacts’ and Environmental Compli ance Costs,” Report prepared by RAP and Synapse Energy Economics. xviii Boyd, GA and JX Pang (2000). “Estimating the lin kage between energy effi ciency and productivity,” Energy Policy , 28: 289-296; Worrell, E. (2011). “Pro ductivity benefits of industrial ener gy efficiency measures.” Lawrence Berkeley National Laboratory Paper LBNL-52727. xix Van Buskirk, R, C. Kantner, B. Gerke et al. The benefits of energy efficiency standards and how policies may accelerate declines in appliance co sts. Proceeding of National Acad emy of Sciences. [forthcoming]DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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xx Woolf, T. W. Steinhurst, E. Malone, K. Takahashi. 2012. “Energy Efficiency Cost-Effectiveness Screening: How to Properly Account for ‘Other Progr am Impacts’ and Environmental Compli ance Costs,” Report prepared by RAP and Synapse Energy Economics. Available at http://www.raponline.org/event/the- importance-of-effective-energyefficiency-cost-effectiveness

. xxi Sixth Northwest Electric Power and Conservation Plan, Northwest Power and Conservation Council. February 2010. Council Document 2010-09. http://www.nwcouncil.org/media/6284/SixthPowerPlan.pdf xxii American Council for an Energy-Efficient Economy (ACE EE). April 2004. Select Stat e-Level Energy Efficiency Policy Opportunities 2016-2030. [forthcoming] xxiii Yu Wang and Marilyn A. Brown. February 2013. Policy Drivers for Improving Electricity End-Use Efficiency in the U.S.: An Economic-Engineer ing Analysis. Energy Efficiency. xxiv Ian A. Bowles. December 29, 2010. Massachusetts Clean Energy and Climate Plan for 2020. Available at http://www.mass.gov/eea/waste- mgnt-recycling/air-quality/green-house-g as-and-climate-change/climate-changeadaptation/mass-clean-energ y-and-climate-plan.html

. xxv Commonwealth of Massachusetts. Global Warming So lutions Act: 5-Year Progress Report. December 2013. Available at http://www.mass.gov/eea/docs/eea/gwsa/ma- gwsa-5yr-progress -report-1-6-14.pdf

. xxvi Ian M. Hoffman, Megan A. Billingsley, Steven R. Sch iller, Charles A. Goldman and Elizabeth Stuart. Lawrence Berkeley National Laboratory. August 28, 2013. Energy Efficiency Program Typology and Data Metrics: Enabling Multi-State Analyses Through the Use of Common Terminology. LBNL-6370E. Available at http://eetd.lbl.gov/news/article/56865 /new-policy-brief-energy-efficie. xxvii U.S. Energy Information Administration Form EIA-861 data files (2012). Available at http://www.eia.gov/electricity/data/eia861/

. xxviii American Council for an Energy-Efficient Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-policy/scorecard

. xxix Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend.

xxx Sciortino, M., et. al. American Council for an Energy-Efficient Economy (ACEEE). June 2011. Energy Efficiency Resources Standards: A Progress Report on State Experience. Report Number U112. Available at http://www.aceee.org/re search-report/u112

. xxxi American Council for an Energy-Efficient Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-policy/scorecard

. xxxii American Council for an Energy-Efficient Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-policy/scorecard

. xxxiii American Council for an Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency Resource Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/ policy-brief/eers-02- 2014.pdf

. xxxiv Consortium for Energy Efficiency. March 28, 2013. 2012 State of the Efficiency Program Industry. Available at http://library.cee1.org/content/2012-state- efficiency-program-industry-report/

. xxxv American Council for an Energy-Efficient Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-policy/scorecard

. xxxvi Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend

. xxxvii U.S. EPA and U.S. DOE. November 2007. Guide for Conducting Energy Efficiency Potential Studies: a Resource of the National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/potential_guide.pdf

. xxxviii U.S. EPA and U.S. DOE. November 2007. Guide for Conducting Energy Efficiency Potential Studies: a Resource of the National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/potential_guide.pdf

. xxxix Sreedharan, P. 2013. Recen t estimates of energy efficiency pote ntial in the USA. Energy Efficiency. xl Eldridge et. al. 2008. State-Level En ergy Efficiency Analysis: Goals, Methods, and Lessons Learned. 2008 ACEEE Summer Study on Energy Efficiency in Buildings. Ava ilable at http://DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014

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xli Electric Power Research Institute (EPRI). April 2014. U. S. Energy Efficiency Potential Analysis through 2035. [forthcoming] xlii American Council for an Energy-Efficient Economy (ACE EE). April 2014. Select Stat e-Level Energy Efficiency Policy Opportunities 2016-2035. [forthcoming] xliii U.S. EPA and U.S. DOE. July 2006. National Ac tion Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf

. xliv U.S. EPA and U.S. DOE. November 2008. Understandin g Cost-Effectiveness of Ener gy Efficiency Programs (Best Practices, Technical Methods, an d Emerging Issues for Policy-Makers): a Resource of the National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/cost-effectiveness.pdf

. xlv Woolf, T., et. al. November 2012. Regulatory Assi stance Project, “Energy Effi ciency Cost-Effectiveness Screening. Available at http://ww.raponline.org/document/download/id/6149

. xlvi U.S. EPA and U.S. DOE. November 2008. Understandin g Cost-Effectiveness of Ener gy Efficiency Programs (Best Practices, Technical Methods, an d Emerging Issues for Policy-Makers): a Resource of the National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/cost-effectiveness.pdf

. xlvii See, for instance, Kriegler, E., J. P. Weyant, G. J. Blanford et al. 2014. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology an d climate policy strategies. Climatic Change, January 2014; Kyle P. , L. Clarke, S. Smith et al. 2011. Th e Value of Advanced End-Use Energy Technologies in Meeting U.S. Climate Policy Goals. The Energy Journal, 32: 61-87. xlviii U.S. EPA and U.S. DOE. September 2009. Energy Effi ciency as a Low-Cost Resource for Achieving Carbon Emissions Reductions: a Resource of the National Action Plan for Energy E fficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/ee_and_carbon.pdf

. xlix McKinsey & Company. December 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? Available at http://www.mckinsey.com/client_service/sustainability/la test_thinking/reducing_us_greenhouse_gas_emissions

. l U.S. EPA and U.S. DOE. November 2007. Guide for Resour ce Planning with Energy Ef ficiency: a Resource of the National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/docu ments/suca/resource_planning.pdf

. li Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility Energy Efficiency Programs. ACEEE Report No. U1402. Washington, DC. Available at http://www.aceee.org/researchreport/u1402

. lii Friedrich, K., M. Eldridge, D. York et al. 2009. Saving Energy Cost-Effectively: A National Review of the Cost of Energy Saved Through Utility-Sector Energy Efficiency Programs,” ACEEE Report No. U092. Available at http://www.aceee.org/re search-report/u092

. liii Kushler, M., D. York, and P. Witte. American Council for an Energy-Efficient Economy (ACEEE). 2004. Five Years In: An Examination of the First Half-Decade of Public Benefits Energy Efficiency Policies. liv Gillingham, K., R. Newell, K. Palmer. 2004. Retrosp ective Examination of Demand-Side Energy Efficiency Policies. Resources for the Future Working Paper DP 04-19 REV. Washington, DC. lv Paul, A., K. Palmer and M. Woerman. 2011. Supply Curves for Conserved Electricity. Resources for the Future Discussion Paper 11-11. Washington, DC. lvi Arimura, T., S. Li, R. Newell, K. Palmer. 2012. Cost-E ffectiveness of Electricity En ergy Efficiency Programs. The Energy Journal 33(2): 63-99. lvii Trains, K. 1994. Estimation of Net Savings from Energy-Conservation Programs. Energy 19 (4):423-441. lviii Joskow, P., and D. Marron. 1992. What Does a Nega watt Really Cost? Evidence from Utility Conservation Programs. The Energy Journal 13 (4):41-74. lix U.S. EPA and U.S. DOE. November 2008. Understandin g Cost-Effectiveness of Ener gy Efficiency Programs (Best Practices, Technical Methods, an d Emerging Issues for Policy-Makers): a Resource of the National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/cost-effectiveness.pdf

. lx U.S. Energy Information Administration Form EIA-861 data files (2012). Available at http://www.eia.gov/electricity/data/eia861/

. lxi American Council Energy-Efficient Economy (ACEEE). February 24, 2014. State En ergy Efficiency Resource Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/po licy-brief/eers-02-2014.pdf

. lxii Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence Berkeley National Laboratory . March 2014. The Program Administrator Co st of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf

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lxiii Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence Berkeley National Laboratory . March 2014. The Program Administrator Co st of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf

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lxiv Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and Electric Power Plan,” Council Do cument 2010-09. Available at www.nwcouncil.org/ener gy/powerplan/6/plan/

. lxv Gordon, Fred, Lakin Garth, Tom Ec kman, and Charles Grist, “Beyond Su pply Curves,” Proceedings of 2008 ACEEE Summer Study on Energy Efficiency in Buildings, August 17 , 2008 available at: http://aceee.org/files/proceedings /2008/data/papers/8_419.pdf

. lxvi Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and Electric Power Plan,” Council Do cument 2010-09. Available at www.nwcouncil.org/ener gy/powerplan/6/plan/

. lxvii Kenji Takahashi and David Nichols, Synapse Energy Ec onomics, Inc. 2008. The Sustainability and Costs of Increasing Efficiency Impacts: Eviden ce from Experience to Date. ACEEE Su mmer Study on Energy Efficiency in Buildings. http://www.aceee.org/files/proceedi ngs/2008/data/p apers/8_434.pdf

. lxviii For example: Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and Electric Power Plan,” C ouncil Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/ ; and McKinsey & Company. D ecember 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? Available at http://www.mckinsey.com/client_service/sustainability/la test_thinking/reducing_us_greenhouse_gas_emissions

. lxix Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend

. lxx Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility Energy Efficiency Programs. ACEEE Repo rt No. U1402. Available at http://www.aceee.org/research-report/u1402

. lxxi State of California Governor’s Office of Planning and Research. July 2002. California Standard Practice Manual: Economic Analysis of Demand-Side Programs and Projects. Available at http://www.calmac.org/events/SPM_9_20_02.pdf

. lxxii American Council for an Energy-Efficient Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-policy/scorecard

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